Modeling of Atmospheric Effects Modeling of Atmospheric Effects 

by 

Richard Shirkey Richard Shirkey 

 

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Battlefield Environment Division Battlefield Environment Division 

ARL-TR-1812June 2000 June 2000 June 2000 

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Modeling of Atmospheric Effects 

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 Dr. R. Shirkey 

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U.S. Army Research Laboratory 

Computational & Information Sciences Directorate 

Battlefield Environment Division 

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Realistically modeling atmospheric effects and their impacts is a difficult task. Atmospheric effects run the gamut from 

small scale, localized effects such as thundershowers, fog, smoke, etc., to large-scale synoptic events such as weather 

fronts. Once these features are modeled they must then be correctly applied and/or interfaced with other, usually larger, 

simulations. To avoid duplicative efforts it is prudent to establish standards in various areas. The Dynamic Atmospheric 

Environments (DAE) category is one of twenty areas established by the U.S. Army’s Modeling and Simulation Office to 

set standards in the area of atmospheric modeling. This document provides the definition, requirements, objectives and 

roadmap for the DAE category. Also presented are those efforts that are currently underway in the U.S. Army that have as 

their goal to render more atmospheric realism in models and simulations in order to ultimately provide the Commander and 

soldier with more accurate choices or predictions on the battlefield. 

15. NUMBER OF PAGES 15. NUMBER OF PAGES 

63 

14. SUBJECT TERMS 14. SUBJECT TERMS 

 

Atmospheric Modeling, Atmospheric Standards, Atmospheric Models, Acoustic 

Propagation, EO Propagation, Transport and Diffusion and Forecast Models 

 

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UNCLASSIFIED 

 

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NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) 

prescribed by ANSI Std. Z39-18-298-102
1 

Preface Preface 

This document provides a summation of ongoing and recent 

developments in the Army Modeling and Simulation Office’s category of 

Dynamic Atmospheric Environments. The determination of standards is 

a cost effective reuse of existing resources. Therefore included herein is a 

brief explanation of the Army Model Improvement Plan and the 

definition, requirements, and objectives of the standards category of 

Dynamic Atmospheric Effects. 
3 

Contents Contents 

 

Preface........................................................................................................................ Preface ..................................1 

Executive Summary .............................................................................................................. Executive Summary .....................5 

 

1.0 Introduction .............................................................................................................. 1.0 Introduction ...........................7 

2.0 Army Model Improvement Plan 2.0 Army Model Improvement Plan Army Model Improvement Plan.......................................................................................................9 

3.0 DAE Standards Category Definition......................................................................................... 3.0 DAE Standards Category Definition .....11 

4.0 DAE Standardization Requirements .......................................................................................... 4.0 DAE Standardization Requirements ....13 

5.0 DAE Objectives............................................................................................................ 5.0 DAE Objectives ......................15 

6.0 DAE Assessment and Accomplishments ......................................................................................17 6.0 DAE Assessment and Accomplishments

 6.1 Assessment.................................................................................................................................17 

 6.2 Accomplishments......................................................................................................................17 

 6.2.1 Modeling of the Ground State in Winter Environments..........................................17 

 6.2.1.1 Objective 6.2.1.1 Objective 6.2.1.1 Objective .............................................................................................................17 

 6.2.1.2 Background 6.2.1.2 Background 6.2.1.2 Background ........................................................................................................18 

 6.2.1.3 Results and Findings 6.2.1.3 Results and Findings 6.2.1.3 Results and Findings ........................................................................................19 

 6.2.2 Light Scattering for War Games and Target Acquisition.........................................20 

 6.2.3 Three-Dimensional Static Environments and Initialization ....................................20 

7.0 Other Army Efforts ........................................................................................................ 7.0 Other Army Efforts ...................23 

 7.1 Weather Tactical Decision Aids ..............................................................................................23 

 7.1.1 Tri-Service Rule-Based Weather Tactical Decision Aids ..........................................23 

 7.1.1.2 The Integrated Weather Effects Decision Aid 7.1.1.2 The Integrated Weather Effects Decision Aid 7.1.1.2 The Integrated Weather Effects Decision Aid...............................................23 

 7.1.2 Tri-Service Physics-Based Weather Tactical Decision Aids.....................................24 

 7.1.2.1 7.1.2.1Target Acquisition Weather Software Acquisition Weather Software Acquisition Weather Software ..........................................................24 

 7.1.3 Acoustic Decision Aids .................................................................................................25 

 7.2 Live and Synthetic Weather ...................................................................................................26 

 7.2.1 The Battlefield Forecast Model.....................................................................................26 

 7.2.2 The Integrated Meteorological System .......................................................................27 

 7.2.3 Microscale Modeling .....................................................................................................28 

7.3 Transport and Diffusion .........................................................................................................30 

 7.3.1 Second Order Closure Integrated PUFF (SCIPUFF)................................................30 

 7.3.2 Vapor Liquid Solid Tracking, Two-Dimensional Transport and Diffusion 

Personal Computer and Transport and Diffusion Simulator ................................30

7.4 Propagation...............................................................................................................................31 

 7.4.1 Acoustics........................................................................................................................31 

 7.4.1.1 Scanning Fast 7.4.1.1 Scanning Fast 7.4.1.1 Scanning Fast-Field Program Field Program Field Program.........................................................................31 

 7.4.1.2 Scanning Parabolic Equation 7.4.1.2 Scanning Parabolic Equation 7.4.1.2 Scanning Parabolic Equation .........................................................................32 

 7.4.2 Electro-Optical ..............................................................................................................33 

 7.4.2.1 Waves 7.4.2.1 Waves 7.4.2.1 Waves ................................................................................................................33 

 7.4.2.2 The Electro 7.4.2.2 The Electro 7.4.2.2 The Electro-Optical Systems Atmospheric Effects Library Optical Systems Atmospheric Effects Library Optical Systems Atmospheric Effects Library .......................34 
4 

 7.4.2.3 Atmosphere Optical Turbulence 7.4.2.3 Atmosphere Optical Turbulence 7.4.2.3 Atmosphere Optical Turbulence...................................................................35 

 7.5 Backgrounds .............................................................................................................................36 

 7.5.1 Smart Weapons Operability Enhancement...............................................................36 

7.5.2 Computer Generation of Realistic Environments with Atmospheres for 

 Thermal Imagery with Optics and Noise..................................................................37 

 7.6 Virtual Dirty Battlefield ..........................................................................................................38 

 7.7 Clutter Statistics of Wet Snow Cover Measured with a Full Maximum 

 Continuous Wave Radar (32 to 35 GHz) ..............................................................................39 

8.0 Priorities................................................................................................................ 8.0 Priorities .............................41 

9.0 Conclusions............................................................................................................... 9.0 Conclusions ........................43 

References..................................................................................................................... References ............................45 

Acronyms and Abbreviations ..................................................................................................... Acronyms and Abbreviations ..........49 

Distribution ................................................................................................................... Distribution ...........................53 

Figures Figures 

1. DAE roadmap....................................................................................................................................15 

2. Probability of detection for a fixed, ground-based sensor (circle).............................................32 

3. MMW scene .......................................................................................................................................37 

4. IR scene...............................................................................................................................................37 

5. Battlefield scene ................................................................................................................................38 

6. Battlefield scene with white phosphorus added..........................................................................39 

7. Refractivity characteristics of a wet snow as a function of depression angle obtained 

 with a FMCW radar operating at 32 to 36 GHz bandwidth.......................................................39 

Tables 

1. DoD M&S objectives...........................................................................................................................6 

2. DAE requirements ............................................................................................................................14 

3. DAE priorities in the near-, mid-, and far-time frame .................................................................40
5 

Executive Summary

The battlefield environment includes many sources of aerosols and 

particulates such as chemical and biological agents, smoke, dust, fog and 

chaff. These add to the natural environment increasing the presence of 

non-uniform aerosol regions. Weather, atmospheric transport and 

diffusion processes, and the attenuating effects of the environment on the 

propagation of electromagnetic energy all impact target acquisition and 

high-technology weapons. The atmosphere and clouds provide cues, 

alter target and background signatures, and produce scene clutter both in 

the real world and in realistic computer-generated simulations. All these 

weather effects and impacts are in the Dynamic Atmospheric 

Environments (DAE) domain and are in harmony with the Department 

of Defense objective representation of the atmosphere. 

Due to the dynamic range of atmospheric processes, the DAE category 

must represent a requirement spectrum ranging from small-scale effects 

found in high-resolution models, such as scene visualization, to largescale, low-resolution, aggregated effects, to correctly represent weather 

impacts. In the high-resolution area physics-based calculations, such as 

the Weather And Visualization Effects for Simulations, are needed to 

represent high fidelity natural and battlefield-induced atmospheric. At 

the other end of the spectrum are the high-level simulations that deal 

with aggregated units. These simulations simply cannot afford to 

include detailed calculations for individual platforms and systems. Thus, 

a new approach is needed to include weather at a realistic level of fidelity 

and still maintain “faster than real time” simulation capability. Such an 

approach may exist in using rule-based programs such as the Integrated 

Weather Effects Decision Aid. DAE requirements, presented in priority 

order, are: 

1. to provide fundamental environmental models for modeling and 

simulation (M&S), 

2. identify requirements for standard atmospheric scenarios, 

3. provide methodologies for determining consistent data sets for 

environmental effects models, and 

4. provide standardized databases for system performance analysis. 

In concert with these requirements, the DAE category has the objectives 

of identifying fundamental gaps in atmospheric M&S, ingesting live 
6 

meteorological data and real-time forecasts into simulations. Additional 

objectives include the development of: 

• fundamental dynamic environment databases to support M&S, 

• standard synthetic natural environment scenarios and backgrounds, 

and 

• standard tools to facilitate weather impact decision aids and system 

performance analyses. 

Models that currently exist or are under development leading towards 

standards in the DAE category, such as the modeling of the ground state 

in winter environments, light scattering for war games and target 

acquisition, and three-dimensional static environments and initialization 

are presented here. Progress relevant in the DAE category is also 

presented in the areas of: weather tactical decision aids, such as the rulebased Integrated Weather Effects Decision Aid, the physics-based Target 

Acquisition Weather Software, and acoustic decision aids (Acoustic 

Battlefield Aid and Battlefield Acoustic Sensor Evaluator); live and 

synthetic weather to include the Battlefield Forecast Model, the fielded 

Integrated Meteorological System, and microscale modeling High 

Resolution Wind (HRW); transport and diffusion models (Second Order 

Closure Integrated PUFF [SCIPUFF], Vapor Liquid Solid Tracking 

[VLSTRACK], Two-Dimensional Transport and Diffusion for Personal 

Computers [D2PC], and Transport and Diffusion Simulator [TADSIM]); 

acoustic (Scanning Fast-Field Program [SCAFFIP], and Scanning 

Parabolic Equation [SCAPE]), electro-optical propagation (Weather and 

Visualization Effects for Simulations [WAVES], Atmospheric Optical 

Turbulence Model [ATMOS], and Electro-Optical Systems Atmospheric 

Effects Library [EOSAEL]); atmospheric optical turbulence (CN2); 

backgrounds (Smart Weapons Operability Enhancement [SWOE], 

Computer Generation of Realistic Environments with Atmospheres for 

Thermal Imagery with Optics and Noise [CREATION]); and other efforts 

(virtual dirty battlefield and clutter statistics). 
7 

1.0 Introduction 1.0 Introduction 

The Department of Defense (DoD) Modeling and Simulation (M&S) 

Master Plan [1] currently has six objectives, shown in table 1, which form 

a framework within which standards development proceeds. In order to 

support these six DoD objectives the Army Modeling and Simulation 

Master Plan [2] introduced and defined the standards development 

process and established the role of standards category coordinators 

within the Army. These 19 categories annually provide the Army 

Modeling and Simulation Office (AMSO) a report in their area on the 

status of standardization, significant progress during the past year, and 

standardization priorities for the next year. One of these 19 standards 

categories is the category of Dynamic Atmospheric Environments (DAE). 

Definitions, objectives, ongoing work, and the status of the DAE category 

are presented here. 

Table 2 Table 2: DoD M&S Objectives : DoD M&S Objectives : DoD M&S Objectives 

DoD M&S Objectives 

• Develop a common technical framework for M&S 

• Provide timely and authoritative representations of the natural 

environment 

• Provide authoritative representations of systems 

• Provide authoritative representations of human behavior 

• Establish a M&S infrastructure to meet developer and end-user 

needs 

• Share the benefits of M&S 

In today’s climate of reduced funding and decreasing budgets, it is 

imperative that models are reused and standards developed. This is 

particularly true in the DAE area since simulation of military operations 

must include realistic representations of the natural environment. 

However, a simulation attempting to emulate the real world around us is 

only as good as the computer models within it. To effectively model the 

atmosphere within variable simulation contexts is a formidable task. 

Atmospheric effects range from small-scale effects, such as smoke plumes 

and turbulence, to large-scale synoptic systems. These effects must be 

dealt with from detailed simulations that require physics-based models 

to high-level aggregated simulations that require a “broad brush” 

outlook and cannot afford the computational burden of detailed models. 
9 

2.0 Army Model Improvement Plan 2.0 Army Model Improvement Plan 

The Army Model Improvement Plan(AMIP) supports the technical M&S 

standards development goals of the U.S. Army by investing in M&S 

projects that will lead to standard algorithms, data, procedures, 

techniques, etc. Each fiscal year, Army Standards Category Coordinators 

(SCC), as designated in the U.S. Army M&S Master Plan, nominate M&S 

projects that will further standards development objectives within their 

respective standards categories. Projects are selected for funding each 

year based on their potential contribution. AMIP program goals are to: 

• further standards development objectives within each category, 

• address Army specific problems, and 

• make use of existing R&D models. 

M&S organizations nominate projects focused on the AMIP Program 

goals. The nominations are selected and prioritized by the appropriate 

standards category team. All projects submitted by each SCC are 

considered by the Policy and Technology (P&T) Working Group (WG), 

which prioritizes them for the Army Modeling and Simulation Executive 

Committee (AMSEC) review. The criteria used by the P&T WG to 

evaluate the various AMIP proposals are: 

• degree to which a proposal addresses the objectives of the proposing 

standards category; 

• applicability across and acceptance by the U.S. Army M&S 

community; and 

• feasibility, technical risk, and management risk associated with the 

project. 

Once reviewed by the AMSEC and approved by the Deputy 

Undersecretary of the Army for Operations Research, projects are funded 

according to their priority and available funds. Projects may be funded 

over multiple years, but must compete each year against all other projects 

submitted for funding that year. The AMIP program is managed by 

AMSO. DAE AMIP funded projects for FY 98 and 99 are presented in 

this report. 
11 

3.0 DAE Standards Category Definition 3.0 DAE Standards Category Definition 

The DAE category for U.S. Army M&S includes those objects, algorithms, 

data and techniques required to replicate weather, weather effects and 

impacts, backgrounds, acoustics, and transport and diffusion (T&D) of 

aerosols and battle by-products. 

The battlefield environment includes many sources of aerosols and 

particulates such as chemical and biological agents, smoke, dust, fog and 

chaff. These add to the natural environment increasing the presence of 

non-uniform aerosol regions. Weather, atmospheric T&D processes, and 

the attenuating effects of the environment on the propagation of 

electromagnetic energy all impact target acquisition and high technology 

weapons. The atmosphere and clouds provide cues, alter target and 

background signatures, and produce scene clutter both in the real world 

and in realistic computer-generated simulations. All these weather 

effects and impacts are in the DAE domain and are in harmony with the 

DoD objective representation of the atmosphere. 

The DAE category does not explicitly cover terrain, but it does cover 

weather influences on terrain. For example, snow cover will change the 

surface albedo; rainfall will alter the ground state affecting mobility. 

Atmospheric radiative transfer affects target and background signature 

propagation, illumination and diurnal heating, which will modify 

background signatures. All these effects fall under the purview of DAE. 

The exception is target signatures, which are handled in the domain of 

the standards category of Acquire, thus, highlighting the 

interconnectivity of standards categories. 
13 

4.0 DAE Standardization Requirements 4.0 DAE Standardization Requirements n Requirements 

The natural environment is important in determining the outcome of real 

battles. Included in this area are weather features (clouds, fronts, 

thunderstorms, etc.) and weather effects such as target contrast changes. 

Meteorological data and weather scenarios are becoming available 

through efforts such as the Defense Modeling and Simulation Office 

(DMSO) funded Weather Scenario Generator [3], the Master 

Environmental Library [4], and the Total Atmosphere and Ocean Server 

[5]. However, converting these meteorological parameters and weather 

features into quantitative effects and impacts that are not 

computationally burdening for simulations is a difficult proposition. 

Due to the dynamic range of atmospheric processes, the DAE category 

must represent a requirement spectrum ranging from small-scale effects 

found in high-resolution models, such as scene visualization, to largescale, low-resolution, aggregated effects, to correctly represent weather 

impacts. In the high-resolution area physics-based calculations, such as 

the U.S. Army Research Laboratory’s (ARL) Weather And Visualization 

Effects for Simulations (WAVES) [6], are needed to represent high fidelity 

natural and battlefield-induced atmospheric effects (e.g., smoke, 

illumination, rain and fog, T&D, etc.) but usually are available only at a 

high computational burden. To reduce this burden a scenario-specific 

natural environmental representation can be pre-computed or prescripted (if time-varying) for later real-time simulations. However, 

embedded environmental processes include battlefield-generated clouds, 

from munitions, vehicles, agents and fires, whose location and time of 

introduction cannot be completely pre-scripted. They are event driven, 

resulting from battle actions and combatant decisions, and thus, can only 

be partly precomputed. These processes are embedded into the natural 

aerosol environment and are generally more localized and dynamic than 

other battlefield effects. Atmospheric parameters and effects from 

embedded processes are thus both super-imposed on and affected by 

input conditions described by the natural environment representation. In 

some cases, the environmental embedded processes will be the dominant 

factors in determining the outcome of a simulation. 

While progress has been made in representing atmospheric effects, 

notably in the Defense Advanced Research Projects Agency Synthetic 

Theater of War–Synthetic Environments (STOW-SE) program [7], such 

efforts require dedicated hardware and precomputed weather effects 
14 

scenarios. The underlying models in these simulations are inherently 

computationally intensive. Engineering level line-of-sight propagation 

models from ARL’s Electro-Optical Systems Atmospheric Effects Library 

(EOSAEL) [8] and the Air Force Research Laboratory’s Moderate 

Resolution Transmission (MODTRAN) [9], while fast, are still 

burdensome considering the playing area, the potential number of linesof-sight between entities and the number of pixels needed to generate 

virtual scenes. 

High-level simulations that deal with aggregated units simply cannot 

afford to include detailed calculations for individual platforms and 

systems. Thus, a new approach is needed to include weather at a realistic 

level of fidelity and still maintain “faster than real time” simulation 

capability. Such an approach may exist in using rule-based programs, 

such as ARL’s Integrated Weather Effects Decision Aid (IWEDA) model 

[10]. This model, based in Army doctrine, provides color-coded matrix 

charts showing the impact weather has on various platforms, sensors, 

and weapons systems thereby allowing for simple and fast assessments 

over large areas without a heavy computational burden. In order to 

provide for these disparate needs (of both high- and low-resolution 

simulations) DAE requirements, presented in priority order in table 2, are 

general in nature. 

Table 2. DAE Requirements Table 2. DAE Requirements 

DAE Requirements DAE Requirements 

• Provide Fundamental Environmental Models for M&S

• Identify Requirements for Standard Atmospheric Scenarios

• Provide Methodologies for Determining Consistent Data sets 

for Environmental Effects Models 

• Provide Standardized Databases for System Performance 

Analysis 

 
15 

5.0 DAE Objectives 5.0 DAE Objectives DAE Objectives 

In concert with these requirements, the DAE category has the objectives 

of identifying fundamental gaps in atmospheric M&S, ingesting live 

meteorological data and real-time forecasts into simulations. Additional 

objectives include the development of: 

• fundamental dynamic environment databases to support M&S, 

• standard synthetic natural environment scenarios and backgrounds, 

and 

• standard tools to facilitate weather impact decision aids and system 

performance analyses. 

Models that currently exist or are under development that will satisfy 

these objectives are embodied in the DAE category roadmap (see 

figure 1). 

Figure 1. DAE roadmap Figure 1. DAE roadmap 
17 

6.0 DAE Assessment and Accomplishments 6.0 DAE Assessment and Accomplishments DAE Assessment and Accomplishments 

6.1 Assessment Assessment 

Modeling efforts leading to the development of standard algorithms in 

the DAE area are, as might be expected, strong in some areas and in need 

of additional effort in others. In FY99, three EOSAEL models were 

approved as standards in the DAE category: 

1. the climatology (CLIMAT) model [11], 

2. the aerosol propagation model scaled transmission (XSCALE) [12], 

and 

3. the smoke model Combine Obscuration Model for Battlefield Induced 

Contaminants (COMBIC) [13]. 

DAE AMIP projects, which include the FY98 Modeling of the Ground 

State in Winter Environments and the FY99 Light Scattering for 

Wargames and Target Acquisition and Three-Dimensional Static 

Environments and Initialization (3DSE), are discussed below along with 

other additional areas where progress has been made this past year. 

Teaming arrangements for the Dynamic Atmospheric Environments 

category include members from ARL, the Army Space and Missile 

Defense Command, the Cold Regions Research Engineering Laboratory 

(CRREL), and the AMTEC Corp. 

6.2 Accomplishments Accomplishments Accomplishments 

6.2.1 Modeling of the Ground State in Winter Environments Modeling of the Ground State in Winter Environments Modeling of the Ground State in Winter Environments Modeling of the Ground State in Winter Environments 

6.2.1.1 Objective 

Cold environments can have drastic effects on U.S. Army operations. 

Current available U.S. Army models and simulations have almost no 

ability to replicate these effects. An inaccurate forecast, or no forecast at 

all, of the impact of cold environments on Army operations can have a 

negative effect on training, resulting in inaccurate planning, faulty 

analysis and subsequent failure of U.S. Army operations. The objective 

was to address the issue of predicting the state of the ground (surface 

temperature, snow cover, snowmelt, and freeze and thaw depths) by 

utilizing CRREL’s snow thermal (SNTHERM) energy balance model. The 

methodology investigated the sensitivity of the ground state to different 

flux model initializations, including a semi-empirical model, a plane 
18 

parallel model, and ARL’s Atmospheric Illumination Module (AIM). 

Model runs for two locations (Grayling, Michigan and Yuma, Arizona), 

three seasons (spring, fall, and winter), and three sky states (clear, partly 

cloudy and cloudy) using the three flux model initializations and 

measured data have been made. The results were inter-compared 

including a comparison with measured ground state information. 

6.2.1.2 Background 

It is a well-established fact that the state-of-the-ground is driven in a 

large part by downwelling solar and infrared (IR) fluxes. Models 

developed to predict the state-of-the-ground for U.S. Army operations 

depend critically on these fluxes for initialization. Unfortunately, these 

fluxes are not routinely measured parameters as is the case with more 

common meteorological parameters like temperature, relative humidity, 

etc. Therefore, indirect methods must be utilized to generate the required 

flux initialization information for state-of-the-ground models. Predicted 

ground temperatures were compared by using the Smart Weapons 

Operability Enhancement Thermal (SWOETHERM) model initialized 

with different solar flux schemes. These initialization schemes used solar 

flux values measured during the Smart Weapons Operability 

Enhancement (SWOE) [14] field programs, and calculated solar flux 

values from Shapiro’s semi-empirical model, a plane parallel model 

(MODTRAN), and ARL’s AIM Model [15]. 

Variations in the level of spatial and temporal variability in the surface 

temperature and solar loading represent clutter. This clutter can degrade 

the performance of weapon systems. The performance impact on 

infrared systems is a direct consequence of this variation in the surface 

temperature and this variability affects systems operating in other 

spectral regions. For example, variations in the solar loading can cause 

variations in the physical characteristics of snow that can have a 

significant effect on active radar systems. With the recent emphasis on 

distributed models for synthetic scene generation, and the development 

of segmentation techniques that facilitate the distributed use of onedimensional (1-D) models, it is imperative that models are developed to 

provide the spatial and temporal variability of environmental parameters 

that drive the energy budget models used to predict the state-of-theground. 
19 

6.2.1.3 Results and Findings 

The response of the surface temperature to different solar flux 

initialization schemes was investigated keeping all other environmental 

parameters constant. It was determined that for clear skies all schemes 

resulted in nearly identical surface temperatures. Thus, a semi-empirical 

model like Shapiro’s has the advantage of computational speed. For 

partly cloudy and cloudy skies, only the AIM model mimicked the 

spatial variability observed with the solar flux and the resulting spatial 

variability in the surface temperature. The Cloud Scene Simulation 

Model (CSSM) [16] was used to determine the spatial variability of the 

clouds. The cloud distributions were then used by AIM to produce the 

variations of the surface solar loading. CSSM also has the capability to 

produce the temporal variations in the cloud fields for short periods of 

time. Thus, it would be possible to use CSSM and AIM to produce the 

temporal and spatial variations in the solar loading. Models like AIM 

frequently incur a large computational burden. In order to reduce the 

computational burden associated with AIM several new procedures were 

implemented and investigation of additional techniques that can be used 

to reduce the model runtime will be continued. Distributed energy 

budget models used to predict the state-of-the-ground for the virtual 

depiction of the battlespace require distributed environmental 

parameters for initialization. Many of these parameters can be obtained 

from mesoscale models like Fifth-Generation National Center of 

Atmospheric Research /Penn State Mesoscale Model (MM5) or databases 

associated with programs like the Integrated Meteorological System. 

However, none of these models or programs provides distributed solar or 

IR flux, a key initialization parameter of energy budget models. Models 

like AIM linked to CSSM, or for that matter any model that provides 

information on the spatial and temporal distribution of atmospheric 

conditions, can be used to provide the spatial and temporal distribution 

of radiative fluxes. In addition to exploring techniques to decrease the 

run time of AIM, the potential use of AIM to generate distributed 

infrared fluxes should be explored. 

Based on the measured solar flux and surface temperatures from the 

Yuma, Arizona SWOE field program, it was found that the surface 

temperature for this semi-arid climate varied by several degrees for two 

measurement sites separated by approximately 100 m for partly cloudy 

skies. The measured solar fluxes varied by a factor of up to two, 

depending on the cloud conditions. Similar variations occurred during 

the Grayling, Michigan field programs. A surface temperature change of 
20 

approximately 4 oC over a period of approximately 12 min was recorded 

at a single site during the Yuma, Arizona field program. The 

corresponding change in the total solar flux was approximately 500 

W/m2

. Overcast sky conditions also produced similar variations in the 

surface temperature and the total solar flux. These variations were 

attributed, in part, to variations in the cloud thickness (cloud optical 

depth). Thus, under most sky conditions, variations in solar loading 

were shown to produce spatial and temporal variations in temperature 

which influences IR signatures of background features. 

6.2.2 Light Scattering for War Games and Target Acquisition 6.2.2 Light Scattering for War Games and Target Acquisition ion 

Light scattering from atmospheric aerosols affects the ability of a sensor 

operating in the visible to acquire targets. This effect, also known as path 

radiance, is embodied in wargames and target acquisition models as the 

sky-to-ground ratio (SGR). The SGR can vary from 0.2 for snow to 25 for 

forested conditions; accurate SGR determinations are needed to assure a 

high confidence for target acquisition ranges used in wargames and test 

and evaluation. 

The AMSO standards category of Acquire has requested a standard code. 

To accomplish this, Combined Arms and Support Task Force Evaluation 

Model’s (CASTFOREM) [17] legacy is being compared with the AIM 

research grade code. A new model is being developed by extracting 

relevant portions from the legacy models and incorporating these with 

improvements determined from the research grade code and advances in 

the literature. This will result in a final model with fewer limitations and 

improved accuracy. This model, along with documentation, will be 

provided to U.S. Army Training and Doctrine Command Analysis 

Center. Potential also exists for application to Night Vision and 

Electronic Sensors Directorate’s sensor performance model, Acquire [18]. 

The final model will also be proposed as a standard in the standards 

categories areas of DAE and Acquire. 

6.2.3Three-Dimensional Static Environments and Initialization Dimensional Static Environments and Initialization Dimensional Static Environments and Initialization 

The operational U.S. Army operates in a four-dimensional (4-D) 

environment. This places upon the modeling community a need to 

provide simulations in a spatially realistic three-dimensional (3-D) 

environment. Typically, the source of environmental data such as 

temperature, wind speed and direction, humidity and level of turbulence 

is a database, which generally contains values at a point or perhaps along 
23 

7.0 Other Army Efforts 7.0 Other Army Efforts 

7.1 Weather Tactical Decision Aids Weather Tactical Decision Aids Weather Tactical Decision Aids 

Weather tactical decision aids (TDA)s come in two flavors: rule-based 

and physics-based. Rule-based TDAs are constructed though rules that 

have been collected from field manuals, U.S. Army Training and Doctrine 

Command (TRADOC) centers and schools, and subject matter experts. 

Physics-based TDAs employ physics calculations that have their basis in 

theory or field measurements. 

7.1.1Tri-Service Rule Service Rule Service Rule-Based Weath Based Weath Based Weather Tactical Decision Aids er Tactical Decision Aids er Tactical Decision Aids 

The U.S. Army’s Integrated Weather Effects Decision Aids is being 

adopted as the model for rule-based weather impact decision aids for all 

the services. A rule-based decision aid provides a general framework 

based on lists of “if-then-else” rules and pre-established critical weather 

thresholds for moderate or severe impacts. The U.S. Air Force, U.S. Navy 

and U.S. Army, with concurrence from the U.S. Marine Corps, are now 

collecting weather impacts using this common format and the current 

database of hundreds of rules are expected to expand to several 

thousands of rules. The U.S. Army IWEDA is designed for the Army 

Common Hardware/Software and the Defense Information 

Infrastructure Common Operating Environment as part of the Integrated 

Meteorological System (IMETS); the Command, Control, 

Communications, Computers and Intelligence (C4I) weather system IS 

currently fielded and being improved for the U.S. Army First 

Digitized Division. 

7.1.1.2 The Integrated Weather Effects Decision Aid 

The IWEDA is a rule-based weather impact model based on U.S. Army 

doctrine. IWEDA allows for simple and fast assessments over large areas 

of weather impact factors on units based on their types of platforms, 

sensors, and weapons. IWEDA provides this information to the U.S. 

Army’s tactical C4I systems in the form of red-amber-green 

(unfavorable/marginal/favorable) 3-D (x, y, time) data grids and as 

common-map overlays. IWEDA is an integral part of IMETS. 

IWEDA is being proposed as the basis for a Joint service rule-based 

tactical decision aid by the U.S. Army Intelligence Center. IWEDA 

includes U.S. Army, U.S. Air Force, U.S. Navy and limited threat systems. 
24 

Not only is information provided on sensor systems and platforms but 

also on other effects of weather, such as impacts on deployment of 

ground-staked antennas due to wind conditions or temperatures too cold 

for diesel vehicles to start. IWEDA generates simple red-amber-green 

tables and overlays with a short text message describing the weather 

impact. The colors highlight the potential for reduced effectiveness. 

IWEDA’s approach differs from a physics-based approach insofar as it is 

based on a large number of weather impact “rules”. Each system has its 

list of relevant rules, and each rule definition includes red-amber-green 

“critical value thresholds” for one, or a combination, of the 

meteorological parameters that affects the system. These weather impact 

rules and critical values have been validated through TRADOC 

organizations, field manuals and National Ground and Intelligence 

Center for the various systems. IWEDA also includes an interactive rule 

editor that allows the user to easily modify rules and critical values. 

ARL is in the process of coupling IWEDA’s rule-based weather impacts 

with information from physics-based tactical decision aids in order to 

provide required performance factors and probabilities applicable to 

aggregated simulations. This effort will then be able to support faster 

than real-time simulation for more efficient play of weather and at a 

fidelity comparable to that used in aggregate simulations. 

7.1.2 Tri- 7.1.2 Tri-Service Physics Service Physics Service Physics-Based Weather Tactical Decision Aids Based Weather Tactical Decision Aids Based Weather Tactical Decision Aids 

Physic-based tactical decision aids, as distinct from rule-based decision 

aids, perform detailed performance calculations for specific systems. Triservice models for electro-optic propagation, such as the Tri-Service 

Target Acquisition Weather Software (TAWS) and models for acoustics, 

are being linked to rule-based TDA’s to provide more detailed effects and 

quantitative information. 

7.1.2.1 Target Acquisition Weather Software

The Tri-Service Target Acquisition Weather Software (TAWS) and its 

predecessor, the U.S. Air Force’s Electro-Optical Tactical Decision Aid 

(EOTDA), are software models that predict the performance of weapon 

systems and direct view optics based on environmental and tactical 

information. The U.S. Army’s TDA, target acquisition (TARGAC), is a 

variant of EOTDA. Performance is expressed primarily in terms of 

maximum detection, recognition, or lock-on range. The EOTDA and 

TARGAC supported systems in three regions of the spectrum: 
25 

1. visible/TV (0.4 to 0.9 μm), 

2. Laser (1.06 μm), and 

3. far IR (8.0 to 12.0 μm). 

TAWS extends this to include the mid-IR (3.0 to 5.0 μm). 

TAWS consists of three essential parts: 

1. an inherent-target contrast model, 

2. an atmospheric effects model, and 

3. a system performance model. 

These basic models are required for any type of device treated by TAWS; 

however, the nature of the models that perform a given function may 

vary considerably depending on the system of interest. For example, the 

inherent contrast for visual and television devices depends on the 

relative reflectances of the target and background. Thus, atmospheric 

scattering and solar illumination [19] are overriding factors. For thermal 

imagers, the inherent contrast consists of the difference in temperature 

between the target and background. Solar loading is the dominant factor 

thus requiring access to previous and forecast conditions required by the 

thermal balance model [20,21]. The nature of the calculations of 

atmospheric propagation is also different in the visual than in the IR. 

Scattering primarily influences the former, while the latter is dominated 

by absorption. The U.S. Army Model and Simulation Office standards 

category model Acquire is used for sensor performance. TAWS will also 

allow for automated meteorological ingest via the Air Force Weather 

Information Network; these meteorological variables may be selected or 

changed through numerous Graphical User Interfaces. 

7.1.3 Acoustic Decision Aids Acoustic Decision Aids Acoustic Decision Aids 

The Acoustic Battlefield Aid (ABFA) is ARL’s prototype acoustic decision 

aid tool developed under MATLAB. ABFA performs realistic 

calculations of probability of detection, direction-finding resolution, 

spatial resolution from acoustic array networks, and other sensor 

performance metrics relevant to U.S. Army applications. It demonstrates 

ARL’s unique capability to assess environmental effects on U.S. Army 

acoustical systems. ABFA also incorporates turbulence effects on 

acoustic sensors in addition to a simple model for the effects of terrain. 

The developments being made in ABFA are being transitioned to the 

Battlefield Acoustic Sensor Evaluator (BASE) (see section 7.4.1). 
26 

7.2 Live and Synthetic Weather Live and Synthetic Weather Live and Synthetic Weather 

Various numerical weather prediction models of interest to the U.S. 

Army are being compared using common initialization and ground-truth 

data sets. The U.S. Army’s Battlescale Forecast Model (BFM) [22], U.S. 

Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System 

(COAMPS) [23], U.S. Air Force’s MM5 [24], Colorado State University’s 

Regional Atmospheric Modeling System (RAMS) [25], and other 

specialized weather forecasting models are being compared by ARL’s 

Battlefield Environment Division. By using common inputs and for 

comparison against actual weather, the U.S. Army will be able to identify 

which models are best under various meteorological conditions and 

hardware/software constraints. 

7.2.1 The Battlefield Forecast Model The Battlefield Forecast Model The Battlefield Forecast Model 

ARL’s BFM is the U.S. Army’s mesoscale model and, as such, is a 

proposed DAE standard. BFM is a meteorological forecasting model that 

is used primarily to obtain 4-D meteorological field parameters (e.g., 

winds, temperature, moisture, cloud, turbulence parameters, etc.) over 

complex terrain. The BFM is currently resident on the U.S. Army’s 

fielded IMETS. The strength of the BFM comes from the high resolution 

of the model (10 km) and its ability to incorporate terrain effects. This 

gives the forecaster in the field a quick understanding of the effects the 

local battlefield terrain will have on the weather. 

The BFM, a hydrostatic, quasi-Boussinesq, 16-layer mesoscale model, is 

initialized with real-time data and forecasted results from a synopticscale model. It consists of several elements including a terrain elevation 

data production program, a three-dimensional (3-D) data analysis 

program of input data for model initialization and data assimilation, and 

a prognostic mesoscale model, called the Higher Order Turbulence 

Model of Atmospheric Circulation. As part of BFM model output, 

interpolation programs for horizontal and vertical displays at desired 

heights and grid locations, graphical user interfaces for data input, 

execution, and display of the meteorological forecast are provided. A 

map background server to ease visualization of execution and forecast 

field display, and a series of algorithms in the Atmospheric Sounding 

Program to forecast visibility, clouds, icing, turbulence, etc. are 

also available. 
27 

7.2.2 The Integrated Mete The Integrated Mete The Integrated Meteorological System orological System orological System 

The IMETS is the meteorological component of the Intelligence and 

Electronic Warfare element of the Army Battle Command System 

(ABCS). IMETS provides commanders at all echelons with an 

automated- weather system to receive, process, and disseminate weather 

observations, forecasts, and weather effects decision aids to all Battlefield 

Functional Areas (BFA)s. 

IMETS will have three configurations: 

1. a command post configuration with IMETS permanently integrated 

into the local area network; 

2. a vehicle mounted configuration for tactical operations where the 

supported echelon moves; and 

3. a light configuration, integrated into a small task force, where light 

weight, easily deployed, core weather functions can be performed 

without having a separate vehicle to shelter the system. 

Each configuration provides automation and communications functions 

to support Staff Weather Officers assigned to echelons from Separate 

Brigades and higher, and to U.S. Army Special Operations Forces. 

IMETS receives weather information from meteorological satellites, the 

Air Force Weather Agency, artillery meteorological teams, remote 

atmospheric sensors, and civilian forecast centers. IMETS then processes 

and distributes weather forecasts, observations, and climatological data 

to produce timely and accurate weather products designed to the meet 

the specific war fighter’s needs. 

The U.S. Army’s BFM and the U.S. Air Force’s mesoscale models provide 

weather forecasts on the scale needed by the U.S. Army; these forecasts 

provide numerous weather parameters at 10 km resolution (higher 

resolution planned). High-resolution forecast data are made available to 

the Staff Weather Officer and other U.S. Army users via two-dimensional 

(2-D) and 3-D displays and IWEDA that displays weather effects on 

friend and enemy tactical systems. 

Significant weather and environmental support for war fighters is found 

in the form of automated tactical decision aids, such as IWEDA, 

produced by the IMETS. These graphics go beyond briefing the weather. 

They display the impact of the weather on current, projected, or 
28 

hypothesized conditions on both friendly and enemy capabilities. 

Instead of reacting to the weather, the war fighter can take advantage of 

the weather and the force multiplier of the effects of the weather can then 

be used by decision-makers for tactics and maneuver. 

IMETS is critical to the war fighter’s decision-making process because it 

provides data directly to many other force multipliers. The other BFAs in 

ABCS depend on IMETS to provide the following: 

• near real-time data for safe aviation operations; 

• the weather feature for the common tactical picture; 

• landing/drop zone data for air assault/airborne operations; 

• current and forecast weather for maneuver, artillery, intelligence and 

electronic warfare, air and missile defense, and combat service 

support planning; 

• wind and atmospheric data for nuclear, chemical and biological 

planning; 

• communications linkage from Army Common User System, Tactical 

Operations Center Local Area Network, weather satellites and 

forecast centers to contingency forces; and 

• terrain mobility/counter mobility weather information. 

IMETS provides first-in weather support to contingency forces, tailored 

weather information for deep fires and precision munitions, and weather 

effects decision aids for the planning and execution of maneuver and 

support activities. 

IMETS actively participates in the Force XXI initiatives ensuring that 

IMETS is an integral part of the U.S. Army’s digitization effort and that it 

will continue to meet the war fighter’s needs into the 21st Century. 

7.2.3Microscale Modeling Microscale Modeling Microscale Modeling Microscale Modeling 

The ARL developed High Resolution Wind (HRW) [26] model is a twodimensional, diagnostic, time independent model which computes 

horizontal fields of the wind components, the mean wind velocity, 

friction velocity, potential temperature, the Richardson number and the 

Power Law exponent. The model typically simulates the flow field over a 

gridded area of some 5 by 7 km with a spatial resolution of 100 m. Model 
29 

initialization requires a single surface value of wind speed and direction, 

temperature, and a corresponding radiosonde type measurement. The 

radiosonde data is used to provide an estimate of the bulk atmospheric 

buoyancy. The model computes this estimate by applying the initial 

single point values at each grid point in the computational array, along 

with digitized terrain elevation. Simulation results are obtained by 

direct-variational relaxation of the wind and temperature fields in the 

surface layer. The solution is reached when the internal constraint forces 

imposed by the warped terrain surface, thermal structure, and 

requirements for flow continuity are minimized. The procedure uses 

Gauss' Principle of Least Constraints, which requires these forces to be 

minimized in order to satisfy the equations of motion. When applied to 

the surface layer, this procedure requires the use of empirical wind and 

temperature profiles. The computational domain size can range from 

2 by 2 km to 20 by 20 km with grid resolutions of 40 to 400 m, 

respectively. Note that HRW is usually run for a nominal 5 by 5 km area 

and a vertical thickness of the computational layer 1/10th the magnitude 

of the grid size. This layer thickness can, however, vary from 2 to 50 m. 

HRW includes the effects of canopies. The effect of vegetation to the 

overall Canopy-Coupled Surface Layer (CCSL) flow patterns is 

analogous to the effect of varying terrain heights. This approach to 

handling the effects of vegetative canopies on the flow has been 

quantified and validated against a number of field trials. Likewise, the 

canopy method for treatment of the urban domains does not include the 

effects of individual buildings but does provide the influence of the 

urban terrain on the large-scale flow. The CCSL technique allows the upand-down scale linkage between flows from mesoscale weather models 

and flows in-and-around buildings. As an AMIP project (see section 

6.2.3), these 2-D surface layer models are currently being extended to 

three dimensions. The original HRW model was designed to be viably 

operable on minimum input data. An improvement, which will provide 

the ability of the HRW model to be partially dynamic and be applicable 

to larger domains, has been initiated. 
30 

7.3 Transport and Diffusion Transport and Diffusion Transport and Diffusion 

7.3.1Second Order Closure Integrated PUFF nd Order Closure Integrated PUFF nd Order Closure Integrated PUFF nd Order Closure Integrated PUFF 

The Second Order Closure Integrated PUFF (SCIPUFF) model [27], which 

uses an internal second order turbulence closure scheme to improve the 

effects of local atmospheric effects on dispersion, has undergone 

additional validation by the Defence Threat Reduction Agency. The 

model, currently driven by interpolated data from large domain 

(mesoscale), is currently being linked by the FY99 AMIP project (3DSE) to 

surface layer wind models to increase the effects of terrain on near 

surface transport and to include the local effects of surface features on 

diffusion. This linkage will provide a very high (100 m) resolution 

capability for M&S T&D capability, but will not carry with it the 

normally high overhead associated with high-resolution meteorology. 

The integrated product will be available in FY00. 

7.3.2 Vapor Liquid Solid Tracking, Two Vapor Liquid Solid Tracking, Two Vapor Liquid Solid Tracking, Two-Dimensional Dimensional Dimensional 

Transport and Diffusion Personal Computer and Transport and Diffusion Personal Computer and 

Transport and Diffusion Simulator Transport and Diffusion Simulator 

The U.S. Army supports three T&D modeling efforts. The approved 

operational models supported by the U.S. Army Chemical and Biological 

Defense Command is a Gaussian puff model, Vapor Liquid Solid 

Tracking (VLSTRACK) and D2PC (2-D T&D for Personal Computers). 

The D2PC model is the only model certified by the U.S. Army Nuclear 

and Chemical Agency for operational use. Both models use climatology 

and/or rudimentary meteorological parameterizations to compute the 

trajectory of a toxic cloud. VLSTRACK and D2PC are both easy-to-use 

programs that provide rapid results on personal computer type 

platforms. The primary causes for error in military applications of T&D 

models are inadequate source descriptions and the treatment of the 

spatial and temporal variability of dispersion. Data sets available for 

validation of dispersion models rarely contain adequate source 

information. The third model, ARL’s T&D Simulator (TADSIM), is a 

suite of models which embodies a methodology to mitigate these 

problems and has been specifically designed to address the space and 

time variability issue. 

VLSTRACK facilitates model initiation and output interpretation through 

its Graphical User Interface. However, the model is restricted due to its 

limited source term flexibility, the lack of terrain-influenced meteorology, 
31 

and its treatment of dispersion, which is tied to a limited categorization 

scheme. The source term selection is based on a library of delivery 

systems and agents. Inherent randomness in the mean flow, which in 

reality is a larger scale dispersion effect influenced by terrain, is driven 

by a random number generated functional variation to the transport. 

VLSTRACK comparisons with field trial data do not always agree. D2PC 

uses a single wind and turbulence parameter for the entire simulation 

domain and time period. Thus, this model can only be considered valid 

for flat terrain, small spatial domains, and short time intervals. 

TADSIM consists of a transport driver utilizing 2-D wind fields at 

variable resolution and user-selectable codes for traditional Gaussian and 

non-Gaussian dispersion for both horizontal and vertical cloud spread. 

This suite of models is comprised of HRW (see 7.2.3); atmospheric 

simulation (AIRSIM), large eddy simulation (LES) model for airflow over 

terrain; airflow simulation (AIRFLOS), a LES model for airflow over 

structures; and an atmospheric chemical/biological simulation 

(ABCSIM), for T&D of the agent cloud. These dispersion codes are 

designed to be specific to the local meteorological and terrain conditions 

rather than using generalized classification techniques. TADSIM is in the 

verification/validation stage of development and employs a unique 

method for handling vertical diffusion called transilient turbulence. This 

technique is particularly suited to the treatment of dispersion in unstable 

atmospheres. The current version of TADSIM has been verified and has 

undergone limited validation. 

7.4Propagation Propagation Propagation 

7.4.1Acoustics Acoustics Acoustics 

7.4.1.1 Scanning Fast-Field Program 

The Scanning Fast-Field Program (SCAFFIP) [28] is an ARL atmospheric 

acoustics propagation model incorporating many of the effects of the 

environment on the sound field such as geometrical spreading, 

refraction, diffraction, molecular absorption, and complex ground 

impedance. It is based on the Fast-Field Program (FFP) with the added 

ability to scan multiple frequencies to predict the propagation conditions 

around the location of a sensor. The FFP is a one-way solution to the 

acoustic-wave equation originally developed for underwater sound 

propagation predictions. SCAFFIP calculates an attenuation table with 

range and frequency for a given geometry and meteorological profile. It 
32 

provides range estimations from the sensor or target for signal-noise of 

-10, -5, 0, and 10 dB (re: 20mPa) with azimuth for a given geometry, 

sound level of target at a given frequency, and meteorological profile. 

The meteorological profile and geometry provide the model the ability to 

calculate the sound-speed profile. The attenuation table is compatible for 

use by acoustic sensor performance models such as the Battlefield 

Acoustic Sensor Integration System (BASIS) and the BASE. BASE will be 

a versatile Unix-based acoustic decision aid the first version of which is 

under development and will be available by the end of FY00. The 

geometry profile is required because of the angular dependence of the 

sound speed; that is, the wind direction is related to the direction of 

propagation. This model works well over a flat-earth and a nonturbulent atmosphere. In the near future this model will be added to the 

EOSAEL. 

7.4.1.2 Scanning Parabolic Equation 

The Scanning Parabolic Equation (SCAPE) model is an atmospheric 

acoustics propagation model incorporating the same effects as SCAFFIP. 

It is based on the Green’s Function Parabolic Equation (GFPE) with the 

added ability to scan multiple frequencies to predict the propagation 

conditions about the location of a sensor. The GFPE is a reformulation of 

the Split-Step Parabolic Equation model used in underwater acoustics. 

SCAPE is currently under development by ARL with the first version due 

in FY00. SCAPE will also calculate an attenuation table with range and 

frequency compatible with the sensor performance models such as BASIS 

and BASE. This model will allow for the incorporation of turbulence and 

terrain effects (see figure 2). Scattering from atmospheric turbulence will 

enhance signal levels at acoustic sensors located in refractive shadow 

zones where very low signal levels are expected. A version of the model 

including turbulence will be available by the end of FY00. 

 

 

 

 

 

Figure 2. Probability of Figure 2. Probability of 

detection for a fixed, detection for a fixed, 

ground- ground-based sensor based sensor based sensor 

(circle). 
33 

7.4.2 7.4.2 Electro Electro Electro-Optical Optical Optical 

7.4.2.1 WAVES 

The WAVES suite of models, being developed by ARL, predicts 

illumination and radiance information for a three-dimensionally variable 

atmosphere as a function of cloud type and amount, including partly 

cloudy skies at visual and IR wavelengths. It also predicts electro-optical 

propagation effects for horizontal and slant paths through the natural 

atmosphere. WAVES output illumination and propagation effects are 

critical to accurate target acquisition and scene generation. WAVES 

computations include direct solar/lunar radiation, multiply scattered 

solar/lunar radiation, optical turbulence, and forward scattering due to 

atmospheric aerosols. 

WAVES was conceived and developed under a series of Tri-Service 

Programs to develop complete modeling and simulation of visualization 

and imaging of the atmospheric environment. WAVES beginnings may 

be traced to the SWOE program, through the Target Acquisition 

Modeling Improvement Program (TAMIP), the DMSO Environmental 

Effects for Distributed Interactive Simulation (E2DIS) program, and is 

now under the DSMO Executive Agent for Space and Atmospheres 

program "Radiometric Validation of the Cloud Scene Simulation Model 

(CSSM) and Boundary Layer Illumination and Radiative Balance Model 

(BLIRB)” where it is currently being integrated and evaluated. 

The WAVES suite of models, (radiative transfer model BLIRB, 

atmospheric optical turbulence model (ATMOS), geometric radiance 

interpreter module View Point (VIEW), and the scene modifier module 

Pixel Modification (PIXELMOD) are integrated together to provide either 

modifications to existing scenes for real-time computer image generation 

or radiance values for a defined scenario. WAVES computes tables of 

solar and lunar multiply scattered radiation under varying atmospheric 

conditions that includes calculations for Rayleigh scattering, scattering by 

background aerosols, and scattering from inhomogeneous clouds and 

partly cloudy skies. WAVES does not attempt to impose any particular 

rendering method, but does provide the 2 and 3-D data to support a wide 

range of possible user implementations. 

WAVES is being integrated with the Total Atmosphere Ocean Server 

(TAOS). This integration will allow High-Level Architecture (HLA) 

federations to have an ability to access the complex 3-D data produced by 
34 

WAVES. Integration with TAOS will also demonstrate that the 

application program interfaces being developed are sufficient to allow 

alternate HLA interfaces. 

Additional ongoing work is evaluating detectability of biological aerosol 

clouds in near-, mid-, and far-IR wavebands. This has required creating a 

high-spectral resolution aerosol optical properties database for use in 

WAVES. This additional data will also be available for hyperspectral and 

other analysis. 

In collaboration with Night Vision, a Tri-Service team is extending the 

capabilities of WAVES and MODTRAN for seamless integration of cloud 

and smoke effects for IR simulation. This work is aimed at providing 

capabilities for real-time IR scene simulation and rendering for realistic 

simulations and scene generation to allow development and testing of 

aided target recognition algorithms in different weather conditions. This 

will aid Night Vision’s Paint-the-Night program and support modeling of 

solar loading for physics based thermal background clutter and target 

modeling. 

The Information Science and Technology Directorate of ARL is 

integrating cloud field, illumination, and visibility results from the 

WAVES suite of models with their Future Tactical Operations Center 

research program. This integration will be used to explore how these 

tools can be used by commanders to better understand the battlefield and 

possible environmental impacts on their missions. Additional research 

will be performed to examine different styles of presentation and their 

effectiveness. 

7.4.2.2 The Electro-Optical Systems Atmospheric Effects Library 

The EOSAEL, developed by ARL, began its development in 1978 and is 

currently considered a mature code. EOSAEL contains 22 computer 

modules that can be separated into eight generic classes: 

1. atmospheric transmission and radiance (low resolution transmission 

[LOWTRAN], ultraviolet transmission [UVTRAN], and nearmillimeter wave [NMMW]); 

2. laser propagation (laser transmission [LZTRAN] and non-linear 

vaporization and breakdown effects [NOVAE]); 
35 

3. tactical decision aids (cross wind integrated concentration [KWIK], 

grenade [GRNADE], helicopter [COPTER], and missile plume 

[MPLUME]); 

4. battlefield aerosols (COMBIC and fire-induced transmission and 

turbulence effects [FITTE]); 

5. natural aerosols (XSCALE and CLIMAT); 

6. TARGAC; 

7. radiative transfer (overcast [OVRCST], illumination [ILUMA], fast 

algorithm for atmospheric scattering [FASCAT], large area smoke 

screen [LASS], refraction [REFRAC], narrow beam scattering 

[NBSCAT], and band-integrated transmittances [BITS]); and 

8. phase function and Mie code support modules. 

The philosophy underlying the development of EOSAEL has been to 

include modules that give reasonably accurate results with the minimum 

in computer time for conditions that may be expected on the battlefield. 

The latest version of EOSAEL is available to approved users through the 

Test and Evaluation Community Network Bulletin Board System 

(TECNET). More information concerning EOSAEL may be found at the 

World Wide Web site URL address http://www.eosael.com. 

7.4.2.3 Atmospheric Optical Turbulence 

A model (CN2) [29] has been developed to allow a quantitative 

assessment of atmospheric optical turbulence. CN2 calculates vertical 

profiles of Cn

2

 for near earth environments. The algorithm uses surface 

layer gradient assumptions applied to two levels of discrete vertical 

profile data to calculate the refractive index structure parameter. Model 

results can be obtained for unstable, stable, and near-neutral atmospheric 

conditions. The CN2 model has been benchmarked on data from the 

radiation and energy balance (REBAL) 1992 field study. The model will 

be added to EOSAEL in the near future. 
36 

7.5 Backgrounds Backgrounds Backgrounds Backgrounds 

7.5.1 7.5.1 Smart Weapons Operability Enhancement Smart Weapons Operability Enhancement Smart Weapons Operability Enhancement Smart Weapons Operability Enhancement 

Terrain, vegetation, and weather effects present a complex, highly 

variable background for IR and Millimeter Wave (MMW) target seekers 

and IR viewers. This is especially true in the presence of snow and 

frozen ground. Captive flight tests are expensive and can account for 

only a limited range of conditions. DoD requires cost-effective methods 

for incorporating the complexities of the battlefield environment into 

training, planning, and weapon system development. 

The physics-based Joint Test & Evaluation Smart Weapons Operability 

Enhancement (SWOE) is a synthetic scene generator. SWOE models can 

produce synthetic scenes for user-specified spectral regions in the visible, 

IR, and MMW regions. The SWOE thermal models consist of a 

soil/snow, vegetation, canopy, and a single 3-D tree model. The thermal 

models consider the exchange of energy via conduction, convection, 

radiation, evapotranspiration, and the mass flux of precipitation. The 

SWOE radiance models consider emitted, primary and secondary 

reflections, bi-directional effects, and atmospheric attenuation. The 

SWOE models are used to generate selected scenes for proposed generic 

validation procedures. SWOE also has an associated high spatial 

resolution measured data base, including meteorological information, 

taken diurnally over four seasons; the measured locations, Grayling, 

Michigan and Yuma, Arizona, represent European and SW Asian 

analogs. 

CRREL researchers have integrated field testing, physics-based 

modeling, and image rendering techniques to generate IR and MMW 

synthetic scenes (see figures 3 and 4). Through the SWOE, a terabyte of 

IR and MMW validation images that span both winter and summer 

conditions have been assembled. An IR scene generation capability has 

been implemented and validated through the Office of the Secretary of 

Defense Joint Test and Evaluation Program. Physics-based MMW 

models of snow and soil processes have been coupled and distributed 

over test areas at high-spatial and temporal resolutions. Predicted 

signatures have been rendered into scenes which are comparable to 

measurements from captive flight tests. These maturing technologies can 

support training, planning, and weapon system development at a variety 

of levels. Applications include building all-weather databases for 
37 

Distributed Interactive Simulation applications, selecting conditions for 

live training, and constructing look-up tables of smart weapons 

performance under winter conditions. 

7.5.2 Computer Generation of Realistic Environments with 7.5.2 Computer Generation of Realistic Environments with 

Atmospheres for Thermal Imagery with Optics and Noise Atmospheres for Thermal Imagery with Optics and Noise 

The ARL developed Computer Generation of Realistic Environments 

with Atmospheres for Thermal Imagery with Optics and Noise 

(CREATION) model is also a synthetic scene generator. CREATION is a 

3-D, multispectral, high-resolution scene generation program, which has 

the capability to simulate IR and visible, static and dynamic synthetic 

images of many diverse real world environments. 3-D geometry inputs 

to CREATION start from digital maps obtained from the Defense 

Mapping Agency that have been interpolated to higher resolution of 

approximately 1 to 2 m. The associated high-resolution feature map is 

developed from high-resolution aerial pictures and road maps to define 

the location of trees, grass, roads, lakes, etc. The thermal signature of 

background components are predicted with the Interim Thermal Model 

(developed under the SWOE program, using the Waterways Experiment 

Figure 3. MMW scene Figure 3. MMW scene 3. MMW scene 

Figure 4. IR scene Figure 4. IR scene 
38 

Station’s background prediction model and the SNTHERM model from 

CRREL); texture variations are also applied to the background 

components to enhance scene realism. 3-D target geometry can be placed 

into the 3-D background map with 6° of freedom. The target signatures 

are derived from either the PRISM or GTSIG signature prediction models. 

The sensor and target can be varied within these 6° of freedom to allow a 

multitude of viewing aspects. Atmospheric and sensor effects can also be 

applied to generate specific meteorological conditions and sensor system 

output. 

7.6 Virtual Dirty Battlefield Virtual Dirty Battlefield Virtual Dirty Battlefield 

The virtual dirty battlefield provides virtual smokes and obscurants 

modeling capability which can be integrated into synthetic scene 

generation of IR battlefield scenes and scenarios. It is being developed by 

Missile Research Development and Engineering Center, ARL, Night 

Vision Electronic Sensors Directive, U.S. Navy, and Industry to support 

all phases of laboratory and field-testing programs (primarily for imaging 

infrared sensors) where realistic dirty battlefield scenes and scenarios are 

required. It also has application to training, mission readiness (direct 

support testing) as well as mission rehearsal. 

This capability utilizes the “emissive smokes” model created at Missile 

Research, Development, and Engineering Center and also makes use of 

the EOSAEL COMBIC model as well as the ARL developed statistics for 

battlefield-induced contaminants (STATBIC) model. These smoke and 

obscurant models represent natural and man-made aerosols, typically 

found in the dirty battlefield environment, and were selected as a result 

of a comprehensive market survey. Examples are shown in figures 5 

and 6. 

Figure 5. Battlefield scene Figure 5. Battlefield scene 

 
39 

7.7 Clutter Statistics of Wet Snow Cover Measured with a Clutter Statistics of Wet Snow Cover Measured with a Clutter Statistics of Wet Snow Cover Measured with a 

Full Maximum Continuous Wave Full Maximum Continuous Wave Radar (32 to 35 Full Maximum Continuous Wave Radar (32 to 35 Radar (32 to 35GHz) 

Snow-covered terrain represents a high-clutter environment for radar 

sensors. Because of the spatial and temporal variations in snow-cover 

properties, radars operating in a winter environment can encounter 

varying clutter statistics. Measurements have been initiated to determine 

the reflectivity characteristics of snow-covered terrain using a Full 

Maximum Continuous Wave (FMCW) radar operating at 32 to 35 GHz 

(see http://www.crrel.usace.army.mil/). The relatively high bandwidth 

allows resolution of the dominant scattering mechanisms in a snow cover 

and allows investigation of the effect of frequency averaging on clutter 

statistics. Initial analysis is limited to wet snow cover where surface 

scattering is the dominant scattering mechanism. Typical backscatter 

results from a wet snow cover are illustrated in figure 7. 

Figure 7. Refractivity Figure 7. Refractivity 

characteristics of a wet snow characteristics of a wet snow 

as a function of depression as a function of depression 

angle obtained with a FMCW angle obtained with a FMCW 

radar operating at 32 to 36 radar operating at 32 to 36 

GHz bandwidth. GHz bandwidth. GHz bandwidth. 

Figure 6. Battlefield scene Figure 6. Battlefield scene 

with white phosphorus with white phosphorus 

added. 
40 

For a wet snow cover, a 10-dB reduction in reflectivity can be expected as 

the radar depression angle decreases from 90 to 50°. The width of 

probability density function (PDF) normalized to the mean reflectivity 

will also decrease as the depression angle decreases. (The width of the 

normalized PDF will increase as the radar bandwidth decreases because 

of reduced frequency averaging.) For wet snow, the frequency-averaged 

PDFs were successfully fitted with Rayleigh statistics and successive 

convolutions of Rayleigh statistics. The analysis of clutter statistics from 

dry snow cover will be complicated by the volume scattering 

contributions from individual snow grains as well as the reflections from 

layers within the snow cover. 
41 

8.0 Priorities 8.0 Priorities 

The priorities for next year include the continuing education of users and 

developers in the methodologies, processes and procedures for DAE. 

This will be accomplished through documentation, presentations, and 

tutorials. The need for standard weather scenarios, the improvement of 

run times for physics-based models, and the inclusion of fast running 

weather impact algorithms for aggregated simulations are high priorities 

for the DAE category. Other priorities in the DAE category are presented 

in table 3. 

Table 3 Table 3: DAE priorities in the near : DAE priorities in the near : DAE priorities in the near-, mid-, and far , and far , and far-time frame time frame time frame time frame 

FYProjects Projects Projects 

Near Weather simulation (WARSIM) weather effects 

Near Acoustics for COMBAT XXI 

Near Icing prediction model 

 

 Areas 

Mid Emissive smokes 

Mid Urban “canyon” winds 

Mid Weather for embedded training 

Mid Weather integration into joint common database 

(JCDB)/M&S 

Mid Remote Sensing Analysis Models 

Mid Forest Effects on Acoustic Sensors 

Far Atmospheric effects for military operation in urban terrain 

(MOUT) 
43 

9.0 Conclusions 9.0 Conclusions 

The environmental community is encouraged to participate in the AMSO 

standard-category DAE area. The DAE category is always desirous of 

outside opinions and people who wish to contribute their time. 

Participation is particularly encouraged via the DAE reflector and at the 

U.S. Army M&S Standards Workshop which meets annually in the 

spring. Further information can be obtained from the DAE home page at 

http://www.amso.army.mil/sp-div/dyn-env.htm or directly from the 

standards category coordinator via email to rshirkey@arl.mil. 
45 

References References 

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http://www.dmso.mil/docslib/mspolicy/msmp/1095msmp/, Defense Modeling 

and Simulation Office, Alexandria, VA, October 1995. 

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army.mil/mstrpln/, Army Model and Simulation Office, Pentagon, Washington 

DC, October 1997. 

3. Lowe, S., et al., Weather Scenario Generator: System Design and Project Status, 

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April 1997. 

5. Whitney, D., et al., TAOS: Providing and Managing Realistic Natural 

Environments for Virtual Worlds, Simulation Interoperability Workshop, 97F-SIW154, http://sisostds.org/, September 1997. 

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Visualization Effects for Simulation, Volume 1: WAVES98 Suite Overview, ARLTR-1721-1, Adelphi, MD, September 1998. 

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5-3100, September 1998. 

8. Shirkey, R. C., L. D. Duncan and F. E. Niles, The Electro-Optical Systems 

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October 1987. 

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Future Plans,” in Proceedings of the 1996 Battlespace Atmospherics Conference, 

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Laboratory, Technical Report, ARL-TR-273-8-ADELPHI, Adelphi, MD, June 1998. 

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13. Ayres, S. D. and DeSutter S., Combined Obscuration Model for Battlefield 

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25. Pielke, R. A., Cotton W. R., Walko R. C., et al., A Comprehensive 

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Laboratory, Adelphi, MD, April 1998. 
49 

Acronyms and Abbreviations Acronyms and Abbreviations 

2 D two dimensional 

3 D three dimensional 

4 D four dimensional 

3DSE Three-Dimensional Static Environments and Initialization 

ABCS Army Battle Command System 

ABCSIM Atmospheric Biological/Chemical Simulation 

ABFA Acoustic Battlefield Aid 

AIRFLOS Airflow Simulation 

AIM Atmospheric Illumination Module 

AIRSIM Atmospheric Simulation 

AMIP Army Model Improvement Plan 

AMSEC Army Modeling and Simulation Executive Committee 

AMSO Army Modeling and Simulation Office 

ARL U.S. Army Research Laboratory 

ATMOS Atmospheric Optical Turbulence Model 

 BASE Battlefield Acoustic Sensor Evaluator 

BASIS Battlefield Acoustic Sensor Integration System 

BFA Battlefield Functional Area 

BFM Battlescale Forecast Model 

BITS band integrated transmittances 

BLIRB Boundary Layer Illumination and Radiative Balance 

Model 

CASTFOREM Combined Arms and Support Taskforce Evaluation Model 

C4I Command, Control, Communications, Computers, 

Intelligence 

CCSL Coupled Surface Layer 

CLIMAT climatology 
50 

COAMPS Coupled Ocean Atmosphere Model Prediction System 

COMBIC Combine Obscuration Model for Battlefield Induced 

Contaminants 

COPTER helicopter 

CREATION computer generation of realistic environments with 

 atmospheres for thermal imagery with optics and noise 

CRREL Cold Regions Research Engineering Laboratory 

CSSM Cloud Scene Simulation Model 

D2PC Two-Dimensional Transport and Diffusion for Personal 

Computers 

DAE Dynamic Atmospheric Environments 

DMSO Defense Modeling and Simulation Office 

DoD Department of Defense 

E2DIS Environmental Effects for Distributed Interactive 

Simulation 

EOSAEL Electro-Optical Systems Atmospheric Effects Library 

EOTDA Electro-Optical Tactical Decision Aid 

FASCAT fast algorithm for atmospheres scattering 

FFP Fast-Field Program 

FITTE fire-induced transmission and turbulence effects 

FMCW Full Maximum Continuous Wave 

GFPE Green’s Function Parabolic Equation 

GRNADE grenade 

HLA High-Level Architecture 

HRW High Resolution Wind 

ILUMA illumination 

IMETS Integrated Meteorological System 

IR infrared 

IWEDA Integrated Weather Effects Decision Aid 
51 

JCDB Joint Common Database 

KWIK cross wind integrated concentration 

LASS large area smoke screen 

LES Large Eddy Simulation 

LOWTRAN low resolution transmission 

LZTRAN laser transmission 

M&S Modeling and Simulation 

MM5 Fifth-Generation National Center of Atmospheric 

Research/Penn State Mesoscale Model 

MODTRAN Moderate Resolution Transmission 

MOUT military operation in urban terrain 

MPLUME missile plume 

NBSCAT narrow beam scattering 

NGIC National Ground Intelligence Center 

NMMW near-millimeter wave 

NVESD Night Vision and Electronic Sensors Directorate 

NOVAE non-linear vaporization and breakdown effects 

OVRCST overcast 

P&T Policy and Technology 

PDF Probability Density Function 

PIXELMOD pixel modification 

RAMS Regional Atmospheric Modeling System 

REBAL Radiation and Energy Balance 

REFRAC refraction 

SCAFFIP Scanning Fast-Field Program 

SCAPE Scanning Parabolic Equation 

SCC Standards Category Coordinators 

SCIPUFF Second Order Closure Integrated PUFF 
52 

SGR sky-to-ground ratio 

SNTHERM snow thermal 

STATBIC statistics for battlefield-induced contaminants 

STOW-SE synthetic theater of war – synthetic environments 

SWOE smart weapons operability enhancement 

SWOETHERM smart weapons operability enhancement thermal 

T&D transport and diffusion 

TADSIM Transport And Diffusion Simulator 

TAMIP Target Acquisition Modeling Improvement Program 

TARGAC target acquisition 

TAWS Target Acquisition Weather Software 

TAOS Total Atmosphere Ocean Server 

TDA tactical decision aids 

TECNET Test and Evaluation Community Network Bulletin Board 

System 

TRAC U.S. Army Training and Doctrine Command Analysis 

Center 

TRADOC U.S. Army Training and Doctrine Command 

UVTRAN ultra-violet transmission 

VIEW view point 

VLSTRACK vapor liquid solid tracking 

WARSIM War Fighter’s Simulation 

WAVES Weather And Visualization Effects for Simulations 

WG Working Group 

XSCALE scaled transmission
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