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PROCEEDINGS
of the

1994

Battlefield

Atmospherics

Conference

Las Cruces. New Mexico

29 November - 1 December 1994

BATTLEFIELD ENVIRONMENT DIRECTORATE
U.S. Army Research Laboratory

White Sands Missile Range
New Mexico

APPROVED FOR PUBLIC RELEASE; DISTRIBUTION IS UNLIMITED,

1 9971 21 5 1 04

NOTICES

Disclaimers

The findings in this report are not to he construed as an official Department of the Army
position, unless so designated by other authorized documents.

The citation of trade names and names of manufacturers in this report is not to be
construed as official Government indorsement or approval of commercial products or
services referenced herein.

Destruction Notice

When this document is no longer needed, destroy it by any method that will prevent
disclosure of its contents or reconstruction of the document.

Proceedings
of the

1994 Battlefield Atmospherics Conference

29 November - 2 December 1994

Sponsor

Battlefield Environment Directorate
U.S. Army Research Laboratory
White Sands Missile Range, New Mexico

Conference Chairmen

Mr. Edward D. Creegan
Mr. John R. Elrick

U.S. Army Research Laboratory

PROGRAM COMMITTEE

Battlefield Environment Directorate
White Sands Missile Range, New Mexico

Conference Manager

Mr. Edward D. Creegan (505) 678-4684

Conference Chairman

Mr. John R. Elrick (505) 678-3691

Conference Advisor

Dr. Richard C. Shirkey

Conference Support

Protocol/Public Affairs Office
Ann Berry
Elizabeth Moyers

Conference Technical Support
Technical Publication Branch
Maria J. Campolla

CONTENTS

Preface . . .

SESSION I: SIMULATION/ANALYSIS

Visualization of Obscuration and Contrast Effects Using the Beams Models . 3

Donald W. Hoock and Patsy S. Hansen, U.S. Army Research Laboratory;

John C. Giever and Sean G. O’Brien, New Mexico State University

A Portable System for Data Assimilation in a Limited Area Model . 15

Keith D. Sashegyi and Rangarao V. Madala, Naval Research Laboratory;

Frank H. Ruggiero, Phillips Laboratory; Sethu Raman, North Carolina
State University

Effect of High Resolution Atmospheric Models on Wargame Simulations . 25

Scarlett D. Ayres, U.S. Army Research Laboratory

An Assessment of the Potential of the Meteorological Office Mesoscale Model

for Predicting Artillery Ballistic Messages . 37

Jonathan D. Turton and Peter F. Davies, Defence Services Division, Meteorological
Office, United Kingdom; Maj Tim G. Wilson, Projects Wings, Royal School
of Artillery, United Kingdom

Results of the Long-Range Overwater Diffusion (LROD) Experiment . 47

Janies F. Bowers; U.S. Army Dugway Proving Ground; Roger G. Carter and
Thomas B. Watson, NOAA Air Resources Laboratory

Modeled Ceiling and Visibility . 57

Capt Robert J. Falvey, U.S. Air Force Environmental Technical Applications Center

A New PCFLOS Tool . 65

K. E. Eis, Science and Technology Corporation

The Influence of Scattering Volume on Acoustic Scattering by Atmospheric

Turbulence . 75

Harry J. Auvermann, U.S. Army Research Laboratory; George H. Goedecke
and Michael DeAntonio, New Mexico State University

Relationship Between Aerosol Characteristics and Meteorology of the Western

Mojave . 85

L. A. Mathews, and J. Finlinson, Naval Air Warfare Center; P. L. Walker, Naval
Postgraduate School

111

SESSION II: OPERATIONAL WEATHER

Evaluation of the Navy’s Electro-Optical Tactical Decision Aid (EOTDA)

S. B. Dreksler, S. Brand, and A. Goroch, Naval Research Laboratory

U.S. Army Battlescale Forecast Model .

Martin E. Lee, James E. Harris, Robert W. Endlich, Teizi Henmi,
and Robert E. Dumais, U.S. Army Research Laboratory; Maj David I. Knapp,
Operating Location N, Air Weather Service; Danforth C. Weems, Physical
Science Laboratory

Development and Verification of a Low-Level Aircraft Turbulence Index Derived
from Battlescale Forecast Model Data .

Maj David I. Knapp and MSgt Timothy J. Smith, Operating Location N, .

Air Weather Service; Robert Dumais, U.S. Army Research Laboratory

Current and Future Design of U.S. Navy Mesoscale Models for Operational Use
R. M. Hodur, Naval Research Laboratory

Combat Weather System Concept . . .

James L. Humphrey, Maj George A. Whicker, Capt Robert E. Hardwick,

2nd Lt Jahna L. Wollard, and SMSgt Gary J. Carter,

Air Weather Service

Small Tactical Terminal Concept and Capabilities .

2nd Lt Stephen T. Barish, George N. Coleman III, and Maj Tod M. Kunschke,
Air Weather Service

Operational Use of Gridded Data Visualizations at The Air Force Global
Weather Central .

Kim J. Runk and John V. Zapotocny, Air Force Global Weather Central

Theater Forecast Model Selection . . .

R. M. Cox, Defense Nuclear Agency; J. M. Lanicci, Air Force
Global Weather Central; H. L. Massie, Jr., Air Weather Service

Air Weather Service: Evolving to Meet Tomorrow’s Challenges .

Col William S. Weaving, Maj Dewey E. Harms, Capt Donald H. Berchojf,
and Capt Timothy D. Hutchison, Air Weather Service

Air Force Weather Modernization Planning .

Lt Col Alfonse J. Mazurowski, Air Weather Service

Uses of Narrative Climatologies and Summarized Airfield Observations

for Contingency Support . 181

Kenneth R. Walters, Sr. and Capt Christopher A. Donahue, U.S. Air Force
Environmental Technical Applications Center

USAFTAC Dial-in Access . 185

Capt Kevin L. Stone and Robert G. Pena, U.S. Air Force Environmental Technical
Applications Center

Astronomical Models Accuracy Study . 191

Capt Chan W. Keith and Capt Thomas J. Smith, U.S. Air Force
Environmental Technical Applications Center

Atmospheric Transmissivity in the 1-12 Micron Wavelength Band for

Southwest Asia . 201

Capt Chan W. Keith, Rich Woodford, U.S. Air Force Environmental
Technical Applications Center

SESSION III: BATTLE WEATHER

Owning the Weather: It Isn’t just for Wartime Operations . 211

R, J. Szymber, M. A. Seagraves, James L. Cogan, and O. M. Johnson,

U.S. Army Research Laboratory

The Real Thing: Field Tests and Demonstrations of a Technical Demonstration

Mobile Profiler System . 221

J. Cogan, E. Measure, E. Creegan, D. Littell, and J. Yarbrough,

U.S. Army Research Laboratory; B. Weber, M. Simon, A. Simon,

D. Wolfe, D. Merritt, D. Weurtz, and D. Welsh, Environmental
Technology Laboratory, NOAA

Characterizing the Measured Performance of CAAM . . 231

Abel J. Blanco, U.S. Army Research Laboratory

Evaluation of the Battlescale Forecast Model (BFM) . 245

T. Henmi and M. E. Lee, U.S. Army Research Laboratory;

MSgt T. J. Smith, Air Weather Service

Verification and Validation of the Night Vision Goggle Tactical Decision Aid . 255

John R. Elrick, U.S. Army Research Laboratory

SESSION IV: BOUNDARY LAYER

Clutter Characterization Using Fourier and Wavelet Techniques . 263

J. Michael Rollins, Science and Technology Corporation; William Peterson,

U.S. Army Research Laboratory

Validation Tools for SWOE Scene Generation Process . 273

Max P. Bleiweiss, U.S. Army Research Laboratory; J. Michael Rollins,

Science and Technology Corporation

The Vehicle Smoke Protection Model Development Program . 281

David J. Johnston, OptiMetrics, Inc.; William G. Rouse, Edgewood Research,
Development and Engineering Center

Development of a Smoke Cloud Evaluation Plan . 291

M. R. Perry, Batelle; W. G. Rouse and M. T. Causey, Edgewood Research,
Development and Engineering Center

Analysis of Water Mist/Fog Oil Mixtures . . 30i

William M. Gutman and Troy D. Gammill, Physical Science Laboratory;

Frank T. Kantrowitz, U.S. Army Research Laboratory

New Millimeter Wave Transmissometer System . 309

Robert W. Smith, U.S. Army Test and Evaluation Command;

William W. Carrow, EOIR Measurements, Inc.

SESSION V: ATMOSPHERIC PHYSICS

Wind Field Measurement with an Airborne cw-COj-Doppler-Lidar (ADOLAR) . 323

S. Rahm and Ch. Werner, German Aerospace Establishment DLR

Behavior of Wind Fields through Tree Stand Edges . 33 1

Ronald M. Cionco, U.S. Army Research Laboratory; David R. Miller,

The University of Connecticut

Transilient Turbulence, Radiative Transfer, and Owning the Weather . 345

R. A. Sutherland, T. P. Yee, and R. J. Szymber, U.S. Army
Research Laboratory

Forecasting/Modeling the Atmospheric Optical Neutral Events Over a
Desert Environment .

G. T. Vaucher, Science and Technology Corporation; R. W. Endlich,

U.S. Army Research Laboratory

SESSION I POSTERS : SIMULATION AND ANALYSIS

Combined Obscuration Model for Battlefield Induced Contaminants - Polarimetric

Millimeter Wave Version (COMBIC-PMW) . 367

S. D. Ayres, B. Millard, and R. Sutherland, U.S. Army Research Laboratory

A Multistream Simulation of Multiple Scattering of Polarized Radiation by

Ensembles of Non-Spherical Particles . 381

Sean G. O’Brien, Physical Science Laboratory

Combined Obscuration Model for Battlefield Induced Contaminants-Radiative

Transfer Version (COMBIC-RT) . 391

Scarlett D. Ayres, Doug Sheets, and Robert Sutherland, U.S. Army
Research Laboratory

Emissive Smoke Modeling for Imaging-Infrared Seeker/Tracker Simulation . 401

Joseph L. Manning, Charles S. Hall, and Sheri M. Siniard, Computer
Science Corporation

SESSION II POSTERS: OPERATIONAL WEATHER

Performance of the U.S. Army Battlefield Forecast Model Performance During

Operation Desert Capture II . ^^3

R. E. Dumais, Jr., U.S. Army Research Laboratory

A Weather Hazards Program Used for Army Operations on IMETS . 423

Jeffrey E. Passner, U.S. Army Research Laboratory

SESSION III POSTERS: BATTLE WEATHER

Comparison of Radiometer and Radiosonde Derived Temperature Profiles Measured

at Wallops Island, VA . 433

Edward M. Measure, U.S. Army Research Laboratory; Dick R. Larson,

Physical Science Laboratory; Francis Schmidlin and Sean McCarthy,

NASA Goddard Space Flight Center Facility, Wallops Island, VA

The Integrated Weather Effects Decision Aid Threat Module . . . 443

David P. Sauter, U.S. Army Research Laboratory; Carl H. Chesley
and Andrew R. Spillane, Science and Technology Corporation

Owning the Weather Battlefield Observations Framework . 449

Richard J. Szymber and James L. Cogan, U.S. Army Research Laboratory

Electro-Optical Climatology Microcomputer Version 2.2 Demonstration (EOCLIMO) 461
Capt Matthew R. Williams, U.S. Air Force Environmental Technical
Applications Center

SESSION IV POSTERS: BOUNDARY LAYER

Technical Exchange with Australia .

James Gillespie and Patti Gillespie, U.S. Army Research Laboratory

... 467

Improvements to Modeling of Polarimetric Scattering .

Michael DeAntonio, National Research Council Post Doc, U.S. Army
Research Laboratory

Atmospheric Acoustic Characterization in Support of BAT- Vehicle
Field Testing .

John R. Fox, U.S. Army Research Laboratory; Prasan Chintawongvanich,
Physical Science Laboratory

ARL Remote Sensing Rover as a Ground Truth Monitor at the XM-21 Challenge
System Field Test .

Frank T. Kantrowitz and Dale U. Foreman, U.S. Army Research Laboratory i
William M. Gutman, Physical Science Laboratory

Lidar Observations During Smoke Week XIV

M. P. Bleiweiss and R. A. Howerton, U.S. Army Research Laboratory

Enhanced Photon Absorption in Multi-Component Aerosol Clouds

Young P. Yee and Robert A. Sutherland, U.S. Army Research Laboratory

Visualization of the MADONA Data Base and Use of Selected Sequences in a

Wind Flow and Diffusion Simulation System .

Harald Weber and Welfhart aufm Kampe, German Military Geophysics Office

SESSION V POSTERS: ATMOSPHERIC PHYSICS

Temperature Profile of the Nocturnal Stable Boundary Layer over Homogeneous
Desert Using LA-Teams .

R. Todd Lines, New Mexico State University; and Young P. Yee,

U.S. Army Research Laboratory

Comparison of Boundary-Layer Wind and Temperature Measurements
with Model Estimations .

R. J. Okrasinski, Physical Science Laboratory; A. Tunick,

U.S. Army Research Laboratory

viii

Optical Turbulence Measurements at Apache Point Observatory . 545

Frank D. Eaton, John R. Hines, and William H. Hatch, U.S. Army
Research Laboratory; James J. Drexler and James Northrup, Lockheed
Engineering and Sciences Company

The APRF SODAR: Bridging the Lower Boundary Layer . 553

John Hines, Frank Eaton, Scott McLaughlin, and William Hatch,

U.S. Army Research Laboratory; G. Hoidale, W. Flowers and
L. Parker-Sedillo, Science and Technology Corporation

A Look at Thermal Turbulence Induced Radar Echoes in or Near

Rain Clouds at the Atmospheric Profiler Research Facility . . 563

William H. Hatch, U.S. Army Research Laboratory

Border Area Air Quality Study . 569

B. W. Kennedy, J. M. Serna, J. R. Pridgen, D. Kessler, J. G. Moran,

G. P. Steele, and R. Okrasinski, Physical Science Laboratory;

J. R. Fox, R. Savage, and D. M. Garvey, U.S. Army Research Laboratory

APPENDICES

Appendix A: Agenda . 579

Appendix B: List of Attendees . 587

Author Index . 607

PREFACE

The 1994 Battlefield Atmospheric Conference was held 29 November through
1 December 1994 at White Sands Missile Range, New Mexico, under the sponsorship of
the U.S. Army Research Laboratory, Battlefield Environment Directorate, White Sands
Missile Range, New Mexico. The conference included oral presentations, posters, and
demonstration sessions on five topics: Simulation and Analysis, Operational Weather,
Battle Weather, Boundary Layer, and Atmospheric Physics. The conference had 219
attendees, including representatives from Denmark, France, Germany, Israel, The
Netherlands, and the United Kingdom.

The genesis of the Battlefield Atmospherics Conference was the Electro-Optical Systems
Atmospheric Effects Library (EOSAEL) and Tactical Weather Intelligence (TWI)
Conference set up to analyze the 1993 Israeli War and the effective use of smoke by the
Israeli forces to defeat electro-optical systems.

In 1991, in an effort to encompass additional aspects of battlefield atmospheric effects
such as acoustic transmission, the conference became known as the Battlefield
Atmospherics Conference.

The reader will find the items related to the conference itself (the agenda and the list of
attendees) in the appendices. An author index is included after the appendices.

Session I

SIMULATION/ANALYSIS

VISUALIZATION OF OBSCURATION AND CONTRAST
EFFECTS USING THE BEAMS MODELS

Donald W. Hoock
Patsy S. Hansen

Battlefield Environment Directorate
U.S. Army Research Laboratory
White Sands Missile Range, NM 88002-5501

John C. Giever
Sean G. O’Brien
Physical Science Laboratory
New Mexico State University
Las Cruces, NM 88003-0002

ABSTRACT

Interest in using highly interactive, real-time computer simulations as development,
analysis, planning and training tools continues to expand within DoD and industry.
Interactive simulations range from graphical manipulations of 3-D scientific data
to realistic 3-D virtual and "fly-through" environments in real-time. Improvement
in both real-time graphics hardware and wider access to off-the-shelf visualization
software has particularly stimulated a user demand for better, "physically correct"
models of processes, effects and appearances. One such improved, physics-based
model for support of battlefield environment simulations is the U. S. Army
Research Laboratory, Battlefield Environment Directorate, Battlefield Emission
and Multiple Scattering Model (BEAMS). BEAMS computes both radiance (color
values) and partial obscuration (opacity) of inhomogeneous battlefield clouds of
obscurants, smoke, dust, haze and fog layers. In achieving a stable, accurate
solution, BEAMS’ long calculations are far from real-time. A full BEAMS 3-D
radiative transfer calculation produces diffuse radiance outputs in 26 directions for
each volume element of its non-uniform cloud concentration distribution. Thus,
for real-time use in representing the "color" and "opacity" of battlefield clouds in
simulators, it is necessary to develop simpler, parametric representations of
BEAMS outputs. These outputs are now being analyzed to produce one of a
number of "environmental representation" products to support real-time
visualization of the battlefield environment. This paper ziddresses relative errors
between; using a simple mean radiance profile derived from many sets of BEAMS
calculations; using actual transmittance distributions through a given cloud along
with the scaled (limiting) path radiance averaged from many BEAMS calculations;
and performing a full BEAMS calculation for the entire cloud at each point in
time. BEAMS outputs for these approaches to cloud visualizations are compared.

1. INTRODUCTION

Given a concentration distribution and a wavelength-dependent mass extinction for the aerosol

oh!rnrlT"i ^ Compute transmittance to an observer’s position for every line of sight through m
bscurant cloud In visualization, one applies cloud transmittance as a 2-D map or array of the
fractions of background radiance that will show through a cloud. However, transmittance is

v3iJeT only be reduced (never increased) by just transmittance,

on y y using transmittance, every cloud appears dark against its background.

Obscurants, one usually also requires an
^lor ^"'itted radiance that gives the cloud its own appearance

(color value) or wavelength-dependent signature. This is called the "path radiance" of the cloud.

epends on external illumination, the cloud concentration and extinction per unit concentration
the relative amount of scattering versus absorption from individual particles, and the wavelength-
• pattern with angle (the phase function) for the type of obscurant particles

m he cloudy Except for optically "thin" clouds, the path radiance is also dependent on the many
possible paths over which the radiant power can be multiply-scattered before emerging.

Scattering Model (BEAMS) (Hoock et al. 1993- O’Brien
1993) IS an approach to computing the steady state diffuse path radiance for finite clouds of non-
uniform concentration. Typical run times for the BEAMS model to compute a 3-D cloud path
radi^ce distribution can be tens of minutes to hours. It gives the path radiance in 26 solid angles
a - elements inside and on the surface of the cloud. A two-dimensional version (BEAMS-2D)

Giever 1993; 1994) for haze and fog In vertically stratified layers
BEAMS-2D requires a few seconds to minutes to compute the multiply-scattered path radiance
distributions in and among the layers m 34 directions (17 upward and 17 downward solid angles).

R^AiIlrQ^ near real-time interactive scene visualizations obviously cannot embed the

BEAMS codes directly into the scene generation process. However, to represent clouds of
obscurants, smoke, dust, haze and fog with physical accuracy one must give them both a correct
transparency (transmittance) and color value (path radiance). Thus, the current approach to

interactive simulations is to pre-compute databases or scenario-
dependent data sets (called environmental representations), such as scene illumination, visibility
and obscurant cloud radiance. It is these tables or simple parametric curve fits that are then used

non-imaging combat simulations. Thus, it is first necessary to
detemme if BEAMS model outputs are general enough to apply to a sufficiently large range of
cloud and illumination scenarios in battlefield environment simulations.

This paper addresses the extent to which BEAMS outputs can be reduced to useful data to
support real-time battlefield scene simulation. The relevant parameters are described in section
Z. Ihe BEAMS methodology is briefly reviewed in section 3, and scenario-dependent inputs are
given m section 4. Sections 5 and 6 are a case study of the analysis of BEAMS outputs. In
particular, the question is to what accuracy can the consolidated outputs of many BEAMS runs

Q represent the path radiance (color values) of real-time simulated battlefield obscurant
clouds. Section 7 gives conclusions.

2. CLOUD TRANSMITTANCE AND RADIANCE

Assume that the obscurant cloud dimensions and mass concentration C(x,y,z) distribution
throughout the cloud are known. These can be from a dynamic model of transport and diffusion.
Or, they can be provided by a mean obscurant concentration model such as the Combined
Obscuration Model for Battlefield Contaminants (COMBIC) (Ayres and DeSutter 1994) and the
2-D or 3-D concentration fluctuations provided by the Statistical Texturing Applied To
Battlefield-Induced Contaminants (STATBIC) model (Hoock 1991). We also assume that the
wavelength-dependent mass extinction coefficient aiX) is known. It can be obtained from the
Electro-Optical Systems Atmospheric Effects Library (EOSAEL) phase function database
(PFNDAT) (Davis et al. 1994) or computed from particle size distribution and wavelength-
dependent refractive index via a shape-dependent particle scattering model, such as the AGAUS
Mie code for spherical particles from EOSAEL (Miller, 1983). Transmittance T at wavelength
X over a path from 0 to L through concentration C(s) = C(fi*S) in direction fl has concentration
length CL and optical depth x:

-t(X) -a{X)CL -aik) f C(s)ds (D

T { X ; X ) = e = e =e o

The change in radiance L(fl*S) along the path in direction Q is given in terms of all incoming
radiances Lin(n’;s), the obscurant and wavelength-dependent single scattering albedo co (ratio of
scattering to"extinction), and a scattering phase function P(n’,0), by the radiative transfer equation

- a ( A. ) C { s)

HQ»S) -« j* s) P(Q; QO dQ'

(2)

If the incoming illuminating radiance has the same relative directional dependence over a finite
path of length As, so that an average incoming illumination can be defined over that path, then
one can define a "limiting path radiance" L5(0) as:

Lg{Q) = <£> j I»iD ( Q' ! averaged over £is) P( Q ; flO <3Q' (3)

The result, in terms of optical depth x, transmittance T and path radiance Lp(Il;s) is:

L(Q;s) = e- i(Q ; 0) = e- L ( Q ; 0 ) + ( Q ; s)

= T{s) LiQio) + [ 1 - r(s) ] L^(Q)

3. THE BEAMS MODEL

Equations 1 through 4 have direct implications to rendering propagation effects of haze, fog and
obscurant clouds. If L(n;0) is the scene background and L(fi;s) is the perceived radiance at the
observer position s after passing through the cloud, then the first term is the transmitted
background radiance through the cloud, and Lp(ft;s) is the radiance (color value) observed from

the cloud itself. Furthermore, L,(0) is the maximum (limiting) radiance from the cloud as the
transmittance goes to zero over the path. It can be used with graphics hardware that allows a
current (background) pixel color value to be blended toward a limiting value (thick cloud L^)
linearly with an opacity factor [l-T(s)]. While is implicitly dependent on optical depth and
position in the cloud (since these affect the incoming illumination Lj^), Lj has far less dependence
on local variations in x than does Lp(n;s). We will exploit this in the following sections.

To compute Lp(n;s) and L,(n) we use the BEAMS model. In the 3-D version of this model the
cloud is gridded into cubical elements, each with its own concentration and scattering properties.
The radiance is broken into 26 solid angles connecting each element with its nearest neighbors.
The phase function P(0’,fi) is integrated over the incoming solid angle and averaged over the
outgoing solid angle to produce a 26x26 transfer matrix of incoming and outgoing radiance which
can be used in place of the integral in the radiative transfer equation. The incident illuminations,
both direct and diffuse, on the outside of the cloud are held constant as boundary conditions.

The internal cloud elements are repeatedly swept over from different directions, redirecting
scattered or emitted radiance out of each element. When internal radiance fields settle down to
"final" values, then the outgoing radiance at the cloud boundaries are the Lp(n,s) radiance of the
cloud. The average L,(fi) is then computed through the cloud to each boundary point. Because
of the many angular averages involved in integrating in each element, specific contributions
of external scene elements to the incoming radiance are not as important as the average diffuse
scene illumination. Strong direct (for example solar) radiation incident on the cloud is used to
determine the direct-to-diffuse radiance source terms in the cloud elements. Thus, the resulting
diffuse radiance from the cloud can usually be determined from basic scene illumination inputs.
The BEAMS-2D version for stratified layers is similar, although standard "doubling techniques"
are used instead of iterations to determine the solutions (Hoock and Giever 1994).

4. SCENARIO-DEPENDENT VARIABLES

Scenario inputs can thus be identified as three types:

o Scene illumination in the form of: Sun Angle (0,(j)) or similar direct beam source; ratio

of direct to diffuse incident irradiance; incident relative diffuse radiance on each element
of the cloud boundary; reflectance or albedo Aj, of the boundary below the cloud (if any).

o Cloud inputs in the form of: Number of cloud elements (Nx,Ny,Nz); length of the side

of each element; concentration array C(i,j,k) for each element.

o Optical inputs in the form of: Obscurant type; phase function P; single scattering albedo
0), mass extinction coefficient ct; steady-state emission source term for each element.

Outputs, as previously described, are Lp(Q„,,Sjj|.) and L5(Q^„Sji|.) for m=l to 26 directional solid
angles, and ijk = coordinates of elements on the surface or interior of the cloud. The optical
depths T(i,j,k) of each element are computed simply by multiplying the concentration, mass
extinction and element size. They are summed along lines of sight for total optical path t’s.

5. CASE STUDY - RADIANCE FROM SMOOTH VERSUS STRUCTURED CLOUDS

Assuming incident scene illumination and obscurant optical properties are fixed or vary slowly,
one would still like to account for radiance changes due to cloud shape and concentration changes
as it evolves and moves downwind. It would be particularly nice, for purposes of cloud rendering
and real-time simulation, if one could reuse pre-computed tables or curves of cloud radiance for
a variety of cloud configurations. Since mainly averages over incoming illumination in all
directions, one would hope that the Lj computed for smooth clouds and average concentrations
(and optical depths) is still approximately valid for structured clouds and fluctuations in cloud
concentration about the mean. Actual cloud output radiance Lp, however, is expected to vary
greatly about the mean, correlating closely to the structure observed in cloud appearance.

To test these assumptions, a series of cases have been run. The obscurant used is white
phosphorus smoke at a visual (0.55 pm) wavelength. The mass extinction for phosphorus smoke
in the visual is 4.3 mVg, the single scattering albedo is 0.9912, and the phase function is from
the PFNDAT database. A terrain surface albedo A^ of 0.3 is assumed. Solar zenith angles of
0, 30, 60 and 90 degrees were run at a fixed azimuth placing it over the positive y-axis (y-z
plane). The ratio of direct sunlight irradiance (normal to propagation) to diffuse irradiance (on
to a horizontal surface) was taken as 10, representing a clear day. The cloud was given a variety
of simple rectangular shapes with coordinates x downwind, y crosswind and z vertical. Cloud
dimensions, as simple ratios of X:Y:Z lengths per side, were assigned as: 1:1:1, 2:1:1, 4:1:1,
2:2:1, 4:2:1 and 4:4:1. The number of blocks used (8 to 64) and optical depths were varied in
runs ranging from 0.25 to 64 across the total Y-dimension of the cloud (crosswind width).

Limiting path radiances for uniform concentration clouds were first computed. Several of the
outputs are shown in figs. 1 through 4. In each case the sun is at an azimuth of 90 deg and a
zenith angle of 60 deg. Figure 1 shows L, computed for rays emerging at normal angles from
the centers of each cloud face. Note the large value when the optical depth is low and the cloud
is between the observer and sun. Figures 2 and 3 are other cases for radiance emerging at
various directions from the cloud. Figure 4 plots the output L, emerging across the face of the
cloud, with sun at the right.

For optical depths below about 10 the variation is small. Above x = 10 distinct darkening or
shadowing appears. This behavior has been found to be parameterized by the simple relation

, perimeter , _

s (5)

Lg ( corrected ) ^ L^i uncorrected ) e

which represents the "escape" of solar radiance through the sides of an optically thick cloud as
the product of the physical (not x) distance s from the major radiance source (sun) into the cloud,
divided by the ratio of the cross sectional area of the cloud perpendicular to s to the perimeter
of this area. (Effectively the exponent is thus the area to volume ratio of the cloud up to the
distance s into the cloud.) These curves were then used to approximate the limiting path radiance
L, for non-uniform clouds of the same average optical depth across the cloud. STATBIC was
used to generate these 3-D cloud concentrations, simulating the statistical properties of

Ls for Uniform Cloud Concentration
X:Y:Z= 1:1:1, Solar Az= 90. Zen = 60. Fdir/Fdif = 1 0. Ag = 0.3

Figure 1. Limiting Path Radiance from Center of 6
Cloud Faces, Normal Angles, Showing Solar Angles.

Ls for Uniform Cloud Concentration
X;Y:Z=1 :1 :1 . Solar Az= 90. Zen = 60. Fdir/Fdif =1 0, Ag =0.3

Figure 3. Ls Limiting Path Radiance as in Figs. I,
and 2, But for Upward and Downward Look Angles.

Figure 5. STATBIC Concentrations Used in
Simulations. Cuts are through the x-y-z Planes.

Ls for Uniform Cloud Concentration
X:Y:Z=1 :1 :1 . Solar Az=90. Zen= 60. Fdir/Fdlf =1 0. Ag =0.3

Figure 2. Ls Limiting Path Radiance as in Figure 1,
but for Outgoing Angles at 45 degrees.

Ls Variation Across Cloud with Optical Depth
X: Y:Z= 1:1:1. Solar Az= 90. Zen = 60. Fdir/Fdif = 1 0. Ag = 0.3

Figure 4. Variation in Output Radiance Across a 16
m Cloud with Darkening Shadows at x > 10.

Figure 6. Radiance example Output from
BEAMS run Using STATBIC Inputs.

concentration fluctuations in homogeneous, Kolmogorov turbulence. Figure 5 shows single x-y-z
plane cross-sections through one of the STATBIC-generated input arrays of 3-D concentration
fluctuations. Brighter regions represent greater concentrations.

As baseline cases, the non-uniform concentrations were run directly in BEAMS to obtain their
resulting radiances. Then, for comparison, the same non-uniform concentrations were used, but
with Lj values for fl and the mean across the cloud. Concentration fluctuations lead to opdcal
depth fluctuations x’. So the proposed rapid (but approximate) cloud radiance calculation is just.

^ cloud

(Q) =Lp(Q;T^ + x') « ll-T{Q:x^ + x^) ] L^(Q;xJ

= [ (T fluctuating but Lg mean)

(6)

Figures 6 and 7 compare the outputs of the full calculation for radiance Lp (fig. 6) and limiting
path radiance L3 (fig. 7). Note that the resulting limiting radiance is quite smooth even with
the cloud fluctuations present. This supports the idea that most (but not all) of the fluctuation
is in the transmittance, not L^. Table 1 quantitatively compares the absolute and mean squared
error between method #1 (complete BEAMS calculation for each fractel cloud realization) and
method #2 (the rapid calculation using Lj for a uniform cloud superimposed on transmission
fluctuations) for representative sets of runs. The most relevant values are those for error in L^.
The fluctuations in Lp from both fill calculation and from the approximate result from Eq. 6 are
proportional to the input fluctuations in transmission, as one would expect.

TABLE 1. Error Analysis of BEAMS Output Diffuse Radiance Comparing Fac
Differences in Full Calculation for Non-Uniform Cloud and Using Ls
from Full Calculation for Uniform Cloud and Fluctuating Transmittanc

e-Averaged

Radiance Case

Solar(Az 90, Zen 60); Fdir/

Fdif= 10; Ag=0.3

Ls(Exac

Ls(Fast

Absolute

Error

t Fluct.)-
Param.)

% RMS
Error

Lp(Exact Fluct)-
Lp(Uniform)

% Absolute
Error

Lp(Exact Fluct.)-
Lp(from Ls Table)

% Absolute

Error

T = 0.14 - 0.16, various rays

0.3-0,9%

0.2-0.7%

9-16%

4-6 %

T = 0.234, Az 180, Zen 90

0.86%

0.77%

1.8%

T = 0.56 - 0.66, various rays

1.1 -2.9%

0.9- 1.9%

8-14%

4-8%

T = 0.995, (180, 90) best case

0.83%

0.79%

1.3%

T = 2.2 - 2.6, various rays

4.0-5.8%

3.8-4.5%

6-12%

4-8%

T = 3.75, (180, 90) best case

1.06%

0.91%

1.0%

T = 8.9 - 10.6, various rays

3-10%

2.5-7%

4-10%

5-8%

T = 15., (180, 90) best case

4.0%

2.9%

4.0%

T = 36 - 43, various rays

4-8%

3-8%

4-8%

6-10%

STATBIC Textured
Curved Surface (Cone)

6. USE OF PARAMETRIC RADIANCE VALUES

Scene visualization of obscurant clouds, haze and fog requires both transparency (transmittance)
and color value (path radiance) from realistic, three-dimensional, non-uniform distributions of
aerosol concentrations. In actual simulators the cloud is rendered into a 2-D screen image by a
variety of techniques. A common approach in real-time simulations is to use a two-dimensional
"billboard" picture of a cloud as it would be seen from the current (and perhaps several) observer
positions. This picture-icon is placed as a small, simple scene object which is kept turned toward
the observer and blended to the background by being totally transparent at its edges. BEAMS
can provide color values and opacity through the entire cloud for this approach, although values
should be changed as the "billboard" is turned. A more ambitious approach to giving the cloud
a 3-D presence, as in fig. 8, uses a semi-transparent cloud image wrapped over a 3-D object cloud
"surface" as a semi-transparent texture. A third method uses many small, flat semi-transparent
disks or planes that represent component, textured "puffs" in the cloud. These are distributed
throughout the 3-D volume of the cloud region and turned to face the observer. They usually
overlap so that one perceives the combined color and attenuation of nearer elements in front of
farther ones. This is shown in fig. 9 for a real-time 3-D fly-through simulation of smoke from
an M2 Bradley. Finally, given enough time, one can fully render the most accurate propagation
representation of the cloud as a complete ensemble of semi-transparent volume elements (voxels)
of different optical depths and color values in a 3-D cloud volume.

Whichever approach is used requires two arrays (over a 2-D surface or in a 3-D volume) of
computer graphics parameters. The first is opacity of the cloud (usually rendered in the cpu or
automatically in the graphics hardware as a random dithered matrix of clear pixels or sub-pixel
points mixed in appropriate ratios with opaque colored points). Physically, the graphics opacity
has a complementary relation (opacity = 1 - transparency) to the physical transmittance
(transparency) of the cloud at the given wavelength. The second array is the color value
(typically RGB) of the cloud itself (Lp) or a limiting blending color (LJ. The latter uses opacity
as a linear interpolator between the unobscured background and the totally opaque cloud. In this
case Lj is the color of the cloud when totally opaque. Opacity and are direct inputs to the
Silicon Graphics "fog function", for example, which renders visibility effects using its internally
computed ranges (z-buffer) from the observer to each scene pixel (Hoock and Giever, 1994).

Figure 10 is from a real-time 3-D simulator of an airfield over Ft. Hunter-Liggett terrain. Haze
effects make a similar use of t (determined from visibility and the Koschmieder relation) with
a solar-angle dependent L^. The "monolith" at the end of the runway is a black cube, 100 m on
a side. It has been placed into this 3-D simulation to measure the accuracy of SGI Performer
real-time software to achieve the objective definition of meteorological visibility when given
physically-correct Computer Image Generator (CIG) inputs. This scene is completely analogous
to the test procedures done on the basic SGI GL language "fog function" presented by us at last
years’ BAG conference (Hoock and Giever, 1993). Figure 11 shows various simulations of
reduced visibility due to haze, solar illumination and fog that can be generated using z and Lj
from the BEAMS-2D program. The scene is from a near real-time virtual 3-D representation of
White Sands Missile Range (USGS terrain) and a rendered tank in the foreground.

7. CONCLUSIONS

Rapid progress is taking place in the incorporation of physics-based environmental effects in
interactive, real-time scene simulations of the battlefield environment. One aspect, dealing with
the visualization of obscurants, smoke, dust, haze and fog, is to properly simulate the obscuration
and radiance effects of these clouds on propagation. These effects impact target acquisition,
weapon engagement, identification friend or foe, concealment, deception, visual cues, mobility
and the general "realism" of scenes. The non-real-time BEAMS model is thus being used to
generate propagation data that can be used as data sets to support real-time 3-D synthetic
environment simulations. We have found that it is feasible to combine tabulated (or parametric)
mean values of limiting path radiance L, (dependent on cloud type, mean transmittance and sun
angle) with simulated fluctuations in transmittance about the mean. In the limited case study
done here, the relative error in using this approach over the full (and very time consuming)
multiple scattering calculations for each cloud realization is overall typically under 14%.

ACKNOWLEDGEMENTS

ARL/BED particularly acknowledges support from the Joint Project Office for Smoke/Obscurants
and Special Countermeasures for the development of the BEAMS models. And, under support
from the Defense Modeling and Simulation Office project Environmental Effects for Distributed
Interactive Simulation (E2DIS), the BEAMS model outputs are being analyzed as one of the
environmental representation products that can support real-time and near real-time 3-D scene
visualization of the battlefield environment. The authors thank Mr. Mario Torres of Science and
Technology Corp. and Mr. Steven McGee of Physical Science Laboratory, NMSU for their help
in generating scenes for figures for this paper.

REFERENCES

Ayres, S. and S. DeSutter, 1993. EOSAEL 92: Vol4. Combined Obscuration Model for Battlefield
Induced Contaminants (COMBIC). In press, U.S. Army Research Laboratory, Battlefield
Environment Directorate, White Sands Missile Range, NM 88002-5501.

Davis, B., A. Wetmore, D. Tofsted, R. Shirkey, R. Sutherland and M. Seagraves, 1994. EOSAEL
92: Vol 19. Aerosol Phase Function Database PFNDAT. In press, U.S. Army Research
Laboratory, Battlefield Environment Directorate, White Sands Missile Range, NM 88002.

Hoock, D., 1991. "Modeling Time Dependent Obscuration for Simulated Imaging of Dust and
Smoke Clouds." In Proceedings of the SPIE, SPIE Conference Vol 1486, pp. 164-175.

Hoock D. and J. Giever, 1993. "Methods for Representing the Atmosphere in Interactive Scene
Visualizations." In Proceedings of the 1993 Battlefield Atmospherics Conference, U.S.
Army Research Laboratory, Battlefield Environment Directorate, White Sands Missile
Range, NM 88002-5501, pp 405-419.

Hoock, D., J. Giever and S. O’Brien, 1993. "Battlefield Emission and Multiple Scattering
(BEAMS), a 3-D Inhomogeneous Radiative Transfer Model." In Proceedings of the SPIE,
SPIE Conference Vol 1967, pp 268-277.

Hoock, D. and J. Giever, 1994. "Modeling Effects of Terrain and Illumination on Visibility and
the Visualization of Haze and Aerosols." In Proceedings of the SPIE, SPIE Conference
Vol 2223, pp 450-461.

Miller, A., 1983. Mie Code AGAUS 82, ASL-CR-83-0100-3, U.S. Army Atmospheric Sciences
Laboratory, White Sands Missile Range, NM 88002-5501. (Now in reprints as EOSAEL
92: Vol 1. Mie Code AGAUS).

O’Brien, S., 1993. "Comparison of the BEAMS 2.2 Radiative Transfer Algorithm with other
Radiative Transfer Methods." In Proceedings of the 1993 Battlefield Atmospherics
Conference, U.S. Army Research Laboratory, Battlefield Environment Directorate, White
Sands Missile Range, NM 88002-5501, pp 421-435.

A PORTABLE SYSTEM FOR DATA ASSIMILATION
IN A LIMITED AREA MODEL

Keith D. Sashegyi and Rangarao V. Madala
Naval Research Laboratory
Washington, DC 20375, U.S.A.

Frank H. Ruggiero
Phillips Laboratory
Hanscom AFB, MA 01731, U.S.A.

Sethu Raman

North Carolina State University
Raleigh, NC 27695, U.S.A.

ABSTRACT

A numerical weather prediction system has been developed for assimilating regional
and mesoscale data in a high resolution limited area model. The system, which can be
run on both high performance workstations and super computers, has been used to
study the assimilation of upper air soundings, surface observations, and precipitation
estimates derived from satellite. The model’s grid system is nested in horizontal with
a fine resolution nest covering the area of interest surrounded by two coarser
resolution nests. An efficient iterative analysis scheme is used for interpolating
atmospheric sounding data and surface observations to the model grid. A sequential
coupling of the mass and wind analyses is used for the upper air data outside of the
tropical regions. Surface observations of wind, relative humidity and potential
tempierature are analyzed on the lowest model vertical level. An iterative adjustment of
the surface fluxes, and the winds, temperature and humidity in the planetary
boundary layer then follows. A normal mode initialization with the diabatic heating
derived from observed precipitation is used to balance the initial mass and wind
fields. During the first three hours of a subsequent forecast, the observed diabatic
heating is merged with the model generated diabatic heating. The major impact of the
assimilation scheme is in the enhancement of the mesoscale circulations and
precipitation in the first twelve hours of model forecast. On the workstation, a twelve
hour period of assimilation followed by a 24 hour forecast can be produced within
two hours of clock time when two grids are used.

1. INTRODUCTION

High resolution regional weather prediction models have been successfully used in the past to
study many mesoscale weather systems (Anthes 1990) and recently to provide operationa
forecasts (Benjamin et al. 1991). Now with the introduction of new high temporal and spatial
resolution observing systems such as the Doppler Radar network, automatic surface observing
stations and the GOES I satellite, there will be a dramatic increase in the amount of data available
for the running of high resolution limited area models. The large volume of data produced by these

new remote sensing systems will limit the amount of data that can be included in the operational
weather prediction systems at central weather forecasting centers. A high resolution weather
prediction model run at a local center would be better able to utilize the data from such a local
observing system for producing short range weather forecasts in the local region. Further with the
increasing computational power and memory now available in desktop workstations, it has become
possible to run a quite sophisticated weather prediction system on a workstation. Recently, Cotton
et al. (1994) have demonstrated the running of the Regional Atmospheric Modeling System
(RAMS) on a RISC workstation at Colorado State University. While the detailed cloud
microphysics used in the model improved the forecasts, this was at an increased cost in CPU time
required to run the model. In the very near future with further increases of computational power in
these workstations, it should be possible to run a high resolution weather prediction system on a
workstation at a local site, utilizing the available high resolution data to produce accurate short
range weather forecasts for the local region.

At the Department of Defense there is a great need for accurate short range regional and mesoscale
weather forecasts in support of military operations which can be used in different regions around
the globe. It is envisioned that a portable weather prediction system that can run on a workstation
will be able to produce high resolution short range 3-12 hour forecasts for this purpose. The short
range forecasts of high resolution would compliment the larger scale forecasts which would
available from a central site such as the from the Navy's Operational Global and Regional
Atmospheric Prediction Systems (NOGAPS, NORAPS). These central site data sets, with high
resolution surface conditions (elevation, sea surface temperature, albedo, etc.) and available
observations could be transmitted by satellite from the central site to the local site, such as a Navy

BattleField Weather Forecast System

high performance
workstation

local high resolution
Satellite data

local data

Local site - analysis, forecasts

data, forecasts^
boundary conditions,
high resol. terrain
>1.5 m^abits/sec

Central Site - Global
and regional forecasts

Figure 1. Illustration of future battlefield weather forecast system based on a high performance workstation on a
Naval ship with high speed satellite communications link to on-shore weather forecasting center.

ship (Fig 1) Then utilizing any local observations and high resolution satellite observations, very
high resolution analyses and forecasts could be run on the local high performance workstation to
support the military operations. Such a system will depend on the availability of high speed satellite
communications between the local military operation and the central weather forecasting site.
Several recent trials have demonstrated that transmission speeds of up to 1.5 megabits/sec can be

achieved between a Navy ship and a shore site (Masud 1994 ). With these speeds, the data needed
for the initial conditions and boundary values could easily be rapidly transmitted to the ship within
10 minutes for use on the workstation system.

At the Naval Research Laboratory we have ported a simplified version of our numerical weather
prediction system to a high performance workstation. As a denionstration of the concept of a local
analysis and forecasting system using current technology, the limited-^ea modeling s^tem is run
on the workstation using upper-air data collected during the Genesis of Atlantic Lows Experiment
(GALE) which was conducted over the southeastern U.S. during the winter of 1986. A 12-hour
period prior to the start of the forecast run, is used to assimilate the observations into the model
using an intermittent data assimilation method as in Harms et al. (1992). A 12 hour prediction with
a 10-laver version of the numerical model utilizing a coarse mesh covering the continent^ U.S and
a fine mesh covering the eastern U.S. took 30 mins of CPU time on the workstation. The analysis
component itself used 12 mins of CPU time to produce analyzes at 19 pressure levels with a

hundred soundings.

2. THE ANALYSIS/FORECAST SYSTEM

The intermittent data-assimilation method is used as in Harms et al. (1992) to assimilate upper air
observations during a 12-hour period prior to the start of the forecast. During the assimilation
period, the numerical model forecast provides the first guess or background for the analysis of the
new upper air data at 3-hourly intervals. A diabatic initialization procedure is used to balance the
mass and wind fields. The assimilation is first started from an operational analysis which is
interpolated to the model grids and initialized. After the assimilation, short range predictions of 12-
24 hours are produced.

2.1 Forecast Model

The forecast model used was developed at the Naval Research Laboratory and is described in detail
in the reports by Madala et al. (1987) and Harms et al. (1992). This is a hydrostatic
equations model in terrain-following sigma coordinates with a triple nested grid network in the
horizontal. Spherical coordinates are used in the horizontal, with the mass and momentum
variables staggered on a C grid. The model uses the split-explicit method of time integration
(Madala 1981). The finite-difference scheme in flux form is second-order accmate, and m the
absence of sources and sinks, conserves total mass, energy and momentum. The model uses
horizontal diffusion of second order and includes large-scale precipitation dry convective
adjustment and a modified Kuo cumulus parameterization scheme. A inulti-level planetary
boundary layer utilizes similarity theory in the surface layer and vertical turbulent mixing above
(Gerber et al 1989; Holt et al. 1990). The mixing is modeled using a turbulent kinetic energy
equation (Detering and Etling 1985). The lateral boundary values for the coarse grid are derived
from 12 hourly operational analyses and forecasts by linearly interpolating in time. Boundary
values for an inner grid are provided by the integrations on the coarser grid. The mode variables at
each grid boundary are updated each time step, using the relaxation scheme of Davies (ly /6).

In the version of the model used on the workstation, 10 equally spaced vertical layers are used and
the boundary layer is parameterized using a single layer with the fluxes computed from

* Government Computer News, July II, 1994, pp 45,47.

generalized similarity theory as in Chang (1981). For the demonstration on the workstation two
nested grids are used, where the model's coarse grid covers the continental U.S. from 40° to
140°W and 10° to 70°N with a resolution of 2.0° latitude and 1.5° longitude. The fine grid covers
the eastern U.S. from 58° to 102°W and 23.5° to 56.5°N with a grid spacing a third less than the
coarse grid (of approx. 50 km). For comparison, the full version of the model with the multi-layer
PBL is run on the Cray super computer with 16 layers in the vertical and a third fine mesh. The
third grid run on the Cray covers the south-eastern U.S. from 90°W to 70°W and 29.5°N to 40.5°N
with a resolution of 2/9 degree in longitude and 1/6 degree in latitude (about 20 km).

2.2 Analysis Method

Our analysis method uses the successive corrections scheme of Bratseth (1986), which converges
to the same solution as that obtained by optimum interpolation. Such iterative analysis schemes are
generally more efficient than the optimum interpolation method, which requires solving a large
linear system of equations (Sashegyi and Madala 1994). The Bratseth scheme, in which the
weights are also based on the statistical correlations of the forecast error, is therefore a very
attractive method for use in a portable system to be run on a workstation. This method has been
successfully applied operationally in the multivariate analysis scheme in Norway by Grpnas and
Midtb0 (1987). In our application of the scheme (Sashegyi et al. 1993), univariate analyses of the
mass and wind fields are initially produced. To provide a coupling of the mass and wind fields, the
mass analysis is enhanced using gradient information derived from estimates of the geostrophic
wind. The wind analysis is used to provide the initial estimate of the geostrophic wind. The wind
analysis is then also updated to reflect the new geostrophic wind. The components of the analysis
method are

(a) data preparation and quality control,

(b) univariate analyses of the mass and wind field,

(d) enhancement of the wind field.

The analysis scheme is described in more detail in Sashegyi et al. (1993) and Harms et al. (1992).
We now briefly describe each of these components in turn.

2.2.1 Data preparation and quality control. Sounding data are smoothed in the vertical
and retained at 50 mb levels. The soundings are sorted into 5° latitude-longitude boxes for each
pressure level from 1000 mb to 100 mb. We perform a "gross" check and a simplified "buddy"
check in which ob.servations with large deviations from the first-guess or from neighboring
obseivations are removed. Observations in clo.se proximity of each other are averaged to generate

super observations and any remaining isolated observations are eliminated. If an operational
analysis is available at the time, bogus data derived from the operational analysis can be used in
regions where we have no soundings.

2.2.2 Univariate analyses of the mass and wind field. Univariate analyses of .sea-
level piessure, geopotential the u- and v- wind components and the relative humidity are conducted
on 19 pressure levels at 50 mb steps from 100 mb to 1000 mb, using a 1.5° latitude/longitude grid,
m the successive corrections method of Bratseth (1986), the analysis weights are derived from the
foiecast error covariance, and include a "local data density", which reduces the weights in regions
of higher data density and prevents extrapolation into data void regions (Bratseth 1986). In the
rnethod, the background field is updated by the latest analysis after each iteration or pass, where
the inteipolated value at an analysis grid point after n such iterations is given by

0a,x(n + l) =

j=l

(1)

where <t)o j is one observation at location rj (of a total of J such observations), Wxj is the weight
for that observation and (l)a,x is the analyzed value at a grid point r^. In the previous successive
corrections schemes, the updated analyzed values ^a,x were then interpolated to the observation
locations using a polynomial interpolation method, in order to compute the observation corrections
for the next iteration. Here, an "observation estimate" is computed instead by using the same
interpolating equation as was used for the analyzed values in eq. (1),

^^.(n + l) =

(n)

j = l

‘0 (n)

(2)

A starting guess for the analysis (!)a,x(l) and observation estimate (t)a,j(l) are derived from the
background forecast (|)b by a cubic polynomial interpolation. Instead of using empirical weights as
in earlier schemes, the weights in each equation are defined in terms of the covariance of the
corrections to the background forecast, which are then reduced by dividing by a local data density

w . =
xj Mj

= ^

_ p. -+£ S--

w. . =

IJ M.

where the local data density is defined by

j='

M. J

mj = ^ = Z

CT j=|

n. . + d

(3)

(4)

(5)

(6)

The Px j and pj \ are the values for the correlation function for the true background forecast errors
rp, - (bbl between values at an observation location rj and at a grid point I'x, and between the
values at observation locations rj and rj, respectively. Here we have assumed that the observation
errors are not correlated with the forecast errors. The variance of the background forecast errors is
a^, is the ratio of the observation error variance to the background forecast error variance
o2’and 5ij is the Kronecker delta function (one for i=j, zero otherwise). The error correlation
function p(r) for the mass and humidity is modeled by a Gaussian function.

p(r) = e

-r^/d^

(7)

which is a function of the distance r and the length scale d is 600 km. For the components of the
wind field the correlation functions are reduced across the direction of the flow using

Pu =

(y-yjr

..2

P(l)

(8)

(9)

(x-x )2‘

Pv= 1 - ^ P(0

where du is 700 km and (x,y) and (xi,yj) are the positions of the analysis grid point and the
observation, respectively. After the first three or four iterations the length scales d, dy are reduced
to 330 km and 380 km, respectively, for one additional iteration, to speed convergence of the
scheme (see also Grpnas and Midtbp 1987).

2.2.3 Enhancement of the geopotential gradient. We use the analyzed wind as an
initial estimate of the geostrophic wind, which is then used to extrapolate the geopotential to the
grid point locations for a further iteration of the geopotential analysis, in a fashion similar to
Cressman (1959). That is,

(10)

where V<j)o j is the gradient derived from the horizontal wind at the observation location using the
geostrophic relation. A fixed correlation length scale of 600 km is used for the re-analysis. An
updated geostrophic wind estimate is then defined by the new geopotential gradient. Three further
iterations of the geopotential are used for the geostrophic wind estimate to converge.

2.2.4 Enhancement of the wind gradient. The geostrophic wind changes produced by
the geopotential enhancement are then used to update the univariate wind analysis as in Kistler and
McPherson (1975), where the updated wind is given by

V* = V + Avg (11)

for geostrophic wind changes Avg. Four additional passes of the wind univariate analysis are then
used to enhance the ageostrophic components of the wind.

The final analyzed corrections on pressure surfaces are interpolated to the horizontal model grids
using a cubic polynomial interpolation. Both the background forecast fields and the new analyzed
fields on pressure surfaces are interpolated to the sigma levels of the model. Analysis corrections
are then recomputed on the sigma levels to update the model forecast fields.

2.3 Surface Analysis and Boundary Layer Adjustment

To utilize the large volume of surface observations which are available, analy.ses of potential
temperature, relative humidity and wind are carried out on the model's lowest sigma layer with a
horizontal grid ot 0.5° resolution that covers the domain of the middle grid. For surface pressure,
the observed and model forecast surface pressures are reduced to sea level following a procedure
similar to Benjamin and Miller (1990). In our case we u.se a lapse rate computed from the virtual
temperature at 255 and 105 mb above the surface, extrapolating the virtual temperature to the
surface to detine an effective mean surface temperature. Univariate analyses are then produced for
the lowest model layer as in the upper air analysis de.scribed in section 2.2.2. For the analysis of
sea level pressure, the Gaussian correlation function in eq. (7) is used with a correlation length
scale d of 300 km, as in Miller and Benjamin (1992). For potential temperature, humidity and the u
and V components ot the wind, the Gaussian correlation functions are similarly modified as in
Miller and Benjamin. The potential temperature and wind in the planetary boundary layer are then
adjusted by a forward integration of the vertical diffusion equation for a number of time steps.

2.4 Diabatic Normal Mode Initialization

In this forecasting system, the updated forecast fields are initialized on the coarse and fine grids for
the first three vertical modes of the numerical model as described in Sashegyi and Madala (1993)
using the vertical mode scheme of Bourke and McGregor (1983). For each vertical mode of the
forecast model, the equations for the vorticity divergence D and a generalized geopotential O
are

^ - f C = Ap

ghkD = A^

(12)

(13)

(14)

where h^ is the equivalent depth for the kth vertical mode, f is the Coriolis parameter, g is the
acceleration due to gravity and the terms on the right hand sides of the equations include the non¬
linear advection, friction and cumulus heating. The generalized geopotential is defined by O =
ps[<t> - <l>s + R T* - (j)*], where ps is the surface pressure, 0 the geopotential, ([is the surface
geopotential, T* and a mean temperature and geopotential profile. The filtering conditions used
to remove the fast inertia-gravity waves are

£D ^ ^(f?- , 0 (15)

<9t ^t

with the further condition that the linearized potential vorticity ^ - f 0/(ghk) is unchanged by the
procedure. The amplitude of the inertia-gravity modes depends only on the divergence D and the
ageostrophic "vorticity" f C - and setting their tendencies zero initially, effectively removes
the inertia-gravity waves. In applying these conditions for the first three vertical modes, the schenae
is solved iteratively. This and other methods which can be used to apply normal mode initialization
to a limited area model are further discussed in Sashegyi and Madala (1994).

For the initialization on the fine grid, boundary values for the mass field and the tangential wind are
updated using the results of the initialization on the coarse grid. As in H^ms et al. (1992; 1993)
diabatic forcing is included as a fixed forcing function in the initialization, where the diabatic
heating rates are computed from a merged field of observed and model- produced rainfall. A
reverse Kuo cumulus parameterization scheme is used to convert these prescribed rain rates into
vertical heating profiles in regions where the lower atmosphere is convectively unstable. Dunng the
first three hours of a subsequent forecast, the prescribed heating rates (used in the initialization)^^
linearly combined with the model generated heating rates. The weighting factor for the prescribed
heating rate is one initially and decreases as a sine function to zero after three hours of integration
(Harms et al. 1993).

3. DISCUSSION

As an example, three hourly upper air soundings, which were collected during the second
Intensive Observing Period (lOP) of GALE, were used to generate analyses and forecasts with our
analysis/forecast system on the workstation. The 1(X)0 mb analysis for 1200 UTC 25 January
1986 is shown in Fig. 2. The cold air damming and the strong temperature gradient along the East
Coast, which were generated by the first guess forecast, are retained in the analysis of the upper air

Figure 2. The analyzed 1000 mb temperature, winds and sea-level pressure for 12 UTC 25 January 1986. The solid
contours of sea level pressure are every 4 mb, dashed contours of temperature every 5°C, and vectors indicate the
direction and magnitude of the winds.

Figure 3. The analyzed sea level pressure, temperature and winds at the lowest model level for 6 UTC 25 January.
Contours as in Fig. 2.

soundings. The low over the Great Lakes, which was too weak in the first guess (Sashegyi et al.
1993) was corrected by the analysis. The surface analysis produced by the 16 layer Cray version
of the model is shown in Fig. 3 for 0600 UTC 25 January as the strong temperature gradient was
developing across the coastline. On this higher resolution grid, the confluence of the flow and the
temperature gradient along the coastline are stronger, but the general features were similar to that
produced on the coarser grid on the workstation. The prediction of rainfall during the first 12-
hours of integration is much improved by using the 12-hour period of assimilation prior to running
the forecast (Harms et al. 1992). In Fig. 4, the rainfall seen over the Carolinas and across the
Florida panhandle in the first six hours of the forecast was produced as a result of the assimilation
on the workstation.

4. CONCLUSIONS

An analysis/forecasting system with a 12-hour period of assiimlation prior to the running of a
forecast was run on a high performance workstation. The intermittent scheme with 3 hour updates
successfully assimilates upper air observations, maintaining the ageostrophic circulations generated
by the forecast model. A higher resolution planetary boundary layer, a third horizontal grid of finer
resolution and a surface analysis in the full model were run on the Cray super computer for
comparison. With 10 layers used in the vertical and quite a coarse operational analysis used as the
starting point for the assimilation, good results were achieved in a reasonable CPU time of two
hours on the workstation when compared with the full run on the Cray.

Figure 4. Six hour forecast of accumulated precipitation in cm valid at 6 UTC 26 January 1986. Contours 0.1 cm,
every 0.25 cm up to 1.0 cm and then every 0.5 cm.

ACKNOWLEDGMENTS

Support for this research was provided by SPAWAR of the U.S. Navy and by basic research programs

at the Naval Research Laboratory. The computing was supported in part by a grant of HPC time from

the DOD HPC Shared Resource Center for use on the Cray YMP-EL at NRL.

REFERENCES

Anthes, R.A., 1990: "Recent applications of the Penn State/NCAR mesoscale model to synoptic,
mesoscale and climate studies." Bull. Amer. Meteor. Soc., 71, 1610-1629.

Benjamin, S.G., K.A. Brewster, R. Brammer, B.F. Jewett, T.W. Schlatter, T.L. Smith and P.A. Stamus,
1991: "An isentropic three-hourly data assimilation system using ACARS aircraft observations."
Mon. Wea. Rev., 119, 888-906.

Benjamin, S.G. and P.A. Miller, 1990: "An alternative sea level pressure reduction and a statistical
comparison of geostrophic wind estimates with observed surface winds." Mon. Wea. Rev. 188
2099-2116.

Bourke, W., and J.L. McGregor, 1983: "A nonlinear vertical mode initialization scheme for a limited
area prediction model." Mon. Wea. Rev., Ill, 2285-2297.

Bratseth, A.M., 1986: "Statistical interpolation by means of successive corrections." Tellus,38A, 439-
447.

Chang, S.W., 1981: "Test of a planetary boundary-layer parameterization based on a generalized
similarity theory in tropical cyclone models." Mon. Wea. Rev., 109, 843-853.

Cotton, W.R., G. Thompson and P.W. Mielke Jr., 1994: "Real-Time mesoscale prediction on
workstations." Bull. Amer. Meteor. Soc., 75, 349-363.

Cressman, G. 1959: "An operational objective analysis system." Mon. Wea. Rev., 87, 367-374.

Davies, H.C.., 1976: "A lateral boundary formulation for multi-level prediction models." Quart. J.
Roy. Meteor. Soc., 102, 405-418.

Detering, H.W. and D. Etling, 1985: "Application of the E-eps turbulence model to the atmospheric
boundary layer." Bound.-Layer MeteoroL, 33, 113-133.

Gerber, H.S., S.W. Chang and T.R. Holt, 1989: "Evolution of a marine boundary layer jet." J. Atmos.
Sci.,46, 1312-1326.

Gr0nas, S. and K.H. Midtb0, 1987: "Operational multivariate analyses by successive corrections."
Collection of papers presented at WMO/IUGG numerical weather prediction symposium, Tokyo,
4-8 August 1986, J. Meteor. Soc. Japan, 61-74.

Harms, D.E., K.D. Sashegyi, R.V. Madala, and S. Raman, 1992: Four-dimensional data assimilation
of GALE data using a multivariate analysis scheme and a mesoscale model with diabatic
initialization. NRL Memo. Rep. No. 7147, Naval Research Laboratory, Washington, D.C., 219dd
[NTIS A256063].

Harms, D.E., R.V. Madala, S. Raman and K.D. Sashegyi, 1993: "Diabatic initialization tests using the
Naval Research Laboratory limited area numerical weather prediction model." Mon. Wea Rev
121, 3184-3190.

Holt, T.R., S.W. Chang and S. Raman, 1990: "A numerical study of the coastal cyclogenesis in GALE
lOP 2: Sensitivity to PBL parameterization." Mon. Wea. Rev., 118, 234-257.

Kistler, R.E., and R.D McPherson, 1975: "On the use of a local wind correction technique in four¬
dimensional data assimilation." Mon. Wea. Rev., 103, 445-449.

Madala, R.V., 1981: "Efficient time integration schemes for atmosphere and ocean models." Finite
D^erence Techniques for Vectorized Fluid Dynamic Calculations, Chpt. 4, Springer Verlag, pp

Miller, P.A. and S.G. Benjamin, 1992: "A system for the hourly assimilation of surface observations
in mountainous and flat terrain." Mon. Wea. Rev., 120, 2342-2359.

Sashegyi, K.D, D.E. Harms, R.V. Madala, and S. Raman, 1993: "Application of the Bratseth scheme
for the analysis of GALE data using a mesoscale model." Mon. Wea. Rev., 121, 2331-2350.

Sashegyi, K.D. and R.V. Madala, 1994: "Initial Conditions and Boundary Conditions." Mesoscale
Modeling of the Atmosphere, Meteorological Monographs, Vol. 25, No. 47, Chpt. 1, Amer.

Meteor. Soc., pp 1-12.

Sashegyi, K.D. and R.V. Madala, 1993: "Application of vertical-mode initialization to a limited-area
model in flux form." Mon. Wea. Rev., 121, 207-220.

EFFECT OF HIGH-RESOLUTION
ATMOSPHERIC MODELS ON WARGAME SIMULATIONS

Scarlett D. Ayres

Battlefield Environment Directorate
U.S. Army Research Laboratory
White Sands Missile Range, New Mexico 88002-5501

ABSTRACT

Battlefield weather conditions have affected, sometimes determined, the outcome
of military conflicts and the resultant global order for generations. An ^ea of
continuing concern for military strategists, the operations research co^unity, an
soldiers throughout history has been atmospheric variability ^ its impact on the
battlefield. The Combat-Induced Atmospheric Obscurants (CIAO) systeni is a
prototype computer-based atmosphere modeling and simulation system designed
to demLtrate the impact of the effects of advanced high-resolution atmo^henc
models on force-on-force wargame simulations, such as the Combined Aims an
Support Task Force Evaluation Model; thus, impacting tactics and doctrine derived
from the simulations. Wargames use low-resolution atmospheric models that tend
to ignore some of the more realistic effects of the battlefield environment and
weather that could prove highly significant on the wargame outcome. In the past
this limitation was necessary because of computer restrictions
unavailability of appropriate atmospheric models. The goal of _

the CIAO system is to determine the impact of advanced high-fidelity, hig
resolution obscuration models on simulated battles. A poster/paper was presented
at the 1994 Battlefield Atmospherics Conference detailing the p^ose ot the
models included in CIAO. This paper illustrates the expected effect ot these
models on the wargame.

1. INTRODUCTION

The Combined Arms and Support Task Force Evaluation Model (CASTFOMM) deals with a
nlane-narallel atmosphere with wind varying neither with direction nor speed m the horizontal
directiL Terrain cm cause a nonlinear inertial character of the flow interacting with temin

surface. Terrain sheltering and channeling, wakes and flow separation f

A r’A^TFORFM uses a fairly detailed smoke model (Combined Ubscuraiion

Ldf foSefieS In™ftonS« (Ayres, DeSutter 1993)) to determine

St^rlriotCsrnission caused by smoke. However. COMBIC uses a ata^nc

boundarv layer model. The wind field direction and horizontal windspeed profile m COMBIC
^“SSrJL ^dTmic everywhere in the scenario. Wind fields and diftaon ~
hv the effects of complex terrain and surface properties in the real world. COMBIC is a tla
ten-ain model It allows only a uniform boundary layer wind field that is assimed to apply over
the entire geographic region. COMBIC smoke flows through hills instead of over and aroimd
them The CIAO system adds high-resolution atmosphenc and modified smoke models to the

CASTFOREM wargames to more realistically simulate the battlefield atmosphere. The models
discussed are SANDIA, High-Resolution Wind (HRW), Onion Skin, and Radiative Energy
Balance Redistribution (REBAR), and the radiative transfer (RT) and polarimetric millimeter
(PMW) version of COMBIC.

2. CASTFOREM

CASTFOREM is a high-resolution, two-sided, force-on-force, stochastic, event-sequenced,
systemic simulation of a combined arms conflict (Mackey et al. 1992). CASTFOREM represents
tactics through the use of decision tables, and it embeds an expert system for battlefield control.
Battle orchestration up to the battalion level is accomplished strictly through the use of decision
tables. CASTFOREM provides extensive line-of-sight (LOS) calculations along various observer-
to-target directions, accounting for terrain, elevation, and vegetation. CASTFOREM also
accounts for intervening atmospheric conditions that can include effects of combat induced
obscurants through the use of the COMBIC model. Digitized terrain is included but is not, at
present, coupled with the meteorological conditions. The original CASTFOREM assumes
homogeneous weather, considered constant in time and space.

3. IMPROVEMENTS
3.1 Effects of HRW

Modifying COMBIC for complex terrain would greatly increase the run time, a fact that could
adversely affect CASTFOREM users. Instead of using a simple wind model with a complex
smoke model, it was decided to use a complex wind model with a simplified smoke model. The
HRW model developed at the Army Research Laboratory can be used to determine wind fields
and, in conjuriction with rudimentary smoke clouds produced by the SANDIA and Onion Skin
models, examines the effects that terrain-induced wind fields can have on the modern battlefield.
The HRW model is a high-resolution micro-alpha scale, two-dimensional, surface layer wind and
temperature model (Cionco 1985; Cionco, Byers 1993). The model supplies high-resolution
calculations of surface layer wind, temperature, and turbulence parameters at selected grid points
over a limited area, considering both the terrain topography and thermal structure. SANDIA and
Onion Skin are highly parameterized smoke obscuration models. SANDIA treats smoke as binary
entities in either of two electro-optical (EO) bandpasses (visible and infrared) (Sutherland,
B^s 1986). Onion Skin models smoke as if it were layered like an onion. SANDIA and Onion
Skin are easily modified to have the smoke clouds follow the wind streamlines, as determined
by HRW, for a particular terrain. The affect on wargaming can be seen in figure 1. Figure la
represents the prevalent smoke representation in CASTFOREM. The smoke blows in a constant
direction, unmodified by the existing terrain. Figure lb illustrates the affect on wargaming when
SANDIA, combined with HRW, produces smoke clouds that follow the complex wind field. The
LOS is obscured by smoke; whereas, it is not obscured in figure la. The smoke follows the
complex wind field when HRW is included; thus, changing position changes the effectiveness of
the smoke screen.

Figure 1. The simplified smoke model (a) allows smoke to blow in one direction.
An advanced atmospheric model like HRW (b) allows smoke to flow with the
complex wind field generated from the complex terrain data.

SANDIA was modified to compute the location of the clouds by utilizing the complex wind fields
generated by HRW. The wind fields are computed for a height of 10 m from the terrain surface.
The standard windspeed profile was used to allow the windspeed to vary with height. The profile
is defined as follows:

l^(z) _

K^refJ

(1)

where

z = height above terrain
z,gf = 10 m

p = windspeed at height z and at 10 m.

P depends on surface roughness and stability.

CASTf OREM passes the initial position of the smoke cloud, the LOS information, and the time
into SANDIA. SANDIA determines the size and new position of the cloud using HRW complex
wind fields. For a given threshold, SANDIA determines if the LOS intersects a cloud and is
defeated. HRW can be run off line to generate the wind field output used to compute the
updated cloud location and size. The updates are used to determine if the LOSs are affected by
the clouds, which saves computer time, an important consideration to CASTFOREM users
SANDIA was ascertained to be twice as fast as COMBIC. The addition of the algorithm to
compute complex wind driven smoke clouds should not slow CASTFOREM.

3.2 Effects of Adding Elevation Data to Smoke Models

CASTFOREM uses an algorithm that determines if the LOS can acquire the target through the
complex terrain, if so, the LOS is passed into the COMBIC or SANDIA smoke model to
determine if the LOS is obscured. The smoke models are utilized as if all the smoke, observers,
and targets are on the same level. The height of the LOS is the height of the sensor above the
ground. However, the observer might be on one hill, the target on another, and the smoke in a
valley between them in a complex terrain scenario. Figure 2 illustrates the effect of complex
terrain on an obscured scenario. Figure 2a presents the normal way of modeling an obscured
battlefield scenario. Figure 2b presents the new methodology for taking terrain elevation into
account. Figure 2b shows that the tank acquires the target because the LOS passes above the
smoke to reach the targets on the hill.

3.3 Effects of Onion Skin-HRW (OS-HRW)

The Onion Skin model is an extension of the SANDIA model previously described; however, the
smoke clouds are not modeled as binary entities, but are resolved into layers representing various
thresholds of optical thickness. Thus, clouds can be played at a higher resolution without much
loss in computational speed. Another advantage is that the cumulative effects of multiple clouds
OS-HRW; whereas, only the binary option for a single cloud can be used with
SANDIA. As with SANDIA, the OS-HRW approach can be made much more compatible with
complex wind models such as HRW. Figure 3 illustrates the Onion Skin concept. Figure 3a

Figure 2. The prevalent methodology in modeling smoke is to pretend the terrain has
no elevation (a). CIAO includes an enhancement that alloivs elevation to be included
in modeling smoke by COMBIC and SANDIA (b).

ONION SKIN MODEL (b)

Figure 3. The LOS does not encounter the SANDIA produced clouds (ellipses) (a) so it
is not defeated. The LOS goes through enough outer edges of the Onion Skin produced
clouds (b) so it is defeated.

shows SANDIA produced clouds specified by an optical depth of three. Optical depA is the
product of the mass extinction coefficient md concentration length (CL). The LOS from the
observer to the target is not defeated because it does not go through any part of the cloud
(represented by the ellipses). However, figure 3b shows that the LOS goes trough enou^^ter
layers of the onion-like cloud to build up an optical depth of three to be defeated, OS-HRW
model increases the number of LOSs obscured in wargames, as compared to the SANDIA model,
which means increased survivability for the targets.

3.4 Effects of REBAR

A large area smoke screen (LASS) that endures a long time can significantly ^
irradiance and drive the local atmosphere toward more stable conditions (Yee, Sutherland 1993).
Smoke operations can be affected because the rise and diffusion of smoke is critically dependent
upon the stability class. Figure 4 shows how critical the Pasquill Category (PC) is m detemming
the height and width of the cloud. The stability of the atmosphere is related to PC as follow:
PC = A, extremely unstable; PC = B, moderately unstable; PC — C slightly unstable, PC ,
neutral; PC = E, slightly stable; PC = F, moderately stable; and PC = G, extremely stable. Note
how width and height increase with increasing instability. The overall concentration decreases
as the cloud increases with size. Thus, any factor influencing the stability should be modeled in
the wargames.

Cloud DM«u«lvti width (2.16 <r)

Par Dilterent PssquUl C»tegari«* ^

Cloud DiffuaivB Height (2.16 .

for Different Paequill CategorlM (O)

Cloud Wldtft <m)

300 400 BOO BOO TOO

Downwind OIttinoe (m)

■i Cat P

y//A Cat c

i\X!^ Cat B

O Cat a

CZ] Cato
^ Cat A

900 1G00

Cat r
Cat C

Cat E
r ] Cat 3

I Cat D
^ Cat A

Figure 4. Width (a) and height (b) of smoke clouds versus downwind distance for
different Pasquill Stabilities.

Figure 5 illustrates the effect of aerosol-induced radiative damping on turbulence and Pasquill
Stability for different optical depths (x). Figure 5a shows that thicker smoke clouds tend to
prevent the atmosphere from becoming more turbulent. Similarly, figure 5b shows the effect of
smoke clouds in increasing the stability of the atmosphere as modeled using the REBAR model.
Notice that at noon (solar elevation = 90) an unsmoked atmosphere would be extremely unstable
(PC = 1). However, the atmosphere becomes only slightly unstable when a dense LASS is
present. The REBAR model is the first attempt to model this important aspect of LASSs. The

CIAO system will use REBAR to determine the impact of radiative damping on the battlefield.
Significant reductions in the amount of smoke are expected to occur if the wargamc developers
^e aware of the depressed conditions caused by the LASS because neutral conditions are often
ideal for smoke deployment and a desired smoke screen can be maintained with less smoke. If
the warg^e developers are not aware that less smoke is necessary, the battlefield might be over
smoked, inhibiting target acquisition on both sides. Inhibition of target acquisition might be
adv^tageous to the side modeled with the best ability to observe through an obscured
environment. A smart commander might create a LASS in the early morning to inhibit the
development of turbulence.

3.5 Effects of COMBIC-RT

Models like CASTFOREM directly relate transmission to EO system performance and smoke
effectiveness by considering only the directly transmitted signal. However, EO systems respond
not only to directly transmitted radiation but also to contrast. The contribution caused by path
radiance, which may be caused by scattering of ambient radiation (sun, sky) into the path of
propagation, emission along the path, or both, must be determined to determine contrast. Path
radiance has a directional nature causing asymmetries to exist between target and observer. The
target or observer has an optical advantage not present when only the direct transmission
component is modeled. The LASS model was developed to model the effects. The radiative
transfer algorithms were integrated with COMBIC-RT to enable COMBIC to compute path
radiance.

Most target acquisition models work by determining the number of resolvable cycles across the
target, which directly relates to the target contrast at the aperture of the nonthermal sensor. It
is possible to determine the probability of acquisition of a given target through a LASS cloud
at any given point in space and time using COMBIC-RT and a target acquisition model like the
one in CASTFOREM (Ayres, Sutherland 1994), providing a direct measure of the effectiveness
of smoke. Figure 6 shows the effect of sun angle on detection probabilities for different optical
depths (t). The probability of detection for t of 1 varies from 34 percent for the sun in front
of the observer to 63 percent for the sun behind the observer, as expected. A force with the sun
behind it has a tactical advantage.

Figure 7 shows the affect that the observer azimuth angle (defined clockwise with respect to
North) can have on contrast transmission. Contrast transmission is shown for five CL values.
The scenario is for early morning and the zenith angle of the observer is 10 . Notice that low
contrast transmission occurs when the observer is looking into the sun (0°) and high contrast
transmission occurs with the sun to the back (180°) of the observer. Further, note that the curve
flattens as the CL increases. The degree to which a force with the sun in their opponent’s eyes,
has a tactical advantage, can depend upon the density of the LASS. However, it must be noted
that very thick clouds can reflect all light and cause inverse situations.

PHOTOSIMULATION EXPERIMENT
LASS MODEL RESULTS

100

*to

:: 'JO

■ 63^0 \

1/^ ... . \

S “

• <0

- ^ \

AVf\

•

. 0 \

SUM rc rno^jT

—

, 1 '

_ j .i_ .. 1 . 1 . 1 .. i. .t. j

0 0.9 1.

1 s _ l

EFFECT OF SUN ANGLE J

jutj ro r?t?4 n

Oetieai 0*«tn

Figure 6. Plot of detection probability as a
function of optical depth for various solar
azimuth angles.

CONTRAST TRANSMISSION

FOR DIFFERENT OBSERVER AZIMUTH ANGLE

150
- CL • .09

Figure 7. Plot contrast transmission versus
observer azimuth angles.

3.6 Effects of COMBIC-PMW

Perhaps the greatest single parameter describing the effectiveness of an obscurant is the mass
extinction coefficient. The mass extinction coefficient is used in Beer’s law, along with the path
integrated concentration, to determine the obscurant optical depth and, hence, transmission for
a specified wavelength. The mass extinction coefficient lumps together electromagnetic (EM)
radiation scattering out of the LOS and EM absorption radiation along the LOS. The mass
extinction coefficient is used by smoke models, such as COMBIC, to determine degradation of

the atmosphere caused by battlefield obscurants. The COMBIC model was originally developed
for Electro-Optical Systems of Atmospheric Effects Library (EOSAEL) to model aerosols for
which spherical symmetry can be assumed to describe the physical and optical properties of the
aerosols. Whereas this is a reasonable assumption when considering the older, conventional
obscurants such as fog oil and white phosphorus, the approximation breaks down for newer
developmental obscurants designed to be effective at longer wavelengths. Many of the new
millinieter wave (MMW) and radar obscurants are highly nonspherical. The propagation of EM
radiation m any medium containing particles is governed by the combination of absorption,
emission, and scattering, making the particles a subject of great importance in determining effects
ot obscurants on EM radiation. Scattering and absorption by particles depend upon the size,
shye, refractive index, and concentration of the particles. Mathematically determining the
radiation field scattered by particles of arbitrary shape at any point in space can be quite difficult.
Exact Malytical solutions ^e only available for the sphere and infinite cylinder. However, the
scattering properties of simple geometries have been well studied (Bowman et al. 1987)
Numerical techniques and approximate analytical methods are used to analyze the properties^
usually, over a limited range of conditions. New techniques are required to model the obscurants

COMBIC-PMW (Ayres et al. 1994), which is a merger between
LUMBIC and the techniques that account for the optical and mechanical behavior of finite
cylinders. The techniques determine EM properties, such as the ensemble orientation averaged
extinction, absorption, and scattering, as well as meehanical properties, such as fall velocity and

angular orientation of the obscurant particles when released into the turbulent atmospheric
boundary layer.

Extinction for MMW obscurants can widely vary depending upon the fiber properties Such
intrinsic particle properties like shape and bulk density, and bulk EM properties (complex indices
ot refraction) must be determined for accurate extinction determination. Furthermore, ensemble
characteristics such as orientation distribution of the obscurant cloud and incident beam properties

polarization must also be included. Orientation distribution is
needed because particle scattering phase functions and attenuation can depend strongly on the
orientation of the particle relative to the polarization of the illuminating radiation. Also, the
direction of the LOS can be of significance in determining obscurant effectiveness, although
current wargames use one value for obscurant extinction per scenario. For example there can
be deferences in extinction for horizontal and vertical LOSs when particles are preferentially
oriented. The vertical LOS is exposed to this preferred orientation, while the horizontal LOS is
exposed to a randomly oriented ensemble if the particles are released in a stable atmosphere and
oriented vnth their long axis horizontal. All the characteristics affect the computation of
extinction for cylindrical obscurants, which can affect the loss-exchange-ratios used to describe
the results of the wargame.

4. SUMMARY

Batfiefield weather eonditions have affected, sometimes determined, the outcome of military
conflicts and the resultant global order for generations. Accounting for atmospheric variability
has been ^ ^ea of continuing concern for military strategists, planners, and soldiers throughout
history. With the emphasis on restructuring the Armed Forces into a streamlined fighting force
equipped with advanced technology weapon systems, it is necessary to develop tactics, doctrine
and weapon systems to minimize friendly and collateral casualties while destroying the enemy’s
capability to fight. Obscurants can be a very important tool on the battlefield. Obscurants are

often described as low technological countermeasures to the high technological weapons of today.

It is imperative that the technological tools can realistically depict the battlefield with accurate
physics and engineering algorithms. This paper shows that the effectiveness of obscurants is
influenced in many ways by the atmosphere; therefore, better atmospheric algonthms must be
included in the wargames that define so much of the tactics and doctrine. The CIAO system is
an important part of the effort to produce a better atmospheric algorithm. In p^icular, the
improved algorithm includes: (a) terrain effects on smoke transport, (b) contrast effects caused
by multiple scattering, and (c) polarimetric effects of nonspherical particles.

ACKNOWLEDGMENTS

The author would like to thank Robert Sutherland and Doug Sheets of the Battlefield
Environment Directorate, Army Research Laboratory and Steve LaMotte of the Physical Sciences
Laboratory, NM for their advice and assistance in developing the CIAO model.

REFERENCES

Ayres S. D., and S. DeSutter, 1993. Combined Obscuration Model for Battlefield Induced
Contaminants (COMBIC) Users Guide. In Press, Department of the Army, U.S. Army Research
Laboratory, Battlefield Environment Directorate, White Sands Missile Range.

Avres S D and R A. Sutherland, 1994. "Combined Obscuration Model for Battlefield Induced
cLaminants-Radiative Transfer Version (COMBIC-RT)." In 1994 Battlefield Atmospherics
Conference, In Press, Department of the Army, U.S. Army Research Laboratory, Battlefield
Environment Directorate, White Sands Missile Range, NM 88002-5501.

Ayres, S. D., R. A. Sutherland, and J. B. Millard, 1994. "Combined Obscuration Model for
Battlefield Induced Contaminants-Polarimetric Millimeter Wave Version (COMBIC-PMW).
In 1994 Battlefield Atmospherics Conference, In Press, Department of the Army, U.S. Army
Research Laboratory, Battlefield Environment Directorate, White Sands Missile Range, NM
88002-5501.

Bowman, J. J., T. B. A. Senior, and P. L. E. Uslenghi, 1987. Electromagnetic and Acoustic
Scattering by Simple Shapes. Hemisphere Publishing Corporation, ISBN 0-89116-885-0.

Cionco, R. M., 1985. Modeling Windfields and Surface Layer Wind Profiles Over Complex
Terrain and Within Vegetative Canopies. The Forest- Atmosphere Interaction. Editors: Hutchison
and Hicks. D. Reidel Publishing Co., Holland.

Cionco, R. M., and J. H. Byers, 1993. "A Method for Visualizing the Effects of Terrain and
Wind Upon Battlefield Operations." In Proceedings of 1 993 Battlefield Atmospherics Conference.
U.S. Army Research Laboratory, Battlefield Environment Directorate, White Sands Missile
Range, NM 88002-5501.

Mackey, D. C., Dixon, D. S., Jensen, K. G., Loncarich, and J. T. Swaim, 1992. CASTFOREM
(Combined Arms and Support Task Force Evaluation Model) Update: Methodologies. U.S. Army
TRADOC Technical Report TRAC-WSMR-TD-92-011.

Sutherland, R. A., and D. E. Banks, 1986. "Smoke Modeling in the Trasana Wargames-The
Comprehensive Smoke Study." In Smoke Symposium X, Volume 1, pp. 259-268, Aberdeen
Proving Ground, MD.

Yee, Y. P, and R. A. Sutherland, 1993. "The Radiative Energy Balance and Redistribution Model,
REBAR." In Proceedings of the 1993 Battlefield Atmospherics Conference. U.S. Army Research
Laboratory, Battlefield Environment Directorate, White Sands Missile Range, NM 88002-5501 .

AN ASSESSMENT OF THE POTENTIAL OF THE METEOROLOGICAL OFFICE
MESOSCALE MODEL FOR PREDICTING ARTILLERY BALLISTIC MESSAGES

Jonathan D Turton, Peter F Davies
Defence Services Division, Meteorological Office
Bracknell, Berkshire, RG12 2SZ, UK
and

Maj. Tim G Wilson

Developments Division, HQ Director Royal Artillery, Larkhill, Salisbury, Wilts, SP4 8QT, UK

ABSTRACT

An assessment of the potential of using data from the Met Office Mesoscale Unified N^el
(MM) for producing artillery ballistic messages was made by the Royal Artillery and the
Meteorological Office, Defence Services during Summer 1994. This paper reports the
results of this assessment.

MM forecasts of vertical profiles for LarkhiU were compared with routine radiosonde
ascents made at Larkhill. Specifically, wind and temperature data were compared over the
various height zones used in artillery ballistics. In addition, both the MM data and the
measurements were applied in a ballistic model to evaluate the likely impact on gunnery
accuracy that would be achieved using MM data for meteorological corrections.

The implications of the results of this assessment are discussed in terms of the potential
use of the MM for making routine ballistic forecasts for (i) artillery training ranges m the
UK and (ii) incorporating model predictions into future Royal Artillery battlefield
meteorological systems.

1. INTRODUCTION

TTie motivation for this work is two-fold. Firstly, Larkhill met office is responsible for providing balli^c
meteorology for a number of Army training ranges around the UK. Many of these ranges are distant from
Larkhill and artillery meteorological (artymet) soundings are seldom made. The only available upper ^
data comes from those trials ranges with an on-site met office and the UK Met Office upper air ne^ork.
Thus there is often no measured upper air data for tiie training ranges, from which to detemme ballistic
messages (SBMM, SCMM). However, the Larkhill met office, through their meteorological informafron
system, the Outstation Display System (ODS), do have access to all the observational data that is avail^le
together witii forecast upper air winds and temperatures at WMO standard levels from the Imuted area
vereion of the Met Office Unified Model (Cullen, 1993). Consequently, the accuracy of these ballistic
messages is very dependent upon the skill of the Larkhill forecasters and their ability to mterprrt tte
available data. It is postulated that the provision of site-specific mesoscale model profiles for tiie UK
training areas could provide valuable information to assist in this task.

Secondly, &ere is the potential application of mesoscale models to enhance the accuracy of in-theatte
meteorological information obtained using the Royal ArtUleiy BMETS (Battlefield METeorological
System), or to improve the quahty of ballistic information when there are insufSciait BMETS deployed
Typically, atmospheric conditions account for some 30% - 70% (depending upon range) of the total error
budget for the accuracy of artillery fire and, as longer range artillery pieces come into service the
requirement for better meteorological data becomes more critical. In the future it is anticipati that
mesoscale models, or rather battlefield-scale models, will be used to provide optimum hattlpfifld
meteorology for artilleiy purposes, lie use of such models is already being investigated by the US Army
^ (Computer Assisted Artillery Meteorology) program (Grunwald, 1993; Spalding et

al, 1993). A possible fiiture concept for UK artymet, CMETS (Computerised METeorological System)
may well embrace this approach and so this woric is a useful precursor to any CMETS studies.

2. RADIOSONDE DATA

m radiosonde currently used at aU UK upper air sounding stations and ranges is lie Vaisala

re^ra ^sto. Tie PC.Cora system has been described by Nash (1991) and uses the standard Vaisala
KS8Q sonde for temperature and humidity measurements. At Larkhdl the winds are determined by traddna
a radar target using a Cossor 353C wind-finding radar. Soundings are usually made several times a day
(typically m summer around 06Z and lOZ), with additional ascents being made as required. The RS80
^perature and humidity sensors have an accuracy of ±0.2 °C and ±2% and give measurements eveiy 2 s
(«10 m) from launch. Wmds at Larkhill are computed from 30 s of radar tracking data, with reported
values updated at 2 s mtervals during flight. Previous studies (Edge et al., 1986) of the reproducibility of
Cossor radar wmds have found that the rms vector errors attributable to the radar are about 0 4 m/s at 20

^ Thus the wind errors are typically 0.4 m/s up to

9000 m height, mcreasmg to 0. 8 m/s at 20000 m height. ^

In this study, archived 2 s data were used to compute winds and temperatures for the ballistic zones These
zones are pven m Table 1. Wmds were computed as mean winds through the zones whilst the pressures
and virtu^ t^peratures were for the zone mid points. The PC-Coia systems at the range stations have
software to pr^uce specialised artillery ballistic data, i.e. Standard Ballistic Met Messages
(SBMM) and Standard Artillery Computer Met Messages (SCMM).

3. THE MET OFFICE MESOSCALE MODEL

The Met Office Mesoscale Unified Model (MM) is integrated within the operational Unified Model (UM)
suite which IS run routinely at the Met Office, Bracknell. The suite, which is described by CuUen (1993)
consists of global, hrmted area" and mesoscale versions of the Unified Model. The global version has 19
yeffical levels up to 4.6 mb (typically 35-40 km) with a horizontal resolution of 0.83® in latitude and 1 25®
m longitude (giv^ a typical grid spacing of about 90 km). The "limited area" model covers an area ‘
ej^nduig from North ^enca m the west to Russia in the east, covering Greenland to the north and North

^^^(^out 5^1^^ ^ horizontal grid length

TTie mesoscale version of the model has a grid length of 0. 15® (about 17 km) on a 92x92 grid covering an
Mea of ^ut^OO lmxl500 km and has 31 levels up to 4.6 mb, with an increased number of levels in the
oposphere. These levels are shown m Table 2, the heights are approximate since the levels are defined

using a hybrid sigma/pressure co-ordinate system. There are 28 levels up to 20000 m as normally r^uired
for artilleiy ballistics. The MM can be run for a number of relocateable ar^, with a standard version
being run for the UK region, together with two "crisis area" windows covering the Gulf and the r^on
around the former Yugoslavia. However, this paper will concentrate on the UK version of the MM which
is run four times each day to produce forecasts out to t+24 (hrs).

Zone

No.

Zonerkige

(m)

midpoint

(m)

surface

0-200

100

200 - 500

350

500-1000

750

1000-1500

1250

1500 - 2000

1750

2000-3000

2500

3000-4000

3500

4000-5000

4500

5000-6000

5500

6000-8000

7000

8000-10000

9000

10000-12000

11000

12000-14000

13000

14000-16000

15000

16000-18000

17000

Zone

No.

Zone range
(m)

midpoint

(m)

surface

0-200

100

200 - 500

350

500-1000

750

1000 - 1500

1250

1500 - 2000

1750

2000 - 2500

2250

2500 - 3000

2750

3000 - 3500

3250

3500 - 4000

3750

4000-4500

4250

4500 - 5000

4750

5000 - 6000

5500

6000 - 7000

6500

7000 - 8000

7500

8000 - 9000

8500

9000 - 10000

9500

10000-11000

10500

11000-12000

11500

12000 - 13000

12500

13000 - 14000

13500

14000 - 15000

14500

15000 - 16000

15500

16000 - 17000

16500

17000 - 18000

17500

18000 - 19000

18500

19000 - 20000

19500

Table 1. Heights of (left) Standard Artillery Computer Meteorological Message (SCMM) zones and
(right) Standard Ballistic Meteorological Message (SBMM) zones.

Level

1365

6800

1600

7900

100

1870

9040

190

2200

10260

300

2600

11750

435

3080

13700

595

3640

16200

770

4300

19700

955

5050

23850

1155

5870

29000

Table 2. Approximate heists of levels m the Met Office MM.

fcthis study site-specific profiles for LarkhiU (51.2^, LS^W) were inteipolated fi^omthe four surrounding
MM grid points and model data were extracted at each grid level. The heights (above 10 m) were then
recomputed using the hydrostotic relationship. (Typically at low levels, at 1000 m, the recomputed heights
differed fi-om the nominal heights by »10 m, whilst higher up, at 2000 m, the increased to

»100 m.) 'nie data were extracted fi-om the midnight run of the model, for 06Z (t+6) and lOZ (t+10) to
coincide with the Laridiill radiosonde ascents. The MM data were then interpolated to the mid points of die
standard levels (Table 1) and used to produce SCMM data (winds and virtual temperatures for the 26
zones up to 20000 m) and SBMM data (ballistic winds and temperatures for the 15 zones up to 18000 m).
Both the MM and radiosonde dato were archived over a two month period July/August 1994. Radiosonde
ascents were only made at LarkhiU on weekdays giving 59 ascents for comparison purposes.

4. COMPARISON OF MODEL PROFILES WITH ACTUAL DATA
4.1 Wind Profiles

For artiUery ballistics the quality of the wind data is the most critical fector and this is generaUy quantified
in terms of the vector wind error. Fig. 1 shows the rms vector wind errors (MM - radiosonde) against
height (for the SCMM zone mid points). Up to 5500 m (zone 12) the errors are similar for both the t+6
and t+10 predictions, being typically 2.5-3 m/s. The errors increase fi-om 6500 m to 12500 m (zones 13 to
19) and here the t+10 winds have larger errors. This region is associated with the jet stream, near the
ttiyopause, where there are strong winds and shears. Above 13500 m (zone 20) the errors reduce to about
2-3 m/s. Over all 26 zones the average rms errors were 3.1 m/s (t+6) and 3.3 m/s (t+10)

In an earlier study examining the potential capability of Met Office models to provide baUistic data
^telaw, 1989) the average model rms error, from the Met Office "fine-mesh" model, up to 20000 m was
3.0 m/s for the analysis (t+0) and 4.7 m/s for a t+3 forecast. The "fine-mesh" model has now been
replaced by the "limited area" model and the mesoscale model has since been integrated into the UM suite.
The accuracy of the available model winds has clearly improved.

rms Vector Wind Error (m/s)

Figure 1. Mesoscale Model
rms vector wind errors (m/s)
against height. The solid
line shows the predictions
for t+6, the dashed line for
t+10.

Wenckebach (1991) looked at SBMM and SCMM data derived from operational models run by the
German Military Geophysical Office, these were a Boundary Layer Model (BLM) for the near surfece
r^on (zones 0 to 2) and a 9-level baroclinic model for zones 3 to 18. The results showed that for zones 3

to 18 the rms vector error was in the range 3 to 5 m/s, and tended to increase with height. The errors for
the lower winds, zones 0 to 2, were typically 2 m/s (zone 0) and 3.5 m/s (zones 1 and 2). At these lower
levels the t+6 forecast winds were better tlm those for t+12, however at die higher levels there was no
.fignifirant difference between the t+6 and the t+12 winds.

4.2 Temperature Profiles

Fig. 2 shows the ims errors (MM - radiosonde) for the temperature data. At levels below 10000 m the rms
errors are generally < 1°C, the t+6 predictions being slightly better than those for t+10, particularly nearer
the surface. At higher levels the rms errors increased up to a maximum of 2.3®C.

0.B 1 1.6 2

rms Temperature Error (C)

Figure 2. Mesoscale
Model rms temperature
errors (®C) against height
The solid line shows the
predictions for t+6, the
dashed line for t+10.

4.3 Ballistic Winds

The ballistic wind is that wind, constant in speed and direction up to a specified zone, which would produce
the same displacement of a shell as the actual wind profile, and can be computed fi-om the actual winds by
applying standard weighting factors to the winds within the various SBMM zones. For each ^osonde
and model profile a ballistic wind profile was computed and the vector differences (MM - radiosonde) were
calculate Fig. 3 shows the profile of the rms ballistic vector wind errors.

Figure 3. Mesoscale
Model rms ballistic wind
errors (m/s) against
height. The solid line
shows the predictions for
t+6, the dashed line for
t+10.

O.s 1 l.s 2 2-S 3

rms Vector Ballistic Wind Error (m/s)

20000

leooo

16000

14000

E 12000

C 10000
?

^ sooo
6000
4000
2000
A

^ j ^ associated with the jet stream. However, the errors

m the balhstic wmds are generally less than those for the actual winds.

of the ways of quantifying the representativeness of meteorological data for ballistic predictions is to
d^cnbe It m terms of its equivalent "staleness". Blanco (1988) gives some simple algebraic formulae
meteorological variability m terms of a time staleness, this is given in Eq. (1) for the

®bw~ 0 061 (1 + 0.03455 v^wr - 0.05846 zj,)* tgj.

(1)

variance m the b^hstic wmd (lets*), Vb^ is the ballistic wind speed (kts), Zb is the top of
^ the ballistic wmd is evaluated (km) and is the staleness (min). The sJeness

can also he equat^ to a spatial separation through the generally accepted relationship that, over feirly level
ten^ a time steleness of 1 hour is equivalent to a spatial displacement of 30 km. Fig 4 shows an
lUu^ration of foe equivalent time staleness for a ballistic wind up to 8 km (line 10), in which a ballistic
wmd speed of 30 1^ was specified. In this illustration a ballistic wind error of 3 kts (1 5 m/s) is

equivalent to a staleness of 1 hour and an error of 6 kts (3 m/s) is equivalent to a staleness of 4 hours ^

Equivalent Time Staleness (hrs)

Figure 4. lUustrating foe
equivalent time staleness of
foe ballistic wind, up to 8000
m (line 10), in terms of foe
rms error in foe ballistic
wind.

staleness based on a statistical analysis of upper air measurements
but that foe stdeness for any p^icular situation will depend upon foe homogeneity of foe atmosphere (i e ’
foe synoptic situation). Given foe errors in foe ballistic winds, as shown in Fig. 3, it is possible to use
Eq. (1) to estimate foe t^ical equivalent time staleness. In doing this foe squL of foeLs vector ballistic
wmd error was applied m Eq. (1). Figure 5 shows foe estimated equivalent staleness for foe MM balhstic
wmds relative to foe on-site radiosonde winds. At foe lowest levels (zones 1 to 3) foe model data has an
Sr r S^^ess of 4 hours or more, this reflects foe fact that site-specific low level winds are
Ki « t t! influenced by foe local topography (which is not resolved in foe

^ fro*" A® foe equivalent

^ ^ ^ ^ to 4 hours for foe t+10

figures are similar to foe results of Wenckebach (1991), who concluded that model derived
messages (above zone 2) were preferable to stale measured ones when foe staleness exceeded 3 hours

20000

Figures. Equivalent time
staleness of ^ mesoscale
model winds, the solid line
shows the staleness for the
winds from the t+6 forecast,
the dashed line for the t+10
forecast.

5. BALLISTIC PREDICTIONS

Whilst the above gives an indication of the accuracy of the forecast winds and temperatures from the MM,
it does not provide any specific information on the likely impact of using model winds for artillery
ballistics. To do this the data were applied in a fire control computer to determine the expected targeting
errors, which were quantified in terms of the range (distance) and line (deflection) corrections. These
comput?»tinns were done for a typical Royal School of Artillety training scenario, for an FH70 gun, firing
charge 8. Other fectors specified were a charge temperature of 21°C and a muzzle velocity of 820 m/s.
The specified target was 23 km due north of the gun. Figure 6 shows the computed range and line
corrections using the on-site radiosonde ascents. These corrections correspond to the miss distances that
would be expected if meteorology was ignored (i.e. assuming a standard ballistic atmosphere) and
a«?su»r>ing that the radiosonde data accurately characterised the meteorological conditions.

Line Correction (m)

Figure 6. Showing the
computed gun corrections
based on the measured
meteorological conditions.

Figure 6 shows that most of the points fiiU in the quadrant for which a negative (reduced) range correction
is needed together with a negative (westerly) line correction. This is because the predominant wind was
from the south-west, with a tendency to carry a shell further towards the north-east. The results give rms
corrections of 12 m (line) and 484 m (range). Similar calculations were also made using the MM data. An

assessment of the accuracy of the ballistic forecasts using the MM data can then be made by comparing the
computed corrections against those made using the radiosonde data. Assuming that all other fectors which
contribute to the artillery error budget are constant, then the differences in range and line corrections (MM-
son^) give an indication of the targeting error introduced by using MM data rather than real-time on-site
radiosonde measuronents. The differences are shown in Figure 7, where the rms errors are 7 m in line and

228 m in range. Thus for a typical training scenario, use of the MM data could reduce the meteorological
error by over 50% .

- - - — - igoo

tooo ■

eoo -

600 -

400 -

200 -

• •

•

• •

1 1 1 f —

!0 -40 -30 -20 -10 .jScfJ

• •

— - 1 * — 1 — 1 —

g • 10 20 30 40 5

-400 *

•

-600 -

-800 -

-1000 -

- - - - — >tgoo

Figure 7. Targeting errors
resulting from using MM
data instead of radiosonde
measuremrats.

Line Error (m)

6. DISCUSSION AND CONCLUSIONS

The above results give an indication of the expected accuracy of ballistic met messages derived from MM
data. The awuracy of these messages is dominated by the quality of the wind data. The results show that
the MM derived winds are least reliable near to the surface (i.e. in the lowest 1000 m up to zone 3). For
ballistic messages, above zone 3, the model winds are equivalent to measured winds with a staleness of 1!4
to 4 hours, this equates to a spatial displacement of 45 to 120 km.

The training ranges are all more than 130 km away from the nearest station making radiosonde acf/»nt«f
(and at weekends when the range stations are closed may be much further from the nearest ascent). Even if
the radiosonde data were available for the time of interest (which is rarely the case), the forecaster would
s^ have to to interpolate between ascents. It is concluded that, there would be a clear benefit in making
site-specific MM data for the training ranges available to forecasters, as it will give them much more
r^resentative meteorological profile information to work with. Plans are now in hand to allow forecasters
to access these data, to provide them with software to view and edit the data, and to automatically produce
SBMM and SCMM. It is considered preferable to allow the forecasters to edit/quality control the data,
rather than provide a totally automated hands-off facility, as there will be occasions when the model
information provides poor guidance (e.g. in mobile synoptic situations when timing errors occur) This
fecility will also aUow them to modify the low level winds on the basis of local surface wind information

6.1 BMETS

BMETS (Battlefield METeorological System) is expected to enter service with the Royal Artilleiy in 1995
and will replace the current, but dated, AMETS (Artillery METeorological System). BMETS will employ
a modem, ground passive RDF radiosonde tracking system to obtain meteorological data. Each BMETS
detachment will consist of 2 light wheeled vehicles with trailers carrying a total detachment of 4 men. One
system will be deployed with each field artillery and MLRS (Multiple Launch Rocket Systran) reginient.
BMETS will provide the capability of generatirig hourly ballistic meteorological messages, Basic Wind
Reports and WMO TEMP messages. Typically 6 BMETS would deploy with a Division and operate
about 20 km apart. BMETS will have an automated interfece to BATES (the Battlefield Artillery Target
Engagement System) which will allow messages to be passed between units.

BMETS will provide much more timely and more densely spaced ballistic meteorological data on the
battlefield. This will lead to significant improvements in the efficiency of artillery fire, both operationally
and during training. However, as artillery ranges increase, the met induced error increases accordin^y and
becomes particularly significant when considering deep operations where instrumental measurement is
particularly difficult. In order to provide more representative meteorological data to support deep and
depth operations, it will be necessary to use meteorological models.

6.2 Future Concepts - CMETS

There is currently a project underway at the Met Office to develop a portable workstation-based high
resolution crisis area model which can be nm in a secure environment (e.g. at the Principal Forecast Office,
HQSTC). This will include a full dynamic data assimilation scheme which will allow it to assimilate all the
available in-theatre data, e.g. firom BMETS and other military sources (which for security reasons cannot
be used in NWP schemes run at Bracknell) and so should be capable of providing the best quality theatre
NWP. This capability is expected to become operational in a few years time and it will then become the
primary source for short-period meteorological forecasts for crisis areas. However, it is envisaged that it
will be a theatre-scale model, with a resolution «15 km, rather than a battlefield-scale capability. The
results presented here suggest that the present MM is capable of giving the most rq)resentative
meteorological information when the radiosonde measurements are between 1 Vi and 3 hours old (or 45 to
120 km distant), although these figures should be reduced for forecasts up to t+6. (It is worth noting that
no upper air data from Larkhill was assimilated into the MM for the forecasts assessed here, such tiiat
these figures are representative of its accuracy away from sources of upper air data.) Thus, away from the
BMETS network, e.g. in the target area, it would be expected that the model would be the best source of
meteorological information.

The CMETS (Computerised METeorological System) concept is to provide "now-casts" (i.e. for 0 to 3 hrs
ahead) of ballistic messages based on meteorological profiles at the point of the vertex of a shells
trajectory, or even along a shell or rocket trajectory. This will necessitate information on battlefield
locations and demands that any processing/modelling is done at a lower echelon. An idea, currently being
considered, is to use a PC/workstation-based battlefield-scale model. This would cover, say, a 200 kmx
200 km area, with a horizontal resolution of 5 km or better, containing orogr^hic information. This model
would receive background fields derived from the portable theatre-scale models run at a higher echelon and
the BMETS data (and any other avaUable target area data) in order to provide an optimum 3-
Himpnginnal analysis of the meteorological Conditions. Additional detail in the boundary layer wind field
could be HiagnnsPiH using a complex terrain model (e.g. based on mass continuity or linear inviscid flow)
which should improve the low level winds (which is where the current MM winds are poorest). Thus the

aim is to provide a mobile computer work-station with the communications &cilities to update the
meteorological field both from strat^c background information from a higher echelon and from tactical

date available m-theatre. Using this information, it is hoped to provide mission specific ballistic data to
artillery users.

REFERENCES

Blanco A J, 1988; Methodology for Estimating Wind Variability, ASL-TR-0225, US Army Atmospheric

Sciences Laboratory, White Sands Missile Range, NM, US.

Cullen M J P, 1993: The Unified Forecast/Climate Model, McteorolMap, n? 81-94.

^eP, Kitchen M, Harding J and StancombeJ, 1986: The Reproducibility of RS3 Radiosonde and
CossorWFMklV Radar Measurements. Observational Services Memorandum OSM No 35
Meteorological OfBce, Bracknell, UK.

(^wdd Maj A A, 1993: Computer Assisted Artillery Meteorology (CAAM). Briefing to the NAAG
ISWG.3 at Army Research Laboratory, Battlefield Environment Directorate on 10 March 1993.

N^h J, 1991: Implementation of the Vaisala PC-Cora upper air sounding system at operational
r^osonde ^lons and test ranges in the United Kingdom. In Proceedings of the 7th Svmpnsiiim nn
Meteorological Observations and Instnunentation^ New Orleans, LA, US. pp 270-275

Spalding JB, Kellner NG and Bonner RS, 1993: Computer-Assisted Artillery Meteorological System

Design. In Proceedings of the 1993 Battlefield Atmospherics Conference. Las Cruces, NM, USA. pp 45-

Wenckeb^h K, 1991: On the accuracy of meteorological messages computed from numerical forecasts.
German Mihtary Geophysical Office. Presentation given at the NATO Panal IV SP2/T.SWG 3 Joint
Symposium on Ballistic Meteorology, May 1991, NATO HQ, Brussels, Belgium.

WhitelawA, 1989: Study of Future Artillery Meteorological Techniques. Technical Note 3 Short Term
Forecasting. Logica Report 242.20103.003.

RESULTS OF THE LONG-RANGE OVERWATER
DIFFUSION (LROD) EXPERIMENT

James F. Bowers
West Desert Test Center
Dugway, Utah 84022-5000

Roger G. Carter and Thomas B. Watson
NOAA Air Resources Laboratory
Idaho Falls, Idaho 83402

ABSTRACT

The Long-Range Overwater Diffusion (LROD) Experiment was a Joint Servic¬
es, multi-agency project to help fill the data gap on the alongwind diffusion
(especially at intermediate to long range) of a vapor or aerosol cloud instanta¬
neously released to the atmosphere. LROD was conducted northwest of the
island of Kauai, Hawaii in July 1993. As described in detail in a 1993 Battle¬
field Atmospherics Conference paper, the experiment consisted of a series of
crosswind line source releases of sulfur hexafluoride (SF5) from a C-130
transport. The tracer cloud was tracked to 100 km using an aircraft-mounted
continuous SF^ analyzer. The SF^ cloud was also sampled by continuous
analyzers on boats at downwind distances of up to 100 km. This paper
summarizes the LROD results and provides an overview of the data that will
be available to researchers and model developers.

1. BACKGROUND

Current atmospheric transport and diffusion models commonly assume that the alongwind
and crosswind diffusion rates are the same because little is known about alongwind
diffusion. However, both short-range diffusion experiments (Nickola, 1971) and
theoretical analyses (Wilson, 1981) indicate that this is a poor assumption. Little data
exist to characterize alongwind diffusion, especially at distances of more than a few
kilometers, because! (1) alongwind diffusion usually is not an issue when modeling
continuous sources of air pollution, (2) total dosages traditionally have been assumed to
be more important for hazard assessments than concentration exposure histories, and (3)
samplers capable of making time-resolved concentration measurements have not been
readily available until recently.

The data gap on alongwind diffusion affects the accuracy of model predictions of the
transport and diffusion of any material from a short-term atmospheric release. These

rcleHscs often present an immediate threat to life and property when they involve the
accidental release of a toxic substance from a failed containment vessel (for example,
rupture of a chlorine tank car). If diffusion models are used in these cases to make
decisions about evacuation routes and priorities, erroneous model assumptions about
alongwind diffusion could have disastrous consequences.

The Long-Range Overwater Diffusion (LROD) Experiment was conducted near Kauai,
Hawaii in July 1993 to help fill the data gap on alongwind diffusion, especially at
intermediate to long range. The experiment was conducted over water rather than land
primarily because it was desired that meteorological conditions be essentially constant
over distances of 100 km or more for days at a time. (Steady-state mesoscale meteoro-
logical conditions were desired to facilitate both experiment conduct and the interpreta¬
tion of experiment results.) Because the experiment was conducted over water, a
secondary objective was to acquire data that will contribute to meteorologists’ under¬
standing of atmospheric transport and diffusion processes over oceans. The design of the
LROD experiment is discussed in a paper presented at the 1993 Battlefield Atmospheric
Conference (Bowers, 1993). This paper briefly summarizes the results of the experi¬
ment.

2. EXPERIMENTAL DESIGN AND CONDUCT

The design and conduct of the LROD experiment are discussed in detail by Bowers et al.
(1994) and summarized in a paper presented at the 1993 Battlefield Atmospheric
Conference (Bowers, 1993). Briefly, LROD consisted of 13 crosswind releases of inert,
nontoxic sulfur hexafluoride (SF^) from a C-130 transport flying 90 m above the ocean’s
surface. The tracer cloud, which formed a 100-km crosswind line source, was tracked to
100 km downwind using a continuous SFg analyzer mounted in a twin-engine aircraft.

The sampling aircraft repeatedly measured the alongwind concentration profile 150 m
above the ocean as it flew through the cloud in a series of downwind and upwind passes.
The SFg cloud was also sampled by continuous analyzers on five boats at downwind
distances of up to 100 km. Because all aircraft- and boat-based SFg concentration
measurements were made near the midpoint of the 100-km line source, the measured
concentrations should be unaffected by diffusion from either end of the cloud, even at
100 km downwind. Meteorological measurements were made from one of the boats and
a specially instrumented single-engine aircraft.

The unseasonably high seas experienced in Hawaiian waters during LROD often
prevented the small sampling boats from going out into open ocean. Even when the
boats were able to leave port, the scientists on the boats were generally incapacitated by
seasickness. Consequently, the boat-based SF^ measurements were quite limited (six
trials with one or more sampling boats). However, the aircraft-based SF^ measurements
were highly successful, yielding over 230 measurements of the alongwind cloud concen¬
tration profile. The only significant problems with the aircraft sampling system were a
data logger problem during Trial 1 and a Global Positioning System (GPS) failure during

Trial 12, which resulted in termination of aircraft sampling at 47 km. Because of the
data logger problem during Trial 1, aircraft sampling data are not available for this trial.

3. LROD EXPERIMENT RESULTS

3.1 SFfi Dissemination

The SFg tracer was released from the C-130 in liquid form. Because SFg has a boiling
point of -63.9 "C, the liquid SF^ vaporized almost instantaneously and was quickly mixed
with ambient air by the aircraft’s wake turbulence. The dissemination rate was measured
by a flow meter, and the SFg cylinders were weighed before and after use. The average
SFe dissemination rate was 12 g/m for Trial 1 and 5 g/m for the remaining trials.

Because the concentrations measured during Trial 1 were much higher than anticipated,
the dissemination rate was reduced after the first trial by decreasing the flow rate and
increasing the speed of the C-130. The dissemination rate standard deviations average
less than 5 percent of the corresponding mean dissemination rates, which indicates that
the dissemination rate was fairly uniform.

3.2 SFg Sampling

The aircraft SFg concentration measurements were paired with GPS position and time
information during data acquisition. During post-experiment data processing, the SF^
concentrations were converted from millivolts to parts per trillion (ppt) by volume using
the calibrations made during each trial. The positions were also adjusted to account for
the measured 9-s delay between the time when air entered the sampling inlet on the
aircraft’s exterior and the time when it reached the continuous analyzer. For conve¬
nience in data analysis, the positions were converted from longitude and latitude to a
Cartesian coordinate system of the type used in Gaussian diffusion models. For each
trial, the origin of the coordinate system was placed at the midpoint of the dissemination
line, the positive x axis extended in the downwind direction, and the y axis was positive
to the right when looking downwind. Because of the easterly trade winds during the
experiment, the x axis pointed approximately to the west and the y axis pointed approxi¬
mately to the north. Time was converted to seconds after the C-130 was at the midpoint
of the dissemination line.

If the SFg cloud had been stationary, the aircraft measurements could be used to estimate
the alongwind cloud width under the assumption that cloud expansion was negligible for
a single pass. However, the cloud transport speed was 10 to 20 percent of the aircraft’s
ground speed. Consequently, it was necessary to correct the downgrid coordinates of the
aircraft concentration measurements to remove the effects of the cloud’s motion.
Assuming that the cloud’s alongwind expansion was negligible during each pass and that
neither the cloud transport speed nor the aircraft ground speed varied during the pass, the
corrected downgrid coordinate of a concentration measured at time t is

(1)

x' = X + u{t^ - t)

where x is the uncorrected coordinate, u is the cloud transport speed, and t,, is the time
when the aircraft passed through the cloud’s center of mass. The cloud transport speed
was determined from the motion of the cloud’s center of mass as determined from the
aircraft measurements. Equation (1) was used to construct the alongwind SFg concentra¬
tion profile at time to for each pass through the cloud.

Figure 1 shows examples of upwind and downwind aircraft SFg cloud concentration
profiles both before and after the correction for cloud motion. Because the concentration
profiles in the figure are from a trial that had one of the highest cloud transport speeds,
the correction for cloud motion is more evident than for most of the other trials. As
shown by the figure, the corrected profile is narrower than the original profile for the
downwind traverse and broader for the upwind traverse.

Airplane data, Trial 12, Pass 01

Airplane data. Trial 12, Pass 02

Figure 1. Corrected (dashed line) and original (solid line) SFg cloud concentration
profiles for aircraft Pass 1 (downwind) and Pass 2 (upwind) through the cloud during
Trial 12.

The LROD data can be used to test or develop a number of types of diffusion models,
but the type of model most frequently used for operational applications is the Gaussian
puff or plume model. As suggested by Figure 1, the individual cloud concentration
profiles usually were not Gaussian in appearance. Nevertheless, most of the profiles
could be described reasonably well by a Gaussian distribution. The LROD report
contains best-fit Gaussian cloud parameters for three different fitting methods, but the
method that appeared to give the best overall representation of the actual profiles was the

peak/area match method. In this method, the fitted peak concentration Xo was set equal
to the measured peak concentration and the Gaussian alongwind dispersion coefficient
was computed from

(2)

where CL is the alongwind-integrated concentration. Note that this fitting method
ensures that the fitted and actual profiles account for the same total mass. Figure 2
shows the peak/area Gaussian fits to the corrected cloud concentration profiles from
Figure 1.

Airplane data. Trial 12, Pass 01

Figure 2. Measured (solid line) and fitted (dashed line) SF^ cloud concentration profiles
for aircraft Passes 1 and 2 of Trial 12. Measured profiles have been corrected for cloud
motion.

The boat continuous analyzer measurements were processed in the same manner as the
aircraft measurements, including conversion to trial grid coordinates and correction for
cloud motion. Each boat continuous analyzer SFg concentration profile was compared
with the aircraft profiles for the two passes made nearest to the boat during that trial.
Figure 3 shows an example of two aircraft profiles superimposed on a boat profile. In
this case, the differences between the two consecutive aircraft profiles are greater than
the differences between the boat profile and the first aircraft profile. A statistical
comparison of the Gaussian model cloud parameters and Xo estimated from the boat
and aircraft measurements showed that the differences are not significant at the 95
percent confidence level. Thus, the aircraft and Xo should be representative of and
Xo near the surface, at least at the downwind distances of the available boat measure¬
ments (60 and 100 km).

itiiiiiiiliiiniiiilmtiintliiiiiiiiilMitiimliiniiiii

Figure 3. Comparison of aircraft and boat continuous SF^ analyzer measurements for
Trial 5, Boat 5 (100 km).

3.3 Meteorological Measurements

Standard surface and upper-air (radiosonde) measurements were made from Boat 1
approximately 10 km downwind from the dissemination line during the first three trials.
Because of high seas, it was not possible to keep Boat 1 in the experiment area for days
at a time during the remaining trials. Beginning with Trial 6, Boat 1 was sent each day
to a position 15-20 km south of Kauai, outside of the island’s wake. The observations
should therefore be representative of conditions in open ocean, but not necessarily of
conditions in the experiment area 200 km to the northwest. Wind, temperature, and
humidity measurements were made at nominal height of 10 m on the boat’s main mast
and sea surface temperature was obtained from a thermometer mounted on the hull. The
wind observations were corrected for pitch, roll, and boat heading. In addition to the
Boat 1 radiosonde soundings, standard synoptic radiosonde soundings are available from
the Lihue Airport on the east shore of Kauai.

The meteorological research aircraft, a specially instrumented Rutan Long-EZ, entered
the experiment area after the C-130 had completed SFg dissemination. Flying a track
approximately parallel to and 45 km south of the sampling line, the Long-EZ began each
trial by measuring the vertical profiles of wind, temperature, and humidity as it slowly
descended from 2500 m above mean sea level (MSL) to about 25 m MSL. The long-EZ
then flew to the dissemination line and back while it measured the vertical fluxes of

sensible and latent heat, momentum, and carbon dioxide (CO2). After completing the
flux runs, the Long-EZ again measured meteorological profiles as it slowly ascended
from 25 to 2500 m MSL during its return flight to the dissemination line. The only
aircraft meteorological measurements included in the LROD final report (Bowers et al.,
1994) are the vertical profiles of temperature and dewpoint and two derived parameters:
potential temperature and equivalent potential temperature. The wind, turbulence, and
flux measurements will be provided in a supplemental report.

Table 1 sununarizes the meteorological conditions at the start of each LROD trial. The
10-m wind speeds and atmospheric stabilities are based on the Boat 1 surface observa¬
tions. Both the Naval Postgraduate School overwater stability classification scheme
(Schacher et al., 1982) and the widely used Turner (1964) scheme give the Pasquill
stability category as the neutral D category. The inverse Obukhov lengths were calculat¬
ed from Boat 1 observations using the bulk methods of Wu (1986). The cloud transport
speeds were determined from the aircraft measurements of the motion of the SF^ cloud’s
center of mass and the mixing depths were calculated from the SFg mass balance at long
downwind distances.

Table 1 . Summary of LROD Trial Meteorological Conditions*

Trial

u,o„, (m/s)

u (m/s)

Stab.

1/L (m ')

H„(m)

8.2

10.5

-0.001

810

9.8

10.1

-0.001

1155

11.1

815

11.5

1495

7.7

10.7

-0.003

735

9.3

12.0

-0.001

1110

10.3

12.7

0.003

1005

10.3

10.1

0.000

665

5.1

9.9

0.002

435

10.3

0.000

635

11.3

13.5

0.002

715

11.3

15.6

0.002

765

* u,om = 10-m wind speed, u

= cloud transport speed. Stab =

Pasquill stability category.

1/L = inverse Obukhov length, = mixing depth

4. DISCUSSION OF RESULTS

Figure 4 shows all of the LROD aircraft measurements plotted as a function of
downwind distance. Figure 4 also shows the Pasquill-Gifford (lateral dispersion
coefficient) curves for the D (neutral), E (stable), and F (very stable) Pasquill stability
categories (Turner, 1970) because many current diffusion models for instantaneous
releases assume that can be approximated by o^. The measured cr^ values range from
near the Pasquill-Gifford curve for D stability to well below the curve for F

stability. Thus, although meteorological conditions were similar during all trials, there
were significant trial-to-trial variations in a,. It therefore appears that the LROD data set
is suitable for use in investigating the quantitative relationship between alongwind puff
growth and meteorological conditions. If this relationship can be established, can be
predicted for other meteorological conditions and other settings, including over land.

Figure 4. Alongwind dispersion coefficient versus downgrid distance for all LROD
trials.

5. SUMMARY

The LROD experiment yielded a unique data set that should contribute to an improved
understanding of both the physics of alongwind diffusion and atmospheric transport and
diffusion processes over oceans. The data presented in the LROD final report will likely
meet the needs of most model developers and researchers. For those who require more
detailed information, the 4-Hz (aircraft) and 1-Hz (boat) continuous analyzer SFg
concentration measurements are available from the Meteorology Division, West Desert
Test Center (formerly the Materiel Test Directorate, U.S. Army Dugway Proving
Ground).

ACKNOWLEDGMENTS

The sponsors of the LROD experiment were the Joint Contact Point Directorate (Project
D049), U.S. Army Dugway Proving Ground, Dugway, Utah; Chemical and Biological
Defense Division, Brooks Air Force Base, Texas; and Naval Surface Warfare Center,
Dahlgren, Virginia.

REFERENCES

Bowers, J. F., 1993: "Overview of the Long-Range Overwater Diffusion (LROD)
Experiment." Tn Proceedings of the 1993 Battlefield Atmospherics Conference,

U.S. Army Research Laboratory, White Sands Missile Range, NM 88002-5501, pp
157-170.

Bowers, J. F., G. E. Start, R. G. Carter, T. B. Watson, K. L. Clawson, and T. L.

Crawford, 1994: "Experimental Design and Results for the Long-Range Overwater
Diffusion (LROD) Experiment." Report No. DPG/JCP-94/012, U.S. Army
Dugway Proving Ground, Dugway, UT 84022-5000.

Nickola, P. W., 1971: "Measurements of the Movement, Concentration, and Dimensons
of Clouds Resulting from Instantaneous Point Sources." J. AppI. Meteor., 8:
962-973.

Schacher, G. E., D. E. Speil, K. L. Davidson, and C. W. Fairall, 1982: "Comparison
of Overwater Stability Classification Schemes with Measured Wind Direction
Variability." Naval Postgraduate School Report No. NPS-6 1-82-0062 prepared for
Bureau of Land Management, Los Angeles, CA.

Turner, D. B., 1964: "A Diffusion Model for an Urban Area." J. ApdI. Meteor., 3:
83-91.

Wilson, D. J., 1982: "Along-wind Diffusion of Source Transients." Atmos. Env., 15:
489-495.

Wu, J., 1986: "Stability Parameters and Wind-Stress Coefficients Under Various
Atmospheric Conditions." J. Atmos, and Ocean. Tech . 333-339.

MODELED CEILING AND VKIBIUnY

Robert J. Falvey
United States Air Force
Environmental Technical Applications Center
Simulation and Techniques Branch
859 Buchanan Street
Scott AFB,IL 62225-5116

ABSTRACT

Modeling distributions of climatological data using mathematical
equations is an effective data compression technique. Since ceiling and
visibility data are not normally distributed, modeling their distributions
is accomplished using the Weibull family of curves. Cumulative
frequency distributions using 20 years of ceiling and visibility data are
analyzed and Weibull curves are fit to the data. The Weibull coefficients
are calculated and stored for use with the microcomputer based MODCV
software. The software uses these coefficients, along with current
conditions and serial correlations in a first-order Markov process, to
produce conditional and unconditional probability forecasts of ceiling and
visibility and joint ceiling and visibility probabilities. The MOEX2V
software uses the standard Microsoft Windows interface which allows the
user to quickly select conditional and unconditional probabilities. The
output consists of bar graphs and tables of ceiling and visibility
probabilities for ten wind stratifications and ei^t user-selected forecast
times up to 72 hours in the future.

1. iNno)ucnoN

In the past, climatological requirements of the typical weather station have been satisfied by
bulky paper copies of the Revised Uniform Standard Surface Weather Observations
(RUSSWO), Wind Stratified Conditional Climatology (WSCC) tables, and ^ Weather
Service (AWS) Climatic Briefs. As microcomputers have become integrated into weather
station operations, the opportunity to complement and/or replace these printed summaries with
electronic climatological databases is possible. The MODeled Ceiling and Visibility
(MODCV) software was originally designed to replace the WSCC tables. However, in the
original version of the program, the data was not wind stratified. Since winds play a vital
role in both ceiling and visibility, this lack of wind stratification made the software fall short
of the end-user's needs. The methodology described below is the same as in the original
version and is taken directly from Kroll and Elkins (1989). The results of the wind stratified
modeling are described in section 3.

2. MEHOXXXXiY

MODCV was developed to provide rapid transportable access to unconditional and conditional
probability forecasts of ceiling and visibility based on climatological data

2.1 Unconditional ProbaHlity

Unconditional climatology data, which is simply the relative frequency of occurrence that a
certain wndition was observed, is easily tabulated at any location that has a representative
observational record. The unconditional probability that the visibility below 1 mile at 12
GMT is calculated by summing the number of times the visibility at 12 GMT was below 1
mile and dividing by the number of observations at 12 GMT. This is the method used to
produce the RUSSWO summaries.

A very powerful alternative to tabulating each condition into frequency tables is to model the
Cumulative Distribution Function (CDF). This process involves the use of mathematical
equations to fit the cumulative frequency distribution. If the variable being modeled is
continuous, the cumulative probability associated with any value of that variable can be
calculated using the equation:

P = Fix) (1)

where F(x) is the function that models the distribution of the CDF. In MODCV, therefore,
given any threshold value of x, this function is used to calculate the unconditional probability
that X will be below that threshold value.

2.2 Conditional Probability

The basic component of the conditional ceiling and visibility is based on the Omstein-
Uhlenbeck (O-U) stochastic process, a first order Markov process for which each value of a
random variable x j is considered a particular value of a stationary stochastic process. The
stochastic model relates a value of x at time f (Xj) to an earlier value of x at time zero (xg).
A frequent assumption in statistical application is that the variable is normally distributed.
Unfortunately, many meteorological variables, such as ceiling and visibility, are not normally
distributed. Non-normal variables can be transformed into normal distributions through a
process called transnormalization. Transnormalization involves expressing the raw variables
in terms of it's equivalent normal deviate (END). This process is discussed by Boehm (1976).
Once the variable has been normalized, the joint density function associated with Xg and Xj
becomes:

f{XQ,X^)

{Xo-p)2-2p(yo-^)

2o2(i-p2)

(2)

where p is the serial correlation between successive values of x and vdiere p and a are the

mean and standard deviation of x. Since we are interested in the probability of x, given the
initial value of Xq, a conditional distribution of x is required. If x can be approximated by
a first-order hferkov equation, then x, is dependent only upon Xq. If successive observations
of X have a bivariate normal distribution, the conditional distribution of x, is normal with a
mean of:

£’[(X(.IXo)] =o+p(Xo-p) (3)

and a variance of:

varlix^lx^)] =o^(l-p^) (4)

Equations 3 and 4 are basic to the first-order Markov equation. A value of x^ can be
calculated using:

Xf. = p+p +oy'l-p2Tlt

If the variable is distributed normally with a mean of zero and a variance of one, equation
5 reduces to:

(6)

where p is the correlation coefficient between Xq and Xj separated by time interval t, and
is a random normal number. The process is considered to be Markov if p = P)‘, where pb is
the hour-to-hour correlation associated with x. If is a constant, then this process is
considered stationary and is known as the Omstein-Uhlenbeck or OU process.

Application of the OU process to meteorological variables is well documented in Gringerton
(1966), Sharon (1967), and Whiton and Berecek (1982). Its use with variables whose time
series have a random component and adhere to the restrictions of the Markov is justifiable.
Stationarity is a feature that is especially favorable for application to weather variables since
predictions derived fi*om stationary processes will converge toward the mean as time
increases. Thus, the conditional probabilities will converge to unconditional probabilities as
the forecast time period increases.

From equation 6, we can conclude that, for a specific value of Xq, the value of will exceed
a minimum value Tv„in as fi*equently as Xt exceeds a minimum value x^^n given an initial value
Xq. In terms of probability.

Now we replace the value of as x(t|0), the normalized value corresponding to file
conditional probability of Xj. Thus, equation 7 becomes:

PiXf-i Xq) P(Xi.>X^^j^\ Xq)

(8)

where P(x,|xo) is the conditional probability of x, given the value of % P(x,) is the
unconditional probability of x at time t, and P(Xo) is the unconditional probability of x at time
zero. MODCV uses equation 8 to calculate conditional probabilities of ceiling and visibility.

2.3 Modeling Qimulative Distributions

Observations for a station's entire period of record (POR) are extracted from USAFETAC's
database and binned by month, hour, and wind direction category (calm, 22.5° either side of
N, ME, E, SE, S, SW, W, NW, and all). The Cumulative Distribution Function (CDF) is fit
using the Weibull family of curves. Since ceiling and visibility distributions are not normal,
fitting their CDF requires the Weibull's flexibility. The use of the Weibull curve for
modeling ceiling and visibility is well documented (Somerville and Bean, 1979; Somerville
and Bean , 1981; and Whiton and Berecek , 1982).

Using the transnormalization process. Equivalent Normal Deviates, or ENDs, are calculated
for each month, hour, wind category. Once the variables are normalized, the Weibull and
Reverse Weibull are used to fit the visibility and ceiling CDF, respectively. The equations
are expressed as:

P=l-e

(9)

and

P=e

(-axf)

(10)

where a and P are the modeling coefficients, is some threshold of ceiling or visibility, and
P is the probability that an actual ceiling or visibility observation (^ will be less than
The values of the empirical cumulative distribution are regressed on the Weibull and Reverse
Weibull distribution frinction.

The modeling coefficients are used by the microcomputer program to calculate normalized
probabilities which are inverse transnormalized to convert the END probability back to an
actual probability. This process allows the user to generate conditional and unconditional
climatological forecasts of ceiling, visibility, and joint ceiling and visibility. The user selects
a wind direction category and in the case of conditional climatology, an initial value of
ceiling and/or visibility. The program then displays the probability of ceiling and/or
visibility at user specified thresholds and times out to 72 hours in either tabular or bar graph
form, as shown in Figures 1 and 2.

Figure 1. MODCV tabular output

Figure 2. MODCV graphical output

3. MCH)Fl.VF3aFlCATia\

Verification of MODCV was conducted for eight locations around the world in order to test
the model under different climatological regimes. MODCV was tested against Wind
Stratified Conditional Climatology (WSCC) tables which are produced by USAFETAC OL-A
located in Ashville, NC. The goal was to provide a compact computer based product that was
at least as good as the bulky WSCC tables.

3.1 Brier Skill (P) Score

The Brier Skill or "P" score (Brier, 1950) is a statisitcal technique used to measure the
amount of skill in the probability forecast. Ihe P-score equation is:

(11)

where r is the number of forecast categories, N is the number of days, f is the probability
forecast of the event occurring in the category, and E takes the value of one or zero according
to whether the ceiling or visibility occurs in that category. P rmges from zero for a perfect
forecast to two for no skill. The number of forecast categories was determined from the
WSCC tables for each of the eight stations.

Verification data was collected during the months of April and November of 1988. Because
of the sheer bulk of the data, only the 3-hour and 24-hour forecasts were verified. For each
day in April and November, MODCV and WSCC forecasts were compared to observed
conditions and a P-score was calculated for each. The MODCV P-scores averaged over the
month were consistently lower than the WSCC P-scores at both the 3-hour and 24-hour
forecast times for both ceiling and visibility. Figures 1-3 show the average P-scores of all

nine st^ions wmbined for the months of April, November and for both months combined,
respertiyely. Note that the scores are consistently lower (better) for the MODCV forecasts
especialty at the 3 hour point. Table 1 lists the numeric P-scores for each of the nine stations
averaged over the 30 days of April and Novemb^. Also included are the averages for all
stations for April, November, and fiDr both months combined.

Table 1. P-Scores for 9 stations for April and November for MODCV and WSCC.

4. SUMMARY

MODCV was originally created to give forecasters an easy-to-use microcomputer prograrn
to make climatological data easier to use. Unfortunately, the fielded program lacked the wnd
stratification needed to provide useful probabilistic guidance. Wind stratification has been
added and the results indicate that MODCV provides more accurate climatological forec^ts
than the WSCC tables. Also, the P-Scores calculated using the new version are smaller than
those calculated using the original version (not shown). Since the conclusion at that time was
that MODCV was practical for operational use, it can be concluded that the new version can

be used operationally.

5. REFERENCES

Boehm, A. R, 1976, Trcmnormdized Regression ProbMity", AWS-TR-75-259 Air
Weather Service, Scott AFB, IL

Brier, G. W., 1950, "Verification of Forecasts Expressed in Terms of Probability" Mon Wea
Rev, 78: 1-3. ’

Gringerton, 1. 1., 1966: A Stochasitc Model of the Frequency and Duration of Weather Events,
United States Air Force Cambridge Research Laboratories, Bedford, MA

Kroll, J. T., and H. A. Elkins, 1989, The Modeled Ceiling and Visibility (MODCV) Pmeram
USAFETAC/TN-89/001, United States Air Force Environmental Technical
Applications Center, Scott AFB IL 62225-5116.

Somerville, S. J. and P. N. Bean, 1979, Stochastic Modeling of Climatic Probabilities,
AFGL-TR-79-0222, United States Air Force Geophysics Laboratory, Bedford, MA '

Somerville, S. J. and P. N. Bean, 1981, Some Models for Visibility for German Stations,
AFGL-TR-8 1-0144, United States Air Force Geophysics Laboratory, Bedford, MA '

Whiton, R C. and E. M. Berecek, 1982, Basic Techniques in Environmental Simulation
USAFETAC TN-82-004, Air Weather Service, Scott AFB, IL

A NEW PCFLOS TOOL

K.E. Eis
5rC-METSAT

Fort Collins, CO 80521, U.S.A.

ABSTRACT

Probability of Cloud-Free-Line-of-Sight (PCFLOS) is a powerful tool used by all
components of the DoD in weapons and sensor development. STC-METSAT has
just completed the development of a medium-resolution (12 to 15 km)
3-dimensional satellite-derived database under DOE sponsorship that provides
PCFLOS interrogation of unprecedented resolution in the Korean and Iraq areas
of interest. The database and PCFLOS extraction software includes the 13 month
period of record from April 1990 through April 1991. This package allows
user-defined target and sensor altitudes and unlike any other PCFLOS analysis
package, can provide azimuthally dependent results. Analysis of PCFLOS using
this package helped confirm earlier investigations into the behavior of PCFLOS
and its dependence not only on mean cloud cover, but also on cloud structure in
the area of investigation. The study includes both temporal and spatial variance
analysis of the new, satellite-based PCFLOS.

1. INTRODUCTION

Airborne sensors such as RAPTOR are effected by both cloud obscuration and clear-air IR
wavelength attenuation. Previous methods of quantifying these degradations have used
statistically-based Probability of Cloud-Free-Line-of-Sight (PCFLOS) algorithms that use little,
or no, satellite data and are dominated by low-resolution surface databases. This
DOE-sponsored study provides a 13-month database over the Iraqi and Korean areas and the
appropriate extraction software to compute PCFLOS directly from satellite data for the period
April 1990 to the end of April 1991. Temporal, spatial, and frequency analyses were generated
from this database.

This 15-km resolution 3 -hourly database can be improved to a 5-km resolution, 1-hour global
analysis using the STC developed Climatological and Historical ANalysis of Clouds for
Environmental Simulations (CHANCES) database (Reinke et al. 1993). This paper will

describe the elements of the tool and outline some of the results obtained from the analysis in
regard to temporal and spatial variations, cloud structure, and azimuthal behavior of PCFLOS.

2. BACKGROUND

The method used to create a 3-dimensional cloud field was fully developed in a Phase I study
(Eis 1993). A geosynchronous IR image is first interrogated for clouds. The IR radiance is then
converted to a blackbody temperature, which is then converted to cloud top height by using an
interpolated rawinsonde value computed using local rawinsonde stations analyzed with a Barnes
algorithm.

The cloud/no cloud algorithm is a modification of the International Satellite Cloud Climatology
Project (ISCCP) and High Resolution Satellite Cloud Climatologies (HRSCC) cloud detection
algorithms. Only IR images were used for cloud detection. Infrared satellite imagery from
GMS (Korea) and METEOSAT 3 and 4 (Iraq) was used for the time period from April 1990
through April 1991. The details of the cloud/no cloud algorithm are beyond the scope of this
paper but are fully developed in the Raptor II report (Eis 1994). Figures 1 and 2 show the

locations of the rawinsonde data used in the estimation of the vertical cloud portion of the data
base.

Figure 1 . Iraq area of interest. Dots indicate upper air data locations.

Figure 2a. Korean (North) area of interest. Dots indicate upper air data locations.

Figure 2b. Korean (South) area of interest. Dots indicate upper air data locations.

The analyzed height and temperature fields at the mandatory levels were merged with the
temperature of the cloudy pixels to determine the cloud-top height. A linear interpolation
between levels, or an extrapolation if the cloud was above the uppermost level, was used to find
the height of the cloud top. Again, the details of the cloud top and base algorithms can be found
inEis(1994).

To determine the cloud base, a check was first made to see if the temperature minus the dew
point at 850 and 700 mb was less than a specified threshold, in this case, 5 K was used for both
Iraq and Korea. If the lower levels of the atmosphere were determined to be moist, the cloud
base was placed at the LCL, on the assumption that the clouds were caused from moisture near
the surface (e.g. a thunderstorm). If the cloud top was less than the LCL, the base was assigned
a value of the top height minus 250 m. This condition could be brought about by a poor
objective analysis, very low clouds with emittance of less than one, or a partly cloudy field of
view of the satellite. The cloud base was never allowed to go below the LCL.

3. ERROR CONSIDERATIONS

There are several sources of error associated with the creation of the 3-dimensional database.
The cloud detection algorithm was developed to match IR images with the eye's response,
consequently, subvisual cirrus will not be detected. The cloud detection algorithm was tuned
for each day. Cloud detection over cold land was manually edited for 146 of the 1259 images
used in this study (106 for the Korean sector and 40 for Iraqi sector).

Cloud height values are the database parameter with the largest possible error. In both the
Korean and Iraqi cases the distances between sampling points is well over 500 km. The result is
that there is significant temporal and spatial spreading of the data to each grid point. If an air
mass discontinuity between two adjacent stations is sensed, we have no way of determining
where along the station-to-station line the front lies.

The cloud base and top values were analyzed statistically for the Iraq data to show some of the
bulk behavior of the cloud/no cloud and top and bottom assignment algorithms. Figure 3 shows
the normalized histogram of cloud top temperatures, as measured with the METEOSAT 3 and 4
satellites for the Iraq area of interest for all 13 months of 0000 UTC interrogated pixels.

Iraq Cloud Top Temp Statistics 390 Days 0000 UTC

Figure 3. Normalized histogram of cloud top temperatures for Iraq at 0000 UTC.

Note the assigned temperatures behave quite well and appear to fall into a Chi-squared
distribution. This is logical since most continuous-boimded meteorological parameters (i.e.,
humidity and winds are bounded by zero and cloud heights by the earth's surface) exhibit this
type of statistical behavior. The "noise" in the trace was expected. It represents local effects
caused by terrain, water bodies, etc. Figure 4 shows the normalized distribution of computed
cloud top heights (in meters) for the same Iraq sector data set. You would expect these
distributions (temperature and height) to be very similar since height is directly derived from
temperature. The only departure from the Chi-squared distribution are the spikes at 100, 2800,
3250, 6000, 7700, 9800, and 16400 meters, due to the dominance of mandatory levels in the

rawinsonde data.

Figure 5 is the distribution of cloud base assignments. Again, the spikes show how the
rawinsonde's mandatory levels effect the cloud base values. Cloud base height clustering is seen
in ceiling statistics, but there is clearly more error associated with base height assignment than
any other part of this analysis.

Iraq 390 Day 0000 UTC Cloud Top Statistics

Cloud Top (meters)

Figure 4. Normalized distribution of computed cloud top heights in meters at 0000 UTC.

Iraq Cloud Base Frequency for 390 Days 0000 UTC

Cloud Base Height meters-ASL

Figure 5. Distribution of cloud base assignments.

4. PCFLOS TOOL DESCRIPTION

Eis (1994) describes the input variables and output format in detail. Basically, the user is
allowed to specify the location and elevation of both the target and sensor. Other specifications
allow the user to integrate over azimuth, time, or date. Range is typically the independent
vanable. The algorithm interrogates each path length for a clear/obscured path and averages
over the user defined parameters range to produce a PCFLOS value. In addition to PCFLOS
the algorithm also computes the mean cloud cover in the circle whose radius is defined by the
maximum range set by the user.

5. ANALYSIS

Several analyses were performed using the new PCFLOS tool. These included: High resolution
composite climatologies (not discussed in this paper, see Reinke (1993)), temporal correlation
analysis, cloud structure effects, spatial analysis, and azimuthal variance.

5.1 Temporal Correlation Analysis

A 3-hourly database (0000, 0300, 0600, 0900, 1200, 1500, 1800, and 2100 UTC) was created
using the November 1990 imagery for both Iraq and Korea. In order to show the temporal
behavior we ran each hour available for a selected day in November 1990 for four points in the
Iraqi and Korean areas. In all cases we used the following parameters: Sensor height = 18 km,
azimuth values used the full 360 degrees, and target altitudes were 0, 2.5, 5, 7.5, 10, 12.5, and
15 km for the low resolution cases and 0, .5, 1, 1.5, 2, 2.5, and 3 km for the high’ resolution
cases. The purpose of this analysis was to explore what changes occur in PCFLOS over short (3
hour) time spans. The following example was for western Iraq where many of the SCUD TELs

were located during Desert Storm. As you will see, there were major differences at the 3-hourlv
intervals.

PCFLOS went from a cloud-free unobstructed view at 100 km to 20 percent PCFLOS at 25 km
just 3 hours later (midnight to 0300 UTC). The rapid variation in PCFLOS and mean cloud
cover indicate a smoothed temporal climatology will contain large errors. Most examples
studied with the limited data set confirm that PCFLOS values vary rapidly and, in fact, vary
faster than the temporal variance in mean cloud cover.

5.2 Cloud Structures Interrogated Over Korea

We interrogated a flight path (sensor at 18 km) over Korea along the 38th parallel. The mean
cloud cover for the January days (sensor at 18 km, target at the surface) is depicted in Figure 6
Again, the rapid variation between 0 and 100 values indicates frontal-produced clouds. The
spatial variance of the PCFLOS is plotted on Figure 7. Note that for a full 360 degree azimuthal
integration and with a target-sensor separation of 100 km, the variance of the daily PCFLOS is
less than the mean cloud cover. This is in contrast to the temporal variance study (Figure 8).

Lastly, Figure 9 depicts the mean cloud cover probability versus PCFLOS scatter diagram. It
shows'a markedly larger scatter on the clear cloud end of the scatter diagram than does the Iraq
case. This is evidence of cloud structure impacting PCFLOS more than the variation in mean

cloud cover.

Jan 91 Date

Figure 6. Mean Cloud Cover over Korea, January 1991.

° Sensor Lat/Lon

January Date

Figure 8. Korea Temporal Variance (Daily).

Figure 9. Korea PCFLOS versus Mean Cloud Freeness.

5.3 Azimuthally Dependent Analysis

Up to now, all of the analyses have used an azimuthally independent method where all PCFLOS
values are derived using target and sensor range and heights. The delivered database can also be
set for an analysis of azimuths integrated over sectors of less than 360°.

In Figure 10, the 0 to 25 km range shows several periods of totally clear cloud cover where all
four quadrants show 100 percent PCFLOS conditions (3-8, 10-12, 15, 16, 19, 20, 22, 23, and
28) for January. On the 2, 3, 9, 18, 24, and 25 of January, the conditions were overcast in all
directions causing CFLOS values to be 0. All the other dates show wide variances in CFLOS
For instance, if Baghdad were to be attacked on January 1, and if the pilot were to approach

^ 25 km, from the southeast the

^FLOS shows a totally obscured condition.

Figure 1 1 shows similar behavior. A clearing trend is evident from January 24-28. Note that
on January 1, a northwest approach would still have a CFLOS value of over 90 percent.

6. SUMMARY

The new PCFLOS tool gives an unprecedented window into the behavior of clouds PCFLOS
and cloud structure as they relate to military operations. In the age of stealth aircraft and smart
munitions, knowledge of the detailed behavior of clouds at a specific target (azimuthally
variable) could be used to mask the delivery system's IR signature. A mission planner could
select an attack profile using day, time, and direction information derived from an advanced
version of this tool. The database and extraction software are on 8mm media and are written in
UNIX based ANSI standard C.

The utility of this tool is even higher in regions of the world with strong terrain-anchored clouds
such as coast lines (navy and amphibious operations) and mountains. The fact that there was
such a strong azimuthal dependence in western Iraq indicates the utility extends to
non-mountainous areas also.

ACKNOWLEDGMENTS

This paper summarizes the work performed under University of California, Lawrence Livermore
National Laboratory Subcontract No. B2 18795. The author wishes to thank Dr. Thomas H.
Yonder Haar and Mr. Donald. L. Reinke of STC-METSAT for their scientific expertise and
assistance in analyzing the data. The valuable support and cooperation provided by Mr. Dennis
Hakala, Technical Monitor, Lawrence Livermore National Laboratory, during the period of this
task are gratefully acknowledged. The authors would also like to thank John M. Forsythe for
developing the database and Mr. Mark Ringerud for developing the PCFLOS algorithm and
extraction code. If the reader would like interaction about the database and extraction code,
please contact the author with a copy of your request to Mr. Dennis Hakala, University of
California, Lawrence Livermore National Laboratory, P.O. Box 808, Livermore, CA 94551.

REFERENCES

Eis, K.E., T.H. Yonder Haar, J.M. Forsythe and D.L. Reinke, 1993: "Cloud Free Line of Sight
Model Differences". Proceedings of the 1993 Battlefield Atmospherics Conference, U.S. Army
Research Lab, White Sands Missile Range, New Mexico 88002-5501, pp. 863-870.

Raptor II - Climatology Studies in Relation to Cloud Type Occurrences, Final Report to the
University of California, Lawrence Livermore National Laboratory, P.O. Box 808 Livermore
CA 9455 1 , by K.E. Eis, March 1 994.

Reinke, D.L., T.H. Yonder Haar, K.E. Eis, J.M. Forsythe, and D.N. Allen, 1993:
Climatological and Historical ANalysis of Clouds for Environmental Simulations
(CHANCES)". Proceedings of the 1993 Battlefield Atmospherics Conference, U.S. Army
Research Lab, White Sands Missile Range, New Mexico 88002-5501, pp. 863-870.

THE BSELUENCE OF SCATTERING VOLUME ON ACOUSTIC SCATTERING

BY ATMOSPHERIC TURBULENCE

Harry J. Auvermann

Army Research Laboratory, Battlefield Environment Directorate
White Sands Missile Range, New Mexico

George H. Goedecke and Michael De Antonio
Dept, of Physics, New Mexico State University
Las Cruces, New Mexico

ABSTRACT

From a complete set of fluid equations, a complete set of coupled linear
differential equations for the acoustic pressure, temperature, mass density,
and velocity in the presence of stationary turbulence may be derived. To
first order in the turbulent temperature variation and flow velocity, these
coupled acoustic equations yield an acoustic wave equation given in the
literature. Further reduction of this wave equation results in a second
equation given in the literature which is good for turbulent length scales a
much greater than the acoustic wavelength X. The length scale of the
scattering volume is found to be just as important as ci and X in predicting
the general behavior of acoustic scattering by turbulence. In particular, if a
<< a„ then the first Bom temperature and velocity scattering amplitudes
for any ratio a/K are the usual ones predicted by the first equation, and both
the forward and backward velocity scattering are essentially zero for
solenoidal turbulent flow velocity. The latter is not tme if a > a,. If a >
a, >> \, then the first Bom scattering amplitudes are those predicted by
the second equation. If X ^ a > a„ other forms result for the scattering
amplitudes. Implications of these findings for predicting results of
acoustical scattering experiments where the scattering volume is often ill
defined are discussed.

1. INTRODUCTION

This is the third paper given at Battlefield Atmospherics Conferences that deals with
acoustic scattering by atmospheric turbulence. In the first paper (Auvermann, Goedecke,
DeAntonio 1992), experimental evidence was presented showing that atmospheric
turbulence near the ground was neither homogeneous nor isotropic, two conditions

required for the usual statistical model of turbulence to be valid. An alternate model
consisting of a collection of isolated vortices of different sizes was proposed. This model
was termed a structural model previously. The more descriptive name of Turbule
Ensemble Model (TEM) will now be adopted. A turbule is an isolated inhomogeneity of
either fluid temperature or fluid velocity. The first paper (Auvermann, Goedecke,
DeAntonio 1992) presented a general formulation of the method by which acoustic signal
levels in shadow zones may be estimated using TEM. The second paper (Auvermann,
Goedecke, DeAntonio 1993) showed how TEM may be used to explain theoretically the
extreme variability of acoustic shadow zone signals that has been documented
experimentally. In TEM, the scattering pattern of the various individual turbules is
assumed known. The analysis proceeds by assuming a distribution function for the sizes,
and then locating the turbules of each size randomly within the atmospheric region of
interest. The shadow zone signal is then the summation of the contributions from each
turbule. To carry out the summation in the first paper (Auvermann, Goedecke,

DeAntonio 1992), a uniform concentration of turbules (number of turbules per unit
volume) was assumed accompanied by a reasonable estimation of the volume from which
the detector could receive signals. The summation in the second paper (Auvermann,
Goedecke, DeAntonio 1993) was carried out directly because a relatively small number of
turbules was postulated, the position and size of each being chosen randomly within
appropriate limits.

In this paper, the problem of determining the volume from which significant scattering can
occur is addressed in a more rigorous manner. Acoustical signals of interest to the Army
are in general low frequency. The import of this is that the wavelengths of interest are
large compared to the dimensions of either source or detector. Therefore, both source
and detector are nearly omni-directional and thus cannot serve to define a scattering
volume. This is a complication not usually experienced in optical scattering scenarios.

For example, optical scattering by atmospheric aerosol is usually modeled with a narrow
beam from a laser source and a narrow field-of-view detector system, the overlap or union
of the two defining a small scattering volume. This geometric construction is the usual
way scattering volume is defined. In this paper (except in section 4), scattering volume
will denote the volume from which significant scattering can occur. An additional
simplification that can be taken advantage of in aerosol scattering is the fact that the
largest aerosol particle dimension is small compared to the dimension of the scattering
volume. Even though acoustic wavelengths are large, turbule sizes can be larger, and
may approach the scattering volume dimension. In section 2, the scattering pattern of
individual turbules is used to define a scattering volume as a function of turbule size.

Then, in section 3, scattering cross-section modified by number concentration is used to
determine the relative contributions from the various size classes. Section 4 contains
general results that may be appUed to determine if surface integrals from scattering theory
may be ignored, as is usually done in optics. Conclusions that may be drawn from this
work are discussed in section 5.

The symbols for the some of the variables, parameters and mathematical operations are
summarized in the foUowing Ust. Others are defined in the text. Bold quantities in the

list (for example r) are three-vectors.

NOTATION

turbule characteristic size, m
asymptotic acoustic wave speed = 344 m s '
a/dXi ; X, = x; X2 = y; Xj = z
time dependence of acoustic wave
acoustic wave frequency = 500 hz
propagation vector, m '
magnitude of k = a)/c„
wavelength, m
position vector, m
magnitude of r

integration variable position vector
time variable, s

turbulence field temperature difference ratio
turbulent flow velocity in the absence of the acoustic wave
i-th component of Vq

scattering volume limit angle for velocity turbule ensemble
velocity turbule ensemble relative scattering cross-section
size parameter = ka
scattering angle between k and r
wave angular frequency = 2irf

2. SCATTERING PATTERN INFLUENCE ON SCATTERING VOLUME

The theory of acoustical scattering from turbules is too lengthy to be covered in this
paper. It is covered elsewhere (Goedecke, DeAntonio, Auvermann 1994a). 'Hie
following is a brief synopsis of how scattering patterns are determined theoretically.
Beginning with a complete set of fluid equations in density, pressure, temperature, and
velocity, each variable is assumed to be made up of a time independent part representing
the inhomogeneous medium plus a small time dependent part representing the acoustic
wave. Expressions representing the above are substituted in the fluid equations and terms
second order or higher in the ratios (vo/c», t) are discarded. Assuming harmonic time
dependence, the resulting wave equation is

Coo

exp(-ja)t)

T(r)

Vo(r)

Voi

+ k2)T7(r) = d.jd^T](j) + 2jo)-'ai(Vo,0,0i)Tj(f) = -47rS(T)r7(r)

(1)

where the summation convention is used for repeated subscripts and

S(T) = StOO + S^(f) = -(47r)''ai(T0i) - (j/27r(o)ai(Vo,a,0i).

(2)

[Sj(f), SyOO] define the scattering operators for (temperature, velocity) inhomogeneities

respectively. ij(r) is the acoustic wave field quantity, the ratio of the acoustic pressure to
the total fluid pressure.

The Green’s function solution for an incident plane wave is written down, and a Bom
approximation is made in the scattering integral. The Bom approximation involves
replacing the field in the scattering integral by the incident field. The result is where

= /d^r,exp(-jkf-r,)S(r,)expO‘ic-r,) (3)

fe is called the scattering amplitude. Equation (3) has been used (Goedecke, DeAntonio,
Auvermann 1994a) to derive the scattering cross-section for temperature and velocity
turbules. The scattering cross-section is equal to the scattering amplitude multiplied by its
complex conjugate, and has units of length squared. Only velocity turbules will be
considered further in this paper.

It has been shown (Goedecke, DeAntonio, Auvermann 1994b) that an isotropic ensemble
of turbules having a given scale length a but arbitrary velocity morphology except for
V vq = 0 can be replaced by an ensemble with velocity given by Vq = n5<ff(r),
where f(r) is a function of the distance r from turbule "center", and fl is a randomly
oriented angular velocity. A Gaussian form of f(r) has proved convenient (Goedecke
1992), so that

VqOO = Ax r exp(-r^/a^) (4)

The scattering cross-section obtained from the theory outlined above was given in the
report (Goedecke 1992). To simplify the presentation in this paper, the cross-section
averaged over orientation angles will be used. This cross-section is (Goedecke 1992)

f— 1

fftax'f

l3 j

, 4kc„

\ /

[sin(i|;)cos(i|;)fexp{-x^[l - cos(i|;)]}

(5)

Two normalized expressions are defined in eq. (6) below for the puipose of illustration of

= 4[sin(i|;)cos(iJ;)pexp{-x^[l - cos(i|;)]}

^vN

j [sin(tlf)cos(il;)]2exp{-x^[l - cos(iJ;)]}

(6)

the behavior of eq. (5). The first contains all of the angular dependence and the second
includes the multiplicative size parameter dependence. These two functions are plotted in

the next figures to show the nature of the velocity cross section in the (x, domain.
Figure 1 shows the angle dependent part, the axis label "Sigma" being Uvn- Hereafter, the
axis label "Chi" is | and the axis label "Psi" is \p. Figure 2 shows the influence of the
size parameter on the cross-section, the axis label "Sigma" being u’vn.

Figure 1. Normalized velocity turbule Figure 2. Velocity turbule scattering

cross-section (angle part only) cross-section

The range of the independent variables x and ^ were chosen in figure 2 to illustrate
interesting features of the function (t’vn. When x is 13.4 and ^ is 0.1, u’vn peaks at
very nearly unity. For \j/ not near zero, the exponential drives a’vN to zero. As x
increases beyond the values in the figure, the peak of continues to increase.

However, the width of the peak decreases and the value of \p at the peak decreases.

These curves for the cross-section are not the entire story because other factors influence
the scattered signal at a detector. Consideration of these other factors is undertaken in the
next section.

3. NUMBER CONCENTRATION INFLUENCE ON SCATTERING VOLUME

In the previous section, the dependence of the cross-section upon size parameter was
shown to emphasize the importance of large turbules which have narrow scattering
patterns. To do an incoherent summation for the signal scattered from an ensemble of
turbules, the first expedient to employ is to simply add the effects of the many by
multiplying by a number concentration. Since the scattering volume as a function of
turbule size is the desired quantity in this paper, the number concentration as a function of
size is necessary. An estimate of this has been made (Goedecke, DeAntonio, Auvermann
1994c). Furthermore, an estimate of the characteristic velocities of the turbules is
necessary. The following power law scaling functions are first assumed:

(7)

N. I

\ 1

f a

JZ|

The meaning of eq. (7) is as follows. The largest turbules are identified by the subscript
1. The largest turbules have the concentration Nj. Their characteristic size is aj. The
largest velocity turbules have characteristic velocities v,. Other sized turbules are
identified by the index a. The exponents (/5, p) are chosen so that a homogeneous
isotropic ensemble of turbules matches the Kolmogorov spectrum. The results are
(Goedecke, DeAntonio, Auvermann 1994c) p = 3, v = 1/3 . An interesting feature of
this result is the concentration exponent being 3. This means that the packing fraction is
the same for all size classes. The velocity scaling exponent is 1/3 as may be derived from
a simple energy cascade calculation.

It is now possible to write the size parameter dependence of the cross-section for each
size of turbules. Multiplying eq. (5) by N„ and using eq. (7) yields for the cross-section
per unit volume for turbule size 3.^ and size parameter

ffy(k,f) = ira,^

48ci

(kai)’/^Xa’’^^rsin(il;)cos(iJj)]2exp{-Xa^[l - cos(4i)]}

(8)

The size parameter subscript will be dropped hereafter. The largest turbule size has been
assumed to be 10 m, which is appropriate for a velocity turbule centered ten meters above
the ground. The size parameter for this size turbule is 91 .325. A velocity turbule is
produced by wind shear with the wind velocity zero at ground level. Further assuming
that the velocity ratio (vj/c„) is 0.01, this size turbule would have a characteristic velocity
of 3.44 m-s *. This velocity would be produced by a wind gradient with this velocity at
turbule center at 10 m height and twice this velocity at turbule upper edge at 20 m height.
The scenario including the ground is more complex than is possible to treat in this paper.
Rather, the assumption will be made that both source and detector are in free space and
that the atmospheric turbulence is homogeneous and isotropic with characteristics of that
at 10 m height. Considering the line joining source and detector a reference line from
which to measure scattering angles and that the source is a long distance away, the
scattering Mgle is the offset angle from the detector to a differential scattering volume.

The total signal received by the detector will be a volume integral of the differential
scattering volume times the expression in eq. (8). A further simplification will be taken in
that no further specification of scenario parameters will be given. Thus, no range
dependent effects will be considered. TTie volume integration will be confined to a shell
around the detector of radius R, and thickness AR^. Figure 3 depicts this geometry on a
plane section through the scattering volume under consideration. The scattering angle is ^
Md the view angle is . These two angles are equal for this choice of source wave
incident direction. The differential scattering volume will then be the ring around the axis

whose cross-section is shown in the figure
and is [2w AR, sin(^’) df]. Equation (9)
below derived from eq. (8) gives the specific
mathematical form for the velocity turbule
ensemble scattering cross-section.

^v(X,C)

Vafv|"(ka|)'”R/AR,N,

, 24c^

c (9)

I'd ilJ 'sin^ (ijr 0 cos^ { 0

exp{-x^[l - cos(ilj')]}

Figure 3. Scattering volume geometry
has been determined and which contains the

The expression involving the size parameter
X and the scattering volume limit angle f in
eq. (8) will be plotted in figure 4 for the
case of f = IT. This is the case in which
the cross-section for an entire spherical shell
maximum of the expression. The maximum

occurs at X = 91.325 and is 3701.82. That which has been plotted in figure 4 is the ratio
of the scattering cross-section to this value, so the maximum in figure 4 is unity. The
curves for which the relative scattering cross-section is 0.01 and 0.95 are plotted in figure

5. A few of the calculated points are listed below:

(88.5592, ir) = 0.95 (91. 3250,0.033716) = 0.95

I (60.2799, it) = 0.50 |,.,„(91. 3250,0.020059) = 0.50

6.2246,7r) = 0.01 (91. 3250,0.005969) = 0.01

These curves are interpreted as an aid to forming a turbule distribution as follows. As an
example limit angle, take 0.05 radian on figure 5 at the largest size parameter. This is
larger than the size for the 0.95 contour. Extend a cone of this central angle out to 200 m
and it will contain an entire 10 m radius turbule. Even if turbules were placed in a
hexagonal close-pack configuration, the six turbules in the next circle can at most
contribute 0.05 to the total scattering. Thus, our distribution need contain only one
turbule of the largest size, or at the most seven. Assume that the shell thickness AR, is
20.0 m, large enough to accommodate a collection of the largest turbules. At the other
end of the distribution, consider those whose size parameter is 6.2246. The radius of
these would be 0.682 m. Some 5,047,000 of these could be packed into the entire
spherical shell at a range of 200 m. However, this entire ensemble would scatter no more
than 0.01 times that scattered by the single tubule of the largest size. Consider now
turbules with size parameter 88.5592 or radius 9.697 m. The complete shell would need
to be filled with turbules of this size to scatter 0.95 times the scatter of the largest turbule.
The number of these would be in the neighborhood of 1,755. For turbules of size

parameter 60.2799 or radius 6.600 m, the number in the shell would be 5,566. These

VELOCITY TURBULB SCATTERINO CROSS- SBCTICW

Figure 4. Relative velocity

scattering cross-section

SCATTBRINC VOLUHB LIMIT ANCXB

/vAirtriKv

Figure 5. Scattering volume limit
angles for velocity

would contribute only 0.50 times the scatter of the largest turbule. Although integration
with respect to size parameter has not been attempted, and therefore it cannot be said
definitely, it does seem likely that the scatter from turbules less than 0.682 m radius will
total a great deal less than 0.01 times the scatter from those of larger radius. This means
that an upper bound has been established above on the number of turbules that need to be
placed in a representative distribution for the scenario considered. It is clear that the
larger turbules dominate the scattering to be considered.

4. APPLICABILITY OF STANDARD SCATTERING THEORY

Standard scattering theory for scattering from a single localized scatterer involves a plane
wave incident on the scatterer and a Green’s function solution of the wave equation. The
quantities obtained are the scattering amplitude and the scattering cross-section in the far
field of the scatterer. In the analysis of the previous section, standard theory results were
used for the scattering from individual turbules, in the Bom approximation. The
summation of cross-sections for the collection of turbules in each size range neglects the
coherence properties of the scattered signals which is appropriate for the ensemble average
scattering by a collection of scatterers having random locations except in the near forward
direction (Goedecke, DeAntonio, Auvermann 1994c). Standard scattering theory for an
ensemble of scatterers is usually applied when the detector is in the far field of the
scattering volume occupied by the ensemble. For the scenario used in this paper, the
detector is assumed to be in the "near field" of the scattering volume, but the far field of
each turbule. This will not actually be tme for the larger turbules in a more realistic
scenario. Also, in many scenarios, the incident wave is not plane. The importance of

deviations from standard far field scattering geometry has not been determined.

Some general conclusions may be drawn from the standard scattering theory, general in
the sense that they do not depend on the form of the temperature and velocity distributions
considered (Goedecke, DeAntonio, Auvermann 1994a). Using the formd result of eq.

(3), it is recognized that the volume integral need only extend over a finite scattering
volume. In this section, the expression scattering volume is thought of as the intersection
of the illuminated region and the detector field-of-regard, perhaps made of limited extent
by the use of a parabolic reflector. Although this volume may have a complicated shape,
a single scale length aj is ascribed to it for convenience. The effect of turbulence outside
the scattering volume rapidly goes to zero. Integration of eq. (3) by parts yields two
terms, the first being essentially the Fourier transform of the turbulence distribution
(either temperature or velocity) and the other a surface integral over the scattering volume
surface. In the commonly treated case where a< <as, there are many turbules in the
scattering volume and the surface integral will be negligibly small. The result is the
standard theory that has been used in the first parts of this paper. If, however, the turbule
scale a is greater than a^, the surface integral is not negligible. The result is the same as
our result above except the cosine factor, which makes scattering at right angles to the
incident wave zero, is not present. In the third case, where the wavelength is greater than
the turbule size which is in turn greater than a,, the surface integral is again not
negligible. The scattering pattern can deviate appreciably from the standard results.

5. CONCLUSIONS

Battlefield scenarios in which acoustic propagation can have significant importance will
often be such that standard scattering theory will not fully apply. This may occur when
the wavelength, the length scale of the turbulence, and the length scales of some of the
turbulent eddies, and the length scale of the scattering volume are of the same order, or
when the detector is not in the far field of the scattering volume and/or the individual
turbules. Thus, definition of the scattering volume is an important element in calculating
turbulence scattering.

Using specific examples of turbule morphologies and a particular simplified scenario, it
was shown that large scale turbulence dominates scattering and that the scattering pattern
of these large scale entities tends to limit the effective scattering volume. The scenario
involved a 4x detector assumed to be in the far field of each turbule in an ensemble of
randomly located turbules of different sizes.

Future work will need to deal with more complex scenarios. However, considerable
information may be derived with the use of standard scattering theory, such information
giving an initial approximation to the true result. Also, to be included are the effects of
ground reflections, shadow zones, and the l/i^ effects of source and scattered fields. All
of these effects may further limit effective scattering volumes and/or modify the current
results.

Concerning the enormous fluctuations of scattered signals measured in shadow zones
(Auvermann, Goedecke, DeAntonio 1993), these must occur because of relative motion
among a moderate number of large entities. This situation requires consideration of
scattering amplitudes rather than cross-sections. Even if the signal from all turbules at
6.22 size parameter were in phase and the signal from all turbules at 91.325 size
parameter were in phase separately, the former could produce only a plus or minus 20%
variation in the overall summed signal. The experiment showed the variation was 100%,
indicating that the relative amplitudes of the different signiflcant contributors are nearly
equal.

REFERENCES

Auvermann, H. J., and G. H. Goedecke, 1992, "Acoustical Scattering from Atmospheric
Turbulence," Proceedings of the 1992 Battlefield Atmospherics Conference. 1 - 3 Dec.,
1992, Ft. Bliss, Texas.

Auvermann, H. J., G. H. Goedecke and M. D. DeAntonio, 1993, "Fluctuations of
Acoustic Signals Scattered by an Ensemble of Turbules," Proceedings of the 1993
Battlefield Atmospherics Conference. 30 Nov. - 2 Dec., 1993, Las Cruces, New Mexico.

Goedecke, G. H., 1992, "SCATTERING OF ACOUSTICAL WAVES BY A SPINNING
ATMOSPHERIC TURBULE," Contractor Report CR-92-0001-2, U. S. Army
Atmospheric Sciences Laboratory, White Sands Missile Range, NM 88002.

Goedecke, G. H., M. DeAntonio andH. J. Auvermann, 1994a, "First-order acoustic
wave equations and scattering by atmospheric turbulence," (submitted for publication in
the Journal of the Acoustical Society of America') .

Goedecke, G. H., M. DeAntonio and H. J. Auvermann, 1994b, "Acoustic scattering by
atmospheric turbulence I: Individual and randomly oriented turbules," (submitted for
publication in the Journal of the Acoustical Society of America V

Goedecke, G. H., M. DeAntonio and H. J. Auvermann, 1994c, "Acoustic scattering by
atmospheric turbulence H: Homogeneous isotropic ensembles," (submitted for publication
in the Journal of the Acoustical Society of AmericaV

RELATIONSHIP BETWEEN AEROSOL CHARACTERISTICS
AND METEOROLOGY OF THE WESTERN MOJAVE

L.A. Mathews and J. Finlinson
Naval Air Warfare Center
China Lake, CA 93555, USA

P.L. Walker

Naval Postgraduate School
Monterey, CA 93943, USA

ABSTRACT

The Visibility Impact Study was an intense, comprehensive project intended to measure aerosol size,
chemical composition and optical properties. Sites at Tehachapi Pass, Antelope Valley and China
Lake were instrumented with nephelometers, aerosol filter samplers, meteorological instruments and
in the case of the Antelope Valley and Tehachapi Pass with aerosol sizing instruments operated
continuously from mid-July through mid-September 1990. Most data collected were for ambient
conditions. Also, data were collected for intensive smog conditions in the Tehachapi Pass and for
windy conditions on the high desert. Four six hour filter samples were collected daily in the
Tehachapi Pass. The purpose of this report is to present some results of analysis of the aerosol data
and to relate the observed aerosol characteristics with meteorological conditions. Usually, polluted
air is transported into the western Mojave from Los Angeles through the Soledad and Cajon Passes
and from the San Joaquin Valley through the Tehachapi Pass primarily during thermal lows in the
San Joaquin Valley and high desert which occur most frequently in the summer. Polluted air at
China Lake originates in the San Joaquin Valley. Fine particle (0-2.5p) concentrations by mass are
35-40% organic carbon and 30% sulfates, nitrates and elemental carbon. The remainder is dust.
The organic carbon component of the Tehachapi aerosols increased dramatically during some
intensive periods. Also, large amounts of sulfur were observed for some of these periods. Wind and
dust conditions occur during Rocky Mountain highs causing flows from the northeast. Dust mass
and composition dependence on wind speed were determined at each of the sites from filter data.
The dust mode aerosols are made of clays and those clays have been identified. Their composition
is wind speed independent for speeds up to 10 m/s, i.e. there is no silicate mode. Dust mass is wind
sjjeed independent up to 6 m/s. Beyond that dust mass is exponentially related to wind speed by m
= 0.55exp(0.59u). Dust mass computed from size distributions also exhibits the 6 m/s threshold.

1. INTRODUCTION

Objectives of the present work are to characterize aerosols of the Mojave Desert and to relate those
characteristics to meteorological conditions. Simultaneous visibility, meteorological and aerosol
data were taken at four wide-spread locations in the western Mojave Desert of California starting
the first week in July and running through the second week in September 1990. Locations of the
sampling sites are shown in the first figure. Size distribution data were taken at Tehachapi Pass and
on Edwards Air Force Base in the Antelope Valley 2 miles south-east of Rogers dry lake. Filter
sampling was performed daily on the China Lake dry lake in the Indian Wells Valley, Edwards and
Tehachapi.

Figure 1 . Topographical Map of the Mojave Desert, San Joaquin Valley, and Los Angeles Basin Marked with the
Measurement Sites at Tehachapi, Edwards Air Force Base and China Lake. Air pollution is transported from the San
Joaquin Valley to China Lake and Antelope Valley through the Tehachapi Pass and from the Los Angeles Basin into the
Antelope Valley through Soledad and Cajon Passes.

The Antelope Valley is part of the southwestern Mojave Desert beginning fifty miles north of Los
Angeles International Airport. The Mojave Desert is in the rain shadow of the Sierra Nevada and
Tehachapi Mountains to the west and the San Gabriel Mountains to the south leaving the desert
relatively dry and cloud free. The Antelope Valley is separated from the Los Angeles and San
Fernando Valley air basins by the San Gabriel Mountains. The Tehachapi Mountains, to the west,
separate the Antelope Valley from the San Joaquin Valley. The Indian Wells Valley lies in the
north-western Mojave Desert 200 km northeast of Los Angeles at the southern entrance to the
Owens Valley. It is bounded by the Sierra Nevada Mountains to the west and the Panamint range
to the northeast.

Combustion aerosols are transported into the Mojave from the San Joaquin Valley through the
Tehachapi Pass and through the Soledad and Cajun passes from the Los Angeles air basin.' Thus
the valley's atmosphere contains a spatially and temporally complex mixture of aerosols of urban.

industrial and desert origin. Combustion aerosols at China Lake originate in the San Joaquin Valley.

The prevailing air flow tends to be from the west or northwest most of the year, with a shift to
southwesterly flows in the summer. The actual flow patterns and wind directions in the lower levels
of the atmosphere are controlled by the locations of high and low pressure systems. In the summer
months solar heating in the desert creates a thermal low pressure area which tends to persist through
the night, generating flow into the desert for most of the day. Winds speeds are typically highest
in the afternoon and lowest in the morning.

The prevailing mesoscale flow patterns tend to be dominated in the lower levels by topography and
thermal effects. Without convective mixing associated with wind, the air in the Mojave tends to be
stable, with mixing depths comparable to, but somewhat higher (in the afternoon) than the heights
of the mountain ranges which separate the desert from the coastal valleys. Thus, the prevailing
westerly flows tend to be channeled into the desert through passes in the mountain ranges most of
the day. Flow into the Indian Wells Valley is from the San Joaquin Valley through the Tehachapi
Pass. This flow bypasses the Indian Wells Valley in the morning, but flows through it in the
afternoon.

2. EXPERIMENTAL PROCEDURE

2.1 Meteorological Data

Radiosonde data were taken daily at 0230 PST at Edwards and at 0530 at China Lake at 1000 foot
intervals from surface to 1(X),000 feet. Wind speed and direction were obtained at China Lake and
Edwards with Handar Wind Speed and Direction Sensors while temperature and dew point were
obtained with a Handar Temp/RH Probe. Climtronics instruments were used to take similar data
at Telhill and Tehachapi. The meteorological instruments were placed ten meters above the ground
and recorded data 24 hours a day.

2.2 Aerosol Filter Samplers

Continuous, twenty-four hour samples were taken with Wedding 2X4 Filter Samplers. These
samplers acquired one coarse and three fine aerosol samples daily. The coarse filter sample and two
of the fine filter samples were collected on Teflon filters for mass, absorption, and elemental
composition. The second fine Teflon filter serves as a data quality check. The third fine particle
filter was quartz fiber and was used for elemental and organic carbon capture.

Two five hour samples were taken daily from 0700 to 1200 and from 1200 to 1700 PDT at Edwards,
Tehachapi and China Lake using NEA Sequential Filter Samplers (SFS). The PMIO size fraction
was transmitted through Sierra-Andersen 2541 size-selective inlet into a plenum. At Edwards and
China Lake the PM2.5 fraction was obtained using a Bendix 240 Cyclone PM2.5 Inlet. At
Tehachapi the PM2.5 fraction was obtained using a Desert Research Institute (DRI) MEDVOL
Model 3030F with Bendix 240 Cyclone PM2.5 Inlet. Two sets of filters were used simultaneously
in both PMIO and PM2.5 SFS. One set consisted of a Teflon-membrane filter which collected
particles for gravimetric and x-ray fluorescence (XRF) analyses. The other fine filter holder
contained a quartz-fiber filter. Deposits on this filter are submitted to ion and carbon analyses.

Nitrate, sulfate, ammonium, chlorine and potassium masses were determined gravimetrically as each
was chemically extracted from the Teflon filter. XRF analysis was performed on Teflon-membrane
filters. Analyses were performed using a Kevex Corporation Model 700/8000 energy dispersive x-
ray (EDX) fluorescence analyzer. The analyses were controlled, spectra acquired, and elemental
concentrations calculated by software implemented on an LIS 1 1/23 microcomputer interfaced to
the analyzer.

2.3 Particle Sizers

Particle sizers were located at Edwards and Tehachapi for the 1990 experiment. The sizers were
mounted four meters above the ground and took 20 minute data twenty-four hours a day. At
Edwards aerosol size distributions were obtained with a TSI Differential Mobility Particle Sizer
(DMPS) for particles with diameters in the range 0.01 to 0.8p and with an APS-33 Aerosol Particle
Sizer for particle diameters ranging from 0.5 to 30p. Aerosol size distribution measurements were
also taken at Tehachapi with a TSI Electrical Aerosol Analyzer (EAA) for particles in the size range
0.01 to 0.6p and a Laser Aerosol Speetrometer (LAS-X model) Optical Particle Counter for particles
in the size range 0.09 to 3 p.

3.0 AEROSOLS DURING AMBIENT METEOROLOGICAL CONDITIONS
3.1 Composition

The average composition of the aerosols captured on the NEA SFS five-hour samplers at Tehachapi,
Edwards and China Lake is tabulated in Tables 1 , 2 and 4. The anomalously high organic content
of accumulation mode aerosols in the western Mojave (Table 1) has been observed several times
before and there has been some concern that this is due to contamination. Therefore, blank quartz-
fibers were randomly examined for organic carbon contamination before use in the field. They were
heated for at least three hours at 900 "C before use and kept refrigerated prior to heating.

TABLE 1. PM2.5 Composition (in pg/m^) Averaged Over the Period of the Project.

Edwards

China Lake

Tehachapi

Intensive

Ion

Mass

Chloride

0.04

0.033

0.037

0.4

.08

.94

Nitrate

0.5

0.34

0.96

9.8

.15

1.76

Sulfate

1.76

1.37

1.61

16.4

2.37

27.8

Ammonium

0.66

6.7

.93

10.9

Organic

4.46

4.57

4.87

49.5

3.13

36.7

Soot

0.78

0.7

1.7

17.3

1.86

21.8

Total Mass

9.84

8.52

Table 1 is a tabulation of PM2.5 aerosol composition captured on glass fiber filters. (PM2.5
aerosols are actually a mix of accumulation and dust mode aerosols.) The optical properties of
sulfate and nitrate aerosols depend upon whether they are present as acids or ammonium
compounds. The acids are clear liquids; whereas, the ammonia compounds are white, hygroscopic
solids. Unfortunately, ammonium ion mass was only recorded for Tehachapi. For both normal and
intensive smog periods there is just enough ammonia present in Table 1 to neutralize the sulfuric and
nitric acids. Thus, the sulfates and nitrates appear as ammonium sulfate and ammonium nitrate. The
source of the organic carbon is yet to be determined. It may reside in the atmosphere in vapor form
which has subsequently condensed on the filters or in aerosol form or some combination thereof.

Table 2 is a tabulation of the elemental composition of PM 10 aerosol at Edwards and China Lake.
Table 3 is a tabulation by per cent of the elemental composition of the most common clays.
Comparison of Tables 2 and 3 show that the best match, except for excess sulfur and calcium, is
with illite clay, which is common to deserts.^ (The compositions of the listed clays are averages
obtained from an extensive review of the literature^ thus, compositional agreement cannot be
expected to be exact.) It remains to be determined whether the excess sulfur and calcium are
associated with the clay. Calcium and silicon masses strongly correlate for both Edwards and China
Lake; so, calcium is a clay component. On the other hand, silicon and sulfur masses are not at all
correlated for Edwards and are only weakly correlated for China Lake. Thus, sulfur is not a dust
mode component at Edwards. Gypsum (CaS04) could be present at China Lake. However, sulfur
and calcium masses do not correlate at China Lake. Other possible sources of dust mode sulfur are
sodium and potassium sulfates. Unfortunately, there was no check for sodium. Sulfur and
potassium are weakly correlated at China Lake in the same way as are sulfur and silicon. The most

TABLE 2. Averaged Mass and Relative Composition of PM10
Aerosols Caught on Teflon Filters. _

Edwards

China Lake

Element

Mass

2.5

20.7

1.62

19.6

5.87

48.5

48.46

0.004

0.03

0.0053

0.06

0.69

5.7

0.63

7.6

0.0076

0.068

0.87

0.55

6.6

0.78

6.4

0.68

8.3

0.11

0.9

0.06

0.7

0.025

0.015

1.28

10.6

0.71

8.6

0.039

0.0004

0.018

0.013

Total Clay

12.1

8.26

likely source of the PM 10 sulfur is accumulation mode sulfates. PM2.5 sulfur masses from sulfates
at both Edwards and China Lake are only slightly less than the PM 10 masses. Thus, at Edwards
sulfur and dust have totally independent origins; whereas, some of the sulfur is associated with clay

at China Lake.

TABLE 3. Composition of Clays Averaged from Data Taken from All Over the World (Reference 5).

PM 10 samples were not taken at Tehachapi Pass. Nevertheless, the composition of dust mode
aerosols needs to be known for this location. Because the China Lake site is located on a dry lake
bed, its dust composition may not be typical of deserts at all. The Edwards and Tehachapi sites are

not on a dry lake bed. The composition of aerosols captured on PM2.5 Teflon filters is tabulated
in Table 4.

TABLE 4. Reconstruction of Composition of Dust Mode Aerosol Captured on PM2 5 Teflon
Filter.

Sulfur mass is in agreement with that captured on the PM 10 Teflon filters, which is to be expected
if most of the sulfur is in the accumulation mode. The clay components are under represented
relative to sulfur since PM2.5 filters capture only part of the dust mode particles. If the sulfur
fraction is adjusted to match that of PM 10, then the distribution of the other elements on the PM2.5

filters falls in line with that of the PM 10 except for a greater abundance of clay trace elements.
Perhaps these elements reside on the surface of alumino-silicate particles so that they represent a
greater fraction of the volume of the smallest dust particles. In conclusion, the dust mode
composition at Tehachapi Pass is also illite clay.

4. METEOROLOGICAL EFFECTS ON AEROSOL CHARACTERISTICS
4.1 Wind Speed Dependence of Aerosol Mass

Wind Speed dependence of the mass (in pg/m^) of PMIO aerosols in Figure 2 was obtained by
plotting the five-hour integrated mass captured on the NEA Sequential Filter Sampler at Edwards
against wind speeds averaged over the same period. The NEA samplers were operated from 07(X)
to 1200 and again, with new filters, from 1200 to 1700 PDT. The U-shaped dashed line in the figure
is a least-squares fit to the data. A more sensible fit would be no Wind Speed dependence below
6 m/sec and an exponential fit that approximates the least-squares curve for greater wind speeds.
This fit is indicated by the solid lines in the figure. Thus, the wind speed dependence of dust mode
mass at Edwards is given by Equation 1.

m = 7 pg/m^

m = 0.13exp(0.66u) pg/m^

for u < 6 m/sec
for u > 6 m/sec

(1)

Figure 2,

Wind Speed Dependence of Dust Mass Figure 3. Wind Speed Dependence of Dust

at Edwards Air Force Base. Mass at China Lake.

Wind speed dependence of dust mass at China Lake is plotted in Figure 3. There is no wind speed
dependence for wind speeds up to 12 m/sec according to the filter measurements. This outcome is
consistent with anecdotal information.

It was hypothesized in the last section that gypsum is a major dust component. The wind speed
dependence of sulfur and calcium for Edwards AFB are plotted in Figure 4. The wind speed
dependence of calcium mass is similar to that of dust as a whole. On the other hand sulfur has no
wind speed dependence. Calcium is probably a dust component; whereas, sulfur has some other
origin. The most likely source of the sulfur is ammonium sulfate and nitrate.

Edwards aerosol mass in the size range .06 to 1 p diameter is dominated by accumulation mode
particles typically made of the substances listed in Table 1. Aerosols in the size range 1 to 10 p are
typically clays. There is supposed to be yet another size distribution mode, due to blowing sand,
made of quartz. Therefore, the composition of the PMIO captured particles might also be wind
speed dependent. Figure 5 is a plot of the ratio of PMIO silicon to aluminum versus the short-term
averaged wind speed. Clearly, not even a part of a silicon dominated sand mode is being captured
even at wind speeds of 9 m/sec.

101

100

101

Figure 4. Different Wind Speed Dependence of

Calcium and Sulfur at Edwards Implies
Different Origins.

Figure 5. Mass Ratio of Silicon to Aluminum

at Edwards Docs not Change with
Wind Speed Indicating the Absence
of the Blowing Sand Mode.

4.2 Size Distribution

The size distributions of Figures 6 and 7 are for pre-storm winds at 0200, maximum aerosol loading
at 1600 with wind speed at 8.9 m/sec, and post-storm at 2400. These size distributions were
calculated from aerodynamic particle data assuming particle specific gravity of 2.7. The pre-storm
aerosol environment was dominated by accumulation mode particles with light dust loading. The
post-storm atmosphere is dominated by residual dust with no apparent accumulation mode particles.
Maximum particulate loading at wind speeds near 10 m/sec is dominated by dust mode clay particles
with the possible presence of a larger mode with mode diameter of about seven microns.

Wind Speed and Dust Loading Calculated from Size Distribution Data Taken at Edwards

5. DISCUSSION

Results from the present study differ in several significant ways from that of Longtin, et al", whose
model is incorporated in LOWTRAN l\ First, the accumulation (or water soluble) mode is
predominantly organic carbon instead of being composed of sulfates. That may be due to the
proximity to large urban, industrial and oil producing and refining regions. Secondly, the Longtin
model does not even include the dust mode, which is the predominant continental large aerosol
mode. It may be that the Sahara Desert from which part of their model is extracted® has been
depleted of small particle clays. Last, there is a hint in the current study of the presence of the so-
called blowing sand mode which is supposed to appear when wind speeds exceed 10 m/sec.
However, the composition of this mode is that of clay, not sand.

6. REFERENCES

1 . Trijonis, J. et al., "RESOLVE Project Final Report", Naval Weapons Center, China Lake,
Calif., December 1987. 179 pp. NWC TP 6869.

2. Pye, K., AeoUan Dust and Dust Deposits. Academic Press, New York, 1987.

3. Weaver, C.E. and Pollard, L.D., The Chemistry of Clav Minerals. Elsevier, New York, 1 973.

4. Longtin, D.L., Shettle, E.P., Hummel, J.R., and Pryce, J.D., "A Desert Aerosol Model for
Radiative Transfer Studies", in Aerosols and Climate,. Hobbs, P.V. and

McCormick, M.P., eds., Deepak Publishing. 1988.

5. F.X. Kneizys, E.P. Shettle, W.O. Gallery, J.H.V Chetwynd, Jr., L.W. Abreu, J.E.A. Selby,
S.A. Clough, and G.P. Anderson, "Atmospheric Transmittance/Radiance: Computer Code
LOWTRAN 7", Air Force Geophysics Laboratory, Hanscom AFB, Mass. 01731,

August 1988. 146 pp. AFGL-TR-88-0177.

6. dAImeida, G.A., "On the variability of desert aerosol radiative characteristics", J. Geophys
Res. 92 D33017 (March 20, 1987).

Session II

OPERATION WEATHER

EVALUATION OF THE NAVY'S ELECTRO-OPTICAL TACTICAL DECISION AH)

(EOTDA)

S. B. Dreksler

Computer Sciences Corporation
Monterey, CA 9393-5502

S. Brand and A. Goroch
Naval Research Laboratory Monterey
Monterey, C A 93943-5502

ABSTRACT

The Naval Research Laboratory EOTDA evaluation program evaluates the
accuracy and utility of the EOTDA in forecasting forward looking infrared (FLIR)
system performances. The program has focused on the collection of performance
data for three specific FLIR systems in use by Naval training squadrons. Both the
model and analysis methods have been refined since the earlier work described by
Scasny and Sierchio at the 1992 Battlefield Atmospherics Conference. The
EOTDA model now uses physical background models rather than empirical
backgrounds. Analysis procedures include a variation of target/background pairs
to allow for typical uncertainties. The results of the analysis are discussed, and
future direction will be described.

1. INTRODUCTION

The strike warfare community requires accurate meteorological analyses and forecasts to
properly plan and effectively execute tactical operations. ^Vhile meteorological information
itself is very important, it is generally of more value to the tactical decision-maker if it is
presented in a tactically relevant form. An example of such an environmental tool is the Electro-
Optical Tactical Decision Aid (EOTDA), under development at the Naval Research
Laboratory, Monterey. This product was derived from the Mark III EOTDA, which was
originally developed by the USAF Phillips Laboratory (Freni, et al., 1993)

EOTDAs are models that predict the performance of electro-optical weapon systems and night
vision goggles, based on environmental and tactical information. Performance is expressed in
terms of maximum detection and lock-on ranges or designator and receiver ranges. The

EOTDAs consist of three microcomputer based programs supporting infrared (IR) (8-12 pm),
visible (0.4-0. 9 pm), and laser (1.06 pm) systems. Each program is comprised of three sub¬
models: an atmospheric transmission model, a target contrast model, and a sensor performance
model.

As part of any development process, it is important to have a good understanding of the
environmental sensitivity of any meteorological decision aid. It is also equally important to
thoroughly evaluate these applications under various environmental conditions to establish their
strengths and weaknesses. This documentation of strengths and weaknesses can assist
operational users and can help direct research and development efforts.

Since the initial phase of this evaluation, the EOTDA has been upgraded to version 3.0 from
version 2.0 and 2.2i. The primary modifications in the EOTDA included different methods of
entering target, background and weather data. Additional generic targets were added so the
user could specify particular parameters for dams, bridges, buildings, bunkers,
Petroleum/Oil/Liquification (POL) tanks, power plants, and runway targets. The number of
backgrounds were reduced from over 150 empirical backgrounds to 6 first principles of physics
generic backgrounds: Vegetation, Soil, Snow, Water, Concrete, and Asphalt. The user can
adjust several parameters for each background. The method for entering meteorology data was
simplified and data are now entered using the standardized Terminal Aerodrome Forecast
(TAF).

2. METHODOLOGY

The Navy's EOTDA evaluation consists of the comparison between calculated and observed
sensor performance. Actual sensor performance data were collected in conjunction with normal
training missions. Navy and DoD test programs, ship deployments, reserve cruises, naval
exercises, and Naval Postgraduate School experiments. This paper focuses on forward looking
infrared (FLIR) data because much FLIR data had been collected to date. Also, our experience
indicates that the IR EOTDA is the most used EOTDA module; therefore, it is the most
important to evaluate.

To collect both operational and meteorology data, a two-sided "knee-board" information card
was distributed to the participants. Scasny and Sierchio (1992) provide a more detailed
description. Side one of the data cards was used by the weapon system operators (WSO)^ for
recording FLIR detection ranges, sensor, target and background information. The WSOs were
asked to choose detection targets that were closest in description to those targets in the
EOTDA Target List or those that could be reproduced using the generic models option. To
obtain the operational sensor detection data, FLIR operators were asked to begin approach of
the target from beyond maximum detection range and to maintain a constant altitude and
airspeed. As the aircraft neared the target, the operator logged the appropriate data. If possible,

Wersion 2.2 was an update to version 2.0 and was specifically used during DESERT SHIELD/STORM

operations.

^The Weapon System Operator (WSO) is the person who records the sensor range and other tactical data. In

different scenarios, the weapon system operator can be the Pilot, the Bombardier/Navigator, an Electronic

Warfare Officer or any other member of the air crew.

multiple passes over the target at different altitudes and approach angles were accomplished^
After the flight was completed, the data card was given to weather personnel for completion of
weather data on the reverse side of the card. In addition to the card, the weather personnel
were requested to provide copies of their observation sheets for the day preceding and the day
of the data collection. This provided approximately 12 to 24 hours of background information
for input into the EOTDA. The meteorology information required is wind, temperature,
humidity, and cloud cover.

The completed data sets were entered into the Mark ffl EOTDA. The EOTDA was mn for
each completed data set and the calculated output was then compared to the observed values of
detection range. Due to the importance of choosing the correct target/background pair, and the
fact that we did not always have exact knowledge of the precise target and background, we ran
the model with different backgrounds and sometimes different targets. For example, if the
target was a POL tank, the model was run with the POL tank as full, empty and half empty. If
the background was described as grass, the model was run with several different vegetation and

soil types.

Data previously run using EOTDA versions 2.0 or 2.2 were rerun using version 3.0^ The
resultant data between the two versions were compared. Data collected since the EOTDA was
upgraded to version 3.0 were not run using previous versions,

3. ASSUMPTIONS AND LIMITATIONS

Our data collections were done without actually interacting with the WSOs prior to or after the
mission Because of this lack of interaction, we would expect the EOTDAs, in many cases, to
predict longer ranges than seen by the WSO, since the WSO may not have reported the target
at the maximum detection range. Additionally, we do not know exactly what targets or
background parameters the WSO saw at detection range, resulting in an uncertainty in selecting
the target-background pair. The interactions and feedback in an operational environment have
been shown to improve the skill of the EOTDAs (Kelly and Goforth, 1994).

Weather biases are always present. In many cases, the weather used by the model was taken at
the nearest reporting station and not at the target site. However, we were able to develop a
fairly accurate 12-24 hour weather history. This weather history is necessary to initialize the
thermal contrast model. In many real-time operations, weather data could be less accurate,
since forecast data instead of archived data would be used as the input to initialize the EOTDA
model.

No temperature or moisture measurements of the targets or backgrounds were available. In an
operational setting, model output, observations and feedback would provide some insight as to
the nature of these variables.

Statistical tests for each sensor were limited because the independence of the data collected
could not be established at this time. This is not a simple task, since independence is a function
of differences in dates/times of data collected as well as differences in target/background pairs,
approach angle, flight altitude of aircraft, time of day, etc.

3.1 EOTDA Limitations

In addition to the data collection limitations, several assumptions were made during the
development of the EOTDA (Dunham and Schemine, 1993). The major model assumptions
are: 1) the target is in the sensor's field of view, and time in view is not an issue, 2) targets are
all ground-based, and operating vehicular targets have been operating long enough to reach
thermal equilibrium, 3) the immediate background around the target is homogeneous, 4) high-
value-targets detection criteria are similar to those used for vehicular targets, 5) the atmosphere
is horizontally homogeneous with only two vertical layers, 6) cloud cover is continuous
(scattered or broken coverages are not modeled), and 7) there exists a cloud free-line of sight
between the sensor and the target.

4. RESULTS AND DISCUSSION

Sensor data from three FLIR sensors have been obtained and preliminary analysis has begun
The present analysis examines the data collected to date. The following paragraphs discuss the
comparison of the observed versus predicted detection ranges. Due to the classification of
sensor data when associated with sensor nomenclature, the sensors will be identified in this
paper solely as sensor 1, sensor 2, and sensor 3. For a complete discussion of the sensor results
refer to Dreksleret.al., 1994.

4.1 Best Choice

As mentioned earlier, for every observed detection range, we ran many input combinations of
the EOTDA varying the backgrounds and the complexity of the scene. In some cases the target
was also varied. Backgrounds were reported by the WSO; however, these were usually brief
comments, such as grass field. Several similar backgrounds were evaluated. For the grass field
example, background selections were made from the following categories: growing states
(intermediate, dormant or growing), coverages (dense, medium or sparse), and soil moisture
(dry, wet, or intermediate).

To help us build the backgrounds, we examined the season and the previous rainfall data. Then
we selected background parameters such as dormant, sparse, dry for California summer or
growing, dense, or wet for New England spring. In many cases we selected three or four
different realistic vegetation backgrounds, varying the soil moisture from wet to intermediate or
varying the coverage from dense to medium. We then made separate EOTDA model runs for
each background. We believe this is a good method to provide a basis for analysis because an
experienced EO forecaster will make many EOTDA runs per mission, and then will decide on

an EOTDA forecast based on his knowledge of what the target-background pair will look like
to the WSO.

A measure of rnodel success that was examined was the best case (where the background was
chosen to provide a range closest to that observed - see Figure 1). We refer to this as "best
choice" and use it extensively during the analyses.

4.2 Background Analysis

Since one of the major upgrades between versions 2.0 and 3.0 was the change in backgrounds
from empirical (version 2.0) to first principles (version 3.0), we were interested to see hov/ well

100

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Figure I. Example of all EOTDA runs for one mission. The best choice is determined by selecting the case
with the smallest error. A best choice case for each EOTDA version (2.0 and 3.0) is selected as annotated.

00 10,0 20.0 30.0 400 50.0 60.0 70.0 80,0 90.0 100.0

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Figure 2. Comparison of EOTDA versions 2.0 and 3.0 best choice selections for the water background.

each background performed. We divided all the best choice runs from all three sensors by
background type. We then compared version 2.0 and version 3.0 backgrounds.

4.2.1. Water Background

Figure 2 shows the results of using the water background for each of the three sensors. For
EOTDA version 2.0, sensors 1 and 2, 50% (3 of 6) of the best choice runs were within 20% of
the observed value and 83% (5 of 6) of the best choice runs were within 50% of the observed
value. By comparison, for EOTDA version 3.0, sensors 1 and 2, 33% (2 of 6) of the best
choice runs were within 20% of the observed value and 50% (3 of 6) of the best choice runs
were within 50% of the observed value. However, for sensor 3, EOTDA version 2.0, all 5
cases had errors in excess of 225% and an average error of 355%. For version 3.0 , the same 5
cases had errors in excess of 159% and an average error of 172%. For sensor 3, the EOTDA
version 3 . 0 average of the 1 0 cases was 1 20%.

Version 3.0 had percent errors 42% lower and root mean square (RMS) errors 25% lower than
version 2.0 for the same 11 cases. But overall, the error using the water background was over
90% for version 3.0. The empirical water background did a better job of representing the actual
background for those cases using sensors 1 and 2. However, when the empirical background
was off, it was far off. The first principles water background appears to be more consistent, but
still produced large errors. The following describes how well the water background compared
with the other backgrounds.

4.2.2. Other Backgrounds

Figures 3 and 4 compare the backgrounds regardless of sensor type. The Y-axis is in percent
for the percent error analyses and in thousands of feet (kft) for the RMS analyses. Comparing
the same 33 cases (Figure 3) for the other backgrounds, version 2.0 was slightly better for soil
and asphalt, while version 3.0 was slightly better for vegetation and water. There was only one
case where concrete was the background and there were no cases that used a snow
background. The soil background (7 cases) gave the best results for each version, 26% error
for version 2.0 and 33% error for version 3.0. Asphalt was the next most accurate background
(3 cases), 79% error for version 2.0 and 94% error for version 3.0. However, the RMS error
from version 3.0 was considerably lower than the version 2.0 error; 39,000 feet versus 61,000
feet. For the water and vegetation backgrounds, version 3.0 had lower percent errors and lower
RMS errors. However, version 3.0 still had over a 100 percent errors for these backgrounds.

These results are supported by the Schemine and Dunham (1993) assessment of the first
principles background signature predictions in the Target Contrast Model. Schemine and
Dunham (1993) showed the soil model provided excellent signature predictions, while the
foliage (vegetation) model produced fairly good predictions and the water and concrete models
were highly inaccurate. The greatest inaccuracies were found in the water background model,
due primarily to two factors. First, the bi-directional effects from the reflection of the sky
ternperature are not included in the water background model. Second, during model
initialization, the water background model sets the water's initial core temperature equal to the
air temperature at the initial input time. Both problems need to be addressed.

102

Total (33/88) Asphalt (3/4) Concrete Soil (7/27) Vegetation Water Snow (0/2)

(1/7) (11/24) (11/24)

Figure 4. Comparison of all best choice selections subdivided by background. The numbers in parenthesis are the number
of cases for that background from EOTDA versions 2,0 and 3.0 respectively. The Y-axis is in percent for the percent
error analyses and in thousands of feet (kft) for the RMS analyses.

5. SECOND PHASE

The second phase of the EOTDA evaluation is designed for controlled operational validation of
the EOTDA. In this phase, each range data point used in the analysis is supervised by the
analyst in cooperation with the WSO and the local weather detachment. The WSO is provided
with a detailed briefing of sensor and weather information and data requirements by the analyst.
After the mission, the WSO is debriefed by the analyst about activity during the mission.

The purpose of this detailed analysis is to determine the detailed chronology of each event,
including detection, classification and identification ranges. These data will identify the detailed
operational and environmental characteristics controlling the utility of the EOTDA. It has been
found in previous analyses that when information gathering duties are added to the already full
workload of air crews and met personnel, data quality suffers substantially.

There are two types of analyses being conducted, aboard ship and at an air station. The
procedure at the Naval Air Station involves the analyst obtaining environmental information
from the weather detachment prior to mission pre-brief At the pre-brief, the analyst provides
support to the weather briefer in identifying conditions expected over the target. The analyst
also obtains details on the mission profile, including aircraft operations plan, target definition,
and the sensor utilization plan. During the mission the analyst remains with flight control noting
location of the aircraft and communications between the aircraft and flight control. It is
anticipated that some of the sensor ranges will be noted by the WSO, and this should be duly
recorded to the nearest second by the analyst. After the mission, the analyst debriefs the WSO,
answering any questions which arise from the mission communications or in the review of data
taken by the WSO. If video records are available of the mission, these are to be reviewed by the
analyst with the WSO.

The aboard ship evaluation will be conducted with the LAMPS SH-60B helicopter as primary
airborne sensor platform, and the Navy Mast Mounted Sight (NMMS) as the ship borne
platform. The helicopter procedures are similar to the procedures at the air station. It is
expected that the weather team deployed aboard the ship will provide similar air- WSO briefings
supported by the analyst. During the mission, the analyst maintains station at Combat
Information Center (CIC) to monitor communications and helicopter location using standard
ship resources. The analyst will debrief the sensor operator to obtain complete information
about each contact. The NMMS is used for surface-surface classification and identification. The
modus operandi includes obtaining a target range and bearing from another source ( radar,
sonar, or pilot report), and then searching the expected target region. The control is usually
mounted on the bridge with video supplied to CIC. With this arrangement the analyst can
maintain a watch over the NMMS, although communication with the NMMS operator may be
limited. An important element of this data set is to recognize when the operator is cued to a
target, and when the operator actually detects the target.

6. CONCLUSIONS AND RECOMMENDATIONS

This paper has described the evaluation of the sensor performance model portions of the
EOTDA for Navy and Marine Corps EO sensors. As previously mentioned, data are limited
and many more data points must be collected under various environmental and operational
conditions to show a statistically sound representation for each sensor's performance. Efforts to

104

collect additional data for these sensors and others are ongoing and will provide the needed
information to produce a useful evaluation of the EOTDA's prediction performance for the full
range of Navy and Marine Corps electro-optical sensors.

We are continually looking for ways to collect more controlled data for more sensors under
different scenarios. Most data were collected from just a few sources with few scientific
controls. No thermal or moisture measurements of the targets or backgrounds were taken.
These data collection efforts require interactions with participants before the data collection
mission, as well as briefings following the missions. We need to measure the weather at the
target and to determine the background parameters at detection, and we need to get all
targeting information. We also need to explore the impact of using atmospheric numerical
output as input to the EOTDAs.

Based on previous sensitivity studies (Keegan, 1990), one of the most important parameters is
the target-background pair. The first principles of physics backgrounds were more consistent,
while the empirical backgrounds were as likely to over-predict or under-predict.

The first principles of physics water background appeared to be more consistent than the
empirical water background, but it still produced large errors. A more detailed examination of
the physics of the water background is needed. As a start, the ability to set the initial sea
surface temperature is needed and also to include the bi-directional effects from the reflection
of the sky temperature. The utility of a nonhomogeneous water background could be studied
for applicability to account for moving targets and target heading changes.

To overcome model limitations cited earlier, the transmissivity model could be examined, and
we could study the effects of improving the vertical resolution and of including near surface
aerosols. Human factors studies could also be examined. As mentioned earlier, the EOTDA is
not concerned with the target search process. It assumes that the target is in the sensor's field of
view. This assumption could lead to over predictions of target detection.

The efforts to collect the data discussed above proved to be an enlightening experience.
Detailed information was obtained about the varying methods used in the different types of
tactical aviation missions. As scientific researchers, one can get lost in the realm of the "way
things should be done" without the legitimate knowledge of the "way things am done" in the
operator's or user's world. Discussions with sensor operators provided insight into the way
things are done" and as is always the case in research, the test plan was adjusted. Continuing
discussions with the aviation community and sensor operators on their tactics will provide the
required information to collect useful sensor detection data.

Strong interaction with the WSOs also provides insights into the way the environmentalist
needs to interact with the tactical users. For example. Air Force weather forecasters at Cannon
AFB NM, working closely with tactical pilots, were able to achieve remarkable success; that is
over 82% of the time, the forecasters predicted detection and lock-on ranges within 10% of
what was observed (Kelly and Goforth, 1994). We need to learn to understand the limits of
these tools as well as the extent of tactical/environmental interactions while using them.

In summary, the EOTDA evaluation program has observed the following: 1) results differed for
each sensor; 2) predicted versus observed errors were particularly large for over water

105

targeting; 3) soil backgrounds gave excellent results; 4) some biases became evident for
selected sensors; 5) more controlled experiments/evaluations are needed to determine causes of
error; and 6) some errors in data sets were induced by mission tactics and not algorithm errors.
The present or second phase of the EOTDA evaluation will attempt to remedy some of the
"lessons learned" during the first phase by including direct interaction with the WSOs prior to
and after the missions. This should provide a firmer basis for assisting operational users as well
as help redirect research and development efforts.

ACKNOWLEDGMENT

The Navy effort in the tri-service development of the Electro-Optical Tactical Decision Aid is
sponsored by the Oceanographer of the Navy (OP-096) through the Space and Naval Warfare
Systems Command Program Office, Washington D.C., program element 0603207N.

REFERENCES

Dreksler, S. B., S. Brand, J. M. Sierchio, and K. L. Scasny, 1994: Electro-Optical Tactical
Decision Aid Sensor Performance Model Evaluation. NRL/MR/7543-94-7216, Naval
Research Laboratory, Monterey, CA 93943-5502. (In Final Review)

Dunham, B. M. and K. L. Schemine, 1993: Intermediate Grade Infrared TDA Analyst's
Manual. Battelle Report for Period March 1993 through September 1993. WL-TR-94-1084,
Wright Laboratory, Wright-Patterson AFB, OH 45433-7409.

Freni, J. M. L., M. J. Gouveia, D. A. DeBenedictis, I. M Halberstam, D.J. Hamann, P. F.
Hilton, D. B. Hodges, D. M. Hoppes, M. J. Oberlatz, M. S. Odle, C. N. Touart, and S-L Tung,
1993: Electro-Optical Tactical Decision Aid (EOTDA) Users Manual Version 3. Hughes-STX
Scientific Report No. 48. PL-TR-93-2002, Phillips Laboratory, Hanscom AFB, MA 02731-
5000.

Keegan, T. P., 1990: EOTDA Sensitivity Analysis. STX Scientific Report No. 44(11). GL-TR-
90-0251 (II), Phillips Laboratory, Hanscom AFB, MA 0273 1-5000.

Kelly, J. L., and B. K. Goforth, 1994: ACC Cannon AFB User EOTDA Experience.
Proceedings of the Weather Impact Decision Aids (WIDA) Conference, Las Vegas, NV, 22-23
March 1994.

Scasny, K. L., and J. M. Sierchio, 1992: Mark III Electro-Optical Tactical Decision Aid Sensor
Performance Model Evaluation. Proceedings of Battlefield Atmospheric Conference, Fort Bliss,
Texas, 1-3 December 1992.

Schemine, K. L., and B. M. Dunham, 1993: Infrared Tactical Decision Aid Background
Signature Model Assessment. Battelle Report for Period March 1993 through August 1993.
WL-TR-94-1064, Wright Laboratory, Wright-Patterson AFB, OH 45433-7409.

106

U.S. ARMY BATTLESCALE FORECAST MODEL

Martin E. Lee, James E, Harris, Robert W. Endlich,
Teizi Henmi, and Robert E. Dumais
U.S. Army Research Laboratory
White Sands Missile Range, NM 88002, USA

Major David I. Knapp

Operating Location N, Headquarters Air Weather Service
White Sands Missile Range, NM 88002, USA

Danforth C. Weems

Physical Science Laboratory, New Mexico State University
Las Cruces, NM 88003, USA

ABSTRACT

The U.S. Army Research Laboratory (ARL), Battlefield Environment
Directorate (BED) is conducting research and development to satisfy Army
Science and Technology Master Plan, Science and Technology Objectives
(STOs) which call for target area meteorology and automated decision aid
capabilities by FY95. This STO technology will be provided to the battlefield
soldier through the Integrated Meteorological System (IMETS) and the Army
Battle Command System. ARL/BED is working to provide the IMETS Block
II an operational Battlescale Forecast Model (BFM) by FY95 that will
accurately forecast target area weather and provide input weather data to
automated weather effects decision aids. This paper describes how ARL/BED
envisions the BFM and subsequent models will be used operationally on the
tactical battlefield, both fi-om a general and technical perspective, and discusses
fiiture improvements that are planned.

1. INTRODUCTION

The U.S. Army Research Laboratory (ARL), Battlefield Environment Directorate (BED) is
conducting research and development (R&D) to satisfy Army Science and Technology Master
Plan, Science and Technology Objectives (STOs) IV.K.l and IV.K.3. STO IV.K.1 calls for
a 12 hour target area weather forecasting capability by FY95, and 24 hours by FY97. STO
IV.K.3 requires development of automated weather decision aids by FY95 and FY97 that use

107

artificial intelligence techniques to provide the Army Battle Command System (ABCS) the
capability to assess and exploit battlefield environmental effects for tactical advantage. This
STO technology will be provided to the battlefield soldier through the Integrated
Meteorological System (IMETS) and the ABCS. Thus ARL/BED is working to provide the
IMETS Block II with an operational mesoscale meteorological model by FY95 that will
provide it the capability to forecast target area weather and provide input weather data to
automated weather effects decision aids.

In meteorology, the mesoscale domain can range from 2,000 km (often referred to as the
regional or theater scale) to 2 km, which is near the microscale. Of primary interest to the
Army is an intermediate mesoscale domain of approximately 500 km which ARL/BED refers
to as the battlescale. ARL/BED's focus therefore is to develop a battlescale model capable of
forecasting battlefield and target area meteorology at the accuracies sufficient to support Army
operations and automated decision aids. A model capable of these accuracies will significantly
improve the intelligence preparation of the battlefield process and specifically the planning and
execution of deep strike fire support missions, increase the first round hit probability of artillery
ballistic systems, and prevent using high cost "smart" munitions and "precision strike" assets
in atmospheric conditions that would render them ineffective.

To achieve the milestones above and build a strategy for future development, ARL/BED has
elected to rely on the Navy and Air Force to perform the basic research of mesoscale model
development, freeing ARL/BED to concentrate its resources on adapting and applying this
research to specific Army applications. This strategy is consistent with the Joint Directors of
Laboratories Project Reliance, whereby, the Navy was assigned the lead in mesoscale modeling
research within the DOD R&D laboratory community, with the Army and Air Force R&D
laboratories agreeing to adapt the Navy's model for service specific applications. The Air Force
is also pursuing an independent initiative outside the DOD R&D laboratory environment to
evaluate mesoscale modeling technology to insure that the most suitable technology, federal
or nonfederal, is adopted for Air Force and Army battlefield weather support.

2. CURRENT SITUATION

Currently neither the Navy Operational Regional Atmospheric Prediction System (NORAPS)
— which is currently being tested for operational cases using 45 km single mesh grids (Liou
et al., 1994) — or the Air Force's Relocatable Window Model are well suited for forecasting
small scale weather features within complex terrain domains ^ 500 x 500 km^ important to
./^y battlefield operations. However, as mentioned above, both the Air Force and Navy have
either in-house research efforts underway or they are supporting research through contractual
mechanisms to develop a model that will support the smaller domain at the accuracies that the
Army requires. Until this technology matures to the point that it can be used operationally, and
to satisfy STO and IMETS milestones, ARL/BED has adapted a hydrostatic model HOTMAC
(Higher Order Turbulence Model for Atmospheric Circulation) which was initially developed
by Dr. Yamada while at Los Alamos National Labs (Yamada and Bunker, 1989). ARL/BED

108

scientists have subsequently, with Dr. Yamada's assistance, improved and tailored HOTMAC
for Army applications. This >^rmy version of HOTMAC is called the Battlescale Forecast
Model (BFM). The BFM uses the hydrostatic approximation, is relatively fast, numerically
stable, easy to use, and has detailed boundary layer physics, a most important feature for Army
operations.

ARL/BED will continue to work jointly with the Navy and Air Force to identify, develop, and
evaluate an objective model capable of the accuracies required by the Army. ARL/BED will
contribute its expertise in boundary layer physics and complex terrain interactions as this
development process evolves. However, until the objective model is considered mature enough
for operational use, ARL/BED will use the Army BFM to satisfy its near term STO and IMETS
milestone requirements. Once the objective model is judged ready for operational use, then
ARL/BED will replace the BFM with the objective model and adapt it for Army applications.
The purpose of this paper is to describe how ARL/BED envisions the BFM and subsequent
models will be used operationally in the field, both from a general and technical perspective,
and to discuss future improvements that are planned.

3. GENERAL CONCEPT OF OPERATIONS

The BFM takes into account local effects on weather patterns which may take an experienced
forecaster years to learn for a particular area. Running a mesoscale model on a workstation
computer offers the SWO the opportunity to produce a fine-tuned local forecast for unfamiliar
areas with accuracy far superior to the large-scale products currently available in the battlefield.
The BFM automatically incorporates knowledge of local terrain, important battlefield weather
observations, and centrally-produced boundary conditions close to the Area of Operations
(AO) to produce its mesoscale forecast gridded fields (see section 4.3.b). The BFM predicts
battle scale weather features causing localized effects often missed by the coarse-grid resolution
output from global and regional models. These large-scale models do not incorporate high
resolution terrain and timely local observations; the BFM does, and thus is able to more
accurately characterize battlefield weather both spatially and temporally.

The BFM will essentially serve as the automated forecast portion of the Local Analysis and
Forecast Program (LAPP). In a battlefield scenario, the BFM will automatically determine the
influence of terrain and local features on atmospheric conditions which the forecaster has
heretofore been determining manually and subjectively in the LAPP process. The BFM
calculates intercepted solar radiant energy that is converted to budgeted atmospheric and
terrestrial thermal energy over complex gridded terrain — which is translated into pressure
gradient driven, mesoscale wind production (e.g., predicted daytime heating of mountains and
high terrain reverses noctumally forced localized downslope drainage flows into upslope flows
and vice versa).

BFM initialization will include all observations from the AO such as data fi’om nearby
airfield^rigade weather teams, soundings from the division or corps Artillery Met teams, other

109

deployed military units' weather observation data, and any indigenous observations being
transrrutted. Data from global or theater scale models are also used in this process, and this is
discussed later. Observations from Automated Meteorological Surface Sensors, Unmanned
Aerial Vehicles meteorological sensors, and meteorological satellites will also be included in
the initialization process as they become available in the future. Initialization can also consist
of only the observation taken from the Tactical Operations Center. In the event that no
observations are available from the AO, the BFM will be initialized using only the gridded
analysis and/or forecast data from global or theater scale models.

Boundary meteorological conditions are automatically input for the region surrounding the
mesoscale AO. Typically, these data would be derived from grid point data closest to the AO
taken from global or regional model output valid at analysis and forecast times of interest. The
BFM forecast is executed using these boundary conditions and AO raw data as initialization
guidance and solves towards the forecast solution dictated by the global/theater scale forecast
boundary condition gridded data. Thus, large-scale flow patterns produced by the BFM will
automatically solve towards the global/theater model's forecast solution.

Looking ahead, we envision the use of mesoscale forecast models as part of the LAFP at most
fixed airfields, for test range and shuttle operations, and in more civilian applications such as
air pollution episodes, natural disasters and emergencies, etc. In the immediate future, the
BFM offers the SWO deployed to an unfamiliar AO the opportunity to accurately predict the
weather on the battlescale in real time at resolutions never before possible.

4. BFM IMPLEMENTATION PLAN

4.1 Technical Characteristics

The BFM selected for inclusion in the IMETS Block II software deliverable was developed to
provide operational short-range (^12 hour) forecasts. The BFM is suitable for use within
battlescale areas (< 500 km x 500 km). The basic equations for the BFM are the conservation
relationships for mass, momentum, potential temperature, water vapor mixing ratio, and mean
turbulent kinetic energy. The composite influence of diumally forced solar, atmospheric, and
terrestrial radiation effects on evolving Planetary Boundary Layers (PBLs) over complex terrain
is accurately simulated by the BFM.

Second-moment^ mean turbulence equations in the BFM are solved by assuming certain
relationships between unknown higher-order turbulence moments and known lower-order
moments. Presently, the BFM assumes hydrostatic equilibrium and uses the Boussinesq
approximation. The Boussinesq approximation is the assumption that the modeled fluid is

^ A second statistical moment is the double correlation, or normalized covariance, of two
turbulent quantities (Stull, 1988).

incompressible to the extent that thermal expansion produces a buoyancy (Huschke, 1959; and
Houghton, 1985). Buoyancy forces are retained in a hydrostatic basic state with respect to
pressure and density [p^ pj via the inclusion of small pressure and density deviations [p', p'].
This assumption holds as long as the (po+p')/po term in the vertical equation of motion is close
to unity (i.e., density variations are only considered when they are closely coupled to gravity).

4.2 BFM Initialization Interface '

The BFM X-window interactive initialization interface, summarized in fig. 1, will provide users
with the following flexibility when initializing all model executions:

a. Users will specify the center of the BFM forecast domain via; i) an input
longitude and latitude; or ii) a user specified hfilitary Grid Reference System
(MGRS) input - consisting of a UTM zone, x, and y coordinate; or iii) if a map
display is active, by using a mouse to graphically point and click at the desired
center point on the map background.

b. Table 1 depicts all possible combinations of grid spacing and grid point array
configurations to produce the BFMs horizontal dimensions. These options will be
specified by users to structure model domains that can range from 40 x 40 km^ to
500 X 500 km^. An estimate of the model run-time for the specified number of grid
points and grid spacing will be displayed on the screen Avith a display of the
selected grid extent before the user executes the BFM program.

c. The BFM is initialized with user selected data inputs. All available and current
data will be listed for the automatically defaulted model initialization time. The
default initialization times may commence at any hourly interval from 00:00 UTC
(i.e. 00, 01, 02, ... 23 Z), which ever is the most relevant to the local IMETS
hardware system time and which can also be supported with currently available
data; this time will be posted on the interface screen. The primary Boundary
Condition (BC) initialization data, as a function of pressure, consists of alternating,
3-dimensional coarse grid (381 km x 381 km grid spacing) sets of 12, 24, and 36
hour forecasts of wind, temperature, water vapor, and geopotential height.

Currently boundary condition data are obtained from the Global Spectral Model (GSM),
which is regularly transmitted by the U.S. Air Force (USAF) Global Weather Central via
the Automated Weather Distribution System (AWDS). This GSM data has alternating
valid times of 00:00 and 12:00 UTC for; i) analyses; and ii) 12, 24, 36, and 48 hour
forecast fields. Future plans call for obtaining boundary conditions from the Navy
Operational Global Atmospheric Prediction System (NOGAPS) if it replaces the GSM
as the AWDS' global model, and finally a higher resolution theater level model, such as
NORAPS, when one becomes operational on AWDS.

Ill

Select center of model domain

Latitude

1 _ 1 Longitude d

MGRS

UTMX/Y

Mouse

point and click on map

_Gri^

Spacing

(in km)

m 2.0
m 5.0
m 10.0

N^umber of X and Y grid points (array size)

21X21

31X31

41X41

51X51

- - - 1

Initialization

Date/Time^our

•O

Estimated
Model Run-Time

« Initial B.C. fbatemmc 1

U Final B.C. \ JDate/Timc 1

■

UA 1 Date/Time 1

SFC 1 l>atc/Time |

Accept setup parametcns
Execute BFM 1

Figure 1. BFM initialization user interface concept.

Table 1. BFM domains in km^ as a function of user-specified BFM grid spacing.

BFM Parameters

Array Number of BFM Grid Points i ||

Grid Spacing i

21x21

31x31

41x41

51x51

2 km

40x40 km'

60x60 km*

80x80 km*

100x100 kirf

5 km

100x100 km'

150x150 km'

200x200 km'

250x250 km'

10 km

200x200 km'

300x300 km'

400x400 km'

500x500 km'

Typically, the set of GSM data necessaiy to produce a 12 hour BFM prediction will
consist of 12, 24, and 36 hour GSM forecasts. This is due to the receipt time-lag of
GSM data in the field being typically ^ 4 hours. For example, a user may desire to run
the BFM for a twelve hour period commencing at 07:00 UTC and ending at 19:00 UTC
on January 28th. To produce hourly boundary conditions for this forecast, three GSM
forecasts will be required (i.e., the 12, 24, and 36 hour GSM forecasts): between 07:00
UTC and 12.00 UTC January 28th hourly boundary conditions will be produced via
interpolation between the GSM 12 hour forecast valid at 00:00 UTC January 28th, and

112

the 24 hour GSM forecast valid at 12:00 UTC January 28th; and finally, for the period
between 12:00 to 19:00 UTC on January 28th, BFM hourly boundary conditions will be
produced by interpolating between the GSM 24 hour forecast valid at 12:00 UTC
January 28th and the 36 hour GSM forecast, valid at 00:00 UTC on January 29th.

During BFM execution, GSM data sets (At > 12 hours) are linearly interpolated in time
and 3 -dimensional space to produce hourly forecast boundary condition data that
coincide with the selected BFM time and space domain. The date/time group (or non¬
availability) of GSM, and/or radiosonde upper-air, and/or surface data, applicable to the
selected model domain, will be listed on the user's display. If GSM data are not
available, radiosonde data with or without surface data can be used as initialization fields
to produce short term forecasts of up to six hours. The user has interactive window
mouse/switch control (e.g., □ = unselected, or S = an activated option) over the
selection of the possible initialization observations, GSM analyses, and forecast data
stream to the BFM in this mode. The capability to review and manually edit radiosonde
and surface BFM input observation data will also exist.

After all initialization inputs satisfy the user's specifications for the model forecast run,
the user starts execution of the BFM via a window interface switch. Actual model
execution begins only after an automated initialization data program transforms GSM,
surface and upper air data into a BFM compatible format. Upon selecting this switch,
if the model domain has been altered from the previous BFM forecast run, the user will
be prompted to load the terrain data from a Defense Mapping Agency compact disk
applicable to the model domain selected. After this operation is completed, model
execution commences.

4.3 BFM Output Interface

Upon completion of the model forecast run a BFM X-window interactive output interface,
summarized in figure 2, will provide users with the following BFM forecast data output review
capabilities:

a. The principle BFM calculations consist of forecasted 3-dimensional: 1) u and
V horizontal wind vector components; 2) potential temperature; and 3) liquid water
potential - which is the combination, within each grid volume, of all predicted:
i)liquid water; and ii) water vapor converted to liquid water. These 3 -dimensional
forecast fields will be saved at 3 hour intervals over a 12 hour forecast period
commencing at the user-specified initialization time.

b. In the field data output mode, users will be able to graphically analyze BFM
wind speed and direction, ambient temperature, and/or the relative humidity via line
and/or shaded contours (including velocity vectors and/or streamlines for wind
fields) at 7 different levels: i) 10 m above ground level (AGL); ii) 250 m AGL; iii)

113

500 m AGL; iv) 1,000 m AGL; v) 1,500 m AGL; vi) at the 700 mb constant
pressure surface; and vii) at the 500 mb constant pressure level.^

c. Users will also be able to graphically review vertical profiles of wind speed and
direction, temperature, and/or relative humidity (in percent) at user specified points
within the model domain. These profiles are constructed by vertically interpolating
results between the model domain terrain ground level and the highest model level,
using a linear interpolation scheme. Users will be able to select any point within
the model domain, to obtain vertical profile outputs, using a mouse to point-and-
click on the map background, and/or from manual keyboard entry of point location
data. And relevant radiosonde and/or surface observation sites within selected
BFM model domains will be graphically identified on the map background display
along with the location of domain bounded GSM grid data points.

d. As indicated in the Parameter Selection decision frame (fig. 2), the option to
select two-dimensional horizontal field predictions of the occurrence of fog and/or
stratus are also planned for inclusion in the IMETS Block II BFM. These outputs
will be presented in planar Cartesian coordinates above sea level - unlike the
remaining parameters, which are in either terrain following Sigma coordinates (10
m - 1,500 m, AGL) or pressure coordinates (700 mb and 500 mb surfaces).

Planar Cartesian coordinates are more correlated to the horizontal stratification of
fog and/or stratus along geopotential surfaces than terrain folloAving Sigma or
pressure surfaces.

5.0 FUTURE PLANS

ARL/BED plans to provide beta releases of the BFM to the Tactical Fusion Systems Branch,
Software Engineering Directorate (SED), Communication and Electronics Command Research
and Development Engineering Center (CERDEC), Fort Huachuca, Arizona during 1-3QFY95
for testing and evaluation. The final version of the BFM for the IMETS Block 11 will be
delivered to SED/CERDEC 4QFY95 with supporting software documentation to include
software design specifications, software test procedures, software user's guide, and verification
and validation statistics/reports. SED/CERDEC will then integrate the BFM software into the
IMETS Block n and, in partnership with Air Force weather personnel, accredit it for operational
use.

^ Mesoscale atmospheric models are typically designed to focus attention primarily on
internal PBL dynamics and interactions near the earth ’s surface. As a result, global or synoptic
scale models are more suitable in making high altitude (e.g, £ 500 mb) transport and diffusion
predictions, which do not fluctuate significantly compared to the same predictions in the PBL.

114

Select time into
forecast period

ANALYSIS

+03FCST

+06FCST

+09FCST

+12FCST

Parameter

Selection

horizontal winds

units

temperature

relative humidity

2-d fog/stratus prediction

Field data

Vertical
Profile data

MODEL

INITIALIZATION
DATE /TIME

lOmAGL

250mAGL

500 ni AGL

Level
Selection 1

1000 mAGL

1500 m AGL

700 mb

500 mb

Contours

Line

Shaded

Wind

Fields

Vectors

Streamlines

Use mouse to select map location
point for vertical profile analysis

Coordinates

Manually define Long/Lat i

profile location MGRS ^

Figure 2. BFM output user interface concept.

Since the objective mesoscale model referred to in paragraph 2.0 will probably not be available
until FY97 or later, ARL/BED plans to improve the BFM for the IMETS Block II and III, with
improvements delivered to SED/CERDEC in FY96 and FY97 as they become available.
Planned improvements include extending the BFM maximum forecast limit of 12 hours to 24
hours in the IMETS Block III delivery, better quality control and editing of input data, and
increased output parameters to include turbulence and icing index, temperature and moisture
advection, vorticity, visibility, precipitation, improved cloud predictions, and meteorological
satellite data assimilation. Other I/O interface refinements, resulting from a maturing "user
feedback-product improvement cycle" anticipated to occur, will also be implemented. Once the
objective model matures then it will replace the BFM, provided its performance in comparison
to the BFM justifies replacement.

115

REFERENCES

Houghton, D.D. (Editor), 1985: Handbook of Applied Meteorology, John Wiley & Sons, 1461 pp.

Huschke, R.E. (Editor), 1959: Glossary of Meteorology, American Meteorological Society, 638 pp.

Liou, Chi-Saan, Hodur, R., and Langland, R,, 1994: "NORAPS: A Triple Nest Mesoscale Model,”
Proceedings of the Tenth American Meteorological Society Conference on Numerical
Weather Prediction, Portland, Oregon.

Stull, R.B., 1988: An Introduction to Boundary Layer Meteorology, Kluwer Academic Publishers,

666 pp.

T. Yamada and S. Bunker, 1989: A Numerical Model Study of Nocturnal Drainage Flows with
Strong Wind and Temperature Gradients, Journal of Applied Meteorology, Volume 28 545-
554.

116

DEVELOPMENT AND VERIFICATION OF A LOW-LEVEL AIRCRAFT
TURBULENCE INDEX DERIVED FROM BATTLESCALE FORECAST MODEL DATA

Major David I. Knapp and MSgt Timothy J. Smith
Operating Location N, Headquarters Air Weather Service
White Sands Missile Range, NM

Robert Duma is

U.S. Army Research Laboratory
White Sands Missile Range, NM

ABSTRACT

Improving the accuracy of low-level aircraft
turbulence forecasts is addressed using high resolution
gridded data and terrain fields to derive localized
horizontal and vertical wind flow patterns. Two
QjjjQQtive upper— level aircraft turbulence indices are
tested at lower levels using mesoscale model data in an
effort to calculate "first guess" estimates of
potential low-level turbulence areas. The Turbulence
Index (TI) is the product of two independent terms,
vertical wind shear and the sum of the horizontal
deformation and convergence. The Panofsky Index (PI)
is a function of horizontal wind speed and the
Richardson Number. The TI, its independent terms, and
the PI are calculated, stratified and evaluated using
multivariate linear regression for specific low-level
layers across three CONUS mesoscale regions for 20
cases from January to April 1993. Using results from
the regression analyses, new low-level turbulence
forecast equations are proposed for future refinement
and verification.

1 . INTRODUCTION

Staff Weather Officers and forecasters supporting U.S.
Army aviation operations provide low-level turbulence
analyses and forecasts for fixed and rotary wing aviation
missions. New advancements in the use of "smart" munitions
and unmanned aerial vehicles sensitive to turbulence make
these forecasts even more important to mission

accomplishment. Forecasters generally rely on empirical low-
level turbulence forecast rules that have been used for
years, resulting in the habit of consistently
underforecasting or overforecasting suspected turbulence

areas.

117

Upper-level instabilities believed to cause turbulence
have been approximated using the components of Petterssen's
(1956) frontogenesis equation. Mancuso and Endlich (1966)
found that the deformation and vertical wind shear components
of this equation were independently correlated with the
frequency of moderate or severe turbulence. Ellrod and Knapp
(1992) went further by deriving a turbulence index (TI) based
on certain assumptions to Petterssen's equation. Assuming
that frontogenesis results in an increase in vertical wind
shear (VWS) , horizontal deformation (DEF) , and horizontal
convergence (CVG) , a similar increase in turbulence
occurrence should also be expected. For a given layer the
index is stated as; '

TI = VWS X (DEF+CVG) , (1)

The range of TI values associated with turbulence occurrence
were found to be model-dependent based on grid resolution and
other physical and dynamic parameter calculations unique to
every model. Typical TI values ranged from 1.0 to 12.0
(xlO- s-2) , with highest values correlated with moderate and
greater turbulence intensities.

At levels below upper-level jet stream maxima, Ellrod
and Knapp found the TI's performance to be unreliable. This
was attributed to coarse synoptic scale model grid-point
resolution that missed the more subtle features in low-level
wind fields contributing to turbulence occurrence. The
resolution problem is solved by deriving the TI from
mesoscale model grid data. The purpose of this study is to
evaluate the TI and its individual component indices at lower
levels. This is part of a project to find or develop an
accurate objective low-level turbulence forecast technique
'£ot future use by Air Force Staff Weather Officers supportincr
the U.S. Army. ^

2. MODEL AND TURBULENCE TECHNIQUE DESCRIPTIONS:

The Higher Order Turbulence Model for Atmospheric
Circulations (HOTMAC) was used to provide a mesoscale nowcast
^ri^lysis of meteorological variables using observed upper— air
and surface data reported at rawinsonde locations. This
analysis is produced by horizontally interpolating observed
rawinsonde wind data onto the mesoscale grid with a
resolution of 20 km. In the vertical, wind observations are
interpolated to predefined levels. Utilizing a
transformation to a terrain— following vertical coordinate
system, 22 staggered levels are retained to allow for high
resolution (Yamada and Bunker, 1989) , with eight levels
within the first 1000 feet AGL, five levels from 1,000-5,000
feet AGL, and eight levels from 5,000-16,000 feet AGL. (NOTE:

118

Hereafter, levels AGL will be in hundreds of feet, i.e., 010-
050 050-160, etc.) Rawinsonde data reported from 00 UTC

orovide the raw input to the mesoscale objective analysis for
this study. The TI is calculated from HOTMAC's u and v wind
components at each grid point in the horizontal and vertical.
Layer average values of VWS, DBF, and CVG are used for the
final TI calculation at every grid point for each prescribed

layer .

3 . TECHNIQUE VERIFICATION

The TI has been tested using data in the Huntsville,
Chicago, and Denver mesoscale regions (Fig 1) for twenty
00 UTC cases during February, March, and April 1993. Point
verification was used to verify the TI's effectiveness using
pilot reports (PIREPs) from 2130 UTC to 0230 UTC for each
case studied. PIREP data included reports of turbulence
level (s) and intensity, as well as reports of no turbulence
for each case. If a PIREP did not contain any turbulence
remarks, it was not counted as a valid report. A program was
written to extract values for the TI and its components at
the grid point closest to each verifying report. Turbulence
reports in the vicinity of thunderstorms were not included in
the verification process. Data from the National Lightning
Detection Network were used to filter out these reports in or
near active cloud-to-ground lightning strikes during the
times studied. Reports from heavier aircraft (i.e., civilian
airliners and military transports) were also eliminated from
the database to keep findings specific to the lighter-weight
aircraft used by the Army. The majority of the reports in
the database were from these lighter aircraft, resulting in
only 5% of the reports being filtered out.

Turbulence reports were assigned numerical values based
on intensity to establish correlation coefficients when
compared to grid point output from each of the technigues.
Intensities, abbreviations, and corresponding numerical
values are listed in Table 1. Correlation coefficients (r)
for the TI at different turbulence intensities in all three
regions studied for the 010-040 and 040-070 levels are shown
in Tables 2 and 3 , respectively . The TI is shown to be a
poor indicator of NEG and LGT intensities. However, for
reports of LGT-MDT or greater turbulence, r-values centered
around .80 show a good relationship between calculated TI
values and intensities for both layers below 070.

119

Figure 1. Mesoscale regions used for turbulence study.
RAOB locations indicated with 3-letter identifiers.

Table 1. Turbulence intensities and abbreviations with
associated numerical values.

pilot numerical

REPORT VALUE

No Turbulence

NEG or NIL

Light

LGT

Light-Moderate

LGT-MDT

Moderate

MDT

Moderate-Severe

MDT-SVR

Severe

SVR

Severe-Extreme

SVR-XTR

Extreme

XTR

Table 2. Correlation coefficients (r) relating
turbulence parameters to turbulence intensities from
010-040 in the three mesoscale regions studied for 20
cases from February-April 1993. INTENSITY values taken
from Table 1. # is the number of pilot reports in the

sample .

INTENSITY

VWS

[DEF + CVG]

VWS AND
[DEF + CVG]

ALL

.58

.21

.51

0-1

.22

.07

.21

.80

.49

.80

.81

.80

.32

.81

.86

Table 3. Same as Table 2, for the 040-070 layer.

INTENSITY

VWS

[DEF + CVG]

VWS AND
[DEF + CVG]

ALL

.58

.24

.58

.59

0-1

.10

.04

.07

.08

.79

.49

.77

.78

.84

.64

.76

.80

4. TOWARD AN IMPROVED TURBULENCE INDEX

In a case study of moderate and greater turbulence
reports occurring across the Huntsville region (Knapp, et. ^
al., 1993), turbulence reports occurred in regions of varied
VWS and DEF+CVG. A report of extreme turbulence was reported
near the center of an area of maximum DEF+CVG with a minimum
of VWS. Reports of moderate and moderate-severe turbulence
occurred in local maxima of VWS with only moderate values of

121

DEF+CVG. No distinctive pattern from either term could be
seen as dominating the other. Based on this case, and on the
19 other cases examined for each mesoscale region, r-values
were also calculated independently for each of the TI's terms
(VWS and DEF+CVG) . These are also summarized in Tables 2 and
3. As for the TI, VWS and DEF+CVG performed poorly as
turbulence indicators for the NEG and LGT intensities, while
statistics dramatically improve for greater intensities. In
both layers, DEF+CVG correlated better with turbulence
intensity than did VWS, implying the importance of horizontal
wind flow changes in low-level turbulence generation. Notice
also that VWS r-values increased significantly in the higher
layer (Table 3) . DEF+CVG compared favorably to the TI as an
independent low-level LGT-MDT and greater turbulence
indicator. Treating the TI's two independent terms together
in a multi-variate analysis shows this new combination
slightly outperforming the TI from 010-040 (Table 2) with the
reverse occurring from 040-070 (Table 3) .

Another index considered was the Panofsky Index (PI) ,
which has been used by the Navy to forecast low-level
turbulence up to 850mb (Boyle, 1990) . The formula for this
index is

PI= (windspeed) ^x [ 1 . O-Ri/Ri^^,] ( 2 )

where windspeed is the average speed in the prescribed layer
in ms'*, Ri is the Richardson Number, and Ri^^it is a critical
Richardson Number. This value should theoretically be .25
for very fine scale data, but in this study (using 3000 foot
layers) as well as for the Navy's purposes (1000mb-850mb
layer), the best empirically derived value is 10.0. The PI
takes into account vertical wind shear as well as the
vertical lapse rate by virtue of the Richardson Number.
Studies for the Huntsville region included the PI as an
additional turbulence index. As an independent term
considered by itself, r-values for the PI were insignificant
for all layers. However, when treating it as an independent
term in a multivariate analysis with DEF+CVG, r-values exceed
all others at both low levels studied (Tables 4 and 5) .

122

Table 4. Same as Table 2, for the 010-040 layer in the
Huntsville Region.

Table 5. Same as Table 4, for the 040-070 layer.

Based on the high correlation coefficients shown in
Tables 2 through 5, new indices for predicting low-level
turbulence intensities are derived as linear regression
equations. Using data for the 010-040 layer which produced
the r-values depicted in Tables 2 and 4 for all turbulence
intensities, the following equations can be derived as a
starting point for future refinements and verification:

Y=.0065(TI)+1.0888 (3)

Y=. 0078 (DEF+CVG)-. 0003 (VWS) +1.074 (4)

Y=.0089 (DEF+CVG) +.0006 (PI) +1.1751 (5)

where Y is turbulence intensity as defined in Table 1; TI,
VWS, and DEF+CVG are in units as depicted in Figs 3, 4, and
5, respectively; and the PI is not scaled.

5 . CONCLUSIONS

A previously validated upper-level aircraft turbulence
index (TI) was studied using mesoscale model gridded analysis
data in an effort to develop a useful objective low-level
index for use by military forecasters. The TI output were
shown to be strongly correlated with LGT-MDT or greater
intensity for light-weight aircraft. A TI case study

123

examining the specific contributions of each term of the TI
was accomplished. This led to further correlating turbulence
occurrence and intensity for each pilot report independently
with each term of the TI. DEF+CVG proved to be comparable to
the TI as a turbulence indicator. Combining DEF+cVG with VWS
as two independent variables in a regression improved
performance as correlation coefficients exceeded those of the
TI from 010-040. Another tool, the Panofsky Index (PI), was
combined as an independent term with DEF+CVG to produce
correlation coefficients which exceed all others.

New turbulence potential tools derived as multivariate
linear regression equations were proposed for further study.
These equations will be refined by increasing the database
from which they were derived with additional data from the
winter 1994 season. Final equations will then be verified
for both intensity and horizontal extent of turbulence
forecast areas using an independent data set from 1992.

REFERENCES

Boyle, J.S., 1990: Turbulence Indices Derived From FNOC
Fields and TOVS Retrievals, NOARL Technical Note 47,
Naval Oceanographic and Atmospheric Research Laboratory
Stennis Space Center, MS 39529-5004. ^

Ellrod, G.P., and D.I. Knapp, 1992: "An Objective Clear-Air
Turbulence Forecasting Technique: Verification and
Operational Use." Wea. Forecasting, 7: 150-165.

Knapp, D.I., T.J. Smith, and R. Dumais, 1993: "Evaluation of
Low-Level Turbulence Indices on a Mesoscale Grid." In
Proceedings of the 1993 Battlefield Atmospherics
Conference, U.S. Army Research Laboratory, White Sands
Missile Range, NM 88002-5501, pp 501-514.

Mancuso, R.L., and R.M. Endlich, 1966: "Clear Air Turbulence
Frequencies as a Function of Wind Shear and
Deformation." Mon. Wea. Rev., 94: 581-585.

Petterssen, S., 1956: Weather Analysis and Forecasting,

Vol 1. McGraw-Hill Book Co., 428 pp.

Yamada, T., and S. Bunker, 1989: "A Numerical Model Study of
Nocturnal Drainage Flows With Strong Wind and
Temperature Gradients." J. Appl . Meteor. , 28: 545-553.

CURRENT AND FUTURE DESIGN OF U. S. NAVY MESOSCALE MODELS FOR

OPERATIONAL USE

1994 BATTLEFIELD ATMOSPHERICS CONFERENCE

R. M. Hodur

Naval Research Laboratory
Monterey, CA 93940-5502

ABSTRACT

The Naval Research Laboratory (NRL), which has developed, implemented, and improved
Navy operational mesoscale models for over 10 years, plans a series of further significant
improvements. The current operational system, the Navy Operational Regional Atmospheric
Prediction System (NORAPS), has produced over 35,000 operational forecasts over the past
12 years. This has been possible through the computer support and cooperation with personnel
at the Fleet Numerical Meteorology and Oceanography Center (FNMOC), co-located with NRL
in Monterey. NORAPS is a complete data assimilation system that contains 4 major
components: 1) quality control to maintain consistency and integrity of the incoming data, 2)
multivariate optimum interpolation analysis, 3) nonlinear vertical mode initialization, and 4)
a hydrostatic forecast model with physical parameterization s for cumulus, radiation, and the
planetary boundary layer. Three major design changes are being developed and tested. The
first is the incorporation of horizontally nested grids, which will allow for high-resolution (10
km or less) over limited domains. The second is the development of a nonhydrostatic
atmospheric model which will replace the hydrostatic model in NORAPS within the next two
years. The third project is a redesign of the operational mesoscale system so that it can be
ported to other mainframes and/or workstations with little or no modifications. This will allow
for use of the prediction system by other labs, universities, regional forecast centers, and
aboard Navy ships. These design changes will ensure that the U. S. Navy maintains state of
the art mesoscale forecasting capabilities, particularly in littoral regions, for the years to come.

1. INTRODUCTION

The U. S. Navy is poised to move into a new era of operational numerical weather prediction
(NWP). The first era began over 20 years ago when FNMOC implemented, and began
running on a twice-daily basis, a hemispheric model based on the primitive equations using a
grid spacing of 381 km and 5 levels (Kesel and Winninghoff 1972). The focus of this model
was to provide 3 day forecasts of systems of synoptic-scale and larger. The next era in U. S.

125

Navy NWP began about a decade later with the introduction of the Navy Operational Global
Atmospheric Prediction System (NOGAPS, Rosmond 1981), the Navy Operational Regional
Atmospheric Prediction System (NORAPS, Hodur 1982), and the arrival of high-speed vector
processors. The purpose of NOGAPS was to provide 3-5 day global forecasts for systems of
synoptic-scale and larger. NORAPS, on the other hand, using a horizontal grid-spacing
approximately one-third that of NOGAPS, was implemented to produce 24-48 h forecasts over
given regions of the world. The NORAPS forecasts were used as an "early-look" when run
before NOGAPS, and to provide mesoscale forecast information due to its higher resolution.
Over the past decade, both NOGAPS and NORAPS have improved due to increased computer
si^ and memory, and also due to improvements in the prediction systems themselves. Now,
with the arrival of another generation of supercomputers featuring vector and parallel
processing, the Navy is preparing for another era of improved NWP products. The
improvements to NOGAPS and computer technology have already been such that NOGAPS
is now capable of mesoscale forecasts previously performed by NORAPS. This implies that
we must push mesoscale modeling to higher resolutions, which calls for a redesign of the
model equations and parameterizations. It is expected that these improvements to our
mesoscale effort will lead to NWP forecasts that can directly support future battlescale
missions.

The purpose of this paper is to describe the current and future design of Navy mesoscale NWP.
A description of the currently used mesoscale model, NORAPS, is given in Section 2. A
description of the system that will replace NORAPS, the Coupled Ocean/ Atmosphere
Mesoscale Prediction System (COAMPS), is given in Section 3. Section 4 presents the work
being conducted to allow for the use of NORAPS and/or COAMPS on a workstation. A
summary is presented in Section 5.

2. NORAPS

The structure of NORAPS is basically unchanged from that described by Hodur (1987), that
is, NORAPS is a mesoscale data assimilation system with four major components: quality
control, analysis, initialization, and forecast model. The system is designed so that it can be
run over any region of the world using any of the following map projections; Mercator,
Lambert Conformal, polar stereographic, or spherical. The grid spacings and grid dimensions
can be set to any value within the speed and memory limitations of the computer system.
Global databases of terrain, surface roughness, albedo, and ground wetness are bilinearly
interpolated to the model grid for each forecast application.

The NORAPS analysis is now based on the multivariate optimum interpolation (01) technique
using the volume method similar to that described by Lorenc (1986). The D-values and u- and
v-components are analyzed at 10, 20, 30, 50, 70, 100,150, 200, 250, 300, 400, 500, 700, 850,
925, and 1000 mb. Wind observations are used from radiosondes, pibals, aircraft reports
including ACARS data, SSM/I, surface reports over the water, and cloud track winds. D-
values and/or thicknesses are obtained from radiosondes, and DMSP and NOAA satellites. All
data is subject to the complete quality control (QC) system which includes the National

126

Meteorological Center’s (NMC) complex QC of radiosondes (Collins and Gandin 1990) as well
as the more traditional QC techniques described in Baker (1992) and Norris (1990).

The NORAPS hydrostatic forecast model is based on the primitive equations using the sigma-p
vertical coordinate system. The horizontal grid is the Arakawa and Lamb (1977) staggered
"C" grid The nonlinear vertical mode initialization described by Bourke and McGregor (1953)
has been’ incorporated as an integral part of the forecast model. Recent improvements of the
physical parameterizations include the use of the Louis (1982) surface layer parameterization
the Detering and Etling (1985) turbulence parameterization and the Harshvardhan et. al. (1987)
radiation scheme, which includes cloud interactions.

Several improvements of NORAPS are currently being worked on. The first is horizontally
nested grids, which will allow for high resolution (10 km or less) over given areas of mteresf
Another advantage of the nested grid structure is to move the boundary zone where NORAPS
and NOGAPS fields are blended together as far away from the area of interest as possible.
The second improvement is the ability to predict aerosols. Currently, we allow for generation
of sea-salt aerosols over water, specifying a point source of aerosols at any point m the grid
advection, diffusion, fallout, and rainfall scavenging. The third improvement is in the PBL
parameterization in which we are examining methods to introduce counter-gradient flux terms
into the model, thereby giving us more realistic temperature and moisture profiles.

3. COAMPS

The use of a hydrostatic mesoscale model, such as NORAPS, with resolutions finer than about
10 km, can pose problems in certain situations. These occur when the hydrostatic assumpUon
is violated, i.e. , when vertical accelerations become significant. Events such as convection,
sea breezes, and topographic flows often exhibit strong nonhydrostatic effects and these need
to be included for proper simulation. To account for these effects, NRL is developing a new
mesoscale model using the fully compressible form of the primitive equations following Klemp
and Wilhelmson (1978). This model is the atmospheric component of the Coupled
Ocean/ Atmosphere Mesoscale Prediction System (COAMPS). The other component of
COAMPS is a hydrostatic ocean model (Chang 1985). The two inodels can be used separate y
or in a fully coupled mode. Although the ocean model plays a vital role in the basic research
we conduct with COAMPS, the remainder of this section will focus on the details and plans
for the nonhydrostatic atmospheric model only.

COAMPS has been designed to make the transition from a hydrostatic model within NORAPS
to a nonhydrostatic model as easy as possible. This has been done by incorporating many of
the details already in NORAPS into COAMPS. This includes the data QC and multivariate 01
analysis. COAMPS also has the same global relocatability features found in NORAPS, and
the user can set the type of grid projection, the number of nested grids (a maximum of 3 is
allowed), as well as the grid dimensions and resolutions of each mesh.

The COAMPS atmospheric model is based on the sigma-z vertical coordinate. The prognostic

127

variables are the u-, v-, and w-components of the wind, perturbation exner function (related
to perturbation pressure), potential temperature, water vapor, cloud droplets, raindrops, ice
crystals, snowflakes, and turbulent kinetic energy (tke). The choice of five moisture variables
allows for the explicit pr^iction of clouds, rain, and snow, using the Rutledge and Hobbs
(1983) explicit moist physics scheme. For resolutions coarser than 5-10 km, such as for the
coarser meshes of a nested grid simulation, cumulus parameterization must still be used. For
this, we have included a scheme developed for mesoscale convective events (Kain and Fritsch
1990, Kain 1993). The 1-1/2 order tke prediction scheme presented by Deardorff (1980) is
used. The Harshvardhan et. al. (1987) radiation scheme, used in NORAPS, is also included
in COAMPS.

4. PORTABILITY

Until very recently, the computational power needed to execute numerical models such as
NORAPS or COAMPS existed only on large mainframes. However, workstation technology
has now improved to the point where these models can be tested on them, although the best
performance is still on vectorized, multi-processor machines. Given the pace of workstation
technology, it is expected that over the next few years, realistic, operationally useful forecasts
will be produced on workstations.

To take advantage of this emerging technology, NRL is leading a program, the purpose of
which IS to make the mesoscale prediction systems, NORAPS and COAMPS, easy to port to
other systems. The benchmark operational systems will still reside at NRL/FNMOC in
Monterey, but execution of a single program can build a file containing all the source code and
the surface parameters databases that are required to run and install either system. This file
can then be sent, via tape, internet, etc., to another machine in which the install program is
used to install the system. At this point, the remote site must have the ability to get fields for
the first guess and boundary conditions, as well as observational data.

Of course, there are certain constraints on the portability of these systems. First, the source
code is written in the FORTRAN language. Currently, we adhere to FORTRAN 77 standards,
but will be transitioning to FORTRAN 90 standards over the next year or so. Second, the
prediction systems require the use of dynamic memory allocation. While this is a standard
feature in FORTRAN 90, it only exists as extensions on some FORTRAN 77 compilers.
Third, it is required that the operating system be UNIX, or a compatible version, such as
UNICOS, on Cray machines. Generalized scripts to execute, build, and install NORAPS and
COAMPS are written for the UNIX operating system. In addition, the generalized database
that we use in these systems is based on the existence of a UNIX environment.

The portability of NORAPS and COAMPS is rapidly becoming a reality. Recently,we have
ported each prediction system to other Cray systems and have been able to perform data
assimilation experiments the same day. Porting to smaller workstations is still under
development. The long term goal is to be able to use NORAPS or COAMPS for data
assimilation at a regional center or onboard a Navy ship. The first step toward accomplishing

128

this goal will be taken in the SHAREM 110 exercise in the Gulf of Oman during February
1995. During this time, NORAPS will be run at FNMOC for the Gulf of Oman area and the
forecast fields will be sent to a remote station near the Gulf of Oman. There will be a
workstation at this location on which the NORAPS multivariate 01 analysis is installed.
Analyses will be generated at this site using the NORAPS forecast fields for the first guess and
all on-scene observations. This will serve as a test for the communication, personnel,
hardware, training, and timing necessary to extend this to a full on-scene predictive capability.

5. SUMMARY

The U. S. Navy is committed to improving its mesoscale NWP capabilities. The major focus
is on improving its efforts in the numerical prediction of mesoscale events in littoral regions.
Recent improvements to our current operational mesoscale model, NORAPS, such as improved
boundary layer and radiation parameterizations and horizontally nested grids make it a practical
choice for now. We are also developing a new mesoscale forecast system, COAMPS, which
will use all the functionality already found in NORAPS, but which is also better suited to
mesoscale prediction since it uses a nonhydrostatic formulation and can perform explicit
prediction of precipitation processes. The operational switch from NORAPS to COAMPS is
expected to occur within the next 2 years. Finally, both NORAPS and COAMPS have been
designed so as to be used on systems other than mainframe supercomputers. This feature
makes these systems attractive for porting to other mainframes for research or to workstations
at other labs, regional centers, or onboard Navy ships.

ACKNOWLEDGMENTS

The support of the sponsors. Office of Naval Research under program element 0602435N, and
Space and Naval Warfare Systems Command under program element 0603207N, is gratefully
acknowledged.

REFERENCES

Arakawa, A., and V. R. Lamb, 1977: "Computational design of the UCLA general circulation
model." Methods in Computational Physics, Vol. 17, Academic Press, pp 173-265.

Baker, N. L., 1992: "Quality control for the Navy operational atmospheric database." Wea.
Forecasting, 7:250-261.

Bourke, W. and J. L. McGregor: "A nonlinear vertical mode initialization scheme for a
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131

COMBAT WEATHER SYSTEM CONCEPT

Mr James L. Humphrey

Science Applications International Corporation
Headquarters Air Weather Service
Scott Air Force Base, Illinois 62225-5206

Maj George A. Whicker, Capt Robert E. Hardwick,
2nd Lt Jahna L. Wollard, SMSgt Gary J. Carter
Headquarters Air Weather Service
Scott Air Force Base, Illinois 62225-5206

ABSTRACT

Current weather information is critical in a deployed environment, however, in a combat
environment it can mean the difference in mission success or failure. Air Force units supporting
Joint Forces Commander, Air Force, and Army combat operations require the means to produce
and apply environmental information to support the employment of military power. The Combat
Weather System (CWS) will enable users to provide combat and support forces the required
timely and accurate global, theater, and local weather information for effective planning,
deployment, employment, and redeployment in response to worldwide crises. The CWS will
integrate highly capable automated weather observing and forecasting systems into a light¬
weight easily transportable system capable of meeting the CWS critical mission, which is, to
support launch and recovery of aircraft. Headquarters Air Weather Service, Directorate of
Program Management and Integration is the CWS Standard Systems Manager and is the
interface with the Implementing Agency, Electronic Systems Center, Weather Systems Division.
There will be two major components of CWS; observing and forecasting. The observing
components will provide accurate observations of more weather elements and distribute these
data more quickly than current systems to the weather forecast system. The forecasting portion
provides a platform on which forecasters can integrate observations and generate tailored
forecast information quicker and make it readily available for operational customers through the
automated Theater Battle Management Command, Control, Communications, Computer and
Intelligence systems. The mission areas supported by CWS will be Air Force; Counter Air,
Strategic Attack, Interdiction, Close Air Support, Strategic Airlift, Aerial Refueling,
Aeromedical Evacuation, Operation Support, Airlift, Electronic Combat, Surveillance and
Reconnaissance, Special Operations, Base Operability and Defense, and Logistics; and Army;
Aviation, Air Defense, Close Combat (Heavy/Light), Land Combat Engineering Support,
Special Operations, Fire Support, Biological, and Chemical operations. The CWS is being
acquired to provide the warfighter, planner, and commander the current, timely, and accurate
weather information.

133

1. INTRODUCTION

Air Force, Army, Joint and Combined warfighters require detailed weather observations and
forecasts across the depth and breadth of the combat zone to refine mission tactics and to
manage combat resources. The system to meet these needs must be a small, lightweight,
modular system for maximum functionality. It must be durable, quickly activated, packaged for
rapid deployment, and field maintainable. The systems' modular design must allow for an initial
deployment capability that can be expanded, as required, to a more capable system.

PROPOSED CWS SYSTEM

Figure 2. 1 Proposed CWS System

134

The system must also provide responsive, reliable, accurate weather information in near real
time directly to the warfighter/decisionmakers. Combat Weather System (CWS) was proposed
and developed to meet these needs. The CWS was to have two components. Tactical Forecast
System (TFS) and Tactical Weather Observing System (TWOS). Recent funding cuts have
canceled all funding for CWS beyond FY95. As a result, CWS, as originally planned, has been
canceled. The system was to be fielded in FY96 to FY99. While Air Weather Service (AWS) is
no longer able to field a deployed weather system as defined by the operational users in the
CWS Opperational Requirements Document (ORD) 21 1-89-I/III, there still remains valid
mission needs. USAF Statement of Operational Need (SON) 211-89, Tactical Weather
Observing Systems (TWOS), 5 Mar 90, and USAF SON 212-89, Tactical Forecast System
(TFS), 3 Aug 90, define the needs. AWS has developed a plan to acquire and field a TFS and
TWOS capability that will satisfy the stated needs for initial combat operations.

2. REQUIREMENTS

The primary objective is to integrate highly capable forecasting and automated weather
observing systems with combat planning and execution systems (Figure 2.1). The TFS and
TWOS will enhance the effectiveness of combat operations by improving the capability of
deployed weather forces to produce comprehensive and timely weather decision products for
combat zone commanders, planners, and aircrews. The critical and most basic mission for the
TFS and TWOS is to support launch and recovery of aircraft by: providing tailored weather
support, which includes receipt of data, ingesting, displaying, processing, and disseminating data
and products (excluding administrative functions); and observing weather elements (cloud
height, cloud amount, surface wind speed and direction, surface visibility, surface free air
temperature, and surface pressure). This primary objective and critical mission must be met
while meeting the users stated set-up/tear-down times, weight and size requirements. The set-
up/tear-down requirement is for two people in full chemical protective clothing to be able to
complete either flmction in 6 or less hours. The size requirement is for the system to fit within
the 2/5 standard 463L airlift pallet. The TWOS will provide accurate observations of weather
elements and distribute these data more quickly than current systems permit to the weather
forecast system and operational customers. In turn, the TFS will integrate these observations
and generate tailored forecast information quicker and distribute it faster to operational
customers through the automated Theater Battle Management (TBM) Command, Control,
Communication, Computer, and Intelligence (C4I) systems. The weather operator will use the
TFS to build weather products to meet the needs of the Air Force's C4I systems. The main
weather data source will be Air Force Global Weather Central (AFGWC) via long haul
communications reach back capability. TFS will be interoperable (be able to exchange data)
with other services; e g.. Navy, environmental support systems at the AF Component Theater
Weather Center (TWC), and at lower levels as appropriate. Additionally, TFS must respond to
single points of failure both within and outside the theater and degrade gracefully to a point
where it will flmction with whatever data is available (e.g., complete data set, set of theater
observations only, or single-station). The TFS must produce the most accurate analysis and

135

forecast fields current state-of-the-art technology and science provides to meet operational
customer requirements.

3. OPERATIONAL CONCEPT

TFS will provide a first-in and eventually a sustainment capability for the conduct of weather
operations and will be interoperable with automated C4I systems such as the Contingency
Theater Automated Planning System (CTAPS), Wing Command and Control System (WCCS),
and Command and Control Information Processing System (C2IPS) (Figure 3.1).

Figure 3.1 CWS Communications and Systems Flow Chart

Figures 3.2 and 3.3 show examples of TFS display screens.

137

Figure 3.2 TFS screen in quadrant view

Figure 3.3 screen with pop-up menu

Weather data in the C4I weather data base will be in a standard relational data base format that
will enable personnel on C4I user positions to overlay weather products on products from any
other functional area. C4I customers can use their systems to access weather information
(observations, forecasts, warnings, and advisories, etc.) and locally generated mission forecast
products. In addition, weather operators using the TFS will make available gridded weather
data fields on the C4I data base for use by automated mission planning and intelligence
applications. The TFS will also allow weather operators to access and display information from
the C4I data base to generate mission-specific forecasts; e g., display flying operations data from
the Air Tasking Order (ATO) and target information from the Intelligence Summaries to tailor
and provide departure, enroute, and recovery flight weather briefings. TFS fielding will allow a
shift in weather operator duties; a decrease in face-to-face support and an increase in weather
data base interpretation, manipulation, and systems management duties. However, weather
operators will still be required to augment automated weather observations and provide direct
support to customers upon their request. Exact configuration of the TFS and TWOS
deployed/employed will vary depending on the mission and customer supported.

4. PROGRAM EXECUTION

Under the revised phased program (Figure 4.1), the software developed under the original CWS
program, exploiting existing Automated Weather Distribution System (AWDS) and Combat Air
Forces Weather Software Package (CAFWSP) software, would become the TFS software
baseline. AWS would then purchase the CAF standard hardware on which the TFS software
would be hosted. Under this plan, the total number of TFSs would be reduced to 44 systems.

PROGRAM SCHEDULE

EVENT

iPIiKl

FTiTin

TFS SOFTWARE BASELINE

TFS HARDWARE 44 SYSTEMS

MOD TFS SOFTWARE

ADDITIONAL TFS HARDWARE

MOD EXISTING TACMET

1L_

Figure 4. 1 Program Schedule

These systems will be single user positions verses the three-position systems in the original
program. This represents a great reduction in total numbers, however, operational users would
have a standard deployable state-of-the-art system by late 1995. This would insure compatibility

139

between MAJCOMs in the deployed environment. In 1997 the plan is to acquire the remaining
TFSs to meet the users requirements. Additionally, AWS would seek to modify the TFS
Software Baseline in the FY98-01 time frame to incorporate changes to the AWDS software
and to integrate a tactical automated observing capability. This tactical automated observing
capability would be achieved via modification programs to replace and upgrade existing
Tactical Meteorological systems (TACMET), i.e., Transportable Cloud Height Detector (GMQ-
33), Tactical Meteorological Observing System (TMQ-34), and Tactical Wind Measuring Set
(TMQ-36). These modified systems may be modular enough to meet most of the original
TWOS requirements.

5. CONCLUSION

In today's environment of shrinking budgets, new and innovative approaches to meeting
operational requirements will have to be considered and employed. As a representative of the
user, AWS is deeply committed to meeting the users operational requirements. With the
planned phased approach a modicum of success can be salvaged from a program broken by
fiscal constraints and a redirection of national priorities.

6. REFERENCES

Air Force PMD 2326(3)/PE0604707F/0305111F/0305117F/0305123F, Program Management
Directive for the Weather System (WXSYS) - IWSM 20 Jun 94.

USAF Statement of Operational Need (SON2 11-89), Tactical Weather Observing Systems
(TWOS), 5 Mar 90.

USAF SON 212-89, Tactical Forecast System (TFS), 3 Aug 90.

CWS Operational Requirements Document I (ORD I), 26 Mar 93.

Air Force Systems Command /Military Airlift Command Mission Area Analysis, Weather 2000,
20 Sep 84.

Air Force System Command Electronic Systems Division Technical Alternatives Analysis
30 Sep 91.

Concept Paper for Weather Support to Air Force Theater Operations 1995-2005, 5 May 92.

140

Page 1 of 10

SMALL TACTICAL TERMINAL (STT) CONCEPTS AND CAPABILITIES

2Lt Stephen T. Barish

Mr. George N. Coleman III, Maj Tod M Kunschke
Directorate of Systems and Communications
Headquarters Air Weather Service
Scott Air Force Base, Illinois 62225-5206

ABSTRACT

Real-time satellite imagery is vital to first-in deployed troops and aircraft; it can enhance the
mission's effectiveness and ensure a maximum safety margin for deployed personnel. It is often
the only source of meteorological data available upon deployment. As such. Air Force weather
teams require the capability to receive, process, and display real-time satellite imagery m order
to support Army, Air Force, and Joint Force combat operations world-wide. The Small
Tactical Terminal (STT) will provide users with a stand-alone platform to receive and process
real-time polar orbiting meteorological satellite data from the Defense Meteorological Satellite
Program (DMSP) and National Oceanographic and Atmospheric Administration (NOAA)
satellites It will receive near real-time geostationary weather facsimile (WEFAX) data from
both foreign and domestic satellites. There are three configurations of the STT, each designed
for use during specific phases of any conflict. The Basic STT is a first-in asset intended for
rapid deployment to the theater and receives low-resolution imagery. The Enhanced STT
consists of a modular kit added to the Basic STT and will be used in the sustainment phase of
the operation. It adds the capability to receive high resolution data from DMSP and NOAA
satellites. The Joint Task Force Satellite Terminal (JTFST) consists of a modular kit added to
the Enhanced STT and is a sustainment phase asset intended to provide weather support to the
theater commander. It adds the capability to receive high resolution data from geostationary
satellites All STT configurations will interface with the following weather forecasting
systems. Tactical Forecast System (TFS), Transportable Automated Weather Distribution
System (TAWDS), and Integrated Meteorological System (IMETS). They will provide the
warfighter, mission planner, and commander real-time satellite imagery for use in operations
from the first-in through sustainment phases of a conflict. Headquarters Air Weather Service,
Directorate of Systems and Communications is the STT User Representative and interface to
the Implementing Agency, Space and Missile Center, Defense Meteorological Satellite

Program.

1. INTRODUCTION

The United States Air Force and Army provide forces for the world-wide conduct of combat
operations. Military personnel operate in both peace-time and war-time scenarios. Past
experience has shown a direct correlation between mission effectiveness and accurate,

141

Page 2 of 10

dependable knowledge of present and future weather conditions. In a war-time scenario, the
ability to observe and forecast weather conditions in both the local area of operations, as well
as over hostile territory, vastly enhances the ability of Army and Air Force pilots to
successfully complete their missions and return home safely. In peace-time, accurate weather
observations give pilots critical information needed to fly sorties safely, while ground forces’
mobility can be enhanced significantly..

One of the most effective sources of real-time weather data, and the sole-source over enemy-
held territory, is meteorological satellite data. The Air Force has long recognized the need of
its combat planners, commanders, and pilots to have recent, accurate, and dependable weather
observations in order to accomplish its mission. However, the Air Force has a capability
shortfall in meeting its combat weather operations commitments.

The lessons learned from Operation DESERT SHIELD/DESERT STORM showed current
tactical direct satellite readout terminals do not meet the full needs of deployed forces. Current
systems do not provide a fast enough data refresh rate, often have limited data reception, and
are too large to be easily transported. Additionally, current systems require too many
personnel to operate. While interim systems were procured to fill the gap in the short run, a
long-term solution was needed. Thus, Headquarters USAF directed Air Weather Service
(AWS) and Space and Missile Center (SMC) to acquire a small, light-weight, tactical, semi-
automated, two-person transportable tactical satellite imagery receiver, the Small Tactical
Terminal (STT).

2. OPERATIONAL CONCEPT

In today's global society, threats to the United State's National Security and foreign interests
can crop up virtually anywhere with very little notice. The way the Air Force mobilizes has
changed to reflect this. Combat forces must now deploy with little to no notice to any location
world-wide, taking with them their support organizations and services. Weather operations
are no exception.

Weather teams in the deployed environment will retrieve their equipment from the airlift or
other mode of transportation used to transport them in-theater. In general, there will not be an
established source of meteorological data. This is particularly true for those Air Force weather
personnel dedicated to supporting Army units. Once deployed to a secure area, a weather
team will set up for operations. The STT will be one of the first weather systems deployed.

It will provide real-time satellite imagery, and enable weather teams to begin performing their
duties within an hour of arrival. It will operate 24 hours per day, for a minimum duration of
30 days without re-supply, with no dependence on outside communications. It is light-weight,
highly reliable, and easily maintainable, all of which enhance its capability as a combat system.

The STT will operate in three configurations, each with its own concept of operations. The
Basic STT (B STT) (Fig 2.1), weighing 470 pounds, is the bare-base system. It is deployed
with the initial cadre of weather forecasters and observers to deployed units in-theater. The
BSTT gives weather teams low resolution satellite data from a variety of sources, both foreign

142

Page 3 of 10

and domestic. It allows weather teams to give mission planners, commanders, and pilots up to
the minute satellite imagery over the theater. _

Figure 2. 1 : Basic STT

The Enhanced STT (ESTT) configuration is composed of the BSTT and an Enhancement Kit.
The ESTT receives high-resolution imagery from polar orbiting satellites in addition to the low
resolution data available on the BSTT. The enhancement kit will typically follow the BSTT to
the theater within the first 30 days of deployment, although it can be deployed at any time.
The ESTT weighs 790 pounds and is intended to operate as a sustainment asset.

Figure 2.2; Enhanced STT

143

Page 4 of 10

The final configuration, the Joint Task Force Satellite Terminal (JTFST), is a modular addition
to the ESTT. The JTFST receives high-resolution data from geostationary satellites. The
JTFST kit will deploy during the first 30 days, and will be used at theater weather centers, and
Joint Task Force command/control centers. This configuration is still in its design phase, but
certain key physical parameters are known. The completed JTFST will weigh less than 3000
lbs. It will receive high-resolution geostationary data. It will allow operators to send satellite
products to remote customers via a facsimile network, or via the Air Force weather link into
the command, control, communications, computers, intelligence (C4I) network.

JOINT TASK FORCE
SATELLITE TERMINAL

APT ANTENNA

RDS/RTD/HRPT

ANTENNA

RECEIVER SUB-ASSEMBLY

HI RESOLUTION DISPLAY MOUSE EXTERNAL KEYBOARD

Figure 2.3; Joint Task Force Satellite Terminal

3. MAINTENANCE CONCEPT

The STT was designed to be a highly reliable system. However, it also had to be capable of
being maintained by weather personnel rather than dedicated technicians. This concept of
Operator Maintenance allows the fielding of a new system without additional maintenance
personnel. This approach provides unique challenges to the program. First, there is a design
issue; weather personnel are not trained as technicians, and the equipment and any
maintenance actions are designed with this in mind. Second, there is a training issue; weather
personnel have never performed Operator Maintenance before, and the learning curve will be
steep initially. Third, there is a maintenance issue; preventative maintenance is essential to
keep any equipment operating correctly. The STT is designed to simplify preventative
maintenance.

The solutions to these problems are found in the maintenance concept and in the system
design. In the field, weather teams will remove and replace large components of the system

144

Page 5 of 10

called Line Replaceable Units (LRUs). The STT LRUs will be large items, such as a receiver
sub-assembly or the computer. Removal and replacement of LRUs will not require weather
teams to open any chassis, or replace any electrical circuit cards or components. All LRUs can
be removed without the use of hand tools. This minimizes the level of technical expertise
necessary to maintain the system.

When a system fails in the field, the operator will initiate a Built In Test (BIT) routine, and
identify the problem. The BIT will isolate the problem, and direct the operator to a set of
actions to correct the malfunction. The operator will remove the faulty LRU, replace it with a
spare, and return the faulty LRU to the supply office at his/her location.

The faulty item will then be shipped back the maintenance depot at Sacramento Air Logistics
Center (SM-ALC), McClellan AFB, CA. The depot will repair the faulty LRU in-house or
send it to the manufacture for repair.

Additionally, when the faulty item is shipped back to depot, a replacement spare will be
shipped back to the deployed weather team. As no single removal/replacement action will take
greater than 30 minutes, the down time of the system will be minimal. In the event an LRU
fails and there is no spare on site, the system is designed to be redundant. If any single source
of data becomes unavailable, the remaining data sources will still be available. This
redundancy assures the operator mission capability in virtually all failure modes.

These maintenance procedures will be extensively addressed during initial skills training, as
well as through an aggressive recurring training program. Additionally, the system's software
has a built in help program designed to provide quick access to these procedures. The training
effort will be minimized as the maintenance actions will be virtually identical with the set up
and tear down actions.

The only other maintenance actions are preventative maintenance instructions. These are
limited to the cleaning and changing of filters, and occasional loading or cleaning of the printer.
Otherwise, the STT requires no preventative maintenance. This addresses the third issue, since
there will be no calibration of the equipment necessary to keep it working properly.

4. THEORY OF OPERATION

The STT system is designed as a direct satellite read out terminal. The system's antennas
receive telemetry and data from polar orbiting and geostationary meteorological satellites. The
receivers then synchronize (bit synch) the data stream and pass the data along to the COMSEC
equipment for decryption (if necessary). Once the data is decrypted, the receiver then sends it
to the processing equipment for framing into an image. The data are processed into visible and
infrared imagery, and assorted meteorological products. The processing equipment is also the
operator's point of interaction. By using a graphical user interface (GUI) the operator can
manipulate and enhance the data, resulting in better observations and forecasts.

145

Page 6 of 10

The STT hardware is designed to be rugged and transportable. It is capable of operating in
extremes from O^C to 55^C (for the computer equipment - antennas are operable down to
- 450C). All connectors are ruggedized, environmentally sealed, and require no tools to
connect. In fact, all fasteners and connectors on the system are captive hardware and cannot
be separated from the system. The entire system can be assembled without using any hand
tools. Size and weight of the hardware were minimized wherever possible without reducing
effectiveness.

Much of the hardware exploits state of the art technology. The computer that operates the
STT software uses a Sun SPARC 10/41 microprocessor, one of the more powerful
microprocessors currently available. This highly capable processor is packaged in a specially
designed, ruggedized laptop, using an active-matrix color 10.2 inch LCD monitor. In the
enhanced configuration, a 1 6 inch color monitor is added, along with an external keyboard and
mouse to allow operators to interact with the machine more effectively. The receivers were
designed specifically for this system. Each receiver is contained on a single PC board, all of
which are contained in a single chassis. Also contained in the receiver sub-assembly are the
Communications Security (COMSEC) devices, and a removable hard drive. The COMSEC
devices were also specially engineered for this system (to minimize weight). Figure 4. 1 shows
a block diagram of the system's design.

Demod/ I - .

Bit Sync/ L-Cti/status _T T kg Status
PacodT ‘

MYK-7

(KQR-46)

— *

O0fairNltirtKi»r ;

10 Inch Internal
Color Display

Data )

USER

. . ' .

tntarnal

Keyboard/

I Trackball

R/F Antenna
Equipment

Controi/Daia

Signals

COMSEC

Equipment

1 182 900 Controlle^P 640 « 480 Controller'

128 MB RAM

Sena! Port

FGB Hart]
_ disk J

Floppy Drive

. .

Processing Equipment

-- H cws

Printer

Power

Inverter

Generator

Transit

Cases

Auxilllary Equipment

iM li Ramovabl* Enhanc*m«nt
I'l [ 'll' Equipmant/Deta

: Basic

: Equlpmant/Data

Figure 4. 1 : Enhanced STT Block Diagram

The robust hardware is complemented by a well-designed and stable software package. The
STT software was designed in accordance with the Software Engineering Institute's principles,
and in accordance with applicable sections of DOD-STD 2167A. This design approach yielded
a mature software package capable of fully exploiting the unique advantages of the system's
hardware.

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The software operates in the Unix environment, is Unix/POSIX compliant, and uses an X-
Windows/Motif interface. The Graphical User Interface (GUI ) allows the operator to quickly
and easily perform a wide variety of meteorological analyses on the data, with little wait time.
The true benefit of the GUI is found in its simplicity. All functions of the software can be
accessed quickly, and with a minimum of instructions. The GUI performs multiple tasks: it
provides an interactive interface with the data, monitors equipment status, informs the operator
of equipment failure, logs significant events occurring in both hardware and software
(including both failure data and corrective maintenance data), and provides a computer based
instruction (CBI) module that covers set up and tear down of the equipment, use of the
COMSEC devices, and maintenance actions.

The software ingests data at the same time the operator is interactively analyzing previous
passes, thus minimizing time delays. Typically, an operator will be able to analyze a pass
within 2 minutes of the end of the pass. Any stored product will be available within 1 minute
of request, and any printed hard copy will be available within 5 minutes of request. In short,
the system provides products in a prompt fashion to allow the operator to brief customers with
the latest data available.

5. IMAGERY AND PRODUCTS

The STT is designed to receive both polar orbiting and geostationary satellites. Table 5.1
shows the satellite data each configuration of the STT will receive.

Satellite

BSTT

ESTT

JTFST

DMSP RDS (Vis/IR)

DMSP Microwave Sensors

NOAA APT (Vis/IR)

NOAAHRPT

Geostationary (WEFAX)

Geostationary (High-Res)

Table 5- 1 ; STT Data Reception

Polar orbiting satellites provide real-time coverage of an area of interest, at a horizontal
resolution that is generally greater than that offered by geostationary satellites. There are two
types of domestic polar satellites received by the STT; Defense Meteorological Satellite
Program (DMSP) satellites, and National Oceanographic and Atmospheric Administration
(NOAA) satellites. The STT also receives selected foreign polar orbiting satellites, including
Russian METEOR and Chinese FENG- YUNG.

Geostationary satellites provide near real-time weather facsimile (WEFAX) data at a
significantly lower resolution than that available on polar orbiting satellites. However, the
geostationary imagery gives weather personnel the capability to give a quick, synoptic scale
view of the weather in the area of interest. Combined with the ability to set up animation
loops, this imagery gives operators a powerful tool to brief their customers. Selected units

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receiving the Joint Task Force Satellite Terminal (JTFST) configuration will also have real¬
time access to high resolution data from geostationary satellites. The STT will receive GOES,
GOES-NEXT, METEOSAT, and GMS satellites.

6. ANALYSIS TOOLS

The STT provides weather teams with satellite analysis tools never before available in the
combat environment. Users will be able to enhance the data using a variety of tools including
image filters and color enhancement. The STT allows the user to zoom into the image, in
effect magnifying the image. Any given image can be displayed in the satellite (overhead)
projection, mercator projection, or polar stereographic north/south projection. This allows the
operator to select the best way to view the data for the given location. The STT also allows
operators to annotate images with meteorological symbols and text, thereby adding to the
information available to the customer. Additionally, unique tools allow the STT to position the
cursor/mouse at specific points of latitude/longitude. This ability is enhanced by the use of the
Global Positioning System (GPS), making the STT's latitude/longitude fixes extremely
accurate.

The most innovative tool provided by the STT is its ability to generate Environmental Data
Records (EDRs) from the Satellite Data Records (SDRs) generated by the microwave sensors
on board the DMSP spacecraft. These EDRs give the operator detailed information about the
environmental conditions of a region. These EDRs can be viewed as images, and enhanced as
such, or they can be viewed as contours, and overlayed on top of DMSP imagery. Again, this
capability can dramatically enhance the quality of forecasts and briefings given to customers.

7. EXTERNAL INTERFACES

All the data in the world is useless if it cannot be used to meet the customer's needs. In the
world of combat weather operations, many of the customers are commanders and planners.
Most of these customers work through the command, control, communications, and computer,
and intelligence (C^I) wide area network set up in theater. There are several combat weather
systems which provide inputs to this network. The Transportable Automated Weather
Distribution System (TAWDS), the Integrated Meteorological System (IMETS), and the
Tactical Forecast System (TFS) are three which the STT interfaces with. These systems act as
primary sources of meteorological satellite data for the entire deployed C^I community.

TAWDS is a currently fielded system that will be modified in late FY95 to accept STT
products. These products will be restricted to images. Satellite Data Records (SDRs - the
raw output of the microwave sensors), and EDRs. The images will be transmitted as rasters,
the SDRs and EDRs as Uniform Gridded Data Fields (UGDFs). This will allow the TAWDS
to overlay these UGDFs on the images and/or other products. IMETS is an Army
communications system providing access to the C^I network. It broadcasts meteorological
data and forecasts to deployed users via a set of high frequency radios. Its software is similar
to that resident on the TAWDS, and will receive the same products. TFS is the combat
forecasting system of the future. It will send weather data from a wide variety of sources to
the local C I network. The STT will act as a front end satellite data receiver/processor for this

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system. It is important to note that while the STT will have a one way interface to TAWDS
and IMETS, it will have a two way interface with the TFS, allowing the TFS to remotely log
on and operate the STT. This automation allows fewer personnel to accomplish more work in
the deployed environment. Raster images and UGDFs will be transmitted to the TFS.

8. CONCLUSION

Combat operations and weather go hand in hand. Accurate weather forecasts enable pilots to
avoid dangerous weather conditions, while ground forces are prepared to find easier routes to
travel, and positions more advantageous to their mission. Knowledge of future weather
conditions is of critical importance when planning aviation missions. Fuel loads, flight safety,
take-off and landing are all central elements of any aviation operation, and all are impacted by
the weather. On the ground operations side, knowledge of the weather and surface conditions
can allow armor divisions to move more rapidly, and prevent them from being bogged down in
soft terrain. Troop safety is enhanced by warning of hazardous weather conditions, thus
enabling commanders to take protective actions.

Many of today’s modem weapon systems employ electro-optical guidance systems. Weather
conditions can have significant impact on the effectiveness of these guidance systems. Even
aircraft relying on free-fall gravity bombs, or Army troops moving through a battlefield are
affected by weather conditions, such as visibility and precipitation. Without a dependable
weather forecast, mission planners may not be able to identify achievable mission goals.
Without accurate knowledge of current conditions, combat commanders may send their crews
into hazardous situations where their mission goals cannot be achieved. In today’s military
where budget limitations force the services to operate more efficiently, these types of
limitations are unacceptable.

Throughout the history of warfare, accurate and dependable knowledge of the weather has
been critical to successful mission execution. When properly utilized, weather forecasts can
act as a force multiplier, enhancing the combat effectiveness of our air and ground forces.
Currently, weather personnel in the combat environment are dependent on sparse sources of
data to support their customers. The field of combat weather operations has a critical
deficiency in meeting the needs of combat planners, commanders, and pilots.

Air Weather Service and Space and Missile Center, is meeting the challenge. The delivery of
the STT, beginning in July 1995, will substantially enhance the quality of today’s combat
weather operations by providing real-time imagery and products in-theater, and by doing so in
a largely automated fashion. This will allow combat weather personnel to concentrate on using
the data, rather than gathering it. The Small Tactical Terminal will become a mission critical
piece of equipment, and in conjunction with expert Air Force weather personnel, will
dramatically improve the safety and effectiveness of the combat operations of the United States
Air Force and Army.

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Page 10 of 10

9. REFERENCES

General Operational Requirement For a Pre-Strike Surveillance/Recon System (PRESSURS),
MAC 508-78, 28 Dec 78.

Air Force Space Command (AFSPC) Mission Need Statement (MNS) for Environmental
Sensing (ES), AFSPC MNS 035-92, 6 Jan 93

Operational Requirement Document (ORD) for the Follow-On Defense Meteorological
Satellite Program (DMSP), AFSPC ORD 03 5-92-1 -A, 27 Dec 93

Program Management Directive (PMD) for Defense Meteorological Satellite Program
(DMSP), PMD 3015(35)/PE030516F/PE0305162F, 3 Sep 93

Small Tactical Terminal (STT) System/Segment
Specification, CDRL A024/DI-CMAN-80008A,

Contract F040701-93-C-0007, 11 Apr 94

Air Force Systems Command /Military Airlift Command Mission Area Analysis, Weather 2000,
20 Sep 84.

Air Force System Command Electronic Systems Division Technical Alternatives Analysis,

30 Sep 91.

Concept Paper for Weather Operations to Air Force Theater Operations 1995-2005, 5 May 92.

150

OPERATIONAL USE OF GRIDDED DATA VISUALIZATIONS
AT THE AIR FORCE GLOBAL WEATHER CENTRAL

Kim J. Runk and John V. Zapotocny
Headquarters, Air Force Global Weather Central
OffuttAFB, Nebraska, 68113

ABSTRACT

In modem weather support operations, the forecaster is forced to process and assimilate
tremendous volumes of data in a short period of time. Thus, it is becoming increasingly
important to provide forecasters with tools which enable them to use that information more
effectively to quickly and accurately assess the state of the atmosphere and evaluate the
meteorological processes affecting the forecast. The Air Force Global Weather Central
(AFGWC) has experimented with several such tools which convert gridded data sets into
image visualizations and animations for use in the operational forecast routine. These
visualization tools have proven to be useful aids for enhancing a forecaster’s ability to
assimilate the data, providing a greater sense of weather system temporal evolution and
numerical model continuity. This paper will discuss and illustrate some of the data
visualization methods which have been developed at AFGWC. Particular attention will be
given to imagery products created from gridded data unique to AFGWC, such as the RWM
(Relocatable Window Model), the HALT (high altitude turbulence) model, and SSM/I
(Special Sensor Microwave Imager) mosaic grids.

1. INTRODUCTION

A large assortment of gridded data sets are produced to support operations at AFGWC. Those
which are built for use on the Satellite Data Handling System (SDHS), which is the primary
delivery system for weather analysis data at AFGWC, are generally formatted into either polar
stereographic projection grids, tropical mercator grids, or cross-sectional grids. Spatial resolution
of these grids ranges from 48km to 381km, depending on the application. A discussion of the
operational grid-to-imagery generation process on the SDHS can be found in Zapotocny (1993).

There are also a number of gridded data sets which are created on Unix-based workstations for the
purpose of providing supplemental data visualizations to AFGWC forecasters. Since these tools
are delivered as prototypes, forecasters can provide critical feedback to programmers, thereby
participating in the definition and refinement of the final software configuration.

151

2. VISUALIZING OBSERVATIONAL DATA AND NUMERICAL MODEL OUTPUT

The ability to highlight, or even isolate key meteorological features through color imaging is gen¬
erally superior to the cluttered appearance presented by overlaying several contoured fields. This
is particularly true when images are animated in time series. During critical decision periods,
when time is short and events are unfolding rapidly, the task of evaluating whether a given region
is becoming more favorably disposed toward a specific weather condition can be made more
manageable through creative use of model derived imagery products.

A number of operational and prototype data analysis techniques developed by Sterling Software
and the Product Improvement Branch at AFGWC provide the forecaster with the flexibility to
interactively define the image display structure. This has been well received by forecasters since
the suite of tools favored by one is not necessarily the same as that which is preferred by another.
One popular technique involves the analyst selecting specific fields, assigning minimum (or max¬
imum) threshold values, then masking out values which do not fall within the assigned range. That
data set is then colored using values defined by another field. As an example, hourly grids of
surface divergence with an absolute value greater than 2x10^ sec'^ could be colored with a palette
defined by all values of moisture convergence or by the surface-based lifted index from the same
array. By employing this form of colorized displays in animation, the forecaster can fashion a
more ordered and focused portrayal of the parameters of interest.

Several visualization tools have been designed specifically for viewing or evaluating in-house
model output. A variety of display formats, including user-defined plan views, cross-sections, and
animations of both observed and derived fields are being developed for operational use.

Four general types of displays are notable for the unique value they add to the forecast process:

(1) Color-enhanced images overlaid with contours. This format enables the user to distinguish
features in animation more easily; patterns and trends often become more distinctive.

(2) Time-height cross-sections of individual RWM fields. These perspectives are often more
revealing than viewing a single level in an instant of time.

(3) Along-track displays of aviation hazards tools. This technique facilitates tailoring briefings
to specific mission requirements utilizing either Global Spectral Model(GSM), RWM
or HALT model grids.

(4) Model error field diagnostics. These displays provide a quick, objective evaluation of
recent model performance, assisting the forecaster in determining the need for, and
scope of necessary adjustments to current model guidance.

152

AFGWC has also begun to explore some new techniques which are very useful for initializing
numerical guidance, and for nowcasting convective development. Superimposing tropopause
level isentropic potential vorticity and low level equivalent potential temperature on water vapor
imagery is one such example. Juxtaposition of these two fields with a well-defined dry prod shows
strong correlation with cyclogenesis. Building composites of hourly changes in various surface-
based indices of static stability, lid strength, and other convective predictors overlaid on visual
satellite imagery is another example.

3. VISUALIZATIONS UTILIZING DMSP IMAGERY

Because AFGWC supports operations and contingencies worldwide, the organization is often
called upon to provide weather forecasts in regions for which data availability is extremely limited.
In such cases, pass-by-pass animations of polar orbiter imagery are valuable aids for identifying
synoptic trends. Animation frames are created by mapping routines which convert images with
different local swath orientation to a common map projection. This permits a stable image looping
capability over a fixed region in areas with limited or non-existent geosynchronous coverage.

For some limited applications, AFGWC has begun to utUize three dimensional data visualizations
in analyzing satellite imagery. The three dimensional view is produced using a combination of
surfacing routines and image mapping. The infrared component is used to produce a wire-mesh
surface whose height values correspond to brightness temperatures. The visual component is then
mapped onto that surface, yielding a three dimensional visualization of the original image. For
extended animation sequences, the IR is generally mapped onto itself since continuity of visual
data is lost at night.

Several analysis techniques exploiting the capabilities of SSM/I data (NRL CaWal, 1991) have
been integrated into operations at AFGWC. When blended with corresponding conventional
observations, these data can provide significant insight into the character of the synoptic setting.

(1) Intercomparison of horizontally and vertically polarized 85GHz with IR imagery.

These perspectives have proven to be useful for detecting thunderstorms concealed
beneath large cirrus canopies. This technique is extremely valuable for positioning
tropical cyclone circulations.

(2) Analysis of multichannel algorithms used to estimate maritime surface windspeeds.

Estimating tropical cyclone gale wind radii or evaluating extent and intensity of wind
fields surrounding large polar storms are primary applications of this technique.

(3) Bichannel differential between 37GHz and I9GHz. Imagery derived from this data permits

qualitative evaluation of surface moisture conditions.

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4. FUTURE DIRECTION

At AFGWC, emphasis will continue to be placed on improving our capability to provide timely
and accurate weather information to the warfighter. For example, several projects are in progress
now to refine our aviation hazards algorithms, such as incorporating the Schultz-Politovich
scheme (Schultz, Politovich, 1992) into our aircraft icing forecast algorithm, and upgrading the
HALT model (based on Bacmeister et al, 1994) to include background shear.

AFGWC will soon implement an upgrade to faster, more powerful microcomputers for operational
production. This robust workstation environment will make widespread application of the types
of techniques discussed in this paper much more feasible. In addition, forecasters will have tools
at their disposal which permit them to interact with the data themselves, to create their own visu¬
alizations and algorithms; in short, to employ new technologies and data sources more creatively
and effectively.

While it is true that our applications development is generally oriented toward operations at a
weather central, many of these display capabilities could be readily adapted to a tactical
environment. In fact, a number of our products are already accessible to deployed troops via the
Air Force Dial-In System (AFDIS). Details regarding the AFDIS are outlined in a companion
paper in these proceedings (Engel, 1994).

REFERENCES

Bacmeister, J.T., P.A. Newman, B.L. Gary, and K.R. Chan, 1994: "An Algorithm for Forecasting
Mountain Wave Related Turbulence in the Stratosphere." Wea. and Fcstg^, 9: 241-253.

Engel, Gregory T., 1994: "Operational Applications of the Air Force Dial-In System."
Proceedings, 1994 Battlefield Atmos. Conf., (in press).

Naval Research Laboratory DMSP SSM/I Calibration/Validation Report, Vol 2.

Coordinated by J.P. Hollinger, 1991: NRL, Washington, D.C., 257 pp.

Schultz, P., and M.K. Politovich, 1992: "Toward the Improvement of Aircraft Icing Forecasting
for the Continental United States." Wea. and Fcstg., 7; 491-500.

Zapotocny, J.V., 1993: "Meteorological Applications Tools for Generating Images from Gridded
Data on the Satellite Data Handling System at AFGWC." Preprints, 10th IntL Conf. on
Interactive Info, and Proc. Sys.for Meteo., Oceano., and Hydro., Nashville, TN,

Amer. Meteor. Soc., 37-38.

154

THEATER FORECAST MODEL SELECTION
R. M. Cox

Defense Nuclear Agency
Alexandria, VA 22310

J, M. Lanicci

Air Force Global Weather Central
OffuttAFB, ME 68113

H. L. Massie, Jr.

Air Weather Service
Scott AFB, IL 62225

ABSTRACT

Recent contingencies including Operation DESERT STORM have shown a need for
a finer-resolution weather forecast capability to aid decision making in theater-level combat
air and land operations. To address this n^, Air Force Weather (AFW) and the Defense
Nuclear Agency (DNA) have begun a joint effort to create a Theater Forecast Model (TFM)
architecture from government and commercial off-the-shelf hardware and software.

The concept calls for global model data (temperature, pressure, geopotential, winds,
and humidity) of approximately 100 km x 100 km horizontal resolution to provide boundary
and initial conditions. The TFM will have a horizontal domain of approximately 2400 km
X 2400 km and a horizontal grid mesh resolution of at least 40 km with a goal of becoming
approximately 10 km. It will run a 36-hour forecast within 1-hour after data assimilation.
The TFM will use in-theater observations as it generates 0 to 36 hour forecast data for theater
applications (clouds, visibility, present weather, aviation hazards, etc.).

AFW and DNA are comparing four mesoscale models to the current Air Force Global
Weather Central Relocatable Window Model to determine which is best suited for theater
operations. The models include the Colorado State Regional Atmospheric Modeling System,
the National Center for Atmospheric Research (NCAR)/Pennsylvania State Mesoscale Model,
the Navy Operational Regional Analysis and Prediction System, and the DNA Operational
Multiscale Environment model with Grid Adaptivity. Comparisons include model numerics,
physics, fidelity, accuracy, sustainability, maintainability, flexibility, and extensibility.

Global model data from NCAR and AFW will provide boundary and initial conditions
for test cases over five topographically and seasonally complex regions. Accuracy
measurements will include interpolated grid point to rawinsonde paired difference root mean
square error, mean absolute error, relative error, bias, and evaluations of standard map sets.

This paper will outline AFW and DNA activities to ensure selection of a model best
suited to joint needs. A brief overview of numerical weather prediction limitations, the TFM
approach, theater requirements, and selection requirements will be presented.

155

1. INTRODUCTION

Recent contingencies including DESERT STORM have shown a need for finer-
resolution weather forecast capabilities to aid decision making for a myriad of theater-level
combat air and land operations. Centralized facilities like Air Force Global Weather Central
(AFGWC) generate much of the theater weather information for these contingencies.
However, the centralized, reach-back approach takes more time to transmit weather
information into and out of the theater.

The use of timely in-theater observations can greatly increase Theater Forecast Model
(TFM) accuracy and value to the decision maker. Because observations are perishable, from
a modeling perspective, most theater observations do not arrive in time to be used in the
centrally run models.

To address the need for timely and accurate theater weather forecasts, including the
benefits from in-theater observations, the Air Force Chief of Staff has approved Air Force
Weather (AFW) Mission Need Statements (MNS) for the Combat Weather System (CWS) and
the Global Theater Weather Analysis and Prediction System (GTWAPS). These programs
require a TFM to supply theater warfighters with theater-optimized weather information.

The Air Weather Service (AWS) pre-screened numerous mesoscale models before
finally settling upon four primary candidates for the TFM. The candidate models for this
study include the Colorado State University (CSU) Regional Atmospheric Modeling System
(RAMS), the National Center for Atmospheric Research (NCAR)/Pennsylvania State
University Mesoscale Model (MM 5), the Navy Operational Regional Analysis and Prediction
System (NORAPS 6), and the Defense Nuclear Agency’s (DNA) Operational Multiscale
Environment model with Grid Adaptivity (OMEGA). After detailed comparison tests, one
of the four models will be selected for adaptation and transition to a Department of Defense
(DoD) standard, theater weather architecture.

2. NUMERICAL WEATHER PREDICTION LIMITATIONS

Despite improvement over the past two decades. Numerical Weather Prediction
(NWP) still has limitations. We will first review some basic NWP limitations before
providing a general discussion on our approach for the TFM selection.

There has always been a trade-off in NWP between resolution and computer power.
Whenever the horizontal grid resolution is increased by a factor of two, the resultant
computer time required to produce a forecast increases by a factor of eight. This happens
because there is change in the x and y direction along with the change in time step. Also,
this limitation is magnified by the necessity to resolve geographically induced meteorological
features. As it is well known, weather patterns are often result from a given terrain feature.
Whether it is a coastal pattern (land/sea breeze) or mountain flows (lee-side cyclogenesis),
to forecast meteorological phenomena in an accurate and timely fashion, the model must be
able to resolve these terrain induced features. However, that resolution requires a fine-scale
numerical grid, which stated earlier requires more computational power. The new generation

156

of computer workstations may well put this limitation behind us in the not to distant future.

Model spin-up time presents another challenge. When gridded data fields are used for
the model’s initial conditions, a 0 - 12 hour period is needed for the model to adjust to
numerical artifacts created by the differences between the initial fields and the model
equations. This time can be remedied by using a data assimilation procedure. Although data
assimilation procedures such as nudging may reduce spin-up time, there is no real "best"
technique.

Many physical processes are simulated in mesoscale models. This simulation or
parameterization is a very challenging task. If the parameterization is in error, then the
resultant atmospheric simulation will be suspect. Several areas which are parameterized in
NWP models include radiation, soil moisture, evapotranspiration, cloud energetics, surface
fluxes, etc. These parameterizations are of significance because often in meteorology we
have unobserved or inadequately observed parameters. Also, the process may exist on a scale
not resolved by the observational network. Most NWP models have the above mentioned
parameterizations; however, the values used for the variable may be in error because it has
not been studied fully to provide a sound basis for the value.

3. APPROACH FOR THEATER FORECAST MODEL SELECTION

These limitations and others can and will have significant impact on the results
produced by a NWP model. To compound that impact, this efforts seeks to port a model that
generally operates on a supercomputer to a workstation. The model must produce a 36-hour
forecast one hour after data assimilation. To accomplish this task, the model must be
effectively downsized. One can develop an engineering version of the model that will operate
quicker than its first principles version. This will require the model will be downsized,
allowing for tradeoffs between run time and accuracy.

Under the auspices of the DNA and AFW, a modeling team is porting and downsizing
candidate models first on a supercomputer and then to a workstation class machine. This
approach will provide insight into what can be optimized within the model to meet the TFM
run times and still achieve acceptable forecast accuracies.

All the models have a four dimensional data assimilation scheme to help model
stability and reduce model spin-up time. The models also have a flexible domain, which is
important when relocating the model operational forecast region.

These models will predict most but not all required theater weather elements out to
36- hours. Other needed weather elements will require the development of applications
models, which will get their basic input from gridded TFM data (pressure, temperature,
winds, and humidity).

157

4. THEATER FORECAST MODEL REQUmEMENTS

The AFW Functional Area Plan (FAP) includes joint operational requirements for the
TFM. These requirements call for a basic capability to have a fine-resolution weather
analysis and forecast capability to support theater combat and non-combat operations.

The TFM will ingest Gridded Data Fields (GDFs) along with additional observational
and model data to calculate "derived" parameters such as present weather, visibility, and
clouds. The TFM will also provide higher resolution GDFs for use in other applications as
directed by theater operations.

The AFGWC will receive GDFs of basic parameters like temperature, pressure,
winds, and dew point from the Navy at approximately 1 degree x 1 degree horizontal
resolution. The relocatable TFM will use these GDFs for its lateral boundary conditions and
initial conditions. Next, the AFGWC will provide additional meteorological information,
e.g., theater observations, satellite imagery, cloud information, etc., for the TFM data
assimilation.

The TFM is required to operate with a horizontal domain of 2400 X 2400 km. Its
horizontal resolution is required to be 40 km with an objective resolution of 10 km. Theater
operations dictate producing a 36-hour forecast within 1 hour run time after data ingest.
Initial plans call for two cycles per day, eventually becoming eight cycles per day.

To identify and assemble government and commercial off-the-shelf technologies to
meet these needs, AFW has developed a coordinated TFM strategy with the DNA, Argonne
National Laboratory (ANL), and the Phillips Laboratory (PL) Geophysics Directorate. This
strategy falls under the auspices of the Electronic Systems Center (ESC) and includes various
proof-of-concept studies on the candidate TFM technologies. The ESC has outlined TFM
requirements in their report number E-1243U, dated 15 December 1993.

5. THEATER FORECAST MODEL SELECTION CRITERIA

A team of scientist from AWS, AFGWC, ANL, PL, and DNA met at AFGWC and
developed the criteria which will be used to ensure the model best suited for theater
operations is chosen. It was the intent of these individuals to provide objective measures,
which could be easily followed for model comparison and subsequent selection. The team
considered model configuration, theaters of operation, and verification criteria.

Initially the team wanted to ensure the models had the necessary numerics, physics,
and run options to fulfil the TFM requirements and that also allow for a graceful degradation
in data denied scenarios. The horizontal grid spacing will be 40 km with a computational
domain of 71 x 71, and a verification domain of 61 x 61. The model will have
approximately 20 vertical levels. AFW and NCAR will provide observational and first guess
data fields for initial conditions and lateral boundary conditions. Each model will be allowed
to use whatever observations and variables it can incorporate in its analysis scheme. Models
will be evaluated out to the 36-hour forecast period and compared at 3-hour intervals.

158

To ensure a relocatable, worldwide TFM capability, test cases will be run over five
topographically and seasonally complex regions. The regions are Alaska, Central America,
Middle East, Korea, and the United States. Data collection for each test case will cover a
72-hour period. Output from the TFM candidates will be compared against each other and
the AFGWC Relocatable Window Model (RWM). To be a viable candidate, a model must
outperform the RWM. The data will cover specific seasons in each of the theaters.

The verification criteria includes the analysis, 6, 12, 24, and 36 hour forecasts for the
u-component and v-component of the wind, temperature, pressure, relative humidity, and
specific humidity. These forecast periods and variables will be evaluated at the surface and
mandatory upper-air levels. The evaluation will include a measurement of accuracy for each
of the variables, forecast periods, and atmospheric levels. Measures of accuracy will include
mean absolute error, relative error, bias, and root mean square error determined from the
paired differences of interpolated model grid point data to rawinsondes. The accuracy
comparisons will also include comparisons of standard map sets from each model for given
time periods. After all the above measures have been complied for each of the models, AFW
and DNA will decide which model best meets the TFM meteorological accuracy
requirements.

The atmospheric accuracy of each model will be evaluated against the computational
requirements and technology transition factors, including the model’s sustainability,
maintainability, flexibility, and extensibility. Each candidate model will have timing statistics
gathered on its operation on a supercomputer and a workstation. The final selection of a
model will depend on which one best satisfies timing, accuracy, and technology transition
requirements.

6. CONCLUSION

Recent DoD contingencies have demonstrated the need for high quality and timely
meteorological information at the theater level of operations. To address this need, the Air
Force Chief of Staff approved a MNS for CWS and GTWAPS. AFW and DNA have
undertaken a joint effort to ensure theater operators in future contingencies will receive value-
added meteorological information when they require it.

The selection of the TFM will take place within the next 12 months after a series of
tests are completed. These tests will compare four leading mesoscale numerical weather
prediction models against each other. The tests will be conducted using data from five
geographically, topographically, and seasonally complex regions. Accuracy comparisons will
involve basic meteorological variables for forecast periods of 0 - 36 hours. The model best
meeting the theater forecast requirements, including technology integration factors, will be
selected.

159

AIR WEATHER SERVICE:

EVOLVING TO MEET TOMORROW'S CHALLENGES

Col William S. Weaving, Maj Dewey E. Harms, Capt Donald H. BerchofF, and

Capt Timothy D. Hutchison
Headquarters Air Weather Service
Scott AFB, Illinois, 62225-5206, USA

ABSTRACT

Air Weather Service (AWS) has undergone a notable evolution since activation on
1 July 1937. Through all the changes, the basic AWS mission remains the same; to
assist the warfighter in any way possible to achieve victory on the battlefield. With
the rapid advancement of technology, and the resulting increase in technical
training requirements, AWS' role as the technical leader for Air Force and Army
weather units is as vital as ever. To ensure military weather capability keeps pace
with technology, a number of major programs and initiatives are underway within
AWS to upgrade its two major production centers. Air Force Global Weather
Central (AFGWC), and the USAF Environmental Technical Applications Center
(USAFETAC). At the headquarters, AWS continues to evolve and adapt to
improve technology transition, equipment acquisition, and training. Working
closely with the Pentagon and other major commands, AWS continues to field a
host of new standard weather systems. With the rapid integration and the limitless
potential of these new weather systems, effective technology transition through
innovative training methods is extremely critical. With this in mind, AWS is
working not only to field these systems, but has also set in place a mechanism for
assuring their maximum exploitation. This paper will concentrate on Air Weather
Service's role in providing centralized products to the warfighter and in improving
those forecaster skills necessary to optimize exploitation of new and existing
technology.

1. INTRODUCTION

During most of our nation's military history, weather personnel have played a vital role in
maximizing the effectiveness of the warfighter by accurately identifying windows of
opportunity for aviators to seize.. Whether it be providing decision assistance in the
Normandy invasion, the Berlin Airlift operation, or hunkering down with the United
Nations coalition forces during Operation DESERT STORM, Air Weather Service has

161

always been ready to provide weather advice to military decision makers. In most
instances, the advice is a key ingredient to mission success. According to the book, Air
Weather Service: A Brief History 1937-1991. during the Berlin airlift, low clouds, fog,
freezing rain, and turbulence frequently impacted airlift activities. The success of the airlift
mission, and in turn, the future , of the residents of Berlin depended on the ability of our
aircrews to deliver ample supplies and break the Soviet stranglehold on the city. As
history shows, despite the frequent occurrences of inclement weather, the Berlin airlift was
a success. Precise forecasts played a major role then and still do today. Despite the
development of all weather" aircraft, low ceilings and visibility, and hazardous weather
still impact mission effectiveness. Additionally, weather can impact the aircrew "rules of
engagement". During DESERT STORM, pilots were required to visually acquire targets
before firing. This requirement made accurate cloud forecasts an absolutely critical
ingredient for mission success.

AWS has gone through many structural and organizational changes since activation on
1 July, 1937. For 54 years, AWS maintained command and control of all Air Force and
Army weather units worldwide. This changed in 1991 as world events dictated major
changes in the direction of national policies.

In August 1991, as part of the 25 percent Department of Defense (DoD) manpower
reduction, a neSv era in AWS began when the Air Force directed the transfer of Air Force
weather field units from Headquarters AWS (HQ AWS) to local operational commanders.
The purpose was to substantially streamline middle management, and assign the weather
field units directly to the local wing commander. This initiative meant the total
deactivation of six AWS weather wings and associated subordinate weather squadrons.
HQ AWS and its remaining subordinate agencies moved out from under Military Airlift
Command and became a field operating agency reporting directly to the Pentagon. Today,
the Directorate of Weather at the Pentagon assumes responsibility for Air Force
atmospheric and space policies, plans, and resources while the focus of HQ AWS and its
subordinate centers is on meeting the present and future operational needs of the Air
Force, Army, and other military and government agencies.

^though AWS has undergone a notable evolution, the basic mission remains the same as
it was over 57 years ago, to assist the warfighter in every way possible to achieve victory
on the battlefield. The AWS role as the technical leader for Air Force and Army weather
units is vital. Internally, AWS continues to evolve and adapt to increase the peacekeepers
ability to use weather to gain all possible advantage. This paper will concentrate on Air
Weather Service's role in providing centralized products to the warfighter and in
improving those forecaster skills necessary to optimize exploitation of new and existing
technology.

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2. THE NEW STRUCTURE: CONCEPT OF OPERATIONS

AWS provides centralized weather products and technical assistance to the Air Force,
Army, selected DoD agencies, and classified programs of the highest priority. Centralized
weather information is critical to operational planners, weapon system designers, and field
units who rely heavily upon centrally produced analyses and forecasts and climatological
studies. Due to the large amount of data and complexity of global, regional, and
mesoscale models, the atmospheric products and services to DoD warfighters are beyond
the resources of the operational military commands. AWS provides these products and
services during peace and war.

Figure 1 depicts the operational weather architecture as the Air Force moves into the 21st
Century. Conceptually, observational data that is collected and used in global
(hemispheric) analysis and forecast models will be sent to a centralized weather facility as
input to theater-scale (mesoscale) forecast models. Here, "theater" refers to a domain
approximately 2500 km by 2500 km centered over the area of interest where military
operations are occurring. The theater model will use this data along with other observed
and model output data to produce finer resolution mesoscale forecasts. The primary goals
are to provide timely, accurate observations and forecasts to help ensure successful air,
ground, and sea battlefield operations.

Figure 1. Weather Support Concept into the 21st Century

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2. 1 Headquarters Air Weather Service

HQ AWS, which is located about 15 miles east of St. Louis MO at Scott Air Force Base
(AFB) IL, provides meteorological technical expertise to the Air Force and the Army, and
directs the operations of its subordinate units. It provides oversight for the
standardization and interoperability of Air Force and Army weather units worldwide, plans
for and fields standard weather systems, transitions new technology to field units, deUlops
standardized training programs, and assesses the quality and technical goodness of
weather information, (AWS Mission Directive 49-1, 1993).

Figure 2. Air Weather Service Organizational Structure

2.2 Centralized Facilities

Essential components of the Air Force weather concept are centralized weather facilities
capable of producing tailored global and theater weather products to enhance operations
worldwide. The current Air Force centralized weather functional architecture is dedicated
towards the synthesis of worldwide weather data, the ingest and manipulation of
numerous meteorological satellite (MET SAT) datasets, daily operational runs of global
weather analysis and prediction models, storage of the data and imagery files within a

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centralized database structure, and the generation of gridded data field, graphical, and
alphanumeric forecast products for global and theater applications. Centralized products
normally will be used to enhance command, control, communication, computers, and
intelligence activities. Although these activities are frequently decentralized, they require
consistent, automated weather information at multiple decision points.

2.2. 1 Air Force Global Weather Central (AFGWC)

Centralized weather information is provided to military forces for planning, training,
resource protection, and operational decision assistance. AFGWC (located at Offlitt AFB,
Omaha NE) primarily provides this information to fixed weather units located at Air Force
bases. Army posts, and tactical units within a theater of operations.

AFGWC is designated as the DoD center for theater-scale weather analyses and forecasts,
meteorological satellite (METSAT) data processing, and cloud analyses and forecasts.
Before the turn of the century, AFGWC will run theater weather analysis and forecast
models for not only the Air Force and the Army, but all warfighters conducting operations
on land, sea, and in the air. The Navy's Fleet Numerical Meteorological and
' Oceanographic (METOC) Center (FNMOC) will provide global model gridded data as
one source of input for the AFGWC theater-scale model.

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Figure 3 depicts the flow of centralized operational weather information in the future.
^GWC will receive Gridded Data Fields (GDFs), and/or spectral coefficient datasets of
"basic" meteorological parameters (e g., temperature, pressure, winds, and dew-point)
from the Navy. AFGWC theater and hemispheric models will use these datasets along
with additional observational, satellite, hemispheric, and internal model data to calculate
derived" parameters (e.g., clouds, visibility, etc.) and provide theater and hemispheric
uniform GDFs (UGDFs). UGDFs will be the field weather teams' primary source of
centralized data.

AFGWC will ingest all available foreign and domestic observations fi'om atmospheric and
satellite data sources to build an accurate environmental database. In addition to currently
available information, these data include automated surface and upper-air meteorological
observations, automated tactical surface observations, wind and thermodynamic profiler
information, and aircraft upper-air meteorological observations.

AFGWC will use regional data assimilation systems which feed atmospheric data into its
theater analysis and forecast models. The atmospheric and satellite data collected will be
processed and incorporated into the AFGWC environmental data base on a standardized
srid. The observational data base will be automatically updated on a regular basis as
newer data become available. Specialists will use weather workstations to tailor the
output of the regional/theater models periodically (e g., every 6 hours) and update the
forecast portion of the data base. The workstations will allow specialists to generate four¬
dimensional visualization of the analysis and forecast fields (Air Force Weather Support
System Concept Paper 2015, 1994).

AFGWC production work centers will consist of personnel trained to produce standard,
routine meteorological products and mission-tailored products serving warfighters' needs
worldwide. Also, AFGWC will operate theater cells as required, which will have the
responsibility for operational execution of theater-scale models for a specified theater. Up
to two cells will be required to cover two regional conflicts simultaneously. Each cell will
manipulate data available from the Centralized Database Management System (CDMS)
and run theater model(s). Each individual cell will tap into the centralized database,
updating information for its respective area of interest. The CDMS itself is the centra!
computer repositoiy for weather information-analyses, forecasts, METSAT imagery, and
cloud information. The CDMS will be capable of simultaneous communications with each
theater cell, as well as any field request for information.

In addition to near real-time forecasting information, there must be a timely response to all
climatological information requests to enhance theater operations and contingencies
anywhere in the world. For instance, military planners needed climatological information
on cloud cover, frequencies of precipitation, and diurnal ranges of temperature for Kuwait
within hours of the initial Iraqi invasion.

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2.2.2 USAF Environmental Technical Applications Center (USAFETAC)

USAFETAC (located at Scott AFB IL) collects, maintains, and applies climatological
information to determine the environmental effects on military operations and systems and
to meet requirements of the Air Force, Army, and other military and civilian agencies.
USAFETAC's data processing and archival center at Asheville NC receives worldwide
observations from AFGWC and combines this information with that received at the
National Climatic Data Center from other sources to continually build and maintain a
climatological archival database available for global and theater applications.

Dedicated USAFETAC personnel provide tailored climatological products for any DoD
mission upon request. Some requests will be supported by products generated
automatically by computer. The majority of requests received (dial-in or message) from
DoD customers, however, will be evaluated and provided tailored support by trained
specialists, particularly those requests requiring significant climatological expertise to
solve. Climatological assistance ranges from preparation of historical area weather data
for contingencies to providing data for combat simulation war games.

Environmental simulation data will provide combat simulation models the ability to
present statistically representative weather in both time and space (ground/air/underwater)
domains. These models will integrate weather information into combat (war gaming) and
weapon simulators, taking advantage of fractals, data-compression techniques, and other
advances in statistics, to provide more realistic training to the warfighter.

2.3 Technology Transition and Training

With the rapid integration of new technology and the limitless potential of new weather
systems, effective technology transition through innovative training methods is as
important as ever in assuring maximum exploitation of new systems. Throughout the
operational weather community, the key to meeting the training challenges of tomorrow
lies in keeping pace with technology. New systems such as the WSR-88D Doppler
weather radar offer a wealth of information, and frequently, users don't have the time or
knowledge to build up quick system expertise. These problems are further compounded
within the military. Besides keeping pace with the rapid technological advances, the
military also contends with shrinking weather unit staffs and a younger forecaster work
force; both of which are at their lowest levels ever. Ironically, as the need for training is
increasing due to new technology, the available manning resources dedicated to providing
the training is decreasing. The challenge of the future is to develop training and
technology transition programs that keep pace with technology and overcome
acknowledged manpower constraints. AWS is working in this area, exploring innovative
training approaches for new weather system acquisitions, and follow-on training, which
are responsive and meet the unique requirements of today's Air Force.

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AWS manages training requirements for all new standardized Air Force weather systems
deployed to the field. Working closely with Headquarters, United States Air Force and
the operating commands, AWS performs new system task analyses using the Air Force
Instructional System Design (ISD) process. In the past, training on new systems was
usually worked out late in the system acquisition phase. The ISD process places more
focus on analyzing the training requirements early in the acquisition phase. Proper
application of IDS principles enables the best mix and match of training for the operator in
today's high tech environment. Recognizing requirements early in the acquisition process
allows for smoother and more efficient implementation of new training concepts such as
on-line training. "On-line" training enables the user to train on the same system used in
day-to-day operations; as a result, it reduces the need for a human trainer, saves operator
time, and provides user help on demand. On-line training will carry new systems
acquisition training into the 21st century.

Besides seeking innovative training methods, AWS is attempting to make standardized
training/transition programs more flexible. The complexity of new computer systems and

their growing number of potential applications makes training/transition program flexibility
critical.

Following the deployment of new weather systems and completion of initial system
training, AWS becomes the focal point for technology transition and exploitation. AWS
provides direct assistance to the field through various training methods by developing and
transifioning new and/or existing meteorological techniques, and by exploiting technical
capabilities of fixed based or tactical weather station equipment. It manages the transition
of new forecast techniques to the field through a regional approach that encourages active

field unit participation through regional working group meetings/conferences, and bulletin
boards.

AWS manages the exchange of technical information between the field units and AWS
through 1 1 regional managers. Regions are broken out based on geographic location and
climatology with six regions in the CONUS, three in the Pacific and two in Europe.

egional managers seek to facilitate the exploitation of technology within a region by
tailoring assistance to a sub-synoptic level and acting as the central focal point for
technology transition and technique sharing within a region. Communication is achieved
through regional bulletin boards, telephone, and annual regional conferences and working
groups. Regional managers assure similar initiatives are not worked simultaneously in
Other regions. The crossfeed concept relieves unit workload by eliminating redundancy in
initiatives and maximizes use of techniques already developed in the field. Additionally,
rather than depending solely on the development initiatives from a centralized facility, the
concept utilizes the intellect of forecasters in the field. Regional managers work closely
with a group of functional experts who specialize in AWDS, WSR-88D, meteorological
computer applications, and theater forecast techniques. These experts provide technical
assi^stance to the regional managers and the field during technique development and assure
technical goodness of the final product.

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AWS also provides on-site technical assistance through Meteorological Enhancement
Teams (METs). The MET is a team of functional and regional experts sent to the field to
present one-day, multi-media presentations on various meteorological topics. AWS visits
units by request only and tailors each MET presentation to meet the needs of the
individual unit. The purpose of the MET is to introduce new or existing meteorological
techniques and to show the forecaster how to exploit the new technology in applying these
techniques. MET teams have already presented over 100 seminars on topics ranging from
"Fog and Stratus Forecasting Problems" to "Severe Weather Forecasting."

AWS is also working to modernize the AFW follow-on training (FOT) program. The new
program exploits the use of interactive courseware (instructional software and hardware)
and Computer Aided Instruction (CAI) replacing the old slide projection technology. In
1991, AWS fielded the first of 253 Multimedia Training Systems (MTS) to weather units
worldwide. The MTS provides interactive video, CD-ROM , and a VCR to run self-paced
POT courseware to enhance forecaster technical skills and supplement formal school and
on-the-job training (OJT). Interactive courseware enhances retainability of information by
engaging the learner's senses and involving the individual in the learning process by
presenting the material through a variety of media such as videotape, compact disk,
videodisk, graphic animation, and sound. Students proceed at their own pace and can exit
and enter the course as required. This feature ensures maximum flexibility so the students
can fit training into their busy schedule. The first videodisk modules fielded included,
"Workshop on Doppler Radar Interpretation" and "Boundary Detection and Convective
Initiation." Military forecasters successfully used both these modules to prepare for
formal WSR-88D training courses.

Development of interactive software is a lengthy process, often taking 1 to 2 years per
module. Occasionally, circumstances require the quick fielding of a training module to
respond to a noted technical shortfall. In these instances, AWS exploits the capability of
CAI. CAI is not as visually effective as interactive courseware, but because development
time is less than a year, it provides an effective method of quickly addressing a critical
technical deficiency. In 1992, a deficiency was noted in the forecasting of icing and
turbulence. AWS quickly developed two CAIs on icing and turbulence forecasts using the
WSR-88D. The WSR-88D Operational Support Facility at Norman Oklahoma also
develops CAIs; they have already developed ten CAIs on WSR-88D algorithms. Both
CAIs and interactive courseware are an integral part of the weather FOT training plan.

AWS is also an active participant with the National Weather Service (NWS) and the Naval
Oceanography Command (NOC) in the Cooperative Program for Operational
Meteorology, Education, and Training (COMET). COMET was originally developed by
the NWS as part of their modernization program to place emphasis on improving the
professional background and operational capabilities of meteorologists to use mesoscale
data. Three COMET programs have been developed to meet the objectives of improving

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mesoscale forecasting: a Distance Learning Program, an Outreach Program, and a
Residence Program.

AWS, NWS, and NOC provide funds as sponsors in the Distance Learning Program. The
objective of the program is to provide professional development education for operational
weather forecasters, university facility, and other meteorologists who do not have the time
or money to attend COMET resident courses. Training is mainly conducted via
interactive courseware that is developed for in-station use. The two modules used to
successfully prepare military forecasters for formal WSR-88D training, "Workshop on
Doppler Radar Interpretation" and "Boundary Detection and Convective Initiation" were
both the result of COMET initiatives.

Additionally, AWS provides funds for research under the Outreach Program and
participates in the Resident Program. The Outreach Program creates partnerships
between the academic research community and operational weather forecasters that focus
on resolving forecast problems of utmost concern to the operational weather community.
For example, under the Outreach Program, North Carolina State University was paired
with AWS to work on nowcasting convective activity during space shuttle launches and
landings. The Resident Program brings meteorologists together with nationally
recognized experts for the purpose of improving their collective understanding of
mesoscale meteorology. The program conducts courses, symposiums, and workshops
that provide operational weather forecasters, hydrologists, and other atmospheric
scientists with new skills and concepts in mesoscale meteorology. Through the years,

AWS' association with COMET has been very productive, and prospects for the future are
just as bright.

3. CONCLUSIONS

Over the last few years, AWS has experienced significant structural and organizational
chants and technological advances. Even though AWS no longer has operational control
of Air Force field weather units, its role in providing decision assistance to the warfighters
has not diminished. AWS still provides centralized analysis and forecast information to
operations worldwide and the technical weather expertise to the Air Force and the Army.

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AIR FORCE WEATHER MODERNIZATION PLANNING

Alfonse J. Mazurowski
Headquarters Air Weather Service
Scott Air Force Base, Illinois, 62225-5206, USA

ABSTRACT

Air Force modernization planning results in the development of documents which evaluate
all aspects of specific functions, pinpoint deficiencies, and demonstrate how the Air Force
plans to affordably satisfy those deficiencies to achieve required capabilities. The Air
Force Weather (AFW) Functional Area Plan (FAP) is a 25-year modernization planning
document that details the programs and laboratory technologies required to enhance
operational capabilities. Laboratory funding will be based, in part, on their role in
satisfying Technology Needs documented in FAPs and Major Command Mission Area
Plans (MAPs). A FAP Integrated Product Team (FAPIPT), consisting of weather
representatives from Headquarters United States Air Force and Army, Army and Air
Force major commands, product centers, and laboratories, was formed to construct the
plan. A FAP Steering Group, made up of senior Air Force leaders in weather, provided
oversight and guidance for the FAPIPT. The FAP addresses the two major segments of
AFW; unit capabilities, which includes fixed-base weather station and tactical unit areas;
and centralized facilities, which includes the Air Force Global Weather Central, United
States Air Force Environmental Technical Applications Center, and the Air Force Space
Forecast Center. The FAP was developed by compiling a comprehensive set of customer
weather requirements, evaluating AFW's capability to provide those products, and
satisfying any capability deficiencies through the development of a modernization
roadmap. The modernization roadmap in the FAP contains planned acquisition programs
for hardware and software in addition to descriptions of Critical Enabling Technologies
that are the contribution of science and technology programs to correct deficiencies. The
FAP is a living document that will be updated annually. Through successful execution of
the modernization roadmap. Air Force Weather will be able to respond to the complex,
evolving requirements of tomorrow's operational missions.

1. INTRODUCTION

During the 1992 Fall Corona, the Air Force Chief of Staff initiated the Air Force's Year of
Equipping by charging the major commands (MAJCOMs) and Field Operating Agencies (FOAs)
to build 25-year modernization plans. These plans are called Mission Area Plans (MAPs).

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Functional areas such as weather, intelligence, communications, and security police were tasked
to develop FAPs, since they are not specifically operational mission areas, but rather are
backbone, cross-cutting functions which support all mission areas. The FAP follows the MAP
format and methodology as documented in Air Force Policy Directive 10-14, Draft, and Air
Force Instruction 10-1401, Draft.

The purpose of the completed MAP/FAP is to guide the development of the Program Objective
Memorandum (POM) and to push technology development through Air Force Material
Command's Technology Master Process.

2. MODERNIZATION PLANNING

MAPs/FAPs are developed through a modernization planning process which uses a mission area
assessment (MAA), mission needs analysis (MNA), and an assessment of possible solutions. The
MAA process evaluates force structure, the operational environment, and the threat we expect to
encounter v/hile conducting the assigned mission. The MAA process uses a strategy-to-task
(STT) evaluation of operational/support mission tasks requiring certain capabilities (current and
programmed). These tasks are derived from the National Military Strategy and identify what
capabilities are needed to achieve military objectives. The STT is a framework used to better

understand and communicate how Air Force Weather's activities support the nation's security
needs. ^

During the MNA, operational tasks are analyzed to determine the factors which impact the
current and programmed capability to accomplish identified operational objectives. The MNA
ultimately identifies deficiencies in current and future capabilities to provide adequate weather
products for operational missions.

After deficiencies are identified, an assessment of possible solutions is accomplished. Non¬
material solutions, such as doctrine, tactics, techniques, procedures, and training, are examined
to determine if changes in these areas can solve the deficiencies. If not, new technologies needed
to improve the warfighting capability in the field are identified and prioritized through interaction
among AFW, the supported operational customers (the warfighters), and Air Force laboratories.

3. DEVELOPMENT OF THE AFW FAP

In August 1993, HQ USAF/XOW tasked Air Weather Service (AWS) to develop an AFW MAP
(which subsequently became the AFW FAP). A FAPIPT, co-chaired by AWS (the user
command) and Electronic Systems Center's system program director for weather programs
(AFMC Product Center) and consisting of weather representatives from Headquarters United
States Air Force and Army, Army and Air Force major commands, product centers, and
laboratories, was charged to develop, document, and continually update the FAP. An AFW
FAPIPT Steering Group, comprised of senior weather functional managers from the Air Force
major commands, the FOA, and Headquarters Air Force and Army, was organized to provide
direction for the FAP planning process.

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In October 1993, the Steering Group directed the FAPIPT to limit the initial focus of the then
MAP to evaluate the Air Force Global Weather Central's (AFGWC's) capability to provide
Theater Battle Management (TBM)-required meteorological and oceanographic (METOC)
products. The FAPIPT’s evaluation of AFGWC's capabilities identified the hardware and
software deficiencies in satisfying the centralized product requirement for wartime operations.
The initial MAP was completed on 18 February 1994.

In March 1994, HQ USAF/XOW directed AWS to expand the scope of the AFW MAP to
include all areas of AFW activities and redesignate it as a FAP. Now the FAP addresses the two
major segments of AFW: centralized facilities and unit capabilities. The centralized area
includes the three meteorological centers within AFW: AFGWC (operational weather products),
USAF Environmental Technical Applications Center (USAFETAC) (climatological products),
and Air Force Space Forecast Center (AFSFC) (space environmental products). The unit area
concentrates on fixed base weather station (BWS) and tactical weather unit operations. In
keeping with the concept of "train in peace as we fight in war," the far-term goal is to combine
the software and hardware functionalities of the fixed BWS and tactical weather unit to the
maximum extent possible.

4. DESCRIPTION OF THE AFW FAP

The AFW FAP is a weather modernization plan which will serve as a roadmap for weather
operations through 2019. It highlights deficiencies that still need to be corrected to meet mission
requirements into the 21st century. This plan is a living document which is updated at least
annually to initiate and validate POM actions. It also provides the basis for technology programs
to be developed which will chart AFMC's science and technology investment strategy.

4.1 Centralized Facilities

4.1.1 AFGWC is designated as the DoD center for theater-scale weather analysis and forecast
models, meteorological satellite (METSAT) data processing, and cloud analyses and forecasts.
The primary mission of AFGWC is to provide centralized weather products to US combat forces
to include unified and specified commands and all Air Force and Army major commands.
AFGWC uses super computers, large computer mainframes, minicomputers, and workstations to
manage the enormous amount of incoming weather data and run complex weather analysis and
forecast models to meet customer needs.

AFGWC was assessed on its capability to provide the required suite of 1340 METOC products
in uniform gridded data field (UGDF) format as identified through the TBM program. As a
result of this assessment, shortfalls in AFGWC's capability to satisfy stated product requirements
were determined. Hardware shortfalls were primarily due to inadequate processing power,
internal communications, and data storage. Software shortfalls included limited or no capability
to provide certain METOC parameters.

Solutions to correct AFGWC hardware shortfalls are planned through a combination of upgrade
and modernization programs:

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Satellite Data Handling System (SDKS) is an interactive computer system within AFGWC for
centralized weather forecast generation and dissemination. The SDKS ingests meteorological
data, both conventional and satellite, for interactive display and manipulation at the forecaster
consoles. It provides the user-interface and computational support for producing specific
meteorological analysis and forecast products required to support AFGWC and its external
customers. The SDHS Upgrade (SDHSU) program provides enhancements to the SDHS and
bridges the gap until the scheduled SDHS replacement program (SDHS II) comes on-line The
enhancements include a logistical upgrade/replacement of the user-interfaces, further
development of the Air Force Global Weather Central Dial-In System, storage expansion
upgrade of processors, and hardware/software modifications to receive new meteorological
satellite data types/formats and foreign meteorological satellite data. In addition to these
enhancements, three more user-interfaces will be included in SDHSU. The SDHSU
alleviate hardware saturation and will completely meet identified TBM
METOC requirements. The SDHS II program will modernize the SDHS hardware architecture
and will support data access and retrieval from a centralized weather data base to meet
anticipated future operational requirements.

Cloud Depiction and Forecast System (CDFS) uses polar orbiting satellite imagery and
continually builds and updates the Satellite Global Data Base (SGDB) upon receipt of the polar
satellite imagery. Also, CDFS builds a global cloud analysis based upon satellite imagery and
conventional meteorological data. CDFS provides all customers with global satellite imagery
cloud analyses, and forecasts products. CDFS II is an acquisition program that will replace
satellite data processing hardware and will develop a new cloud analysis model therebv
improving cloud forecasts for TBM. ’

Global Theater Weather Analysis and Prediction System (GTWAPS) will replace the Automated
Weather Analysis and Prediction System (AWAPS) with an open system architecture
workstation environment which will host advanced, high-resolution theater weather analysis and
orecast models to meet TBM resolution requirements for theater-scale operations. The CDFS
I and GTWAPS programs, along with other software initiatives (e g., laboratory technology
e orts, utilization of Navy data, AFGWC software development), will meet TBM METOC
product requirements.

Weather Information Processing System (WIPS) is made up of two areas; Weather Information
Processing-Production (WIPP) and the Weather Information Processing-Development (WIPD)
WIPP pnmanly receives and processes conventional weather data. Conventional weather data
surface and upper air data accumulated from weather stations around the world.
WIPD serves as a development system and back-up to some of the hardware on WIPP. The
WIPS Expansion (WIPS-E) Program and the WIPS processor modification program procure

alleviate current saturation and expand the capability of WIPS and

uSTc requirements. The WIPS-R program will replace the existing

WIPS computer hardware and operating system to satisfy future operational requirements.

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HYPERchannel (HYPER) is the means by which required information is transported from
computer system to computer system within AFGWC and ultimately to the Communications
Front-End Processor for transmission to the field. The HYPERchannel Replacement Program
procures the hardware and software to replace the existing HYPER communication link which is
not compliant with Air Force Open System Standards and does not have sufficient throughput
capability to meet TBM METOC product requirements.

Software enhancements are planned to support modernization plans and future improvements in
AFGWC's capability. Key technologies are needed to meet TBM METOC requirements for
clouds, surface visibility, present weather conditions, snow depth, soil moisture, turbulence,
icing, thunderstorms, volcanic ash, and layered visibility.

4.1.2 USAFETAC is the AFW center for global climatological products. Their mission is to
archive worldwide atmospheric and space environmental data and to prepare analyses and studies
from manual and computer manipulation of this data for DoD applications. Air Force and Army
combat planning and employment decisions, development of weapon systems, and national
programs are examples of their products. USAFETAC uses two large computer mainframes,
minicomputers, workstations, and large amounts of mass storage to manage the tremendous
amounts of climatological data required for product generation.

USAFETAC product evaluation used customer identified, coordinated climatological product
requirements. The following assessed product categories attempt to cover all types of customer
needs: climatic summaries, descriptive climatology, electromagnetic propagation, engineering
climatologies, studies/product improvement, tailored operational support, forensic studies, and
simulation/modeling. Hardware shortfalls included inadequate processing power and mass
storage. Software shortfalls were primarily due to the lack of worldwide data availability and
insufficient simulation models.

Hardware upgrades through the USAFETAC-Replacement (ETAC-R) program are planned to
satisfy the above deficiencies. ETAC-R will replace/upgrade computer systems required at
USAFETAC and AFGWC in order to provide climatological products to DoD customers
worldwide. At USAFETAC, the program replaces its existing mainframe computer system and
associated storage with a cluster of workstations and state-of-the-art storage devices. This
equipment will provide necessary processing required to run high resolution mesoscale models,
which may partially solve data availability, and storage for rapidly expanding databases. At
AFGWC, ETAC-R upgrades the AFGWC Centralized Database (CDB) and allows the new
ETAC systems access to the CDB.

Software solutions include, in-house training programs, increasing the availability of additional
worldwide data, and development of advanced climatic models.

4.1.3 The AFSFC provides space environmental forecasts, warnings, and anomaly assessments
to enhance the capability of DoD forces worldwide. As the basis for these products, the AFSFC
collects data from a mix of global networks of DoD-unique, national, and international ground-
and space-based instrumentation which monitors the solar and near-earth environment. This data

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IS processed into a wide range of products by various computer algorithms which model the
space environment. Assistance includes alerts of solar and geomagnetic events and assessments
of event impacts on satellite drag, satellite performance, radar correction, early warning radar
and space surveillance performance, and communications effectiveness.

AFSFC uses three computer systems, large databases, and communications in both their
hardware and software operational clusters. These systems run specific application programs for
customer products while a separate computer cluster is used for unclassified software
development. The assessment of hardware capabilities led to a determination of shortfalls in
computer processing and storage capabilities. Software shortfalls include the inability to provide
solar and geophysical alerts, analyses, and forecasts to the level of accuracy required by users.

The solution to the hardware and software limitations is the AFSFC Replacement(AFSFC-R)
Program. The AFSFC-R program provides for replacement of AFSFC's four Digital VAX
computer systems (clusters) and current database environment with a system of new computers
(to include replacement of the Uninterruptable Power Supply). Additionally, the program
includes upgraded external communication interface hardware and software for each of the
operational clusters. The program also contains software transition and integration support for
application software to be transitioned from the current AFSFC Digital VAX environment to the
new vendor's hardware. The new hardware will include the capability to satisfy software
solutions by processing new model software being developed under the Space Environmental
Technology Transition (SETT) program. This program also includes development of the follow-
on SETT models. The program will comprise an integrated effort beginning in the early 2000s to
accomplish both the Operational Software Development (OSD) and the software maintenance
for the Ionospheric Models, the Magnetospheric Model, the Neutral Atmospheric Models, the
Integrated Space Environmental Models (ISEM), and for the coupling of these follow-on space
models. These improved, follow-on space models will concentrate on the advanced development
of algorithms that will use new space measurements to improve model accuracy.

4.2 Unit Areas

Unit Areas include both tactical and fixed BWSs. Functions of these units include weather
warning, observing, forecasting, briefing, and resource protection. Weather personnel use a
combination of visual observations and equipment-sensed measurements to observe, record, and
report weather elements. Fixed BWSs take advantage of state-of-the-art computer hardware and
software technology improvements to provide operational products at Army posts and Air Force
bases worldwide. Tactical units currently rely on less sophisticated systems to provide decision
assistance to deployed combat forces.

Assessments were made from a hardware and software viewpoint to determine the tactical and
fixed BWSs capability to satisfy customer requirements for METOC products. In the near term
there will be different solutions to achieve desired capability at tactical and fixed BWSs.

However in the far term, the software and as much of the hardware solution as possible should
be the same.

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4.2.1 AF Systems' Enhancements

The following programs are planned to satisfy Unit Area shortfalls:

Tactical Forecasting System (TFS) is a hardware/software upgrade of AFW tactical forecasting
capability. TFS must be a small, lightweight, modular system that is rapidly deployable, durable,
quickly activated, and field maintainable. TFS' modular design must allow for an initial
deployment capability that can be expanded, as required, to a more capable system. TFS will
provide responsive, reliable, accurate weather information in near real time. In addition, the
system's products will flow to other systems locations using the in-theater communication
system. The system's modules at the in-theater forecast center must provide a theater-scale
weather analysis and forecast model capability that will produce tailored, accurate, and reliable
forecast products in a more responsive fashion than is currently possible.

Modifications to existing tactical observing systems, GMQ-33s, TMQ-34s, and TMQ-36s, are
required to provide an automated capability to determine: cloud amount, cloud heights,
visibility, surface pressure, surface wind speed and direction, surface temperature, surface dew
point, and precipitation (amounts and type) and the capability to add additional sensors to
determine lightning (direction and range), nighttime illumination, present weather, soil moisture
and soil temperature, precipitation (fall rates), and cloud type. They must automatically collect
and transmit data directly to the TFS. The TFS will then transmit the quality controlled data to
the C4I systems making it available to operational customers.

Modifications to the current upper air observing system, MARWINs, must be a lightweight,
deployable subsystem that determines vertical profiles of wind speed and direction, temperature,
pressure, geopotential heights, and dew point. The base station will transmit this data to the

TFS.

Modifications to the current manual surface observing system, Belt Weather Kits, must be
single-person portable, consisting of components that can be hand held or ground mounted to
manually determine cloud heights, visibility, surface pressure, surface wind speed and direction,
surface temperature, surface dew point, precipitation amount, and infrared visibility.

The solution for remote, automated capability must be an expendable, lightweight, and small
sensor system deployable to remote areas under friendly or enemy control. It must automatically
determine cloud heights; visibility; surface pressure; surface wind speed and direction; cloud
coverage; cloud type; infrared visibility; precipitation type, rate, and amount; surface
temperature; and surface dew point. These determinations will be automatically transmitted to a
designated TFS location.

Meteorological Operational Capability (MOC) develops and procures observing and data
processing systems to meet Army and Air Force operational requirements in the fixed BWS
environment. This program replaces and improves existing fixed meteorological observing and
processing systems, improving support to the planning and execution of aerospace operations,
while satisfying critical flight safety and resource protection requirements. The MOC will build

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upon technological advances developed under the TFS and tactical observing programs. The
transition of tactical systems technology back into the fixed base/post environment supports the
train in peace as you fight in war" concept, ensuring combat and peacetime support systems are
as similar as possible; ultimately reducing unit wartime training requirements. The MOC must
ingest all available sources of METOC information and, from a single user position, quality
control, format, display, process, analyze, and archive all required observed and forecasted
weather data and products. The MOC must disseminate this meteorological information to local
C4I systems and worldwide weather communications systems. Improved weather observing
capabilities must provide continuous and automatic sensing, collection, quality control, and
display of local weather conditions. Also, new automated observing capabilities must provide
lightning detection for ground refueling, munitions safety and support to base and post central
computer facilities, measurement of wind and temperature vertical profiles for wind shear
detection and warning, and measurement of slant range visibility to improve flight safety.
Improved forecasting capabilities must include the integration of a local environmental forecast
model (designed to improve short-range forecasting), replacement or upgrade of existing
meteorological data manipulation and display systems, and an integrated platform dedicated to
foe timely collection, assimilation, processing, and dissemination of all required METOC
information.

NEXRAD is a hardware/software development and upgrade to current weather radar systems.
The NEXRAD will replace a majority of current fixed radars (FPS-77s and FPQ-2Is) and
improve weather forecasts. Doppler and computer technology will allow better storm detection
and assessment of severity, improve warning accuracy, increase warning lead-times, and permit
the automated exchange of digital radar data with civil agencies.

Automated Weather Distribution System Pre-Planned Product Improvement (AWDS P3I)
program develops, procures, installs, and maintains evolutionary AWDS improvements. A
timely processing improvement will increase the responsiveness and processing abilities of the
original system to meet increasing system demand and operational requirements.
Communications and computer systems interfaces between AWDS and customer C4I systems,
weather satellite receiving systems, and other weather systems will allow timely forecasting and
dissemination of critical weather information to customer decision makers. A Remote Briefing
Capability (RBC) will allow AWDS to provide selected alphanumeric and graphic products to
customer facilities both on and off base/post for briefings. Software improvements include
severe weather algorithm calculations, solar and lunar data calculations, toxic corridor
calculations, high resolution grid processing, improved archival abilities, and model climatology
processing. AWDS P3I will support the migration of this software to an open systems
environment.

4.2.2 Army Systems' Enhancements

The following Army programs are planned to satisfy tactical unit shortfalls;

The Integrated Meteorological System (IMETS), AN/TMQ-40 is predominately a non-
developmental item that provides automation and communications support to Air Force Weather

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Teams assigned to Army Intelligence (G2/S2) Sections at echelons from separate brigade
through the echelon-above-corps level and Special Operations Forces. IMETS will receive,
process, and collect weather forecasts, observations, and climatological data used to produce
timely, accurate products tailored to meet supported commander's requirements for state-of-the-
art weather support. IMETS produces, displays, and disseminates, over the Army Tactical
Command and Control System (ATCCS), weather forecasts and decision aids that compare the
impact of current, projected, or hypothesized weather conditions on both friendly and enemy
capabilities.

The Meteorological Measuring System (MMS), AN/TMQ-41 is under development by the Army
Research Laboratory (ARL), Battlefield Environment Directorate. The system will have the
capability to provide meteorological support to Army artillery operations. The information
provided will be the same that is provided by radiosonde-based systems through the use of
profiling radars, ground based sensors, and meteorological satellites. This system will provide
more frequent atmospheric profiles then are currently provided.

4.2.3 Science and Technology Programs

Key technologies are needed to support modernization plans and future improvements in
capability for meeting shortfalls in wartime and peacetime requirements. Software development
research for battlefield operations is needed for the following;

A Theater-Scale Analysis Procedure (TAP) capable of ingesting and fusing observations
available in-theater, including ground, upper-air, and satellite data in a nearly continuous manner
and of providing timely analyzed values of wind, temperature, moisture, and surface pressure.

Analytical, statistical, and artificial intelligence (AI) (e.g.. Expert Systems and Neural Network)
techniques and models to serve single station and regional data analysis and forecast scenario
requirements, including battlefield models capable of predicting/inferring precipitation rates, soil
moisture, vertical density variations (atmospheric refractive (atmospheric refractive index
structures), ceiling, visibility (obscurants, e.g., dust and smoke), height of the low-level
(atmospheric boundary layer) inversion, and severe and extreme low-level turbulence (e.g., due
to downslope windstorms).

In addition, a theater-scale numerical weather prediction model will be selected, prototyped,
evaluated, and validated. The model will have the capability of providing reliable, very high
resolution forecasts of atmospheric elements, including wind, temperature, pressure, and
moisture.

Research is also needed to develop improved battlefield atmospheric sensors. Sensors,
algorithms, and strategies necessary to automate the detection of the elements of the surface
observation will be developed.

R&D is needed to develop improved algorithms for the NEXRAD. The following research
efforts are needed to meet customer requirements;

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Severe Storms (automated mesocyclone identification and prediction of supercell
tornadoes)

Aviation Hazards (icing and turbulence)

Storm Structure (automated wind analysis and non-severe weather)

Tropical Cyclone Analysis (storm strength and wind analysis)

5. FUTURE FAP PLANNING

Currently, several studies are underway that will determine future requirements and operational
concepts of AFW. One example is a study scheduled for completion in December 1994 to
provide options on the capability of a centralized weather unit on the battlefield. A second
example is the AFW architecture study scheduled for completion in December 1995 to determine
several functional and physical model options for future AFW development. A third effort is the
study to determine joint-level communication connectivity needs. These studies will be used to
update and modify the FAP's operational concept and adjust future modernization plans. These
may form additional customer requirements and initiate the need to start acquisition and
technology programs to solve the newly created deficiencies. The modernization planning
process described is continually evolving to optimally meet the changing requirements of our
nation's defense. As new technological improvements enhance and change the mission
capabilities of AFW's customers, our challenge is to keep AFW at the forefront of technology
and poised to provide the warfighter with the best possible decision assistance.

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USE OF NARRATIVE CLIMATOLOGIES AMD SUMMARIZED AIRFIELD
OBSERVATIONS FOR CONTINGENCY SUPPORT

Kenneth R, Walters. Sr. and Christopher A. Donahue
U. S. Air Force Environmental Technical Applications Center
Scott Air Force Base, Illinois 62225-5116

ABSTRACT

A recurring problem for the United States military is supporting
contingency operations worldwide. Integral to effective planning
of such operations is detailed knowledge of regional climate and
weather. USAF Environmental Technical Applications Center
(USAFETAC) fills this void by providing tailored narrative studies
and summarized airfield observational statistics. Planners use
the narrative studies to ascertain weather associated-problems for
the entire area in question. Summarized airfield observational
statistics give detailed airfield information for both ground and
air operational planning. Additional tailored specialized studies
are provided as requested, packages are prepared and transmitted
electronically to worldwide users within as little as eight hours
of request receipt. Examples will be presented at the conference
for various recent real world contingencies.

1. INTRODUCTION

The Readiness Support Branch of the United States Air Force
Environmental Technical Applications Center (USAFETAC) serves as
the focal point for climatological support to contingency
operations levied on the USAF and USA. Products go through the
Unified and Specified Commands to all subordinate United States
military commands as determined by the tasking Unified Command
senior meteorological/oceanographic (METOC) officer. Such
tailored, point or area specific products are often the only
climatological information available to planners who are
responding on very short notice to unforeseen deployment of the
United States military. Where possible, these products are
operationally tailored.

2. SCOPE AMD CONTENT OF SUPPORT

Two products form the core of such support: a narrative study and
summarized airfield weather observations for airfields to be used
either in the area of operations (AOR) or by forces enroute.

The focus, of the narratives varies greatly from one study to the
next, both spatially and temporally. These normally range from
"point” studies, which cover the weather for a city-sized area, up
to "small area" studies such as that done for the former

181

Yugoslavia. Such studies may concentrate on a specific time
period or cover the complete annual cycle. The emphasis is placed
on weather affecting the type of operations planned, which can
cover the gamut of military operations.

Such studies begin by covering, in as much detail as possible, the
terrain, vegetation and, if required the flora and fauna of the
area of interest. Greatest detail is in the small point studies,
such as the heights of nearby hills, ridges, and mountain ranges;
a discussion of current speeds and flooding potential of local
rivers and drainage systems; and a discussion of indigenous flora
and fauna. This last item is not always possible.

Next a very brief discussion of the synoptic, mesoscale, and local
meteorological factors which drive the local climate is provided.
The discussion is aimed at the audience specified by the
requestor. Normally, at least a limited meteorological background
is assumed, but some have been tailored for non-meteorologists.

The core of the study is a description of the weather cycle for
the requested time period. These are usually divided by local
seasons — which are not necessarily the classical temperate zone
ones. Conditions that impact military operations are highlighted.
In some studies, only those factors (fog, low clouds, heavy rain,
high winds, flood and so on) that have adverse affects are
discussed. Included are discussions and frequencies of the "rare
events” — severe thunderstorms, dense fog, heavy snows, and the
like.

Finally, a subjective "confidence factor” is assigned. As most of
these studies concern areas for which both studies and raw data
are sparse, such confidence factors are necessary if the users are
to fully integrate the information into contingency operations.
This allows us to discuss our evaluation of the quantity and
quality of the information used in preparing the study.

Source material encompasses the total USAFETAC information base.
The primary source is the superb collections of USAFETAC 's Air
Weather Service Technical Library. Its over 500,000 documents
make it arguably the largest dedicated atmospheric sciences
library in the United States. Summarized numerical data is
extracted from the Air Weather Service Climatic Database
maintained by USAFETAC 's Operating Location A (OL-A) at Asheville
NC. Located in the Federal Climate Complex with the National
Climatic Data Center and the Navy's Fleet Numerical Meteorology
and Oceanography Detachment Asheville, OL-A has immediate access
to the combined data bases. If we are lucky, the area of interest
is within the area covered by one of our regional climatologies.
Research is simplified greatly by such a coincidence.

These studies are normally transmitted via secure
telecommunication links to the requestor (s) ; illustrations are
necessarily kept to a minimum due to the very short amount of time

182

allowed to complete such studies. During "DESERT SHIELD”, for
example, such studies were routinely researched, prepared, and
sent within 24 hours after receiving the request. Studies are
under 10 pages.

The second product is one or more airfield climatological data
summaries. Such summaries are highly desirable for those
locations where flight operations are planned. These give not only
standard mean and extreme monthly temperature and precipitation
information, but also percent frequencies of occurrence of
selected joint ceiling and visibility values by three hour blocks
(00-02 local time, 03-05, etc), prevailing and extreme winds, mean
number of days with fog, dust, and so on. Such a summary can be
prepared, if sufficient data is available in the database, within
4 to 8 hours after request receipt. Observation availability is,
of course, key in preparing such summaries. Data receipt from
many Third World areas is somewhat limited. Observations may not
be available for nighttime hours; often observations are
transmitted only every three hours even during the day. Such
constraints often mean that only a "limited hour summary" can be
prepared. In the worst, and most frustrating, cases insufficient
observations are available to prepare such a summary.

Other products may be provided as requested by the senior METOC
officer. For example, climatological refractive index profiles
can, under certain conditions, be extremely useful in determining
radar performance. Electro optical climatologies are vital for
determining just what sensors will work effectively under various
weather conditions. Wet-bulb globe temperature climatologies are
crucial in tropical regions in establishing deployed personnel
work schedules. Conversely, climatological wind chill factors for
certain seasons and areas of the world are equally vital.
Engineers tasked to build everything from runways to ports in the
AOR require specialized temperature and precipitation
climatologies.

3. USES

Effective planning for any military operation require a thorough
knowledge and integration of the effects of weather and/or climate
into such plans. Papers in the 9th Applied Climatology Conference
discuss the uses of climatology in long term, or "deliberate"
planning. Here we focus on the rapid, or "contingency," response
to unforeseen, rapidly developing operations. Few military
operations are not affected by weather.

The specialized climatological packages discussed in this paper
allow planners to modify deploying equipment to effectively
operate in the particular conditions of the small area of
interest. Often all this requires is slight modification of
already existing plans. On occasion, it may require more
comprehensive changes. Such information becomes especially
important now that most operations occur in Third World areas

183

where little current meteorological data is available, both the
Somalia and the Rwanda Humanitarian Relief Operations are
excellent examples.

Experience has shown the narrative studies are best for general
discussions of area conditions and for ground operations.
Summarized point data are best for air operations over a specific
point, if such observational data are available. In some cases,
such climatological contingency packages provide the major portion
of the on-site weather information available to deploying,
including weather support, forces. This is particularly true for
Special Operations Forces. These packages not only provide the
deploying weather personnel with badly needed background
information on the area concerned, they also provide packages
that, often with little modification, can be given to flying and
ground operations personnel to acquaint them with expected general
conditions. Such packages do not take the place of real-time
weather support; they do provide invaluable information which
allows effective planning for immediate operations.

Such packages mark a departure from standard climatological
support as it has been provided for the past 20 years. The
combination of computer sophistication and the availability of a
large atmospheric sciences library allows preparations of such
packages on very short notice and rapid dissemination to DoD units
worldwide. Such centrally prepared and distributed climatological
packages ensure that weather personnel and operations staff at all
levels are provided with identical information. To quote General
of the Air Force "Hap" Arnold, "Weather is the essence of
successful air operations." Given its effect on present and
future weapons systems, this is now true for all operations. Our
climatological contingency packages are designed to help maximize
weather support quality.

184

USAFETAC DIAL-IN ACCESS

Kevin L. Stone and Robert G. Pena
USAF Environmental Technical Applications Center
Scott AFB, Illinois 62225

ABSTRACT

Describes the USAF Environmental Technical Applications Center's
(USAFETAC's) Online Climatology Dial-ln Service (Dial-In). Dial-In allows
Department of Defense (or any U.S. Government agency) users to gain direct
access, using a PC and modem, to certain climatological applications available on
USAFETAC's IBM 3090 mainframe computer. Dial-In uses a batch-type
communication technique called "Advanced Program-to-Program Communication
(APPC)." Dial-In works cooperatively with commercial APPC software to allow
information exchange between a PC and the IBM mainframe. A menu system
allows users to run pre-selected programs to receive standard output. Applications
currently available are divided into three categories: Surface (12 applications).

Upper- Air (2 applications), and Utilities (3 applications).

1. INTRODUCTION

The USAF Environmental Technical Applications Center's (USAFETAC's) Online Climatology
Dial-In Service (Dial-In) allows Department of Defense or any U.S. Government agency users
direct access, using a personal computer (PC) and modem, to certain climatological applications
available on USAFETAC's IBM 3090 mainframe computer. Dial-In, which became operational
in 1992, makes climatological data quickly and easily available to USAFETAC's customers.

A user can login to Dial-In by entering a valid user ID and password. The TM.ystem allows users
to run pre-selected programs and receive standard output. The output can be downloaded to the
remote PC. The program features "point and click" commands to control 3-D buttons that
prompt the user with dialog windows for required input information.

Dial-In has a messaging capability that allows communication between remote users and
USAFETAC. Seventeen applications are currently available and are divided into three
categories: surface (twelve applications), upper-air (two applications), and utility (three
applications).

2. HARDWARE/SOFTWARE CONFIGURATION

Dial-In uses a batch-type communication technique called "Advanced Program-to-Program
Communication (APPC)." Dial-In works cooperatively with commercial APPC software to

185

allow information exchange between a PC and the IBM mainframe. To access Dial-In the end
user needs an IBM or compatible 286 based PC with 640KB main memory, 1.5MB of available
hard-disk space, MS DOS version 3.2 or better, EGA or better graphics display (256KB)
memory, and Hayes compatible 2400 baud or better modem. The end user connects to a PC
at USAFETAC which serves as an asynchronous controller. The controller contains a
coprocessor board and control program allowing up to eight simultaneous users. The
commercial APPC software communicates with the mainframe computer host through the
asynchronous controller.

3. MAIN DISPLAY

The Dial-In main display screen (see Figure 1) consists of two sections: Control Buttons and
the Log Area.

Surface Apps

Utility Apps

Mieu Job Status

Uieu Results

Delete Results

Uieu Message

Send Message

Help

Quit

Shell to DOS

I Upper Air rtpps |

Dounload Results

USAFETAC DIALIN Uex*sion 1.0

12:28:54 Sending Station Locator run cards
12:28:56 Request successfully transnitted
12:29:05 Sending Mean Coincident Wet Bulb run cards
12:29:07 Request successfully transnitted
12:29:14 Sending Uind Speed Sunnary run cards
12:29:16 Request successfully transnitted
12:29:23 Sending Distribution Sunnary run cards
12:29:25 Request successfully transnitted
12:29:31 Sending Sunnary of the RUSSUO run cards
12:29:33 Request successfully transnitted
12:29:39 Sending Sads Data Interpolator run cards
12:29:41 Request successfully transnitted
12:30:40 Sending Station Locator run cards
12:30:42 Request successfully transnitted
12:32:39 Dounloading UINDSPD.TXT
12:32:40 UINDSPD.TXT uas doun loaded successfully

<F1> Help <F10> Quit <Entey> Select I te» <Down/Up> Next/Prev i ous Selection

Figure 1. Main Display

3.1 Control Buttons

The Control Buttons line the left side of the main display. Activating the control buttons allows
the user to enter into any of three types of applications (surface, upper air or utility), a job status
viewer, a module to download the job results, other modules to view and delete results, and a
message viewer allowing the user to read messages stored on the mainframe computer. The
user can send a message to USAFETAC by activating the Send Message button.

3.2 The Log Area

The log area appears on the right two-thirds of the screen displaying a summary of transactions.
The log is copied to a file and stored on the user's PC for a history of the Dial-In session.

186

4. APPLICATIONS

Surface applications run against the DATSAV2 surface data set. The data set consists of
worldwide weather observations collected through the USAF Automated Weather Network
(AWN); decoded at the Air Force Global Weather Central (AFGWC), Offiitt AFB, Nebraska;
and stored on magnetic tape at USAFETAC, Scott AFB, Illinois and at USAFETAC's Operating
Location A (OL A), Asheville, North Carolina. The database contains synoptic, METAR,
SMARS, AMOS, AERO, MARS, and airways observations.

Upper air applications run against the DATSAV Upper- Air data set which contains rawinsonde
and pilot balloon observations derived from reports received at AFGWC over the AWN. These
observations are quality checked before they are sent to USAFETAC.

Utility applications are general purpose utilities which help with locating Block Station
information. These applications run against the Air Weather Service Master Station Catalog
(AWSMSC) which is a comprehensive listing of environmental observing sites current to the
last nine months. For each station, it lists the name, identifier, location, types of data reported,
field elevation, pressure reporting locations, equipment types, etc.

4.1 Surface Applications

4.1.1 A Summary (Weather Conditions)

This program provides output that is equivalent to part A of the Surface Observation Climatic
Summary (SOCS). The program provides for both a total occurrence count and a percent
frequence of occurrence for a specified period of record (FOR) for the following weather
categories: thunderstorms, rain and/or drizzle, freezing rain and/or drizzle, snow and/or sleet,
hail, fog, smoke and/or haze, blowing snow, dust and/or sand.

4.1.2 Conditional Weather Summary

This program provides the mean number of days a selected surface weather element (e.g., fog,
rain, precipitation) or a combination of two elements occurred for each month of a specified
FOR. The data are arranged in hourly and 3-hourly groups in local time. A range of values
may be used to further describe an element (e.g., visibility from 4800 to 8000 meters).

4.1.3 Distribution Summary

This program prints hourly, monthly, or annual cumulative frequency distributions for density
altitude, pressure altitude, or dry bulb temperature for a specified FOR.

4.1.4 Ceiling Durations

This program provides the duration each time the ceiling is below a specified level for a
specified FOR. The beginning and ending date and hour are provided for each duration.

187

4.1.5 Mean Coincident Temperature

This program gives the mean frequency of occurrence of a primary temperature with a mean
coincident secondary temperature for each primary temperature range. It provides the number
of occurrences within a range of a temperature type and the average corresponding value of
another specified temperature type. This information may be used in temperature or design
studies. The possible primary and secondary temperature types are dry bulb, wet bulb, and dew
point.

4.1.6 Percent Cloud Free Line of Sight

The Percent Qoud Free Line of Sight program provides matrices of percent probability of cloud
free line of sight above a selected location. The matrices give average percent values by month
for three hourly periods. The angles above the location are computed every 10 degrees from 0
to 80 degrees. Surface-derived databases require a specified Block Station number. Satellite-
derived databases can be run for any latitude and longitude point.

4.1.7 Phenomena Summary

This program supplies output that is equivalent to part A of the Surface Observation Climatic
Summary (SOCS). The program accesses data already in the computer database; however, the
period of record may not be as complete as what is available for the "A Summary" program
which runs all available tape data. The program provides both a total occurrence count and a
percent frequency of occurrence for a specified POR for the following weather categories:
thunderstorms, rain and/or drizzle, freezing rain and/or drizzle, snow and/or sleet, hail, fog,
smoke and/or haze, blowing snow, dust and/or sand.

4.1.8 Precipitation Summary

The Precipitation Summary presents precipitation, temperature, and sky cover data for a selected
station and POR. The data is derived from the DATSAV database and can be obtained for any
reporting station back through the year 1973. The program is relatively fast and produces a

large amount of data but is limited to an 1 1 year POR per request due to size limitations in the
program.

4.1.9 Surface Package

The Surface Package program produces a percent frequency of occurrence for specified elements
and POR. A list of over 50 elements may be compared against each other for a specified block
station. The program is designed for either a one element percent frequency of occurrence or
a detailed comparison of many elements.

188

4.1.10 Temperature, Relative Humidity, and Wind Climo Summary

This program provides the following tables of climatological statistics using a specified POR not
to exceed 30 years.

1) Monthly/annual temperature and relative humidity statistics.

2) Percent frequency of occurrence of wind direction and wind speed (knots) for both
sustained winds and gusts.

3) Monthly/annual winds (knots).

4) Maximum wind occurrence - the five highest values per year.

4.1.11 Wind Chill

The Wind Chill program provides the percent frequency occurrence of equivalent chill
temperature (wind chill). The frequency distributions are given for user specified temperature
categories and POR.

4.1.12 Windspeed Analysis

The Windspeed Analysis program provides the five strongest wind speeds (sustained or gust)
for each year and month of a specified POR.

4.2 Upper Air Applications

4.2.1 Probability of Icing

The Probability of Icing program computes icing information for a selected rawinsonde site and
determines the probability of icing at the following levels: 1000, 850, 700, 500, and 400 mb.
The probabilities are multiplied by a correction factor from AWSM 105-39 (AWS/TR-80/001)
based on 20,000 aircraft flights in icing conditions to obtain the potential for icing.

4.2.2 Upper Air Reader

The Upper Air Reader program extracts RAOB data for a particular station from USAFETAC's
climatic database. Pressure, temperature, moisture, and wind data are interpolated to either
pressure or height. Pressure interpolation is from the surface to 100 mb in 100 mb intervals.
Height interpolation is from the surface to 50,000 feet MSL in 1,000 foot intervals.

4.3 Utility Applications

4.3.1 Nearest 50 Stations

The Nearest 50 Stations program provides the 50 closest active weather stations to a given
point. The program searches the Air Weather Service Master Station Catalog and helps locate
stations which may be used for climatological studies.

189

4.3.2 Station Locator

Station locator helps find the best reporting station(s) for either surface or upper air data. The
program selects and displays reporting stations in specified areas and provides the frequency of
reports for surface or upper air data. Five degree squares or less are the optimum input,
especially in data dense areas.

4.3.3 TAFVER II Statistics

The TAFVER II program is an automated tool designed to measure the quality of weather
forecasting support provided by the Air Force weather community. The program verifies all
Terminal Aerodrome Forecasts (TAFs) issued by Air Force weather forecasters, providing the
corresponding observations are available.

5. SUMMARY

The Dial-In service was fielded to provide USAFETAC's customers with more responsive
support. The system allows remotely located users to access USAFETAC's mainframe
computer, which benefits USAFETAC and the customer. A menu system allows the user to run
pre-selected programs to receive standard output. Programs construct commonly requested
summarized profiles of meteorological variables. The output can then be downloaded to the end
user's PC. The Dial-In service was implemented to augment the traditional system to help
relieve backlogs and provide information quickly, not as a replacement to the traditional system.

REFERENCES

Pena, Robert G., 1994: USAFETAC Online Climatology Dial-ln Service Users Manual,
USAFETAC TN-94002, USAF Environmental Technical Applications Center, Scott Air Force
Base, IL, 80 pp.

190

page 1 of 10 pages

ASlRO^aVUCAL MC80ELS ACCURACY STUDY

Chan W. Keith and Thomas J. Smith
USAF Environmental Technical Applications Cento*
Scott AFB, Illinois 62225-5116

ABSTRACT

Several astronomical routines and their accuracy in calculating solar and lunar
event times were compared. The Naval Observatoiy MICA (Multiyear Interactive
Computer Almanac) model was used as ground truth and assumed to be correct.
Programs compared to MICA were the USAFETAC Nitelite for Windows model;
LightPC version 4.2, developed by Sgt Schweinfiirth of Detachment 5, 5th
Weather Squadron; the Ilium routine used in the USAFETAC program INSOL;
and the Army model, NVG versions 4.0 and 5.1. Model output for a series of
latitudes between the equator and 80 degrees north were compared.

1. INTRODUCTION

The USAF Environmental Technical Applications Center (USAFETAC) performed a study to
determine the accuracy of various astronomical event time calculation routines. Model accuracy
was determined by using the models to calculate sunrise, sunset, moonrise, moonset, and various
twilight times over a three year period, and comparing these values to an acceptable standard.
The Naval Observatory's MICA (Multiyear Interactive Computer Almanac) model was used as
ground truth in the study. Programs compared to MICA were (a) the USAFETAC Nitelite for
Windows model, (b) Li^tPc version 4.2, developed by Sgt Schweinfiirth of Det 5, 5th WS, (c)
the Ilium routine used in the USAFETAC program INSOL and the Electro-Optical Tactical
Decision Aid (EOTDA) software, and (d) versions 4.0 and 5.1 of the Army model, NVG.
Statistical parameters including average error, root mean square error, standard deviation, and
total error were computed. Model output for a series of latitudes between the equator and 80
degrees north were compared. The Ilium routine was the most accurate model compared to
MICA. Accuracy degraded only slightly at the extreme northern latitudes. The next most
accurate model was LightPC. Nitelite performance was nearly equivalent to LightPC at latitudes
below 60 degrees north, but accuracy decreased rapidly at the hi^er latitudes. The latest version
of NVG performed comparably with Nitelite in computing the sunrise, sunset and moonrise, but
performance degraded somewhat for the other event times. The average errors for all of the
models except NVG were less than 3 minutes for all of the latitudes and event times tested.

All of the models are capable of running on a desktop PC, although our version of ILLUM 92
was run on an IBM R/S 6000 workstation using IBMs XL FORTRAN compiler, which defaults
to double precision for floating point calculations. Output from LIGHTPC version 3.2 and earlier
is assumed to be identical to NITELITE, since they used the same algorithm for computing the
sun and moon locations.

191

page 2 of 10 pages

2. MODEL DESCMPnaVS

2.1 NITELITE

NITELIIE is the latest USAFETAC-developed program produced for calculating astronomical
data It is the only program tested that is available in a Windows version. It computes the
beginning and ending of nautical twilight, sunrise and sunset, moonnse and moonset, and percent
illumination information. The output is in an easy to read graphical format, but the program will
not output the data to a file in a tabular format. As a result, a slight modification was made to
the program to save the data to a file for comparison purposes.

The algorithm that calculates the solar and lunar data was originally developed by A. C. van
Bochove (1982) in FORTRAN; the NITELITE version contains a few minor corrections. The
algorithm was converted to visual BASIC and adapted for use within the Windows program on
the desktop computer. The program is maintained by USAFETAC/SYS. NITELITE uses the
same algorithm (ILLUM, 1987) as LIGHTPC version 3.2, an USAFETAC program, and we
assume LIGHTPC v3.2 performance would be nearly identical to NITELITE.

2.2 UGfflPC

This program is an updated version of the original LIGHT program developed by ILt's D. Payne
and J. Morrison from USAFETAC. This version was produced by Sgt J. Schweinfiirth, of Det
5, 5WS, and was developed specifically for determining solar and lunar event times, and for night
vision goggle support. In addition to computing event times, it can compute percent illumination
and nighttime (tokness data. The program is user friendly and menu driven. Processing time,
however, is somewhat slow for large amounts of data (i.e. more than several months).

LIGHTPC calculations are based primarily on the methods developed by A. C. van Bochove
(1982), with a number of corrections and updates. The solar semidiameter was assumed to be
a constant 16 minutes (') of arc. The lunar semidiameter and parallax were individually
computed and included as correction terms in the calculations of event times. Upgrades to this
program can not be readily obtained.

2.3 II1IJM92

Capt M. Raffensberger, in USAFETAC/SYT, developed the INSOL program in 1994, primarily
to compute the daily cumulative insolation at the surface and the top of the atmosphere as an aid
for forecasting fog dissipation. The original program did not provide the solar and lunar
information directly, but since it made the astronomical computations and used the data
internally, the program was modified to output that information so that its accuracy could be
determined.

The algorithm is based on the program developed by A. C. van Bochove and Erlich (1982) and
was modified by Sidney Wood (1986), Paul Hilton (1987), Maria Gouveia (1989) and Dan

192

page 3 of 10 pages

DeBenedictis (1992) of Hu^es STXL The ILLUM subroutine within the INSOL program was
originally used in the Electro-Optics Tactical Decision Aid (EOTDA) program by Hu^es SJX;
it computed solar position at 15 minute intervals, but was modified to make the confutations
every 20 seconds to ensure the event times we obtained were to the nearest minute. Solar and
lunar locations were adjusted to account for a standard refractive index produced by the
atmosphere, which corresponded to 34' of the solar or lunar path arc length. The sm's
semidiameter (16') and moon's semidiameter (16') were also included in the adjustment, since
event times are based on the upper limb of the disk. A search was then conducted to find the
positions and times corresponding most closely to die appropriate solar and lunar locations
described within the definitions of the various twilights and rise and set times.

2.4 NVG

The Night Vision Goggles program (NVG) was developed and recently updated (version 5.1) by
the Army Research Laboratory (ARL), primarily for computing nighttime natural illumination
values. User inputs include observer location, date and time, and current meteoroloj^cal
information for specific parameters. The model is capable of calculating solar and lunar position,
rise and set times, two forms of twilight times, and illumination information. The calculated sun
and moon positions are based on the methods developed in 1982 by A. C. van Bochove.

The program is menu driven and some background knowledge of the program is helpful, since
some of the input parameters require a specific format. Wraknesses of this software include:
(1) calculations are limited to the area between 64° S and 64° N, and (2) the prograrn will only
send the output to the screen or printer, and not to an output file for further processing. (ARL
provided a modified version of the program that did allow for data to be sent to an output file.)
One year of data from v4.0 was also used in the study. This model includes horizontal refection
and disk semidiameter corrections (34' and 16', respectively) in the event time computations.

According to D. Sauter (personal communication), NVG searches for the beginning of nautical
and civil twilight (BNT, BCT), sunrise and sunset (SR, SS), then uses the time differential
between BNT and BCT from SR to get an approximated end of civil and nautical twilight (ECT,
ENT). While this procedure is a useful time saving tool, it can cause a non-occurring event to
be predicted at higher latitudes. This occurs when the sun goes above (below) 12° elevation
angle and stays above (below) 12°, for example, for many weeks in the summer (winter).

2.5 MICA

This Naval Observatory program superseded their Floppy Almanac in 1992, and was created for
astronomers, surveyors, meteorologists, and navigators to provide high precision astronomical
ftata for a variety of astronomical objects, including all of the planets, the moon and many stars.
Stated accuracy is less than 1 minute of time. A sample of the parameters that be calculated
are three forms of twilight times, rise and set times, positions of celestial bodies, and fi:action
illumination. The calculated celestial body positions, as described in the Mica Users' Manual,
are based on methods presented by C. A. Smith et al., and ephemeris data provided by E. M.

193

page 4 of 10 pages

Standish, Jr. The progi^m is user fiiendly, fast, and menu driven. A constant solar semidiameter
of 16' of arc and a con^t horizontal refraction (34') are assumed. The program individually
computes the lunar semidiameter and parallax and includes them in the event time computations.

3. ME1MC®)S Table 1. Latitudes for conputing event times

^ „ used within this study for each of the models.

3.1 Scope

Each program's data, except that from NVG
v4.0, was compared to output from MICA for
the 3 year period of record from 1 Jan 94
through 31 Dec 96. Output from NVG v4.0
was limited to 1 Jan 94 throu^ 31 Dec 94.

The locations for which event times were
calculated for each model corresponded to a
constant longitude of 90° W and the latitudes
given by Table 1. The latitudes were limited to
the northern hemisphere. It was assumed that
the results obtained apply to the southern
hemisphere as well, and that model errors did
not vary for different longitudes. Latitudes
above 80° N were not used in this study
because MCA accuracy decreased within a few

Table 2. Event times predicted by each model
for purposes of this study.

NITE

LITE

ILLUM

LIGHT

NVG

BNT

✓

Bcr

✓

Ecr

✓

ENT

✓

LAT

NITE

LITE

ILLUM

UGHT

NVG

✓

degrees of the poles. Event categories
indicated in Table 2 are available from the
models. The program times were compared
with the MCA times, and the statistical
analyses described below were performed.

3.2 Statistical Ptirameteis

Several statistical parameters were computed
for each latitude for the results generated by
the models. The following statistical
parameters were computed (Wilmott, 1982).

194

page 5 of 10 pages

Total eiror - the total number of minutes of error over the period of record;

hp.-o). «

i=l

where N is the total number of observations for the period of record, Oj is the MICA predicted
event time, and P; is the model predicted event time. The total error provides an indication of
the model bias, whether the model consistently predicts events to occur either before or afta the
MICA model. To determine the bias of model results, it is necessary to consider the magnitude
of the total error, in conjunction with the magmtude of the total absolute error, desaibed below.

Total absolute error - the total number of minutes of the absolute value of the error over the
period of record;

i=l

The total absolute error describes the overall magnitude of the error for each model. This
statistic is not normalized by the number of events and will vary significantly with the number
of events in the period of record.

Aveix^e error - the total number of minutes of the absolute value of the error over the period
of record divided by the total number of observations;

(3)

j=i _

This is referred to as mean absolute error by Wilmott (1982), and it weights all errom equally
in its determination. This statistic is normalized by the number of observations, so it will not
change if a larger period of record is used, as long as the sample size is representative.

Root Mean Square Error (RMSE) - the square root of the sum of the individual errors squared
divided by the total number of observations;

(4)

The RMSE weights larger individual errors more heavily than the smaller errors. The RMSE is
also normalized by the total number of observations.

195

4. RESULTS

page 6 of 10 pages

4.1 Solar Events

In general, the models predicted the solar event times much more accurately at the low latitudes
than hi^ latitudes, particularly above 60 ° N. All of the models exhibited some seasonal trend.
The models tended to work best near the equinoxes (March and September) and worst near the
solstices (June and December). Results from NVG v5.1 did exhibit a sli^t degradation of
performance over time.

Figure 1 depicts the frequency, in percent,
that each model predicted the sunrise time
within 1 minute of MICA, as a function of
latitude. Below 60 ° N, all of the models
except NVG were within 1 minute at least
80 percent of the time. NVG v4.0
performed worse than version 5.1, and since
the latest version is strictly an update of
v4.0, only v5.1 results are shown. For all
latitudes and events, ILLUM 92 predicted at
least 90 percent of the events correctly. At
60 ° N and below, the number of correctly
predicted events generally exceeded 95
percent for this model. LIGHTPC was the
second best performing model, and
predictions were within 1 minute over 90
percent of the time over the entire range of latitudes. There is a decreasing trend in the
performance of NITCLITE with increasing latitude. NITELITE correctly predicts as little as 30
percent of any specific event at 80 N. Predicted event times were within 1 minute of the MICA
determined tiine at le^t 67 percent of the time and within 2 minutes at least 83 percent of the
time for any given latitude. NVG sunrise times within 1 minute decreased to 53 percent for the
higher latitudes, but the model was within 2 minutes at least 80 percent of the time. NVG
predicted sunset more accurately than sunrise.

Table 3 provides error statistics for a typical midlatitude. Statistical analyses from other latitudes
showed similar results. The ILLUM 92 model performs the best of the group, compared to
MICA. The maximum absolute error did not exceed 3 minutes for ILLUM 92, except for one
occurrence of a 31 minute error at 65 ° N. Average errors were under 0.1 minute. The total aror
for the beginning event times are slightly negative, meaning the model forecasted the beginning
times early more often than late overall. It also predicted the event ending times slightly early.

Error statistics for LIGHTPC show it to be the next best performing model. Total errors indicate
a positive bias, meaning the model predicted event times were slightly later than MICA predicted
event times. Average errors ranged from 0.02 minutes at the equator to 0.64 minutes at 50° N.

NITELITE

ILLUM

LIGHTPC

NVG

Figure 1. Frequency of model predicted sunrise
times within 1 minute of MICA sunrise times.

196

page 7 of 10 pages

Table 3. Statistical analysis results for the model predicted solar event times for 40° N.

40 N

Tot

Obs

Tot
Abs Err
(min)

Tot

Err

(min)

Avg

Err

(min)

Nfex
Abs Err
(min)

RMSE

(min)

Num

Non

Occ

MTELITE BNT

1096

506

404

0.46

0.76

ILLIM 92 BNT

1096

-5

0.01

0.12

LIGHTPC BNT

1096

496

410

0.45

0.74

NVG BNT

1096

478

-370

0.44

0.66

ILLUM 92 BCT

1096

-9

0.01

0.11

LIGHTPC BCT

1096

324

138

0.30

0.54

NVG BCT

1096

423

-209

0.39

0.64

NITELITE SR

1096

422

120

0.39

0.62

ILLUM 92 SR

1096

0.02

0.14

LIGHTPC SR

1096

394

116

0.36

0.60

NVG SR

1096

1066

294

0.97

1.14

NITELITE SS

1096

436

0.40

0.63

ILLUM 92 SS

1096

0.03

0.17

LIGHTPC SS

1096

449

0.41

0.64

NVGSS

1096

705

283

0.64

0.83

ILLUM 92 ECT

1096

0.02

0.13

LIGHTPC ECT

1096

302

-16

0.28

0.52

NVG ECT

■1096

2084

1482

1.90

2.19

NITELITE ENT

1096

478

-244

0.44

0.68

ILLUM 92 ENT

1096

0.02

0.16

LIGHTPC ENT

1096

490

-234

0.45

0.69

NVG ENT

1096

2183

1603

1.99

2.28

Event errors are distributed much more widely for NITELITE, and some of the events show a
skewed distribution in the statistics. Below 60 ° N, the errors were approximately the same
magnitude as those of LIGHTPC but at the higher latitudes, LIGHTPC performance was
significantly better than NITELITE.

Error statistics for NVG v5.1 were the least accurate in the study, and they exhibited a definite

197

page 8 of 10 pages

bias for several of the events. The model predicted the beginning of nautical and civil twili^t
^ly. Likewise, the model predicted the ending of civil and nautical twilight late, on average,
^ors for sunnse were larger than those for sunset. NVG does not produce results poleward of
64 , so study results are limited to 0 ° N through 60 “ N latitude.

4.2 Lunar Events

^erall, the lunar event time predictions followed trends similar to those of the solar event times.
All of the models exhibited a decrease in accuracy with increasing latitude. The results suggest

a mmor penodic trend for all of the models. None of the models exhibited a decrease in
accuracy over time.

Except for NVG v5.1, there were fewer
correctly predicted lunar event times than
the solar event times, but as Figure 2
demonstrates, the number of moonrise
times within 1 minute is nearly equivalent
to those of sunrise times. NVG predicted
moonrise much more accurately than
sunnse. All of the models were within 1
minute at least 80 j^cent of the time for
latitudes below 65 ° N. The ILLUM 92
predicted event times were within 1
minute of the actual event over 95
percent of the time for any latitude.

LIGHTPC and NVG v5.1 performed as
well as ILLUM 92 at the low and middle
latitudes, but LIGHTPC degraded more
quickly at the higher latitudes. NITELITE
was within 1 minute over 90 percent of the time up to 60 ° N.

The m^mum absolute error for ILLUM 92 was only 12 minutes and this model performed best
mpredictmg li^ events. Table 4 provides results for a typical midlatitude. The maximum
absolute error for LIGHTPC was only one minute from the equator through 65 ° N, and there
were no missed events for that range of latitudes. Prediction times r^idly deteriorated above
umt latitude and the maximum absolute error increased to as much as 26 minutes. The maximum
^smute error for MTELITE was only 1 minute throu^ 40° N, and 4 minutes throu^ 65 ° N.
IwG v5.1 moonrise errors were comparable with NITELITE, however, NVG performance
degraded somewhat in predicting moonset.

5. CmCLUSIONS

^thou^ the four models tested used variations of the same algorithm, updates and
improvemOTts in processing speed caused large variations in model performance results.

NITELITE

^UM

LIGHTPC

NVG

Figure 2. Frequency of model predicted moonrise
times within 1 minute of MICA moonrise times.

198

page 9 of 10 pages

Table 4. Statistical analysis results for the model predicted lunar event times for 40 N.

40 N

Tot

Cfcs

Tot
Abs Err
(min)

Tot

Err

(min)

Avg

Err

(min)

Max
Abs Err
(min)

RMSE

(min)

Num

Mssd

Evnt

Num

Non

Occ

NITELITE MR

1058

340

0.32

0.57

ILLUM 92 MR

1058

0.08

0.28

LIGHTPC MR

1058

0.08

0.29

NVG MR

1058

375

-279

0.36

0.60

NITELITE MS

1059

343

117

0.32

0.57

ILLUM 92 MS

1059

-17

0.08

0.28

LIGHTPC MS

1059

0.07

0.27

NVG MS

1059

861

-861

0.81

0.99

The most accurate model compared to the Naval Observatory's model, MCA, was the ILLUM
92 routine found in the USAFETAC-sponsored INSOL and Hughes STX provided EOTDA
programs. This routine consistently produced the smallest average and root mean square error^
However in its current state, the routine is not readily suitable for directly computing and
displaying astronomical event times. The program needs to be capable of determining event
times more efficiently. Some time saving methods could be incorporated mto the routme to
allow the model to determine event times more quickly. The program also needs to be converted
into a user iSiendly format, such as making it Windows compatible.

LightPC version 4.2 is the next most accurate model. The high accuracy mode at the extremely
high latitudes and the easy to use menus make this model a solid performer. A significarit
drawback to this program is the inaccessibility of the program code for maintenance and upgrade
purposes, such as a foil time high accuracy mode and changes to the output form.

Nitelite for Windows works fairly well at the low and middle latitudes but accuracy decreases
rapidly above 60° N. The program is driven by friendly prompts and has the capabrhty to
display the data in an easy to read graphics format, but rnore optiom, such as the ability fo save
to a file and the computation of civil twilight, may be desirable. This program should be updated
by the newer ILLUM 92 routine for improved accuracy if it continues to be used.

The Army model, NVG, had the largest errors for all latitudes when compared to MCA.
Presumably, much of the error for several of the events can be eliminated by a simple correction
factor within the program itself The model uses prompts and Help Screens for input par^eto
that it requires. Although the documentation that comes with the program claims that the
program is not for operational use, a few modifications to the program would nj^c it slight y
more accurate. An increase in accuracy for all of the event times and the capability to wnte to
an output file are two changes that could be made.

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page 10 of 10 pages

All of the models assumed a constant correction for the effects of refiuction (34') wiien
computmg event times. Variations in the refractive index occur due to natural atmospheric
vanations, such as mversions or stable layers, causing all of the models, including MCA, to
produce erroneous results. s ^

The magnifede of these errors are partially a function of the magnitude of the atmospheric
vanations m the index of refraction; in the tropics variations are relatively small- at the
nudlatitudes ^d higher, variations will be larger. A more important contributor to the magnitude
of the emors is the sun's apparent path through the sky, or more precisely, the maximum zenith
^gle of the sun. This zenith angle decreases with increasing latitude. At the higher latitudes
the sun remains near the horizon for a longer period, so that variations in the refractive index will
produce a larger error than the same refractive index variation at lower latitudes. Near the poles
this variation forces larger errors and may be sufficient to incorrectly predict the occurrence or
nonoccurrence of an event.

This report was designed to provide accuracy information to users or potential users of the
models studied to allow more informed decisions be made, based on model output. If the reader
IS lookmg for simple and accurate event times or fraction illumination information in tabular
format, we suggest obtaining MCA from the address in the bibliography. If a graphical format
IS needed then MTELITE will be required. For specific support and output requirements needing
a hi^ de^ee of accuracy, such as support to night vision goggle users, additional programs or
moditications to the existing programs will have to be sought.

BBIJOGRAPHY

^can, Louis D., and David P. Sauter, Naurd Illumination under Redistic Weather Conditions
Atmospheric Sciences Laboratoiy, ASL TR-0212, White Sands Mssile Range, NM 88002, 198?!

Duncan, I^uisD. and Gavino Zertuche, Night Vision Goggles (NVG) Software Use/s Guide

^^fsion 4.0, U.S. Army Research Laboratoiy Battlefield Environment Directorate, White Sands
Mssile Range, NM 88002-5501.

MCA for DOS User's Guide, Astronomical Applications Department, U.S. Naval Observatorv
Washington, DC 20392-5420. woi>crvaiory,

vari Bochove, A. C., The Computer program "ILLUM": CdcuMon of the Positions of the Sun
a^ Moon and the Naturd Illumination, Physics Laboratoiy TNO, National Defense Research
Organization INO, P.O. Box 96864, 2509 JG The Hague, The Netherlands, 1982.

Willmott, Cort J., Some Comments on the Evaluation of Model Performance Bulletin of the
Amencan Meteorologicd Society, 63, 1 1, 1982, pp 1309-1313.

200

page 1 of 8

ATMOSPHERIC TRANSMISSIVITY IN THE 1 TO 12 MICRON WAVELENGTH BAND

FOR SOUTHWEST ASIA

Richard A. Woodford and Chan W. Keith
USAF Environmental Technical Applications Center
Scott AFB, Illinois 62225-5116

ABSTRACT

The USAF Environmental Technical Applications Center (USAFETAC) performed
a study to analyze the atmospheric transmittance for the path between a variety of
target altitudes and a satellite based sensor over southwest Asia during Desert
Storm, and to compare these results to transmittances computed for different
climate regimes. The atmospheric transmittance computer model, LOWTRAN 7,
was used to compute transmittances for specific atmospheric conditions. Model
target height varied between the surface and 10 km above ground level. Actual
sounding data from Baghdad, Iraq; Howard AFB, Panama; and Pyongyang, North
Korea for the period from January 1973 through December 1988 was used in the
model to provide a climatological reference for the analysis. Atmospheric slant
path model (ASPAM) output for Iraq during January and February 1991 and
climatologically averaged soundings from specific climate regions provided data
sources for additional LOWTRAN 7 computations. Using the individual data sets,
transmissivities were computed for the wavelength bands 1-2.5 pm, 3-4 pm, 1-4
pm, 1-8 pm, 8-12 pm and individual wavelengths between 8 and 12 pm. Results
indicate that the ASPAM generated data set had considerably lower
transmissivities than the climatological average over Baghdad, but were not as low
as typical values computed for the tropical (Howard AFB) or midlatitude
(Pyongyang) environments.

1. INTRODUCTION

This report is a synopsis of two separate studies evaluating atmospheric transmittance values at
select locations in Southwest Asia. The objective of the first study was to derive atmospheric
transmittance values for the 8 to 12 micron wavelength band, then determine if those values were
lower than "normal" or expected values for the area. The second study extended the range to
include the 1 to 8 micron wavelength band. The atmospheric transmittance model, LOWTRAN7,
(Kneizys, et al, 1988) was used to generate both average and total transmittance values for the
bandwidth in question.

LOWTRAN7 was run using two sensor/source geometries. The first series of model runs held
the sensor directly above the source (i.e., at nadir). The second series increased the transmittance
path length by moving the sensor to a 30 degree viewing angle (i.e., 30 degrees off-nadir).
Source altitudes were varied incrementally from the surface to 10 km above the surface.

201

page 2 of 8

2. DATA SOURCES

The study was accomplished using three primary data sources. We will refer to the data
throughout the rest of this report as datasets 1, 2 and 3. Dataset 1 included the January/February
91 vertical temperature/moisture profiles for several select Southwest Asia locations. For this
set actual sounding data were not available at the locations requested by USAF Environmental
Technical Applications Center's (USAFETAC's) customer for the study, so vertical temperature
^d moisture profiles were generated for those locations by the USAFETAC's Atmospheric Slant
Path Analysis Model, ASPAM (Koermer,1984). Dataset 2 consisted of average climatological
profiles of temperature and dewpoint; profiles not restricted to Southwest Asia, but representative
o several different geographic regions. Dataset 3 was made up of actual sounding data covering
a year period of record (POR) from January 1973 through December 1988 for the following
locations. Baghdad, Iraq; Howard AFB, Panama; and Pyongyang, North Korea. These sites were
selected because US AFETAC believes they are representative of a desert, tropical, and
continental-type climate, respectively.

3. BACKGROUND

The overall distribution of atmospheric gases and aerosols determine the spectral absorbency of
the atmosphere. Any change in a beam of radiation passing through a layer of air is determined
m part by the concentration and temperature of these resident constituents. Atmospheric

1 to 12 micron wavelength band is greatly affected by tri-atomic
K absorption. The absorption however, is not continuous across the

band. There are regions where the atmosphere is nearly transparent to electromagnetic radiation
and absorption is at a minimum. These windows are located roughly between 1 to 2.5 microns

3 to 4 microns, 8 to 9.5 microns, and 10 to 12 microns (Environmental Research Institute of
Michigan, 1978).

USAFETAC s customer for this study originally supplied datasets 1 and 2, and requested an
eva uation of atmospheric transmittance values based on those sets. We felt that because dataset
1 consisted of atmospheric profiles which might have been overly smoothed, dataset 1 might
misrepresent actual conditions at the associated locations by, for example, eliminating temperature
inversions. Dataset 2 was generated by compiling several hundred soundings averaged to produce
one mean temperature, pressure, and moisture profile per geographic region noted. These
soundings are mean values for the selected geographic regions, and also may not be
representative of actual conditions. The temperature and moisture profiles have been smoothed
considerably; subsequently, the resulting vertical profile may never actually occur. It is for this
reason that USAFETAC suggested providing statistical distributions based upon atmospheric
transmittance values generated by inputting actual soundings into LOWTRAN7.

The LOWTRAN7 computer code is capable of modeling many atmospheric parameters of
transmittance and radiance over wavelengths from 0.2 microns to infinity (Kneizys, et. al. 1988)-
owever, in this study, LOWTRAN7 was used to determine atmospheric transmittance only.

202

page 3 of 8

4. APPROACH

Datasets 1 and 2 were input directly into LOWTRAN7, and model runs made. Both total and
average transmittance values for each location in each dataset were generated. Only the average
transmittance values per bandwidth specified were used in the analysis. Dataset 3 consisted of
USAFETAC's archived soundings for Baghdad, Howard AFB, and Pyongyang. USAFETAC
soundings for those three stations covering a 15 year POR were input into LOWTRAN7 to
produce both total and average transmittance values. The results of these model runs were then
sorted by location, month, viewing angle, bandwidth, and source altitude. They were then
processed through a statistical analysis package, SAS (SAS INSTITUTE, 1994), to create
frequency distributions! Values derived from these distributions were then compared to the values
computed for datasets 1 and 2.

5. ASSUMPTIONS / LIMITATIONS

A no cloud/no rain scenario was assumed for all datasets, however the presence of water vapor
in the atmosphere was the primary source of atmospheric extinction. Adding both cloud cover
and rain would significantly reduce transmissivity.

The LOWTRAN7 model assumed default atmospheric profiles for O3, CH4, NjO, CO, COj, O2,
NO, SO2, NO2 NH3, HNO3, and for other background aerosols, such as dust and smoke. No
effort was made to adjust the modeled extinction for higher or lower concentrations of these
constituents.

Assumptions specific to datasets 1, 2 and 3 are as follows: (1) Station elevations were assumed
to be 24 meters above mean sea level. NOTE: This input into LOWTRAN7 is used to modify
aerosol profiles below 6 km. (2) Dataset 1 consisted of atmospheric profiles interpolated to
needed levels by ASPAM. The boundary layer wind speed was extracted from the dataset-
supplied surface wind speed. (3) Dataset 2 was generated by compiling several hundred
soundings averaged to produce one mean temperature, pressure and moisture profile, per
geographic region noted. (4) In dataset 3, a 6 meter per second surface wind speed was used to
initialize the boundary layer aerosol model within LOWTRAN7 for Baghdad and Pyongyang.
A 3 meter per second surface wind speed was assumed for Howard AFB.

6. RESULTS

Tables 1, 2 and 3 on the following pages compare dataset 1 average surface transmittances with
those calculated for dataset 3. In the I to 8 micron band, water vapor is the dominant absorber
with reduced transmittances indicated at approximately 2 and 3 microns. This held the average
surface transmittance for dataset 1 to approximately 0.45. When compared to climatological
records (dataset 3), 0.45 is expected to occur only 1% of the time at Baghdad. At Pyongyang,
average transmittances could be expected to be less than 0.45 26% of the time, while at Howard,
we would expect values to be at or below this average (0.45) 90% of the time. (All comparisons
are being made to January dataset 3 data).

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page 4 of 8

TABLE 1; 50th Percentile Values of Average Transmittance for Baghdad vs Average
Transmittance Values for Dataset 1 .

MICRON BAND

BAGHDAD

1- 8

1-2.5

3-4

1-4

JANUARY

0.54

0.65

0.62

0.58

FEBRUARY

0.53

0.63

0.60

0.56

BEST

0.54(JAN)

0.65(JAN)

0.62(JAN)

0.58(JAN)

WORST

0.50(MAY)

0.59(MAY)

0.54(SEP)

0.54(MAY)

ANNUAL

0.52

0.60

0.58

0.55

DATA SET 1

0.54

0.48

0.49

♦FREQUENCY

'T' _

_ ’aa _ _ T7 1

’■Percentile ranking of Average Transmittance Values for Dataset 1 vT Baghdad's January
Cumulative Frequency of Occurrence.

TABLE 2. 50th Percentile Values of Average Transmittance for Pyongyang vs Average
Transmittance Values for Dataset 1.

MICRON BAND

PYONGYANG

1- 8

1-2.5

3-4

1-4

JANUARY

0.48

0.54

0.65

0.51

FEBRUARY

0.47

0.53

0.64

0.50

BEST

0.47(JAN)

0.54(JAN)

0.65(JAN)

0.51(JAN)

WORST

0.35(JUL)

0.42(AUG)

0.40(JUL)

0.37(JUL)

ANNUAL

0.42

0.47

0.54

0.44

DATA SET 1

0.45

0.54

0.48

0.49

♦FREQUENCY

***Pprr* An+1 1 o rortb-it-ir*

26%

i-k-P T' _

50%

- _ Aj _ _ _ 't 7 1

42%

’■Percentile ranking of Average Transmittance Values for Dataset 1 vs Pyongyang's Janumy
Cumulative Frequency of Occurrence.

In the 1 to 2.5 micron band, water vapor is still the dominant absorber with reduced transmittance
starting to show up at 2 microns. The effects are quite pronounced at 2.5 microns. The average
surface transmittance for dataset 1 across this band was 0.54, 20% higher than the 0.45 average
arrived at when considering the entire 1 to 8 micron band. Based upon dataset 3 runs, 0.54 is
expected to occur only 1% of the time at Baghdad. At Pyongyang, average transmittances could
be expected to be less than this value (0.54) 50% of the time, while at Howard, 94% of the time
we would expect values to be at or below 0.54.

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page 5 of 8

TABLE 3: 50th Percentile Values of Average Transmittance for Howard AFB vs Average
Transmittance Values for Dataset 1.

MICRON BAND

HOWARD

1- 8

1-2.5

3-4

1-4

JANUARY

0.40

0.51

0.41

0.45

FEBRUARY

0.41

0.52

0.42

0.46

BEST

0.41 (FEB)

0.52(FEB)

0.42(FEB)

0.46(FEB)

WORST

0.38(JUN)

0.45(JUN)

0.36(JUN)

0.40(JUN)

ANNUAL

0.39

0.47

0.38

0.42

DATA SET 1

0.45

0.54

0.48

0.49

♦FREQUENCY

90%

94%

96%

95%

*Percentile ranking of Average Transmittance Values for Dataset 1 vs Howard AFB's January
Cumulative Frequency of Occurrence.

Similar results were noted when evaluating the 3 to 4, 1 to 4, and 8 to 12 micron bandwidths.
In general, the dataset 1 values would be expected to occur more often at Howard, rather than
at Pyongyang or Baghdad. In the 8 to 12 micron wavelength band, there are two noticeable
regions where the transmissivity values dropped considerably. They were at 9.5 and 12 microns.
Ozone absorption was at a peak near 9.5 microns. The 12 micron wavelength was affected by
absorption due to water vapor.

Atmospheric transmittance values are presented in tabular form for each location. Data from the
months of January and February are evaluated, since this period corresponded with the dataset
1 time frame. Also presented are data for the month with the best transmissivity, the month with
the worst transmissivity, and finally an average transmissivity for all months. The best month
is defined as the month with the lowest cumulative percentage of occurrence of transmissivities
equal to or less than 0.75. Based on this definition, the best month contained the fewest
occurrences of transmissivity values at or below 0.75. Similarly, the worst month was defined
as the month with the highest cumulative percentage of occurrence of transmissivity values at or
below 0.75. Transmissivity in the 8 to 12 micron band at Baghdad is highest during the months
of January and February. In general, over half of the transmissivities reported at all altitudes
were greater than 0.80. The two poorest months were judged to be August and September,
August being the worst. August surface transmissivity values were less than 0.75 more than
50% of the time, but 0.65 or less only 10 percent of the time. Transmissivity values rapidly
improved with an increase in target altitude above the surface boundary layer. Transmissivities
at 2 km and above were generally 0.70 or greater for any month.

Data from Howard AFB, was used to demonstrate transmissivity values from a tropical regime.
The month of February was the month with the highest transmissivity values, and in general, over
50% of the time were greater than 0.50, for all altitude.

The two poorest months were June and July, July being the worst. A dramatic increase in low

205

page 6 of 8

level moisture gave July a transmissivity value of less than 0.40 at the surface more than 50%
of the time. Transmissivity values rapidly improved above the surface boundary layer, and
generally exceeded 0.65 at and above 2 km.

Data from Pyongyang demonstrated a much more seasonal bias in the transmissivity than did the
other locations. January and February were the months with the highest reported transmissivity
values, January being the best. In general, over 50% of the transmissivities reported at all
altitudes were greater than 0.80. The poorest month was July, where a dramatic increase in low
level moisture resulted in a transmissivity value of less than 0.45 at the surface more than 50%
of the time. Transmissivity values again rapidly improved as altitudes above the surface

boundary layer were evaluated, and nearly always exceeded 0.60 at source altitudes of 2 km or
greater.

USAFETAC evaluated the 50th percentile frequency of occurrence to estimate the mean. In the
I to 8 micron case, surface transmissivity is highest during the month of January at Baghdad
(0.54). The poorest month was May (0.50). The annual average surface transmissivity was 0.52.
Transmissivity values rapidly improved with altitude.

Data from Howard AFB, Panama showed February had the highest transmittance value of 0.41,
while June was the poorest month at 0.38. The abundance of low level moisture keeps
transmissivity values near the 0.40 value.

Pyongyang data demonstrated a much more seasonal bias in transmittance values. January had
the highest transmittance of 0.48, while July was the poorest at 0.35. Annually, values were less
than 0.42 at the surface 50% of the time.

Transmissivity values again rapidly improved as source altitudes above the boundary layer were
evaluated. Similar results were found on the 3 to 4, 1 to 2.5, and 1 to 4 micron cases. Values
of absolute humidity from dataset 1 were compared with the absolute humidity values from the
Baghdad sounding, and are depicted in Table 4. The average surface absolute humidity for
dataset 1 was 11.032 grams per cubic meter (g/m^), and ranged from 6.121 g/m^ to 13.89 g/m^

The surface absolute humidity from the Baghdad sounding averaged approximately 7.5 g/m^, and
humidities greater than 11 g/m^ occurred 1.8% of the time. At approximately 2 km (data used
was taken from 7000 feet), the absolute humidity from dataset 1 averaged 4.48 g/m^ and ranged
from 0.9232 g/m^ to 7.021 g/m^ compared to the average Baghdad humidity of 2.6 g/m^. At this
level, humidities greater than 4.0 g/m^ occurred 11.8% of the time. This demonstrates that
dataset 1 atmospheric profiles, at these 2 levels, do contain higher concentrations of water vapor
than the average Baghdad 15 year POR sounding, but they do not exceed the extremes. Above
2 km, the humidities from dataset 1 continue to be slightly higher than the Baghdad soundings;
as a result, transmissivities are slightly lower. However, the transmissivities from dataset 1 fall
within the range of the expected transmissivity values for January.

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page 7 of 8

TABLE 4. Absolute humidity for dataset 1, and for the archived data for Baghdad _

DATA SET 1
LOCATION

SURFACE ABSOLUTE
HUMIDITY (g/m^) .

UPPER LEVEL (7000 FT)
ABSOLUTE HUMIDITY(g/m^)

OlA

9.064

3.836

02A

13.89

6.013

03A

6.121

3.396

04A

8.657

2.116

05A

8.261

3.574

06A

9.099

0.9232

07A

11.10

7.021

BAGHDAD

7.5

2.6

7. CONCLUSIONS

Review of preliminary results of combinations of transmissivity vs wavelength, altitude, and as
a function of sensor view angles show no great surprises: (1) Dataset 1 atmospheric profiles do
contain higher concentrations of water vapor than the average Baghdad 15 year FOR sounding,
but they do not exceed the extremes. Transmissivities fall within the range of expected
transmittance values for January. (2) Transmissivity values increased as a function of source
altitude. The decrease in water vapor with increasing distance from the surface contributed to
the increase in transmissivity. (3) Transmissivity values for all cases were lower given an
increased path length in the 30 degree off-nadir case. (4) The selection of the bandwidth interval
affected the average transmittance values calculated. There are select regions in the 1 to 12
micron range band that are "opaque" to electromagnetic wave propagation. We ran LOWTRAN7
over the entire 1 to 12 micron bandwidth interval. Transmittance values calculated were
substantially lower than values calculated in select subintervals of the 1 to 12 micron band. The
reductions ranged anywhere from 7% to nearly 20%. This reduction was primarily due to
inclusion of the "opaque" regions mentioned above. Also, when we used a large step size, on
the order of 1 micron, for the calculations, the "opaque" regions were effectively masked in the
model output. To help eliminate this bias, we calculated transmittances in selected spectral bands
located within the atmospheric windows.

There were differences between the interpolated dataset 1 cases provided by USAFETAC's
customer, and the desert source soundings selected by USAFETAC (Baghdad). January Baghdad
surface transmissivities (8 to 12 micron band) were 50% of the time greater than 0.80.
Transmissivities for dataset 1 averaged near 0.59 for sources at the surface. This value (0.59)
has a very small probability of occurrence (1%) at Baghdad, but is more likely to occur (10%)
at Howard AFB.

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page 8 of 8

Dataset 1 showed lower surface transmissivities throughout the year when compared to a
Baghdad 15 year FOR, but did not approach the reduction experienced in a true tropical
environment such as at Howard AFB. A tropical environment shows surface transmissivities on
the order of 0,40, depending on the time of year.

It should be noted that dataset 1 was based upon ASPAM-derived data. Previous research at
USAFETAC (0 Connor, 1994) has shown ASPAM-derived data to have a slight bias to report
higher absolute humidity values than may actually be present.

The data extracted from the Pyongyang upper-air soundings show a definite seasonal bias, with
lower transmissivities in the summer months, all tied to an increase in absolute humidity.'

This study was an example of how climatological data might be used in determining expected
atmospheric transmittance values. USAFETAC plans to apply this approach to geographic
locations worldwide.

REFERENCES

Environmental Research Institute of Michigan for the Office of Naval Research, Department of
the Navy, The Infrared Handbook, 1978.

Kneizys, F.X., et. al., AFGL-TR-88-0177, 1988, Users Guide ToLowtranJ, Air Force Geophysics
Laboratory, Hanscom AFB, MA.

Koermer, James P., and J.P. Tuell, Improved Point Analysis Model (IPAM) Functional
Description, 1984, Internal Working Document, Air Force Global Weather Center Offut
AFB, NE.

O'Connor, Lauraleen, and Charles R. Co^^\n, Atmospheric Slant Path Analysis Model Baseline
Study, 1994, USAFETAC/PR-94/001, Scott AFB, IL.

SAS Institute, Cary, NC, SAS/STAT User's Guide, Version 6, 1990, FOURTH EDITION

208

Session III

BATTLE WEATHER

209

OWNING THE WEATHER:

IT ISN’T JUST FOR WARTIME OPERATIONS

RJ. Szymber, M.A. Seagraves, J.L. Cogan, and O.M, Johnson
U.S. Army Research Laboratory
White Sands Missile Range, NM 88002-5501

ABSTRACT

Owning the Weather (OTW) technologies that provide state-of-the-art weather
support for Army tactical operations and battlefield simulations may also be
used to support certain Army Operations Other Than War (OOTW), as well as
civilian and commercial applications. Types of OTW technologies and products
that may be used in applications other than tactical situations include remote
sensing, atmospheric characterization, scene visualization, and atmospheric
models. OTW products can be used in Army humanitarian assistance and
disaster relief, peace enforcement, and peacekeeping operations, as well as in
civilian applications such as air and noise pollution control, environmental
cleanup, global climate change programs, transportation, forestry, and
agriculture. Unique OTW meteorological testbeds are used in product
development. Interactions and partnerships with other government agencies and
private industry help to pave the way for technology transitions.

1. INTRODUCTION

In his essays on "The Art of War" written more than 2,000 years ago. Sun Tzu asserted,
"Know the enemy, know yourself; your victory will never be endangered. Know the ground,
know the weather; your victory will then be total." Historically, weather has decisively
impacted battlefield success, and future warfighters prepared to exploit weather and terrain
effects will also benefit in battle. Today’s Army doctrine (Dept, of the Army, 1993) states,
"The commander who can best measure and take advantage of weather conditions has a decided
advantage over his opponents. By understanding the effects of weather, seeing the opportunities
it offers, and anticipating when they will come into play, the commander can set the terms for
battle to maximize his performance and take advantage of limits on enemy forces. "

Owning the Weather (OTW) is the Army vision for improved battlefield weather support to
Force XXI, the force projection Army of the 21st century. It is critical to out-thinking the
enemy and winning the information war, and in executing precision strikes. OTW is defined
as the use of advanced knowledge of the environment and its effects on friendly and enemy
systems, operations, and tactics to gain a decisive advantage over opponents. The OTW
strategy involves the observation, collection, processing, forecasting, and distribution of timely

211

battlefield environmental conditions. This information is transformed into weather intelligence
and decision aids for final battlefield exploitation of the weather.

OTW will provide a digitized picture of battlefield weather and its effects for Intelligence
Preparation of the BattleHeld (IPB) to support mission planning, situation awareness, synchro¬
nized battle management, and advanced decision and execution support. The Integrated
Meteorological System (IMETS) will collect data from various sources and distribute timely
battlescale weather information to multiple command elements via the All Source Analysis
System. This information will be used in tactical decision aids (TDA’s) resident on computers
in all battlefield functional areas to provide commanders and soldiers with real-time and
predicted environmental effects on missions and systems.

OTW capabilities and products that provide state-of-the-art weather support for Army tactical
operations and battlefield simulations are ideal for supporting Army force projection operations
and joint military missions, including operations other than war (OOTW). OTW technologies
also have important dual-use civilian, and commercial environmental applications that can
contribute to defense conversion and technology transfer.

1.1 OTW Battlefield Sensing

Weather conditions must be observed before they can be forecast and converted into weather
intelligence. A suite of complementary and synergistic space-based, airborne, and ground-
based sensing systems provides real-time observations at required accuracies, resolutions, and
coverage. All available data are collected, validated, and assimilated to build a complete
horizontal and vertical picture of the atmosphere over friendly and enemy controlled territory.

Battlefield sensing systems and technologies include:

- meteorological satellites;

- Automatic Meteorological Sensor System;

- Meteorological Measuring Set;

- Target Area Meteorological Sensors System (TAMSS), that is, the Mobile Profiler
System (MPS), Unmanned Aerial Vehicles (UAV) with meteorological sensors and dropsonde
payloads, and Computer Assisted Artillery Meteorology software; and

- remote sensing and data fusion techniques.

1.2 OTW Processing, Analysis, and Dissemination

The IMETS receives, processes, analyzes, and distributes mission-specific observations,
forecasts, and weather intelligence. The IMETS is a mobile, tactical, automated weather data
system designed to provide timely weather and environmental effects forecasts, observations,
and decision aid information to appropriate command elements through the Army Battle
Command System. It includes a battlescale (mesoscale) forecast model and satellite
communications.

212

1.3 OTW Battle Decision Aids and Displays

TDA’s permit commanders to rapidly war game courses of action; determine probable effects
on friendly and enemy systems, tactics, and doctrine; and incorporate weather effects into
tactical planning and operations. Decision aids not only provide information about weather
effects, but also show the commander if and when weather conditions give a competitive edge
over the enemy.

IPB, TDA’s, and war gaming enable the commander to quickly and accurately analyze the
effects of weather on impending operations. Examples of these types of products are:

- Integrated Weather Effects Decision Aid,

- IPB weather analysis overlays,

- mobile generator smoke screen TDA,

- night vision goggles TDA, and

- electro-optics TDA’s.

1.4 OTW Technology Exploitation of Weather

Training, combat simulations, weapon development, and system testing and evaluation are all
areas where the exploitation of weather-related technology results in having advantages over
threats and in making adverse weather a force multiplier. Some examples of these exploitation
technologies and products include:

- atmospheric scene visualization of battlefield obscurants (smoke, dust, haze, and fog),

- atmospheric transport and diffusion modeling over urban and complex terrain,

- target contrast change characterization, and

- simulation of optical turbulence effects.

2. OPERATIONS OTHER THAN WAR (OOTW)

The Army has evolved from the Cold War doctrine and structure to a new strategic era of
force projection, and has consequently modified its weather support architecture to support new
missions. War, that is, a major regional conflict, remains the baseline objective for OTW
support to the Army. However, many new missions are now likely and a major regional
conflict is only one of many contingencies for which the Army must provide weather support.
These new missions require more flexible, mobile forces to respond to the wider range of
unpredictable threats and situations. Tailored weather information is vital to the success of
these noncombat operations. Planning now considers and integrates components of other
service into joint task force meteorological and oceanographic support. Split-base operations
provide support from the CONUS or theater to complement capabilities deployed with the joint
task force.

OOTW are military activities during peacetime and conflict that do not necessarily involve
armed clashes between two orgcinized forces (Dept, of the Army, 1993). Today’s Army
conducts OOTW as part of a joint team and usually in conjunction with other government
agencies. The Army has participated in OOTW supporting national interests throughout its

213

history. However, the pace, frequency, and types of OOTW have increased over the last 25
years. Furthermore, the future will likely see a growing percentage of the Army’s activities
committed to OOTW (Eden, 1994).

In general, OOTW have weather support requirements different from those for war. During
war, the peacetime weather infrastructure is usually not available and all indigenous sources
of local weather data in the war zone may be denied or lost. Therefore, the full range of OTW
support capabilities is required in a war situation. In OOTW, availability of weather data from
the existing peacetime indigenous sources will likely continue, allowing a much smaller
weather support element to deploy to support missions with a greater reliance on indirect
support from the CONUS or theater weather facilities. The exception to this is OOTW
conducted in remote, under-developed areas where no weather infrastructure exists, such as
in Rwanda, Generally, noncombat missions enable a small weather team deployed to the
contingency area to incorporate all available indigenous weather information relayed to the
center, integrate it with data from other sources, tailor and repackage it, and transmit it in
minutes. Also, OOTW may occur in relatively benign environments where weather support
concepts and procedures are much different from those in high- to mid-intensity conflicts.

2.1 Disaster Relief

Disaster relief operations occur when emergency humanitarian assistance is provided by DoD
forces to prevent loss of life and destruction of property resulting from man-made or natural
disasters. The diverse capabilities of the Army make it ideally suited for disaster relief
missions. Assistance provided by U.S. forces is designed to supplement efforts by civilian
agencies who have primary responsibility for such assistance.

OTW technologies are especially well-suited to provide support during severe weather and
other weather-related disasters such as hurricanes, tornado outbreaks, flash flooding, windstorm
fires, and toxic air pollution episodes. For example, weather support was critical during the
disaster relief the Army provided during the landfall and aftermath of Hurricane Andrew in
1992 in southern Florida. In this type of operation, a detailed knowledge of predicted weather
conditions and their effects on coastal zones, infrastructure, transportation, and public safety
is important in quickly achieving stated objectives, while avoiding added injuries and
destruction related to the weather.

2.2 Peace Enforcement and Peacekeeping Operations

Peace enforcement is military intervention designed to forcefully restore peace between
belligerents engaged in combat. Peacekeeping operations use military forces to supervise a
cease-fire and/or separate the parties at the request of the disputing groups. OTW support is
required to initially project the force and for subsequent ground and aerial reconnaissance
efforts to collect intelligence. During these operations Army combat power that benefits from
the OTW capabilities may need to be applied.

214

2.3 Noncombat Evacuation Operations

Noncombat evacuation operations relocate threatened civilians from hazardous locations in
foreign countries. These operations usually involve U.S. citizens whose Uyes are in danger.
OTW support is critical to the success of these operations, as the failed Iranian hostage rescue
mission demonstrated in 1980. The failure of this operation was directly attributed to the
weather, specifically, the effects of unexpected dust and sand storms encountered along the
helicopters’ flight paths and at staging points.

OTW supports overall mission planning and execution, to include elements of concealment and
surprise, by predicting weather conditions and their effects along planned flight paths and
loading zones. During the successful 1983 Grenada action, for example, weather was an
important factor in that overcast cloud conditions prevented Russian satellites from observing
our aircraft and ships, adding to the element of surprise.

3. NONMILITARY/CIVILIAN APPLICATIONS

3.1 Air and Noise Pollution

Of the many civilian and commercial applications for OTW technologies, the problem of air
pollution is one that impacts a great number of people. The air pollution problem has reached
such a significance that urban areas that do not show positive efforts to meet Environmental
Protection Agency standards will be penalized by reductions in federal funding. The pollution
problem along segments of the U.S. -Mexico border has been noted as quite serious,
particularly in the El Paso-Ciudad Juarez area. It often becomes evident at White Sands
Missile Range (WSMR) when southerly wind flow carries the dark brown layer of contami-
n'ants northward into New Mexico. Concern for this regional situation has prompted state
agencies of Texas and New Mexico, and city governments of El Paso and Ciudad Juarez to
form the "Paso del Norte Task Force," dedicated to cooperation in attempting to correct the
problem. The Task Force welcomed U.S. Army Research Laboratory (A^) proposals to
enter into Cooperative Research and Development Agreements (CRD A), which would benefit
all partners by pooling resources and technologies in efforts to alleviate the mutual problem.
The partnerships augment the civilian atmospheric monitoring capabilities with state-of-the-art
direct and remote sensing instruments, which can continually monitor the state of the
atmosphere. Some of the capabilities are fixed in place at WSMR, but many of the instruments
can be transported to observation sites where needed. Major benefits to the Army include the
testing and evaluation of transport and diffusion models in an urban and complex terrain
environment, and the display of results on a computer-based geographic information system.

The Mobile Profiler System (MPS) proved its value as a primary source of atmospheric data
during the Los Angeles Free Radical Experiment, a multi-agency and bi-national (U.S. and
Canada) air pollution experiment in the Los Angeles basin during September 1993. The MPS
consists of a radar wind profiler, a Radio Acoustic Sounding System, a ground-based
microwave radiometer, and other instruments, as well as a meteorological satellite receiver.
It provided vertical profiles of wind and temperature nearly non-stop throughout the four weeks

215

of the experiment, at an unprecedented level of detail in time and space as shown, for example,
in figure 1. These profiles were averaged and displayed as often as every 3 minutes at vertical
resolutions as fine as 100 m from the surface to 3-5 km (wind) and to 0.8-1. 6 km (virtual
temperature). Combining these profiles with those derived from satellite data extended the
maximum height to over 14 km. The profiles from the MPS were used with measurements
of concentrations of pollutant species (ozone, particulates, etc.) to describe the transport and
diffusion of those pollutants within the local area.

Meteorological models of the type found in the IMETS use these MPS profiles to derive
descriptions and forecasts of atmospheric conditions throughout a mesoscale region. These
analyses and forecasts, in turn, provide essential input into regional transport and diffusion
models also being developed as part of the OTW effort. More information on the experiment
and the role of the MPS may be found in Wolfe et al. (1994), and Cogan et al, (1994).

. . . -i.mcinr-uiw 120000 OS-SEP-93

Figure 1. Time/height display of the MPS radar wind profiler data. Wind arrows have
conventional meaning except a full barb represents 10 m/s and a half barb represents 5 m/s.
Sounding derived from 15 min of data displayed every half hour.

216

Noise pollution is another area where OTW technologies can benefit the civilian community.
An acoustic testbed is available to validate acoustic models that predict noise impacts of
planned urban developments, such as airport placement, industrial development sites, and
traffic routing. The testbed is located in an isolated area where an exceptionally wide range
of frequencies may be produced without public disturbance. Scientists performing acoustic
research have also provided assistance in dealing with the noise pollution associated with
munitions testing in the Aberdeen Proving Ground area.

3.2 Environmental Cleanup

Environmental cleanup operations include the transportation and disposal of toxic/hazardous
materials that have the potential for environmental disasters in the event of accidents. OTW
technologies with potential in this area include the continuous remote monitoring of hazardous
waste sites and the prediction of toxic corridors resulting from potential and actual chemical
spills or nuclear radiation releases. The monitoring capability was recently demonstrated at
WSMR when routine operation of the Remote Sensing Rover, a portable Fourier transform
spectrometer, detected the presence of ammonia gas as a byproduct in the smoke from a forest
fire at a distance of 8 km from the Rover. The gas was determined to have been produced
from ammonia fertilizer compounds used in slurry for fighting the fire. While the concentrat¬
ion of the gas was not hazardous, its detection demonstrated one of the many areas for potential
civilian applications.

3.3 Global Climate Change

Understanding climatic change and effects of human activities requires an intensive effort to
monitor the atmosphere, generate essential input to environmental models, and provide data
to update and check the quality of those models. OTW technology can assist in fulfilling those
requirements. The MPS demonstrated the ability to continuously monitor the atmosphere over
a period of nearly a month. The future MPS will have the ability to provide high-quality data
for more extended periods. These data will feed mesoscale meteorological models in the
IMETS, permitting high-resolution analysis and short-term forecasts over regional scales. Both
the MPS and the IMETS can be deployed in remote areas not normally accessible by more
conventional measurement and analysis systems. The mobility of these systems allows them
to be placed in a variety of locations on short notice at a relatively low cost. At the same time,
their durability and reliability result in lower costs of operation, repair, and maintenance.

The measurements provided by a network of MPS’s may be supplemented by meteorological
sensors and dropsondes carri^ by small manned or unmanned aircraft. The use of these
instruments on, or deployed by, aircraft provides detailed, quantitative data over wide areas
during experiments or unusual weather. The dropsondes can also become small meteorological
ground (or sea-surface) stations when they reach the surface. Measurements from these sensors
serve as input to analysis and forecast systems such as the IMETS, further increasing the
ability of mesoscale models to accurately depict the atmosphere.

217

Global monitoring will be carried out by future space earth-observing systems that provide
^de-area coverage. To obtain accurate and detailed measurements, satellite remote sensors
require calibration both before and after launch, and periodically during their lifetimes. This
calibration and validation (cal/val) process depends on having high quality "ground truth"
measurements. The MPS and airborne sensors can provide these data with both high accuracy
and wide coverage. The MPS can be moved to a large variety of locations around the globe,
and the airborne sensors and processors may be fitted in light military and civil aircraft. These
systems will allow, for the first time, cal/val over many different climatic regions at an
affordable cost, as opposed to the common practice of taking data over a few limited areas and
extrapolating those results for the entire earth. The unique earth-target provided by the White
Sands National Monument, the world-class ARL Atmospheric Profiler Research Facility, and
a vast array of other meteorological instrumentation make WSMR an ideal site for satellite
sensor cal/val.

3.4 Transportation

OTW sensors can provide real-time data for aircraft safety and hazard avoidance. The MPS
can generate wind profiles at airports that will enable rapid response warnings of hazardous
conditions such as down-bursts and other sudden changes in wind speed or direction, including
vertical motion. Current experimental sites at airports such as Denver, CO provide horizontal
wind information as often as every 15 min. However, even this relatively rapid refresh rate
may not be adequate for rapidly developing situations, for example, such as a gust front or
sudden down-burst. The 3 min refresh time of the current MPS enables the detection of these
events in time to provide adequate warning. Another useful MPS capability is the production
of accurate wind data in the presence of overflying aircraft or birds, either of which produce
erroneous results in current types of radar wind profiling systems. In the presence of strong,
rapidly-changing convective conditions, radar profilers may not produce reliable winds!
Nevertheless, the MPS will generate information that will indicate that the wind data are
unreliable, and will provide estimates of probable errors that may be used as indicators of
hazardous conditions. Unlike many lidars, the MPS will produce profiles through clouds and
fog. It will be able to move to a location most suitable for the active runway and start
operations in less than an hour. The MPS satellite receiver will help provide advance warning
of potential severe weather that could affect airport operations. IMETS models may be used
to extend the area of coverage to the mesoscale region around the airport, taking advantage of
other sources of data, for example, from the National Weather Service. Civilian versions of
decision aids will permit controllers and others to perform their functions more efficiently.

The models and decision aids of the IMETS will be able to provide short-term warning of
hazardous conditions for ground transportation. Examples include ice on roadways, fog, high
winds, and flooding. Data can be extracted from existing systems, such as rawinsondes and
surface stations, and specifically deployed systems such as the MPS. Several MPS’s, and
perhaps light aircraft with meteorological sensors and dropsondes, would be sent to locations
with a high potential for hazardous weather events. The mobility and consequent low cost of
deploying OTW systems would allow the movement of these systems to locations of interest,
even in remote areas.

218

ARL is in the process of making technologies available to civili^ efforts in the development
of the Intelligent Vehicle Highway System (IVHS). A low-visibility warning system being
developed as part of the IVHS will provide early warning to transportation officials and traffic
of rapidly deteriorating highway visibility due to the sudden onset of blowing dust, snow,
smoke, fog, etc. To reduce development costs resulting from waiting for natural obscunng
phenomena in which to validate the system, the developer plans to make use of some of ARL’s
technologies such as an artificial fog generator and remote measurement systems.

3.5 Forestry

Application of OTW systems to forestry is closely related to environmental momtonng and
providing short-term warning of hazardous conditions. Of particular concern are the
atmospheric conditions that affect the spread of forest and brush fires. During the unusually
hot and dry period during the summer of 1994 at WSMR, NM a series of brush fires bum^
much of the vegetation covering the Organ and San Andreas mountains. Sudden and frequently
unexpected changes in wind speed and direction caused the fire to spread rapidly, at one point
threatening the WSMR main post area. Other recent examples include fires in the Los Angeles
area in 1993, in Yellowstone National Park in 1988, and near Glenwood Springs, Colorado in
1994 The fact that sudden changes in atmospheric conditions cause fires to rapidly spread
when they may have been thought to be under control is well known to many charged with
fighting and controlling fires. The combination of high temperature (> 40 "C), very jow
relative humidity (10-15%), and highly variable wind conditions led to a highly volatile fire
weather" situation that fed the fire at WSMR. In the Colorado fire, a sudden shift in wind
direction over rugged terrain led to the death of 14 fire fighters.

The MPS and other OTW sensors can provide invaluable information for analysis and
prediction of atmospheric conditions that affect forest fires. Especially valuable is the mobility
of the MPS and its ability to reach remote areas accessible only by 4- wheel drive vehicles.
The essential capability is the production of profiles of wind for the lowest few kilometers
every 3 min, tracking sudden changes during highly variable conditions. The MPS also
includes instruments that can provide equally frequent data for temperature and total moisture
in the lowest kilometers. The satellite data acquired by the MPS will provide a general picture
of the atmospheric situation over a large area around the location of the fire. The airborne
instruments being developed may be carried or dropped by fire fighting or observation aircraft
to provide required atmospheric data above the fire location. The very low weight, size, and
power requirement of these instruments permit them to be added on a non-interference basis,
such as in a removable pod.

The data gathered by these OTW sensors and more conventional instruments may be analyzed
by software in the highly mobile IMETS to provide rapid analyses and predictions for the area
of and nearby the fire. The predictions will enable fire fightep to place personnel and
equipment in places where the fire is expected to spread ahead of time, and to avoid potential

219

high-risk areas. The overall result is the ability to control and extinguish fires quickly with
less expenditure of resources, and with less danger to personnel.

3.6 Agriculture

Agriculture has always been extremely dependent upon weather conditions, especially floods
droughts, and freezes, but OTW technologies promise some applications that are not as readily
evident as the daily weather reports and forecasts available through the news media. For
example, ARL’s FM-CW radar is used primarily to provide profiles of atmospheric turbulence
but IS so sensitive that it can detect airborne insects. The atmospheric transport of adult moths
IS a cntical concern in combating infestations of the Fall Army Worm, for example. This
capability is advantageous for detecting and monitoring insect migration, thereby permitting

more timely, effective, and efficient use of pesticides and minimizing contamination of the
environment by chemicals.

4. CONCLUSIONS

State-of-the-art OTW technology that shows great promise in providing land warfare weather
sup^rt has many applications outside of wartime operations. Knowledge of atmospheric
conditions and their effects is essential to success in Army OOTW, such as disaster relief,
pe^ enforcement, peacekeeping and noncombat evacuation operations. OTW is the next
weighting edge for enabling land force dominance by leveraging the power of information
and t^hnology to increase the lethality, survivability, and tempo of operations in war and
CWW. In addition, much of this technology will be extremely useful in a wide variety of
civilian applications such as air and noise pollution control, environmental cleanup global
climate change analyses, transportation safety, forest fire control, and agriculture.

REFERENCES

Cogan, J. L., E. M. Measure, E. D. Creegan, D. Littell, and J. Yarbrough, 1994, "The Real
Thing: Field Tests and Demonstrations of a Technical Demonstration Mobile Profiler
System." In Proceedings of the 1994 Battlefield Atmospherics Conference, U.S. Army
Research Laboratory, White Sands Missile Range, NM 88002-5501.

Dept, of the Army, 1993, Field Manual 100-5 Operatinns U.S. Army Training and
Command, ATTN; ATDO-A, Fort Monroe, VA 23651-5000.

Doctrine

Eden, MAJ Steve, 1994, "Preserving the Force in the New World Order." Military Review
No. 6, pp 2-7. ’

Wolfe, D., B. Weber, D. Wuertz, D. Welsh, D. Merritt, S. King, R. Fritz, K. Moran, M.
Simon, A. Simon, J. L. Cogan, D. Littell, and E. M. Measure, 1994, "An Overview
of the Mobile Profiler System, Preliminary Results from Field Tests during the Los
Angeles Free-Radical Study." Submitted to Bull Amer. Meteor. Soc.

220

THE REAL THING: FIELD TESTS AND DEMONSTRATIONS OF
A TECHNICAL DEMONSTRATION MOBILE PROFILER SYSTEM

J. Cogan, E. Measure, E. Creegan, D. Littell, and J. Yarbrough
U.S. Army Research Laboratory
Battlefield Environment Directorate
White Sands Missile Range, NM 88002-5501

and

B. Weber, M. Simon, A. Simon, D. Wolfe, D. Merritt,
D. Weurtz, and D. Welsh
Environmental Technology Laboratory
National Oceanographic and Atmospheric Administration
Boulder, CO 80303

ABSTRACT

A near real-time sounding of the atmosphere from the surface to >30 km over a battlefield
may be obtained by combining atmospheric profiles from an array of ground-based remote
sensors and meteorological satellites. This type of capability is essential for optimum use of
Army assets such as artillery and defense against biological and chemical attack, as well as a
variety of civilian applications. This paper briefly describes the techmcal demonstration (TD)
Mobile Profiler System (MPS), and outlines the method for merging data from the satellite and
ground-based systems. The processing software allows acquisition of valid ground-based data
with a refresh time as short as 3 min and in the presence of overflying birds or aircraft.
Results from field tests at sites in White Sands Missile Range, NM; near Los Angeles, CA;
and at Ft. Sill, OK indicate the capability of the TD MPS to generate useful atmospheric
soundings for the field artillery and a variety of other military and civilian users.

1. INTRODUCTION

The Mobile Profiler System (MPS) is being developed to provide military and civilian users
with atmospheric soundings in close to real time. Applications include avoidance of hazardous
wind conditions at airfields and training ranges, and obscurant and pollution monitoring.
Szymber et al. (1994) discuss potential military applications in operations other than war and
related civilian uses. The type of systems found in the Technical Demonstration (TD) MPS
are described in Cogan and Izaguirre (1993), Miers et al. (1992), their references, Weber and
Weurtz (1990), Hassel and Hudson (1989), and Strauch et al. (1987). This paper briefly
describes the TD MPS, provides an outline of the combined sounding technique, and presents
examples of actual data.

221

2. SYSTEM DESCRIPTION

The TD MPS consists of 3. 924-MHz radar profiler operating in a five-beam mode for winds,
a Radio Acoustic Sounding System (RASS) for virtual temperature (T^), a ground-based
microwave radiometer for T^ and humidity, a small ground station for temperature, pressure,
humidity, and wind velocity, and a small satellite receiving system for acquiring and processing
satellite sounder data for temperature and humidity. Satellite sounding heights are computed
for the standard pressure levels, and wind velocity is calculated using the geostrophic
assumption. Temperature is converted to T„ as required. Pressure versus height is computed
from the measured sounding data and, in the future, may be measured for the lower part of the
sounding using the microwave radiometer. The main electronic components and some of the
sensors of the TD MPS are housed in or on a 9-m trailer. The radar antenna, the four RASS
sources, the satellite antenna, and the microwave radiometer are deployed around the trailer.

A single workstation controls the satellite terminal and processor, while a PC operates the
radar ^d collects data from the remaining ground systems. A second workstation serves as
the primary processor and data manager. Up to two balloon systems may be run from the
trailer to obtain comparison data, as during the Los Angeles Free Radical Experiment (LAFRE)
in the Los Angeles basin. Further tests have been held at White Sands Missile Range
(WSMR), NM; near Boulder, CO; and at Ft. Sill, OK.

3. PROCESSING AND COMBINING METHOD

New algorithms help operate the ground-based sensors and considerably enhance the quality
control of the output. Examples include updated software for the radar wind profiler and
RASS. Entirely new routines eliminate most of the problems arising from birds or aircraft
flying through the main radar beam or side lobes (Merritt 1994). In the few cases in which
the atmospheric return cannot be separated out of contaminated data, the output for those
particular times and altitudes can be flagged as unreliable. New techniques under development
inckde neural-net-based methods for converting radiances from' the ground-based microwave
radiometer into T^ profiles and for converting satellite radiances into temperature and dewixtint
profiles (e.g., Bustamante et al. 1994).

Merging algorithms are described in Cogan and Izaguirre (1993) and their references. Ground-
based systems provide detailed soundings for the lower troposphere, while a satellite sounder
covers the atmosphere from about 2 or 3 km up to >30 km. Profiles from ground-based
systems are combined to form a single, multivariable sounding. The satellite sounding is
weighted relative to the MPS location and time and merged with the ground-based sounding.
Normally, satellite and ground-based profiles overlap; if not, satellite data for each variable
are extrapolated down to the uppermost level of each ground-based profile. For each variable,
routines within the merging program adjust the satellite profiles starting at the satellite sounding
level immediately above the highest level of each ground-based profile. The merged profiles
are entered in a single file to form a combined sounding.

222

4. DATA

This section presents samples of data acquired at WSMR, NM and Ft. Sill, OK. Figure 1
presents a plot from the Ft. Sill data of radar profiler winds in the form of smndard wind barbs
in which speeds are in meters per second instead of knots. The abscissa is time in hours UTC,
from 1130 to 1330 on 15 September 1994. The included scale (original in color) at the bottom
of the chart also gives an indication of wind speed. Radar wind profiles appear every 15 min,
but satellite wind profiles generally are available every 2 to 6 h. For each satellite pass, the
same satellite sounding is input into the processing program until the next satellite pass or until
the current satellite sounding reaches a maximum time staleness (e.g., 6 h). Adjustments to
the satellite winds are only slight as a consequence of the very light wind at uppermost radar
heights (about 4 km in figure 1).

' /

aHe: Fi. Sill _ WINDS inainj ment: Cogon Intaqrolion

A A

r r
r f

i .

[

A A

r r
r f

rf '/ 1 '/ '/ '/ '/

Figure 1. Time-height display of combined wind velocity
profiles derived from radar profiler and satellite data.

The TD MPS can generate a variety of useful profiles. Figures 2 through 7 show profiles of
wind (15 and 3 min) and temperature (15 and 3 min) as well as 3-min graphs of vertical
velocity and an indicator of error (error estimate). Figure 2 presents 15-min wind profiles
from the 924-MHz radar profiler from 1600 to 2000 UTC on 29 July 1994. The surface layer
(up to 1 km) shows light and variable winds capped by a layer of westerly winds around 5 m/s
that gradually veer with height reaching speeds near 10 m/s from the northeast at about 4 km.
Toward the end of this period the wind becomes variable around 2 to 3 km. Figure 3 shows
3-min winds for part of the period of figure 2 (i.e., 1830 to 1930). The general pattern is
roughly the same as for the 15-min winds for the same period even though more variability is

223

apparent (e.g., more variable wind direction and changes in maximum height). Particularly
noticeable is the zone of missing data around 1920. It did not appear in figure 2 because the
15-min average displays a sounding if at least one 3-min profile occurs within the averaging
period. This smoothing effect also is apparent in the soundings from the RASS. The
15-min averages (figure 4) smoothed the 3-min values (figure 5), especially near the surface,
eliminating some holes in the data created by removal of questionable values by the quality
control algorithm. Figures 4 and 5 show useful RASS data to about 1.5 km. Under
unfavorable conditions the maximum height may reach only up to 0.7 to 1.0 km.

224

from the 924-MHz radar profiler.

225

Virtuol 1

ent. Wind Profiler

Figure 5. Time-height display of 3-min profiles of profiles
from the RASS.

Figure 6. Time-height display of 3-min vertical velocity
proHles.

226

aKe; While Sends Miaale Renqe _ Error EaUmotc fm/s) Inalrurnent: Wind Profiler

Figure 7. Time-height display of 3-min profiles of error
estimate.

In the TD MPS the software computes and displays two other usefiil quantities, vertical
velocity (figure 6) and error estimate (figure 7). Vertical velocity in this system is computed
from the four oblique beams. The vertical velocity may be used in investigations of
atmospheric dynamics, as well as providing a correction to the RASS measurements of T^.
More details on the algorithm may be found in Weurtz et al. (1988). Figure 7 presents a
measure of the error in the computed horizontal wind in the form of error estimates
(Weurtz et al. 1988). These estimates primarily indicate the nonuniformity of the wind at the
specified height interval over the averaging period (e.g., 100 m and 3 min). For example, the
radial wind velocity from the north beam should be of equal magnitude and opposite sign of
that from the south beam for a uniform wind field in a particular layer. Normally, the
assumption of uniformity is not exact, especially at higher altitudes, but in the absence of
strong convection should be close enough to allow useful measurements. If the error estimate
is high relative to the horizontal wind speed, the user at least knows that the measurement at
that height and time is unreliable.

The profiles presented in figures 2 through 5 suggest that for many atmospheric situations
15 min averaged profiles may be sufficient for certain applications in which relatively small,
very short term changes are not critical. An example would be routine analyses and forecasts
for mesoscale areas. However, for applications such as detection of hazardous winds at
airfields, defense against chemical attack, or fighting forest fires, 3-min profiles may be
extremely important. Szymber et al. (1994) present some applications for operations other than
war that require profiles with a very short refresh time.

227

5. COMPARISONS

Ty profiles from RASS, satellite, and combined RASS and satellite were compared with
soundings from rawinsonde using the LAFRE data. A limited set of comparisons for 3 days
indicated standard deviations of differences (sdd) between combined soundings and rawinsondes
of around 1.5 to 2.8 K. The sdd for the combined sounding was lower for the satellite alone
but usually higher for the RASS. The magnitudes of the mean differences (mmd) for combined
soundings relative to rawinsondes were <0.7 K. The one case of large sdd (2.8 K) and mmd
(0.7 K) may, in part, be a result of the large sdd and mmd of the satellite profile (2.4 and
2.8 K, respectively). The sdd and mmd for radar profiler wind speeds were in line with
values reported in the literature (Miers et al. 1992), about 1.5 to 2.5 m/s and <1 m/s,
respectively . Wind speeds derived from satellite data relative to those from rawinsonde varied
widely depending on atmospheric conditions and time and distance from the ground-based
sounding, ranging from about 3 to 4 m/s to over 15 m/s. Wind direction differences varied
from around 10° to over 70°. These differences are in line with values found in
Miers et al. (1992) and others. A possible method for reducing differences in wind speed and
direction is the use of an analysis model to produce a satellite sounding at the location (and
possibly time) of the ground-based profiles (e.g., Caracena, 1992).

To gain an idea of the quality of the rawinsonde data, wind soundings from two similar
systems receiving data from one sonde were compared: (1) MARWIN and (2) Cross Chain
Loran Atmospheric Sounding System (CLASS). Usually, wind speeds and directions tracked
each another within 1 m/s and 10°. However, poor agreement occurred occasionally, with as
much as 2 to 3 m/s and 70° for 100-m layers. A possible partial explanation is that the
MARWIN software has more extensive built-in checks and somewhat smooths the data.
Nevertheless, the user should make sure each sounding contains valid data and apply
appropriate quality controls.

6. CONCLUSION

The TD MPS is a mobile system that combines the capabilities of an array of remote sensors
to provide atmospheric soundings with a rapid refresh rate that can greatly reduce the error
caused by time staleness. The MPS is a true dual-use system, capable of providing data that
have a variety of applications. The rapid refresh capability is of great value for fire support,
airfield operations, and chemical and biological defense. The ability to generate a picture of
very short term flow and T^ patterns can lead to a better understanding of the atmosphere and
to better modeling at smaller scales. As shown in the LAFRE, this system can be invaluable
for pollution studies. Use of the MPS, especially if tied to prognostic models, could help
reduce damage from forest fires, and lower the cost of fighting them in both lives and material.

228

7. REFERENCES

Bustamante, D., A. Dudenhoffer, and J. Cogan, 1994. "Neural Network Derived Thermal
Profiles: Analysis and Comparison with Rawinsonde Data." In Proceedings of the
1994 Battlefield Atmospherics Conference, White Sands Missile Range, NM, (in press).

Caracena, F. 1992. "The Use of Analytic Approximations in Providing Meteorological Data
for Artillery." In Proceedings of the 1992 Battlefield Atmospherics Conference, Ft.
Bliss, TX, pp 189-198.

Cogan, J., and A. Izaguirre, 1993. A Preliminary Method for Atmospheric Soundings in Near
Real Time Using Satellite and Ground Based Remotely Sensed Data. ARL-TR-240,
U.S. Army Research Laboratory, White Sands Missile Range, NM.

Hassel, N., and E. Hudson, 1989. "The Wind Profiler for the NOAA Demonstration Network.
Instruments and Observing Methods Rep. No. 35." At Fourth WMO Technical
Conference on Instruments and Methods of Observation. (TE-CIMO-IV), Brussels,
WMO/TD, pp 261-266.

Merritt, D.A., 1994. "A Statistical Averaging Method for Wind Profiler Doppler Spectra."
J. Atmos. Ocean. Tech., submitted.

Miers, B., J. Cogan, and R. Szymber, 1992. A Review of Selected Remote Sensor
Measurements of Temperature, Wind, and Moisture, and Comparison to Rawinsonde
Measurements. ASL-TR-0315, U.S. Army Atmospheric Sciences Laboratory, White
Sands Missile Range, NM.

Strauch, R. G., B. L. Weber, A. S. Frisch, C. G. Little, D. A. Merritt, K. P. Moran, and
D. C. Welsh, 1987. "The Precision and Relative Accuracy of Profiler Wind
Measurements." J. Atmos. Oceanic TechnoL, 4:563-571.

Szymber, R. J., M. A. Seagraves, J. L.Cogan, and O. M. Johnson, 1994. "Owning the
Weather: It Isn’t Just for Wartime Operations." In Proceedings of the 1994 Battlefield
Atmospherics Conference, White Sands Missile Range, NM, (in press).

Weber, B. L., and D. B. Weurtz, 1990. "Comparisons of Rawinsonde and Wind Profiler
Measurements." J. Atmos. Oceanic Technol, 7:157-174.

Weurtz, D. B., B. L. Weber, R. G. Strauch, A. S. Frisch, C. G. Little, D. A. Merritt, K.
P. Moran, and D. C. Welsh, 1988. "Effects of Precipitation on UHF Wind Profiler
Measurements." J. Atmos. Ocean. Tech., 5:450-465.

229

CHARACTERIZING THE MEASURED PERFORMANCE OF CAAM

Abel J. Blanco

Army Research Laboratory
Battlefield Environment Directorate
White Sands Missile Range, New Mexico 88002-5501, USA

ABSTRACT

The Computer Assisted Artillery Meteorology (CAAM) provides a
proposed artillery meteorological (MET) message that can
significantly improve predicted artillery fire. The CAAM design
allows the artillery commander to use tailored MET messages
computed by an advanced physics model using recent MET data input
rather than his stale dedicated station message for adjusting a
first-round-hit artillery fire mission. This paper presents two
important kinds of estimates describing the performance of CAAM
using data collected in the desert and mountains of southern New
Mexico. These include the best single estimate and the
confidence interval estimate derived from measured upper air data
versus nowcasted and forecasted results. In complex terrain the
confidence interval improves with the number of available
initializing MET stations. Simulated cannon impact displacements
effected by wind, virtual temperature, and pressure parameters
are tabulated for the evaluation of an analytical objective
analysis algorithm and a physical, time dependent, three-
dimensional hydrodynamic forecasting model used in CAAM.

1. INTRODUCTION

The Computer Assisted Artillery Meteorology (CAAM) research was designed
to include a two phase approach. The first phase included the solution for
the immediate need of improving the accuracy of the current cannon/rocket
systems and the developmental long range weapon systems. A short suspense
for supporting the actual firings of an engineering development weapon system
led to the design and implementation of the Time Space Weighted (TSW) CAAM.
This proposal is described as a met message manager, "nowcasting" technique
(Blanco, etc., 1993). Based on centralizing all available met data, this
objective analysis algorithm automatically tailors a best met message for a
particular user. Through a peer review including the Army designer, the
weapon development contractors, and the Army Research Laboratory (ARL) , the
Project Office selected the TSW algorithm from available proposals. The
selected technology was portable and required a PC environment. This

231

methodology derives the expected met variability due to time staleness and
space separation of the collected data. It follows the same science applied
by the U.S. Army Materiel System Analysis Activity (AMSAA) in their
development of the Cannon Artillery Delivery Accuracy Model (CADAM) which is
been used to defined met accuracy requirements in the development of the new
weapon systems (Reichelderfer , etc. 1993). The TSW assigns weights to the
available field data and takes advantage of applying functional relationships
such that the time staleness follows more important role than the space
separation in computing the best met to be disseminated.

The other phase, a longer term, proposal involves a time dependent,
three dimensional Higher Order Turbulence Model for Atmospheric Circulations
(HOimc) CAAM. Because of the complexity of this model the required platform
consists of a HP 9000 Series 750 computer. The HOTMAC CAAM computer runs a
complex suite of software that will manage all tactical commimications and
data sharing. HOTMAC is a computer code that forecasts wind, virtual
temperature, and pressure over complex surface conditions. This model is
based on a set of second-moment turbulence equations and can be used under
quite general conditions of flow and thermal stratification. Effects of
short and long wave solar radiation, tall tree canopies, and topography are
included in the model. The surface temperatures are computed from a heat
conduction equation for the soil and a heat energy balance equation at the
surface. The model assumes hydrostatic equilibrium and uses the Boussinesq
approximation. A terrain-following vertical coordinate system is used in
order to increase the accuracy in the treatment of surface boundary
conditions. Vegetation plays an active role in the apportionment of
available heat energy between convective (sensible and latent) and conductive
(into the soil) components.

Based on the U.S. Army Field Artillery School requirements and funding
from the Project Manager, Electronic Warfare, Reconnaissance, Surveillance,
Target Acquisition, ARL defined and monitored the development of two research
proto-type systems that allow significant meteorological (met) accuracy
improvements for the field artillery. These systems provide state-of-the-art
software and hardware developments that allow automated field data
integration, meteorological modeling, and dissemination techniques. The Fire
Control Centers (FCC) are automatically refreshed with met data that can
enhance the first roimd hit capability for predicted fire. No longer will
the FCC be delayed in waiting for final met adjustments because CAAM will
automatically refresh met messages for the particular user. The TSW CAAM was
developed in-house (Vidal etc., 1994); and the HOTMAC CAAM was developed by
the Physical Science Laboratory under contract with the ARL, Battlefield
Environment Directorate (Spalding etc. 1993).

This report defines a methodology that demonstrates the worth of the
proposed CAAM models. Available met data from White Sands Missile Range, New
Mexico, has been utilized to test and evaluate CAAM performance. To
qualitatively demonstrate the long term capability, data from the Target Area
Meteorology Data Experiment (TAMDE) conducted during July - September 1992,
is used^ to derive a spatial analysis for a 60 by 220 km area. The CAAM
forecasting performance capability is also evaluated with this data set. The
other set of data collected during the Proto-type Artillery Sub-System (PASS)
Field Experiment conducted within the same area during November and December
1974 is used to quantitatively reveal the significant improvement over the
current doctrinal method of adjusting artillery fire for met variations.
Simulated lS5mm rocket assisted round impact displacements are tabulated and

232

analyzed results are graphically presented to demonstrate the significant
improvement afforded by each proposal. This improvement enhances the
predicted fire accuracy such that the new capability approaches the accuracy
afforded by registration/transfer fire techniques. Both proto-type systems
are automatic and all modeling input and output are transparent to the
operator except for met data editing and final recommendation to disseminate
the best met messages. The systems were designed so that a high school
graduate can effectively operate and efficiently interact with the FCC.

2. STATEMENT OF PROBLEM

All effective artillery fire includes meteorological (MET) aiming
adjustments to compensate for the variations of atmospheric wind, temperature
and density. Many times the current doctrine of utilizing data from a
dedicated met station is not representative of the actual met effects
experienced by unguided projectiles. This has become most noticeable for
extended range artillery. The classical mechanics for predicting unguided
projectile trajectories are well known and automated at artillery FCC. Under
standard conditions, this simulation science is assumed to be exact. Since
the atmospheric conditions are rarely standard, the U.S. Amy Field Artillery
deploys met teams to measure atmospheric conditions within the battle area.
These teams are not co- located with the artillery fire systems, and the
balloon-borne sensor may drift away or towards the point of application
depending on the general wind flow within the battle area.

MET data time staleness must be significantly reduced if the artillery
commander is to maintain effective predictive fire for future long-range
targets. CAAM provides the field artillery with inexpensive techniques for
automating representative met corrections by retrieving, analyzing, and
disseminating best met data. All battlefield MET messages are received
through existing tactical field equipment. These messages are cataloged with
respect to time staleness and space separation from the point of application.
The CAAM design allows the artillery commander to use tailored MET messages
computed by an objective analysis or an advanced physics model using recent
MET data input rather than the normally stale dedicated station message.
This derived message enhances the first round hit probability for current and
future artillery fire systems. Aiming adjustments will accurately deliver
carrier projectiles and compensate for met effects on target area parachute
delivered sub -munit ions , scan and search patterns, chemical bursts, and wind
gliding warheads. CAAM provides a proposed artillery MET message that can
significantly improve predicted artillery fire. With the CAAM the field
commander can review simulated results revealing his expected artillery
accuracy before his mission engagement.

3. EVALUATION METHODOLOGY

The two data bases utilized in the comparison evaluation are the
following: the 1992 TAMDE (Grace, 1993) and the 1974 PASS (Blanco etc.,
1976). The tactical scenario addressed was a battle area covering 60 by 220
km. The emphasis was placed on a 60 by 40 km area corresponding to a more
representative application of cannon/rocket artillery. Both data base
experiments were conducted to help define meteorolgical effects on unguided

233

projectiles. Temporal and spatial variability of atmospheric conditions were
the focus in these programs. The data bases may not be representative of
other climates and regions, but their uniqueness is that the sets contain
simultaneous upper air sounding from as much as nine stations with artillery
computer messages simultaneously collected at 2 hours intervals, over a time
of as much as ten hours, with the start and ending times varying with each
day. The TAMDE data base is used to qualitatively demonstrate the analysis
over a 60 by 220 km area that includes complex terrain. It is also used to
demonstrate the forecasting capabilities.

Paired (measure/estimate) statistics is used in quantitatively comparing
the accuracy and confidence limits in evaluating the worth of proposed CAAM
solutions for improving the artillery accuracy. The emphasis is placed on
the artillery miss instead of the actual met parameter. CAAM does not have
to exactly predict the weather conditions, but it is designed to accurately
predict artillery fire accuracy. The projectile's weight, velocity, and
flight time determine the met effect it experiences along the trajectory.
For example, the fine weather conditions that effect smoke particles have
minor effects in aiming a 95 lbs artillery shell. But understand that the
met IS a major contributor in the total artillery error budget, and that the
gross 0.2 - 2.0 km averages are used in adjusting artillery fire for met
variability.

oAA« actual artillery firings have not been completed with the proposed

CAAM solutions, expected accuracies are simulated using a demonstrator
Battery Computer System (BCS) fire control. For example, a measured met
message and a 27 km target range firing problem are inputed to the BCS, and
the aiming solution is then assumed to represent the "truth" impact.

Following a similar procedure using the derived aiming angles and the

estimated met message, a new impact is computed. If the estimated and
measured met messages are the same then the computed impacts should be

Identical. A bad estimated met message should then produce a large impact

difference from the one derived by using the measured met message. The best
solution IS identified when the paired accuracy difference is equal to zero
or the difference is well within the lethal radius of the delivered warhead
Note that the individual difference is derived from the comparison between

computed impacts using the estimated (nowcasted TSW or the forecasted
HOTMAC) and the measured met messages.

A day from each of the met data base experiments is selected to describe
the worth of the proposed C.^ solutions. All data days will be analyzed and
results will be documented in the final report. The purpose of this report
IS to present preliminary results and provide the status for the on-going
applied research. The most variable weather day from each experiment was
selected to reveal a maximum improvement. September 2, 1992 is selected to
present the HOTMAC spatial analysis and forecasting capability over a 60 by
220 km, complex terrain, battlefield area. December 7, 1974 is used to

present the expected improvement afforded by the HOTMAC and TSW CAAM
solutions .

With this constraint the sample size is limited to analysis of computer
met messages simultaneously collected at 2 hours intervals over a time of ten
hours. Table 1 presents the met message pairing used in deriving time
staleness results for the current doctrine of adjusting artillery fire to
compensate for met variability. Table 2 presents the met message pairing
used in deriving the TSW estimates. For example, if station 1 is considered
the truth station, then the following staggered releases define the expected

234

time staleness: to estimate Stn 1 at 0715 use Stn 1 at 0515 and Stn 2 at
0715; and to estimate Stn 1 at 0915 use the above releases plus Stn 3 at
0915. TSW always uses a fresh message except for the case of simulating six
hour staleness referenced to Stn 1. In reality at the sixth hour the
commander using Stn 1 would have realtime data because the release cycle is
repeated maintaining a new message every 2 hours among the three available
stations. The same cycle is repeated to derive other replicates defining the
sample size used in this preliminary analysis. One can start at 0715 and
pair messages to increase the replicate size. All HOTMAC estimates are
derived from the first, 0515, met message. The hourly forecasts are
dependent on only one message. The same cycle is repeated to derive other
replicates defining the sample size; for example use the 0715 to forecast,
then use the 0915 to forecast, etc..

Table 1. Pairing met messages for deriving time staleness sample size.

Message

Staleness(h)

Local time

0515

0715

0915

1115

1315

1515

Total

Sample

Table 2. Pairing met messages for deriving TSW estimates.

Local time
Stn 1
0515
0715
0915
1115

Staggered Releases
Stn 1 Stn 2 Stn 3
X

Time Staleness
2 h 4 h 6 h

0715

0915

1115

1315

0915

1115

1315

1515

Total

333 Sample

235

4. PERFORMANCE CHARACTERISTICS

The first performance check is on HOTMAC CAAM's ability to provide a
qualitative spatial analysis for an area 60 by 220 km using only one
initializing met message. Frame 1 on figure 1 presents the contour map for
the desert and mountainous region. The reporting stations are identified as
circles within the simulated battle area. Notice that the open circle
located at the lower left corner at about the (6,8) coordinates indicates the
station used to start HOTMAC forecasting hourly computer met messages for the
entire area. The darken circles represent the location of the other stations
used to evaluate the accuracy of the computed estimates for each of the met
parameters effecting the artillery accuracy. The index represents a
normalized 4 km grided universal transverse mercator unit internal to CAAM.
All^ terrain features are retrieved at this interval; however, CAAM is
designed to compute the met message on an 8km grid. Each complete square in
the map represents 40 by 40 km and the entire area contains more than the 60
by 220 km requirement. In this case the required area is oriented due north
but CAAM has the capability to rotate the desired area in any direction.

Using the September 2, 1992 (fifth day - TAMS) data, frame 2 displays
the wind vector plot for the terrain following 1227 m level. Note the
speeding up of the wind at the location of the elevated terrain. The wind
starts as westerly with changing direction as it travels through the mountain
canyons. The current doctrine assumes that the met message collected at the
open circle location is representative of the entire area. One can realize
the CAAM improvement is already significant. Frame 3 reveals a more
representative description because there are three open circles representing
three met messages initializing the HOTMAC CAAM. In this case the two
darkened circles represent available data for comparison with the generated
estimates at the corresponding locations. Examining the station at about
coordinate (14,32), one can see that the one station run over estimates the
wind speed and predicts the wrong direction. The mountain provides a
stronger sheltering than the model predicted. Frame 4 represents the most
realistic description of the wind. Here all stations are used to initialize
CAAM and simulate the effect of dropsondes in the target area. This case
study reveals that space interpolation results contain the best confidence in
the estimated results. An application for knowing the target area winds is
the management of air delivery of supplies or personnel to a particular
sector within the battle area.

The other qualitative performance check on CAAM is how well can it
forecast. Figure 2 presents the terrain contour, 0600, and 0900 wind vector
plots. Generally, CAAM estimates a persistence forecast and for this case
the wind field was indeed following this same pattern. The pattern is much
smoother at 0900 than at the initialization time at 0600. In the following
section one can quantitatively see that the forecasting capability is
significantly better than the current doctrine of using stale data that may
be as much as 6 hours old.

5. EXPECTED ARTILLERY IMPROVEMENTS

Using the December 7, 1974 (julian day 341) data from the other met data
base one can quantify the improvement afforded by the two proposed CAAM
solutions. Figure 3 presents the station locations on a 20 km grid. Using

236

Figure 1. Terrain contour and wind vectors special analysis.

. \ W i i f f /
^ / c y / / / /

\ \ \ \
k k k k\
M ♦ ♦ ♦

I 1 ! I ( M M 4 M M M M / ^

\ { { "t n \ \ M y y 4 4 y 4 4 4 M / /

mmm'4

apu] Buil]1JO[^i

the Table 1 scenario, the stations are identified as follows: Stn 1 is tsx;
Stn 2 is oro; and Stn 3 is meg. The Table 2 staggered releases are followed
to predict the actual met messages measured at tsx. To compute the current
met accuracy one pairs the appropriate met messages outlined in Table 1 and
inputs into the BCS to compute a firing angle solution for a 155mm rocket
assisted round fired at a 27 km range target. After completing the
differences from the simulated impacts as described in the above section,
Table 3 presents the comparison results. ’

Table 3. The tsx delta BCS output for jday 341.

current doctrine tsx analysis

stale(h)

pairs

Range (m)

Cross (m)

QE(mil)

0715

0515

116

547.2

0915

0515

1115

0515

228

112

0915

0715

-59

-44

541.9

1115

0715

111

1315

0715

333

133

1115

0915

174

544.7

1315

0915

397

181

1515

0915

452

254

1315

1115

220

537.0

1515

1115

274

150

1515

1315

527.6

0715

HOTMAC tsx0515
0515 -68

forecasting tsx

-40 541.9

0915

0515

-35

8 544.7

1115

0515

-210

-83 537.0

TSW_tom nowcasting tsx

0715

-32

541.9

0915

544,7

1115

0915

-92

-79

537.0

Note that the deltas for the two hour staleness varies with the time of
the day. For the range component the smallest variabilities are listed as
occurring during the 0915-0715 and 1515-1315 periods. The -59m and the 55m
represent the expected error for firing at 0915 and 1515 with met aiming
adjustments from 0715 and 1315. Another observation is that the results for
one case reveal the four hour stale data (0915-0515) providing more accurate
results than the two hour stale data (0715-0515). This is the behavior of
the weather; it is unpredictable and never standard. As we group the results

240

in a small sample and derive the mean and standard deviation, the results can
be presented in another arrangement. The root -mean- square values are fitted
to a fxmction of the time staleness raised to the one-half power. Figure 4
presents the statistical results and demonstrates the accuracy of the fit
with the data located on the derived curve. The solid line curve represents
the accuracy of the current doctrine. If one aims with two hour stale data,
one can expect about 150m miss in the range and about 75m miss in the cross.
The other two curves represent the expected accuracy afforded by the two
proposals: HOTMAC using one met message at the start of the day, and TSW
using all messages available from the staggered balloon releases. The
scenario presented in Table 2 is used in deriving the Figure 4 results. TSW
is always using current data collected at the other stations except for the
six hour staleness.

Table 4. The hms delta BCS output for jday 341.

current doctrine hms analysis

stale(h)

pairs

Range (m)

Cross (m)

QE(mil)

0745

0545

544.6

0945

0545

133

1145

0545

261

0945

0745

540.8

1145

0745

174

1345

0745

287

170

1145

0945

122

538.5

1345

0945

232

146

1545

0945

331

230

1345

1145

105

533.3

1545

1145

208

174

1545

1345

106

529.1

HOTMAC

tsx0515

forecasting hms

0745

0515"

-88

30 540.8

0945

0515

-150

15 538.5

1145

0515

-269

-34 533.3

TSW tom nowcasting hms

0745

0715

-7

540.8

0945

0915

-42

538.5

1145

0915

-162

-40

533.3

Table 4 present the results for estimating met data at the hms station
from using data from the tsx, oro, and meg stations. Again the HOTMAC using
only one station and TSW using the staggered release schedule reveal a
significant improvement over that expected from the current method of using a

241

dedicated met station that may provide 6 hour stale data. Figure 5 presents
the graphical comparison that used historical met data to estimate the met at
a 50 km location. Comparing figure 4 and 5 reveals that the range (wind,
temperature, and pressure) variability at hms is lower than that at tsx.
Note that the solid line curves are derived from actual measurements at each
station as defined in Table 1. The other curves represent how well the
estimates do in predicting the actual measurements. A general conclusion is
that the two proposals do better when estimating closer to where the
initializing data are collected. The cross component comparison in Figure 5
indicates that the HOTMAC temperature and pressure forecasts are not as
accurate as those esimated by TSW using the 0715 and 0915 observations.
Perhaps the single station HOTMAC can be improved by using surface
observation in order to adjust for large changes in the pressure due to
fronts moving in. TSW estimates at a distance greater than 50 km reveal a
significant improvement over the current doctrine of using stale data from a
dedicated station.

6. SUMMARY

The ARL, Battlefield Environment Directorate, has completed the
development of two proto- type CAAM systems. The performance of the CAAM two
phase approach has been qualitatively characterized for spatial analysis and
forecasting. For a single, variable weather, day quantitative results have
been tabulated. Graphical comparisons reveal impressive results. The TSW is
already implemented in the software of an engineering development fire
control weapon system. It is also under review and evaluation for inclusion
in future FCC. This preliminary analysis presents results that quantify the
improvement afforded by TSW under variable met conditions. The TSW
improvement is significant and the algorithm is portable and requires no
change to the actual tactical procedure in adjusting artillery fire for met
variability. There are other days that reveal no significant improvement
because of the homogeneity of the weather; the wind remains strong and
persistent in its direction through the day. For very light wind days the
expected improvement is also insignificant. For example under these
conditions the time staleness takes a minor role because the four hour old
message continues to be a good estimate of the present weather conditions.
The final report will document all cases from available TAMDE and PASS data
and present the general capability of TSW. Based on the on-going research
ARL is already compiling a list of improvements for TSW.

The HOTMAC using a single met message has been demonstrated to
significantly improve upon the current method of adjusting artillery fire.
In the same variable met day that TSW was evaluated, HOTMAC revealed
impressive results. The added advantage is that the staggard release met
message schedule is not required in enhancing the first round hit capability.
This means that the artillery commander can continue with his dedicated met
station and update or forecast his met message until he receives fresh met
data. However, this is not the case for the spatial analysis for the 60 by
220 km. One needs more initialization observations in order to obtain
accurate results. If the application is over a time when no fronts are
passing then the single message initialization may yield acceptable results.
Many areas of improvement have been identified and the final report will

242

document the status and future plans for HOTMAC. This approach in the CAAM
research has a longer implementation schedule.

Actual artillery firing using these two proposals are being planned.
The quick implementation of TSW was accepted by the weapon system developer
in order to show the required accuracy during the initial system technical
demonstration. The on-going research findings can easily be incorporated by
installing the new revision into the weapon's fire control subroutine. The
CAAM specifications, requirements, and design were established to allow
portable revision control. Because of the modular development, the HOTMAC
can be the final revision of CAAM by replacing TSW, the intermediate CAAM
solution.

REFERENCES

1. Blanco, Abel, Edward Vidal, and Sean D'Arcy, 1993: "Time and space
weighted computer assisted artillery message" , Proceeding of the 1993
Battlefield Atmospherics Conference, Army Research Lab, WSMR, NM.

2. Blanco, A. J. and L. E. Traylor, 1976: " Artillery meteorological
analysis of project PASS," ECOM-5804, U.S. Army Atmospheric Sciences
Laboratory, WSMR, NM

3. Grace, John, 1993: "TAMDE - The variability of weather over an army
division size area," Proceeding of the 1993 Battlefield Atmospherics
Conference, Army Research Lab, WSMR, NM.

4. Reichelderfer , Magan and Craig Barker, 1993: "155 -mm howitzer
accuracy and effectiveness analysis". Note DN-G-32, U.S. Army
Materiel System Analysis Activity, Aberdeen Proving Ground, MD.

5. Spalding, John B. , Natalie G. Kellner, and Robert S. Bonner, 1993:
"Computer-assisted artillery meteorology system design". Proceeding
of the 1993 Battlefield Atmospherics Conference, Army Research Lab,
WSMR, NM.

6. Vidal, Edward, 1994: Personal communication. Army Research
Laboratory, Battlefield Environment Directorate, WSMR, NM.

7. Yamada, T. , and S. Btmker, 1989: "A nximerical Model Study of
Nocturnal Drainage Flows with Strong Wind and Temperature Gradients",
J. Appl. Meteorol., 28:545-554.

243

EVALUATION OF THE BATTLESCALE FORECAST MODEL (BFM)

T. Henmi and M. E. Lee
U.S. Army Research Laboratory
White Sands Missile Range, NM 88002-5501, USA

T. J. Smith

Operating Location N, Air Weather Service
White Sands Missile Range, NM 88002-5501 , USA

ABSTRACT

The performance of the Battlescale Forecast Model (BFM), developed at the
U.S. Army Research Laboratory (ARL) to produce an operational short-range
( < 12 h) mesoscale forecast, is evaluated. The model test domain centers on
the White Sands Missile Range (WSMR), NM where observation data from
Surface Automated Meteorological System (SAMS) 10-m towers and
Atmospheric Profiler Research Facility (APRF) profilers are available. Three
different initialization approaches are examined to identify optimal model
initialization methods. Statistical parameters such as mean residual and
standard deviation of residual are calculated for hourly forecast fields of
surface wind and temperature from comparisons of corresponding observations
and twenty five 12-h forecast calculations. Results indicate that incorporation
of surface wind observation data into the initial field is essential to produce
good, short-range BFM forecasts.

1. INTRODUCTION

The Army Research Laboratory (ARL) developed the Battlescale Forecast Model (BFM) to
produce an operational, short-range ( < 12 h) forecast over an area of < 5(X) x 500 km. The
BFM will become a major component of the Integrated Meteorological System (IMETS)
Block 2 software. The BFM is composed of two major programs. A program called 3DO^
creates initial and boundary values for the forecast model by processing selected U.S. Air
Force Global Spectral Model (GSM) forecast field output data, and/or upper-air sounding and
surface observation data, if available. The BFM was adapted from a mesoscale
meteorological model called the Higher Order Turbulence Model for Atmospheric Circulation
(HOTMAC) (Yamada, Bunker 1989). HOTMAC has been used extensively at ARL
(Henmi et al. 1987; Henmi 1990; 1992) to simulate the evolution of locally forced
circulations caused by surface heating and cooling over meso-jS and 7 scale areas. HOTMAC
is numerically stable and easy to use, and thus suitable for operational use. Details of the
BFM are described in Henmi et al. (1993; 1994).

245

In this study, the forecasting capability of the BFM is evaluated by comparing forecast results
with surface and upper-air data observed by the White Sands Missile Range (WSMR) Surface
Automated Meteorological System (SAMS) and Atmospheric Profiler Research Facility
(i^RF) profilers, respectively. To find an appropriate method to initialize the model, three
initialization methods were selected, and 25 comparisons between 12-h forecasts and
observations were made at hourly intervals. The purpose of this paper is to describe the three
initialization methods and the method evaluation results.

2. MODEL DOMAIN AND OBSERVED DATA

The study area centered on WSMR, NM. Figure 1 shows the terrain elevation distribution
of the selwted BFM domain, covering a 250 x 250 km area. The latitude and longitude of
the domain are 33.20“ N and 106.41“ W, respectively. Meteorological variables are
calculated at 51 x 51 horizontal grid points x 16 vertical grid points with a unit horizontal
grid distance of 5 km. The upper atmosphere model boundary is 7000 m above the highest
surface terrain elevation in the domain. The locations of selected WSMR SAMS sites are
marked by Arabic numbers in figure 1.

Figure 1. Selected WSMR BFM model domain (250 x
250 km). Contour lines are drawn every 200 m. The
locations of SAMS sites are marked by Arabic numbers.

GSM output are reported on grid points spaced 381 km apart on mandatory pressure surfaces.
A three-dimensional objective analysis of GSM data is made over an area covering
800 X 800 km centered on the BFM domain.

246

Twelve-hour forecast computations producing hourly outputs were made for 25 cases ^l^ted
from the months of February and I^ch 1994. Hourly averaged values of surface wmd and
temperature were used for comparison.

3. INITIALIZATION METHODS

Based on the case study reported in Henmi et al. (1994), the BFM was initialized by toee
different methods described in sections 3.1 through 3.3. Additionally, two computed data,
mentioned in sections 3.4 and 3.5, are compared with observations.

3.1 Initializatioa Using GSM

GSM uses a normalized pressure a = p/p, vertical coordinate. GSM analysis and 12-hour
forecast values of horizontal wind components, temperature, dew-point depression, and
geopotential height on mandatory pressure levels were used to produce three-dimensional
fields for BFM initialization and time-dependent boundary values.

HOTMAC uses a z* vertical coordinate, and is defined in the following manner:

z* = H

(1)

where

z* = the transformed vertical coordinate

z = the Cartesian vertical coordinate

Zj = the ground elevation above mean sea level (MSL)

H = the material surface top of the model

H = the corresponding height in the Cartesian coordinates.

For simplicity, H is defined as

H = W

(2)

where is the maximum value of

247

Because different vertical coordinates are used in GSM and HOTMAC, the following two
steps are needed:

(1) HoriTOntal interpolation of wind components (u,v), temperature, mixing ratio, and
geopotential height froni GSM grid points to BFM grid points on constant pressure surface.
Barnes’ method (1964) is used for horizontal interpolation.

(2) Vertical interpolation of the variables from BFM constant pressure surfaces to z* surfaces
at BFM grid points using a linear interpolation method.

GSM synoptic scale variations of meteorological variables are incorporated into the model
equations by nudging (Hoke, Anthes 1976).

For 12-h forecasting, both the current analysis and the 12-h forecast fields from the GSM are
analyzed using the above method, and hourly data are generated by a linear interpolation
between the two time periods. The first hourly analysis field data are assimilated by using
the nudging method for the hour preceding the initiation of forecast computation. The next
hourly data are assimilated into the forecast 1 h into the forecast period; the process is
repeated hourly over the 12-h forecast period.

Out of 16 vertical layers, nudging was applied only in the 9 upper layers (corresponding to
z heights > than 151 m). ^

3.2 Initialization With GSM and Mean Snrface Wind Direction and Speed

Wind directiotis reduced from GSM data in layers near the surface were frequently and
significantly different from those observed. Thus, to improve the agreement between
computed and observed wind vectors in short-range forecasts, mean surface wind data is
incorporated into initial fields. From all the selected SAMS data obtained over WSMR, mean
surface wind vector components were calculated at the initial time of forecast, and logarithmic
wind profiles were assumed from the surface (z* = 10 m) to the seventh layer (z* = 151 m).
Linear profiles were then interpolated between the 7th to the 10th model layers, above which
only GSM data is used to initialize BFM grid points.

3.3 Nudging of Individual Surface Wind Data at Initial Time

In addition to the m^ surface observation process described in section 3.2, individual SAMS
site wind observation data obtained at initialization times are assimilated into model
calculations at the grid points adjacent to the SAMS locations. The method of surface wind
data assimilation is described in Henmi et al. (1994).

3.4 Surface Data Nudging Every Three Hours

In section 3.3, surface wind data is nudged only at the first hour of model computation. This
method was extended in a way such that SAMS wind data are assimilated into model
(^culations every 3 h. For instance, the data observed at 5, 8, 11, 14, and 17 local standard
time (LST) are nudged for 1 h starting at 4, 7, 10, 13, and 16 LST.

248

3.5 Linear Interpolation of GSM Data

For comparison purposes, the three-dimensional GSM data set, creat^ by the method
described in section 3.1 at two time periods, is linearly interpolated in time, and resulting
data (at hourly intervals) is compared with observation.

4. STATISTICAL PARAMETERS

To examine the differences in the results using methods described in sections 3.1 through 3.5,
the following statistical parameters are calculated hourly by using the data from the 25
different cases.

4.1 Mean Residual

The difference between observed and forecast values of a meteorological parameter can be
written as

p = F

res obs for

where F represents a meteorological parameter and and represent residual,

observation, and forecast, respectively.

A mean residual for 12 forecast hours is defined as

E. E,

wxn

(4)

where ni represents the number of forecast cases, and n represents the number of SAMS data
at forecast time t.

4.2 Standard Deviation of Residual

The standard deviation of residual of a meteorological parameter is defined as

mxn

(5)

where F^Jt) is the standard deviation of residual at forecast time t.

Improved forecast calculations result in mean residuals converging to zero in conjunction with
smaller standard deviations of residual. Perfect agreement between observation and forecast
results in zero values for both parameters.

249

5. RESULTS

In figures 2 through 4, the mean residuals (mean curves) and standard deviations (upper- and
lower-bound curves) are plotted as a function of time.

(b)

Figure 2. Temporal variations of mean residual (mean curves) and standard deviation (upper-
and lower-bound curves) for methods in sections 3.1(a) and 3.2(b). Upper plots represent
the surface x wind vector components, middle plots are the y wind vector components, and
bottom plots are of surface temperature.

Comparisons of figures 2 through 4 reveal the following:

(1) Differences between figures 2(a) and 4 indicate that the BFM produced significantly
pproved forecast fields of wind and temperature (section 3.1) compared to the linear
interpolation of GSM data (section 3.5). In gener^, the values of mean residuals and
standard deviations are smaller in figure 2(a) than in figure 4. The physical scheme of the
model produced better agreement with observation data than simple interpolation of GSM data
m time and space.

(2) From figures 2(a) and (b), the initialization using the mean wind speed and direction
(section 3.2) produced better forecast fields than the GSM data initialization (section 3.1).
Substanti^ improvements in both x and y components of wind vectors were obtained. As can

s^n in figure 2(e), the meEn residuEl vElues of both wind vector components were
negative. This means that BFM forecast calculations initialized with GSM data produced

250

larger wind vector components than observed. Conversely, the mean r^idual values in figure
2(b) are much closer to zero, indicating that on average the BFM, using mean wind speed
and direction at model initialization times, produced surface wind vector component
magnitudes similar to those actually observed. Even temperature mean residuals m
figure 2(a) show larger negative values throughout the 12-h forecast calculation Aan m
figure 2(b). Initial temperature fields in the methods in sections 3. 1 and 3.2 are identical for
all 25 simulations. The boundary layer scheme of the BFM produc^ improved temperature
predictions using the method in section 3.2, compared to the method in section 3. 1 . Although
it is not clearly understood, logarithmic profiles of wind components assumed in Ae method
in section 3.2 may coincide to produce good surface temperature profile predictions in the
boundary layer. Further studies are needed to understand this problem.

(3) Comparison between figures 2(b) and 3(a) indicates that nudging the surface wind vector
components at the forecast initial time (section 3.3) produced better forecast results in wind
fields for a few hours during the early stage of calculation, but during the later stage of
calculation the forecast method in section 3.2 produced superior agreement betw^n predicted
and observed parameters. This can be inferred from larger standard deviations in both x Md
y wind components in the last several hours of forecast calculation. Nudging of surface wind
components that are not dynamically balanced with the numerical schemes of the model may
be the reason for the results of the method in section 3.3. Temperature fields show little
differences between the methods in sections 3.2 and 3.3.

251

(4) The method in section 3.4 produced the best agreement between predicted and observed
parameters. In this method, observed wind vector components were assimilated into model
calculations by nudging every 3 h. Figure 3(b) shows smaller standard deviations at 3, 6,
9, and 12 h when the data were nudged during the previous 1 h. It should be noted that,
although the nudging of dynamically unbalanced wind vectors is done repeatedly, the
numerical scheme of the model is stable enough to prevent numerical instability.

for temporal and spatial interpolation of GSM
data (section 3.5).

6. SUMMARY

Comparison of forecast results using the method in section 3.1, with space and time
interpolation of GSM data, clearly shows that the BFM produced substantially improved
forecast fields over methods using a simplistic interpolation of GSM data. Initialization by
methods in sections 3.2 and 3.3 produced further improvement over the method in
section 3.1, confirming that incorporation of observed data into initial fields is important.

In the present study, all the cases simulated were in February and March, 1994, Forecast
fields of moisture were not compared with observation because observed data were not
reliable. In a future study, cases will be simulated for the summer for which the data have
been archived.

252

7. REFERENCES

Barnes, S. L., 1964. "A Technique for Maximizing Details in Numerical Weather Map
Analysis." J. App. Meteor., 5:396-409.

Henmi, T., R. E. Dumais, Jr., and T. J. Smith, 1993. "Operational Short-range Forecast
Model for Battlescale Area." In Proceedings of 1993 Battlefield Atmospherics Conference,
BED, U.S. Army Research Laboratory, White Sands Missile Range, NM, pp 569-578.

Henmi,T., M. Lee, and T. J. Smith, 1994. Evaluation Study of Battlescale Forecast Model
(BFM) using WSMR Observation Data, U.S. Army Research Laboratory, White Sands
Missile Range, NM 88002-5501.

Hoke, J. E., and R. A. Anthes, 1976. "The Initialization of Numerical Models by a
Dynamic-Initialization Technique." Mon. Wea. Rev., 104:1551-1556.

Yamada T., and S. Bunker, 1989. "A Numerical Study of Nocturnal Drainage Flows with
Strong Wind and Temperature Gradients." J. Appl. Meteor., 28:545-554.

253

VERIFICATION AND VALIDATION

OF THE

NIGHT VISION GOGGLE TACTICAL DECISION AID

John R. Elrick

U.S. Army Research Laboratory
Battlefield Environment Directorate
White Sands Missile Range, New Mexico, 88002-5501, USA

ABSTRACT

The night vision goggle (NVG) tactical decision aid (TDA) is a computer
software application used to determine the suitability of NVG use based
on existing or forecast meteorological conditions. The TDA combines
solar and lunar ephemeris data with the general effects of clouds and
precipitation on illumination levels based on the weather data contained in
standard weather observations. This TDA is one of a suite of TDAs that
was delivered in the Block I, Integrated Meteorological System (IMETS)
release to the Program Executive Office Command and Control Systems,

Project Director, IMETS. Although the NVG TDA was acceptance tested
before it was released, it was never formally verified and validated. The
verification and validation (V&V) described here are results of Battlefield
Environment Directorate efforts to include V&V as part of future software
releases to U.S. Army weather support personnel. The accurate, early
identification of software problems and their correction prior to operation^
applications are integral parts of providing physically and theoretically
sound products to the end user. The methods that were used in the V&V
of the NVG TDA are discussed and some of the pertinent findings are
presented.

1. INTRODUCTION

The night vision goggle (NVG) tactical decision aid (TDA) is part of a suite of computer
software applications that is designed to be included in the Integrated Meteorological System
(IMETS). It is a product that will provide battlefield decision makers with detailed
information about the solar and lunar illumination levels at user-specified locations, with
general consideration for cloud cover and precipitation. It was one of the TDAs that was part
of the block I IMETS release from the U.S. Army Research Laboratory’s Battlefield

255

Environment (BE) Directorate to the Program Executive Office Command and Control
Systems (PEO CCS) headquartered at Fort Monmouth, NJ. The block I release was delivered
to the PEO CCS project director (PD) IMETS as the initial step in a three-block transition
process to field an operational IMETS. The final version of the IMETS will be used by U.S.
Air Force Staff Weather Officers (SWOs) to support future Army operations with modem
hardware and software specifically tailored to the concept of a highly mobile fighting force
capable of worldwide deployment. The IMETS will be used at the echelon-above-corps level
down to the separate brigade and special operation force level where SWO support is
necessary and defined by Army doctrine.

2. DEFTNITIONS

The following definitions, taken from Army Regulation 5-11, "Army Model and Simulation
Management Program" (1992), were used in the V«&V of the NVG TDA (M&S in these
definitions refers to model and simulation):

"a. Verification.

(1) Verification is the process of determining that M&S accurately
represent the developer’s conceptual description and specifications.
Verification evaluates the extent to which the M&S has been developed
using sound and established engineering techniques. The verification
process involves identifying and examining the stated and pertinent
unstated assumptions in the M&S, examining interfaces with input data¬
bases, ensuring that source code accurately performs all intended and
required calculations, reviewing output records, performing structured
walk-through techniques to determine if M&S logic correctly performs
intended functions, and performing M&S sensitivity analyses. Unexpected
sensitivity (or lack of sensitivity) to key inputs may highlight a need to
review the M&S algorithm for omissions or errors.

(2) Verification also includes appropriate data certification and M&S
documentation (e.g., programmer’s manual, user’s guide, and analyst’s
manuals).

(3) Verification should normally be performed by an independent V&V
(IV&V) agent but remains the responsibility of the M&S proponent to
ensure accomplishment.

b. Vqlidqtion. Validation is the process of determining the extent to which
M&S accurately represent the real-world from the perspective of the
intended use of the M&S. The validation process ranges from single
modules to the entire system. Ultimately, the purpose is to validate the
entire system of M&S data, and operator-analysts who will execute the

256

M&S. Validation methods will incorporate documentation of procedures and results
of any validation effort."

The above document cites the types of validation that may be used in the process described
here. "Face Validation" or the determination that an M&S, based on the software
performance, seems reasonable to people knowledgeable about the system being modeled was
used in this V&V effort in conjunction with "Peer Review" where people who are very
familiar with the technical area being modeled evaluate its internal representativeness and the
accuracy of the output of the M&S.

3. TECHNICAL DOCUMENT REVIEW

A comprehensive review of the technical references used in the NVG TDA research and
development (R&D) effort was made along with a complete review of the technical
documentation associated with the release of the NVG TDA to PD IMETS. This review was
necessary for the validation of the physical principles used in the computer model. The
validation described is of the "conceptual" variety described by Dale K. Pace in his article
"Modeling and Simulation" (1993) because of the maturity level of the TDA. The following
paragraphs present the findings of this review.

The first major document reviewed was the basis for the illumination calculations. The
computer program ILLUM (van Brochove 1982) was the technical basis for all illumination
values reported by the NVG TDA. This program was used for all solar and lunar ephemeris
computations. A reasonable "constant" value for natural illumination without solar or lunar
contribution is presented. There is a full explanation of the FORTRAN computer code used
to develop the model. ILLUM calculates the illumination based on the geographical altitude
and longitude of an earth-based observer (user) for clear skies. Infrequent solar and lunar
phenomena, such as eclipses, are considered and the application warns of their occurrence.
A natural illumination value of 1.1 X 10'^ lux (lumens(lm)m-2) of natural illumination and the
illumination value for the full moon of 0.267 are consistent with the RCA Electro-Optics
Handbook (1978).

Another major contributor to this development was AFGL-TR-82-0039, Solar Radiance Flux
Calculations from Standard Meteorological Observations (Shapiro 1982). Shapiro’s work and
associated computer models were used to include the effects that clouds have on the
illumination reaching the ground. In its most complex form, the computer model described
will calculate the solar radiation incident at or near the earth’s surface through n-layers of the
atmosphere through a system of 2n -I- 2 linear equations. These equations comprise a closed
set of equations that account for the physical processes of reflection, absorption, and
transmission of the electromagnetic radiation along its path.

To be consistent with the standard methodology of reporting cloud cover, the n-layers are
taken as three discrete cloud layers representing low, middle, and high clouds. Specific
radiative transfer coefficients were developed that are dependent on cloud amount and

257

thickness for each layer. The effects of the earth’s surface (albedo) are considered. Shapiro
tested the model he d^ribed against independent data and found it to be accurate. The
c^culations presented in this work are based on simple scattering theory and Monte Carlo
simulations. Nine cloud types were chosen to represent those clouds commonly observed.
The World Meteorological Organization Synoptic Cloud Code recognizes other cloud types
but they are seldom observed. ’

The case where precipitation is occurring is the least reliable of the solar flux calculations
used. Only a small number of precipitation events led to a small number of case studies.
During precipitation, the cloud types present can be very complex and ground-based
observers can see, and therefore report, cloud types and amounts up to and including the
lowest overcast layer. Because of this, worst-case thick clouds are assumed at all levels when
precipitation is occurring.

Neither Shapiro’s work nor the NVG TDA computer model accounts for such radiative
transfer processes as aerosol and molecular scattering and absorption. Ozone and water vapor
absorption are treated only in the most rudimentary way. This seems a most reasonable
approach in light of the other simplifying assumptions and the accuracy of the input
observational data. Even with the simplified treatment presented by Shapiro, the process of

radiative transfer is very complex and is handled in a physically realistic and rieorouslv
complete way. ^

Finally, the Technology Exploitation Weather TestBed (TEWTB) User’s Onirtp. and T^rtiniV^i
Reference fpr the Block I Integrated Meteorological System riMETS'i (Elrick et al. 1992) was
reviewed for content and consistency. This document was prepared by scientists and
engineers from the Physical Science Laboratory and the BE Directorate. It is intended as a
reference manual for individuals who are unfamiliar with the IMETS but who possess some
basic computer operating skills.

This document has some inconsistent references to "night vision devices" that could include
such low-light-level equipment as starlight scopes and tank gunner’s sights. The NVG TDA
is geared toward aviation applications for nighttime flying operations.

Occasionally, computer jargon is used in the text. This could present a limitation to its use
by operators who are not familiar with computer nomenclature. In other instances incorrect
reference is made to operations being "GO" or "NO GO" based on the existing or forecast
weather. Conditions should be identified as being "FAVORABLE, MARGINAL, or
UNFAVORABLE;" TDAs are not intended to be directives; they are intended instead to be
intelligent planning guides for battlefield decision makers based on existing doctrine and
equipment limitations.

This document has a final weakness. It does not contain definitions for some of the terms
used. Terms such as nautical and civil twilight need to be defined for operators who are not
famili^ with them. Inclusion of a complete set of definitions will make this document a
valuable reference guide that will stand on its own merit.

258

4. SOFTWARE TESTING

During the period 1-18 November 1994, the NVG TDA computer software was exercised for
a total of 11.5 hours in seven separate sessions. This testing was done on the system known
locally as the ACCS4, which is a nonrugged commercial version of the Army common
hardware system. The most current version of the block I IMETS baseline software resides
on this computer and is identical to the baseline version on the Army Command and Control
System. The purpose of this testing was to test for accuracy of the software application and
its "user friendliness."

Several errors in the software were noted and documented according to the established
configuration management practices employed in the BE Directorate. There are undoubtedly
other errors that were not detected because of the inability to examine every possible
scenario. The ephemeris data that are an output of the TDA were compared with data used
at the official meteorological forecasting and observing station at White Sands. All ephemeris
data were found to be within plus or minus 5 minutes of the data provided to the local station
by the Nautical Almanac Office (1993). This is well within the acceptable operating envelope
for most Army operations.

For this stage of the IMETS R&D effort, the software is acceptable. The minor errors found
and documented during this test should be fixed before final fielding. Other errors that
surface need to be documented and corrected before future baseline releases.

5. CONCLUSIONS AND RECOMMENDATIONS

The NVG TDA is complete and accurate based on its stage of development in the IMETS
release cycle. It is based on sound physical principles, and it is certainly usable and
trustworthy for limited operational considerations. Before the software can be fielded, it must
be made absolutely "user-friendly" and any errors noted in this and future V&V efforts must
be corrected. The TDA must be fully tested and independently evaluated in each baseline
stage of its development before it is fielded as part of a fully operational IMETS. As in the
past, software developers must periodically test changes and upgrades to verify the
correctness of the computer code and its interaction with its associated computer hardware
platform. IV&V must be thoroughly conducted, as part of sound configuration management
practices, before the IMETS blocks II and III releases.

REFERENCES

Some references that are listed here were not referenced in this paper but were cited in the
original V&V effort. They are included here for completeness.

Army Model and Simulation Management Program. Army Regulation 5-11, Headquarters,
Department of the Army, Washington, D.C., 10 June 1992

259

Burks, J. D., 1993, Verification. Validation, and Assessment for the Technology Ryplnitarinn

Weather_TestBed (TBWTB). PSL-93/57, Physical Science Laboratory, Las Cruces,
NM.

Electro-Optics Handbook, Technical Series EOH-11, RCA, Solid State Division, Electro-
Optics Devices, Lancaster, PA, reprinted 5-78

Elrick, J. R., D. C. Shoop, P. V. Laybe, R. R. Lee, J. E. Passner, J. B. Spalding, J. D.
Brandt, D. C. Weems, A. W. Dudenhoeffer, G. E. McCrary, and S. H. Cooper,
1992, Technology Exploitation Weather TestBed rTEWTBl User’s Guide and
Technical Reference for the Block I Integrated Meteorological System aMETSV
PSL-92/60, Physical Science Laboratory, Las Cruces, NM

Harris, J. E., 1992, "A Technology Exploitation Weather TestBed for Army Applications,"
Proceedings of the Eighth International Conference on Interactive Information and
Processing Systems for Meteorology. Oceanography, and Hydrology. American
Meteorological Society, 45 Beacon Street, Boston, MA 02108-3693, pp. 5-9

Lunar Ephemeris Tables for White Sands Missile Range, NM, for 1993, Nautical Almanac
Office, U.S. Naval Observatory, Washington, D.C.

Pace, D. K., 1993, "Modeling and Simulation," Phalanx. The Bulletin of Military Onerafinns
Research. 26(3):27-29, ISSN 0195-1920

Shapiro, R., 1982, Solar Radiative Flux Calculations from Standard Surface Meteorological
Observations, AFGL-TR-82-0039, Scientific Report No. 1, Systems and Applied
Sciences Corporation, Air Force Geophysics Laboratory, Hanscom, MA.

van Brochove, Ir. A. C., 1982, The Computer Program ILLUM: Calculation of thp.
Positions of Sun and Moon and Natural Illuminatinn PHL 1982-13, Physics
Laboratory TNO, National Defence Research Organization TNO (the Netherlands)

260

Session IV

BOUNDARY LAYER

261

CLUTTER CHARACTERIZATION USING FOURIER AND WAVELET TECHNIQUES

J. Michael Rollins

Science and Technology CorpoiaticMi
Las Cruces, New Mexico 88011, U.S.A.

William Peterson
U.S. Army Research Laboratory
Battlefield j^vironment Directorate
White Sands Missile Range, New Mexico 88002, U.S.A.

ABSTRACT

Clutter is a feature of scene content that can confuse an observer or
automatic algorithm trying to locate and track a particular target object. It
consists of variations in the radiance field that either camouflage an object or
divert perceptual attention away from the object location. The ability to
measure and quantify clutter is playing a growing role in reliably estimating
target acquisition ranges.

Two methods for characterizing clutter are presented. They involve Fourier
and wavelet analysis. Results of clutter analysis on terrain images are shown
and the merits of the two characterization methods are discussed.

1. INTRODUCTION

An important aspect of scene characterization is measuring to what extent natural features
of the terrain interfere with the ability to distinguish manmade objects. Scene characteristics
that malffi such discernment difficult are qualitatively called "clutt^." Various quantitative
metrics for the assessment of clutter have been proposed, most of which are related in some
way to the scene variance. Other types of analysis may provide metrics giving similar
information, such as the fractal dimension and parameters derived from wavelet power
distributions.

The presrace of significant intensity variation in an image does not alone constitute clutter.
The spatial »trat of patches of intensities significantly differrat from the average intrasity
is also an important factor. If bright or dark patches are presoit that are of the same
goieral spatial dimrasions as an object of interest, the discernment of the object is the most
challenging, especially if the patches are of the same brightness as the object of interest.
If a measure can provide a reliable goieralization about the rough dimensions of clutter, it
is a useful tool in the validation of terrain simulators that might be used to predict detection
and recognition ranges in the presrace of clutter of varying spatial structure.

263

The simplest approach to clutter characterization is simply to analyze the scene in terms of
pixel blocks of a certain size— most often, the size of a real or hypothetical object of
interest. To further study scene features that constitute clutter of the same spatial dimension
as a manmade object of interest, techniques that describe the spatial extent of scene
information can be useful. Such techniques include Fourier analysis and wavelet analysis.
The first is used to describe scene information in terms of a superposition of two-
dimensional sinusoids and is most useful when the image pattern to be analyzed is
distributed homogeneously across the entire image region of interest. The second describes
scene information in terms of a superposition of very localized functions called wavelets,
Md is most useful in characterizing discrete features of limited spatial extent within the
image region of interest.

Fourier techniques are attractive because they have been used, studied, and interpreted so
thoroughly. Wavelet techniques are attractive because wavelet analysis appears to have
similarities to the human visual decomposition process. The more closely an analysis
procedure emulates biological vision, the more directly predictions can be made of visual
performance under variable environmental conditions. Both Fourier and wavelet techniques
have an advantage over the clutter metric in that each frequency or wavelet band contains
completely unique and independent information about the size and relative weighting of
scene features. The full set of Fourier and wavelet information gives a complete description
of the image.

This paper presents the results of clutter analysis of two different types of terrain— forest
and desert. The terrain images were obtained at two field tests sponsored by the Smart
Weapons Operability Enhancement Joint Test and Evaluation (SWOE JT&E) program at
Grayling, Michigan, and Yuma, Arizona. The analysis involves four metrics: direct
measurement of clutter in terms of a hypothetical object five pixels wide and three pixels
high, autocorrelation function (ACF) slope correlation length and fractal dimension, and the
Haar wavelet horizontal centroid. The ACF and fractal dimension are derived from Fourier
frequency analysis. The latter three metrics are not sought for replacing the first as contrast
measures, but rather for providing information on the spatial extent of clutter. Special
attention is given to the various measures in the case where the clutter metric is relatively
high. Finally, problems specific to the type of wavelet analysis used are discussed,

2. SIMPLE CLUTTER METRIC

A simple, often-used metric for clutter is based on the horizontal and vertical dimensions
of a target in the image field. This metric is given by Schmeider and Weathersby (1983)
as

C =

1 "

MZ-f "i
i-1

(1)

where M is the number of image blocks obtained by partitioning the image into pixel blocks
whose horizontal and vertical dimensions are twice those of the object and is the variance

264

within each block. If no target is present in the image, a block size can be specified based
on a hypothetical target at the center of the image. For instance, in this study, pixels in the
center of the image represent a distance of approximately 0.5 m (horizontally) and a
hypothetical object five pixels wide and three pixels high was used in the specification of
the image block partitioning. This metric is easy to calculate and produces results that agree
with visual assessment. Unfortunately, while the block size is related to the target size, the
variances are calculated on a pixel basis and the relationship of this metric to image features
of specific sizes is rather tenuous.

3. FOURIER ANALYSIS

A number of metrics based on the Fourier power spectrum can be used to describe scene
data. One of the most popular is the correlation length, which is a measure of local
uniformity. The correlation length can be determined in two ways— by integrating over the
power spectrum and dividing by the variance or by modelling the ACF as a decaying
exponential and determining the semilog slope of the decay. The latter method has been
found more useful in this study.

The autocorrelation function in one dimension (t) is given by

ACF(t) « «(-»") (2)

where a is a constant.

Figures 1 and 2 show typical radiance fields for the Grayling and Yuma sites respectively.
For a small region of interest in the Yuma scene (figure 3) the two-dimensional
autocorrelation is shown in figure 4. The ACF (0,0) in Ae upper left comer is the most
intense pixel and the function decreases from this point monotonically in each direction.
The decaying exponential model is well suited to this function.

A somewhat different measure is the fractal dimension, which is an indicator of the
"roughness” of the image texture. The fractal dimension is based on the slope of the power
spectrum (Peitgen and Saupe, 1988). More accurately, assume the power spectram can be
characterized such that

s(/) - -7 w

where / = and jS is some constant {k and / are the two-dimensional Fourier

transform indices).

265

(5)

Taking the log of both sides and defining the fractal dimension as

7 - P
2

the result is

log[S(/)] = (2/J, - 7)log/

(6)

If the downward slope of the power spectrum is substantial, implying principally low-
frequency components, the fractal dimension is low. Conversely, when the slope of the
power spectrum is very shallow, approaching white noise, the fractal dimension is very
high. Most natural terrain exhibits a between 1 and 3. Since the fractal dimension is a
measure of the "roughness" of a signal, it is a convenient measure for analyzing the clutter
content of an image. A fractal dimension is a real-valued metric that has the clearest
meaning when it happens to correspond to an integer value such as 1 or 2. A signal with
a fractal dimension of 2 is represented by a simple two-dimensional topographical surface.
As deviations from one pixel to the next increase in intensity, the roughness increases, as
does the fractal dimension.

4. WAVELET PROCESSING

In addition to the one-dimensional power spectrum analysis, a study was made using wavelet
analysis. Fourier analysis is concerned with the frequency (and phase) content of an image;
wavelet analysis is concerned with its scale (and translation) content. Wavelet analysis
recasts image content in terms of objects of various sizes (scales) and positions
(translations). As this area of study is new, convenient metrics for encapsulating trends in
the wavelet representation have not been developed and tested as thoroughly as has the
fractal dimension in Fourier analysis.

In the previous section, a Fourier analysis of terrain images was described. The power
q)ectra provide a catalogue of the frequency content of the images. The Fourier transform
compares image patterns to sinusoidal basis functions differing in frequency and phase.

While there is a relationship between object sizes and frequency content, the Fourier-based
methods applied to real scenes match periodic basis functions with nonperiodic data. The
result is that objects of finite extent in space have a very widespread, nonlocalized signature
in the frequency domain.

A logical response to this situation is to search for a basis of functions that are themselves
at least somewhat local in space and then to catalog image content according to this basis.
Since the basis functions are of finite extent, the word "scale" is more meaningful than
"frequency" in indexing the functions. If an orthonormal basis of local functions is

267

available, a transform analogous to the Fourier transform can be developed. One such
transform developed recently is the continuous wavelet transform, defined in one dimension
as (Chui, 1992):

(7)

where c is a scaling factor and is a shift (translation) along the axis of support. The
function ^ is a wavelet; W^,f is the wavelet transform; overscore denotes the complex
conjugate.

Most of the energy in the function ^ is concentrated in a small interval [c,d]; that is,

- d

/ « ||i|r(x)p<fe (8)

For scale p and translation q, the Haar wavelet defined on the interval [0,1] is given by
(Jain 1989)

W = —

2(9 , ^ < g_.:

Jf) q-Vz

^ X

2P 2^

0 , elsewhere

(9)

where

0 <: p ^ logj (iV) - 1

q - 0,1 for p = 0 (10)

1 ^ q ^ 2^ for p * 0

The Haar coefficient index k is given by

k = 2P + q - 1 (11)

As with the Fourier analysis, it is instructive to use a metric that can represent the extensive
information contained in the wavelet transform of an image. For this study, a metric was

268

used that roughly characterizes the mean horizontal scale of image features. This is the
horizontal centroid given in equation 12 (Bleiweiss et al. 1994; Rollins et al., 1994).

Hor. cen. =

k)fe(A01ofe(A0

2"-l 2»-l \

E E

« E E

m-0 11=0

^ t=/NT(2"-‘) /=/NT(2‘-') J

N-\ S-\

E E

i'O /=0

(12)

where W{k, 1) is the two-dimensional wavelet transform coefficient at indices k,l.

This equation partitions the wavelet transform domain into regions (bands, indexed by m and
n) of the same wavelet scale and obtains the energy in each band. The horizontal centroid
specifies the center of mass of energy location in terms of the horizontal band index. The
value gives a rough indication of dominant horizontal feature sites in the logarithmic
domain. No pair of indices should be simultaneously zero in this expression to avoid
considering global brightness offsets (i.e., DC term). The centroid expression is only valid
for the Haar wavelet, because it is the only wavelet for which the DC information is
captured entirely within a single coefficient.

While the Haar wavelet and others commonly used are convenient for image analysis, they
lack an important quality of Fourier power spectrum analysis— shift invariance. The Fourier
power spectrum signature of an image feature does not change with small shifts. The
wavelet transform and wavelet power spectrum of an image feature, however, can change
with even a one-pixel shift.

5. RESULTS

In this study, terrain images were selected from Grayling, Michigan, and Yuma, Arizona,
sites. In each scene, areas containing grass and/or bare soil were treated separately from
regions containing thick foliage or trees, resulting in four regions of interest. In ^ch
region, the five metrics were calculated within a 32x32-pixel window advancing 1 pixel
horizontally at a time. The following paragraphs discuss some salient results of the study.

5.1 Merit Criteria

In assessing the effectiveness of the metrics, several types of criteria were used. Visual
assessment, level of sensitivity to scene changes, and correlation with the simple clutter
metric can be used can all provide information about the metric’s validity in representing
the spatial extent of scene clutter.

For the metrics whose results can be cast conveniently in terms of size, such as correlation
length and wavelet centroid, it is useful to compare the metric values with a visual
assessment of scene feature sizes. The scenes were assessed with respect to 32x32-pixel

269

regions of interest, and objects smaUer than about 3 pixels are difficult to discern visually
m these images, l^erefore, values for these metrics larger than 32 pixels or smaUer than
5 pixels would obviously call the validity of the metric into question.

T^e regions examined in these studies were chosen to be visually homogeneous. If a metric
displays a large standard deviation about its mean across a given region, that would suggest
that the metric is too sensitive to very small changes in the scene.

The simple clutter metric varies in its accuracy with the size of the clutter being examined.
As scene features approach sizes for which the clutter metric is increasingly responsive, it
is desirable that the other metrics have a strong correlation with the clutter metric. Such
correlation would show that the sizes each measures accurately reflect the same scene
features.

5.2 Simple Clutter Metric

The clutter meMc indicated negligible clutter for the Grayling images while showing
considerable activity for the Yuma images, with a large number of discrete objects in the
Mze order of Ae imaginary target. For the Yuma images, the clutter metric was of higher
mtensity than m the Grayling images, indicating that distinct background features here were
more often of similar size to the imaginary target and had higher contrast than those in the
Grayling scenes.

5.3 ACF Correlation Length

For the Grayling images, the average value for the correlation length was 15.56 pixels in
the area with foliage and had a standard deviation of 0.58 pixels. For the bare/grassy area
^ mean correlation length was 8.90 pixels and had a standard deviation of 0.87 pixels!
The DC term was removed before the generation of the ACF, so that the discolorations
within the bare areas are more important than the average intensity. Thus the correlation

lengm demonstrated significant class separation between the thickly vegetated and
bare/grassy areas.

correlation lengths for vegetated areas and bare areas were very similar,
(6.02 ± 0.76 ^d 6.38 ± 0.47 pixels, respectively) due to the small average size of the
vegetation. With a high average intensity for the clutter metric, a correlation between the
other metrics and the clutter metric indicated a strong relationship between the correlation
Iragth and the clutter metric (|p| = 0.93 for vegetation and |p| =0.80 for bare soil).
The ^nsitiyity of the clutter metric and correlation length to each other demonstrated the
useful auxiliary information that can be provided by the correlation length to further
nieasure the clutter content in geometric extent when the clutter present is close to the size
of the target. In essence, in conditions of significant clutter where the contrast is
appreciable, the information the correlation length conveys is rather specifically about the
clutor, whereas the other metrics are providing scene information somewhat less specific
to the clutter. For the other metrics, |p | was less than about 0.35 in every case.

270

5.4 Fractal Dunension

For the Grayling images, the fractal dimension had a mean of 1.81 in the vegetated area and
2.06 in the bare area with a standard deviation of 0.08 in each. The lower fractal
dimension within the trees indicates the presence of larger, more distinct features than in
the grassy regions.

For the Yuma images, the fractal dimension of the bare soil area had a mean of 2.00 and
a standard deviation of 0. 17, while the area with foliage had a mean of 1.89 and a standard
deviation of 0.28. The fractal dimension was again slightly lower in the area of thick
foliage. In general, the fractal dimension means did not differ appreciably between regions
of apparently different clutter content.

5.5 Wavelet Metrics

In the GrayUng region containing a fir tree, the horizontal centroid of the wavelet energy
bands had a mean value of 0.74 with a standard deviation of 0.04. This value is between
band 0 and band 1, closer to band 1. Band 0 represents the DC component (i.e., average
intensity in the horizontal direction) whereas band 1 represents features of one half the
breadth of the 32x32-pixel region of interest. A value of 0.74 for this centroid indicates the
presence of a feature of width somewhat greater than 16 pixels, which is in fair agreement
with visual assessment and in rough agreement with the correlation length. In the grassy
area, the centroid mean increases to 1.25 with a standard deviation of 0.02, indicating the
presence of clutter between 8 and 16 pixels in breadth. This result is in fairly strong
agreement with the correlation length.

In the Yuma region with foliage, the horizontal centroid had a mean of 1.34 and a standard
deviation of 0.09, indicating the presence of features between 8 and 16 pixels in breadth,
somewhat larger than the 6.02 pixel correlation length. In the bare soil region, the centroid
had a mean of 1.16 and a standard deviation of 0.10. There are no prominent discrete
features here, however, and the result is somewhat ambiguous in descriptive meaning in
terms of the presence of discrete clutter.

6. CONCLUSIONS

In conclusion, metrics derived from both Fourier and wavelet representations of the image
provide concise and useful descriptions of clutter content in terms of clutter size. This study
indicates that in the Fourier transform-based analysis, the correlation length is more reliable
than the fractal dimension in ascribing a rough size to clutter content in a scene, at least in
terms of separation of means between classes and in terms of agreement with visual
assessment. The centroid obtained from the wavelet analysis also gives useful information
regarding the horizontal size of discrete clutter, but has the disadvantage of correlating scene
features with wavelet basis functions that are frozen in specific positions. This problem

271

may be somewhat alleviated by the use of complex-supported harmonic wavelets, which
allow phase shifting of the basis functions and thus better congruence to scene features. An
investigation into the use of harmonic wavelets in assessing clutter is ongoing.

Specification of a number such as the correlation length can provide an additional parameter
in a scene generation process. For instance, a two-dimensional autocorrelation map can be
generated corresponding to equation 2 in each direction. The map is then Fourier
transformed and the square root of the resulting power spectrum is taken. This proems
produces a transfer function that can be used to filter another two-dimensional random map
of white Gaussian noise, producing a synthetic "clutter" map with the desired correlation
length. Such clutter maps can then be used to modify probability of detection and
recognition of synthetic targets in the presence of clutter of various size distributions.

REFERENCES

Bleiweiss, M.P., J.M. Rollins, and C. Chaapel, 1994. "Analysis of Infrared Background
Scenes from the Grayling I SWOE JT&E Field Test." In Proceedings of the 1993
Battlefield Atmospherics Conference, U.S. Army Research Laboratory, White Sands
Missile Range, NM 88002, pp 281-295.

Chui, C.K., 1992. An Introduction to Wavelets. Vol. 1 of the series Wavelet Analysis and
its Applications, Academic Press, San Diego, California.

Jain, A.K., 1989. Fundamentals of Digital Image Processing, Prentice-Hall, Englewood
Cliffs, New Jersey.

Peitgen, H.-O., and D. Saupe, eds., 1988. The Science of Fractal Images. Spiinger-
Verlag, New York.

Rollins, J.M., C. Chaapel, and M.P. Bleiweiss, 1994. "Spatial and Temporal Scene
Analysis." In Characterization and Propagation of Sources and Backgrounds, SPIE
Proceedings, International Society for oi)tical Engineering, Vol. 2223, W.R Watkins
and D. Clement, eds, pp 521-532.

Schmeider, D.E., and M.R. Weathersby, 1983. "Detection Performance in Clutter with
Variable Resolution." IEEE Trans. Aerospace & Elect. Syst., 19(4):622-630.

VALIDATION TOOLS FOR SWOE SCENE GENERATION PROCESS

Max P. Bleiweiss
U.S. Army Research Laboratory
Battlefield Environment Directorate
White Sands Missile Range, New Mexico 88002, U.S.A.

J. Michael Rollins

Science and Technology Corporation
Las Cruces, New Mexico 88011, U.S.A

ABSTRACT

The Smart Weapons Operability Enhancement (SWOE) scene generation
process involves the simulation of infrared radiance fields produced by a
variety of terrain and vegetation environments under diverse weather
conditions. As part of the validation procedure, an ensemble of land-based
and airborne terrain images were captured at two locations— Grayling,
Michigan, and Yuma, Arizona. These mid and far infrared images were
compared on a frame-by-frame basis to synthetic images of the same scenes
generated by the SWOE process. Statistical procedures as well as metrics for
feature segmentation were implemented in the comparison to assess the
accuracy with which the SWOE process generates a facsimile of the real scene
radiances. A description of the image registration process, the analytical tools
used, and qualitative results for the SWOE process are presented.

1. INTRODUCTION

This paper describes some of the image analysis methodology used in the validation of the
Smart Weapons Operability Enhancement (SWOE) Joint Test and Evaluation (JT&E) scene
generation process. A diverse collection of measurement and analysis techniques was applied
for the dual purpose of comparing real and synthetic images and of investigation methods for
automatic segmentation of homogenous regions of interest within images of natural terrain.

The primary means of validation involves the comparison of real and synthetic image
histograms using the chi square statistic. A secondary effort, reported here, seeks to develop
a tool chest of various metrics that can be used to compare and contrast image features within
real images and between real and synthetic images. Metrics that display invariance to
temporal changes in environmental conditions are of special interest in this paper.

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2. REAL IMAGE ACQUISITION

As described previously (Bleiweiss et al. 1994a), a large number of paired far and mid
infrared images of natural terrain were acquired using an AGEMA BRUT 880 system. The
imagery was acquired at sites near Grayling, Michigan, and Yuma, Arizona. The acquisition
took place during periods of greatest seasonal change (near the spring and fall equinoxes) to
capture as wide a range of variability in scene radiances as possible.

A subset of the images acquired was selected randomly and was used for comparison with
corresponding synthetic images generated by the SWOE process. A typical real image is
shown in figure 1, with its synthetic counterpart in figure 2. A comprehensive presentation
and analysis of the results is given in a report that is being prepared by the SWOE JT&E
office. Qualitative comparison results are discussed in this paper to highlight the utility of
the various scene characterization metrics used in this image analysis.

Figure 1. Mid infrared image from Figure 2. Synthetic connterpart of image
Grayling site. in Figure 1.

3. IMAGE COMPARISON PROCESS

The metric comparison of real and synthetic images involved the following process:

1. Based on knowledge of the artificial scene generation process, designate
appropriate image analysis tools as determined from experience and literature
searches. These tools should be able to provide succinct, objective, and
intuitively meaningful descriptions of image regions of interest for comparison
between real and synthetic counterparts.

274

2. Align images through objective registration process so that compariMns will
always involve exactly the same scene features. Without proper registration,
objective comparison is impossible.

3. Apply each designated metric to the same regions of interest in each real and
synthetic image.

4. Investigate the differences between the real and synthetic metrics for
corresponding images and determine a useful measure of significance for Ae
differences. If the metrics are for contrasting dissimilar types of terrain,
measure the mean and standard deviation for each homogeneous type of terrmn
and establish whether or not the metrics demonstrate sufficient discrimination
between classes. If the metrics or the histogram values are to be used to
establish whether regions of interest are from the same population, use
techniques to compare distributions such as chi-square techniques.

5. Based on the resulting agreement or disagreement from the comparisons,
observe the performance of the scene generation process in terms of each
metric and propose corrective modifications to the process as necessary.

4. IMAGE REGISTRATION

In order to make valid comparisons between real and synthetic images, it is necessary to
register each image from a site to a given reference image. This process ensures that the
regions of interest selected for analysis in each image correspond to the same area of tennin.
Otherwise, the comparison becomes meaningless with offsets greater than 1 to 2 pixels.
During the registration process, the initial misregistration was t^ically 5 to 7 pixels in both
the horizontal and vertical directions. If the region of interest is within a tree, this amount
of offset is enough to mistakenly include a large number of pixels from the background,
which is of a considerably different statistical population in terms of radiance values ^d
texture. Since different images of a scene may contain different features near the boundaries,
only a subset of each image is common to all images. In this registration process, a 128x128-
pixel region was cropped from the original larger image.

The method of registration ultimately employed in this effort involved the use of normaliz^
cross correlation between a given image and the reference. In essence, the image is
converted to its two-dimensional finite Fourier transform (FFT) and normalized at each
coefficient to have a magnitude of 1. The same operation is perform^ on the reference
image. The FFT arrays are multiplied at each coefficient, and the resulting array is inverse

transformed.

The registration process was two tiered. The first tier consisted of the display of the
reference image and the image to be registered superimposed over *e s^e area of the
screen. Arrow keys were assigned movements that allowed the registration image to be
moved with respect to the reference image. In this way, a specific tree as seen in both

275

images, for instance, could be visually aligned. This process requires that the registration
image have enough contrast to contribute useful feature information to the superposition.
Even when contrast is comparatively high, the sharpness of the imagery is usually not
sufficient for visual registration better than 2 or 3 pixels. Thus the visual registration process
is followed by the slower but more accurate phase correlation process (Tian and Huhns 1986)
in the second tier. The ouq)ut array from the phase correlation process represents the
normalized correlation between the two images. The position of maximum magnitude
determiiies the amount of misregistration in each direction. The offsets required for
registration can then be recast as phase shifts for each FFT coefficient for the image to be
registered. Once the phase shifts are performed in the frequency domain, an inverse
transform is implemented and the resulting image is registered correctly.

The frequency domain registration techniques can be used to implement registration accurate
to the sub-pixel level. In the normalized phase correlation map, a sub-pixel location of the
true m^imum can be determined (accurate to about 0.1 pixel) by using two-dimensional
quadratic interpolation over the pixel with the maximum amplitude and its eight nearest
neighbors. In our prot^ure, we returned misregistration offsets with quarter-pixel accuracy
to the frequency domain and implemented the corresponding phase shifts.

It is important to note that phase shifts corresponding to quarter-pixel translations require a
four-fold lengthening of the signal vectors in the frequency domain. Suppose, for instance,
that a given row in the frequency domain has N = 128 elements. The row must be
lengthened to 512 elements, the first and last 64 being the same as before, but those in the
center being assigned a value of zero. This represents the same information on a finer
resolution grid. The center values’ being set to zero correctly indicates that no information
of frequencies higher than the 63rd harmonic are present. The phase shifting is implemented
and the resulting data are inverse transformed, subsampled to the original resolution, and
stored as the registered image.

Suppose a high-resolution grid had not been used in the frequency domain, but a quarter-pixel
phase shift had been implemented. The resulting image upon inverse transforming would be
complex, not real. This is due to the fact that phase shifts corresponding to sub-pixel
amounts destroy the frequency domain arrangement in which the coefficients above N/2 are
complex conjugates of those below N/2. This arrangement is required to represent real
images.

The ability to use the frequency domain in shifting the images was also important in some
ca^s where the images could not otherwise be registered because the real image scenery was
a little tTO far to the left of the synthetic scenery to have a 128x128 pixel intersection. Using
the cyclical representation of the image in the frequency domain, such images were scrolled
slightly into an adjacent cycle so that as many features in the main image cycle could align
with those in the reference image as possible.

Positions of certain features in the newly registered images were noted and compared to the
positions of the same features in the reference images. It was found that the phase correlation

276

process worked very well in most cases. Conditions for which the visual registration
overrode the phase correlation results were rare and occurred principally in the mid infrared
images when the sun angle was low enough to cause bright outlines and shadows, changing
the apparent structural content of the images. Occasionally, when the signal-to-noise level
of an image was very low, the phase correlation method indicated a maximum at an obviously
incorrect position. However, the phase correlation method in general was much more
accurate in measuring the offset or indicating correct registration than the purely visual
superposition method.

5. METRICS USED

In a previous paper (Bleiweiss et al. 1994a), several metrics for the characterization of
histogram information, texture and structure of image features were demonstrated. These
included the mean, median, maximum, minimum, variance, standard deviation, absolute
deviation, skewness, kurtosis, autocorrelation integral correlation lengths, variant-based
clutter, gray-level co-occurrence matrix (GLCM) statistics, and wavelet compaction. In
addition to these, autocorrelation function (ACF) slope correlation lengths and fractal
dimension are discussed here.

The correlation length determined from the slope of the autocorrelation function is based on
the rQiresentation of the ACF as an exponential (Ben-Yosef et al. 1985):

ACF(x) «

where r is the offset from ACF(0). The correlation length may thus be represented as

I = (2)

The slope a is determined numerically by calculating ln[ACF(r)J close to r = 0 and
performing a linear regression with respect to r. The fractal dimension is similarly based on
the slope of the power spectrum, and is discussed further elsewhere (Rollins and Peterson

these Proceedings).

First-order statistics are useful in assessing the macroscopic performance of the thermal
radiance and diffusion model employed in the SWOE process. The emphasis here is on the
accuracy of the temporal aspect of the model and its abUity to predict thermal evolution as
a function of time based on initial conditions. Second-order statistics such as the GLCM
metrics and correlation lengths are useful in assessing reliable texture simulation and provide
information on the accuracy of fine spatial detail simulated by the SWOE process. S^nd-
order statistics are also used for their characterization of the structure present in a region of
interest. Second-order statistics that demonstrate invariance to the diurnal cycle are desirable.

277

6. IMAGERY MEASUREMENTS AND COMPARISON RESULTS
6.1 Evaluation of Metrics

At this time, the analysis of real-to-synthetic measurement comparisons for significance is
ongoing. The results will be given in the final SWOE report. The first-order comparison
made between histograms of the real and synthetic images uses the chi-square statistic at 95%
confidence to accept or reject the hypothesis that the samples come from the same
distribution. A secondary effort is the evaluation of the metrics themselves as useful tools
in ihe compmson of like regions or contrast of dissimilar ones. A complete set of images
registered with sub-pixel accuracy has been produced and compared to their corresponding
synthetic images. Based on initial correlation analysis, the first-order histogram measures
correlate well between the real and synthetic images and better than the second-order
measures. The second-order measures showing the highest correlation were the GLCM
entropy and the ACF slope-based correlation length. The wavelet-based metrics showed poor
agreement.

In some cases, it may be desirable for image metrics to exhibit invariance to environmental
chwges. For example, table 1 gives results for some of the metrics in regions containing
foliage over the full testing period, during which seasonal and diurnal changes took place.

Table 1. Selected Metrics from Grayling and Yuma Sites

Metric

Mean

Gravling
Std. Dev.

Ratio

Mean

Yuma

Std. Dev. Ratio

Clutter

0.07

0.09

0.778

0.37

0.33

1.121

Correlation Length

2.43

1.82

1.335

3.87

0.97

3.990

Fractal Dimension

1.72

0.59

2.915

1.80

0.32

5.625

Horizontal Centroid

1.12

0.33

3.394

1.30

0.26

5.000

GLCM Entropy

0.67

0.95

0.705

3.46

1.07

3.234

GLCM Contrast

0.19

0.18

1.05

2.91

3.43

0.848

The data in table 1 show that the fractal dimension and the horizontal centroid display more
inv^Mce to diurnal effects than the other measures, if the ratio of the mean to the standard
deviation is used to indicate invariance.

It should be noted that even though the Grayling and Yuma regions of interest included
foliage, the Grayling region was completely within a single coniferous tree, while the Yuma
region contained as much foliage as possible but also contained bare soil, with a significantly
different apparent radiance. Thus the Grayling region can be characterized as a radiance field
with dull, morphous structures, while the Yuma region is described by stark contrast
between foliage and the background. As such contrast is necessary for visual identification

278

Figure 3. Plot of GLCM contrast with Figure 4. Plot of fractal dimension with
diurnal cycle from Yuma site region of diurnal cycle from Yuma site region of
interest. interest.

of discrete objects, examining regions with both foliage and background has its own
importance in designing simulators to be used in predictions of recognition range. Such
contrast for boundaries between different textures is also important to the reliability of the
correlation length as an intuitive measure of structure extent in these images.

Plots of the GLCM contrast and fractal dimension for a Yuma region with thick foliage are
given in figures 3 and 4, respectively. The sensitivity of the contrast to the diurnal cycle is
clear, while the fractal dimension seems to indicate a greater invariance to diurnal changes.

7. CONCLUSIONS

In this paper, a description of some of the ongoing SWOE image analysis has been presented.
The registration technique has been demonstrated to be both necessary and accurate. We
have shown that the chosen image metrics are uniquely descriptive of scene content. Certam
metrics have been found to show a degree of spatial invariance (Bleiweiss et al. 1994b) or
temporal invariance, as seen above. For instance, this work has shown that the fractal
dimension and the horizontal centroid retain much of the same information about Ae texture
and structure in a scene from trial to trial, regardless of changes in thermal conditions.

REFERENCES

Ben- Yosef, N., K. Wilner, S. Simhony, snd G. Feigin, 1985. "Measurement and Analysis
of 2-D Infrared Natural Background." Applied Optics, 24(14):2109-2113.

Bleiweiss, M.P., M. Rollins, and C. Chaapel, 1994a. "Analysis of Infrared Background
Scenes from the Grayling I SWOE JT&E Field Test." in Proceedings of the 1993
Battlefield Atmospherics Conference, U.S. Army Research Laboratory, White Sands
Missile Range, New Mexico, pp 282-295.

Bleiweiss, M.P., M. Rollins, C. Chaapel, and R. Berger, 1994b. "Analysis of Real Infrared
Scenes Acquired for SWOE JTifeE." In Proceedings of the 1994 International
Geoscience and Remote Sensing Symposium, in press.

Rollins, J.M., and W. Peterson, these Proceedings. "Clutter Characterization Using Fourier
and Wavelet Techniques. "

Tian, Q., and M.N. Huhns, 1986. "Algorithms for Subpixel Registration." Computer
Vision, Graphics, and Image Processing, 35:220-23.

280

THE VEHICLE SMOKE PROTECTION MODEL DEVELOPMENT PROGRAM

David J. Johnston
OptiMetrics, Inc.

Bel Air, Maryland 21015-6181

William G. Rouse

U.S. Army Edgewood Research, Development, and Engineering Center
Aberdeen Proving Ground, Maryland 21010-5423

ABSTRACT

This paper reports on work in progress to adapt existing methodologies to
develop a Vehicle Smoke Protection Model. The objective of this effort is to
produce a data-rich model that will become the standard technique for simulating
on-vehicle smoke protection systems. Other types of obscurants and dispensing
mechanisms may also be included. Software will be designed and constructed
using object-oriented techniques so that the simulation modules can be used in a
stand-alone mode or adapted for use in other applications, including distributed
interactive simulations.

1. INTRODUCTION

In 1993, the Defense Science Board convened a Task Force on Simulation, Readiness, and
Prototyping to assess the impact of simulation technology on U.S. forces. In its findings, the
task force enthusiastically embraced distributed interactive simulation (DIS) for training
applications and strongly encouraged its continued use. In addition, it recognized that DIS
could transform the acquisition process if it were used to support materiel development, combat
development, training development, and operational testing. As a result, several advanced
technology demonstrations (ATD) have been planned which will make extensive use of this
simulation technology.

A major effort is now underway to enhance the DIS architecture so that it can be used in ATDs
and similar applications. This is being accomplished through a rigorous standardization process
with the voluntary cooperation of numerous organizations from government, industry, and
academia. The architectural enhancements are required because DIS cannot currently simulate
complex battlefield interactions in a physically realistic manner and it has insufficient resolution
for detailed system studies. When DIS is finally ready to support dynamic effects, many
operations will be added to the DIS environment that cannot currently be modeled with

281

acceptable fidelity. This includes the employment of smoke and obscurants on the virtual
battlefield.

A number of techniques have been developed over the years to model the production, transport,
diffusion, and effect of smoke and obscurants on the battlefield. Two of these, GRNADE and
COMBIC, have gained considerable acceptance and are part of the Electro-Optical Systems
Atmospheric Effects Library (EOSAEL). GRNADE simulates multiple-round salvos of tube-
launched grenades (L8A1 and M76) and is used for self-screening analysis (Davis, Sutherland
1987). COMBIC is a more comprehensive model that simulates several obscurant sources,
including: high explosive dust; vehicular dust; phosphorus and hexachloroethane munitions;
diesel oil fires; generator-disseminated fog oil and diesel fuel; and, other screening aerosols
(Hoock et al. 1987). It is used in numerous and diverse applications.

While GRNADE and COMBIC are accepted standards, they are somewhat dated and do not
explicitly simulate many of these systems, sources, and materials currently in service or under
consideration. In addition, a number of deficiencies have been noted which influence their
fidelity. Given the expanded role of simulation technology in research, development, and
acquisition, an immediate need exists for an updated standard. This is particularly true for on-
vehicle smoke protection systems because they are most likely to be included in DIS simulations.

OptiMetrics, Inc. is addressing this need by developing a Vehicle Smoke Protection Model.
This effort is being conducted under contract to the U.S. Army Tank-Automotive Research,
Development, and Engineering Center (TARDEC) and in cooperation with the U.S. Army
Edgewood Research, Development, and Engineering Center (ERDEC). This paper reports on
that work in progress.

2. OBJECTIVES AND SCOPE

The objective of this program is to produce a data-rich model that will become the standard
technique for simulating on-vehicle smoke protection systems. This will be achieved by building
upon and enhancing the standard methods for simulating smoke and obscurant production (i.e.
GRNADE and COMBIC) and related models. Self-screening systems wUl be emphasized, but
other types of obscurants and dispensing mechanisms may also be included.

The goal is to increase the resolution of the simulation process, improve its overall fidelity, and
package the product in a manner that will facilitate its use in a wide variety of applications,
including DIS simulators. The program is focused on smoke production. Consequendy,
exis&g predictive techniques for transport, diffusion, radiative transfer, etc. will be used to the
maximum extent possible and the Vehicle Smoke Protection Model will nQi deviate significantly
from current procedures. Puffs and plumes wiU, for example, still be described by three-
dimensional Gaussian distributions.

Rapid obscuration systems (ROS) wiU be addressed first, followed by obscuration reinforcing
systems (ORS) and aU other dispensers. Any vehicle, dispensing system, grenade, or obscurant

282

may be modeled from user-specified parameters, but a descriptive database will be constructed
and it win include most fielded and developmental items. The vehicle database wiU, for example,
include the : Ml family of main battle tanks; M2/3 family of fighting vehicles; M88A1E1
Improved Recovery Vehicle; CATTB/CCATTD; Breacher; Heavy Assault Bridge; Armored
Gun System; and, M93 Reconnaissance Vehicle (FOX).

3. APPROACH

The Vehicle Smoke Protection Model development program is being conducted in three phases:
analysis, design, and development. The program is currently in the analysis and design phases,
which are being conducted concurrently.

In the analysis phase, GRNADE, COMBIC, and related models are being examined to identify
the simulation techniques that are used for different sources and materials. The algorithms and
parameters will be described in a set of flow charts with supporting documentation. They wiU
also be evaluated using the Concentration and Path Length (CL) Product Visualization Utility
(paragraph 4) to determine how well they simulate the smoke production process. If the
algorithms produce satisfactory results, they wiU be included in the Vehicle Smoke Protection
Model without modification. Otherwise, an alternative simulation technique will be sought

In the second phase, the Vehicle Smoke Protection Model is being designed using object-
oriented techniques (Coad, Yourdon 1991). A preliminary design is described in paragraph 5.
The results of this phase will be documented in a report and submitted to ERDEC and the Army
Research Laboratory - Battlefield Effects Directorate (ARL-BED) for evaluation.

The Vehicle Smoke Protection Model wiU be coded and implemented in the development phase
using object-oriented programming techniques. The model will be completely self-contained and
may be used in a stand-alone mode to support the analysis of smoke and obscurant effectiveness.
In addition, its classes may be used independently to provide similar functionality in other
applications. This wUl be particularly useful in the DIS arena.

4. CL-PRODUCT VISUALIZATION UTILITY

As described in paragraph 3, a CL-Product Visualization Utility has been developed to aid in the
analysis of smoke production algorithms and investigate alternatives. This utility operates on
IBM or compatible personal computers and runs under Microsoft Windows™. It was adapted
from the CL computational routines in GRNADE (EOS AEL- 1992 version).

The CL-Product Visualization Utility operates on a set of files that record puff and plume
information in a specified format. These files can be produced by GRNADE and COMBIC
(with proper modifications) or by any other application (e.g., a spreadsheet) that can describe
puffs and/or plumes as a function of time. For a given puff or plume and for each sample (time
increment) in the descriptive data, the utility computes a CL-product matrix for the front, side,
and top views as depicted in Figure 1. The results are then displayed in accordance with a

283

mapping scheme that associates a range of CL-products with a specified color. This enables the
user to visualize how the obscurant material is distributed in three-dimensional space and how
that distribution changes with time.

Figure 1. CL-Product Visualization Utility computational procedure.

To illustrate how this utility is being used in the analysis phase, consider the manner in which
GRNADE simulates the M76 grenade. The model computes a detonation point that is thirty
(30) meters from the launcher along a calculated azimuth and fom (4) meters above the ground.
The detonation occurs 0.75 seconds after launch and a simulated smoke cloud is formed.
GRNADE models this cloud as a small spherical initial burst puff (Figure 2) that grows larger
and moves downwind as a function of time. The CL-Product Visualization Utility displays these
changes in a series of fi'ames, such as those depicted in Figures 3 and 4.

Figure 2. M76 Grenade modeled as a spherical puff.

It has been suggested that this simulation does not accurately model the manner in which the
M76 grenade functions, particularly in the initial build-up phase. Field tests have demonstrated
that the obscurant is released very rapidly after the grenade is detonated and a large smoke cloud
is formed almost instantaneously. Furthermore, the cloud does not have a spherical shape; the

284

Figure 3. CL-Product Visualization Utility sample output (M76 Grenade modeled as a spherical
pirff one second after launch).

Figure 4. CL-Product Visualization Utility sample output (M76 Grenade modeled as a spherical
puff ten seconds after launch).

285

obscurant material is generally distributed about the detonation point to form a toroidal puff. To
experiment with alternatives, GRNADE was modified to model the resulting smoke cloud as a
collection of six small spherical sub-puffs, which are distributed about the detonation point to
form a torus (Figure 5). The CL-Product Visualization Utility was then used to examine this
approach and determine if it improved the fidelity of the simulation process (Figures 6 and 7).

Figure 5. M76 Grenade modeled as a toroidal puff with six spherical sub-puffs.

Figure 6. CL-Product Visualization Utility sample output (M76 Grenade modeled as a toroidal
puff one second after launch).

286

Figure 7. CL-Product Visualization Utility sample output (M76 Grenade modeled as a toroidal
puff ten seconds after launch).

5. VEHICLE SMOKE PROTECTION MODEL PRELIMINARY DESIGN

A preliminary design has been developed for the Vehicle Smoke Protection Model and it wiU
serve as the foundation for the evolving design. In its simplest form, the model consists of the
six classes depicted in Figure 8.

Figure 8. Vehicle Smoke Protection Model preliminary design.

In this design, vehicles are loaded with expendable material, such as smoke grenades and fog oil.

The vehicles carry this material until an initiation event occurs, at which time the obscurant is

287

released and a smoke cloud is produced. The formation and fate of the cloud is influenced ly
the terrain and atmosphere. This simple representation has been expanded, as depicted in Figure
9, and additional details will be added as the design matures.

In the expanded design, the vehicle class comprises numerous components, which (for a given
vehicle) might include grenade discharger tubes and/or smoke generators. The location and
orientetion of these components must be known when a grenade is launched or obscurant
material is released because they determine where, in three-dimensional space, the smoke cloud
is formed. This be particularly important when the Vehicle Smoke Protection Model is
included DIS applications where vehicle position and orientation cannot be known a priori.
Similarly, the state of rotating and articulated components cannot be predicted in advance.
Consequently, the Vehicle Smoke Protection Model must include a general method for
computing component location and orientation given: a mounting hierarchy; vehicle location
and orientation; and, the state of components in the mounting hierarchy. Note: the mounting
hierarchy might be different from the parts breakdown structure. This methodology must be
compatible with the DIS standard (Institute for Simulation and Training 1994).

This point is illustrated by the MlAl tank in Table 1 and Figure 10. The vehicle has a number
of components, each of which can be considered to have its own coordinate system.
Components are mounted with an offset (three-dimensional translation) and orientation (three-
dimensional rotation) in accordance with the vehicular design. However, some components are
also free to move within specified constraints. The vehicle (i.e., its huU) is free to move about
the terrain and assume any orientation that the topography permits. The turret is free to rotate
in any direction about its axis.

Table 1. Example MlAl parts breakdown stracture and mounting hierarchy.

Whole

Parts

Mounted On

Translation (in)

Rotation (deg)

Yaw

Pitch

Roll

MlAl

Tank

Turret

MlAl Tank

-30

0-360

M250 launcher

VEESS

MlAl Tank

-156

-26

180

-30

M250

Launcher

■

Turret

-24

-12.6

Discharger

RHtube#!

RHtube#2

RH discharger

-10

RHtube #3

-20

RH tube M

-30

RHtube #5

RH discharger

-40

RHtube #6

RH discharger

-50

M250 Launcher (right discharger)

Figure 10. Example MlAl mounting hierarchy.

289

Given this configuration, consider tube #3 on the right-hand discharger. Its location and
orientation is dependent upon: (1) the location and orientation of the vehicle (hull) with respect
to the terrain; (2) the offset and orientation of the turret with respect to the hull; (3) the offset
and orientation of the right-hand discharger with respect to the turret; and, (4) the offset and
orientation of the tube with respect to the right-hand discharger. The DIS standard specifies
how this information will be expressed and reported to networked simulators through message
protocols. The computational procedures for calculating component location and orientation
are well established and widely used in such fields as robotic control (Paul 1981).

7. SUMMARY

Starting from vehicle position and attitude in variable-terrain scenarios, the Vehicle Smoke
Protection Model will be able to provide the location, orientation, and initial cloud
characteristics for diffusion and transport in any battlefield model. The resolution can be varied
to support small-scale one-on-one simulations or large-scale organizational wargames. The
software will be developed using object-oriented techniques so that it can be readily used in
many applications.

REFERENCES

Coad, P. and E. Yourdon, 1991: Object-Oriented Design, Prentice-Hall, Inc., Englewood
Cliffs, New Jersey.

Davis, R.E. and R.A. Sutherland, 1987: EOSAEL 87, Volume 14, Self-Screening Applications
Module GRNADE. U.S. Army Laboratory Command, Atmospheric Sciences
Laboratory Technical Report, TR-0221-14, White Sands Missile Range, New Mexico.

Hoock, D.W., R.A. Sutherland, and D. Clayton, 1987: EOSAEL 87, Volume 11, Combined
Obscuration Model for Battlefield-Induced Contaminants (COMBIC). U.S. Army
Laboratory Command, Atmospheric Sciences Laboratory Technical Report, TR-0221-
11, White Sands Missile Range, New Mexico.

Institute for Simulation and Training, University of Central Florida, 1994: Proposed IEEE
Standard Draft, Standard for Information Technology - Protocols for Distributed
Interactive Simulation Applications, Version 2.0 (Fourth Draft), Orlando, Florida.

Paul, R.P. 1981: Robot Manipulators: Mathematics, Programming, and Control. The

Computer Control of Robot Manipulators, The MIT Press, Cambridge, Massachusetts.

290

DEVELOPMENT OF A SMOKE CLOUD EVALUATION PLAN

M. R. Perry
Battelle

Columbus, Ohio, 43201, USA
W. G. Rouse and M. T. Causey

Edgewood Research, Development, and Engineering Center (ERDEC)
Aberdeen Proving Ground, Maryland, 21010, USA

ABSTRACT

This paper describes a methodology for field test design intended to achieve
repeatability in smoke cloud evaluation. The objective is to establish standard test
and data analysis procedures for the characterization of smoke clouds. Obscurant
output rates, dissemination durations, and obscurant particle characteristics will be
related to effective cloud size and duration for visible, infrared (IR) and millimeter
(MM) frequency regions of the spectrum. If relationships can be established, they
may be used later within a standardized Test Operations Procedure (TOP) for smoke
generator cloud characterization. The Research and Technology Directorate,
Armored Systems Modernization Team of ERDEC will be conducting a field test
using visible, infrared, and millimeter wave smoke/obscuration generator systems at
Dugway Proving Ground (DPG), UT, in September 1994. Three types of smoke
generators will be used during the trials: XM56, MM Cutter, and MM Wafer Storage
and Dispensing System (WSDS). The XM56 produces visible screening with a
visible to near-IR (NIR) obscurant disseminated at two temperatures, IR screening
with two types of visible to far-IR (FIR) obscurant, or a combination of both visible
and IR. The Cutter and WSDS produce MM screening clouds by disseminating two
types of MM obscurants of various lengths and diameters. More than 23
combinations of obscurant will be disseminated. There will be four main categories
of equipment: cloud monitoring, aerosol sampling, obscurant consumption
monitoring, and meteorological monitoring equipment. The test approach will focus
on measurement of aerosol parameters near the point of generation only, and on
measurement of the macroscopic obscurant cloud properties down range. This will
lead to an ability to evaluate smoke generator performance without meteorological
constraints on production testing.

291

1. INTRODUCTION

The goal of this task is to establish standard test designs and data analysis procedures for the
characterization of smoke clouds. Obscurant output rates, dissemination durations, and obscurant
particle characteristics will be related to effective cloud size and duration for visible, IR, and MM
obscuring clouds. If relationships can be established, they will be used later within a Test
Operations Procedure (TOP) for smoke generator cloud characterization.

1.1 Test Objectives

This paper will define repeatable methods for determining effective cloud size/duration,
dissemination parameters and obscurant parameters that will be used to test the following
hypotheses:

Effective cloud size = /, (Generator parameters. Aerosol properties)

Effective cloud duration = /2(Generator parameters. Aerosol properties)

The details of /, and /2, and any other independent parameters influencing cloud effectiveness will
be assessed once a relationship has been confirmed. The specific objectives that will test the above
stated hypotheses are listed below:

a. Establish procedures for determining effective cloud size and effective cloud duration.

b. Establish procedures for determining smoke generator operation parameters (effective
cloud formation time, delay time, generation time, dissemination duration, and feed rate).

c. Establish procedures for monitoring obscurant parameters (size distribution and
condition).

d. Evaluate effective cloud size as a function of generator and obscurant parameters.

e. Evaluate effective cloud duration as a function of generator and obscurant parameters.

1.2 Approach

To meet the above objectives, the test approach focuses on measurement of aerosol parameters at
the point of generation only, and on measurement of the macroscopic obscurant cloud properties
down range. The expectation is that the resulting obscurant cloud can be predicted with substantial
accuracy from the characterization of the generator output. This will enable developers to evaluate
the performance of smoke generators free from the usual meteorological constraints.

292

1.3 Test Scope

This paper describes procedures for determining size and duration of smoke/obscurant clouds.
Included are descriptions of the field test equipment needed to provide the required data from the
field tests. In addition, this paper describes the method for analyzing the field test data. The
procedures are appropriate for existing and developmental smoke/obscurant clouds that screen
visible, near, mid, far infrared, and millimeter wavelengths.

2. Test Equipment and Material

2.1 Test Location and Grid Layout

The test will be conducted at the Romeo Grid at Dugway Proving Grounds, UT. The test grid
layout, shown in Figure 1, illustrates the location of the monitoring equipment and the smoke
generators within the test grid. The smoke generator(s) will operate from the north or south launch
pads (LP2 and LPl, respectively) based on wind direction, to allow the smoke cloud to travel in
front of the cloud monitoring equipment. All particle sampling equipment will be located within
5 meters of the launch pads.

2.2 Cloud Generating Equipment

Three types of smoke generators will be used during the trials; XM56, MM Cutter, and MM Wafer
Storage and Dispensing System (WSDS). Table 1 summarizes the screening spectrum for each
generator. The following sections describe each of the smoke generators.

Table 1. Summary of the intended screening spectrum of each of the smoke generators being used
during the field test

Smoke Generator

Intended Screening Spectrum

Vis-NIR

Vis - FIR

XM56

Cutter

WSDS

2.3 Field Test Equipment

There are four main categories of field test equipment: cloud monitoring, aerosol sampling,
obscurant consumption monitoring, and meteorological equipment. Tables 2-5 list all the field test
equipment, the parameters monitored, the organization(s) that will support the equipment and the
applicable spectrum(s).

293

294

Figure 1. Test Grid Layout. Romeo Test Grid, Dugway Proving Ground, Utah.

Table 2. Listing of the cloud monitoring equipment that will be used during the field test

Equipment

Parameters Monitored

Org.

Applicable Spectrum: j|

Vis

Millimeter wave Radar

Obscurant Characterization

System (MROCS)

MM backscatter and 2-way attenuation for heights
of 1, 3.5 and 6 m over a 40 degree horizontal FOV.

Eglin

AFB‘

■

Atmospheric Transmission
Large-Area Analysis System
(ATLAS)

FIR transmittance over a 20 degree horizontal

FOV.

ASL^

■

Mobile Image Processing System
(MIPS)

Visible and FIR cloud growth.

DPG^

■

Multi-Path Transmissometer/
Radiometer (MPTR)

Visible and IR 1-way attenuation at 3.5 m height
over a 26 degree horizontal FOV.

ASL

■

Research Visible and Infrared
Transmissometer (REVIRT)

Visible, IR, and MM one-way signal attenuation at
3.5 m height.

ASL

Full Grid FOV Camera

Visible images of the entire grid during smoke
generator operation.

DPG

■

Tank Thermal Sight
(TTS)/Visible Split Image
Recording System

TTS and visible images will be combined,
producing a visible/infrared split-image video of
the test grid during the smoke generator trials.

DPG

■

* = Eglin Air Force Base
^ = Atmospheric Science Laboratory
^ = Dugway Proving Ground

Table 3. Listing of the aerosol sampling and analysis equipment that will be used during the field
test _

Equipment

Parameters Monitored

Org.

Applicable Spectrum: ||

Vis

Cascade

Impactor/Microbalance

Measures size distribution of visible-NIR obscurant
particles.

Battelle

Cyclone Sampler/Elzone
Analysis

The Cyclone sampler captures samples of visible-
FIR obscurant. Elzone analysis measures size
distribution and concentration of the particles.

ERDEC"

Guillotine/Hercules

Radar Chamber

The Guillotine sampler captures MM obscurant on
sticky paper. Optical and radar analysis of samples
will provide number of particles per unit area and a
relative measure of dissemination effectiveness,
respectively.

ERDEC/

Hercules^

Electrostatic Ball/Pulse
Counter

Measures size distribution and concentration of

MM obscurant.

ETI‘

^ = Edgewood Research, Development, and Engineering Center
* = Hercules, Inc.

^ = Engineering Technology, Inc.

295

Table 4. Listing of the meteorological parameters that will be monitored during the field test

Parameter

Height of Measurements (m)

Horizontal Wind Speed and Direction

2, 4, 8, 16, and 32

Vertical Wind

Temperature

Dew Point

Pasquill Stability Category

All parameters will be monitored at a 1 Hz rate, except for the vertical wind components

which will be monitored at a 10 Hz rate. DPG will be responsible for monitoring
meteorological conditions.

Table 5. Listing of the obscurant consumption monitoring procedures that will be implimented
during the field test

Procedure

Purpose

Org.

Applicable Spectrum;

Vis

Identify Trial Timing From
Smoke Generator Monitoring
Camera Video

Determine effective cloud formation time,
delay time, generation time, dissemination
. duration, and feed rate

DPG

Weigh Auxiliary Visible-NIR
Container

Determine amount of visible-NIR obscurant
consumed during trial.

ERDEC

■

Weigh Visible-FIR Obscurant
Required to Reload XM56
Hopper

Determine amount of visible-FIR obscurant
consumed during trial.

ERDEC

Weigh MM Obscurant For

Cutter

Determine amount of MM obscurant
consumed during trial.

ERDEC

■

Count Number of Wafers
Disseminated by WSDS

Determine amount of MM obscurant
consumed during trial.

ERDEC/

Battelle

■

2. ANALYTICAL PROCEDURES

This section describes how the acquired field test data will be used to satisfy the test objectives.

2.1 Test Objective a: Establish procedures for determining effective cloud size and effective
cloud duration.

Effective cloud size is defined as the horizontal extent (m) of a cloud that is ^ a predetermined
height and attenuation level. Effective cloud duration is defined as the maximum time in which
there axe consecutive effective cloud sizes. MROCS and MIPS data will be the primary data
sources used to determine cloud size and duration in the MM and visible/IR spectral regions,

296

respectively. ATLAS and MPTR data will be used to approximate visible and IR signal
attenuation levels associated with the MIPS data.

MROCS Data- The MROCS MM attenuation and backscatter data will be used to calculate
effective MM cloud size and duration. The MROCS data will be analyzed using Battelle's
"Computer Program for Analysis of Millimeter Wave (MMW) Attenuation Data", dated December,
1993. The program may require modifications if the MROCS data format has been changed. In
addition, the MROCS data will be compared to the REVIRT data for validation.

MIPS Data: The MIPS visible and FIR cloud growth data will be used to calculate effective visible
and FIR cloud size and duration. Cloud length, height, and duration values will be taken directly
from the 3DCAV cloud dimensioning program. These data will be compared with ATLAS and
MPTR data for establishing the MIPS cloud transmission level.

ATT , AS Data: The ATLAS FIR transmittance contour plots will be used to calculate effective FIR
cloud size and duration. Cloud length, height, and duration values will be measured directly off
the contour plots. The size scaling factors used in the measurements will be calculated from the
ATLAS contour plot frame radians and the distance from the cloud center to ATLAS. ATLAS
transmittance contour plots will be compared with MPTR FIR transmittance data. In addition,
ATLAS plots will be used to establish the FIR transmittance level of the MIPS cloud dimensioning

data.

MPTR Data: The MPTR FIR transmission data will be compared with ATLAS contour plots. The
MPTR visible and FIR transmission data will be used to attempt to establish the transmission level
of the MIPS cloud dimensioning data.

2.2 Test Objective b: Establish procedures for determining smoke generator operation parameters
(effective cloud formation time, delay time, generation time, dissemmation duration, and feed rate).

Table 6. Definitions of the smoke generator operation time parameters that will be monitored
during the field test _ — _ ^

Time difference between .

Cloud formation time generator start

Delay time

Generation time

generator start

Dissemination duration initial obscurant
dissemination

formation of an effective
cloud _

initial obscurant
dissemination _

generator stop _ _

final obscurant dissemination

Generator Dnta Sheets: Information from the Smoke Generator Data Sheets will contain
trial specific parameters for every trial which will be used in correlating smoke generator operation

297

with particle results and cloud screening, size, and duration results. The data sheets will also
contain obscurant consumption weights which are required for determining feed rates. Key
information from the data logs will be imported into a summary table.

Smoke Generator Mpnitoring Camera Videos: Video recordings of the smoke generator during
operation will be used to determine the delay time, generation time, and dissemination duration of
the smoke generators for each trial. Smoke generator start and stop times will be indicated by the
smoke generator operator.

Fgsdj^ will be determined by dividing the weight of the obscurant materials consumed by the
dissemination duration.

2.3 Test Objective c: Establish procedures for monitoring obscurant parameters (size distribution
and condition).

Cascade Impactor Visible Obscurant Samnler Datf.- Visible-NIR obscurant sampling data will be
used to characterize the obscurant as it exits the XM56. Average concentration and size
distribution data for the hot and cold obscurant will be compared.

ELzone Analysis of Horn Samples: Elzone analysis of the Horn samples will be used to
characterize the two types of visible-FIR obscurants as they exit the XM56. Particle size
distribution data will be compared.

Optical and Radar Chamber Analysis of Guillotine Samples: Optical and radar chamber analysis
of the Guillotine samples will be used to characterize the MM obscurant materials as they exit the
Cutter and WSDS. Optical analysis will provide number of particles per unit area and percent
clumping. The radar chamber analysis will provide MM attenuation data which will be compared
to MM obscurant standards available at Hercules.

Electrostatic Ball MM Obscurant Samnler Data- The Electrostatic Ball detector data will be used
to characterize the MM obscurant as they exit the Cutter and WSDS. Concentration and length
distribution data will be reported.

2.4 Test Objective d: Evaluate cloud size as a function of generator and obscurant parameters.

Effective cloud size results from the MROCS and MIPS data will be compared with smoke
generator p^ameters and obscurant particle results. The primary goal is to determine if there is
a relationship between feed rate and effective cloud size for each type of obscurant material.

298

2.5 Test Objective e: Evaluating cloud duration as a function of generating time and obscurant
parameters.

Effective cloud duration results from the MROCS and MIPS data will be compared with smoke
generator operation timing and obscurant particle results. The pnmary goal is to detenmne if there
is a relationship between generation time and effective cloud duration for each type of obscurant

material.

2.6 Additional information that will be analyzed from the data.

2.6.1 Homogeneity of multispectral screening clouds.

RF.VTRT Data- The REVIRT visible, IR and MM signal attenuation data will be used to determine
the multispectral screening effectiveness of clouds containing multiple obscurants. Attenuation
levels and screening times will be compared. REVIRT data will also be us^ to validate
MROCS data. REVIRT LOSs will be correlated with MROCS comer reflectors and the M

attenuation data will be compared.

2.6.2 Approximate Obscurant Dissemination Velocity.

SmnVp Generator Timid Monitoring: Video images will be used to estimate the velocity of the
exiting obscurant material. As the initial obscurant exits the ejector, the distance it travels per units
time will be monitored. Obscurant travel distance will be approximated using maps with Imovm
dimension within the FOV. Travel time will be approximate because of the limitation of the 3U

frame/second video image speed.

2.6.3 Additional Support data

Fr.ii rvrift FOV Moniinrincf Camera Videos: The Full Grid FOV video tapes will be used to
qualitatively assess each trial. Selective images will be used m the final briefing package.

TTS/Visihle Split Image Videos: Selective split image frames will be used in the final bnefing
package. The images incorporated into the briefing package will be of each of the screening clouds
produced (i.e., visible, IR, MM, and combinations).

Data: Wind speed, wind direction, temperature, dew point, md stability category
data will be used to assess the effects of the meteorological conditions on the generated clouds.

3. QUICK-LOOK RESULTS

This paper was submitted for the Battlefield Atmospheric Conference just two weeks after the
compktion of the above described field test. As a result, the quantitative field test data was not

299

available for analysis. Listed below are qualitative assessments of the quick-look data that were
available during the field test.

3.1 MPTRData

Preliminary MPTR data (Valdez 1994) were reviewed to compare the screening effectiveness of
the visible-NIR obscurant disseminated hot and cold. The quick-look data suggest that the hot

disseminated obscurant screened visible-NIR signals more effectively and for a longer period of
time.

3.2 MROCSData

Preliminary MROCS data (Mijangos 1994) were reviewed to compare the effectiveness of the
various lengths and diameters of the MM obscurant. The quick-look data suggest that the shorter

lengths and shorter diameter particles screened the MM signals more effectively and for a longer
period of time.

3.3 Smoke Generator Data Sheets

The Smoke Generator Data Sheets accurately documented the obscurant consumption during each
trial. This information will significantly increase the ability to relate the dissemination parameters
with the resulting cloud.

4. OVERVIEW

This paper illustrates repeatable procedures which can be used to monitor and analyze
smoke/obscurant source parameters, aerosol characteristics, and effectiveness (size, duration,
attenuation, and wavelength). These procedures will provide data that can be used to evaluate
cloud size/duration as a function of generator and obscurant parameters. The point of this effort
IS to demonstrate that by recording generator parameters and point-of-exit aerosol data, you can
adequately define generator performance in terms of anticipated cloud effectiveness.

REFEREENCE

Perry, M., Kuhlman, M., Kogan, V., Rouse, W., and Causey, M., 1994: Study of Test Methods for
Visible, Infrared, and Millimeter Smoke Clouds - DPG: Sept. 1994. Test Plan, Battelle and

ERDEC, Contract No. DLA900-86-C-2045, Task 182.

Mijangos, Adrian, 1994: MROCS Quick-Look MM Attenuation Plots, Unpublished, Supplied to
Michael Causey (ERDEC) during DPG Field Test, Eglin AFB, Florida.

Valdez, Robert, 1994: MPTR Quick-Look Visible-IR Transmittance Plots, Unpublished, Reviewed
by Mark Perry (Battelle) during DPG Field Test,

300

ANALYSIS OF WATER MIST/FOG OIL MIXTURES

William M. Gutman and Troy D. Gammill
Physical Science Laboratory

New Mexico State University, Las Cruces, New Mexico 88003
Frank T. Kantrowitz

Anny Research Laboratory Battlefield Environment Directorate
White Sands Missile Range, New Mexico 88002

ABSTRACT

The Army Research Laboratory Mobile Atmospheric Spectrometer (MAS) has been used to
optically characterize obscurants at numerous tests over the past several years. These have
included Smoke Weeks XIII, XIV, and XV as well as the recent Large Area Smoke Screen
Experiment (LASSEX). The MAS spectrometers are usually operated as transmissometers at
4 cm-i spectral resolution and this configuration provides approximately 200 measurement
channels in the 8-12 pm region and 600 in the 3-5 jixa region.

At LASSEX, the principal MAS line-of-sight was approximately parallel to the nephelometer line
but offset by approximately by 30 m. This was sufficiently close to permit realistic time-adjusted
correlation comparisons between MAS transmittance measurements and nephelometer-based
mass loading data fw most materials. Time adjustment was necessary to corr^t fca- the time for
the materia] to be transported from the nephelometer line to the MAS line-of-sight or vice versa.
One of the generator systems tested at LASSEX could combine water mist with fog oil smoke.
Trials were conducted with that generator system with the separate materials and with the
combined materials. Nephelometers normally cannot distinguish between components of a multi-
component mixture. By using distinctive absorption features of separate components, however,
MAS transmittance data offer a means to estimate mass loading for the separate components,
although the difficulty of collecting water mist with a filter sampler introduces considerable
uncertainty into the calibration of the nephelometer data. MAS transmittance spectra were used to
investigate the properties of water mist/fog oil smoke mixtures, and results of that investigation
are presented.

1. INTRODUCTION

For the past several years, the Army Research Laboratory Mobile Atm^pheric Spectrometer has
been used to characterize the infrared transmissive properties of various obscurant materials.
Measurements have been made at Smoke Weeks XIII*, XIV^, and XV as well as at the Large
Area Smoke Screen Experiment (LASSEX) which was conducted at Eglin Air Force Base,
Florida during May, 1994. Over the period of time spanned by these tests, steady improvements
have been made in the data acquisition repetition rate, the signal-to-noise ratio of the spectra, and
the processing algorithms.

2. DATA ACQUISITION AND REDUCTION

As currently configured, the primary spectroscopic instruments in the MAS are two Fourier
transform spectrometers. The original instrument is capable of 0.04 cm' spectral resolution. A
second instrument that is capable of 0.5 cm * spectral resolution was added prior to LASSEX, but
collectal with the original instrument are ihe subject of this paper. The original spectrometer
is particularly well suited to the field measurement environment. The instrument uses comer

301

reflectors rather than flat mirrors, and it is, therefore, essentially immune to thermally-induced
misalignment that can severely Umit the reproducibility of flat-mirror systems. A comer reflector
spectrometer achieves this immunity without the complexity of a dynamic alignment system.

2.1 Test Conflguration

At LASSEX, m^t of the MAS data were collected in transmissometer mode. A source was set¬
up on the west side of the test grid at “Ml” while the MAS van containing the spectrometer was
set up on the e^t side of the grid at “S3.” The source was a 1000 °C temperature-controlled
blackbody colUmated with a modified 60-inch searchlight. The receiver optics for the
spectrometer consisted of the main 31-inch Coudd-mounted Cassegrain telescope. The Coudd
telescope mount greatly facilitates spectrometer repointing when required, for example, to collect
radiance spectra of munitions set on the test grid. As will be discussed below, a rotating-blade
shutter was used to block the source on command from the receiver in order to obtain background
and path radiance spectra. The source and spectrometer were set up so that the line-of-sight was
approximately parallel to, and 30 m south of, the nephelometer line. All transmittance spectra
were collected at 4-cm ‘ spectral resolution.

2.2 Measurement Methodology

The transmittance of a sample of material at radiation frequency v is defined as the ratio of the

radiant power at frequency v exiting the material to the radiant power at that frequency incident
upon the material, i.e.

T(v) =

Absolute atmospheric transmittance is quite difficult to measure over a path of significant length
(of the order of hundreds of meters) because, except in special cases, it is impossible to collect
the entire beam of radiation at the receiver. The beam spreads because of the finite size of any
non-laser source, because of diffraction, and because of atmospheric turbulence. The only
satisfactory broadband absolute atmospheric transmittance measurement methodology that has yet
been demonstrated is to measure the transmittance at discrete frequencies using a low divergence
laser whose spot size and spread and wander footprint at the receiver are small enough to allow
collection of the entire beam. Even with this approach, a large collecting aperture is required for
most path lengths of interest, especially during high turbulence parts of the day. The
transmittance at one or more discrete frequencies, however, can be used to normalize the result of
a broadband relative transmittance measurement. The broadband measurement is made by
comparing a spectrum collected at the desired path length with a spectrum collected over a very
short atmospheric path. This is usually called a zero-path spectrum. Dividing the long path
spectrum by the zero-path spectrum corrects for the shape of the instrument response function.
The resulting spectrum is the unnormalized transmittance of the part of the path that is different
between the two measurements. Normalization with the laser measurement corrects for the
spreading of the beam. Measurements of this type are extremely difficult.

In the case of an otecurant measurement, what is usually required is not the absolute
transmittance of the entire path, but rather the transmittance of the path with the obscurant relative
to the clear air path. The spreading factors and the instrument response function remain
unchanged when comparing the path with the obscurant to the path without the obscurant, and so
a simpler measurement methodology can be applied. In the absence of path radiance and
background effects, the relative transmittance of the obscurant is the point-by-point ratio of the
obscurant sp^trum to the clear air spectrum. Path radiance and background radiation, i.e.
scattered sunlight, and radiation emitted by the obscurant, by the optical elements in the beam

302

path, and by the part of the background that is not obscured by the source, may contaminate the
raw signals and thus must be removed. I.e.

~ ^ clear ^bkg

Therefore,

TO =

c _ c

^obscurant paih
^clear ^bkg

It is understood that all of the quantities on the right hand side of Eq. 1 are functions of t>. .

The MAS measurement methodology is to measure these four quantities over ^ short a time
interval as possible. It is especially important for the obscurant and path radiance data to close
together in time because of the rapidly changing nature of the typical obscur^t cloud. Clear air
data typically were acquired both before and after each obscurant release. Whenever possible
pre-trial clear air data were used in the analysis because they were less likely to be cont^mated
by residual obscurant that may not have been apparent visually. Obscurant spectra were altermt^
with path radiance spectra. Path radiance spectra were obtained by blocking the source with the
remotely controlled rotating shutter. Two way communication with the shutter hel^ to ensure
that it was always in its correct state. Each spectrum was the result of the addition ol two
interferograms. This had the effects of reducing the signal variability resulting from atoosphenc
turbulence and of improving the signal -to-noise ratio. The acquisition of ^h coadded
interferogram required approximately 0.5 s, but because of the overhead associated with tiai^ier
and storage of the data, the time between spectra was approximately 1.5 s. Because ol the
requirement to alternate between obscurant and path radiance spectra, the overall measurement
period was approximately 3 s. Transmittance spectra at LASSEX were collected at a nominal
spectral resolution of 4 cm ‘ and covered the transparent parts of the atmospheric spectrum
between 800 and 3000 cm ' (12.5 and 3.3 ptm). Data acquisition was controlled by a computer
program to ensure that opening and closing of the shutter were properly time with res^t to the
data acquisition. Fourier transformation of interferograms into spectra was accomplished alter the
conclusion of each trial. Computation of transmit^ce spectra from the raw spectra was
controlled by a computer program. Several computational aids have been develop^ to lacilitate
analysis of the data. These include a program to display time sequence movies of spectra in a
simulated three-dimensional space.

3. COMPARISON WITH NEPHELOMETER MEASUREMENTS

The proximity of the MAS line-of-sight with the nephelometer line permits realistic correl^on
comparisons between MAS transmittance measurements and nephelometer-bas^ m^s loading
for most materials. The correlation function expressed as a function of the time between the
spectral measurement and the nephelometer measurement can be defined as

Cit)=la{T)T(t-t)dT

where a is the nephelometer signal and T is the transmittance measured with the MAS. The
correlation function normally has its peak value at r = — where x is the displacement between

V I

303

the MAS Ime-of-sight and the nephelometer line, and Vj^ is the mean magnitude of the wind
component ^^ndicular to the line-of-sight. Previous work has demonstrated good correlation
with the nephelometer measurements under some meteorological conditions.

3. MULTICOMPONENT SMOKES

Ob^urant ^rfonnance is often enhanced by combining materials. The combination of graphite
with fog oil, for example, provides good obscuration from the visible through the infeed
, region. Measmement of the individual components of multicomponent smokes can be
ditticult. By using distinctive absorption features of the separate components, however
spectrally-resolved transmittance data offer a potential means to estimate the contribution from the
separate components. Obviously, for this approach to be successful, at least one component of a
two-component mixture must have some distinctive spectral feature.

3.1 Water Mist/Fog Oil Mixtures

^e of the generator systems tested at LASSEX could combine water mist with fog oil smoke.
The water component is particularly troublesome to measure. Because of its high volatility it is
not possible to use filter samplers to obtain reliable mass concentration estimates.

A number of trials were conducted at LASSEX with the water mist/fog oil system. During most
of the trials, both components were generated. One trial was conducted with pure water mist and
numerous fog oil spectra have been collected with the MAS in support of other trials.

3.2 Measured Spectra
3.2.1 Fog Oil

Figure 1 IS a pure fog oil spectrum. Fog oil is a poor infrared attenuator, and that fact is obvious
trom this figure. The strong absorption band at the high frequency end of the spectrum is
charactenstic of fog oil.. The band originates from the C— H single bond and is, therefore
common to most hydrocartons. This absorption band in fog oil spectra is probably toe result of
gaseous material that vaporizes in the generator or that evaporates from the droplets in the fog. In
general toe strength of this feature correlates with toe overall attenuation level in the spectra. The
depth of this future, then can be used to estimate toe contribution of fog oil to the attenuation in
other parts of toe spectrum, to estimate the fog oil contribution to the attenuation of a mixture and
to estimate toe fog oil concentration-length product. The feature between 2000 and 2200 cm * that
appears to be a weak a^iption band is a weak water vapor feature that did not ratio out perfectlv
because of small vanation in toe water content between toe clear air and toe obscurant spectra. It
IS not uncommon to see this band in MAS obscurant transmittance spectra.

3.2.2 Water Mist

u ^ spectra of water mist. These spectra were collected during LASSEX Trial

50. There are several interesting characteristics common to these spectra;

1 . The overall transmittance level is fairly low, at least compared with fog oil. Water mist
can be a fairly good infrared obscurant.

absorption band that was discernible in the fog oil spectrum is
slightly more evident in toe water mist spectra. It is not surprising that toe water vapor
content of toe air might be enhanced by the presence of water droplets in toe path.

3. The low frequency end of the spectrum is up-turned slightly. The origin of this effea
IS unknown at this time, but it seems to be common in toe water mist spectra. Its
strength appears to be directly related to toe overall attenuation level. Although it is not

304

particulariy strong, it offers promise as an aid in estimating the concentration-path
length product of water particles.

4. The high frequency end of the spectra contain an absorption featui^e simil^ to the
hydrocarbon absorption band but much weaker. This feature probably results from
unbumed hydrocarbons in the turbine exhaust of the generator.

The weak absorption band between 2000 and 2200 cm holds little promise as a potential 3*^ o
analyze the separate water vapor and fog oil concentiutions be<rause it may be present m both
spectra. The slightly up-turned transmittance near 850 cm' , although weak, is unique to the
xiater spectra. The height of this feature above the baseline correlates with the oyei^l
transmittance level, and so it should be usable to aid the analysis. Because this feature is relative y
weak the uncertainty level in the resulting estimate of the transmittance and concentration-path
length product would be relatively high. The presence of the weak hydroc^ton absorption in the
water mist spectra does not appear to represent a serious problem because it is so weak compared
with ihe fog oil spectra.

LASSEX Fog OU Spectrum

Figure 1. Typical transmittance spectrum of pure fog oil collected during LASSEX Trial 075.

305

1.0

0.9

0.8

1 0.6

2 0.5

■a 0.4

^ 0.3

0.2

0.1

0.0

LASSEX Water Mist Spectra

500

1000 1500 2000 2500 3000

Spatial Frequency (cm‘^)

Figure 2. Three water mist transmittance spectra collected during LASSEX Trial 050. These
spectra exhibit among the strongest attenuation observed for water mist.

3.2.3 Fog OilAVater Mist Mixture

Figure 3 is a spectnim of the combined water mist and fog oil smoke. Both the strong
hydrocarbon absorption band of fog oil and the up-turned baseline at 850 cm * of water mist are
evident m the spectrum.

3.3 Analysis

By correlating the strength of the up-turned baseline in the pure water mist spectra with the
average transmittance, and the depth of the hydrocarbon absorption with the baseline, it is
straghtforward to amve at estimated values for the transmittance levels that would result from the
individual c^^nents of the mixture if present at the same concentration alone. These values are

the water ^d 0.96±0.02 for the fog oil. The product of these
numters is 0.83^.05 and the actual combined transriiittance was measured to be appioximatelv
0.9. Given Ae limited data set that has so far been examined, this appears to be reasonable
agreement. No attempt was made to estimate flie concentration-path length products for the

sep^te com^nente. This step will require detailed analysis of nephelometer data sets for both
water mist and log oil.

306

LASSEX Fog OilAVater Mist Spectrum

Spatial Frequency (cm’^)

Figure 3. Typical spectrum of fog oil/water mist mixture collected during LASSEX Trial 076.

4. CONCLUSIONS AND FUTURE DIRECTIONS

Spectrally resolved transmittance measurements appe^ to offer an eff^tive m^s ^

opSal properties of the individual components of mixed obscurants for v^hich no ottier meth(^
hL been d^onstrated. Future work in this area will be directed confirming and refining these
results, and toward obtaining concentration-length products for the separate components.

REFERENCES

1 Peterson W A D. M. Garvey, and W. M. Gutman, “Spectrally Resolved Transmittance
■ Measurements at Smoke Week XIII,” Proceedings of the 1991 Battlefield Atmospherics

Conference, U. S. Army Atmospheric Sciences Laboratory, White Sands Missile Range,
New Mexico.

2 Kantrowitz, F. T., W. M. Gutman, T. D. Gammill, and J. V. Rice, “High Resolution
Spectroscopy at Smoke Week XIV,” Proceedings of the Smoke/Obscurant Symposium
XVII, Johns Hopkins University, Laurel, Maryland.

307

NEW MILLIMETER WAVE TRANSMISSOMETER SYSTEM
ROBERT W. SMITH

U.S.Artny Test and Evaluation Command
Ft. Belvoir Meteorological Team

WILLIAM W.CARROW
EOIR Measurements , Inc
Spotsylvania , Va

ABSTRACT

The TECOM Ft Belvoir Meteorological team and the Night
Vision and Electronic Sensor Directorate of CECOM contracted
with EOIR Measurements , Inc to develop a new instrument which
would provide atmospheric transmission data in the 3 5 giga¬
hertz region. The desired instrument would have complete
redundancy, long path length, compact size, stable microwave
pgrf ormance , easy field setup and alignment, standard data
output, low development risk, and above all, low development
cost. The design by EOIR consists of mostly off the shelf
components with a design goal of measuring 1 percent transmis
sion over a 5 km path in a rainfall of 64 millimeters per
hour. To achieve simplicity of design and field use, and to
]^gQp the cost down, two innovations have been made. Fir^t, a
new antenna design that uses optical refraction principles
replaces the large and cumbersome parabolic antennas and
second, an open loop frequency design, as opposed to a fre-
quency tracking receiver , allows for the use of less expensive
transmitters and receivers. In this paper we will describe
the instrument, present the test procedure and look at some of
the data.

■ INTRODUCTION

For many years the Army's Ft Belvoir Meteorological Team
has been making measurements of atmospheric transmission in
the visual and infrared regions of the spectrum. With the
increasing interest in the millimeter wave region, we have
been asked to extend our capability. Propagation effects are
very important to millimeter wave systems. They include atten¬
uation and scattering by precipitation, fog and dust and clear
air absorption by water vapor and oxygen. Millimeter wave
propagation models do exist but it remains difficult to make
accurate predictions of attenuation due to differences in data
bases and in the assumptions which are made during the calcu¬
lations. Some of the measurements required for predictions
have significant uncertainties which can effect the results.
These include rainrate,drop size and distribution, water

309

content of fog, and the extent of precipitation or fog. Addi-
tionally, predictive models do not handle rapidly changing
conditions caused by either changing weather conditions or by
battlefield obscurants. Because of these considerations, we
decided to obtain a system which would measure the attenuation
loss directly. A brief survey of existing systems found many

problems but primarily their cost exceeded the resources
available .

3 . DESIGN GOALS

- The process of developing our system started with a set
features which had to be met. We required a
path length of at least 5 kilometers, a compact size, stable
microwave performance, easy field setup and alignment, output
capable of computer processing, low development risk, high

above all, low system cost. We will expand of
each of these considerations. ^

system has been built with complete redundancy since
we have two stand alone source units and two stand alone

computer is a standard pc, we can

f I’pJThi another. This gives the additional

flexibility of measuring two separate paths if desired.

Size and weight have been carefully controlled so that
the entire source or radiometer are contained in environmental
housings four feet long and eight inches in diameter. These

weigh about 30 pounds each will be mounted on

^ 1 provides a very simplified representation of
the transmissometer system.

performance design goal for this system is to allow
tne user to make path loss measurements accurately with 1%
transmission over 5 km path length. This corresponds to oper¬
ating in over 64 mm/hr of rainfall. ^

_ The two primary mechanisms responsible for unstable

( power and frequency drift) are changes
ambient temperature and power supply. To
tZwtt power problem, extensive use of tightly regulated

components use two stages of power
regulation to assure isolation from power line or generator
flu^ctuations. Temperature stability is obtained by the use of

housing for the microwave head and
of^ Calculated frequency drift over a range

of 20C to +40C ambient is on the order of 5 Mhz. This worst

accommodated by the 10 Mhz
the radiometer. Worst case power drift is calcu¬
lated to be on the order of 0.4% over the same range of tem-

310

peratures. Because of excellent power supply regulation, the
teinperature induced variations will dominate the performance
considerations.

The system has about a 70 mr field of view (4 degrees) .
While this is considered narrow for radio systems, it is
significantly wider than the 3 mr used on our optical trans-
missometers and greatly eases alignment considerations. In
addition each unit is provided with an alignment system con¬
sisting of a narrow beam intense spotlight and a high quality
24 power aiming scope.

Data output from the radiometer to the user will be 0 to
10 volts analog and RS232 serial digital. These signals
represent 0 to 100 % transmission. The power source provides a
0 to 10 volt power monitor signal where 10 volts represents a
relative 100 % power output. We do not anticipate using lock-
amp processing. Finally the source and radiometer have rear
panel displays to assist in alignment.

All key components of this system are commercially avail¬
able. The most critical components, the microwave transmit and
receive heads, are derived from police traffic radar systems
which have had over 20 years of in the field use. The entire
system, consisting of two complete separate transmissometers
is the result of an exhaustive survey of the U.S. microwave
industry, is about 1/2 the cost of the nearest competitor ' and
it is specifically designed for the needs of the Ft Belvdir
Meteorological Team.

4 . SYSTEM CONCEPT

The initial concept for this transmissometer system
borrows heavily from the present Barnes Optical transmissome¬
ter system presently being used by the meteorological team.
That is, a known amount of energy of the desired frequency is
transmitted into space towards a companion radiometer .The
radiometer is separated from the source by a known path
length. After correction for free space loss over the path
length, and any system loss, the amount of energy received by
the radiometer is considered to represent the propagation loss
of the path.

The free space loss is calculated from the formula: path
loss = 1/(4 pi R^). System loss will be measured by a method
of suitably attenuating a very near field signal such that
path atmospheric loss can be considered zero. This calibration
procedure will be more fully developed once we have field
tested the unit.

311

Considerations unique to microwave systems require some
departure from classical optical concepts. However the system
prepnted here, for the most part, adheres to the above con-
cepts. The source for this system uses a 34 Ghz police radar
head of about 50 mw output power. The difference in signal
attenuation between 34 and 35 Ghz can be assumed to be very
small since these frequencies lie in an absorption minimum for
water vapor and the small difference will not affect rainfall
attenuation. A power monitor circuit has been included on the
source which allows the user to monitor the power output of
the source and to make periodic corrections to the calibra-
tion factor used to compute the per cent transmission. At a
® modulate the output power information onto

the 34 Ghz signal to give the capability to monitor the source
performance from the receiver site. Finally, the microwave
section, or "front end" of the source environmental housing is
temperature stabilized at about 35 degrees C (+/- 3 deg) to
help obtain the required frequency and power stability. A
cutaway view of the environmental housing is provided in
rigure 2 and a block diagram of the system is at figure 3.

the 34 Ghz radiometer is also tempera-
^ ^he same manner and for the same reasons as

at the source. The receiver signal handling method used is
known as super-hetrodyne detection. This is the same method
used in all modern radio and tv receivers. Super-hetrodyne
detection provides superior signal to noise performance rela-
unhetrodyned lockamp assisted radiometers such as used
on the optical transmissometer system. The primary benefits of
super-hetrodyned detection are three. First, the received
signal IS immediately down converted (or hetrodyned) to a more
riendly frequency (34 Ghz to 30 MHz in this case) in the
microwave head. The new frequency contains all of the informa-

in the original frequency. The lower interme¬
diate frequency (i.f.) of 30 MHz, however, eliminates the need
^°JJ^ ^hain of difficult to tune microwave circuits.
Second, 30 Mhz amplifiers and filters are standard electronic
Items, readily available at low cost. This is an important
consideration for field maintenance. Third, in general i.f.
amplifiep are used because they provide a stable tuned cir¬
cuit with very high gain and low noise.

A second frequency conversion takes place when the 30 Mhz

chopped at 1 KHz. ( we will evaluate
aJf ” ^ ^ reference signal in at the source

evaluation but it is difficult and may not be
fild ^ signal is then further filtered and ampli-

ried. Finally, a precision demodulator circuit converts the 1

^ voltage representing the received signal

strength. The system uses six inch diameter dielectric lenses

312

to collimate and collect the transmitted energy. The efficien¬
cy of these lenses allows them to replace more costly and much
more cumbersome parabolic metal dish antennas.

System performance has been calculated for a 5 kilometer
path length. A free space (no attenuation) signal to noise
ratio of 36 db is predicted. Assuming that a minimum signal
to noise ratio of 3 db is needed to make a usable reading, we
have a dynamic range of 33 db, of from 100% down to 0.05%
transmission. It is likely that other factors will cause the
minimum readable signal to go to the ,0.1 to 1.0 per cent
range, but this is a healthy performance range. Calculations
indicate that it should be possible to make 5 kilometer path
measurements in precipitation in excess of 64 mm/hr. Our
field evaluation of the system will of course confirm these
predictions.

^ DATA PROCESSING

The software for the transmissometer was developed by
Chris Wolf son of EOIR Measurements , Inc who was also one of the
development engineers. The voltage from the receiver output is
sent to a computer by RS 232 where it is converted _ into
transmission values. The software has a number of configura¬
tion files which are accessed by menus. The main menu offers
the following selections:

OPERATION MENU - collects and processes data
CONFIGURATION MENU - sets the test parameters
CALIBRATION MENU - saves calibration and setup data
PRINTOUT MENU - controls printout of selected data

The first step after setup and alignment uses the calibration
menu. In this step the calibration distance, signal strength
and attenuator setting are recorded along with the specific
run values for distance and attenuator setting. This menu is
followed by the configuration menu is used to set run

ID, sampling and recording intervals. Finally the operation
menu is called. Here one has the choice of timed start/stop,
user commanded start/stop, or continuous readout. When in the
continuous mode, the data is only displayed, not stored. The
sample interval can be varied from many times a second to a
few seconds. The recording interval specifies the averaging
period. The last five recording period data points are dis¬
played.

The transmission values are calculated using the follow¬
ing equation:

T = S^* 100

313

where T is transmission in percent corrected for path loss
S is signal strength
A is attenuator setting
D is distance or path length

the subscripts c and m refer to calibration or measured

The software contains several diagnostic routines, however
these are not on a menu at this time.

FIELD EVALUATION

The instrument was set up at the NVESD compound at FT
A.P. Hill, Va on 30 August. The day was very clear and dry.
The first step was calibration and checkout. We first wanted
to demonstrate the received signal intensity under good

transmission conditions followed the free space loss curve. To
do this we set up at 500,2000, and 5000 meters and did the
necessary alignment procedures. We then took intensity data at
each of the attenuation settings from each distance. The
calibration distance was the 500 meter point , assuming that
there we would have negligible transmission loss. We normal-
data collected at each distance and calculated the
standard deviation. Figure 4 shows the results with the aver¬
aged data for each distance and the 3 sigma standard devia¬
tion. While the data did not fall exactly on the free space
loss curve, the shape appears the same and the losses repre¬
sent a constant percentage regardless of range. The differenc¬
ial attributed to system noise and less than perfect

calibration. Also small errors in calibration distance can
significant errors at the longer ranges. Armed
with this system verification we proceeded to the collection
of transmission data.

DATA PRESENTATION

Data will be presented for three rain events. The first
event was on 2 6 and 27 September. This was the first day of
rain after we set up the instrument. There were four measure¬
ment sites down range at 1,2,3, and 5 km. The rainrate data in
^ shows three periods of significant rain fall. The
millimeter wave transmission data shown in figure 6 clearly
was affected during these periods. However the data dropped
below zero percent, which was bothersome. After invest igatinq
this data we discovered that the problem was in a lineariza-
ro'4tine which had been turned on in the software. This
routine had the effect of incorrectly distorting the received
signal below about the 5% level. This routine proved to not
only be erroneous but also unnecessary and was removed. Anoth-
er interesting result was the reduction in transmission after
tne rain ended. Figure 7 shows visibility data for that

314

period. Most of the time it was below 5km in fog. A few days
after the first event we noticed some problems with power
stability and response to calibration so the instrument was
returned for maintenance. After its return we again set up and
calibrated. After a period of no rain, we finally had another
chance on October 20. Here about 3 mm/hr of rain reduced the
transmission to about 18 %. Data for this event is shown in
figures 8,9, and 10. The noise in the millimeter data ^ after
2230 hrs is caused by over ranging which we did not edit out.
This was probably due to the wet ground. On October 23 we
captured another rain event as shown in figure 11. Figure 12
provides the transmission data. Again the periods corre¬
sponded well. Figure 13 provides the visibility data. On this
occasion our PMS precipitation probe was functional and some
of the size distribution data is presented in figures 14 and
15. After this day, we had to pack the instrument for shipment
to Alaska where it was to be used in snow conditions. However,
this test was canceled, so snow data will be collected later.

CONCLUSION

We are very encouraged by the first data from the milli¬
meter wave transmissometer but we have a lot to learn about
its use. The addition of the millimeter wave transmissometer is
eagerly awaited by the Ft. Belvoir Meteorological Team. It
v/iii extend their transmission measurement capability into an
important region of future system development. The system has
already been scheduled for two field tests. It is important to
understand that this new system has been designed to fit in
the overall data collection system used by the team so that no
additional resources will be reguired for operational use. The
second unit is nearly completed and incorporates changes
identified during the testing of the first unit.

315

Fig 1. 34 GHZ TRANSMISSOMETER FIG 2. ENVIRONMENTAL

SYSTEM HOUSING

fIG 3. SYSTEM BLOCK
DIAGRAM

FIG 4. INITIAL FIELD

PERFORMANCE TEST

316

FIG 5. SEPT 26,94 RAINRATE VS TIME

FIG 6. SEPT 26,94 MILLIMETER WAVE TRANSMISSION

FIG 7

SEPT 26,94 VISIBILITY VS TIME

Session V

ATMOSPHERIC PHYSICS

321

WIND FIELD MEASUREMENT WITH AN
AIRBORNE CW-CO2-DQPPLER-LIDAR (ADOLAR)

S. Rahm and Ch. Wemer
German Aerospace Establishment DLR
82234 Oberpfaffenhofen, Germany

ABSTRACT

The small scale wind fieldin the boundary layer is an important parameter e.g.
for the detection of fluxes from pollutants. For this purpose a compact cw 002
Doppler lidar has been developed that can perform measurements from the
ground as well as from an aircraft. In the airborne setup this instrument can
easily be installed in the research aircraft FALCON F20 of the DLR. The
instrument consists of two racks, one electronic rack (size 56 X 66 X 96 cm)
and an optical rack (41 X 62 X 125 cm), which carries the transceiver, the laser
and the interferometer optics all together mounted at two sides of an optical
breadboard. The transceiver consists of of an 150 mm diameter off-axis-
telescope and a Germanium wedge, which provides the conical scan with a cone
angle of 60“. The interface to the atmosphere is a Germanium window installed
in the bottom of the aircraft. One critical part is the elimination of the Doppler
shift due to the platform motion. This can be done at a low flight level by the use
of the ground return. At higher flight level, where the ground return is not
available, the built in inertial reference system (IRS) of the aircraft will be used
for this task. With this instrument was tested, that the wind field can be
measured from the aircraft. For battlefield operations (ground based or airborne)
the system should have an automatic operation mode. The wind measurement
requirements are: wind speed 1-30 m/s with an accuracy of 1 m/s and 5° of the
direction. The time of one measurement to get the mean wind depends for the
ground based system on the atmospheric stability and surface roughness length.

It will not exce^ 60 s in the worst case.

1. INTRODUCTION

The knowledge of the three dimensional wind field is mandatory for the description of transport
phenomena e.g. fluxes of pollutants or dust and also for small scale meteorological effects. To
obtain this wind field at the condition of clear air (no rain or fog) a Doppler lidar is the
appropriate instrument. For the continuous wave (cw) (X>2 lidar, the energy is focused by the
telescope into the region of investigation. Some of the radiation is scattered back by small
aerosol particles drifting with the wind speed through the sensing volume. The back scattered

323

radiation is collected by the telescope and detected by coherent technique. With the laser Doppler
method one gets the radial wind component along the beam axis. To determine the magnitude
^d direction of the wind, some form of scanning is required. With a ground based Doppler
lidar the wind of only a small region can be observed. Therefore an airborne system is a good
approach to obtain the information about the wind of a larger area in a relative short time. On the
other hand with an airborne system one has to deal with some additional problems as there are
the influence of vibrations, the safety requirements and most important, the elimination of the
platform motion by an appropriate signal processing. This presentation will deal with these
problems as well as with the system design and the results of a first test flight

2. THEORY OF THE WIND EVALUATION

One possibility to measure this wind field is the use of a conical scanning Doppler lidar. The
principle of such a lidar is quite simple. Monochromatic light is transmitted into the atmosphere
and scattered back by aerosols. At this process the line of sight (LOS) component of the velocity
causes a Doppler shift [eq. (1 )].

Av = -.yLOS

^ (1)

At a wavelength X = 10,6 }im, 1 m/s LOS velocity corresponds to a Doppler shift Av =
189 kHz. The Doppler shift is detected by an optical heterodyning technique. If the local
osciUator has the same frequency as the transmitted light, the lidar system is called homodyne,
and in the other one heterodyne. If several measurements during one conical scan are evaluated,
it is possible to calculate the tree dimensional wind field by applying a sinus fit for example. For
this the wind field is assumed to be homogeneous in each level over the measured area. This
technique is well approved for ground based systems (Schwiesow et al. 1985), (Bilbro 1980).
On the other hand only a few attempts have been made to integrate a Doppler lid^ into an aircraft
(Bilbro 1980)(Bilbro et al. 1986)(Woodfield et al. 1983), and none of these systems were
applying a conical scan. If the laser Doppler system is used on board an aircraft, the speed of the
aircraft modifies of the Doppler shift [eq. (2)J.

^OS = '^LOs(wind) + a)Los(carrier) ' (2)

where -Olos (carrier) carrier speed, and speed both with respect to

the lidar line-of-sight (LOS). Normally the wind field is the interesting parameter, therefore the
platfom motion has to be subtracted by the means of signal processing. This will be done at a
low flight level by the use of the ground return. At higher flight level, where the ground return is
not available, the built in inertial reference system (IRS) of the aircraft can be used for this task,
"^e platform velocity contribution is the main problem together with the pointing accuracy, but
like shown below the data of the IRS can fulfil these strong requirements.

3. SYSTEM SETUP

The Doppler lidar ADOLARis a homodyne system. This means, that the magnitude of the wind
field can be detected but not the sign. The principal setup of this system can be seen in figure 1.

324

Figure 1 . Principal layout of the transceivCT and the inter¬
ferometer optic. At the real system, the telescope is mounted
at at the front side of the breadboard and the interfCTometer
optic at the rear.

The laser is aCMlOOOfrom Laser Ecosse with anouq)ut power of approximate 3.5 W cw. The
laser is operating at single longitudinal mode (SLM), transversal at TEMqq, and is p-polarised.
The laser beam passes the lens L^, which is used to adopt the beam parameters of the laser to the
telescope, the Brewster window B, and the quarter wave plate which converts the polarisation to
a circular one. After passing the beam splitterBS (R = 10 %) the radiationis coupledinto the 15
cm off-axis- telescope from Lambda/Ten Optics, which focuses the light to 180 m distance. The
wedge scanner provides the conical scan with half a cone angle of 30°. At the measurements
described below, one revolution of the scanner needs 20 s. The back scattered light goes tfie
same way back, passes again the quarter wave plate, where its polarisation is converted to the s-
state, so that it can be focused onto the detector via the Brewster window B, the mirror and
the lens 1^. The local oscillator (lo) is realised by the beam splitterBS, the CaF2 plate to adopt
the power, and the mirror Mg. The lo-beam is also focussed on the detector and tii^
heterodyned with the back scattered light. The mixing efficiency m (Kingston 1978) is estimated
to m » 0.3 out of the calculated beam parameters (Gaussian for the lo and an Airy pattern for the
received light). The detectoris a LN, cooled MCT-diode with an active size of 200 X 200 |xm,
and a quantum efficiency 11 = 0.53 from Kolmar. The electric beat signal gets amplified with a
bandwidth from 1 to 20 MHz. The low cut off frequency is necessary to eliminate EMI from the

325

laser power supply and the high cut off frequency to reduce the effect of aliasing of noise. The
amplified signal is ^gitised with a sampling rate of 20 MHz and a resolution of 8 Bit. One
measurement contains 8 kByte of data which represent a duration of about 0.4 ms. The
repetition rate at these measurements was with 2 Hz quite low. One thing to be mentioned is,
that the Nyquist frequency was with 10 MHz rather low due to a malfunction of the ADC. But
the aliasing effect which occurred at some few measurements has been resolved.

To obtain a mechanical stiff and robust setup, the interferometer optic together with the laser is
mounted at one side of an optical breadboard 900 X 300 mm from Newport, and the telescope is
fixed at the other side. The breadboard itself is mounted in a light weight aluminium frame,
which compensates the average pitch angle of 5.5° of the aircraft. The scanner is fixed below the
telescope at the frame. Figure 2 shows the installation of the optical part in the Falcon F20
aircraft.

To reduce the influence of vibration the whole instrument is connected with shock mounts to the
aircraft. The electronic equipment, like the cooling unit for the laser, the A/D converter, the
computer, the spectrum analyser for quick look etc. are mounted in a standard electronic rack of
size 0,55 X 0,65 x 0,95 m in front of the operator.

326

4. MEASUREMENT OF THE WIND FIELD

In the case of an airborne Doppler lidar we had to deal with some problems. The most important
one is the elimination of the platform motion in the detected Doppler signal. AI^LAR was
originally designed as a test bed to gain experience concerning the points ^scribed above.
However the results of the first test flight, discussed below, were so encouraging, that it is now
planned to upgrade this cw-system to an operational airborne Doppler lidar for the detection of
small scale wind fields. A flight test was performed on May 19, 1994. Test results of the
different signatures are shown in figure 3. During this test flight measurements at several height
level have been performed. The most interesting scenario, that will described here, was a part of
the flight in 315 m height over the ,Ammersee“, a lake in Bavaria. At this day a rather strong
wind was blowing, which was ideal for the test of the system, for each measurement contains
information about the LOS velocity from both, the aerosols, and the ground return mostly at
different frequencies, so that they can be differed from each other. The algorithm for the signal
processing is quite simple. The data set of one measurement (8 kByte) is divided into 1 6 parts of
length 512 Byte. Each of them is processed with a FFT and the resulting power spectra ate
averaged.

LOS [m/s]

Figure 3. Doppler lidar signals for different targets

Figure 3 shows 3 single measurements at different scan direction and different time (flight
altitude) for an first overview. There is a land and sea surface return each together with an
aerosol ( wind ) signal. This is caused by the focal distance of 200 m and the stronger return of
land and sea surface from outside the focal volume (figure 2). The wind si^al is left from the
land signal and right from the sea signal caused by the scanning and the aircraft velocity. The
LOS difference is in the order of 8 m/s. The third signature comes fiwm a cloud.

327

The strong narrow peaks in the spectra (figure 3) are belonging to to the ground return and the
weak broad peaks to the aerosol signal. The spectral width of the peak is an indicator for the
coherence time which corresponds to the turbulence. The ground return is therefore narrow, and
the peak belonging to the aerosol is rather broad due to the velocity distribution in the focal
regime. The iritensity of the ground return in the case of water is rather low. A ground return
from the land is normally about 10 - 15 dB stronger. As it can be seen (figure 3) are the two
peaks ch^ging their absolute position as well as their relative position to each other during the
scan. This effect and the estimation of an average wind vector will now be discussed more in
deual (figure 4).

corresponds to the position of die scanner.

a) Doppler shift of the ground return with IsinI fit

b) Doppler shift of the aerosol with IsinI fit

c) Difference b)-a).

d) Corrected Doppler shift of the wind field.

The centre frequencies of both peaks (aerosol and ground return) are estimated for the
measurements of 3 scanner revolutions (figure 4a and 4b). To each couple of points a IsinI

328

function has been fitted with a least square fit procedure. At a homodyne system, the influence
of the platform motion cannot be eliminated by calculating the difference aerosol - ground return.
This would lead to the rather confusing result shown in figure 4c. Therefore the graph (figure
4c) was divided into two groups of areas, where the sign of one ^itrarily chosen group was
changed (shaded in figure 4c). This operation leads to the graph in figure 4d. There it can be
seen, that mostly the measured values and the sin fit are lying quite close together, only a few
points do not fit to the sinus. There are different reasons possible for this effect. First, changes
of the attitude of the aircraft and small changes of it's speed are not considered at all at this
discussion, and second due to the strong wind and the low level of measurement, there can be
expected a lot of turbulences and variations of the wind speed during the observation time.

Out of the coefficients of the IsinI fit an average wind vector was estiniatedin reverence to the
aircraft x-axis and the result has been compared with the data from the inertial reference system
(IRS) of the aircraft. This results are shown in table 1.

Table 1. All values in m/s if not other indicated. Comparison of the

measured wind field. The results in the level of 159 m over ground has been

obtained bv the Doppler lidar, and the resul

Its at the aircraft level by the IRS.

Lidar

IRS

ground speed

104,5

104,1

horizontal wind speed at 315 m

19,2

horizontal wind angle at 315 m

61,7°

vertical wind speed at 315 m

0,1

horizontal wind speed at 159 m

13,5

horizontal wind angle at 159 m

74,3°

vertical wind speed at 159 m

0,9

The results of the ground speed from the lidar and the IRS fit good together. The difference in
the wind parameters is due to the different levels, 159 m over ground for the lidar and 3 15 m for
the aircraf^t.

These points are the most important results of this campaign. The good coincidence between the
lidar and the IRS concerning the ground speed is the basis for the evaluation of a tl^
dimensional wind field with an airborne Doppler lidar. Furthermore it has been proved that it is
possible to measure a Doppler shift from aerosols with this instrument. Before the next flight the
following points will be changed or improved. An acousto optic modulator will be integrated to
obtain a heterodyne system so that magnimde and sign of the Doppler shift can be measured. In
dependence of this the sampling rate of the ADC must be higher (« 100 MHz). To establish a
more sophisticated signal processing the information about the attitude, the velocity of the
aircraft, and the exact scanner position are required. These parameters have to be stored
simultaneously together with the digitised data. With all these points improved, it should be
possible to establish an operational Doppler lidar for the measurement of small scale wind
phenomena.

329

REFERENCES

Bilbro, J. W., 1980. “Atmospheric laser Doppler velocimetry; an overview". Ootical
Engineering, 19: 533-542. ^

Billwo, J. W., DiMarzio C., Fitzjarrald D., Johnson, S., and Jones, W., 1986. “Airborne
Doppler lidar measurements". Appl. Opt., 25: 3952-3960

Kingston, R. H., 1978. Detection of Optical and Ir^ared Radiation.. Vol. 10 of Optical
Sciences, Springer Verlag, New York, 24 pp.

Schwiesow, R. L., Kopp, F ., and Werner, Ch., 1985. “Comparison of cw-Lidar Measured
Wind Values Obtained by Full Conical Scan, Conical Sector Scan and Two-Point-
Technique”. Joum. Atmospheric and Oceanic Technology, 2: 3-14

Woo^eld, A^., and Vaughan, J. M., 1983 “Airspeed and Wind Shear Measurements with
Airborne OO2 CW Lasef“. International J. Aviation Safety, 1: 207-224

330

BEHAVIOR OF WIND FIELDS THROUGH TREE STAND EDGES

Ronald M. Cionco
Battlefield Environment Directorate
US Army Research Laboratory
White Sands Missile Range, NM, 88002-5501 USA

David R. Miller

Natural Resources Management and Engineering Department
University of Connecticut
Storrs, CT, 06269-4087, USA

ABSTRACT

Recently several investigators have indicated that the forest edge effect involves
the generation of form drag forces, the appearance of a large pressure gradient,
the upward (or downward) deflection of mean flow, the transport of momentum
into the leading edge of the canopy, and the advection of the flow characteristics
conditioned by the upstream surface across the edge. The purpose of this paper
is to quantify the effects of atmospheric stability and wind regime on these edge
flow processes. To analyze these effects, the huge Project WIND canopy flow
and micrometeorological data base collected by the USA ASL (now the US
ARL). The WIND data are tailored for this type of study. Other known data sets
are notably limited for this purpose. These raw wind data sets were conditionally
selected for periods when the wind was +/- 20 degrees of perpendicular to the
stand edge. This procedure resulted in 132, 30 minute periods at the orchard
edge and 94 periods at the forest edge. The 30 minute data runs were classified
by z/L into three categories; free convection (z/L<-l), mixed convection
(-1 <z/L<0), and stable (z/L>0) as similarly defined by Panofsky and Dutton.
Results of this research demonstrate that the airflow properties conditioned by the
upwind surface such as friction velocity, mixing length, and turbulence
characteristics are advected for varying distances across and through the tree
stand edge depending on the atmospheric stability.

1. INTRODUCTION

Albini (1981), Li et al (1990), Miller et al(1991) and most recently Klaassen (1992) have
modeled the air flow across forest edges. They have indicated the edge effect involves the
generation of form drag forces, the appearance of a large pressure gradient, the upward (or
downward) deflection of mean flow, the transport of momentum into the windw^d edge of the
canopy, and the advection across the edge of the flow characteristics conditioned by the
upstream surface. Very few field measurements are available to verify these and subsequent

331

models. Those that are available were made for limited studies (Miller et al 1991-
Raynor 1971; Thistle, 1988; Wang, 1989; Kruijt, 1993). The data sets are therefore difficull
to transfer to other sites and conditions because of instrumentation, spatial sampling, and fetch
wnstramts. None are comprehensive enough to analyze the interactions of the forest edge and
me state of the atmospheric boundary layer (stability, etc.) on the local mean wind field except
for the Project WIND data base.

During Project WIND, comprehensive (spatial and temporal) micrometeorological
me^urements were made across the edge of an orchard and a pine forest in north central
California (Cionco, 1989) conducted by the US Army Research Laboratory (formerly the USA
AtmosphOTc Sciences Laboratory). The purpose of this paper is to use the measured wind
fields at these two edges to quantify the effects of the atmospheric stability and wind regime
on the m^ wind flow through and over the tree stand edges. Note that although the forest
setup will be mentioned, this paper will limit its scope to reporting on the results of the
analysis of the Orchard Site.

2. METHODS

2.1 Field Measurements

^ Project >^ND were conducted in and about the Sacramento River Valley

o Northern California during the period beginning June 1985 and ending October 1987
(Cionco, 1989). One site was a geometrically uniform, almond orchard on the flat terrain of
the Sacramento River Valley. The other site was a more complex coniferous forest on the
west slopes of the Sierra Nevada Mountains.

In each phase, data were collected over a two week time span for selected periods resulting

in two full sets of daytime (1000 to 1600 hrs), nighttime (2200 to 0400 hrs), transition (sunrise

^ Mnset) periods and two full 24 hour diurnal periods (1000 to 1000). The four phases of

T ® conducted during synoptic meteorological regimes of weak marine incursion -

c" /lif activity - Jan/Feb 86, shallow convection - Apr/May 86, and subsidence -

oCp/Oct o7.

Identical sets of eight-level micrometeorological towers were located at both orchard and forest
sites at three positions during each phase as presented in Table 1. One tower (OT3) is located
deep into the canopy 24 tree heights (H) from the canopy’s edge. The second tower (OT2)
IS placed just inside (2.5H) the canopy’s edge. The third tower (OTl) was on the extensive
and uniformly cut open field 24 H from the canopy’s edge in the clearing. Note that OTl
provides the reference profile of the surface layer ambient flow for this study. The sensor
heights, vmables measured, measurement frequencies and tower locations were reported in
de^l by Cionco (1989). Complete profiles of the wind components (u,v,w), temperature (T)
Md relative humidity (RH) wete measured at each tower. Note that the orchard canopy was
8m tall whereas the forest canopy averaged 18m tall.

332

Table 1. Micrometeorological Tower Instrumentation for the Orchard

SENSOR HT

OTl

OT2

OT3

2.0TH

1.7

1.45

1.25

l.OOTH

0.75

0.50

0.25

Sfc

uvw,T/AT,RH
uvw,T/4.T,
uvw,T/AT,
UVW,T/^T,
uvw,T/4T,
uvw,T/^T,
UVW,T/aT,
uvw,T/A,T,RH
Rn, P
Hs

uvw,T/AT,RH

uvw,T/aT,RH

uvw,T/AT,RH

uvw,T/aT,RH

uvw,T/AT,RH

uvw,T/AT,RH,Rs

uvw,T/AT,RH

uvw,T/AT,RH,

uvw,T/AT,RH,Rs

2.2 Data Reduction

The raw data sets were conditionally sampled to select all the periods, at least one hour long,
when the wind direction was within + or - 20 degrees of normal to the stand edge. The data
were then split into even 30 minute run periods. This procedure resulted in 132 thirty minute
periods at the orchard edge (60 into the edge and 72 with the wind out of the edge) and 112
thirty minute periods at the forest edge (19 in and 93 out).

Mean fluxes of heat (6,) and moisture (q.) and mixing length (IJ and the resulting stability
parameter (z/L) were calculated from the above canopy profiles by Monin-Obukhov surface
layer similarity (Obukhov, 1971) where the method of Rachele and Tunick (1992) and Tunick
et al.,(1994) was used in place of the diabatic influence functions of Paulson (1970) and Benoit
(1977) as follows.

The 30 minute data runs were classified by z/L (measured in the open field) into three
categories; free convection (-l<z/L), mixed convection (-1< =z/L< =0), and stable
(z/L>0). These are similar to the stability classes defined by Panofsky and Dutton (1984),
except we group periods when mechanical turbulence dominates and when mechanical
turbulence is dampened as follows;

z/L

Panofsky and Dutton This

Interpretation Classification

Strongly negative
Negative, but small
Zero

Slight positive
Strong positive

Heat convection dominant Free convection
Mechanical turb. dominant Mixed convection
Solely Mechanical turbulence Mixed convection
Slight damping of turbulence Stable
Mech. turb. severely reduced Stable

These groupings were used because the number of 30 minute runs with slightly positive or zero
z/L were limited.

333

Rawindsondc measurements of the upper air profiles were available every two hours. The
stability classifications determined from the surface tower data were compared to the static
stability of the boundary layer determined from the rawindsbnde data by the method of Stull
(1993). Only runs in which both measurements of stability agreed were used. Table 2 lists the
number of 30 minute runs in each stability/wind direction category.

Table 2. Numbers of 30 minute runs in each Wind Direction/Stability Category used in the
Analysis.

Wind Direction

Free

Mixed

Stable

Orchard:

Into edge

Out of edge

Forest:

Into edge

Out of edge

3. RESULTS

The forest edge and orchard edge results were very similar. Therefore the results are presented
here for the orchard only to meet space limitations.

3.1 Profile adjustments across the edge

Figure 1 presents average vertical profiles from all of the half hour periods for the three
stability classes (stable, mixed convection, free convection) at each tower location with the
wind blowing either into (figure 1 a,b,c) or out of (figure 1 d,e,f) the stand edge. For
comparison, all the profiles, even those outside the orchard, are scaled by the wind at 8
meters which is the height of the orchard canopy.

In the open field (tower OTl, figure la,d) the measurements were all in the surface layer well
above the short crop canopy and the scaled profiles demonstrate the effects of stability on the
surface^ layer flow. The profiles from mixed and free convection conditions are very similar
with slightly less shear during free convection conditions. The profiles in stable boundary
layers diverge drastically from the profiles during convective boundary layer conditions with
the overall shear much larger as expected in conditions with little vertical turbulent mixing.

Comparison of the OTl profiles with wind into and out of the edge shows essentially no
difference during convective conditions, but during stable conditions the mean profile has
significantly less shear. Apparently the greater mixing capacity above the orchard is

334

Figure 1. Mean Wind Profiles: With wind into edge: a. at OTl; b. at OT2; c. at OT3
and With wind out of the edge: d. at OTl; e. at OT2; f. at OT3.

transported further than 24 tree heights out of the edge during stable boundary layers but is
transported less than 24 tree heights during convective and neutral conditions. Therefore the
assumption of infinite fetch is violated at this location during stable conditions but not in a
convective boundary layer.

The wind flowing into the edge at 2.5H inside the orchard edge (figure lb), shows high sh^
above the canopy as the wind compresses over the top of the canopy and a relatively high wind
penetrating the subcanopy trunk space as noted in previous edge studies. Stability effects the
magnitude of these flows significantly. In free convection conditions, the below canopy
penetration is maximized while in stable conditions the above canopy shear is maximized.
Obviously the strength of vertical mixing in the upwind surface layer has a significant effect
on momentum absorption and wind penetration into the edge.

At 2.5H inside the leeward orchard edge (OT2, figure le), the mixed convection profile is
essentially the same as inside the canopy. But the stable and free convection profiles are
showing the effect of the relaxation of drag 2.5H downstream from their position. The free
convection profile shows greater shear above the canopy and the stable profile shows less shear
above the canopy than is present inside the orchard away from the edge. Obviously the
downward vertical motion at the leeward edge overshadows the effects of stability at this
location.

335

At 24H windward inside the orchard edge (OT3, figure If), the relative effects of stability are
similar to outside the canopy when no edge effects are present. The mixed and free convection
profiles are very similar while the stable profile shows greater shear above the canopy. Below
the canopy , no subcanopy maximum is present and the least penetration of momentum occurs
during stable conditions, as expected. Comparison of this to figure Ic, the same position but
leeward of the edge, shows the same profile during mixed convection conditions but the stable
and the free convection profiles diverge significantly from figure If. The free convection has
less shear and the stable profile has more shear above the canopy than when the edge was
downwind. These characteristics were developed at the edge (figure lb) and apparently were
transported horizontally further than 24H into the edge.

3.2 Scaling Parameter Adjustment Across the Edge

Table 2 presents the mean values of the friction velocity, U*, calculated from the profile data
above the canopies, for each stability and wind direction classification at the three orchard
tower locations. Comparisons of OTl and OT3 values shows that U* in the roughness
sublayer above the orchard canopy, at OT3, was about two to three times that in the surface
layer above the open field, reflecting the greater mechanical turbulence above the rougher
surface. In all cases the greatest difference between the open and orchard friction velocities
was during stable conditions and the least difference was in free convection conditions.

Table 3. Mean U*, 6* and q* Values Across The Orchard Edge In Different Stability Classes.

Wind

OTl

Dir

Free

Mixed

Stb

Into

Edge.

,22

.39

.15

-.6

-.27

.005

.0007

Out of Edge

,21

.25

.03

-1.1

-.8

.04

.004

.002

.004

OT2 OT3

Free

Mixed

Stb

Free

Mixed

Stb

— — — —

—

. 19

.28

.24

.39

.83

.41

-1

-.7

.08

-10

-1.2

-.006

-.0003

-.0002

.012

.22

.46

.10

.30

.62

.42

-1.1

-.17

.04

-6.5

-1.0

.11

-.002

-.0009

-.002

-.018

-.003

-.018

The OT2 edge tower data were intermediate between the open and orchard. But the edge
tower generally shows U* closer to the open conditions when the wind is from the open field
and closer to the orchard values when the wind was from the orchard, reflecting the horizontal
advection of the upwind conditions. This is demonstrated visibly when the values in Table 2
^e plotted as horizontal profiles in figure 2, The plots show concave curves when the wind
IS from the o^n field and convex curvature when the wind is from the orchard. The exception

to this is during stable conditions which shows an opposite trend when the the flow is out of
the edge.

Figure 3 compares the stability parameters measured simultaneously above the smooth field

336

z-d/L above orchard. z-d=8m

mechanical mixing (i.e. orchard (z-d)/L < open z/L). In a convective boundary layer the air
and rough orchard. In stable conditions, the flow over the field is significantly less turbulent
than that above the orchard. Thus conditions are less stable above the orchard due to higher
above the orchard tended to be more unstable than that above the open field when the open z/L
was less than -1, Apparently when these conditions of strong convection (high heat flux and
low wind speeds) occurred, the drag of the orchard canopy slowed the wind to nearly zero and
thus induced nearly free convection above the orchard. When mixed convection dominated
the boundary layer (-l<z/L<0), the above relationship was reversed as shown in the inset
graph in figure 3.

Vertical profiles of momentum flux inside the orchard (OT3) and just inside the edge (OT2)
are shown in figure 4. The open area tower is not shown because all the measurement levels
were in the surface layer and the momentum flux was essentially constant. The orchard tower
(OT3) with homogeneous conditions showed profiles similar to other tree stands in the
literature. With maximum momentum flux above the canopy and decreasing values below the
canopy top.

Remembering that the momentum flux values at the edge tower are not vertical, but are
calculated perpendicular to the stream lines, the edge tower shows a maximum at the bottom
of the canopy and a minimum just above the canopy top when the wind is horizontal
penetration and downward deflection of momentum below the canopy. The above canopy
minimum reflects the speed up over the top of the canopy, an increase in horizontal advection
and a subsequent reduction in cross streamline turbulent transport.

When the mixed convection wind is from the edge the edge profile is similar to the profile
inside the canopy except that the profile is more vertical and showed lower values at all but
the below canopy level. The lower vertical momentum transport is a reflection of the relaxation
of the flow as it diverges and pours overs the edge.

The 6* values show that the sensible heat flux was higher over the orchard and lowest over
the open field. The very low (==0) q* values reflected the lack of ET in the arid open field.
The orchard was periodically irrigated during the growing season and therefore generally
showed a non-zero humidity gradient.

4. DISCUSSION

4.1 Mixing Length Adjustments

IGaassen (1992) pointed out that the mixing length (1,J adjustment across the edge was non¬
linear with advection of the mixing length from a different height a significant influence. He
modeled the change in 1„ across the edge using advection and adjustment:

Momentum flux profiles
wind into orchard

where the advection is:

dx u dz

and the adjustment term is:

^=0/1--^)
OX I

(2)

(3)

where q is the "rate of adjustment" constant and is the fully adjusted mixing length.
Klaassen fit the above equation to the data of Bradley and arrived at a value of for q.
Using the profile data presented here 10"^ is a better fit and there is no change with stability.
There is, however, a significant change in the advection term with stability because the ratio
w/u changes. Mean values of w/u at the edge (2.5h inside the edge) and interior (24h inside
the edge) are plotted in figure 5 for each stability class.

Outside the edge in the open the mean flow was horizontal and parallel at all levels. The
slight non-zero values in these average profiles are the average leveling errors of the
anemometers. In the interior canopy with no edge effects present (OT3) the mean flow above
the canopy is essentially horizontal. Below the canopy there is a general downward motion
reflecting the periodic penetration of gusts from above.

Near the edge with the wind blowing into the stand (OT2, figure 8b) the general upward flow
over the top and the upward movement of air that penetrated the side of the stand below the
canopy are apparent. The large positive below canopy values during stable flow are reflections
of the relative absence of turbulent drag as air is forced into the side of the stand and then
moves upward. In convective conditions, the kinetic energy of air forced into the side of
the stand is dissipated more rapidly by the turbulence and the mean flow at 2.5 h inside the
edge does not move upward as readily.

Near the edge with the wind blowing outward (OT2, figure 5), the above canopy flow was
essentially the same as the interior flow except when the mechanical mixing was strong (mixed
convection) where a slight upward motion can be seen. Below the canopy, the motion was the
opposite of the interior canopy where a general upward motion can be seen during convective
conditions. The exception is during stable conditions where the flow has changed from
downward in the interior to horizontal near the edge. The general upward motion during
turbulent conditions ahead of the drag release at the edge was accompanied by a slowdown of
the wind at this position.

The streamline slope is an important indicator of how rapidly the wind field is adjusting to the
height change in the new surface. Thus from figure 5, when the air is flowing out of the
orchard edge, we can see that the flow reacts to the drag release at the edge well before

340

Deviation of Streamline from Horizontal
wind into orchard

Deviation of Streamlines from Horizontal
wind out of orchard

-50-40-30-20*“ 10 0 10 20 30 -50-40-30-20-10 0 10 20 30 -50-40-30-20-10 0 10 20 30
W/U (degrees) W/U (degrees) W/U (degrees)

TOWER-OTt TOWER-0T2 T0WR-0T3

Figure 5. Profiles of the streamline angle, W/U, with wind into the stand (a) and with wind
out of the stand (b). Horizontal bars indicate standard deviations.

341

reaching the edge. We can infer that the adjustment length of the vertical component is
longest above canopy in stable conditions and shortest in free convection.

5. CONCLUSIONS

Stability has a major effect on the canopy-air interaction at tree stand edges. In a stable
boundary layer, the air over the tall canopy is less stable than that over the oi)en field due to
higher turbulent mixing induced by the rougher canopy. In a convective boundary layer, the
absolute wind speed interacts with the two canopies differently. At very low wind speeds, the
canopy drag reduces the wind enough that free convection conditions occur above the orchard
while mixed convection dominates the open field. At moderate and high wind speeds, the
higher turbulence over the orchard keeps the air very close to neutral while the open field is
dominated again by mixed convection.

The strength of vertical mixing in the upwind surface layer has a significant positive
correlation with momentum absorption and wind penetration into the edge. Whereas at the
leeward edge, the downward vertical motion overshadows the effects of stability at this
location and the flow reacts to the drag release at the edge well before reaching the edge.

The greatest difference between the open and orchard scaling parameter u* was during stable
conditions and the least difference was in free convection conditions. The temperature scaling
parameter, 6,, showed that the sensible heat flux was higher over the tall canopy and lowest
over the open field, q* was zero over the non-irrigated open fields, but indicated measureable
latent heat flux occurring over the tree canopies.

Upwind conditions are advected horizontally across the edges and the adjustment length
depends on stability. The adjustment length of both the u and w components are longest above
the orchard in stable conditions and shortest in free convection.

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Vegetation Cover Layers. J Appl. Meteorol. 20:1325-1335.

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Kruijt, B. 1994. Turbulence Over Forest Downwind of an Edge. PhD Dissertation, Dept, of
Physical Geography, University of Groningen, The Netherlands. 156p.

342

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Panofsky, H. A. and J. A. Dutton. 1984. Atmospheric Turbulence. John Wiley, N. Y.

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White Sands Missile Range, NM 88002-5501.

Raynor, G. 1971. Wind and Temperature Structure in a Coniferous Forest and a Contiguous
Field. Forest Science 17:351-363.

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Thistle, H. W., Jr. 1988. Air Flow Through a Deciduous Forest Edge Using High
Frequency Anemometry. Ph.D. Dissertation. Department of Natural Resources Management
and Engineering, University of Connecticut, Storrs, CT 06268. 211 pp.

Tunick, A., H. Rachele, F. V. Hansen, T. A. Howell, J. L. Steiner, A. D. Schneider and S.
R. Evett. 1994. Rebal ’94 - A Cooperative Radiation and Energy Balance Field Study for
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Wang, Y. 1989. Turbulence Structure, Momentum and Heat Transport in the Edge of Broad
Leaf Tree Stands. Ph.D. Dissertation. Dept, of Natural Resources Management and
Engineering & Civil Engineering, University of Connecticut, Storrs, CT 06268 137 pp.

343

Proceedings o£ the 1994 Battlefield
Atmospherics Conference, 26 Nov - 1 Dec, 1994
White Sands Missile Range, New Mexico

TRANSILIENT TURBULENCE, RADIATIVE TRANSFER,
AND OWNING THE WEATHER

R.A. Sutherland, Y.P. Yee and R.J. Szymber
U.S. Army Research Laboratory
Battlefield Environment Directorate
White Sands Missile Range, New Mexico 88002-5501

ABSTRACT

A major technical barrier encountered in modeling radiative processes in the
atmospheric boundary layer involves making proper account of turbulent and
radiative interactions. Exact solutions are not possible due to the problem
of closure of the underlying differential equations and the complexity of
both the turbulent and radiative processes. The most direct effect of the
radiative interaction is to alter the energy balance at the surface and cause
differential heating in the aerosol layer. These effects then alter the local
vertical profiles of temperature, aerosol concentration, and other meteoro¬
logical variables which have an effect on the overall stability of the layer.
However most conventional micro-meteorological models either ignore radiative
processes entirely or utilize sub-grid parameterization schemes which may not
be applicable to the modern, aerosol -laden, "dirty battlefield" environment.
On the other hand many conventional radiation models ignore the turbulent
interaction by focusing only on cases where the turbulent-radiative heat flux
ratio is small. In this paper we offer an approximate solution to handle both
radiation and turbulence using a modified two-stream radiative transfer
scheme of McDaid (1993) in combination with a relatively new " transilient"
turbulence theory of Stull (1987) and others. In this paper we extend the
Stull method to incorporate radiative interactions making special account for
such radiative processes as absorption, extinction, thermal emission, and
multiple scattering. The research is relevant to Army applications involving
modeling and simulation of boundary layer processes and contributes to the
scientific basis of programs in “Owning the Weather" and limited weather
modification.

1. INTRODUCTION

The Army has had a longstanding interest in simulating and modeling the
effects of the "dirty battlefield" on boundary layer micro-meteorological
processes. The main emphasis in the recent past has been on the direct effect
of aerosols on electromagnetic propagation. More recently it has been
realized that these same processes can have a significant effect on critical
environmental parameters (Yee, et.al. 1993a, b) and boundary layer destabil¬
ization processes (Lines and Yee, 1994; Grisogono and Keislar, 1992;
Grisogono, 1990 and Telford, 1994) . Other relevant work on a larger scale and
not directly involving the turbulent reaction has been published for fogs by
Bergstrom and Cogley (1979) and Saharan dust by Carlson and Benjamin (1980) .
More recently the idea of "Limited Weather Modification" through the use of
aerosol obscurants and artificial fogs has been considered in the ARL "Own
the Weather" program (Szymber and Cogan, 1994) . Also the relevance of such
a capability to the Army mission is discussed in the recent STAR 21 report
published by the National Research Council (STAR 21 - Strategic Technologies
for the Army of the Twenty-First Century, 1993) . However one of the major
technical barriers in modeling the relevant physical processes has been in
the understanding of the nature of turbulent-radiative interactions and the
attendant effects on the structure of the atmospheric boundary layer and it
is this problem that we are addressing here.

345

2. PHYSICS OF THE PROBLEM

One of the questions that arose during the initial phases of this work
concerned the magnitude of the radiative effect as compared to the turbulent
effect. Whereas it is certainly true that for strong winds the effect of
turbulence will dominate the radiative effect, it is not so clear which is
more significant at the lower wind speeds. We can gain some insight into this
(^estion by examining fogs where the radiative effect has been studied. This
is done in the plot of figure 1 which is based upon an expression from
Oliver et. al. (1978) which we have modified slightly to express the ratio
of radiative-to-turbulent heat flux density as a function of the friction
velocity, u.

Figure 1. Plot showing the radiative-to-turbulent heat flux ratios as a
function of friction velocity for fog thickness of 10, 50, and 100 meters.

As expected, the plot shows the radiative flux to dominate at low wind speeds
(friction velocity) and to diminish as the wind speed increases. Note, for
example, that for a layer thickness of 50 meters that the radiative flux is
greater than the turbulent flux for all values of u less than about 0,20 m/s
and is at least 10 percent of the turbulent flux out to a value of 1.0 m/s.
The calculations, although approximate, do give a qualitative indication of
the significance of radiative effects.

The physics ^ of the problem is explained with the aid of the sketches in
figure 2 which sho\v a hypothetical example of the effect of radiation and
turbulence on profiles of temperature, T, and aerosol concentration, C.
Figure la sets the initial condition of a hypothetical early morning
temperature inversion over an isothermal layer near the surface. It is
assumed that, at some height, the inversion gives way to a lapse condition
iriciicated by the upper dashed line. In figure la we also assume a Gaussian
aerosol concentration profile with a maximum in the isothermal layer.

The effect of the solar radiation is to first set up an "energy balance" at
the surface that results in an increase in the sensible heat flux density,
H. The second effect is to cause heating of the entire layer at a rate
dependent upon the concentration and radiative properties of the underlying
aerosol . Because of the presence of the higher aerosol concentration near the
surface, the change in the profile due to radiative heating will be most
affected near the surface. This is also where surface induced radiative and

346

turbulent heat fluxes are most significant. The overall effect is shown in
figure 2b as an increase in the temperature of the isothermal layer and the
development of an unstable lapse condition near the surface. In this step the
upper level inversion remains almost unaffected by the direct heating due to
the lower aerosol concentration at this level. Also during this step the
aerosol concentration is not directly affected by the radiative heating.

Figure 2 . Sketch demonstrating the stages of the radiative turbulent
interaction and the effects on profiles of . temperature , T, and aerosol
concentration, C.

In figure 2c we show the combined effect of radiative cooling and the induced
turbulence which tends to counteract the radiative forcing by producing an
upward “mixing" of the hot air from the surface with the relatively colder
air above. This step may be viewed as a (turbulent) reaction to the unstable
layer created near the surface. Note from figure 2c that the overall effect
results in a tendency toward neutral . Note also in figure 2c • that the
concentration profile has also changed due to the actual movement during the
mixing process. The final step of the process that takes place simultaneously
with the turbulent reaction is due to radiative cooling by thermal
emission to an extent dependent upon both the temperature profile and the
concentration levels.

In this paper we present our results in modeling the processes sketched in
figure 2 using a combination of radiative transfer theory and a relatively
new " transilient " approach to modeling the turbulent interaction due to Stull
and co-workers at the University of Wisconsin (Stull, 1984, 1986, 1993; Stull
Sc Takehiko, 1984; Stull & Driedonks, 1987; see also Cuxart, et.al., 1994).

3. RADIATIVE MODELS

The radiative transport model consists of two parts; one to treat the effect
of radiative heating of both the air column and the surface (i.e. radiative
"forcing") and one to account for radiative cooling due to thermal self
emission (i.e. radiative "reaction"). The radiative transport^ model is
composed of two parts; one treating solar band (shortwave) radiation and

347

another treating thermal band (longwave) radiation. In both cases effects
of multiple scattering and absorption are treated using a modified two— stream
fo^ulation originally due to Adamson (1975) as modified by McDaid (1993).
This particular model has the advantage of relative simplicity and can be
modified to treat inhomogeneities using first order corrections developed bv
Sutherland (1988).

Both models, and the turbulence model described later, assume a five level
aerosol layer as illustrated in the sketch of figure 3. Each layer is assumed
to be homogeneous and described by a single value for temperature, wind
speed, humidity, and aerosol concentration.

Figure 3 . Sketch describing of the five layer model . Note that the
optical depth, t, is referenced positive downward.

The equations for calculating the radiative fluxes at each layer interface
are written in general as follows:

Shortwave :

) ^^0^0 1 '■ “o' ; &)„, g) ]

Longwave :

F Ut) (X ; g) -R' {z ! g) ] (2)

(T^-x)

"^0^0 f ^ o-'f ' “o' '• g)

]

where t is the optical depth at any level inside the layer and is the

348

total optical depth of the layer. In eq. (1) is the solar band irradiance
incident at a zenith angle 0„ [vi„= |Cos (GJ | ] at the top of the layer and in
eq. (2) D is the total thermal band downwelling hemispherical irradiance at
the top of the layer. In both expressions is the surface albedo and is
the downwelling surface irradiance both taken as appropriate to the
particular bandpass' of interest (i.e. shortwave or longwave) . Other
quantities are; aerosol scattering albedo, Uq, and^ the optical phase function
asymmetry parameter, g, both of which are a function of the aerosol type and
the bandpass under consideration. In both expressions EjCx) is the well known
exponential integral and the functions R' and T* are the diffuse transmission
and reflection operators which are, strictly, complex functions of the
optical depth that account for effects of multiple scattering and absorption
and described in greater detail elsewhere (Sutherland 1988) . For purposes
here we use a less accurate but nevertheless useful approximation based upon
a modified two-stream approximation due to McDaid (1993) . Some typical values
of the R* and T* functions are plotted in figure 4.

Optical Depth (t) Optical Depth (r)

Figure 4. Representative plots of the multiple^ scattering functions
for diffuse reflection, R*, and transmission, T*, .

In all of the above expressions the surface irradiance, G^, is approximated
to account for the effects of the aerosol layer as :

Shortwave :

Longwave :

The net radiative flux at any level in the layer is determined by repeated
application of eqs. (1) and (2), then the time rate of change of temperature
due to radiative heating for each level i is given by:

349

dT^

~dt

(5)

dT^

"dF

pCpAZ

; i=l

(6)

where the quantity pC^ is volumetric specific heat of air, Az is the sub¬
layer thickness and AF^ is the net radiative flux density entering the i'”
layer for either the shortwave (superscript s) or longwave (superscript 1)
spectral regime. As indicated, the second expression applies only to the
layer nearest the surface and utilizes the modeled surface heat flux density,
H .

In practice the above expressions are used in a matrix formulation relating
temperature rate of change to height. For the radiative forcing terms this
results in a diagonal matrix. For the self emission terms however there is
a need to account for transfer of (thermal) radiation from one level to the
next as well as the self emission. This results in a full matrix with
elements approximated by:

R..-e.e.(aT*) E^\ z .-t .\ ; i*l ,g .

R,j^e^e.{aT*)E^\z.-z.\;i^l

where o is the Stefan Boltzmann constant and the layer emissivity e- is
given simply as {e^ = [1 - coj [1 - } where At is the layer optical
thickness. As before the first layer (i = 1) is an exception and requires
accounting for surface emissivity,

4. TURBULENT REACTION MODEL

We now turn attention to the turbulent reaction model where we borrow
^hrongly from the theory of "transilient turbulence" developed over the years
by Stull and co-workers at the University of Wisconsin. A* complete
description of the theory can be found in the cited references and in the
following paragraphs we give only a cursory description. The direct effect
of the radiative forcing is to alter the temperature profile, and this, in
turn, results in the creation of an unstable sub-layer region as explained
in the discussion of figure (2) . The creation of this instability then sets
up (turbulent) motions in the layer which tend to oppose the cause of the
forced instability. The degree to which this happens, and whether or not the
reaction will be turbulent or non— turbulent , depends upon several factors
including the temperature and wind speed profiles and the general
®^v^^onmental conditions. One quantifiable measure of the strength of the
instability is the Richardson Number given by:

(g/T^) dT/dt
(dU/dz)^

where T is air temperature, u is wind speed, g is acceleration of gravity,
and is a reference temperature. The Richardson Number represents the ratio

350

of the thermal (static) to mechanical (turbulent) fluxes. Large values imply
a stable layer and small values imply an unstable layer, pother important
quantity is the turbulent kinetic energy, ^turh^ which is a complicated
function of both the thermal and mechanical forces and is given in one
dimensional differential form as:

£H£^=.U V'— -u V'— w'e'-e

turb

(12)

where u', w' , 0' represent turbulent fluctuations in wind and potential
temperature and U,V, 0 represent their time averaged counterparts. The
quantity 6^^^, is the turbulent energy dissipation rate.

In a classic series of papers, Stull and co-workers have worked up a
theoretical scheme that utilizes the above expressions in a formulation
37Qpj^QS©nting the time dependent turbulent reaction effect ^ on any scaler
property. The result, when adapted to our five layer model, is expressed in
matrix form as :

(13)

(14)

where [T,j] and [C^] are five component column vectors representing the initial
(subscript i) and final (subscript j) temperature and concentration profiles
and is the time dependent “transilient turbulence" reaction matrix.

For all of the work here we calculated the turbulent reaction matrix using
the FORTRAN program described by Stull (1986) which we applied to the wind
components as well as concentration and temperature.

5. RESULTS AND DISCUSSION

As a test of the full radiative- turbulent theory we used the "clear air"
example described by Stull (1986) as our baseline and added an assumed
aerosol concentration profile and reworked the example to include the
radiative effect. The various aerosol and environmental parameters used in
the study are listed in Table 1 and results are shown in table 2. For the
example shown the momentum forcing fluxes were assumed to be zero and all
inputs were assumed constant in time.

Short wave flux density
Long wave flux density
Surface albedo (shortwave)

Surface albedo (longwave)

Aerosol albedo (shortwave)

Aerosol albedo (longwave)

Asymmetry parameter (shortwave)

Asymmetry parameter (longwave)

Aerosol concentration

Extinction coefficient _ _

Table 1. Aerosol and environmental parameters used in the study.

In table 2, the case 1 example shows the effect as calculated ignoring

aerosol loading (i.e. the "clear air" approximation) and case 2 shows the

results as calculated using the full radiative- turbulent model without self

w/m
50 w/m'
0.15
0.10
0.60
0.20
0.750
0.000
0.003 g/m^
1.00 km“^

351

emission. Case 3 results include self emission and Case 4 includes self
emission but omits the turbulent reaction. The final column represents
Stull's original model using our calculated heat flux density for the clear
air case (22.1 W/m^) .

From comparisons between case 1 and case 2 in table 2 we see that for this
particular example the effect of the aerosol loading gives rise to an overall
radiative contribution of about 1/2° C per hour. Comparing all cases we also
see that the initial change is largest near the surface and tends to decrease
with height and that the profile tends to isothermal as time proceeds. It is
important to note that the results here were extrapolated over time assuming
a constant solar and _ infrared loading. In applications there would be some
variation over this time span. For simplicity we have also assumed a constant
aerosol concentration.

_| HEIGHT [INITIAL | CASE 1 I CASE 2 I CASE 3 I CASE "4 I STULL '

1 HR

450

350

250

150

18.0

16.0

15.0

18.00

16.00

15.17

15.21

15.27

18.67

16.58

15.58
15.60
15.63

17.94
16.07
16.08
16.24

16.95

17.94

16.19

15.33

15.39

18.47

18.00

16.00

15.17

15.21

15.27

2 HR

450

350

250

150

18.0

16.0

15.0

18.00

15.73

15.45

15.48

15.64

19.34

17.16

16.18

16.20

16.23

17,

18.

17.88

16.38

15.66

15.77

22.07

18.00

15.68

15.50

15.53

15.58

4 HR

450

350

250

150

18.0

16.0

15.0

18.00

15.83

15.85

15.88

16.04

20.68

18.32

17.39

17.40
17.44

18,

19,

19.

20.

17.76

16.75

16.33

16.54

29.70

18.00

15.68

15.87

15.90

15<96

6 HR

450

350

250

150

18.0

16.0

15.0

18.00

16.15

16.17

16.20

16.36

22.02

19.49

18.59

18.61

18.64

20,

20.

21,

21.

78
92
07

17.64

17.11

16.98*

17.31

37.99

18 . 00
16.18
16.20
16.23
16.28

Table 2. Results of the modeling exercise showing temperature profiles.

Perhaps the most marked result from the study is the effect of ignoring the
turbulent reaction as evidenced by case 4 where the temperature change is in
excess of 20 C for the lowest level. This unrealistic result represents the
case of ignoring any exchange at all and as such represents an extreme
example. It is also interesting to note from comparing case 2 and case 3 that
the effect of adding the radiative reaction is to cause cooling at some
levels and heating at others. This may appear unusual at first because this
term generally implies cooling by self emission. This occurs because, in our
model there is an added term due to multiple scattering which tends to trap
the radiation, however, the most significant cause of the increase is due to
increased transport in the first layer due to radiation from the surface.

5. SUMMARY AMD CAVEATS

The modeling exercises reported here have demonstrated the significance of
both the radiative and turbulent heating effects in boundary layer modeling,
and the importance of treating both in micro-meteorological models. In
particular the radiative, or "aerosol loading", component has been shown to
be more significant that some have assumed for "dirty" atmospheric
conditions. On the other hand there is more work to be done in developing the
model for applicability over a wider set of scenarios and in comparing with
measurements and in the theoretical treatment of wind profile effects .

352

ACKMOVniEDGEHENTS

Portions of this work were funded under the 1994 ARL Directors Research
Initiative Program. We also wish to aclcnowledge the contribution of Dr. David
Miller, University of Connecticut, for first bringing the subject of
transilient turbulence to our attention.

REFERENCES

Adamson, D. , 1975, The Role of Multiple Scattering in

Radiative Transfer, NASA Technical Note (NASA TN D-8084) ,
Center, Hampton, VA.

One -Dimens i onal
Langley Research

Bergstrom, R.W. and A.C. Cogley, 1979. "Scattering of Emitted Radiation from
Inhomogeneous and Nonisothermal Layers. "J. Quantitative Spectroscopy and
Radiative Transfer, 21:279-292.

Carlson, T.N. and S.G. Benjamin, 1980. "Radiative Heating Rates for Saharan
Dust . "Journal of the Atmospheric Sciences, 37:193-213.

Cuxart, J.P. Bougeault, P. Lacarrere,
Between Transilient Turbulence Theory
Approaches ." Boundary Layer Meteorology,

and J. Noilhan, 1994. "A Comparison
and the Exchange Coefficient Model
67:251-276,

Grisogono, B. and R.E. Keislar, 1992. Radiative
Nocturnal Boundary Layer over Desert . "Boundary Layer

Destabilization of the
Meteorology, 52 : 221-225 .

Grisogono, B., 1990. "A Mathematical Note on the Slow Diffusive Character of
Long-wave Radiative Transfer in the Stable Atmospheric Boundary Layer ,
Boundary Layer Meteorology, 52:221-225.

Lines R.T. and Y.P. Yee, 1994, "Temperature Profile of the Nocturnal
Boundary Layer over Homogenous Desert Using LA-TEAMS" . Proceedings of
1994 Battlefield Atmospherics Conference, U.S. Army Research Laboratory,
White Sands Missile Range, NM 88002-5501 (in press)

McDaid, W.J., 1993. A Modified Two-Stream: Improvements over the Standard^
Stream. Master’s Thesis, New Mexico State University, Las Cruces, NM 88005.

Oliver D.A., W.S. Lewellen and G.G. Williamson,- 1978. "The Interaction
Between Turbulent and Radiative Transport in the Development of Fog and Low
Level Stratus ." Journal of the Atmospheric Sciences, 35:301-316.

Stull R.B., 1984. "Transilient Turbulence Theory. Part I: The Concept of

Eddy-Mixing across Finite Distances ." Journal of the Atmospheric Sciences,
41(23) :3351-3367.

Stull, R.B. and T. Takehiko, 1984. "Transilient Turbulence
Turbulent Adjustment. "Journal of the Atmospheric Sciences,

Theory. Part II:
41(23) :3368-3379.

Stull R.B. and T. Takehiko, 1984. "Transilient Turbulence Theory. Part III:
Bulk Dispersion Rate and Numerical Stability . "Journal of the Atmospheric
Sciences, 41(l):50-57.

Stull, R.B. 1986. "Transilient Turbulence Algorithms to Model Mixing Across
Finite Distances", Environmental Software, 2(1):4-12.

Stull, R.B, and A.G.M. Driedonks, 1987, "Applications of the Transilient
Turbulence Parameterization to Atmospheric Boundary-Layer Simulations. ,
Boundary Layer Meteorology, 40:209-239.

Stull, R.B, 1993, "Review of Non-local Mixing in Turbulent Atmospheres:
Transilient Turbulence Theory ", Boundary Layer Meteorology, 62:21-96.

353

Sutherland, R-A-/ 1988. "Methods of Radiative Transfer for Electro-Optical
??rSr221-23°4 the 1988 Army Science Conference Vol.

Szymber, R.J.and J.L.Cogan, "Owning the Weather Battlefield Observations
Framework Proceedings of the 1994 Battlefield Atmospherics Conference, U.S.
Army Research Laboratory, White Sands Missile Range, MM 88002-5501 (in
pir©s s ) .

Telford, J.W., 1994. "Comment on Radiative Destabilization of the Nocturnal
Boundary Layer over the Desert." Boundary Layer Meteorology, 68:327-328.

Yee, Y.P.,^ R.A. Sutherland, R.E. Davis, S.W. Berrick, and M.M. Orgill 1993.
"The Radiative Energy Balance and Redistribution (REBAR) Proaram" '
Proceedings of the 1993 Battlefield Atmospherics Conference, U.S. Army
Research Laboratory, White Sands Missile Range, NM, 88002-5501, pp. 181-194

Yee, Y-P,, R.A. Sutherland, H. Rachele, and A. Tunick, 1993. "Effects of
Aeroso]^-Induced Radiative Interactions on Stability and Optical Turbulence"
Proceedings of the Society of Photo-Optical Instrumentation Engineers.

?QQT Technologies for the Army of the Twenty-First Century,

1993. Prepared by the Board on Army Science and Technology, National Research
Council, National Academy Press, Washington, D.C,

354

FORECASTING/MODELING THE ATMOSPHERIC OPTICAL NEUTRAL EVENTS

OVER A DESERT ENVIRONMENT

G.T. Vaucher

Science and Technology Corporation
White Sands Missile Range, New Mexico 88002, U.S.A.

R.W. Endlich

U.S. Army Research Laboratory
White Sands Missile Range, New Mexico 88002, U.S.A.

ABSTRACT

Optical turbulence can degrade seeing conditions over long paths, especially horizontal
paths near a desert floor. Forecasting the onset and duration of optical turbulence
minima, which we call neutral events, requires knowledge of the local energy bailee
through the heat flux cycles. At the High-Energy Laser Systems Test Facility
(HELSTF), White Sands Missile Range, New Mexico, we collected two months of
morning and evening neutral event data sets. From these, we determined first-iteration
models for forecasting the timing of the morning and evening turbulence minimum.

We provide a general definition for the atmospheric optical neutral event, a description
of the morning and evening neutral event models over desert terrain, and an “ideal and
a "less than ideal" set of case studies for the model.

1. INTRODUCTION

For years, the degrading effects of atmospheric optical turbulence (AOT) have plagued scientists dealing
with light/laser propagation. With the declassification of the adaptive optics techniques developed by
Starfire Optical Range (SOR) scientists, astronomers and atmospheric optical/laser propagation
researchers now have a viable alternative to these degrading atmospheric effects along a slant path
(Fugate and Wild 1994). For those unable to benefit from SOR’s technology, we offer this study,
which integrates the properties of AOT and AOT neutral events (NE) into a forecastable phenomenon.

The AOT NE forecasting model we developed is based on a near-surface AOT data set collected along
a 1-km horizontal desert path in the Tularosa Basin, White Sands Missile Range (WSMR), NM.
Though the 1-km path is essentially flat, about 50 km to the east lie the Sacramento Mountains, a flat-
topped range that rises about 1.5 km above the basin floor. About 25 km to the west are the San
Andres Mountains, a much more jagged range also with maximum elevation around 1.5 km above the
desert floor. To the north and south of the site, the terrain is relatively flat with no major obstructions.

1.1 Atmospheric Optical Turbulence Defined

Light propagates through the atmosphere in the form of a wavefront, "a surface over which an optical
disturbance has a constant phase" (Hecht and Azjac 1974). Fermat’s Principle describes the optical path
length primarily as a function of the index of refraction. When a wavefront encounters random
irregularities in the index of refraction, a well-acknowledged characteristic of the atmosphere, phase
distortions occur. An accumulation of random phase differences degrades light propagation and image

355

system perfoimance. Depending on the beam size and the characteristics of the index of refraction
inhomogeneities, the results take the form of laser beam centroid wander, scintillation, image breakup
and blurring.

1.2 Measuring Atmospheric Optical Turbulence

Quantifying AOT requires an understanding of the AOT phenomenon, as well as of the assumptions
necessary to express its effects in terms of a measurable quantity. The following sections provide a
brief summary of AOT theory and a description of the main sensors used for this study.

1.2.1 Atmospheric Optical Turbulence Parameters

Atmospheric optical turbulence is a random process. Therefore, to quantify AOT characteristics, we
use statistics. The primary parameter employed in our study was the index of refraction structure
function, C„\ By definition,

C2 = <(”i -

" ^2/3

(1)

where <(n, - is an ensemble average of the atmospheric index of refraction differences

(effectively Ae index of refraction variance), and r is the separation between Uj and n2. An alternative
equation, using more easily measured meteorological elements, is

c . [79xl0-‘^rc/ (2)

where P is pressure, Tis temperature, and C/ is the temperature structure function (Tatarski 1961)
By definition,

<(r, - T^)^>

(3)

where < (T, - T^) > is the ensemble average of temperature differences. When using these structure
functions, we assume (1) horizontal homogeneity and isotropy within path r and (2) that the separation
between sample points is within the turbulence inner and outer scales. (Tatarski 1961- Kolomogorov
1961; Clifford 1978). ’

1.2.2 Atmospheric Optical Turbulence and Meteorological Sensors

The AOT sensors used to collect the C/ data were Lockheed Model IV scintillometers. These
instruments essentially measure the log amplitude variance of a beam transmitted along a 1-km
horizontal path at 8 and 32 m above ground level (AGL).

Aspirated thermistors and three-component anemometers measured temperature and wind profiles on
32^ towers at 2, 4, 8, 16, and 32 m AGL. Temperature differences between the 16- and 2-m levels
(AT) were used to characterize the heat flux. These AT values were observed at the 0-, 0.5- and 1-km
positions along the scintillometer path.

356

1.3 Neutral Events Defined

To best understand the AOT "neutral event," one must first understand the AOT diurnal cycle. The
following describes a typical sequence of AOT conditions over a desert valley floor under cle^ skies.
The 24-hr cycle, described below, begins at 0000 hours local time. Figure 1 displays a "typical"
diurnal AOT time series along with temperature and insolation for the same time period.

Under clear skies and calm winds, the desert basin atmosphere at 0000 hours (loc^ time) is stably
stratified, with the coldest temperatures at the lowest levels {AT > 0); the heat flux is negative. ^ "Hie
AOT is low. With the slightest wind, such as a katabatic flow, the stable layers ovei^rn, mixing
atmospheric layers with different indices of refraction. The resulting mdlange of density variations
increases the AOT. If winds decrease, AOT decreases.

As the sun rises under clear skies, the sun’s rays begin to warm the soil. Over time, the soil radiates
this warmth into the lowest layers of the atmosphere. The heat flux increases, passing through zero,
and the previously stable atmosphere evolves into an adiabatic or neutral-stability atmosphere. A C„
minimum is observed; this is the morning AOT NE.

As morning progresses, the sun continues to warm the ground. The ground in turn warms the
atmosphere, resulting in a deeper boundary layer. The vertical temperature difference (AT) becomes
increasingly negative, indicating an unstable atmosphere. increases. Atmospheric convection
attempts to rebalance the unstable conditions by mixing the near-surface warm air into cooler air aloft.
The heat flux is positive, and the atmosphere is unstable, with a super-adiabatic lapse rate. This
persistent mixing intensifies the atmosphere’s density (temperature and index of refraction) variations,
increasing AOT. The peak AOT occurs around midday, or soon after.

In the late afternoon, reduced insolation decreases the magnitude of negative AT values. AOT also
decreases. Just before sunset, the atmosphere briefly becomes adiabatic, the heat flux goes to zero, and
AOT reaches a minimum. The second NE of the day occurs.

Twilight evolves into night, and the warmed soil strongly emits the solar radiation absorbed during the
day. AT becomes positive. Though the atmosphere is stable, colder and heavier air from the
surrounding mountains and hills drains into the valleys. The unequal cooling and drainage create
mixing, which moderately increases AOT throughout the night.

The NE is clearly associated with sunrise and sunset. The sunrise NE occurs as the stable nighttime
atmosphere makes the transition to the unstable atmosphere of the daytime (AT changes from a positive
to a negative value). The sunset NE takes place as the daylight’s unstable conditions progress into the
night’s stable state (AT changes from a negative to a positive value). The common factor in the two
cases is that the atmosphere briefly becomes dry adiabatic, exhibiting the smallest index of refraction
variations along horizontal and vertical paths. In terms of actual field measurements, we found that the
AT values were slightly negative during the NE. This observation is consistent with the dry adiabatic
lapse rate of 9.8 °C km '.

2. FORECASTING ATMOSPHERIC OPTICAL TURBULENCE NEUTRAL EVENTS

Figure 1 (Vaucher and Endlich 1993) is a "typical" AOT desert floor diurnal cycle. "Hie relevant
features are the two C/ minima and their correlating insolation and vertical temperature time series.

357

^ ^ xTAaicu inaex or reiraction structure function

C„ . Semors sampled at 1 m AGL, along a 1-km path, (b) Temperature and insolation
Temperature sensors at 2, 4, 8, 16 m, insolation sampled at 32 m.

The quasi-isothermal condition and C/ minima occurred about 1 hr after sunrise (when insolation begiris
to increase), and again about 40 min before sunset (when insolation is approaching zero). Does this
NE timing, observed near the vernal equinox, also occur when the sun’s position is higher in the sky,
or when clouds block the rising and setting sun? In the next few sections, we refine the typical
March observations.

2.1 Statistical Model for Predicting the Neutral Events

AOT data were collected between April and May 1994 along a 1-km path in the desert environment of
the Tularosa Basin, WSMR, NM. Based on the AT and 8-m Q time series, the closest minute or
minutes of the NE were tabulated. A NE was considered to have occurred when the C„ value reached
a minimum below m ^'^ and the AT was near zero or slightly negative. When the minimum C„
value remained constant over an extended period, the midpoint of the period was listed as the NE time
The reference point used to standardize the neutral event statistics was the astronomical sunrises and
sunsets tabulated for Holloman Air Force Base, about 20 miles northeast of our site. Differences
between the astronomical sunrises (sunsets) and the NE times were calculated and averaged, and a
minimum and maximum NE time (with respect to tabulated sunrise or sunset) were determined.

Based strictly on the April/May 94 data set, the average occurrence of the morning NE was about
70 min after sunrise. The sunrise-NE time difference ranged between 40 and 133 min after sunrise.
The evening NE occurred an average of about 60 min before sunset, with the sunset-NE time difference
ranging between approximately 98 and 12 min before sunset. During the calculations and subsequent
analysis, variables were identified that directly influenced the NE times. These parameters are
discussed in the next section.

2.2 Field Observations

The "ideal" atmospheric conditions selected consisted of clear skies and low wind speeds. In analyzing
the "less than ideal" cases, we noted the effects of cloud cover, moist soil, and mountain shadowing.

The greatest cause for variation in NE timing was cloud cover. Specifically, an isolated stratified cloud
deck obscuring the sun at sunrise or sunset tended to delay the sunrise NE and cause an earlier sunset
NE. The more extensive the cloud cover from the horizon to the site, the more ill-defined the NE.
In fact, the C/ minima for these shrouded sunrise/sunset cases were often significantly shallower than
those observed under clear skies.

The muddling effects of clouds on the NE can be partially explained in that the sun goes through an
estimated 25 times more atmosphere at the horizon than at the zenith. Thus, any difference fi'om fully
clear sky would result in a diffused and more irregular insolation. This weakened and erratic warming
of the terrain translates into a sluggish evolution between stable and unstable atmospheric conditions.

Damp ground was another major influence on NE timing and duration. When the site experienced rain
within the previous 12 hr, AOT tended to be suppressed, with the NE occurring earlier. That is,
sunrise NE would occur sooner after sunrise; the sunset NE would occur longer before sunset.

Mountain shadowing was not taken into account when the table of astronomical sunrises/sunsets was
calculated. The actual on-site sunrise occurred about 10 min after the calculated sunrise. For
consistency, we have expressed all NE time measurements with respect to the calculated table of
astronomic sunrises and sunsets.

359

Mountain shadowing affected the local NE times. West of the site is a very jagged mountain range,
the San Andres Mountains. The extremely irregular horizon had the same effect as cloud cover In
Ae northern hemisphere, mid-latitude location of sunset is to the north in the summer and to the south
in the winter. The exact sunset location with respect to the mountain silhouette at the local site had to
be taken into account before the local NE forecast could be issued. The evenness of the mountain range
on the eastern horizon minimized this effect for the sunrise NE.

We conducted a month-by-month review of the average NE timing for April through June. The sunrise
NE was selected for study because of the more ideal eastern horizon. When the NE average and range
were tabulated, we found that the average time separating sunrise and NE was about 50 min near the
vernal equinox and around 85 min near the summer solstice. Each succeeding month displayed an
increase of approximately 12 min. The fact that the NE occurred further from sunrise as the sun’s
position moved northward may seem inconsistent at first. The following explanation assumes clear
skies and light winds.

It is true that northern hemisphere summer temperatures are warmer than winter temperatures. The
AOT NE, however, is concerned with temperature differences and heat fluxes (density variations). In
the winter, morning air masses are cooler than they are in summer. Therefore, the solar heat flux
required to create an adiabatic environment near the surface (AOT minimum) in the winter is less than
it is in summer, when the air mass over the terrain is warmer.

3. CASE STUDIES

Two case studies are presented below. The first is a sunset NE under "almost ideal" atmospheric
conditions; the second is a "less than ideal" sunset NE case study.

3.1 Clear Skies Case Study

On 15 June 1994, the site had a high-pressure area to the south and a low-pressure area to the north
causing westerly winds to persist throughout the day. The skies overhead were mostly clear during the’
^y, though scattered high clouds moved across the horizon from the northwest shortly before sunset.
These clouds were well to the north of the sunset horizon. A fire on a mountain range to the southwest
released large quantities of smoke visible from the site; however, westerly winds kept the smoke well
to the south during the entire period. During the forecasted NE, the temperature at 2 m AGL was
around 32 °C, the winds at 8 m AGL were from the west at about 5 m s ', and the dew point at 2 m
AGL was around 0 °C.

The sunset horizon was free of clouds, as were the atmosphere between the sunset horizon and the site
and the sky east of the site. Applying the statistical model to these "almost ideal" conditions the NE
time range forecast for ISrJune 1994 was the following;

Astronomical sunset: 1914 mST

Local NE based on average: 1814 MST

Range in which the NE could occur: 1736 - 1902 MST

Fi^re 2 displays the C/ and AT time series for this June case. Placing the NE threshold at IQ-'*
m- , the NE at both 8 and 32 m begins around 1810 MST and ends around 1848 MST. During this
period, the AT hovers around 0 °C. The single C/ minimum occurs around 1824 MST, about 10 min
later than the statistical average for April/May, but well within the anticipated NE range.

360

Delta— T (deg C)

: SOURCE

ZERO 32m
ZERO 8m

.15^ ? -4^1^ / : 0’ ’ • -1

b. : c < •

• 'M ? : ; T: *
r

‘ *??

18:00

Time (MST)

19:00

SOURCE

HALF KM
ONE KM
ZERO KM

Sunset

17:00

18:00

19:00

Time (MST)

Figure 2. (a) C„^ and (b) Ar time series for the "almost ideal" 15 June 1994 case study

3.2 Effect of Overcast Skies

On 12 May 1994, a low-pressure area centered over eastern Arizona brought moist, unstable air over
the site from the south. Thunderstorms, rain events, and considerable cloud cover dominated this 24-hr
period. The local thunderstorm activity began soon after 0300 MDT and continued until about sunrise.
Moisture and cloud cover over the site persisted throughout the day, leading to an ill-defined and
extended evening NE. Winds around sunset were firom the north at about 5 m s ‘. The statistical
evening NE model forecast the NE time and range as follows:

Astronomical sunset: 1854 MST

Local NE based on average: 1754 MST

Range in which the NE could occur: 1716 - 1842 MST

Figure 3 displays the C/ and AT time series for this ill-defined NE. In contrast to the 15 June case,
the 32-m and 8-m magnitudes tended to coincide. They also lacked a single point minimum. In
fact, there were four turbulence minima. The "best" 32-m level minimum (1708 MST) occurred before
the "best" 8-m Cf minimum (1754 MST). Note, however, that the "best" Cf minimum at 8 m
coincides with the forecast NE. The AT magnitudes hover around the 0 °C mark throughout the
extended NE period.

4. SUMMARY

Atmospheric optical turbulence (AOT) was observed in order to develop a model for predicting the time
of AOT neutral events (NE), which occur shortly after sunrise and shortly before sunset in a desert
environment. The parameter used to quantify the AOT was the index of refraction structure function.
The assumptions made when using Cf are horizontal homogeneity and isotropy within the path
and that the distance separating the two sampled points is within the turbulence inner and outer scales.
Principle sensors used for this study were Lockheed Model IV scintillometers to determine Cf and the
aspirated thermistors to measure the AT (16-m - 2-m AGL temperature differences). All sampling was
done along a 1-km horizontal path.

Cf and AT data for periods near sunrise and sunset from April-May 1994 were collected. Using the
astronomical sunrise and sunset for a local Air Force base, the difference between sunrise (or sunset)
and the NE was calculated. An average of the time differences combined with the range in the time
of occurrence allowed us to refine the forecasting model.

Based strictly on the April/May 94 data set, the average occurrence of the morning NE was about
70 min after sunrise. The difference in time between sunrise and the associated NE ranged between
40 and 133 min after sunrise. The evening NE occurred an average of about 60 min before sunset,
with a sunset-NE difference ranging between approximately 98 and 12 min before sunset.

The statistical NE model was tested and a subsequent analysis identified additional factors that directly
influence the NE. The greatest cause for variation in the NE timing was cloud cover. A shroud of
clouds during the sunrise or sunset period tended to delay the local NE. In some cases, a shallower
AOT minimum was also observed. Since the sun travels through more atmosphere at the horizon than
at the zenith, one can expect a clouded horizon to affect NE timing.

362

: SOURCE

ZERO 32m
ZERO 8m

% Tmrn

17:00

Time (MST)

18:00

SOURCE

16:00

HALF KM
ONE KM
ZERO KM

Sunset

17:00

18:00

Time (MST)

Figure 3. (a) C„* and (b) AT time series for the "less than ideal" 12 May 1994 case study

A second influence was soil moisture, which retarded the effects of insolation. A third influence was
mountain shadowing from the surrounding horizons. The orographic profiles affected the exact timing
of local sunrise or sunset, and jagged terrain occulting the sun had a similar dulling effect on the NE
to that of cloud cover.

A month-by-month analysis of the sunrise NE occurrence showed the sunrise-to-NE time differential
to increase by about 12 min per month during the spring. The greater heat flux required in the summer
months to produce the low-level adiabatic environment associated with a C„^ minimum helps to explain
the longer sunrise-to-NE separation. An "almost ideal" (clear skies) and "less than ideal" (cloudy) case
were presented. When skies were clear, the forecasted NE generally agreed with the model. During
cloudy and showery conditions, the effect of nonuniform radiation and latent heat made the simple
statistical model difficult to use.

5. RECOMMENDATIONS

The above study is by no means an exhaustive investigation of AOT NE forecasting/modeling.
Collecting and analyzing a full annual cycle of AOT NE data and quantitatively linking cloud cover,
heat flux, and ground moisture with the AOT NE would greatly enhance the AOT NE forecast model.

ACKNOWLEDGEMENTS

A special thanks to T. Jameson for daytime observations of 12 May 94; to A. Rishel and J. Niehans
for data management and assistance with the figures; and to C. Vaucher for text critique.

REFERENCES

Clifford, S.R., 1978. "The Classical Theory of Wave Propagation in a Turbulent Medium." Topics
in Applied Physics — Laser Beam Propagation in the Atmosphere, v. 25, Springer-Verlag,
Berlin, Germany, 325 pp.

Fugate, R.Q., and W.J. Wild, 1994. "Untwinkling the Stars - Part I." Sky and Telescope, May 1994,
24-31.

Hecht, E., and A. Azjac, 1974. Optics, Addison-Wesley Publishing Co., Reading, MA, 565 pp.

Kolomogorov, A., 1961. Turbulence, Classic Papers on Statistical Theory, ed. by S. Friedlander and
L. Topper, Interscience, New York. NY, 151 pp.

Tatarski, V.I., 1961. Wave Propagation in a Turbulent Medium. Dover Publications, Inc., New York
NY, 285 pp.

Vaucher, G. Tirrell, and R.W. Endlich, 1993. "Intercomparison of Simultaneous Scintillometer
Measurements over Four Unique Desert Terrain Paths." Eighth Symposium on Meteorological
Observations and Instrumentation, American Meteorological Society, Boston, MA.

364

Session I Posters

SIMULATION AND ANALYSIS

365

COMBINED OBSCURATION MODEL FOR BATTLEFIELD

INDUCED CONTAMINANTS - POLARIMETRIC

MILLIMETER WAVE VERSION (COMBIC-PMW)

S. D. Ayres, J. B. Millard, and R. A. Sutherland
Battlefield Environment Directorate
U.S. Army Research Laboratory
White Sands Missile Range, New Mexico 88002-5501

ABSTRACT

The COMBIC model was originally developed for Electro-Optical Systems of
Atmospheric Effects Library (EOSAEL) to model aerosols for which spherical
symmetry can be assumed to describe both the physical and optical properties of
the aerosols. This is a reasonable assumption when considering older,
conventional obscurants such as fog oil and white phosphorus; this approximation
breaks down for newer developmental obscurants designed to be effective at
longer wavelengths. Many of the new millimeter wave and radar obscurants are
highly nonspherical. New techniques are required to model nonspherical
obscurants. COMBIC-PMW is a merger between COMBIC and the techniques
that account for the optical and mechanical behavior of these nonspherical
battlefield aerosols. These new techniques determine electromagnetic properties
such as the ensemble orientation averaged extinction, absorption, and scattering
as well mechanical properties such as fall velocity and angular orientation of the
obscurant particles when released into the turbulent atmospheric boundary layer.

This paper describes COMBIC-PMW, its function, and how to use it. The paper
also describes the range of conditions under which the model is applicable.

1. INTRODUCTION

1.1 Model Purpose

Millimeter wave (MMW) radars were developed to provide greater accuracy than conventional
microwave (centimeter wave) radars even though MMW radars do not have the same all-weather
capability. Although MMW radars have superior penetrability through smoke, fog, and rain
over their electro-optical (EO) counterparts, they do not have the high resolution of EO systems
(Sundaram 1979). MMW systems represent a compromise in which most of the advantageous
characteristics of the microwave and EO regions are available and the disadvantageous effects
are minimized. MMW radar systems are also much smaller because component size is related
to wavelength. MMW systems are of considerable interest for applications in which size and

367

weight restrictions are important, as in aircraft and smart munitions. With the development of
the MMW systems, the Army turned to countermeasures that can defeat these radars. The
prevalent thought in the Army is that conventional battlefield obscurants hardly affect MMW
(Knox 1979). The Army is developing obscurants that can defeat the MMW systems. These
obscurants are different from the obscurants that can defeat conventional EO systems. Their
primary dimension is approximately the same as the wavelength of the system (i.e., on the order
of millimeters). Furthermore, the obscurants are not spherical, like the more conventional
obscurants. The combination of these effects create situations not present with the traditional
smokes. Atmospheric turbulence can affect the orientation of nonspherical particles. The
orientation and scattering from nonspherical particles leads to different scattering intensities at
different angles. New models are required to simulate these obscurants.

1.2 COMBIC

One of the original purposes for developing COMBIC-PMW was to assist in modeling the
effectiveness of smoke screens used in wargame simulations. The COMBIC computer
simulation predicts spatial and temporal variation in transmission produced by various smoke and
dust sources. It models the effects of reduction in electromagnetic (EM) energy by combining
the munition characteristics with meteorological information of an idealized real world.
COMBIC produces transmission histories at any of seven wavelength bands for a potentially
unlimited number of sources and lines of sight. It also computes concentration length, which
is the integration of the concentration over the path length. Previous smoke models, like
COMBIC, adequately model older conventional smokes such as fog oil and white phosphorous;
this is not true of the newer developmental obscurants. Since the original development of
COMBIC, which treats only spherical obscurants, new obscurants have been developed for
effectiveness in the MMW regime. These obscurants are severely nonspherical in shape;
therefore, new algorithms are required. The traditional simplifying assumption of spherical
symmetry to describe the optical and mechanical properties of the obscurants is no longer valid
for the newer obscurants. The new obscurants are actually nonspherical and require different
methodologies to compute their effect on the battlefield and the effect of atmospheric turbulence
on the obscurants.

1.3 MMW Obscurants

A wide variety of MMW obscurants, such as graphite, have been developed in recent years.
The most efficient are the fibers modeled as finite cylinders. Very few MMW obscurants have
made it to the inventory list. The fibers can be either prepackaged, precut and packed in parallel
arrays having packing densities as high as .8, or precut fibers loosely packed in powder form.
The Army favors the first method (Farmer, Kennedy 1991). MMW obscurants can exist in a
multitude of complex shapes including helix, coils, disks, flakes, cubes, antennas, and their
aggregates.

368

1.4 Dissemination Methods

In a dissemination system, such as a grenade or rocket, the fibers are packed coaxially in disks.
Disk thickness corresponds to fiber length. The aspect ratio of diameter to fiber length is
typically 1 to 1000. The disks are stacked on a center-core burster-unit that is used to break the
packaging binder and spread the fibers. For artillery-shell packaging, the disks must be
reinforced with a steel or aluminum superstructure. This design approach assumes that when
the disks burst, the disks separate into single fibers of the same length and diameter as the
original fibers used to make the disk. Electrostatic charge and other factors can cause clustering
of fibers to stick together along the long axis or to agglomerate into randomly oriented sets of
particles resembling bird nests. The large clusters tend to fall out of the obscurant cloud at a
much greater rate than single fibers. The large clusters decrease cloud obscuration efficiency.
The decrease in obscuration efficiency results from a reduction in extinction efficiency for the
individual clusters relative to single particles and from a reduction of the numbers of individual
fibers available for effective obscuration. COMBIC-PMW does not model this directly except
through an empirically derived munition efficiency factor.

In a fiber cutter MMW smoke generator system, the obscurant material comes from the factory
in multiple strand ropes, called tows, wound on spools. The material is often graphite, although
other materials have been employed. The number of fibers per tow can vary from 1000 to
48,000, and there are 10 to 30 tows per belt. In a typical system, the belt material is fed to a
fiber cutter consisting of two rollers in contact, in which one contacts the cutting blades at fixed
spacing (typically 6.25 mm or 1/4 in). The fiber length can be varied by changing the blade
spacing. The motor speed is variable allowing fiber belt speeds from 0 to 12 ft/s. Proper
selection of belt speed and belt size can produce throughputs of 0 to 10 Ib/min. A coanda flow
ejector consists of a short cylindrical shell with a high-speed sheath (generated by air pressure
expelled axially at the inside edge of the cylindrical shell). Momentum is then transferred to the
air within the cylindrical shell. This device can be used to produce air flow without mechanical
interference and within which shear flow can be carefully controlled. The coanda flow ejector
separates and disseminates the fibers by accelerating them to very high speeds resulting in a
nearly uniform nonbuoyant cloud.

A wafer storage and dispensing smoke generator disseminates fibrous material from wafers
containing fibers. Wafer storage and dispensing consists of a cartridge magazine, four wafer
cartridges, and a pneumatic indexing mechanism. The wafer cartridges are inserted into four
bores in the cartridge magazine. In a prototype system tested at the Dug way Proving Ground
(Perry et al. 1994), the wafer cartridge can contain up to 54, 6.25-mm- (1/4 in) thick wafers.
Each wafer is approximately 20 g (.044 lb). Pistons in the wafer cartridges discharge the wafers
into slots in the wafer turret motor that includes two rows of wafer slots. The wafer turret
motor rotates until the wafer lines up with the exit from the turret housing. There a spring
strikes the wafer’s rear surface, ejecting the compact fibers into the turret housing. Ambient air
enters at a high velocity and mixes with the aerosol and forms the exiting cloud.

369

2. DEFINITION OF PROBLEM

2.1 Scattering

The propagation of EM radiation in any medium containing particles is governed by the
combination of absorption, emission, and scattering. Particles are a subject of great importance
in determining effects of obscurants on EM radiation. Scattering and absorption depend upon
the particle size, shape, refractive index, and concentration. Mathematically determining the
radiation field scattered by particles of arbitrary shape at any point in space can be quite
difficult. Exact analytical solutions are only available for the sphere and infinite cylinder. The
scattering properties of simple geometries have been well studied (Bowman et al. 1987).
Numerical techniques and approximate analytical methods are used to analyze these properties,
usually over a limited range of conditions. In this first attempt to more effectively model MMW
obscurants, one is limited to modeling finite cylinders. A recently held workshop entitled
Second Workshop on the Electromagnetics of Combat Induced Atmospheric Obscurants examined
all aspects of the scattering problem to determine status of existing models, measurement
capabilities, field-model comparison, and where research needs to be focused.

Long-wavelength theory predicts that parallel rays encountering a cylinder are scattered axially
symmetric. Short-wavelength theory predicts that parallel rays encountering a cylinder are
scattered into a conical shell with a half angle equal to the angle between the incident rays and
the cylinder axis. For cylinders nearly parallel to the incident radiation, the scattered radiation
is contained in a small cone near the forward direction. The cone half angle increases as the
angle increases between the incoming radiation and the cylinder. For an extreme case with the
cylinder at right angles with the incoming radiation, the conical wave becomes a cylindrical
wave propagating in a direction perpendicular to the particle axis. (Note, in this case, that
backscatter can only occur when the particle is aligned perpendicular to the viewing angle.)

2.2 Polarization

2.2.1 What Is Polarization

Light waves are transverse in the far-field approximation. The displacements of the electric and
magnetic vectors are not along the line of travel, like sound waves, but are perpendicular to it.
For example, if the direction of travel of a given light beam is east, the electric vibrations may
be up and down, or north and south, or along some other line perpendicular to the east- west
axis. The transverse electrical (TE) and transverse magnetic (TM) fields are mutually
perpendicular at any point in space. Polarized light is light in which the transverse components
vibrate in a preferred manner. Unpolarized light is light that exhibits no long-term preference
as to vibration pattern. Partially-polarized light falls somewhere in between.

370

2.2.2 Effects of Polarization

Laboratory obscuration effectiveness in the MMW regime is strongly dependent upon the system
polarization mode. Note that in figure 1 this particular obscurant is much more effective against
horizontally-polarized systems. Figure 1 shows mass extinction coefficients versus concentration
for horizontal- and vertical-polarized radiation. The mass extinction coefficient average is
0.2 m^/g for vertically-polarized radiation and 0.8 mVg for horizontally polarized radiation.
Even unpolarized light can become polarized after encounters with scatterers, although the
opposite usually occurs. Figure 2 shows plots of the (relative) magnitude of the scattered
intensity as a function of the cone azimuth angle for various values of the incident angle for the
TE and TM polarization modes (Sutherland, Millard 1994). When 0i„c is perpendicular to the
cylinder, scattered TM radiation reaches a minimum at 180° (backscatter). This is not true for
TE radiation which reaches a minimum at 79° and shows a significant amount of backscatter.
Potential counter-countermeasures that can take advantage of scattered radiation polarization
characteristics can be identified through studies of a phase-function plot for both vertical and
horizontal polarization.

Figure 1. Chaff particles polarization effects. Mass extinction coefficients vary with
polarization mode being higher for horizontal polarized incident radiation.

371

2.3.1 What Causes Particles to Orient

Under certain conditions, nonspherical particles tend to adopt a preferred orientation when
falling through the atmosphere. For long cylindrical-shaped particles used to approximate
MMW obscurants the stable mode occurs when the particle is oriented with long axis horizontal.
Figure 3 shows the laboratory measured orientation distribution of chaff particles 2 and 10 s
after release. At first the particle orientation is nearly uniform; however, after 10 s the
aerodynamic and gravitation forces tend to shift the distribution to the more stable model. The
degree to which the particle orients will also affect the polarimetric extinction properties of the
ensemble. The fall velocity of a nonspherical particle is significantly lower than for an
equivalent spherical particle of the same mass (Sutherland, Klett 1992).

372

2.3.2 Role of Turbulence

Nonspherical particles tend to adopt a preferred orientation when falling through the atmosphere
under quiescent conditions. Atmospheric turbulence can cause a perturbation to the stable-fall
mode that can result in random tumbling for extremely turbulent conditions. The degree of
perturbations depend upon the level of turbulence and cylinder length as well as the aspect ratio
(ratio of particle diameter to length). Figure 4 shows results of the computation of stable-fall
mode for Reynolds number versus cylinder length for different aspect ratios. Note that the small
Reynolds number for stable-fall mode means the dominance of viscous forces over inertial forces
for MMW obscurants. The current Army belief is that stable-fall modes are the exception rather
than the rule in the turbulent atmospheric boundary layer. This belief is increasingly being
challenged.

373

REYNOLDS NUMBER vs PARTICLE LENGTH
Stable Fall Mode (Broadside to Flow)

Figure 4. Reynolds number versus particle length for stable-fall
mode for three different aspect ratios.

2.3.3 Effect of Particle Orientation

MMW particles will rarely all have the same orientation. The expected orientation distribution
of particles in a cloud will probably fall somewhere between completely oriented to completely
random for a turbulent atmosphere. The problem is not to compute obscuration efficiency for
a particle at angle B but to determine obscuration efficiency for a cloud of particles oriented at
all different angles but possibly having a preferred orientation. Sutherland and Klett (1992)
created a model that estimates the degree of orientation of various sized particles falling though
the turbulent atmosphere. The problem is difficult and, like most problems involving the real
atmosphere, is not exactly solved. Sutherland and Klett (1992) assumed that the mean square
tilt angle of a large ensemble of particles is proportional to the magnitude of turbulent pressure
fluctuations. Results for cylindrically-shaped particles are described elsewhere (Sutherland
Mdlard 1943). The theory is valid only in the inertial subrange of turbulence where the
behavior of the vanous microscale parameters are fairly well known. Some results of Sutherland
and KleU’s model are shown in figures 5a and 5b. Figure 5a gives estimates of the root mean
square tilt as a function of particle length for various levels of the turbulent dissipation rate e.
The vertical scale represents the calculated mean square tilt varying from a value of 0° (full
horizontal orientation) to 90° (near total uniform random orientation). It is evident from
figure 5a that larger particles tend toward the stable orientation mode {bB = 0) is intuitively
expected. Note that as the turbulence level increases in figure 5b, the width of the function

increases to the point where the distribution becomes nearly uniform (flat) at the highest
turbulence levels.

374

ANGLE (degrees)

(a) (b)

Figure 5. Modeled particle orientation statistics for long cylindrical fibers (a) mean square tilt
as a function of particle length and (b) particle orientation distribution.

3. PMW RESULTS

Unlike their spherical counterparts, the extinction efficiency of nonspherical obscurants depends
upon the viewing angle and the level of atmospheric turbulence. It becomes necessary to model
these factors. The PMW model uses the Wentzel-Kramers-Brillouin (WKB) method to calculate
the values of the ensemble averaged efficiencies and the differential scattering cross section for
fibers with lengths much less than the wavelength (i.e., the Rayleigh regime). The WKB
method and the quasistatic model (used to calculate the absorption efficiency), as used by PMW,
are described in papers by Klett and Sutherland (1992); Evans (1991); Pederson, Pederson, and
Waterman (1985); and Pederson, Pederson, and Waterman (1984). The subroutine WKB
requires the following inputs: M, the complex index of refraction; R, the cylinder radius
(microns); L, the cylinder length (millimeters); W, the wavelength (microns); Pq, the incident
polarization angle; B, the tilt distribution parameter (as shown in figure 5); D, the maximum tilt
angle (as measured from the X-Y plane); and the zenith angle (degrees). The outputs of WKB
are the absorption, extinction, and scattering ensemble averaged efficiencies and the differential
scattering cross section for <^ = tt and 0 = 0 with the vector polarizations. The polarization
angle is the angle measured from the vertical, clockwise, in a plane perpendicular to the incident
direction. The vector polarizations are the in the same XYZ coordinate system as the incident
angle and the maximum tilt angle.

375

4. COMBIC-PMW

4.1 Description

COMBIC-PMW is made up of two models: one that treats transport and diffusion (the original
COMBIC) and another that models the mechanical and optical properties of MMW obscurants
(PMW). COMBIC calls PMW as a subroutine and passes the parameters that define the MMW
obscurant such as length, diameter, and complex index of refraction as well as the polarization
information described in section 3, The output is the extinction efficiency, absorption efficiency,
scattering efficiency, phase function, and vector polarization for both backscatter and angle of
interest. Only the extinction efficiency is used by COMBIC. Future research will make use of
other parameters.

4.2 Inputs

All input data for COMBIC-PMW are entered in standard EOSAEL format, A4,6X,7E10.4.
Input data are entered through 80-character, order-independent, "card" images. Tables 1,2, and
3 describe the new input cards used in addition to the original COMBIC input cards.

Table 1. The PMWO card describes the properties of MMW obscurant. The first line
shows the parameters of the record. The second line gives a typical example.
Explanation of the parameters follows.

PMWO

FLENG FDIAM

3.4 1.0

FINDEX

FDNSTY

1.8

NAME

UNITS

FLENG

FDIAM

FINDEX

FDNSTY

fim

Length of fiber

Diameter of fiber

Complex index of refraction
Density of the fiber

Table 2. The TURB card describes the turbulence and lists frequencies of interest.
The first line shows the parameters of the record. The second line gives a typical
example. Explanation of the parameters follows.

TURB EPS GHz(l) GHz(2) GHz(3) GHz(4)

TURB 10.0 220 140 94 70

NAME UNITS _

EPS Turbulence parameter (10 = low turbulence, 100 = medium turbulence, 1000 =

high turbulence)

GHz(l-4) GHz COMBIC-PMW computes the transmission at 6 default MMW frequencies (220,

_ 140, 94, 70, 35, and 24 GHz). The user can change the first four.

376

Table 3. The TLOC describes target location and specifies if the sensor is a
sensor. The first line shows the parameters of the record. The second line gives a

typical example. Eixplanation of the parameters follows.

TLOC OBSN XTAR YTAR ZTAR TARN

TLOC 1 2000 3 1

OBSN

PANG

NAME

UNITS

OBSN

XTAR

YTAR

ZTAR

TARN

PMW

PANG

User assigned number matching an observer
Target X location
Target Y location
Target Z location

User assigned target number. One observer can have many targets.
If greater than zero, then the sensor works in MMW frequencies.
Incident polarization angle _ _

4.3 COMBIC-PMW Results

Figures 6 through 9 are for identical clouds. The first two plots are crosswind views of three
generators producing graphite. The second two plots are top-down views of the same thr^
generators. In the first three examples, the atmospheric turbulence is high (e = 1000), and in
the fourth example, the atmospheric turbulence is light (e = 10). The first, third, and fourth
examples are for an incident polarization angle (PANG) of 0° and the second example is for an
incident polarization angle of 90°. The incident angle is the only difference between the first
two examples. The only difference between the third and fourth example is the turbulence
parameter. Notice how the effectiveness of the exact same clouds changes with incident
polarization angle and also with atmospheric turbulence.

5. CONCLUSIONS

Past models that assume spherical symmetry are not capable of treating effects of either viewing
angle or atmospheric turbulence, which are highly significant according to the model of
Sutherland and Millard. In general, the angular scattering pattern produced by nonspherical
obscurants is much more complex than the spherical counterparts.

377

C]rtc=1.038 Horlz LOS

378

REFERENCES

Ayres, S. D., and S. DeSutter, 1993. Combined Obscuration Model for Battlefield Induced
Contaminants (COMBIC) User’s Guide. In Press, Department of the Army, U.S. Army
Research Laboratory, Battlefield Environment Directorate, White Sands Missile Range, NM.

Bowman, J. J., T. B. A. Senior, and P. L. E. Uslenghi, 1987. Electromagnetic and Acoustic
Scattering by Simple Shapes. Hemisphere Publishing Corporation.

Evans, B. T. N., 1991. Laboratory Technical Report MLR-R-11231. Commonwealth of
Australia Department of Defence Materials Research, Ascot Vale, Victoria 3032, Australia.

Farmer, W. M., and B. Kennedy, 1991. Electro-Magnetic Properties of RADAR/MMW
Obscurants. Contract Report DAAL03-86-D-0001, Bionetics Corporation, Hampton, VA.
Sponsoring agency: U.S. Army Research Office, Research Triangle Park, NC.

Fournier, G. R., and B. T. N. Evans, 1991. "Approximation to Extinction Efficiency for
Randomly Oriented Spheroids." Applied Optics, 50(1 5): 2042-204 8.

Klett, J. D., and R. A. Sutherland, 1992. "Approximate Methods for Modeling the Scattering
PropertiU of Non-spherical Particles: Evaluation of the Wentzel-Kramers-Brillouin Method. "
Applied Optics, 27(3): 373-386.

Knox, J. E., 1979. "Millimetre Wave Propagation in Smoke." In IEEE EASCON-79
Conference Record, Vol.2, pp 357-361.

Pederson, N. E., J. C. Pederson, and P. C. Waterman, 1984. Recent Results in the Scattering
and Absorption by Elongated Conductive Fibers. Panametrics, Inc., 221 Crescent Street,
Waltham, MA 02254.

Pederson, N. E., J. C. Pederson, and P. C. Waterman, 1985. Absorption and Scattering by
Conductive Fibers: Basic Theory and Comparison with Asymptotic Results. Panametrics,
Inc., 221 Crescent Street, Waltham, MA 02254.

Perry, M. R., M. R. Kulman, V. Kogan, W. Rouse, and M. Causey, 1994. Test Plan - Study
of Test Methods for Visible, Infrared, and Millimeter Smoke Clouds. ERDEC-CR-115,
Edgewood Research Development and Engineering Center, Aberdeen Proving Ground, MD.

Sundaram, G. S., 1979. "Millimetre Waves - The Much Awaited Technological Breakthrough?"
International Defense Review, 1 1 {2) '.211-211.

379

Sutherland, R. A., and J. D. Klett, 1992. "Modeling the Optical and Mechanical Properties of
Advanced Battlefield Obscurants." In Proceedings of the 1992 Battlefield Atmospherics
Conference.

Sutherland, R. A., and J. B. Millard, 1994. "Modeling the Optical and Mechanical Properties
of Advanced Battlefield Obscurants: Alternatives to Spherical Approximations." In
Proceedings of the 19th Army Science Conference.

Sutherland, R. A., and W. M. Farmer, 1994. Second Workshop on the Electromagnetics of
Combat Induced Atmospheric Obscurants. In Press, U.S. Army Research Laboratory,
Battlefield Environment Directorate, White Sands Missile Range, NM 88002-5501.

380

A MULTISTREAM SIMULATION OF MULTIPLE SCATTERING OF
POLARIZED RADIATION BY ENSEMBLES OF NON- SPHERICAL PARTICLES

Sean G. O'Brien
Physical Science Laboratory
New Mexico State University
Las Cruces, New Mexico 88003-0002

ABSTRACT

The Battlefield Emission and Multiple Scattering (BEAMS) model has been
niodified to allow for simulation of the multiple scattering of polarized
incident radiation by both spherically symmetric and non-spherical
scatterers. The modified Stokes vector representation is used to
characterize the incident and scattered radiation streams . The new
model uses multiple scattering Mueller phase matrices to describe the
interaction between the incident radiation and the spatial volume
containing scattering particles. The theory behind necessary
modifications to the BEAMS model is described, along with comparison
examples of the modified model with the previous scalar version for
spherical (Mie) particles. Comparisons of total scattered power between
the new and scalar BEAMS versions show good agreement, indicating that
the coding and normalization of the new version are fundamentally sound.
BEAMS simulation examples for preferentially-oriented ensembles of non-
spherical particles are also provided. Interesting features and
applications of these results are discussed.

1 . INTRODUCTION

The practical simulation of interactions of electromagnetic radiation sources
with the environment has been an enduring topic of interest to military systems
analysts and climatological modelers. One more difficult aspect of such
simulations is the accurate depiction of radiative transfer through realistic
atmospheres composed of scatterers of varying size, shape, and number density.
By necessity, computer models developed to consider this class of problems
represent compromises in both execution speed and accuracy. Scene visualization
for infrared (IR) and millimeter wave (MMW) sensors in battlefield environments
populated by dense inhomogeneous clouds of aerosol obscurants is a particularly
demanding enterprise. Any model used in this application must have reasonably
high spatial and angular resolution, and be efficient enough to allow time-
stepped (but not necessarily real-time) calculations simulating relative motions
of the clouds, sensors, and targets.

The BEAMS series of models (Hoock, 1987, 1991; Hoock et al . , 1993; O'Brien 1993)
represents an evolving effort to provide practical and efficient means for
performing radiative transfer calculations used in scene visualizations. The
03^2;72.y versions of the BEAMS models simulated the multiple scattering of
monochromatic scalar (unpolarized) radiation from infinite beam (e.g., solar) and
finite beam sources. This scalar scattering treatment provided a foundation for
one of the major goals of the BEAMS development project, which is to model the
multiple scattering of arbitrarily polarized radiation by finite, inhomogeneous
aerosol clouds. The latest version of the BEAMS model (version 4.0) realizes
this objective by describing the incident and propagated radiation in terms of
modified Stokes 4-vector streams. These 4-vector streams replace the single
scalar intensity streams of the scalar model. In place of the phase matrix used

381

“9" JtJ/ariSo

workings of BEAMS 2 . 2 will thus b/br?ef of the

2 . THEORY AND IMPLEMENTATION

2.1

Review of the BEAMS 2.2 Scalar Multistream Approach

Tt ^TJ‘ -a^-rical solution

oubioal volume elements or "ceUs- eJS e?em\nt Ts ontie^

its own volume and may opticallv differ optically homogeneous within

interactions between aTjacent ^cenf are^^L Radiative transfer

gror

immediate neighbors usina the scathf^-ri nrr i / r, «4- ^ isrerrea to its 26

to its neighbors' oppositely-directed s^ream^c, stream output powers as inputs

Strple"“s“att°erTn“ “^ular shape of that matrir due to

In practice, the later versions of the j

isiilps£sssi

2.2 The Mueller Matrix for Single Scattering

roaftL^t^ i“fde;rm“d“S

aL??tjf derived from the transverse electrfrUector' (or

a desortptil??r?^f JlUpti^nvToA^^^ ‘’‘d“? process begins with

ellipse swept out by the tS elictS ™ct^ r f“ ^ ‘he

may be represented by the relations

= Ejg sin (to t - ej)

= E^g sin (tot - e^)

(1)

where

total

the corresponding intensities
intensity is given by = l

are given by and = E^,," and the

- Ii + If. It is convenient to define the

382

ratio of the minor axis to major axis
as the tangent of a parameter : tan P
= a/b. The total E vector may be
decomposed into components along the
major (E,^) and minor (E^^ axes of
the polarization ellipse:

EL = sin (ot cos P
" (2)
^X*n/2 = COS Wt sin P

Projecting the components and ♦ 1/2
onto the parallel and perpendicular
scattering planes, and expanding the
Eq. 1 relations, it is seen that

El = (sin a>t cos P cos % - cos wt sin p sin x )

= Eig (sin o)t cos - cos wt sin Sj )

E^ = E^ (sin wt cos P sin x + cos wt sin P cos x )

= Ejo (sin o)t cos - cos ot sin )

Equating the coefficients of the sin wt and cos cot terms in Eq. 3, and using
simple trigonometric identities, the amplitudes and phases in Eq. 3 are seen to
obey the relations

Eio = (cos^ p cos^ X + sin^ P sin^ X )

Ej.^ = E^ (cos^ P sin^ X + sin^ p cos^ % )
tan Ej = tan p tan %
tan Ej. = -tan p cot x

Figure 1. Polarization ellipse for
elliptically polarized plane wave.

The components of the modified Stokes vector F - {li/ It< U, v} can then be
defined in terms of either their fundamental forms, involving amplitucies and
phases, or one composed of intensities and the geometry of the polarization
ellipse :

Ii = eIo = I (cos^ P cos^ X + sin^ P sin^ X )

= I (cos^ P sin^ X + sin^ P cos^ X ) ^5)

U = 2 Ei^ E„ cos {Ej - ) = I cos 2P sin 2x

y = 2 Ejo E„ sin (e^ - e, ) = I sin 2P

The intensity form of the Stokes vector is convenient for use by a flux transport
model like BEAMS, because each of its components may be treated as an
independently-propagating stream. The 4x4 transformation matrix R that defines
the scattering process converts an incoming Stokes vector F into an outgoing
vector F' = {li' , Ir' / U' , V' } ; F' = R F.

383

"models calculate the polarized scattering properties of
an aerosol particle or collection of particles in the form of 2x2 amplitude

aS outgoinrw^ies! ® amplitudes of the incoming

S2 s,]

matrices may be transformed to the intensity (Mueller)
form through the amplitude definitions for the Stokes vector components The

a given S into a corresponding R is given by (van de Hulst,

where

' M2

C23

-Dyy

^>41

-l>4i

2<?24

2(?31

^21 ■*'^34

-£>21+1);

,21524

2P3,

*^21 '^■^34

^21

= Qj„ = (Sj 5; + Sj, S;)/2
-^ky = = i (5j. Si* - Sj, s;)/2

The asterisk superscript in Eg. 8 denotes complex conjugation of the S matrix

elements, which are in general complex -valued. The elements of the Mueller
matrix R in Eg. 7 are real -valued. wueiier

2.3 Change of Coordinates between Planes of Incidence and Scattering

representation given by Egs. 5-8 is directly usable
y plane of scattering is studied. If a fixed scenario coordinate

employed (as is the case for BEAMS), the incoming and outgoing
p pagation directions of the Stokes vector are essentially arbitrary. In that
matrices must be employed to rotate the Stokes vector defined in
out of scattering plane before the scattering event and

plane after scattering. The procedure for constructing such
matrices is straightforward (Chandrasekhar, 1960) . Looking in the
direction of propagation, a clockwise rotation of the reference axes about an

S! L: the

ellipse. The defining relations in Eg. 5 then become

I'l = I (cos2 p cos^ (x-a) + sin^ P sin^ (x-a) )

Ir = I (cos^ P sin^ (x-a)
U' ^ I cos 2P sin 2 (x-a)
V' = I sin 2P

sin^ P cos^ (x-a)

expressions, grouping terms, and making appropriate
Identifications from Eg. 5, Eg. 9 defines a rotation matrix L that expSss?7Se

384

cos^ a

sin^ a

-isin 2a

sin^ a

cos^ a

- — sin 2a

(10)

-sin 2a

sin 2a

cos 2a

The geometry for a scattering event in the BEAMS model is shown in Figure 2 . The
nomenclature for angles used here follows that given by Chandrasekhar. Referring
to Fig. 2, the component of the E field parallel to the meridian plane containing
the Z axis (Z assumed vertical) is labeled V; the perpendicular component (which
is parallel to the XY plane) is labeled H. The spherical angle between meridian
Diane 1 (which contains the vertical Z axis and the line of incidence through the
- - - - - - - origin) and the scattering

I nci dent

^"0 ^

- ►

5cat t er i nq
Point

plane

(which contains the lines of incidence
and scattering through the origin) is
denoted by ii. The spherical angle ±2
is formed by meridian plane 2
(containing the line of scattering
through the origin and the Z axis) and
the scattering plane. The scattering
angle between the incoming and
outgoing directions is 0, and (d^, <^i) ,
(^2, 02) are the respective polar and
azimuth angles for the incoming and
outgoing directions . The transfor¬
mation angles ii and ij may then be
obtained from the cosine law for a
spherical triangle (Smart, 1977) :

Figure 2 . Geometry for Stokes vector
scattering in BEAMS 4.0.

cos

cos 02 - cos 03^ cos 0
sin^ sin 0

cos 22 =

cos 01 - cos 02 cos 0
sin 02 sin 0

(11)

A Stokes vector scattering from meridian 1 to meridian 2 in Fig. 2 must be trans¬
formed by the linear transformation L(-ii) (Eq. 10) prior to the scattering
event. After scattering, another rotation LCir-ij) is performed in order to
express the scattered Stokes vector in terms of the orthogonal (V, H) components
in meridian 2. The Mueller phase matrix for scattering from meridional plane
1 to meridional plane 2 may then be stated as

Eq. 12 is the fundamental result that allows the transition from the scalar
scattering model of BEAMS 2.2 to the polarized scattering treatmesnt of version
4.0. The user now must input new parameters that specify the fraction of
incident flux that is polarized, the polarization angle X/ and the axial ratio
parameter |0 of the polarized component. These quantities are used in conjunction

385

with Eq. 5 to construct the Stokes vector for both the polarized and unpolarized
(Ii - Ir = 1/2, u = V = 0) portions of the incident radiation. After propagation
through the rectangular array of cubical scattering cells, the resulting Stokes
vectors may be analyzed to yield degree of polarization, polarization angle, and
ellipticity of polarization information for radiances at the boundary of any
individual scattering cell or for any group of such cells.

2.4 The Multiple Scattering Mueller Matrix

The Stokes vector formalism allows for the construction of a Mueller phase matrix
that reflects multiple scattering effects. The method used in the BEAMS 4.0
package is essentially identical to that employed by the scalar BEAMS 2.2. A
single scattering model is first used to generate the scattering amplitude matrix
S. The scattering plane Mueller matrix R is next generated by averaging over the
BEAMS input streams and applying Eqs . 7 and 8 . The single scattering Mueller
phase matrix P is then generated for stream- to- stream scattering geometries with
the relations of Eqs. 10-12. This result is stored in a file (named POLOXJT.MAT)
with a format that is directly usable by the BEAMS 4.0 program.

As in the scalar version of BEAMS, a dedicated version of BEAMS (named MSPPHMX)
was created to generate the multiple scattering phase matrix. This version does
not have the normal BEAMS output routines and takes its input from files
containing the single scattering Mueller matrix P (POLOUT.MAT) and a uniform
aerosol concentration parameter fixed at a value of unity. The code computes the
stream output Stokes vector radiances for a uniform cubical 5x5x5 array of cells.
The axial optical depth t of the identical component cells is varied to give
results for different total axial optical depths 5t of the cubical array. The
output radiances, when renormalized under energy conservation, provide the
multiple scattering Mueller matrix for the 5t cubical array. This matrix result
can be used for an individual cell in a nonuniform rectangular scenario array in
a BEAMS 4.0 production run.

The demand that creation and storage of the BEAMS 4.0 multiple scattering Mueller
matrix places upon computer resources is considerable. MSPPHMX loops over 15
optical depths, creating a result for each depth. At each optical depth, the
BEAMS code is executed once for each input stream direction (for a total of 26
separate runs) . If the aerosol scatterers under study are spherical, display
some degree of shape symmetry, or are randomly- oriented, then clearly the number
of such runs could be reduced by application of symmetry. Such reductions are
inconvenient because they must be applied on a case-by-case basis and require
care to avoid errors caused by inappropriate symmetry assumptions . For this
reason, the MSPPHMX software only considers the general case where no symmetry
is assumed.

A Mueller phase matrix is computed by MSPPHMX at each of 15 optical depths, is
stored in a file named MSPPHMX. MAT, and contains 26x26x4x4 = 10,816 elements.
In a binary file format, a file of 15 such phase matrices slightly exceeds half
of a megabyte in size. Thus, on any capable modern computer system, file size
is seldom a problem. However, because BEAMS logarithmically interpolates phase
matrix elements over optical depth, the entire phase matrix data set must reside
in memory during a BEAMS 4.0 run. The required Mueller multiple scattering phase
matrix storage, combined with that required for the scenario array of cubical
aerosol scattering elements, makes the BEAMS 4.0 model impractical to use on
personal computer platforms for scenario arrays with over a few thousand cells.

386

3 . 0 APPLICATIONS

3.1 Hie Scattering - Comparison of BEAMS 2.2 and BEAMS 4.0

A Mie scattering aerosol was chosen to compare the far field scattered power
predicted by the scalar BEAMS 2.2 code with the total (Ii + 1^) scattered power
yielded by version 4.0 of BEAMS. The 2x2 scattering amplitude matrix for the
Deirmendjian Cloud C.l aerosol (at a wavelength of 0.45 ^m) (Deirmendjian, 1969)
was employed for this purpose. A uniform cubical cloud with an edge length of
5 m was constructed for this case, with an axial optical depth of 5. The top (+Z
face) of the cube was illuminated by an unpolarized plane-parallel infinite beam
with a flux value of 1 W/m^ Figure 3 shows how the scalar and polarized

scattering versions of the BEAMS model
compared. The quantity compared is
the total scattered power exiting from
the boundary surfade of the cube into
a solid angle equal to 47r/26
steradians. It is apparent that the
total scattered power predicted by the
two versions of BEAMS compare
reasonably well for this case. The
BEAMS 4 . 0 results indicate that the
scattered power in the forward
hemisphere (scattering angles less
than 90 degrees) is not strongly
polarized. The backward hemisphere
shows a marked enhancement in the
horizontal polarization component,
which is consistent with the
, polarization properties of Mie

Figure 3 . Comparison of detected power scatterers
in the XZ plane for BEAMS 2.2 (scalar)
and BET^S 4 . 0 (polarized) multiple
scattering phase matrices for
Deirmendjian C.l aerosol.

Vertical

Horizontal

Vert. + Horiz. Pwr.
Scalar BEAMS

-1.0-0.8-0.6-0.4-0.2 0.0 0.2 0.4 0.6 0.8 1.0
Cosine of Scattering Angle

3.2 Ice Crystals

The BEAMS 4 . 0 model can be used to predict the scattered power from an ensemble
of preferentially-oriented or randomly-oriented non-spherical aerosol particles.
For the example shown here, a solid cylindrical ice rod with a 20:1 (length-to-
diameter) aspect ratio was chosen. In order to simulate results for a randomly-
oriented particle, the Digitized Green Function (DGF) model (Goedecke and
O'Brien, 1988) was used to generate an averaged scattering amplitude matrix for
a set of orientations of this particle. The ice rod length was set at 0.98 mm,
the illuminating wavelength was fixed at 3 mm, and a complex refractive index of
1.78 + 0.00387 i was used. Figures 4 and 5 show the BEAMS 4.0 radiances for a
32mx8mx8m uniform cloud of such crystals. The optical depths along the
X, Y, Z axes of the cloud were 6.4, 1.6, and 1.6, respectively. The
horizontally-polarized (H) collimated illuminating source had a uniformly-
illuminated aperture of 0.5 m diameter pointing in the +X direction. Beam power
was set at 10 W, and the entry point of the beam was at X = -16 m, Y = +0.5 m,
and Z = +0.5 m. The emergent radiances shown here are actually the total
scattered power emitted from the cloud into a solid angle of 4ir26 steradian.

387

Figure 4. Orthographic perspective
view of emergent radiances from ice
crystal cloud: V polarization.

Figure 5 . Orthographic perspective
view of radiances from ice crystal
cloud: H (incident) polarization.

The viewing direction chosen for the emergent radiance is looking down (at a

degrees) at an azimuth of 0 degrees (in the +X direction) .

oblique scattering in the backward
emisphere. It can be seen that the backscattered radiance from the cross-
polarized (V) component (Fig. 4) is rather weak and diffuse compared to the H
(incident) component (Fig. 5) . Also, note the growing strength and spread of the

P^'^^t^tes into the cloud. The relatively thin transverse
optical depth of the cloud appears to be the cause of this trend.

3.3 Graphite and Metal Fibers

Preferentially-oriented graphite and copper fibers with very large aspect ratios
may be used to illustrate possible applications of the BEAMS 4.0 model. The

for such fibers (Klett, 1994; Sutherland and iciett,
1994) may be created for average particle orientations at different mechanical
Jewels . Under low turbulence conditions, an aerosol fiber will
fre<^ently fall with its long axis wobbling in a small envelope about the
horizontal (Sutherland and Klett, 1992) . Both fibers were 3 mm long, with the
graphite and copper having respective diameters of 7 /xm and lO urn. Both fibers
were also assigned bulk material densities of 2.5 g/cm\ The graphite fiber
diameter was such that it fell with a larger amplitude of wobble than that of the
copper wire fiber. The same size and optical depth were used for the aerosol
cloud as in the previous (ice crystal) example, as were the source location,
orientation, aperture size, power, and polarization. Figures 6 and 7 show the
emergent radiance results for the graphite, and Figures 8 and 9 show the copper
wire results. The direction of the emergent radiance is the same as in the
previous (ice crystal) case. It can be seen that the cross-polarized radiance
IS considerably stronger for both the graphite and copper fibers than it is for

although the more preferentially-oriented copper wire
scatterers show an appreciable enhancement of the H polarization radiance over
that or the V component.

388

Figure 6 . Emergent radiances for
graphite cloud (V polarization) .

Figure 8 . Emergent radiances for
cloud of copper wire scatterers (V
polarization) .

4 . CONCLUSIONS

Figure 7 . Emergent radiances for
graphite cloud (H polarization) .

Figure 9 . Emergent radiances for
cloud of copper wire scatterers (H
polarization) .

Modifications for treating Stokes vector scattering to BEAMS 2.2 have produced
a model (BEAMS 4.0) that compares well with its predecessor for Mie scattering.
Preliminary BEAMS 4.0 results for scattering from randomly and preferentially
oriented particles are consistent with expectations. However, more testing will
be made to confirm the validity and consistency of the new model. One difficulty
with the BEAMS 4.0 code is that it places considerable demands upon system
resources, even in production mode. For rectangular scenario arrays (e.g.,
64x32x32) with large numbers of elements, a BEAMS 4.0 execution may take several
hours on a fairly capable machine (i.e., a Silicon Graphics Onyx) . This does not
represent a problem for the intended applications of the BEAMS model, where
multiple scattering radiance statistics are examined. Nevertheless, in cases
where polarization effects are not significant, it is still preferable to use one
of the faster, scalar versions of BEAMS.

REFERENCES

Chandrasekhar, s., i960. Radiative Transfer, Dover Publications, New York, NY,

York,' scattering- on Spherical Polydieperaicna,

"Spattering by irregular inhomogeneous
particles via the digitized Green's function algorithm", Appl . Opt., 27:2431.

Hoock, D.W., 1987. "A Modeling Approach to Radiative Transfer through

Clouds", Proceedings of the 7th Annual EOSAEL/TWI
Conference, Las Cruces, NM, pp. 575-596. '

Hoock, D.W., 1991. "Theoretical and Measured Fractal Dimensions for Battlefield
Aerosol Cloud Visualization and Transmission", Proceedings of the 1991
Battlefield Atmospherics Conference, Ft. Bliss, TX, pp. 46-55.

Giever, and S.G. O'Brien, 1993. "Battlefield Emission and
tiple Scattering (BEAMS) , a 3-D Inhomogeneous Radiative Transfer Model"
Proceedings of the SPIE Vol . 1967, Characterization, Propagation, and
Simulation Conference, Orlando, FL, pp. 268-277.

Klett, J.D., 1994. Scattering of Polarized Light by High Conductivity Fiber
erosol in Turbulent Air, Final Report, Contract No. DAAD07-91-C-0139 PAR
Associates, 4507 Mockingbird St., Las Cruces, NM, 88001.

2-2 Radiative Transfer Algorithm
Radiative Transfer Methods", Proceedings of the 1993 Battlefield
Atmospherics Conference, Las Cruces, NM, pp. 421-435.

Spherical Astronomy, Cambridge University Press,

Sutherland, R.A., and J.D. Klett, 1992. "Modeling the Optical and Mechanical
Properties of Exotic Battlefield Obscurants", Proceedings of the 1992
Battlefield Atmospherics Conference, Ft. Bliss, TX, pp. 237-246.

Sutherland, R.A., and J.D. Klett, 1994. Private communications.

1981. Light Scattering by Small Particles. Dover
Publications, New York, NY, 470 pp.

390

COMBINED OBSCURATION MODEL FOR BATTLEFIELD
INDUCED CONTAMINANTS-RADIATIVE TRANSFER VERSION (COMBIC-RT)

Scarlett D. Ayres, Doug Sheets
and Robert Sutherland
Battlefield Environment Directorate
U.S. Army Research Laboratory
White Sands Missile Range, New Mexico 88002-5501

ABSTRACT

The COMBIC model was originally developed for the Electro-Optical Systems
Atmospheric Effects Library (EOSAEL 84) to model effects of direct transmission
(i.e. Beer Law) only and ignored the more complicated effect of contrast
transmission. COMBIC-RT represents an improvement in the radiative transfer
algorithm to account for single and multiple scattering, and hence contrast
transmission. COMBIC-RT is a merger between COMBIC and the Large Area Smoke
Screen (LASS) model developed in 1985. COMBIC-RT reverts to normal COMBIC if
the RT option is not exercised. If the RT option is exercised then the outputs
of the model are symbolic maps displaying the direct and diffuse components of
scene transmission as affected by a large-area smoke screen or a contrast
transmission history. The model can be exercised with various optional inputs
to determine the effects of solar angle, solar flux density, sky radiance,
surface albedo, etc. The COMBIC part of the model applies the Gaussian
diffusion approximation to compute obscurant concentration path length (CL
product), and the LASS part applies the plane-parallel approximation to compute
target-background contrast and contrast transmission. The radiative-transfer
algorithms are unique to LASS and COMBIC-RT in the use of the extensive
radiative-transfer tables originally published by Van De Hulst that are used
together with novel scaling algorithms to account for effects of single and
multiple scattering along arbitrary slant path and horizontal lines of sight
(LOS). The model does not treat thermal emission and is thus restricted to
visible and near-infrared regions. The obscurant phase function is taken to be
of the Henyey-Greenstein form and can account for various degrees of anisotropic
scattering as well as isotropic scattering. The model accounts for scattering
of the direct solar beam, uniform diffuse skylight, and diffuse reflection from
the underlying (earth) surface.

1. INTRODUCTION

One of the original purposes for developing COMBIC-RT model was to assist in
modeling the effectiveness of smoke screens used in wargame simulations. Large
area self-screening smokes are feasible at large fixed and semifixed military
installations such as air bases, air fields, and ammunition supply points where
attack by nap of the earth aircraft is a possibility. The commander of these
military installations need to know to what degree a LASS deployment will
protect his station from enemy aircraft as well as know how the LASS will effect
friendly aircraft. The wargame simulations will ultimately impact the doctrine
the commander will use. In these type of scenarios, contrast reduction caused
by scattering of light is the major acquisition defeat mechanism. This
scattering of light into the path in real world scenarios can often be of
overriding significance in affecting perception. A natural example is the
apparent disappearance of stars in daytime. Another common example is the
backscatter from headlights when driving through fog with the brights on. The
degree to which scattering can be important is indicated by the optical
properties of the medium; the mass extinction coefficient a which combines
absorption and scattering out of the path of propagation into one term; the
single scattering albedo (wq) which indicates the fractional amount of
scattering, and (l-c5o) which indicates the fractional amount of absorption.
Conventional visible band obscurants such as fog oil has indicating a

predominance of scattering.

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COMBIC-RT is made up of two sub-models: one that treats transport and diffusion
(the original COMBIC) and another that treats radiative transfer (the radiative
transfer algorithms of LASS)* COMBIC uses a Gaussian formalism to calculate,
for potential unlimited number of smoke clouds, the obscurant path-integrated
concentration (CL) for either parallel LOSs over the extent of the entire screen
or for just individual LOSs* The radiative transfer segment performs extensive
radiative transfer calculations by using the plane parallel approximation that
essentially transforms a CL map into a radiative transfer map of contrast
transmission* Since the output of COMBIC-RT includes path radiance and
downward-directed hemispherical surface irradiance, digital maps of these
quantities may also be generated with minor code modifications* The model is
primarily applicable to situations in which the observer (for example, an
aircraft) is located above the screen and the target is located on the surface*
The LASS computer model provides a tool for the study of large area screening
systems applications and effects.

2 • BACKGROUND

Models like CASTFOREM directly relate transmission to Electro-Optical (EO)
system performance and smoke effectiveness by considering only the directly
transmitted signal:

s(f)=s(fjr (1)

where S(f) is the optical signal received by an observer at {£) from a target
at (fo) . The transmission (T) includes effects of both scattering out of the
path plus absorption along the path. However, EO systems respond not only to
directly transmitted radiation but also to contrast. Equation (1) is thus
modified to include a term representing path radiance as:

5(i')=5(f^)r+5^(f) (2)

where the contribution due to path radiance (5p) may be due either to scattering
of ambient radiation (sun, sky) into the path of propagation or emission along
the path, or both. Path radiance has a directional nature causing asymmetries
between target and observer . One or the other has an optical advantage
not present when one models only the direct transmission component. The LASS
model was developed to model these effects. The radiative transfer algorithms
were then integrated with COMBIC-RT to enable COMBIC to compute path radiance.

Most target acquisition models work by determining the number of resolvable
cycles across the target. This directly relates to the target contrast X at
the sensor's aperture for non-thermal sensors, and for a slant-path LOS:

Tc(±u.^)

Ajb€Xp(“T)

(3)

where is the direction cosine directed upward, -|i is the directional cosine
directed downward, ii> is the azimuth, x is the optical depth, refer to the
target and background albedos. P*(±n,<|)) is called the Duntley factor, after the
Pioneering work of S.Q. Duntley, (Duntley, 1948) and reduces to "sky— to— ground"
for a horizontal LOS.

The probability of acquisition may be calculated using the integral:
where n- is the number of resolvable cycles across the target for an acquisition
probability of 50 percent, and a is the standard deviation of the number of
resolvable cycles across the target. Using COMBIC-RT and a target acquisition

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® exp(-x^/2) dx

^ y/SnJ^m

model like the one in CASTFOREM, it is possible to determine the probability of
acquisition of a given target through a LASS cloud at any given point in space
and time. This provides a direct measure of the effectiveness of smoke. Figure
1 shows the effect of sun angle on detection probabilities for different optical
depths (t). The probability of detection for x of 1 varies from 34% in the
case of the sun to the front of the observer to 63% for the sun behind the
observer. This is as expected. Most of the time, it is easier to ”see” with
the sun to the back.

Figure 2 shows the effect that the observer azimuth angle (defined wrt North)
can have on contrast transmission. Contrast transmission is shown for five CL
values. The scenario is for early morning and the zenith angle of the observer
is 10 degrees. Notice that low contrast transmission occurs when the observer
is looking into the sun (0^) and high contrast transmission occurs with the sun
to the back (180®) of the observer. Further, note that the curve flattens out
as the CL increases.

3. DEFINITION OF THE PROBLEM

In a typical obscuration scenario, the problem is to compute the total radiance,
both direct and diffuse, reaching an observer and emanating from the direction
of the target (oi^ background) . The direct radiance includes light either
emitted or reflected by the target (or background) then transmitted (with some
loss due to extinction) along the LOS to the observer. The diffuse radiance is
the path radiance emitted and scattered by suspended material (obscurants) at
all points along the LOS then transmitted (again with some loss due to
extinction) a remaining distance to the observer. The scenario, including the
large-area screen, is assumed to be irradiated from above by diffuse sky
radiation and from below by diffuse surface radiation (see Figure 3). For
daytime scenarios, the direct solar beam is included in the sky component. For
nighttime, the direct source may be the Moon, and starlight may be included in
the diffuse component.

The radiance incident at the observer propagating from the direction of the
target is the combination of the direct and diffuse component or more formally:

J(r;n,4>) e“"+ f V(r' ; n , <|)) (5)

J 0

where ji»|cos0| is the zenith angle of the observer with respect to the target.
The geometry is shown in Figure 4. The first term on the right-hand side is the
familic^r Beers law and represent radiance transmitted directly from the target
to the observer (the direct component). The second term on the right hand side
represent the diffuse component. It represents contributions due to scattering
of ambient radiation into the path of propagation at all points along the path.
This equation has been extensively studied but the major difficulty is the
optical-source function, J’(r / which is itself a function of incoming
radiance from all directions so that the formal solution is really quite
complex.

The LASS model makes several simplifying assumptions that allow rigorous
solutions of Equation 5, including all orders of multiple scattering. A major
simplification is the plane-parallel approximation where the optical depths for
a slant path at angle 0 is equal to the vertical optical depth divided by the
cosine of the angle.

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PHOTOSIMULATION EXPERIMENT

Figure 1 Plot of detection
probability as a function of optical
depth for various solar azimuth
angles.

SKY

Figure 2 Plot contrast transmission
vs. observer azimuth angles.

SURFACE

Figure 3 Typical LASS scenario.

The optical source function is dependent upon the phase function p{u,<b;|i-,<k.)
which mathematically describes the angular-scattering properties of the
obscurant. For inventory smokes, the phase function is best approximated with
the Henyey-Greenstein form.

(6)

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where !|r is the scattering angle and g is the asymmetry parameter that
determines the overall shape of the scattering phase function and can vary from
-1 for strong backscatter, to zero for isotropic scattering^ an on to near +1
for strong forward scattering. The use of the Henyey-Greenstein form presumes
a spherical aerosol, which is reasonable for many obscurant types, especially
fog oil. Plots of the Henyey-Greenstein phase function for various values of
the asymmetry parameter are shown in Figure 5.

Figure 4 Geometry of the path

propagation.

0 30 60 90 120 150 180

SCATTERING ANGLE

of Figure 5 Plot of the Henyey-

Greenstein phase function for
various asymmetry.

4. REFLECTION AND TRANSMISSION FUNCTIONS

The major computational problem in modeling contrast transmission is the
determination of the diffuse transmission and reflection functions. These
functions account for effects of multiple scattering within the obscurant cloud
which gives rise to the DIFFUSE component of the radiation field and should not
be confused with the more familiar DIRECT component which is treated with the
simple Beer's Law. In general the diffuse component is difficult to calculate,
even under the simplifying assumption of a plane parallel atmosphere. In COMBIC-
RT we use a combination of precomputed look up tables based upon rigorous
solutions (Sutherland & Fowler, 1986) and special scaling algorithms to
approximate the full angular dependent transmission reflection functions
accounting for both absorption and scattering (Sutherland, 1988).

In Figure 6 we give an example for a Henyey-Greenstein asymmetry parameter of
0.750 which is typical of conventional visible band obscurants such as fog oil.
The reflection operator, denoted in general as i2(T, jio'4>o) f represents that
fraction of an incident plane parallel beam that is diffusely "reflected" into
the direction denoted by polar angles 0 (^=|cos0|) and <|>, where the incident
beam is from the direction denoted by (|iQ=|cos0o|) and The transmission
operator, ^ same except that it accounts for diffuse
"transmission".

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Figure 6 Plots of diffuse reflection and transmission as a function of
optical depth for several azimuth angles. Solar beam zenith direction is
uo=0.50 and viewing angle is u=0.10.

For example, the plots on the left in Fig. 6 show the value of the reflection
operator as a function of optical depth assuming a solar incident beam direction
|io=0.50 (00=60°) and <|>o=0°. This example corresponds to the case of an airborne
observer looking downward and solar radiation "reflected” upward from the
obscurant cloud. Note that, in gen