OFFICE OF THE FEDERAL COORDINATOR FOR

METEOROLOGICAL SERVICES AND SUPPORTING RESEARCH

U.S. DEPARTMENT OF COMMERCE/ National Oceanic and Atmospheric Administration

JOINT ACTION GROUP FOR ATMOSPHERIC

TRANSPORT AND DIFFUSION MODELING

(RESEARCH AND DEVELOPMENT PLAN)

FCM-R23-2004

Washington, DC

September 2004
THE FEDERAL COMMITTEE FOR

METEOROLOGICAL SERVICES AND SUPPORTING RESEARCH (FCMSSR)

VADM CONRAD C. LAUTENBACHER, JR., USN (RET.) 

Chairman, Department of Commerce

DR. KATHIE OLSEN

Office of Science and Technology Policy

DR. RAYMOND MOTHA 

Department of Agriculture

BRIG GEN DAVID L. JOHNSON, USAF (RET.)

Department of Commerce

MR. ALAN SHAFFER

Department of Defense

DR. ARISTIDES PATRINOS

Department of Energy

DR. PENROSE C. (PARNEY) ALBRIGHT

Science and Technology Directorate

Department of Homeland Security

DR. ROBERT M. HIRSCH

Department of the Interior

MR. RALPH BRAIBANTI

Department of State

MR. RANDOLPH LYON

Office of Management and Budget

MR. NORMAN FUJISAKI (Acting)

Department of Transportation

MR. DAVID MAURSTAD (Acting)

Federal Emergency Management Agency

Department of Homeland Security

DR. GHASSEM R. ASRAR

National Aeronautics and Space

 Administration

DR. MARGARET S. LEINEN

National Science Foundation

MR. PAUL MISENCIK

National Transportation Safety Board

MR. JOHN W. CRAIG

U.S. Nuclear Regulatory Commission

DR. GARY FOLEY

Environmental Protection Agency

MR. SAMUEL P. WILLIAMSON

Federal Coordinator 

MR. JAMES B. HARRISON, Executive Secretary

Office of the Federal Coordinator for

Meteorological Services and Supporting Research

THE INTERDEPARTMENTAL COMMITTEE FOR

METEOROLOGICAL SERVICES AND SUPPORTING RESEARCH (ICMSSR)

MR. SAMUEL P. WILLIAMSON, Chairman

Federal Coordinator

MR. THOMAS PUTERBAUGH

Department of Agriculture

MR. JOHN E. JONES, JR.

Department of Commerce

RADM STEVEN J. TOMASZESKI, USN

United States Navy

Department of Defense

BRIG GEN THOMAS E. STICKFORD, USAF

United States Air Force

Department of Defense

MR. RICKEY PETTY

Department of Energy

MS. NANCY SUSKI

Science and Technology Directorate

Department of Homeland Security

MR. LEWIS T. MOORE

Department of the Interior

MS. REGINA MCELROY

Federal Highway Administration

Department of Transportation

MS. LISA BEE

Federal Aviation Administration

Department of Transportation

DR. JONATHAN M. BERKSON

United States Coast Guard

Department of Homeland Security

MR. JEFFREY MACLURE

Department of State

DR. S. T. RAO

Environmental Protection Agency

MR. JOHN GAMBEL

Federal Emergency Management Agency

Department of Homeland Security

DR. RAMESH KAKAR

National Aeronautics and Space

 Administration

DR. JARVIS MOYERS

National Science Foundation

MR. DONALD E. EICK

National Transportation Safety Board

MS. LETA A. BROWN

U.S. Nuclear Regulatory Commission

MS. ERIN WUCHTE

Office of Management and Budget

MR. JAMES B. HARRISON, Executive Secretary

Office of the Federal Coordinator for

Meteorological Services and Supporting Research

Front Cover Graphic: On Friday, February 21, 2003, a fuel barge that was unloading fuel at a storage

facility on Staten Island exploded. The barge was unloading 4 million gallons of unleaded gasoline. 

Smoke and flames climbed hundreds of feet into the air. A plume of smoke originating from the fire was

carried over lower Manhattan and was captured in this photograph by Gregory Bull (AP Photo)
FEDERAL COMMITTEE FOR 

METEOROLOGICAL SERVICES AND SUPPORTING RESEARCH 

WORKING GROUP FOR 

ENVIRONMENTAL SUPPORT TO HOMELAND SECURITY 

JOINT ACTION GROUP FOR ATMOSPHERIC TRANSPORT AND DIFFUSION 

MODELING (RESEARCH AND DEVELOPMENT PLAN) (JAG/ATD(R&DP)) 

Federal Research and Development 

Needs and Priorities for 

Atmospheric Transport and Diffusion Modeling 

FCM-R23-2004 

September 2004 

Office of the Federal Coordinator 

for Meteorological Services and 

Supporting Research (OFCM) 

8455 Colesville Rd, Suite 1500 

Silver Spring, MD 20910 
ii Federal R&D Needs and Priorities for ATD Modeling
Federal R&D Needs and Priorities for ATD Modeling iii

iii 

FOREWORD

In August 2002, the Office of the Federal Coordinator for Meteorological Services and 

Supporting Research (OFCM) published the comprehensive report, Atmospheric 

Modeling of Releases from Weapons of Mass Destruction: Response by Federal Agencies 

in Support of Homeland Security. This publication, prepared by the OFCM’s Joint 

Action Group for the Selection and Evaluation of Atmospheric Transport and Diffusion 

Models (JAG/SEATD), provided a comprehensive summary of Federal capability to 

provide atmospheric transport and diffusion (ATD) modeling support and has become a 

valuable resource in support of the homeland security/defense missions. 

The JAG/SEATD report also made a number of recommendations for future work, 

regarding ATD modeling support, which was endorsed by both the Interdepartmental 

Committee for Meteorological Services and Supporting Research (ICMSSR) and the 

Federal Committee for Meteorological Services and Supporting Research (FCMSSR). 

Among the recommendations was the need to address the research and development 

required to advance the state-of-the science of ATD modeling in support of critical 

homeland security/homeland defense activities. 

In October 2003, the OFCM established the Joint Action Group for Atmospheric 

Transport and Diffusion Modeling (Research and Development Plan) 

(JAG/ATD(R&DP)) to address this recommendation head on. Each agency that 

participated in the JAG/ATD(R&DP) shared the common goal to: Identify the most 

pressing research needs facing the Federal ATD modeling community as it strives to 

support the homeland security mission and to recommend a strategy that will eventually 

satisfy those needs. 

This report, Federal Research Needs and Priorities for Atmospheric Transport and 

Diffusion Modeling, represents a commitment by each of the responsible Federal agencies 

in the OFCM Federal coordinating infrastructure to work in a collaborative and 

synergistic way to address this critical homeland security issue, and the report’s 

recommendations are the result of careful deliberation by the members and are based on 

their collective skills, experiences, and vision. 

The next step is for the participating agencies to work together in a collaborative and 

cooperative manner to incorporate these recommendations into agency plans and 

programs in order to improve Federal ATD modeling capabilities. The completion of this 

task in a timely and coordinated manner is vitally important to the Nation. Close 

coordination with the academic and private organizations will also be required, and the 

user community must be involved in the process from start to finish. 

I wish to extend my deepest appreciation to the JAG members, alternates, technical 

advisors, and subject-matter experts who brought unprecedented knowledge and 

experience to the table and who demonstrated outstanding teamwork in developing this 

report. I am also deeply grateful for the outstanding leadership of the JAG cochairs, Dr. 

Walter Bach and Ms. Nancy Suski. The quality and comprehensiveness of the report 
iv Federal R&D Needs and Priorities for ATD Modeling

reflects highly on the insight, professionalism, and dedication of all the participants and 

the report provides a solid foundation for future R&D efforts, regarding environmental 

support to homeland security. 

Samuel P. Williamson 

Federal Coordinator for Meteorological Services 

and Supporting Research 
September 23, 2004

MEMORANDUM FOR: Mr. Samuel P. Williamson

Federal Coordinator for Meteorology

SUBJECT: Federal Atmospheric Transport and Diffusion Modeling

Research and Development Plan

FROM: Cochairpersons

The Joint Action Group for Atmospheric Transport and Diffusion Modeling (Research

and Development Plan) (JAG/ATD (R&DP)) has completed the tasks assigned in the

JAG/ATD (R&DP) Terms of Reference. We are pleased to provide the subject report,

titled:

.

Federal Research and Development Needs and Priorities for Atmospheric

Transport and Diffusion Modeling

Mr. Bruce Hicks

Director, Air Resources Laboratory

National Oceanic and Atmospheric

Administration

Dr. Walter D. Bach, Jr., Cochairperson

Program Manager,

Environmental Sciences Division

Army Research Office

Ms. Nancy Suski, Cochairperson

Director, Emergency Preparedness and

Response Portfolio

Science and Technology Directorate

Department of Homeland Security

Dr. Paula M. Davidson

Environmental Hazards Planning Lead,

Office of Science and Technology

National Weather Service

National Oceanic and Atmospheric

Administration

Ms. Jocelyn Mitchell

Senior Level Technical Advisor,

Office of Research

Nuclear Regulatory Commission

Ms. Debbie Payton

Hazardous Materials Program Manager for

Fate and Transport Modeling,

National Ocean Service

National Oceanic and Atmospheric

Administration

Office of Response and Restoration

Federal R&D Needs and Priorities for ATD Modeling v
Mr. John S. Irwin

Meteorologist,

NOAA Air Policy Support Branch

Environmental Protection Agency

Office of Air Quality Planning and Standards

CDR Stephanie W. Hamilton, USN

Program Manager,

Consequence Assessment Branch

Defense Threat Reduction Agency

Mr. James F. Bowers

Chief, Meteorology Division

West Desert Test Center

Dugway Proving Ground

United States Army

Mr. Stephen D. Ambrose

Program Manager for Homeland Security

Applied Sciences Program

Office of Science

National Aeronautics and Space

Administration

Mr. John J. Perry

GIS / Remote Sensing Coordinator

FEMA Response

Emergency Preparedness and

Response Directorate

Department of Homeland Security

Mr. Rickey C. Petty

Program Manager,

Environmental Meteorology Component

of the Atmospheric Science Program

Department of Energy

vi Federal R&D Needs and Priorities for ATD Modeling
Federal R&D Needs and Priorities for ATD Modeling vii

TABLE OF CONTENTS 

Foreword 

Executive Summary 

1.0 Introduction...............................................................................................................1 

1.1 Purpose...............................................................................................................2 

1.2 ATD Models in a Consequence Assessment System ........................................3 

1.3 Scope and Context of the R&D Planning Activity ............................................5 

1.4 Structure.............................................................................................................8 

2.0 User Needs ...............................................................................................................11

2.1 The Emergency Preparedness and Response Environment.............................11 

2.2 User Needs in Other Applications of ATD Modeling Systems.......................14 

2.2.1 Military Applications, 14 

2.2.2 Air Quality Monitoring Applications, 15 

2.3 ATD Information Required for Hazard Response Decisions ..........................16 

2.4 Constraints and Tradeoffs in ATD Modeling to Meet User Needs .................17 

2.5 “If I Can’t Have Certainty, Tell Me How Bad It Could Be, and Where”........18 

3.0 Model Capabilities Required to Meet User Needs...............................................21 

3.1 Consequences of Model Scale .........................................................................22 

3.1.1 Consequences of Scale in Atmospheric Data, 24 

3.1.2 Consequences of Scale in Concentration Data, 25 

3.2 Requirements and Capabilities by System Component...................................26 

3.2.1 Source Term, 27 

3.2.2 Meteorological Inputs, 27 

3.2.3 ATD Input Processing, 32 

3.2.4 The Transport and Diffusion Code, 33 

3.2.5 Deposition (and Other Removal Mechanisms), 35 

3.2.6 Concentrations Downwind, 37 

3.3 Transitioning New Research and Development Capability to 

Operations........................................................................................................37 

3.3.1 Usability, 38 

3.3.2 Training, 39 

3.3.3 Connectivity to Data Sources, 40 

3.3.4 Results Communication, 40 

3.3.5 Evaluations of Modeling System Performance, 40 

3.3.6 Software Testing and Evaluation Including Performance Evaluation, 42 

3.3.7 Documentation, 43 

4.0 Modeling and Measurement Research Needs ......................................................45 

4.1 R&D Prioritization Methodology ....................................................................46 
viii Federal R&D Needs and Priorities for ATD Modeling

4.2 Improving ATD Meteorological and Concentration Models...........................47 

4.2.1 The Meteorological Modeling Components of ATD Modeling 

 Systems, 47 

4.2.2 R&D Needed to Improve ATD Modeling Components, 49 

4.2.3 Approaches to Model Improvement, 58 

4.3 Improving Measurement Technologies ...........................................................61 

4.4 Model–Data Interface Challenges....................................................................66 

4.4.1 Source Characterization by Sensor Fusion, 66 

4.4.2 Data Representation and Data Assimilation, 68 

4.4.3 Model Performance Evaluation Issues, 71 

4.5 Summary of R&D Needs.................................................................................73 

5.0 An R&D Strategy to Meet User Needs..................................................................75 

5.1 The Goals for ATD Modeling R&D................................................................75 

5.2 The R&D Aspects of Interpreting Uncertainty Implications for Users ...........79 

5.3 Capture Existing Data Sets for New Objectives..............................................81 

5.4 Foster Evaluation Standards for Modeling Systems and Components............82 

5.5 Coordinate Programs for Improved Measurement Technologies....................84 

5.5.1 Tracers, 85 

5.5.2 Meteorological Data, 86 

5.5.3 Implementation, 87 

5.6 Establish Multiple ATD Test Beds..................................................................87 

5.6.1 R&D to be Supported by ATD Test Beds, 88 

5.6.2 The Role of ATD Test Beds in Transitioning New Capability to 

 Operations, 90 

5.6.3 Why Test Beds in Cities? 91 

5.6.4 Requirements for Effective Test Bed Implementation, 92 

5.6.5 Test Bed Management Issues, 94 

5.7 Plan and Conduct Special Studies and Experiments........................................94 

6.0 Recommendations...................................................................................................99 

6.1 Quantify Model Uncertainties and Interpret Their Implications to Users .....100 

6.1.1 Improve ATD Modeling Systems to Routinely Quantify 

 Uncertainties, 100 

6.1.2 Effective Communication of Model Uncertainties to Users, 101 

6.2 Capture and Use Existing Data Sets ..............................................................102 

6.3 Implement ATD Testbeds..............................................................................103 

6.4 Develop Standards for Model V&V ..............................................................105 

6.5 Improve the Spatial and Temporal Scale Interactions between 

Meteorological and ATD Models ..................................................................105 

6.6 Improve Measurement Technology ...............................................................106 

6.6.1 Coordinated Measurements Technology Development Program, 106 

6.6.2 Instrument Siting and Networking, 107 

6.7 Design and Conduct Special Studies and Experiments .................................108 

References.......................................................................................................................111 
Federal R&D Needs and Priorities for ATD Modeling ix

Appendices 

A. Historical Perspective 

B. Federal Capabilities and Research and Development Programs 

C. Atmospheric Transport and Diffusion Modeling Considerations 

D. Acronym List 

E. List of JAG/ATD(R&DP) Participants 

FIGURES 

1. The functional components of a complete consequence assessment system, 

with its embedded ATD modeling system shown by bold lines. ............................3 

2. Questions of concern to users of ATD modeling predictions for emergency 

preparedness and response.....................................................................................12 

3. Scale definitions and different atmospheric processes with characteristic time 

and horizontal scales (adapted from Orlanski, 1975). ..........................................23 

4. Concentration field of a simple flow (top) versus time-averaged distribution 

(bottom). ................................................................................................................25 

5. The functional components of a complete consequence assessment system.........26 

6. Mean absolute error in wind speed and wind direction, measured over a 

relatively simple terrain. ........................................................................................30 

7. Transport and diffusion scales and model grid sizes. ...........................................48 

8. Six R&D objectives need to be achieved to support the ultimate goals of 

quantifying uncertainty and communicating its implications to users ..................76 

TABLES 

1. Spatial Scale and Oberservation Frequency of Common U.S. Meteorological 

Observing Systems ................................................................................................24 

2. Example of Prioritization Factor Ratings for a Hypothetical R&D Need .............46 

3. Prioritization Factors for Bridging the Modeling Gap from Mesoscale to 

Microscale..............................................................................................................49 

4. Prioritization Factors for Improving Characterization of Surface Conditions 

and Input Data Sets................................................................................................51 

5. Prioritization Factors for Testing and Refining the Physical Basis for SubGrid-scales Parameterizations................................................................................52 

6. Prioritization Factors for Characterizing Dispersion in Complex 

Environments.........................................................................................................54 

7. Prioritization Factors for Developing Methods and Technologies to Improve 

Ensemble Construction and Interpretation ............................................................56 

8. Prioritization Factors for Developing and Testing Techniques to Better 

Estimate Wet and Dry Deposition .........................................................................57 
x Federal R&D Needs and Priorities for ATD Modeling

TABLES—continued 

9. Prioritization Factors for Development and Use of Physical and High-Resolution 

Computational Models...........................................................................................60 

10. Prioritization Factors for Improving Tracer Materials and Measurement 

Technology ............................................................................................................63 

11. Prioritization Factors for Improving Boundary-Layer Measurement 

Technology ............................................................................................................65 

12. Prioritization Factors for Improving and Evaluating Sensor Fusion 

Techniques.............................................................................................................67 

13. Prioritization Factors for Improving and Evaluating techniques for Data 

QA/QC for Model Fit and Data Assimilation, for Both Initial and Boundary 

Conditions..............................................................................................................70 

14. Prioritization Factors for Developing Physics-based Model Evaluation 

Methods .................................................................................................................73 

15. Summary Table of R&D Needs with Prioritization Factors..................................74 
xi 

EXECUTIVE SUMMARY 

Atmospheric transport and diffusion (ATD) modeling is a challenging and dynamic field 

of research, and the Federal agencies have played a critical role in applying advances in 

the field to satisfy pressing national needs. Federal resources have proven to be vitally 

important to these efforts because ATD models typically must describe atmospheric 

processes in the most changeable and complex part of the atmosphere. For homeland 

security applications, the end-user’s need for timely and accurate ATD information in the 

urban environment is one of the most pressing national needs. 

In October 2001, the Office of the Federal Coordinator for Meteorological Services and 

Supporting Research (OFCM) established the interagency Working Group for 

Environmental Support to Homeland Security (WG/ESHS) at the request of the Federal 

Committee for Meteorological Services and Supporting Research (FCMSSR). In January 

2002, the WG/ESHS formed the Joint Action Group for the Selection and Evaluation of 

Atmospheric Transport and Diffusion Models (JAG/SEATD) to study the nonproprietary 

atmospheric transport and diffusion (ATD) modeling systems in use by federally funded 

operations centers. In August 2002, the JAG/SEATD’s definitive report was published, 

and its recommendations were endorsed by FCMSSR. 

Among the recommendations was the need to further study the research and development 

(R&D) requirements related to ATD modeling. In October 2003, the Joint Action Group 

for Atmospheric Transport and Diffusion Modeling (Research and Development Plan) 

(JAG/ATD(R&DP)) was formed under the WG/ESHS and charged to: 

• Develop a methodology for characterizing and prioritizing the research and 

technical needs, and for linking those needs to stated operational requirements. 

• Consult with subject-matter experts as required (based on the needs of the JAG 

members). 

• Identify the tools required for transitioning successful research results into 

operations through interagency cooperative efforts like observational and 

modeling test beds, field and urban studies/experiments, and a common model 

evaluation methodology. 

• Develop a comprehensive plan that documents the research and technical needs of 

the ATD modeling and operational communities. The plan should prioritize the 

most pressing R&D needs and provide a roadmap to address those needs within 

the OFCM coordinating infrastructure. Expanded feedback on the plan was 

solicited during the 8th Annual George Mason University Conference on 

Atmospheric Transport and Dispersion Modeling on July 14, 2004, and the 

OFCM Urban Meteorology Forum, Challenges in Urban Meteorology: A Forum 

for Users and Providers, on September 21-23, 2004, which included participation 

from the academic, public, and private sectors. 

This report is the culmination of the JAG/ATD(R&DP)’s efforts. The report describes the 

research and development needs, based on user-community needs, and recommends a 
xii Federal R&D Needs and Priorities for ATD Modeling

number of strategies to address those needs in order to provide a reliable capability to use 

atmospheric transport and diffusion as an instrument of local and national emergency 

response or planning. The principal topics addressed by the report include: 

• A discussion of user needs for consequence assessment systems (a general name 

for typical applications in which ATD models are employed, including but not 

limited to emergency response/recovery and preparedness planning applications). 

• Interpretation of the ATD modeling capabilities required to support what users 

need from their consequence assessment systems. 

• Analysis of the gaps between the required capabilities and current Federal ATD 

modeling capabilities (requirements pull), plus opportunities for new and 

emerging science and technology to fulfill user needs better in the future 

(technology push). 

• Strategies to fill the gaps and provide improved capability through an interrelated 

set of coordinated R&D activities implemented by Federal agencies with ATD 

modeling programs or related research, development, or technology transition 

programs. 

• Prioritized recommendations for implementing the R&D strategies. 

User’s Needs 

The emergency preparedness and response environment includes a number of activities 

during which consequence assessment of an airborne hazard is relevant. Planning 

activities start in anticipation of a specific incident to help everyone prepare to respond. 

Response activity begins when an incident occurs. Response activity then transitions into 

recovery activity. Incidents that involve the release of an airborne hazardous material can 

range from a relatively straightforward situation that that can be handled by local 

responders to a complex situation that involves elements of all three activities described 

and requires resources from many different organizations; i.e., incidents of national 

significance. 

Users recognize that ATD models of the consequence assessment system are imprecise. 

They desire—and create—ways to work with the limitations of the information. Models 

give little or no expressions of probability and uncertainty, so they are insufficient to help 

many users make sound decisions. This fact imposes two complementary demands on 

model development. First, we must provide a reasonable measure of the uncertainty in a 

prediction or its probability distribution, and then secondly, we must communicate the 

implications of quantifying the uncertainty in ways the users can apply. 

Research Needs 

Advances in current ATD modeling are likely to come from improvements in 

meteorological model predictions and from measurements at the scales of interest. The 

former are closely related to better representations of atmospheric boundary layer (ABL) 

processes by improved parameterizations, initial conditions, boundary conditions, and 

representations in complex, especially urban, environments. As existing modeling and 
Federal R&D Needs and Priorities for ATD Modeling xiii

observing capabilities are improved, the realization that the ATD process is partly 

stochastic, rather than entirely deterministic, will enable uncertainties in the modeling 

process to be quantified. The modeler must then learn with the user how to communicate 

this uncertainty information to the user in ways that are relevant to the user’s decisions. 

 

Models and data must come together and complement one another. Techniques to 

localize and/or quantify source characteristics by fusing information from concentration 

sensors, ATD models, and other measurements are lacking or untested. To meet user 

requirements for timely modeling predictions, faster methods are needed to determine the 

quality of observed data, merge the acceptable data into modeling frameworks, and 

estimate concentrations rapidly across several scales of motion. Finally, to ensure the 

quality of the model estimates and provide the benchmark for improvements, the skill of 

the prediction and its robustness need to be assessed on a continuing basis. 

To advance the state-of-the-science in ATD modeling, we must meet these R&D needs: 

• Bridge the gap from mesoscale to microscale/urban scale. 

• Improve characterizations of surface boundary conditions in model 

parameterizations and in input data sets (initial conditions and boundary 

conditions). In particular, better methods are needed to obtain, maintain, and 

apply land cover data for urban and surrounding environments. 

• Test and refine the physical basis for sub-grid-scale parameterizations. 

• Characterize dispersion in complex environments. 

• Develop methods and technologies for improving ensemble construction and 

interpretation. 

• Develop and test techniques to better estimate wet and dry deposition and 

chemical interactions. 

• Improve tracer materials and measurement technology. 

• Improve boundary layer atmospheric measurement capability. 

• Improve and evaluate data acceptance and assimilation techniques for both initial 

conditions and boundary conditions. 

• Develop physics-based evaluation metrics that recognize the fundamentally 

different sources for variations in observed and model-predicted values of 

downwind hazard concentration. 

A tabular summary of these needs and priorities follows. 
xiv Federal R&D Needs and Priorities for ATD Modeling

Research and Development Strategies 

The JAG identified two capstone goals for the R&D plan: quantifying uncertainty and 

interpreting the implications of this uncertainty to users. The JAG then identified six 

program elements needed to support the capstone goals. To achieve the goals, it is 

essential that the elements of the strategy be sustained, evaluated, and allowed to evolve 

as the knowledge base grows and the capabilities for ATD modeling improve. 

Capture and Use Existing Data Sets. This element focuses on assembling the available 

(open access) data sets from the many years of ATD experiments and model testing into a 

modern data format. The data are in various forms and available on many types of 

storage media. Both the data and the expertise of the participating scientists are at risk of 

being lost. These rich data files are the only source of concentration data which can 

currently be used to quantify uncertainty in ATD models. 

R&D Need Time 

Sensitivity 

Short-Term Gain Overall 

Level of 

Effort 

Lead Time Ultimate Gain 

Potential 

Bridge the modeling gap near term average moderate average exceptional 

Characterization of surface 

conditions & input data sets 

near term average high average exceptional 

Test and refine physical 

basis for sub-grid-scale 

parameterizations 

longer term average moderate average exceptional 

Characterize dispersion in 

complex environments 

immediate average high average high 

Improve ensemble 

construction and 

interpretation 

immediate minimal high short exceptional 

Techniques to better 

estimate wet and dry 

deposition 

Physical and high-resolution 

computational models 

near term 

near term 

average 

average 

moderate 

moderate 

average 

average 

high 

exceptional 

Improve tracer materials and 

measurement technology 

immediate high moderate short exceptional 

Improve boundary-layer 

measurement technology 

immediate high high short exceptional 

Improve and evaluate 

sensor-fusion techniques 

immediate high moderate moderate high 

Data QA/QC for model fit 

and data assimilation 

immediate average moderate moderate high 

Develop physics-based 

model evaluation methods 

near term high low average exceptional 

Table ES-1. Summary Table of R&D Needs with Prioritization Factors 
Federal R&D Needs and Priorities for ATD Modeling xv

Model Evaluation Standards. Procedures for evaluating the performance of ATD 

modeling systems are not standardized across the Federal agencies or ATD model user 

communities. Further, the existing procedures may not fully deal with the complexities 

introduced by comparing calculations and observations having different inherent space 

and time averaging. Without common reference standards, model development and 

implementation tends to remain “stovepiped” within the developing agency, while other 

development efforts are discounted or go unused. 

Bridge the Modeling Gap. Top-down modeling (large to small scale) and bottom-up 

modeling (small to larger scales) do not merge across scales from 50 meters to 5 

kilometers in realistic environments. This fact points to a fundamental lack of knowledge 

of how to model the processes at these scales. In all models, there is an element of 

turbulence carried in the smallest scales and the unresolved processes. Although there is 

perpetual optimism that higher-resolution models will give better results, current 

operational experience does not substantiate the optimism. 

Improved Measurement Capabilities. Measurements are fundamental to advancing the 

realism of a science-based description or prediction. In ATD modeling, improving the 

capability to measure concentrations of tracers and atmospheric variables (e.g., wind 

velocity, turbulence, temperature) at the scales of ATD model use is essential to R&D 

leading to better ATD models. Quantifying the uncertainty in model variables requires in 

situ and/or remote measurements at the modeled scales. Most applications of ATD 

models are at much finer scales than are the available data, especially in populated areas. 

To develop better parameterizations, measurements are needed to understand processes 

not resolved within models. Measurements are also needed to help bridge the model gap. 

Tracer measurement capabilities are needed to provide data for quantifying the 

uncertainty in ATD model predictions. 

Local/Regional Siting of Instrumentation. Each locality has a unique morphology. 

Many localities want to provide a network of instruments, within budget limitations, that 

will reasonably represent wind and turbulence fields needed for ATD concentration fields 

in emergency conditions. No one plan fits all these sites. Strategies are needed to make 

reliable recommendations through a combination of modeling exercises, optimization 

processes, and experience in other areas. 

ATD Test Beds. Most ATD model studies come in defined field campaigns operated to 

maximize the probability of success. Within this context, benign, simple, and non-taxing 

weather conditions were preferred although terrain conditions may have been complex. 

Controlled tracer releases are sampled and atmospheric measurements are made as 

densely as capabilities and resources permit. As accidental releases and terrorist 

incidents are not scheduled, little is known about performance of ATD models under 

daily conditions. Recently, fledgling infrastructures for routine ATD forecasting based on 

model and local information, such as NOAA’s DCNet, have developed in several urban 

areas. The JAG proposes a strategy of establishing test beds in appropriate areas across 

the country to address and test ATD models, model needs, and model capabilities on a 

continuing basis. By operating and performing evaluations continuously, by testing new 

ideas and instruments, and by interacting regularly with users, an ATD test bed becomes 
xvi Federal R&D Needs and Priorities for ATD Modeling

a crucible in which ATD modeling is made robust and refined from an art into a 

demonstrated and verified operational capability. The initial number of test bed 

installations should be limited so that operational procedures can be developed and 

refined without squandering scarce resources. Once the prototyping lessons have been 

learned, the set of installations could expand to cover more locations of priority interest, 

with each additional location chosen to represent different challenges from those already 

in place or being installed. 

Figure ES-1. Six R&D objectives need to be achieved to support the ultimate goals of quantifying 

uncertainty and communicating its implications to users. 

COORDINATED AGENCY SUPPORT & FUNDING

GOALS

MEASUREMENT 

MODEL EVAL 

CAPABILITIES

STANDARDS

ATD TEST BEDS

BRIDGE THE 

SCALE GAP

CAPTURE AND USE 

EXISTING DATA SETS

LOCAL/ REGIONAL 

MEASUREMENT SITING

Routinely Quantify Uncertainty

Interpret Uncertainty

OBJECTIVES

COORDINATED AGENCY SUPPORT & FUNDING

GOALS

MEASUREMENT 

MODEL EVAL 

CAPABILITIES

STANDARDS

ATD TEST BEDS

BRIDGE THE 

SCALE GAP

CAPTURE AND USE 

EXISTING DATA SETS

LOCAL/ REGIONAL 

MEASUREMENT SITING

Routinely Quantify Uncertainty

Interpret Uncertainty

OBJECTIVES
xvii 

Recommendations 

The recommendations are intended to support and guide Federal agency efforts to 

prioritize and obtain the necessary resources for their most pressing research needs. 

Additionally, they were developed to encourage multiagency collaboration and 

cooperation on shared objectives while helping to facilitate the participation from the 

academic and private sectors and the coordination of Federal activities with state, 

regional, and local governmental agencies. 

The R&D elements will require a robust, coordinated effort by the multiple Federal 

agencies engaged in research, development, or application of ATD modeling systems. No 

one agency holds all the capabilities needed to affect the recommended course of action. 

Shared responsibilities, shared vision, and shared resources are essential to success. 

Without the resource base and sustained direction that a well-coordinated Federal effort 

can provide, the R&D needs cannot be met within time horizons consistent with national 

policy priorities. 

Implementing recommendations are provided for the following seven areas: 

• Quantify model uncertainties and interpret their implications to users. 

• Capture and use existing data sets. 

• Implement ATD test beds. 

• Develop standards for evaluating modeling system performance. 

• Improve the spatial and temporal scale interactions between meteorological and 

ATD models. 

• Improve measurement technologies. 

• Design and conduct special studies and experiments. 

Implementing the recommendations will require a sustained effort over more than a 

decade. Some of the implementing actions will produce returns in the near term—within 

the next 2 years. Other actions will provide some benefits at intervals along the way, even 

though the most significant benefits may require a decade or more to be realized. Many 

of the limitations in capability identified in the report, however, are systemic, resulting 

from a lack of coordinated effort across agencies and agency programs, but they can be 

successfully overcome with the commitment and coordination of resources and facilities, 

particularly the agency teams of individuals dedicated to advancing the operational stateof-the-science of ATD modeling. 
xviii Federal R&D Needs and Priorities for ATD Modeling
1 

1.0 INTRODUCTION

The fundamental reason for modeling atmospheric transport and diffusion (ATD) is to 

predict the concentration in the atmosphere of hazardous material released from a source 

or sources (one or more points of release) as the material moves from the source to other 

locations. The predictions from an ATD model of concentration as a function of space 

(location) and time, plus other information, can be used for a variety of purposes. 

Hypothetical releases at particular points and under different conditions (the planning 

scenarios) can be used by planners to identify potential zones of hazardous threats and 

prepare effective responses to these scenarios. In an actual release situation, the threat 

may require emergency responders to take immediate action to protect health and safety 

of those in the zones of hazardous threats or to provide aid to those injured or exposed. 

Model predictions of where concentrations did (and did not) reach levels of concern are 

also important during the recovery phase, which may extend long after the immediate 

emergency. For less immediate dangers, such as those from continuing release of air 

pollutants with potential long-term effects, the planning, response, and recovery activities 

may weave into one another. Response activities, in particular, will periodically rise and 

fall as concentration levels of the hazardous material rise and fall over time. 

At the most basic level, an ATD model predicts how motions in the atmosphere—wind 

and turbulence fields—transport and diffuse the material of interest after its release. 

Perfect prediction of the smallest motions in the atmosphere is not possible. Inherent 

constraints arise from limited information about the source, the atmosphere, and the time 

available to generate a prediction. The information needed about the source and the state 

of the atmosphere is always limited. Furthermore, some of the motions involved must be 

described stochastically or as nonlinear dynamic processes. Consequently, getting useful 

results from an ATD model is always a compromise between timeliness and completeness in portraying how the atmosphere acts on the released material. This tradeoff 

between timeliness (or resources for the modeling activity) and completeness is starkest 

for emergency response to an actual incident. No matter how much better the ATD 

modeling results could be in an hour or two, by then they are likely to be too late to help 

the first responders. Even the planner cannot wait forever or invest unlimited resources in 

a single model run. If planning for the one situation that does occur is to be appropriate, 

many scenarios must be considered and evaluated. Timeliness (and to a lesser extent, 

resource constraints) are less of an issue for long-term recovery, but the completeness 

standard often rises very high in that context of use. 

Another practical demand on many real-world applications for ATD models stems from 

the consequences that variations in the prediction of hazard zones can have on large 

numbers of people. In urban areas, planners and responders are often faced with difficult 

triage decisions: who most needs help and needs it most quickly? When the complex 

morphology of urban areas is added to the prediction task—the irregular land-water 

interfaces of coastal bays and inlets, mountain-valley structures, or just the height and 

spacing variations of the modern urban built environment—the demands on the ATD 

model to identify the hazard zones accurately become extreme. ATD models typically 
2 Federal R&D Needs and Priorities for ATD Modeling

must describe atmospheric processes in the most changeable and complex part of the 

atmosphere. 

The events of September 11, 2001, dramatically sensitized the American public to the 

magnitude and range of potential terrorist actions aimed at civilian populations. The 

Nation is now far more aware of the potential threats from airborne technological 

hazards, such as releases of chemical, biological, radiological, and nuclear (CBRN) 

materials, not only from a deliberate action with hostile intent but also from industrial 

and transportation accidents. There is also an increased (and appropriate) expectation that 

all levels of government will improve their capabilities to share information, coordinate 

responses, and collaborate on preparations to better protect the public. Thus, there is a 

new sense of urgency associated with the research needs identified in this report. 

1.1 Purpose 

Given the objectives of ATD modeling and the constraints and current concerns as 

sketched above, there is value in a systematic approach to determining the most effective 

ways to lessen the constraints while making ATD modeling systems more useful for their 

intended applications, particularly applications of most pressing concern. This report 

presents a research and development (R&D) plan for providing the ATD modeling 

capabilities needed to meet established needs of the user communities, with special 

emphasis on enabling the National strategy for responding to domestic CBRN incidents. 

Although the report emphasizes homeland security and homeland defense applications, 

many of the capability improvements identified here will benefit other applications as 

well, such as air quality monitoring or emergency preparedness planning and response for 

accidental releases of hazardous materials. 

The report includes: 

• A discussion of user needs for consequence assessment systems (a general name 

for typical applications in which ATD models are employed, including but not 

limited to emergency response/recovery and preparedness planning applications); 

• Extraction of ATD modeling capabilities required to support the users needs; 

• Analysis and prioritization of the gaps between the required capabilities and 

current Federal ATD modeling capabilities (requirements pull), plus opportunities 

for new and emerging science and technology to fulfill user needs better in the 

future (technology push); 

• A strategy to fill the gaps and provide improved capability through an interrelated 

set of coordinated R&D activities implemented by Federal agencies with ATD 

modeling programs or related research, development, or technology transition 

programs; and 

• Recommendations for next steps in implementing the R&D strategy. 

The R&D plan and recommendations presented here are intended to support and guide 

Federal agency efforts to fund their most pressing research needs and to encourage multi-
Chapter 1: Introduction 3

agency collaboration and cooperation on shared objectives. The plan will help to 

facilitate participation from entities in other sectors (academia and industry) and 

coordinate Federal activities with local, regional, and state governmental entities. 

1.2 ATD Models in a Consequence Assessment System 

For all the applications of ATD models mentioned above (and discussed more fully in 

chapter 2), users actually work with a complete consequence modeling system (or the 

functional equivalent of such a system, composed of several pieces). Figure 1 shows how 

an ATD modeling system fits within a complete consequence assessment system. The 

functions typically considered as part of the ATD modeling system are represented by the 

bold black boxes. The other functions are in lighter boxes. 

FIGURE 1. The functional components of a complete consequence assessment system, with its 

embedded ATD modeling system shown by bold lines. 

The purpose of a consequence assessment system is to assess the consequences of 

specific hazards on people and the environment. To do this, the functional components 

must work together, passing information from one component to the next as shown by the 

arrows in figure 1. 

• The source term component includes information about the identity and physical 

state of the hazard or hazards for which consequences are being assessed, the 

release mechanism(s) involved, and the mass of hazard released per unit time. For 

CBRN weapons, the release mechanism is the agent delivery system. For an 

industrial accident, it could be a leaking transfer line or burning truck trailer. 

• Meteorological inputs, in simple terms, describe the local weather conditions at 

the time a source term release occurs and forecasts for these conditions through 

the time the substance is airborne. At a minimum, ATD models require inputs on 

wind speed and direction and a measure of turbulent activity, with the implicit 

assumption that these conditions do not change. A more complete meteorological 

Meteorological Inputs:

 • Observations

 • Forecasts

Source Term

(where, when,

how much)

ATD Input

processing

Transport and

Diffusion Code

Human Health

Effects and

Environmental

Effects

Concentrations

Downwind

Deposition (vapor,

liquid, and solids)
4 Federal R&D Needs and Priorities for ATD Modeling

specification may include clouds, precipitation, temperature, pressure, humidity, 

surface heat and momentum fluxes, and more complex turbulence parameters. 

• ATD input processing involves processing the available meteorological forecast 

information and observational data to prepare it for use in the modeling done by 

the transport and diffusion code. Input processing might, for example, include a 

diagnostic model in which available wind observations are used to estimate threedimensional wind fields that reflect the impact of local terrain and conserve mass. 

It may also involve data quality acceptance and quality control (data QA/QC), 

such as applying criteria for whether additional observational data are accepted 

into the model run after initialization. ATD input processing may be used when 

meteorological observations are ingested into a localized wind field forecast. It 

may also be used to generate a nowcast (forecast for the next 1 to 6 hours) using 

the forecast fields from a prognostic meteorological model as a first guess and 

refining them by assimilating observational data that were not available at 

initialization. Sometimes these input processing functions are not considered part 

of the ATD modeling. For reasons that will emerge in chapter 3, this report 

includes them as a component within the ATD modeling system. 

• The transport and diffusion code is the software engine that computes 

advection (transport solely by the mass motion of the atmosphere) and diffusion. 

The code describes, in sets of computation instructions to a digital computer, the 

combined effects of time-averaged transport (which has traditionally been viewed 

as a deterministic process) and diffusion. The principle mechanism of diffusion is 

turbulence, which has traditionally been represented as a stochastic process. A 

deterministic process is governed by and predictable in terms of definitive laws, 

such as dynamic equations. A stochastic process evolves in time according to 

probabilistic equations; that is, the behavior of the system is determined be one or 

more time-dependent random variables. 

• Deposition refers to the way in which the ATD modeling system represents 

processes that remove the hazardous material of interest from the air carrying it 

and deposit it on the Earth’s surface (land or water). Substances released into the 

atmosphere will continue to reside there, continually diluted by mixing processes, 

until they are removed by reactions with other components of the atmosphere or 

are deposited to the surface. In some instances, deposited materials have the 

potential for subsequent resuspension by wind or volatilization. 

• Concentrations downwind refers to the model’s prediction of how much of the 

hazard of interest (what concentration) will be in the air at particular locations and 

times after the release. 

• Human health and environmental effects are the consequences of ultimate 

interest to most users of a consequence assessment system. From the prediction of 

concentrations downwind and other information, potential impacts on human 

health and safety and on the environment are estimated. 

In conformance with the terms of reference under which this R&D plan was prepared, the 

functional requirements for characterizing the source term or the human health and 
Chapter 1: Introduction 5

environmental effects components of a complete consequence assessment system will not 

be analyzed. R&D needs are not defined for the capabilities needed in those components, 

nor does the R&D plan include activities to address capabilities needed in those areas. 

However, these components are considered from the perspective of being, respectively, 

an essential input to and output of the ATD modeling system. As such, they do influence 

the capabilities required within the ATD modeling components and the R&D to provide 

those capabilities. 

The analysis in chapter 3 will return to figure 1 to analyze in detail the capabilities 

needed for each component of an ATD modeling system. For the moment, however, the 

principal message of figure 1 is that, for the purposes of this R&D plan, an ATD 

modeling system is always a tool for the larger purpose of a consequence assessment 

application. Differences in the specific objectives of that application will often mean that 

the ATD modeling system must be tailored to fit the tool to its task. 

1.3 Scope and Context of the R&D Planning Activity 

The activity leading up to this ATD R&D plan began shortly after the terrorist attacks on 

the World Trade Center and the Pentagon on September 11, 2001. In December 2001, 

under the direction of the Federal Committee for Meteorological Services and Supporting 

Research (FCMSSR), the Federal Coordinator for Meteorology established the Joint 

Action Group for the Selection and Evaluation of Atmospheric Transport and Diffusion 

Models (JAG/SEATD). The task of the JAG/SEATD was to evaluate the ATD modeling 

systems available to address threats to homeland security. The group’s final report, 

published in August 2002, included a list of candidate research needs and concluded that 

the current ATD modeling systems available for Federal agency and military use should 

be refined and prioritized to reflect operational needs (OFCM 2002). The FCMSSR 

concurred with this recommendation, as documented in Action Item 2002-2.1 of the 

Record of Actions from the FCMSSR meeting of October 12, 2002. The Federal 

Coordinator for Meteorology then initiated a phased effort to address the 

recommendation. 

For the first phase, the Federal Coordinator established the Joint Action Group for 

Atmospheric Transport and Diffusion Modeling (Research and Development Plan), or 

JAG/ATD(R&DP), and charged it to perform the following tasks: 

• Review the proceedings from the Office of the Federal Coordinator for 

Meteorology (OFCM) special session at the George Mason University (GMU) 

Transport and Dispersion Modeling Conference (OFCM 2003) and identify any 

additional research needs that resulted from the conference. 

• Review the results of the Joint Urban 2003 experiment at Oklahoma City 

(DTRA/DOE 2003). 

• Identify any scenarios that are not addressed satisfactorily by the modeling 

systems documented in the JAG/SEATD report. 
6 Federal R&D Needs and Priorities for ATD Modeling

• Develop a methodology for characterizing and prioritizing the research and 

technical needs and for linking those needs to stated operational requirements. 

• Consult with subject-matter experts as required (based on the needs of the JAG 

members). 

• Identify the tools required for transitioning successful research results into 

operations through interagency cooperative efforts like observational and 

modeling testbeds, field and urban studies/experiments, and a common model 

evaluation methodology. 

• Develop a comprehensive plan that documents the research and technical needs of 

the ATD modeling and operational communities. The plan should prioritize the 

most pressing R&D needs and provide a roadmap to address those needs within 

the OFCM coordinating infrastructure. 

This R&D plan is the response of the JAG/ATD(R&DP), hereafter referred to as “the 

JAG,” to the above terms of reference. Expanded feedback on the plan was solicited 

during the 8th Annual GMU Conference on Atmospheric Transport and Dispersion 

Modeling on July 14, 2004, and the OFCM Urban Meteorology Forum on September 21-

23, 2004, which included participation from the academic, public, and private sectors. 

Research areas that were considered within the scope of phase 1 included but were not 

limited to meteorological inputs and input data processing, directly measured dispersion 

inputs, and transport and diffusion calculations. Research needs associated with but not 

limited to source characterization, common default source terms, chemical mixtures, 

chronic health effects, and common frameworks for the display of results in geographic 

information systems (GISs) will be included in a later phase. 

To understand existing Federal operational modeling capabilities, the JAG has relied 

heavily upon the earlier JAG/SEATD study (OFCM 2002). The JAG/SEATD explored 

these capabilities in considerable detail but with limitations imposed by incomplete 

understanding of each modeling system. The JAG/SEATD determined that, of the 29 

distinct ATD modeling systems it studied, many emphasized processes and factors that 

were peculiar to a specific application. Some of these systems provided a commendable 

representation of atmospheric dispersion processes. Many were integrated into 

consequence assessment systems with extensive source characterization and health 

effects for specific substances. These capabilities reflected investments by the Federal 

agencies that developed the ATD modeling systems over a significant period. 

The current JAG studied the R&D needs listed in the JAG/SEATD report, as well as the 

report’s recommendations. It also reviewed the proceedings from the OFCM special 

session of the 7th Annual GMU Transport and Dispersion Modeling Conference (OFCM 

2003) and the National Research Council report, Tracking and Predicting Atmospheric 

Dispersion of Hazardous Material Releases, Implications for Homeland Security (NRC 

2003). From the review of these documents, the JAG developed a preliminary list of 

research needs. 
Chapter 1: Introduction 7

Next, the JAG conducted teleconferences and several panel sessions dedicated to specific 

topics, to facilitate as much participation as possible by Federal agency representatives 

and subject-matter experts in drafting early versions of the R&D Plan. Subject-matter 

experts were invited to assist in reviewing and revising the list of candidate R&D needs. 

The JAG also reviewed the Joint Urban 2003 field experiment (DTRA/DOE 2003) and 

discussed a framework for transitioning successful research results into operations 

through interagency cooperative efforts. Among the cooperative efforts considered were 

test beds for observing systems and modeling, field studies and experiments including 

ones conducted in urban areas, and a common model evaluation methodology. 

The JAG considered whether there were incident scenarios of recognized importance for 

which all of the current ATD modeling systems were totally unsuited. The conclusion 

was that all of the identified scenarios can be at least minimally addressed by one or more 

modeling systems. The JAG also discussed the challenges from releases of CBRN 

material due to high-altitude intercepts of ballistic missiles. In consideration of these 

realities, the R&D plan proposed in this report makes thorough and imaginative use of all 

available tools to address the dispersion issues confronting the Nation. Overall, the R&D 

plan reinforces the need for expanded theoretical and physical modeling studies, for 

dedicated field studies conducted in the circumstances of intended application, and for 

close coupling of all such activities with the development of improved models. These 

elements of an overall strategy accept and build upon the standard approach of continuing 

exploration of contributing processes. They acknowledge the powerful needs of current 

times and the urgency with which new and refined products are desired. They recognize 

that rapid transfer of the findings to the user community is necessary. To accelerate this 

transfer to operations, an essential element of the proposed strategy is to deviate from the 

usual approach to meteorological research and couple the user community more closely 

with the R&D program for ATD modeling. 

The JAG prepared a draft R&D plan in time for review within the OFCM coordinating 

infrastructure prior to the 8th Annual GMU Transport and Dispersion Modeling 

Conference in July 2004. The main elements of the plan, including the recommendations 

for next steps in implementing it, were presented at that forum during a special OFCM 

session. This session enabled representatives from the public, private, and academic 

sectors to comment on them. Their comments were used to revise the report as 

appropriate. 

The R&D strategy and recommendations in this report were presented at the OFCM 

Urban Meteorology Forum in September 2004 to solicit feedback from non-Federal 

organizations. This forum provided an opportunity for commercial interests and academic 

institutions to consider how their resources complement or supplement the approach to 

research and advanced development described in this document. 

The report will be submitted to FCMSSR, through the Federal Coordinator and the 

Interdepartmental Committee for Meteorological Services and Supporting Research, for 

its endorsement of the report’s recommendations. After the FCMSSR review, the 

responsible agencies within the Federal meteorological community will coordinate an 

approach for implementing the recommendations. 
8 Federal R&D Needs and Priorities for ATD Modeling

1.4 Structure 

The report consists of six chapters and five appendices. Chapter 1 provides an 

introductory view of the purpose of ATD modeling systems and introduces the plan’s 

scope, context, and general argument structure. Chapter 2 describes the operational needs 

of users of ATD modeling systems from their perspective, with particular attention to the 

similarities and differences in operational context and consequence assessment 

requirements of the major segments of the user community. 

Chapter 3 interprets the operational user needs into required capabilities of ATD 

modeling systems and the inputs to those systems. Chapter 3 also begins the analytical 

task of comparing these requirements with existing capabilities, to identify gaps between 

what current systems can now do and what users need them to do. Chapter 4 goes further 

into this assessment of capabilities against requirements. It focuses on three broad areas: 

• Improvements to both ATD models and the meteorological models that typically 

provide input data for them; 

• Improvements to measurement technologies, to provide both the data needed to 

improve results with current modeling capabilities and the data required to take 

full advantage of the proposed modeling improvements; and 

• Improvements required at the interfaces between data and models—areas that 

require a joint analysis and will involve accommodation and innovation from both 

sides. 

Chapter 5 presents the set of interlocking program elements that the JAG adopted as the 

best approach for addressing the R&D needs identified and prioritized in chapters 3 and 

4. The exposition of each program element covers the capabilities to be achieved through 

it, the rationale for this element as the best approach to meet user needs, and the 

relationships among the elements as components of an overall R&D Plan. 

Chapter 6 contains the JAG’s recommendations for actions to implement the plan in 

Chapter 5. Most of the recommendations include specific implementation actions to be 

taken if the basic recommendation is endorsed by FCMSSR. 

The appendices include supporting details for the main lines of argument in chapters 3 

through 5. Appendix A provides a historical perspective on the current state of 

meteorological and ATD modeling capabilities. It includes brief summaries of a set of 

past ATD field studies, which the JAG considers to be prime candidates for capturing 

existing data for new R&D objectives (section 5.3). Appendix B is a summary of current 

ATD modeling capabilities and R&D programs. It supports the analysis of gaps and 

opportunities in chapters 3 and 4. Appendix C is a more technical treatment of some of 

the key considerations underlying the general argument of chapter 3 and section 4.1 on 

the necessity to incorporate more probabilistic methods, representations, and output 

information in ATD modeling systems. Appendix D is a glossary of acronyms used in the 

report. Appendix E lists the JAG participants. 
Chapter 1: Introduction 9

Reference information for the technical literature and other source citations in the body of 

the report (chapters 1 though 6) is listed after chapter 6 and before Appendix A. Each of 

appendices A through C contains its own reference list. 
10 Federal R&D Needs and Priorities for ATD Modeling
11 

2.0 USER NEEDS

The consequence assessment systems introduced in chapter 1 can serve a variety of 

applications. While the scope of this document is primarily focused on the emergency 

preparedness and response needs of the homeland security community, consequence 

assessments are important in other applications as well. All of these applications utilize 

the fundamental building blocks of figure 1 and face the challenge of properly employing 

ATD modeling systems in complex environments, such as urban and coastal areas. 

This chapter explores the range of user activities and their specific needs within the area 

of emergency preparedness and response, while identifying common needs that extend 

across these diverse application areas. These needs are the principal drivers that 

determine what the ATD modeling system must provide within any specific application’s 

consequence assessment system. This statement foreshadows a major theme of this 

report: early and continuing involvement of the user community is essential in the 

development and product improvement process. 

The JAG invited users from local, state, and Federal agencies to discuss their current use 

of ATD modeling systems, shortfalls in their ability to perform consequence assessment, 

and how their needs and requirements could be better met. These representatives included 

firefighters, state emergency managers, and Federal emergency responders and managers. 

Their perceptions of current capabilities and needs are the basis for most of this chapter. 

2.1 The Emergency Preparedness and Response Environment 

The emergency preparedness and response environment includes a number of activities 

during which consequence assessment of an airborne hazard is relevant. Planning 

activities start in anticipation of a specific incident to help everyone prepare to respond. 

Response activity begins when an incident occurs. Response activity then transitions into 

recovery activity. Incidents that involve the release of an airborne hazardous material 

(HAZMAT) can range from a relatively straightforward situation that can be handled by 

local responders to a complex situation that involves elements of all three activities 

(response, planning, and recovery) and requires resources from many different 

organizations. 

The same personnel may be (and often are) involved in planning, response, and recovery 

activities. These personnel include: 

• The local, state, and Federal emergency responders—law enforcement officers, 

firefighters and HAZMAT technicians, emergency medical personnel, and onscene commanders; 

• Government officials and emergency managers, many of whom will have 

important decision-making roles; and 

• Federal agency decision makers, executing their operational missions. 
12 Federal R&D Needs and Priorities for ATD Modeling

For domestic incidents of national significance, the roles and responsibilities of the 

Federal agencies are defined in the National Response Plan and its associated annexes 

(DHS 2003). The incident management process is described in the National Incident 

Management System. Requests for Federal assistance and information flow up through 

the local jurisdiction’s (e.g., city or county) emergency operations center to the state 

level, and then to the Department of Homeland Security (DHS). In domestic incidents of 

national significance, such as terrorist incidents and other high-visibility, multijurisdictional events, DHS may designate a Federal officer to serve as the local DHS 

representative and provide senior leadership, strategic guidance, and Federal operations 

integration (DHS 2004). 

FIGURE 2. Questions of concern to users of ATD modeling predictions for emergency 

preparedness and response.

For an incident involving the atmospheric release of a hazardous material, these users all 

need answers to the questions shown in figure 2. These fundamental questions remain 

relevant whether the hazardous release incident is small in scale and handled entirely by 

local response personnel or becomes an incident of national significance, spanning 

multiple jurisdictions and requiring Federal response support.1

 Current capabilities to 

answer these questions vary among Federal, state, and local response agencies. Locales 

that have industrial or manufacturing facilities, containing substantial amounts of 

hazardous materials, may be better prepared in terms of personal protection equipment, 

sensors, and stand-alone modeling capability than those without such hazards. Military 

and other Federal installations that store or handle hazardous material may have 

capability to respond to CBRN releases. The military may also provide support services 

to civilian agencies as an integral element of its homeland defense mission. 

Four common themes recurred in the comments from most of the users who spoke with 

this JAG: 

 

1

 The 2002 annual report of the National Atmospheric Release Advisory Center (NARAC 2002) states, 

“Effective preparation for and response to the release of toxic materials hinge on accurate predictions of the 

dispersion, ultimate fate, and consequences of the chemical or biological agent. Of particular interest is the 

threat to civilian populations within major urban areas, which are likely targets for potential attacks.” 

Release Event

What was released?

When?

Where?

How Much?

Observations

Direct

Sensor

Casualty

Risks

How many?

For how long?

Consequence

management

How to minimize

and/or manage

impact?
Chapter 2: User Needs 13

• The safety of people is the first priority. 

• All emergencies are local, and most are short-lived. 

• The end user of the ATD results needs actionable information. 

• Inconsistent data products and distribution protocols can cause confusion and 

inefficiencies in the emergency response. 

Not surprisingly, one of the first and most important needs expressed by emergency 

responders is the need for effective communication and early indication of where the 

contaminant is going. Equally important is gaining an understanding of where it is likely 

not to go. A communications link between the HAZMAT or other emergency personnel 

and personnel with ATD expertise is often preferred to having the modeling capability 

“onboard” or at the scene. A “reach-back” capability is desirable because responders are 

busy dealing with public safety issues including medical response, potential evacuation, 

and incident characterization. They do not have the resources to run models and interpret 

results. 

Emergency responders requested more useful and uniform products and a standard 

protocol for distribution and display of hazard predictions to the response community. 

They emphasized the need for established procedures to rapidly disseminate hazard area 

predictions to all levels of the response team. The use of GIS-based displays with 

overlays of near real-time hazard information could be especially important for largescale incidents, involving multiple response agencies and potentially affecting large 

segments of the local and/or regional population. 

The products need to be easily displayed and uniform in content. While users wanted a 

reach-back capability when needed (for example in a major incident), they also want an 

on-scene capability for making rapid decisions during smaller scale incidents. Above all, 

emergency responders asked for the capability to deliver the information needed into the 

hands of those working to save lives. 

The user panel expressed the following key needs. 

• Improvements are needed in the national capability for consequence assessments 

for preparedness and response applications: 

 Capability for modeling more than one substance at a time; 

 Realistic planning scenarios that can be built quickly and simply, to include a 

variety of hazards and local weather scenarios; 

 Scenarios that include common industrial chemicals, especially ones that 

could be weaponized; 

 Planning scenarios for CBRN agents, utilizing local building terrain and 

weather profiles; 

 Improved infiltration models; and 

 Capability for modeling flammability and explosivity. 
14 Federal R&D Needs and Priorities for ATD Modeling

• Information must be displayed in the emergency manager’s display system (which 

may or may not be GIS-based) in accordance with the organization’s standard 

operating procedures. 

• A standardized set of products should be tailored to the needs of the particular end 

user: 

 Multiple product sets for multiple users; 

 Regular updates; 

 Realistic predictions which depict forecast uncertainty; and 

 Timely products—the key to saving lives. 

• Training and coordination among Federal, state, and local responders are critical 

to efficient communications during an event. 

• Users must be brought into the development process early and often. 

2.2 User Needs in Other Applications of ATD Modeling Systems 

2.2.1 Military Applications 

The primary military requirements for ATD modeling in theater are for force protection 

and civilian population protection. CBRN materials are expected in modern military 

encounters on the battlefield. In data-denied areas, the military needs improved 

capabilities to sense weather and hazard parameters within an operational area. Accurate 

guidance on the use of hazard protection equipment (protective garments, masks, etc.) to 

defend against chemical or biological agents is critical for conducting military operations. 

The key questions of importance to emergency preparedness and response (figure 2) are 

still the questions that military users need to answer, but the form and content of a useful 

answer may be different. For CBRN incidents, military applications involve planning and 

response phases similar to those for civil emergency preparedness. However, the resultant 

actions may differ and will be driven by military needs. 

Meeting the needs specific to military applications will require: 

• Improving current capabilities of weather-parameter and hazard-sensing systems 

that can be remotely deployed and monitored; 

• ATD planning for installation protection and for joint and coalition operations, 

covering both deliberate releases (hostile actions) and accidental releases (e.g., 

destruction of enemy munitions containing CBRN and accidents during the 

removal or storage of CBRN-containing materiel); and 

• Enhancing the capability to acquire and process the weather and hazard-sensor 

data; including assimilating the data into meteorological and ATD models. 
Chapter 2: User Needs 15

2.2.2 Air Quality Monitoring Applications 

The 11th Prospectus Development Team of the U.S. Weather Research Program 

observed in its July 2003 final report that there are at least three groupings of users of air 

quality information: the public, decision makers, and researchers (Dabberdt et al. 2004). 

The public is the largest user group, and the broadcast media provide the means of 

disseminating air quality information from its official source to the public. Decision 

makers that use air quality forecasts include Federal agencies, civil authorities (e.g., state 

and local departments of health), emergency response organizations, and the private 

sector (power, transportation, health care, and others). Among the Federal agencies that 

use these forecasts are the U.S. Environmental Protection Agency (EPA), the U.S. Forest 

Service in the Department of Agriculture, and the National Park Service in the 

Department of the Interior. Both public and private sector organizations use air quality 

forecasts to authorize and plan operations such as prescribed burns or pollutant-emitting 

operations at power plants and chemical manufacturing plants. Researchers who use air 

quality forecasts include air quality and environmental scientists, regulatory scientific 

advisors, and atmospheric scientists conducting field measurement studies. 

The key questions of importance to emergency response also apply to air quality users; 

however, the form and content of a useful answer may be different. There is likely to be 

more emphasis on the capability for planning, and some of the decision elements 

involved, such as regulatory considerations, differ because the reasons for interest in air 

quality only partially overlap with the objectives of emergency response. Forensic 

applications in air quality are similar to those used in emergency response when ATD 

models are used to backtrack from observed concentrations to a release point. This 

application of ATD modeling may be necessary when the actual amounts and chemical 

makeup of the source material are unknown, as in an accidental release. Other uses of 

ATD models in air quality assessment are distinct from the way these models are used in 

a consequence assessment system. 

For air quality forecasting, three-dimensional meteorological and chemical observations 

and advanced data assimilation methods are essential. In addition, meeting ATD 

modeling needs specific to air quality applications will require: 

• Improved physical understanding of the atmospheric boundary layer (ABL); 

• Better land-surface models; 

• Better representation of winds and turbulence across varied spatial and temporal 

scales; 

• Better understanding of clouds and cloud processes that affect chemical fate and 

transport; and 

• Improved capabilities for estimating uncertainty and predictability and for 

evaluating models. 

Information about other pollutants, including particulate matter, is important when 

considering the long-term health impacts on the population. For example, the air quality 
16 Federal R&D Needs and Priorities for ATD Modeling

standards for fine particles are calculated using an annual average because national 

ambient air quality standards are set for pollutants that the public is generally exposed to 

for long periods of time. A shorter-term average standard would be more helpful when 

assessing the impact from short-term events or peak concentrations in long-term events. 

In the case of a fire or some other large source of particulates, the general practice is to 

move people away from the most intense concentrations of smoke. In responses to 

events that last on the order of days to a year (e.g., a landfill fire), however, managers 

need guidelines to decide how much short-term exposure a population can tolerate at any 

given time. 

2.3 ATD Information Required for Hazard Response Decisions 

The preceding sections showed that, across a range of applications for ATD modeling 

systems, the basic questions are the same. The user needs to know what the hazard is, 

when and where it is a threat to people (or other consequences), and the seriousness of 

the potential health and safety impacts to people and the potential environmental impacts. 

For both health and environmental issues, the consequences can range from acute effects 

of short-term exposure to more slowly developing consequences of long-term exposure to 

levels too low to produce acute effects. For acute effects, spikes in the concentration of 

an airborne hazard are typically of interest. Therefore, the ATD model prediction needs 

to provide information on the spatial variability in hazard concentration on a time scale 

consistent with the time required to produce an acute effect. To assess effects from 

longer-term exposure to lower concentrations of a hazard, the time-averaged 

concentration of the hazard is needed. Many potential airborne hazards, including most 

CBRN agents, can potentially have acute and long-term effects, so both kinds of 

information are often relevant to the user. 

Flammable materials are another class of potentially hazardous materials. When mixed 

with air in the right concentration range (i.e., between lower and upper concentration 

limits for the particular material), the material can explode if a source of ignition is 

present. If other combustible materials are within or near the explosion volume, the 

explosion can set off a rapidly growing fire. Users of a consequence assessment system 

for this class of hazards want to know when and where the concentration could be within 

the explosivity limits. This requirement is much like that for acute exposures—spikes in 

the airborne concentration at a time and place can be enough to reach the lower limit of 

explosivity. 

For all of these consequences, the bottom line for the ATD modeling system is that the 

users want to know about the concentration of the hazard as a function of space and 

time. In many instances, the user will want to know about both the peaks in the 

concentration at small time and space scales and the time-averaged concentration at 

spatial scales relevant to the consequence of interest. ATD predictions of concentration as 

a function of space and time must meet accuracy objectives for all places and conditions 

of concern, especially coastal urban areas. Physical processes to which ATD models are 

sensitive must be adequately treated, including land-sea breezes, urban heating effects, 
Chapter 2: User Needs 17

and urban effects on local winds. In densely populated regions of concern, small changes 

in the predicted hazard area can have substantial impacts on user decisions.

2.4 Constraints and Tradeoffs in ATD Modeling to Meet User 

Needs 

Planning, response, and recovery activities have different constraints on timeliness for 

receiving a relevant prediction and on the comprehensiveness and completeness required 

for a prediction to be useful. The optimal tradeoff among these constraints will be 

different for different activities, different applications, and even specific characteristics of 

an incident (e.g., the hazard released and its consequences of interest, the amount 

released, the location of release, and the areas potentially affected). 

As emphasized above, timeliness is the most important constraint for the responder but 

not the only one. One of the most important needs that emergency responders expressed 

to the JAG is for early indication and effective communication of the plume direction. 

Equally important is gaining an understanding of where the plume is likely not to go. 

Most responders desire a comprehensive reach-back capability, which allows the local 

ATD modeler (or the on-scene user) to access technical support in getting predictions 

from the ATD modeling system that reflect the specific characteristics of the incident at 

hand. A single, direct line of contact from the incident command post to the supporting 

expertise is needed, rather than a complicated system that requires an expert to operate at 

the front lines. Most responders prefer an ATD modeling tool that is simplified for the 

response environment. They are too busy dealing with the immediate threats to health and 

safety to run complex models or to analyze results that do not tie directly to the decisions 

confronting them. 

Specific emphasis on meteorological studies for planning purposes should be given to 

coastal zones, complex terrain, and urban environments where local heterogeneities have 

significant impact on dispersion. Users were especially aware of the special challenges 

raised by urban environments. In urban areas, the presence of buildings and other 

structures affects not only the flow fields but also the structure and intensity of 

atmospheric turbulence. The R&D and test and evaluation communities need to seek user 

input on these urban challenges, the practical approaches users have found to dealing 

with them, and the kinds of information that would be of most benefit. Accurate 

databases on the built environment are required to model these surface-atmosphere 

interactions at scales relevant to ATD in urban environments. 

Standard dispersion methodologies are based on descriptions of processes developed in 

the absence of buildings and urban street canyons. The influence of such urban 

complexity is known to be major, but relatively little is known concerning the best way to 

capture the consequences of site-specific surface features in the descriptions of 

turbulence used in dispersion calculations. In addition, building infiltration and 

exfiltration should be represented. 
18 Federal R&D Needs and Priorities for ATD Modeling

2.5 “If I Can’t Have Certainty, Tell Me How Bad It Could Be, and 

Where” 

Emergency responders do not want ATD model predictions couched in terms of 

mathematical measures of uncertainty or highly technical statements about probability. 

They do not know how to use such information. These measures of uncertainty bear no 

immediate and clear relationship to the decisions for which the users want information on 

hazard concentration as a function of space and time. This point was made most strongly 

by the first responders among the users who met with the JAG. While some decision 

makers may understand how to use uncertainty estimates, this fact also generally applies 

to planners and recovery operators as well. 

The fundamental issue for the model developer can be expressed simply: Users want 

certainty in the information they get, so they can act quickly and decisively. The nature of 

the modeling situation (to be discussed in section 3.1), however, means that no ATD 

modeling system can provide predictions with certainty, at least not for situations of 

interest to real-world users. 

Because users understand that there are limits to ATD modeling capabilities, they apply 

their intuitive estimate of uncertainty in order to err on the side of safety. A better option 

than relying on an intuitive safety margin is for users to have information on uncertainty 

interpreted into a form they know how to use. Rather than mathematical measures of 

uncertainty or probability, users asked for answers to these kinds of questions: 

• What is the [reasonable] worst case to prepare for, and where could it occur? 

• What areas are safely out of danger? 

• Where could thresholds of interest (e.g., concentrations with lethal or other acute 

effects, longer-term exposure thresholds, explosive concentrations) be exceeded 

and when? 

Many users are aware of the uncertainties in source characterization, in other model 

inputs such as fine-scale winds and land cover, and in modeling simplifications made to 

get results within time constraints. In many cases, users are dealing with the same types 

of uncertainty in their decisions. Knowing how users understand and work with these 

uncertainties can help developers find useful ways to present prediction uncertainties and 

probabilities to the user. 

Reducing the uncertainty in ATD model predictions is an obvious goal for model 

developers. At the same time, model developers, users of model predictions, and all who 

assess progress in improving consequence assessment capabilities must understand that 

there will always be uncertainties in modeling complex, dynamic systems. The task for 

the modeling system researcher-developer is first to identify the sources of uncertainty in 

a given modeling system and provide reasonable measures of the uncertainty in a given 

set of predictions from the system. The second task—which may be the harder of the two 

because we know less about it—is to find ways to communicate to the user the 

implications of these uncertainties for that user’s decisions. It is not enough to provide 
Chapter 2: User Needs 19

measures of uncertainty that are defensible within the model developer’s world. 

Developers and representative users from the range of applications to be served will have 

to work together to determine how to make this essential information useful. The next 

task is to provide users with tools that meet their needs, which must be accomplished as 

part of development. The transition of R&D products into working tools must begin 

while the tools are still under development and before they are declared operational. 
20 Federal R&D Needs and Priorities for ATD Modeling
21 

3.0 MODEL CAPABILITIES REQUIRED TO MEET USER NEEDS

Chapter 2 showed how the needs of those who use consequence assessment systems lead 

to the requirement that an ATD modeling system within the larger consequence 

assessment system predict accurately and usefully the concentration of the airborne 

hazard (or hazards) as a function of space and time. The predictions must be relevant to 

the actual conditions at the time of an incident. Users typically need to know who is not 

endangered, as well as who may be in danger. They want to know which locations are 

likely to be within a hazard area, which locations are safely outside the hazard zone, and 

the quality of these estimates. 

Users know that ATD modeling systems do not produce perfect predictions. They 

desire—and find—ways to work with the limitations of the information, but the 

modeler’s measures and expressions of probability and uncertainty are insufficient to 

help many users, particularly emergency responders and managers, make sound 

decisions. This user need imposes two complementary demands on the model developer. 

The first demand is to provide a reasonable measure of the uncertainty in a prediction or 

its probabilistic distribution. The second is to communicate the implications of this 

uncertainty measure or probabilistic 

distribution in ways the user can apply. 

This chapter interprets all of the above 

user needs into requirements on ATD 

modeling systems, in terms relevant to 

assessing the further R&D that should 

be done. Section 3.1 describes how the 

temporal and spatial scales for which 

models have been designed limit their 

applicability to other scales, either to get 

input for the model or to apply its 

results in the real world. Section 3.2 

returns to the major functional 

components of a consequence 

assessment system, as introduced in 

chapter 1, to examine how the 

requirements on the ATD modeling 

system flow down to requirements on 

each of its components. It describes 

current capabilities in each component, 

compares them with what is required, 

and identifies both challenges and 

opportunities in meeting the 

requirements. Section 3.3 examines, 

from the standpoint of actions available 

to the research, development, and 

test/evaluation communities, ways to 

Uncertainty in ATD Model Predictions 

The total model uncertainty is measured by the variance 

between the predicted and the observed quantity over a large 

number of events that have similar properties (an ensemble). 

In a recent discussion of the mathematical basis for 

understanding model uncertainty (Rao 2004), the components 

of the total model uncertainty are divided into: 

(a) Internal factors such as the numerical approximations 

to the governing equations, modeling errors, and the 

treatment of dynamical processes; 

(b) External factors such as data used to execute and 

evaluate the model, model parameterizations, and the 

initial and boundary conditions; and 

(c) The stochastic component or inherent uncertainty, due 

to the natural variability of the atmosphere. 

The model developer can minimize the first two components 

of uncertainty by addressing the several factors contributing 

to each. The third component cannot be eliminated and is only 

quantifiable in a statistical sense. Furthermore, we can expect 

inherent uncertainty to vary as a function of averaging time, 

location, and the ensemble parameters. 

For the analysis of R&D needs, the essential relationship 

between measurements (observations) and identifying, 

quantifying, and minimizing model uncertainty must be 

embraced. The inherent uncertainty cannot be estimated 

without measurements. Progress toward reducing the first two 

components of uncertainty also depends on having 

appropriate observations and on continually improving the 

techniques used to obtain them. 
22 Federal R&D Needs and Priorities for ATD Modeling

undertake the task of improving the transition of modeling capability into useful tools for 

users. In effect, it analyzes capabilities, gaps, and opportunities at the output interface 

from the ATD modeling system to the consequence assessment system. 

The exposition in this chapter draws on two prior reviews of ATD modeling capabilities, 

each of which included recommendations on R&D needed to address deficiencies. The 

National Research Council (NRC 2003) reviewed current capabilities in dispersion 

modeling, identified deficiencies and research needs, and recommended actions to 

provide more accurate information. The 11th Prospectus Development Team of the U.S. 

Weather Research Program addressed meteorological research necessary to improve air 

quality forecasting (Dabberdt et al. 2004). The JAG performed its own survey of current 

capabilities, which are summarized in Appendix B. 

3.1 Consequences of Model Scale 

Atmospheric processes are classified by the horizontal dimension and time periods of 

typically observed phenomena. Choosing an appropriate ATD model requires knowledge 

of the physical processes that should be treated for the intended application. It also 

requires an appreciation of the uncertainties associated with the tradeoffs made by the 

developer in constructing a model of the physical processes that are dominant or relevant 

at a particular scale. 

For purposes of ATD modeling, there are three major scales of interest: 

1. Macroscale applies to processes having spatial dimensions of 2,000 km or 

greater and influencing temporal variations of 3 days or longer. 

2. Mesoscale applies to processes having spatial dimensions of 2 km to 2,000 km 

and influencing temporal variations of 1 hour to 3 days. 

3. Microscale applies to processes having spatial dimensions of 2 km or less and 

influencing temporal variations of 1 hour or less. 

These three are further subdivided by decades of distances, from larger to smaller, 

indicated by α (alpha), β (beta), and γ (gamma), as shown in figure 3. 

As the scale becomes smaller, the effects of some processes become increasingly more 

difficult to treat explicitly or deterministically. Depending on the horizontal scale of 

interest, different atmospheric processes become significant. Turbulence—the gustiness 

superimposed on the mean wind—can be visualized as consisting of irregular swirls of 

motion called eddies. Eddies produce effects at the microscale. The small-scale 

phenomena associated with the microscale are so transient in nature that deterministic 

description and forecasting of individual eddies is virtually impossible. 

The scales of atmospheric motions are interconnected and nearly continuous. Macroscale 

processes drive mesoscale and microscale processes as energy is transferred from larger 

to smaller scales. Conversely, small-scale processes can organize to develop larger-scale 

systems, such as convective storms. Many of the phenomena of interest for ATD occur in 
Chapter 3: Model Capabilities Required to Meet User Needs 23

the troposphere—the portion of the atmosphere from ground level up to approximately 13 

km. Most applications of ATD models are for incidents occurring in the atmospheric 

boundary layer (ABL)–the lowest few kilometers of the troposphere where people live. 

However, there are situations in which transport and diffusion in the upper atmosphere 

become critically important for ATD modeling. 

Plumes

Roughness

Turbulence

Dust

 Devils

Thermals

Wakes

Tornadoes

Deep

 Convection

Short gravity

 waves

Thunder-

 storms

I.G.W.

C.A.T.

Urban Effects

Nocturnal

 low-level jet

Squall lines

Inertial waves

Cloud clusters

Mtn. & Lake

 breezes

Fronts

Hurri-

 canes

Baroclinic

 waves

Tidal

waves

Ultra long

waves

Standing

 waves

Micro γ Micro β Micro α Meso γ Meso β Meso α Macro β Macro α

1 Min

1 Hr

1 Day

1 Month

1 Year

Micro Scale

Meso Scale

Macro Scale

20 

M

200 

M

2 

Km

200 

Km

2,000 

Km

10,000 

Km

20

Km

FIGURE 3. Scale definitions and different atmospheric processes with characteristic time and 

horizontal scales (adapted from Orlanski, 1975). C.A.T is Clear Air Turbulence and I.G.W. is 

Inertial Gravity Waves. 

The horizontal and temporal dimensions of the incident to be modeled define the 

appropriate scale of the ATD model. The chosen ATD modeling approach should be 

appropriate for the circumstances, providing a comprehensive and concise description of 

effects at a particular scale of interest. Note: the horizontal grid increment is not the scale 

of the model. Full representation of the phenomena at the desired scale requires five or 

more grid increments. Appendix C contains a fuller discussion of ATD model 

construction and selection related to considerations of scale. 
24 Federal R&D Needs and Priorities for ATD Modeling

3.1.1 Consequences of Scale in Atmospheric Data 

Atmospheric measurements may show scaling by their horizontal spacing or by the 

frequency of observations. Both the spatial and the temporal scale are important to 

understanding the relevance of observations and their applicability to models. Unlike the 

continuum of atmospheric motions, measurement scales show little continuity in space. 

Table 1 lists common ground-based measurement systems used in the United States and 

some of their characteristics. The spatial scales they generally represent are also 

indicated. As the table indicates, the only systems that are truly available nationally are 

surface weather observations, the rawinsonde upper air system, aircraft data from the 

Aircraft Communications Addressing and Reporting System (ACARS), and the Doppler 

weather radar (WSR-88D) system. All of these systems are applicable to measurements 

of the meso-alpha and meso-beta scale processes. The specialty systems and tracer 

measurement capabilities are applicable to smaller scales but are available in a relatively 

few locations and for limited times. 

TABLE 1. Spatial Scale and Observation Frequency of Common U.S. Meteorological Observing 

Systems 

Spatial 

Scale 

Observing 

System 

Observation 

Frequency 

Vertical 

Range 

Spatial 

Separation 

Spatial 

Range 

Spatial 

Resolution 

In Situ Measurements 

Meso-α Rawinsonde 12 hourly Surface to 30 

km 

400 km 

Meso-β Weather 

observations 

Hourly 2–10 m 60 km Local Local 

Meso-β Aircraft platform 10 to 1 Hz Surface to 20 

km 

Variable Continental 

scale over time 

Platform 

dependent 

Meso-γ Tethered 

balloon 

variable 10-

30 min 

1 km Irregular Local N/A 

Multiple Tracer 1s to 30 min Local Irregular None Irregular 

Micro-γ Sonic 

anemometers 

10 Hz Tower height Irregular N/A Tower spacing 

Remote Measurements (Excluding Satellite-Based Systems)*

Meso-β WSR-88D 

weather radar 

~100 Hz 100 m to 

> 15 km 

200 km 250 km 1 km 

Micro-α Radio 

frequency 

sounders 

15 min 100 m to 

>5 km 

Irregular Vertical only Irregular 

Micro-β Acoustic 

sounders 

10 to 30 s 20 m to 3 km Irregular Vertical only Irregular 

Micro-β Doppler lidar ~500 Hz ~4 km, 

aerosoldependent 

Irregular 3 to 12 km 3 to 75 m range 

gate 

Micro-β Radio Acoustic 

Sounding 

System 

~1 min 100 m to > 5 

km 

Irregular Vertical only Irregular 

* Satellite-based observing systems are applicable to many of the scales listed but were not included 

among remote observing systems in this table. 
Chapter 3: Model Capabilities Required to Meet User Needs 25

FIGURE 4. Concentration field of a simple flow (top) versus time-averaged distribution 

(bottom). Two images of the same release. In (a) we see a photograph of an instant during a 

point source release of smoke within a wind tunnel (view is taken looking down on the plume), 

where large and small swirls have distorted the plume into serpentine twists and turns. In (b) 

we see a time-average photographic exposure of the smoke release, where the time-average 

of the individual chaotic swirls are seen to have the “traditional” Gaussian plume shape used 

in ATD plume dispersion models. (Photographs are courtesy of U.S. EPA/NOAA Fluid 

Modeling Facility). 

3.1.2 Consequences of Scale in Concentration Data 

ATD models attempt to describe hazard zones by their boundaries and temporal extent. 

The meteorological portion of a model attempts to describe where material would go if 

the source was known, but the spatial and temporal distributions of the concentration are 

highly variable. As shown in figure 4, a single realization of a concentration field in a 

simple flow may bear little resemblance to a time-averaged distribution. At a given 

location, changes in concentration over time will depend on the sampling frequency of 

the sensor and its sensitivity. For high sampling rates, a sensitive sensor can detect a few 

intervals of large values and longer periods of low or no concentration. Depending on 

application, the time-averaged value may be more relevant or entirely inapplicable. 

Short-term peaks in concentration, which are needed to assess acute effects or 

explosivity, are microscale phenomena. Many other characteristics that affect ATD 

predictions, such as concentration eddies in the vicinity of walls and urban canyons, are 

at the microscale. Meteorological models that are used to initialize ATD models are 
26 Federal R&D Needs and Priorities for ATD Modeling

typically mesoscale models. Issues arise because of the scale differences between the 

meteorological model’s process representations and grid spacing compared with the 

microscale representations needed by the ATD model. 

For long-term health and environmental effects, time-averaged concentration is useful. 

Wind transport at local scales, however, has a large stochastic component that makes the 

time-averaged concentration a probability distribution with respect to space and time 

rather than a point value. To improve the information given to the user, the model 

researcher-developer needs to represent these stochastic processes realistically in the 

model and produce a probabilistic prediction that includes measures of the uncertainty in 

the point estimate (the probability distribution). Then effective ways need to be found to 

communicate to the user the implications of concentration as a probabilistic function of 

space and time. 

Relevant to the components of an ATD modeling system, these scale issues affect many 

of the capability requirements and contribute to many of the capability gaps. Discussions 

of scale will recur repeatedly in section 3.2 and chapter 4, as the specific capabilities, 

gaps, and the R&D required to address the gaps are presented. 

3.2 Requirements and Capabilities by System Component 

The functional components of a consequence assessment system, which were introduced 

in chapter 1, are shown again in figure 5. Each component of the ATD modeling system 

(within the bold boxes) has its own requirements to become a functional part of the 

whole. These requirements can be compared with current capabilities in that functional 

component. Where the requirements are not fully met with existing ATD models 

(capability gaps), promising directions for further R&D can be identified on a 

component-by-component basis. 

FIGURE 5. The functional components of a complete consequence assessment system. The 

embedded ATD modeling system is shown by bold lines. 

Meteorological Inputs:

 • Observations

 • Forecasts

Source Term

(where, when,

how much)

ATD Input

processing

Transport and

Diffusion Code

Human Health

Effects and

Environmental

Effects

Concentrations

Downwind

Deposition (vapor,

liquid, and solids)
Chapter 3: Model Capabilities Required to Meet User Needs 27

3.2.1 Source Term 

The source term component of a consequence assessment system includes information 

about the identity and physical state of the hazard, the release mechanism, and the mass 

of hazard released per unit time (emission rate). When ATD modeling is used in 

emergency situations, the characterization of the source term and the local transport and 

diffusion conditions are typically the largest sources of uncertainty. For users, the four 

questions listed in Figure 2 for the release event are source term questions essential to 

consequence assessment: What was released? When? Where? How Much? The mass of 

hazard released per unit time, or emission rate, is the key input derived from the source 

term that the ATD model needs to answer users’ questions about where the hazard is 

going and in what concentration (concentration as a function of space and time). 

To characterize near-field (less than 3 km) dispersion, it is critical to know the dilution 

and buoyancy of the source emissions in the immediate vicinity of the release. 

Uncertainties in the emission rate and initial dilution volume greatly increase the 

uncertainty in the near-field impact estimates. 

As noted in section 1.2, this report does not address R&D needs for source term 

characterization. However, ATD modeling techniques can be coupled with concentration 

measurements made by sensors at some unknown distance from the exact location of the 

source term to back-calculate to a more precise estimate of the source location and 

emission rate. This approach, called sensor fusion, can be defined for the purposes of this 

report as the combination and synthesis of information from networked sensors and 

predictive models to obtain more information about a chemical, biological, or 

radiological event than would be available from any individual sensor or diagnostic 

model alone. The networked sensors can include multiple sensor types, including in situ 

sensors and remote sensors, and other relevant sensors such as meteorological 

instrumentation. Section 4.4.1 explores sensor fusion techniques and their potential for 

reducing uncertainty in ATD model predictions downwind from the source location. 

3.2.2 Meteorological Inputs 

The meteorological inputs to an ATD modeling system may be data from observations, 

the output from a meteorological model, or a combination of observations and model 

output. At a minimum, ATD models require wind speed and direction and a simplified 

turbulence parameter as their meteorological inputs. A more complete specification of the 

meteorological parameters of interest may include input data on clouds, precipitation, 

temperature, pressure, humidity, surface heat and momentum fluxes, and a more complex 

characterization of turbulence. Mass structure and winds can also be measured directly, 

using a variety of in situ and remote-sensing systems and processing techniques. 

In the absence of such detail, ATD models make assumptions to characterize 

meteorological conditions. Providing as much pertinent meteorological information as 

possible will improve ATD model predictions by decreasing the number of assumptions 

that must be made by the model. Before mesoscale meteorological model output was 

available, ATD modelers used surface and upper air meteorological observations from 
28 Federal R&D Needs and Priorities for ATD Modeling

sites near the release location. In cases where the nearest available observations did not 

represent the meteorological conditions at the release site, the modeler would estimate the 

wind and turbulence conditions. 

Mesoscale Models for Meteorological Inputs 

While mesoscale meteorological models are executed at much finer grid resolutions than 

are macroscale (synoptic, global) meteorological models, they typically ingest boundary 

conditions from a macroscale model. With the sustained growth of computational 

resources, mesoscale meteorological models that provide acceptable descriptions of 

mesoscale atmospheric motions and turbulence were developed. These models have now 

been run operationally for over a decade, and the output from these models is used as 

input to the ATD model in cases where direct observations of local atmospheric 

conditions were not available and for the prediction of changes in conditions during 

transport and diffusion. The use of mesoscale meteorological model output has also 

allowed ATD model developers to account for additional atmospheric processes with 

self-consistent input. Although mesoscale meteorological models have proven capable of 

describing mesoscale atmospheric motions and accounting for atmospheric processes at 

the mesoscale, they have not yet been optimized for ATD models. Methods to refine 

these meteorological products before using them as input to the transport and diffusion 

code component are explored in section 3.2.3. 

An advantage to using mesoscale meteorological model output (as opposed to macroscale 

models) to drive ATD calculations is the potential for improved resolution of localized 

wind patterns. Worldwide, many population centers are located near coastal regions with 

highly variable wind patterns. Thermally driven flows associated with land–sea interfaces 

and complex terrain, which are not resolvable by the coarser grid of macroscale models, 

can present significant challenges to the accuracy of ATD model predictions. Mesoscale 

models with horizontal grid lengths of about 12 km or less are capable of capturing some 

of the time evolution of such flows, potentially improving the accuracy of ATD 

computations for regions with these wind patterns. 

For consequence assessment applications, modeling surface-layer fluxes, winds, and 

temperatures, even in a mesoscale meteorological model, is a challenge for many regions 

of interest. Surface fluxes are currently parameterized in numerical weather prediction 

(NWP) models using Monin-Obukhov similarity theory (Stull 1988). The atmospheric 

surface layer is defined as the inner region of the ABL, having approximately constant 

flux with height. It is generally on the order of 10 to 40 meters in depth for neutral to 

unstable conditions but can be considerably thinner in stable conditions. Because the 

atmospheric surface layer can be observed continuously using instrumented towers, there 

is a long history of studies measuring it under a variety of surface and atmospheric 

conditions. These observational studies have supported the development of detailed 

theoretical descriptions; however, as originally detailed, these theories are applicable to 

flat surfaces having uniform roughness, albedo, emissivity, moisture, and thermal 

conductivity. Real conditions, particularly in populated areas, often deviate significantly 

from these idealized conditions. So modeling the surface-layer fluxes, winds, and 
Chapter 3: Model Capabilities Required to Meet User Needs 29

temperatures in real cases is difficult, even if the larger-scale winds (scales from tens to 

hundreds of kilometers) could be predicted exactly. 

As an example of particular interest to many consequence assessment scenarios, surface 

irregularities (roughness elements) due to land use (trees, buildings, etc.) are a major 

challenge for modeling surface-layer properties. Especially in urban areas, large changes 

in surface conditions (parks, high rises, rivers, industrial zones, residential areas, etc.) can 

occur within distances of a kilometer or less. This variability affects the local state of the 

atmospheric surface layer. In major urban centers, tall buildings create “urban canyon” 

effects. The different types of surface irregularities found in urban areas are difficult to 

treat in a mesoscale model with a single practical theory for representing the surface 

layer. In fact, the flaws in current theory for modeling uniform surfaces may be small in 

comparison with uncertainties due to the effect of spatial surface irregularities found in 

major urban areas. 

Another problem associated with high-resolution mesoscale modeling involves how 

information is passed from coarser to finer scales when models are nested (a smallerscale model taking its initialization data and boundary conditions from a larger-scale 

model). For example, if there is an inconsistency in the nested models’ terrain or urban 

information databases, errors will propagate to all levels of a simulation. Some models 

currently in development have two-way feedback, which creates even more sensitivity to 

the initialization data. 

Limitations in Using Model Fields for Meteorological Inputs 

Although driving ATD calculations with mesoscale model predictions can, under 

favorable conditions (i.e., in other than complex environments), improve simulations of 

transport and diffusion due to localized wind flows, this approach is not without pitfalls. 

Slight misrepresentations of the temporal evolution (i.e., the timing) of local wind flows 

can severely degrade the accuracy of the predictions. Predicting the timing of 

meteorological events, whether synoptic (macroscale) or mesoscale in nature, is one of 

the greatest challenges in NWP. In these cases, ATD modelers should include phase 

errors as a contributory source of uncertainty and consider how best to quantify the 

uncertainty in the prediction stemming from this uncertainty in timing of key 

meteorologically driven events. Modelers must also have effective ways to communicate 

the impact of that uncertainty to users; for example, by showing plume development with 

and without the meteorologically driven event. 

Forecast or diagnostic models at horizontal intervals greater than about 300 meters are 

incapable of explicitly representing ABL circulations, which are dominated by buoyancy 

and vertical wind shear. In daytime, buoyancy-driven circulations have lateral and 

vertical scales of the same order as the mixing height, which is typically one to two 

kilometers. These processes (which are turbulent from a larger view) mix the contents of 

the ABL. The nocturnal ABL is typically nonbuoyant, and stability resists vertical 

motion. It is poorly represented in current models because the lateral motion is typically 

weak, moving material without mixing. Intermittent turbulent events occur almost 
30 Federal R&D Needs and Priorities for ATD Modeling

without local causes. This extremely complex and poorly understood environment is not 

modeled with skill. 

The atmospheric surface layer occupies roughly the lowest tenth of the daytime mixed 

layer. Although the atmospheric surface layer is relatively well defined during the day it 

is less defined during the night. It is a zone of interaction, where heterogeneities in 

energy, momentum, and moisture dominate ATD processes. Eddy sizes in the 

atmospheric surface layer are proportional to the eddy’s height above the surface. More 

than half of the energy fluctuations are unresolved. Since the ATD processes cannot be 

resolved, deterministic models do not apply. Predictions of concentration in this layer are 

the most important for consequence assessment because this is where human exposure 

occurs, but they are also the most difficult to make accurately. 

The problem of accuracy applies even to relatively simple terrain. Hall and Basara (2004) 

found that operational mesoscale model predictions of wind speed and directions for the 

Oklahoma City airport had mean absolute errors in wind speed on the order of 2 ms-1 for 

forecast periods of 6 to 36 hours (figure 6). The mean absolute errors of wind direction 

were larger than 20 degrees. Other studies of model performance during different seasons 

and varied terrains found that wind speed errors are typically greater than 2 ms-1 and 

standard deviations in wind direction are greater than 50 degrees (Henmi 2003; Fast 

2004). Although operational mesoscale models may have a small bias over many 

predictions, the predictions for appropriate wind speeds and direction for a given time 

and place can be expected to differ from concurrent observations. 

Clouds affect transport and diffusion of airborne materials in several ways. Diminished 

solar radiation from cloud cover reduces surface heating and convective mixing. 

Nocturnal cloud cover, even at high altitudes, reduces radiative cooling and influences 

the development and structure of the stable boundary layers. Insolation also can affect the 

chemical activity of various agents. Convection in clouds assists the mixing of air above 

and below the boundary layer: a process that contributes to the dilution of concentration 

levels at lower levels of the atmosphere. To reduce errors in the prediction of radiative 

fluxes, cloud information can be assimilated into a model rather than being represented 

by simple parameterizations. For example, remote-sensing methods can provide cloud 

FIGURE 6. Mean absolute error in wind speed and wind direction, measured over a relatively simple 

terrain. Source: Hall and Basara 2004. 

Best Possible Wind Speed (Mean Absolute Error) 

Over Time (From All MOS)

1.4

1.7

2

2.3

2.6

2.9

3.2

6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60

Projection (Hours)

Wind Speed Error (in 

Knots)

Best Possible Wind Direction (Mean Absolute 

Error) Over Time (From All MOS)

15

20

25

30

35

40

6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60

Projection (Hours)

Wind Direction Error (in 

Degrees)
Chapter 3: Model Capabilities Required to Meet User Needs 31

mapping data, including inferred measurement of cloud height, for assimilation into 

mesoscale models. 

Ensemble Forecasting of Meteorological Inputs 

To provide some sense of the probabilistic variability of ATD outcomes, it is becoming 

more common to link ATD models to statistical information constructed from ensembles 

of mesoscale meteorological models. Means, variances, and correlations of 

meteorological parameters to be used in the ATD evaluations can be obtained by 

considering results from the multiple model realizations in an ensemble rather than 

relying on a single set of point-estimate input assumptions. Ensemble statistics can be 

obtained by including realizations in the ensemble either from differing models (a multimodel ensemble) or from multiple realizations of a particular model (a single-model 

ensemble). Multiple distinct realizations from one model can be obtained in various ways 

such as perturbing the initial conditions, varying the parameterization schemes, using 

combinations of these first two methods, or varying the grid resolution. Regardless of the 

ensemble building method, the objective is to characterize quantitatively the range of 

possible outcomes. 

Significant research is still needed in this area. In particular, work is needed to determine 

the optimal number and types of ensemble members to produce a statistically 

significantly improved result. Advanced techniques for creating individual members of 

the ensemble are also of interest. Much development work is needed to link ensemble 

results from mesoscale meteorological prediction systems to corresponding ensemble 

systems of dispersion models and to evaluate the resulting probabilistic predictions. Most 

important, to make ensemble techniques useful to the user, research is needed on how to 

merge the probabilistic information in ensemble mesoscale meteorological solutions with 

ATD modeling systems to yield user-tailored probabilistic decision aids. 

The WRF Modeling System 

With support from multiple Federal agencies, the new Weather Research and Forecasting 

(WRF) mesoscale modeling system has been developed through an interagency 

collaboration of the atmospheric science research and operational communities. The 

National Oceanic and Atmospheric Administration’s National Weather Service (NOAA/ 

NWS) is currently preparing WRF applications for operational implementation. The 

initial WRF system in the High Resolution Window domains will be run as an ensemble 

of six to eight model versions developed with two dynamical cores, multiple choices of 

physical parameterizations, and different anomalies in initial and boundary conditions. 

The number of ensemble members is expected to increase over time with planned 

increases of computational capacity. By the end of 2005, NOAA/NWS plans to 

implement WRF at 10–12 km resolution over all of North America. This North American 

WRF is expected to be replaced by an ensemble system as soon as computational 

resources allow. The WRF system is designed for applications with grids as fine as 1 km 

or smaller. Current computer capabilities allow WRF ensembles to be run at high 
32 Federal R&D Needs and Priorities for ATD Modeling

resolution on regional or subregional domains that are smaller than the national weather 

forecasting requirements. 

3.2.3 ATD Input Processing 

Processing the input for an ATD model, for purposes of this report, includes refining 

meteorological inputs, whether from observational or model sources, to prepare them for 

use in the transport and diffusion code. Input processing techniques attempt to solve (or 

at least mitigate) several kinds of problems. 

One set of problems addressed in input processing comes under the heading of data 

representation. Does a data value, whether an observation from an instrument or a model 

output value, truly represent the conditions that the model assumes it represents? For 

ATD modelers, data representation questions such as Are these data representative?

typically mean how well do the data meet the assumptions that this model makes 

regarding the data? 

The second type of input processing problem is data quality—how well does a 

measurement system capture the conditions it is intended to measure? The raw data in the 

direct signal output from a sensor can be in many forms. Most common signals are in the 

form of an electrical impulse, voltage, current, or resistance or a change in one of these 

properties. Quantifying the physical principle of the signals to a concentration, wind 

velocity, or pressure is the first step in ensuring data quality and is part of the sensor 

design. Data quality also depends on the sensitivity of the physical property it is intended 

to measure, changes in that property, and confounding environmental conditions. 

Calibration of the mean and variance of the measurement instrument to known references 

sets the precision of the measurement. 

When each sensor is well calibrated and working properly, the measurement system as a 

whole may or may not be providing a realistic “observation” of the patch of reality it is 

intended to observe. At the level of accepting a set of data values from a measurement 

system, data quality acceptance/quality control (QA/QC) processes may identify outliers 

as potential instrument errors, interpolate for lost or missing data, or compensate for 

timing errors or irregularities. Furthermore, the exposure of the instrument and the 

heterogeneity of the instrument location must be factored into the assessment of 

acceptable data. 

An ATD model generally assumes a correlation or coherence among the input data. In 

some instances, data incoherence arises from a data quality or data representation 

problem; in other instances, it results from incompatability of different input sources 

(observations and forecast models). When those data do not support the coherence 

assumption, the ATD model must provide rules for acceptance or rejection. 

Development of guidelines for observation networks is one of the R&D needs that 

emerges from issues of data quality and data representation. In some ATD modeling 

systems, evaluating input data for either data representation or data quality is 

incorporated in the input processing operations. In other modeling systems, these 
Chapter 3: Model Capabilities Required to Meet User Needs 33

characteristics must be examined independently. Data quality and data representation 

concerns are further magnified when the data are used to calculate derived quantities such 

as fluxes, scaling parameters, mixing height, wind shear, or thermal stability. The user of 

the input data needs to know the temporal or spatial averaging that has been used to 

produce the derived quantity. 

 

Data assimilation is another concern in processing data for input to an ATD model. It 

overlaps with data representation and data quality but can also derive from other 

complications in the data–model interface. Weather forecast models are re-initialized at 

regular intervals using previous forecast fields and recent observations. In some 

instances, large differences between the forecast and the observation may occur. The 

initialization procedure is designed to weigh the forecast field and the observation within 

the context of expected variance in the values and the governing equations of motion. 

The new initialization may not include, or assimilate, the observation because doing so 

would violate other model constraints. This rejection of the observations by the model’s 

rules for assimilating data may result from data representation problems (data that are not 

representative of scales that the model can represent), data quality problems, model errors 

(representations or parameterizations that deviate from the real processes being modeled), 

or a combination of these factors. The data quality and representation problems can be 

either in the data the model is now trying to assimilate or in data previously used for 

initialization or assimilation. 

As this limited discussion illustrates, ATD input processing quickly becomes complex, 

consuming both time and resources (computational and human capital). While automated 

input processing is appealing, one approach does not fit all models or even all 

circumstances for the same model. As new instrumentation is developed, a major concern 

should be for internal consistency checks and usability (suitability) of the measurements 

in data input processing. 

3.2.4 The Transport and Diffusion Code 

The transport and diffusion code describes in algorithms the combined effects of timeaveraged transport (which has traditionally been viewed as a deterministic process) and 

of atmospheric diffusion (which has traditionally been represented as a stochastic 

process). The entire set of computations is sometimes called the “ATD model,” but that 

term is also sometimes used to mean the way in which transport and diffusion processes 

are represented when the set of instructions (the code) is run with an appropriate set of 

initialization data. 

Federal agencies, the academic community, and others employ a large number of ATD 

modeling systems for a variety of purposes, including regulation, research and 

development, and emergency operations.1

 However, the JAG/SEATD report identified 

only a few basic types of operational ATD models, or transport and diffusion code 

approaches, in the modeling systems assessed by the JAG/SEATED (OFCM 2002, p. 1-

 

1

 The JAG/SEATD reported that the FEMA Insurance and Mitigation Administration identified more than 

140 ATD modeling systems in an internal report (OFCM 2002, p. 1-2). 
34 Federal R&D Needs and Priorities for ATD Modeling

4). These basic model types—box, plume, segmented plume-puff, Lagrangian particle, 

Eulerian grid, and computational fluid dynamics (CFD)—are defined and discussed in 

Appendix C. There are also only a few types of diffusion characterizations in use; the 

most common are empirical, statistical, similarity, eddy diffusivity, and second-order 

closure. The profusion of ATD modeling systems arises from the variations and 

combinations of these approaches combined with specializations made to handle unique 

problems, such as plume impaction on elevated terrain, concentration within the wakes of 

buildings, or heavy-gas effects. 

Because of the stochastic component, all ATD modeling with a transport and diffusion 

code must be considered a forecast of possible outcomes. In addition, the sets of 

deterministic and probabilistic equations implemented in any given model provide only 

an approximation to the complex atmospheric conditions the model is meant to represent. 

Consequently, ATD modeling is always a compromise between getting a useful solution 

in an appropriate amount of time and realistically portraying the transport and diffusion 

of a released material within the atmosphere. These uncertainties introduced by the 

inherent probabilistic nature of the processes and by the compromises to make the model 

useful are in addition to the uncertainties in the input data. 

Several techniques are used to apply ATD models to complex environments such as cities 

or coastal areas. The top-down approach uses multiple nests of finer-scale models within 

coarser grids to approximate the mean transport and turbulent flow at short temporal and 

spatial scales. This approach is useful when appropriate observation systems at the 

smaller scale are lacking. The bottom-up approach uses physical models—based on wind 

tunnels or flow channel experiments for example—or high-resolution computational 

models (discussed in section 4.2) to capture the larger-scale effects of the complex region 

being modeled and the fine-scale features of flows within the region. Physical models are 

used principally to build a knowledge base about a specific location and to provide 

appropriate data for improving the understanding of processes that are not measurable in 

the natural environment. Their advantage is that experimental conditions can be 

controlled; their disadvantage is that each experiment is only one possible realization of 

the stochastic variability. A middle ground, computational fluid dynamics (CFD) models, 

uses CFD codes adapted from the aerospace industry for examining turbulent 

atmospheric flows around single or multiple obstacles. In a sense, CFD models are 

numerical surrogates for wind tunnels or flow channels. 

An emerging option for model refinement is to use remotely sensed data of actual 

conditions. Remote sensing can inform and update a model of the physical landscape on a 

recurring basis, allowing natural and manmade changes to be incorporated. 

Finally, one of the principal user needs identified in Chapter 2 is seldom met at present. 

Most of the current operational ATD modeling systems for consequence assessment in 

civil emergency response applications are unable to provide information on the 

variability of hazard concentration on the shorter time scales needed to assess such 

consequences as acute effects of exposure or explosivity. A few modeling systems 

attempt to estimate the probability that such events could occur but are not specific as to 

when or where. Even CFD estimations cannot predict the exact stochastic pattern of 
Chapter 3: Model Capabilities Required to Meet User Needs 35

dispersion. As a result, a forecast from even a very sophisticated ATD model has a large 

single-event uncertainty. At present, even ensemble-based ATD modeling systems 

predict only the ensemble-average dispersion pattern (the average over the multiple 

realizations in the ensemble) and the range of predicted ensemble variables, not the 

complete event-to-event variability. Because this variability can represent substantial 

uncertainties with respect to human health and safety risks, the ATD R&D community 

must do better at quantifying the uncertainty and communicating its implications 

effectively to those making emergency response decisions (or other decisions based on 

assessing consequences sensitive to this uncertainty). 

3.2.5 Deposition (and Other Removal Mechanisms) 

Substances released into the atmosphere will stay there, continually dispersed and diluted 

by mixing processes, until they are removed by reactions with other components of the 

atmosphere or are deposited on the Earth’s surface. Consideration of in-air reactions is 

essential in modeling the ATD of gaseous and biological agents. Nerve agents, for 

example, interact with atmospheric oxidants and with other constituents of the 

background air, gradually reducing the total amount of the hazard remaining in the air. 

Biological agents tend to be susceptible to ultraviolet radiation; hence, their active 

residence time in the air is largely controlled by their exposure to sunlight. Atmospheric 

reactions during transport can also be important for reactive liquids and solids. 

Deposition Mechanisms 

Precipitation is one of the most efficient mechanisms for removing pollutants and other 

substances from the air. The two precipitation-related processes of importance are rainout 

and washout. 

Clouds serve as dynamic systems for processing air that passes through them, 

concentrating most pollutants in cloud droplets, which then coalesce and eventually fall 

to the surface (ground or water) as precipitation. This process of in-cloud scavenging is 

commonly referred to as “rainout.” For rainout to be an efficient removal mechanism, the 

hazardous material or pollutant must become directly entrained into a cloud. The 

scavenging efficiency depends on the chemical and physical properties of the pollutant in 

question, as well as on the dynamic characteristics of the cloud. Not all of the materials 

entrained in cloud circulations will be removed and deposited in precipitation on their 

first pass through a cloud. Sulfate particles, for example, are likely to pass through 

several clouds before being scavenged. For the gaseous and biological warfare agents of 

current concern, rainout appears likely to be important but has not been extensively 

studied. 

Hazardous materials that are dispersing in the air near the surface will be scavenged by 

raindrops or other hydrometeors such as snowflakes, in addition to any rainout 

scavenging by clouds from which the precipitation derives. This process, called 

“washout,” is relatively inefficient for liquid or solid hazards unless the particles being 

scavenged are close to the size of the droplets scavenging them. Gaseous hazards, if they 
36 Federal R&D Needs and Priorities for ATD Modeling

are soluble in water, can also be removed by falling hydrometeors, sometimes quite 

efficiently. 

Dry deposition to the surface continues at all times, regardless of whether precipitation is 

occurring. Dry deposition is a far less efficient process than wet deposition but often 

removes similar amounts of material solely because the process is continuous, albeit 

slower. 

The amount of deposition as a function of space and time is complex and difficult to 

predict in detail. For example, the factors of timing, amount, and location of precipitation 

are very important for wet deposition of dispersing materials. Prediction of clouds and 

precipitation mechanisms are a major focus of high-resolution mesoscale models. 

Although the best current models still have problems predicting the location and intensity 

of precipitation at scales of interest to potential users of the predictions, they do better 

when the driving forces are strong. Quantitative data on cloud-mixing processes and 

deposition are needed but are difficult to obtain. 

Descriptions of deposition processes, particularly quantitative descriptions, need to be 

refined and tested. In future field studies and experiments on ATD, a component to 

measure deposition rates should be included wherever possible. This necessary work can 

build on the long history of relevant studies. 

Resuspension 

The arrow in Figure 5 from the Deposition box back to Transport and Diffusion Code 

represents the resuspension of hazardous material particles. For ATD modeling purposes, 

resuspended particles can be treated in the ATD model as a new release or emission to 

the atmosphere. In some instances, deposited materials will remain at the surface, with 

potential for subsequent resuspension into the air. Resuspension can be a major 

consideration for consequence assessment; radioactive particles in surface dust are a good 

example. In practice, resuspension of deposited materials will occur only when 

mechanical or volatility forces on the deposited material are sufficiently energetic. Such 

forces may be associated with vehicular traffic, foot traffic, or simply the wind. 

Health and Environmental Consequences of Deposition 

Atmospheric deposition provides the linkage between air concentrations of hazardous 

materials and surface environmental consequences. Although deposition constitutes a 

major sink for removing airborne hazardous materials, it is also a major source for 

studying and assessing environmental effects of hazards. Hazardous substances deposited 

from the air to the underlying surface are likely to enter into the biosphere. For example, 

if a nuclear or radiological material were deposited on the ground and inserted in an 

environmental pathway that led to human food sources, there would be human health 

consequences from this route of exposure. 
Chapter 3: Model Capabilities Required to Meet User Needs 37

3.2.6 Concentrations Downwind 

Prediction of the concentration of a released hazardous substance as a function of space 

and time is the reason why consequence assessment systems incorporate an ATD 

modeling system. Once all the appropriate information about “concentrations 

downwind” has been delivered, the ATD modeling job is done; other components or 

players take that information as input for assessing the human health and safety 

consequences and environmental effects. A major theme of this report, however, is that 

deciding what information about concentrations is appropriate is not the sole province of 

either the ATD modeling community or the user community. Much work remains to be 

done by both communities to meet the user needs set forth in chapter 2. 

3.3 Transitioning New Research and Development Capability to 

Operations 

The term “operations” refers to the application of ATD prediction capability by a user to 

support that user’s decision-making process. As discussed in chapter 2, consequence 

assessment tools are designed to support a range of operational planning, response, and 

recovery efforts. The ATD modeling system is likely to be only one component within a 

larger system for the overall consequence assessment. Transitioning ATD codes or 

systems from development to operations requires an understanding of the operational 

requirements, as well as how the ATD prediction capability will be used and how it will 

be integrated into the larger concept of operations. 

Experience has proven that hazard assessment and decision information must get to the 

right people at the right time. The “right time” means that information must flow to the 

decision maker before it is too late for the mitigating action to be relevant. The “best” 

hazard analysis, if too late, is useless for response decisions, although it may still be 

relevant to forensic analysis during recovery activities. In addition to timeliness, the 

information must be operationally relevant. 

For new ATD prediction capability to be successfully transitioned from R&D to 

operational use, the following areas must be addressed: usability; training; data 

connectivity; results communication; operational testing and evaluation, including 

production readiness; and documentation. Each area is discussed separately below, but 

there are major interrelationships among them that are critical to successful R&D. The 

successful program manager applies sound risk-management processes to invest in and 

coordinate activities in these areas. Keeping in mind that risks range from low probability 

of occurrence to high probability of occurrence and from small consequence to huge 

consequence, it is clear that the risk management plan must describe the risks to the 

program and prioritize them by degree of importance to the success of the program. The 

risk management plan should address all of the applicable risks, including acceptability; 

schedule; and technical, cost, and program risks. 

The task of development is not complete until the new capability has been proven useful 

in operations. The work of the researcher must be guided by what users need and by what 
38 Federal R&D Needs and Priorities for ATD Modeling

current capabilities cannot give them. To transition a new capability into operations in the 

time desired to meet national goals of preparedness, upfront and continuing interactions 

between users and researchers-developers must replace the leisurely, phased approach to 

research, followed by development, followed (perhaps) by operational deployment. No 

longer can the researcher or the developer walk away from the issues of transition as 

being someone else’s problem. 

3.3.1 Usability 

Usability refers to the relationship between tools and their users. An effective tool allows 

the intended user to accomplish a given task in the best way possible. For ATD model 

codes that are either new or modified as a result of new research, the intended users 

should be clearly stated. As the level of user expertise with predictive modeling codes 

moves away from trained meteorologists and dispersion modelers, the need increases for 

more complex intelligence to be built into the modeling system to guide the user. For 

example, both novice users and advanced but infrequent users will probably need simple 

graphical user interfaces with standard defaults. More-expert users will want to use 

shortcuts and have more control over input parameters. Emergency response use will 

generally require a model that adapts to quickly changing conditions, provides clear 

guidance on input, and allows for unambiguous output. Regardless of user expertise, online help and error and range checking embedded in the modeling system software should 

be part of any operational system that will be used under stressful conditions. 

In using dispersion models for planning or post-event analysis, the user friendliness of the 

modeling system is generally less critical. In nonemergencies, the more flexible time 

scale for providing an answer typically allows the user to analyze input and output more 

closely, get additional expertise or data, and explore a broader range of scenarios. 

Without a clear understanding of the intended model use, the model user, and how 

information must flow to get relevant information to the right people at the right time, 

research-derived model enhancements will fail the usability requirement for transitioning 

to operations. Proper usability testing and implementation is critical for ATD models 

designed to define hazard areas where lives may be in danger. Usability testing should 

address a number of factors including fitness (how well the functionality fits the user 

need), ability to perform the intended task correctly, and how well the application fits the 

user expectations. Achieving this level of usability requires iterative interaction between 

users and developers, beginning well before a modeling product is ready for operational 

testing. 

Prototyping can be an effective means to manage this and other technical risks. The 

user’s inputs should be incorporated in the design of ATD modeling system 

improvements, and user feedback should be incorporated in subsequent prototype 

development cycles. In considering tradeoffs between capability and cost, a sensitivity 

analysis of new approaches or parameters should be part of the prototyping effort. The 

preferred software engineering methodology incorporates risk management techniques 
Chapter 3: Model Capabilities Required to Meet User Needs 39

and engages users and other stakeholders throughout the software system life cycle. An 

example of such a methodology is the spiral model.2

3.3.2 Training 

The model end user is rarely the model developer. Training of both the person who runs 

the model and the person responsible for making a decision using the model’s output is 

critical for appropriate model use. Unfortunately, the decision makers often do not have 

the time and resources to be trained to use every tool intended to help them make a 

decision. Therefore, it is even more critical that the person running the model understand 

how to convey the implications for the decision maker of a forecast from the model (or 

from a set of models, depending on the user). As model forecasts become more 

sophisticated—for example, by incorporating reasonable and useful measures of 

uncertainty—the forecast itself must be presented in ways that are immediately 

meaningful to the decision maker. Model developers can no longer rely on the expertise 

of the person running the model to interpret this complex, sophisticated information and 

convey it concisely yet correctly to the decision maker. This means the developer (and 

behind the developer, the researcher) need to be “trained” on the user’s decision-making 

environment just as much as those who run the model or make decisions using model 

output need training on the tool. In effect, the model must talk the decision maker’s 

language. Therefore, those who create the modeling capability must also understand and 

“speak” that language. 

Analogous to forecasting the weather with a meteorological model, any given ATD 

model has strengths and weaknesses, depending on the scenario and the environmental 

conditions known at that time. The forecaster needs to understand the model and the 

scenario details well enough to know how to adjust the forecast product. Unlike 

meteorological models that are run daily, thereby generating forecasts that can be 

evaluated every day, those who run ATD models are often intermittent users. They 

seldom have adequate data to evaluate the model or enough experience to make 

reasonable adjustments to the model output. Infrequent model use creates a unique set of 

problems, some of which can be addressed by usability in the model development. Others 

can be addressed through appropriate training. Training must address the entire range of 

users for whom the modeling system is intended to be an appropriate tool. 

Although there are a variety of users, most operational objectives share a common 

requirement—generating consequence assessment information. At present there is no 

overall certification process for training personnel in ATD modeling. The most-effective 

training will cover more ground than just using a given model. It may, for example, 

include learning about the operational environment, exploring the basics of how air 

moves particles, understanding forward deployable technical solutions and expert reachback services, and learning strategies for managing the risks of CBRN hazards. 

Workshops, formal courses, computer-based or on-line training, and tutorials are all 

mechanisms for providing training that should be considered when new ATD prediction 

 

2

 The spiral model was initially described by Barry Boehm in 1988. It is a “risk-driven determination of 

process and product; growing a system via risk-driven experimentation and elaboration; and lowering 

development cost by early elimination of nonviable alternatives and rework avoidance” (Boehm 2000). 
40 Federal R&D Needs and Priorities for ATD Modeling

capability is being transitioned into operations. In chapter 5, the advantages of test beds 

for ongoing and interactive training of both the users and the researcher-developers will 

be emphasized as the “hands on,” experiential learning to complement these conventional 

approaches to user training. 

3.3.3 Connectivity to Data Sources 

Figure 5 identifies the major components of an ATD modeling system as meteorological 

inputs and input processing, transport and diffusion code, and deposition (fate of the 

dispersed material), with concentrations downwind as the output. The larger consequence 

assessment system includes source term characterization and effects on human health and 

safety and the environment from the dispersed material. All of these components may 

contribute data to the ATD model. The ability to connect to different data sources for 

inputs requires an information infrastructure to answer such questions as: 

• Are the data available? 

• Through what mechanisms are they available? 

• What are the temporal and spatial scales for data retrieval? 

• Are there standard formats? 

Data connectivity also assumes an understanding of how the model will use the data 

input. An ideal operational system will have a seamless mechanism for both inquiry 

about access to potential data sources and utilization of the data received by the model. 

3.3.4 Results Communication 

Requirements for an operational ATD modeling system to communicate a forecast of 

hazard zones will depend on whether the forecast is for planning, response, or recovery 

(including post-event assessment). The situations with higher stress for users and less 

flexibility in timeliness of decisions require more emphasis on standardized, easy-tointerpret output. In emergency response, for example, standardized products for similar 

categories of threat (radiological, biological, chemical) will aid in the time-critical use of 

predictions. Planning and post-event assessment provide more opportunity for discussion 

and alternatives for presenting model output. Whether output is deterministic (a single 

best guess), probabilistic (probability distribution), or ensemble (combinations of 

different model outputs), communicating what the particular output conveys and its 

associated confidence or uncertainty should be considered integral and essential features 

of an operational system. In addition, an operational capability should provide interfaces 

for both the most widely available and the latest technologies for communicating output. 

3.3.5 Evaluations of Modeling System Performance 

This document uses “modeling system performance evaluation” to refer to a collection of 

engineering and scientific processes that enable modeling system developers to establish 

the degree of correctness of the software, how well the physical models and databases 
Chapter 3: Model Capabilities Required to Meet User Needs 41

represent reality, and the fitness for use of an ATD modeling system. There are 

established guidelines and consensus approaches for evaluating ATD modeling system 

performance that must be incorporated in the overall processes of system development, 

evaluation, and transition to practice, especially when the ATD modeling system is 

integrated into a consequence system. 

The manner in which a modeling system performance evaluation is conducted should 

depend on a number of factors, including the intended application, whether the modeling 

system will interface with a mission-critical system, and the amount and type of 

evaluation processes that were previously applied to the parts of the ATD modeling 

system. The processes in a modeling system performance evaluation include: 

• Science peer reviews. During science peer reviews, the model’s key constructs 

must be shown to be reasonable and defensible for the defined uses. A key part of 

the scientific peer review will include the comparison of modeled and observed 

evaluation objectives over a range of model inputs (e.g., maximum concentrations 

as a function of estimated plume rise, stability, or distance downwind). 

• Diagnostic and performance evaluations. Diagnostic and performance 

evaluations are two types of statistical evaluations that are typically performed to 

assess different qualities of how well a model is performing. Both are needed to 

establish credibility within the client and scientific community. Diagnostic 

evaluations examine model capability to simulate individual processes that affect 

the results (e.g., droplet fall velocity using small-scale data sets, such as those 

from special field experiments, wind tunnels, or other laboratory equipment). 

Performance evaluations, particularly those conducted in circumstances of the 

intended application; enable one to decide how well the model simulates the 

average temporal and spatial patterns seen in the observations. Work is underway 

to develop a new generation of evaluation metrics that takes into account the 

statistical differences (in error distributions) between model predictions and 

observations. 

• Supportive analyses (e.g., software verification, sensitivity, and uncertainty 

analyses). Software verification is the process of determining that a model 

implementation accurately represents the developer's conceptual description and 

specifications. These supportive analyses should be applied to ensure that the 

following four key tasks are completed: 

1. Modeling assumptions, limitations, and errors are adequately documented. 

2. The software development effort is well managed and controlled. 

3. Results produced by the modeling system are stable and predictable. 

4. The results of diagnostic and performance evaluations are well understood. 

In summary, numerical comparison of model predictions with observed field data 

provides only a partial means for assessing model performance. Due to the limited supply 

of evaluation data sets, there are severe practical limits in assessing model performance. 

In this context, conclusions reached during the scientific peer reviews and the supportive 

analyses have increased significance in deciding whether a model can be applied in the 
42 Federal R&D Needs and Priorities for ATD Modeling

circumstances defined by the model evaluation objectives. Therefore, setting up an 

evaluation program might include publishing peer-reviewed papers, hosting technical 

review boards, and having independent third-party reviewers. 

3.3.6 Software Testing and Evaluation Including Production Readiness 

When the ATD modeling system is integrated as a component system of the larger 

consequence assessment system, software testing must be conducted at all phases of the 

modeling system’s life cycle, starting with unit-level testing and continuing through 

systems-integration testing. These activities provide confidence that the modeling 

system’s performance requirements have been met and determine the degree to which the 

modeling system represents the real world in the context of the intended use of the model.

The JAG/SEATD report reviewed the procedures currently in use by Federal agencies for 

testing and evaluating ATD modeling systems (OFCM 2002), and those procedures need 

not be reviewed again here. 

Established test and evaluation procedures, including the model performance evaluation 

processes discussed in section 3.3.5 and their documentation, are essential parts of the 

process of transitioning from an R&D result to an operational tool. Implementation of 

new research results into new and existing ATD modeling systems should ensure that the 

following conditions are met: 

1. New products of research should make a measurable improvement in and 

increase the value of the model results to the end user. 

2. Software verification and validation procedures should be employed to ensure 

that new algorithms and techniques perform as intended. If the modeler and the 

researcher are not the same, then the model developer needs a mechanism to 

confirm that the new enhancement is being correctly implemented. 

3. Usability testing has been completed, and the modeling system meets the needs 

of all its intended users. Operational test and evaluation should focus on the 

operational effectiveness of the system and its suitability for operational use. 

4. Production readiness has been achieved by demonstrating reliable, sustained 

production. Production readiness also includes providing results within the 

required time constraints and providing backup against single points of failure 

in production, communication, and connectivity. 

5. Comparisons with field data have produced no surprising discrepancies. To the 

extent model results are available, they should be compared with field data or 

historical data sets. While there will often be differences, the evaluation should 

be able to explain why the differences are acceptable. 

6. Model-to-model comparisons are consistent for different modeling systems used 

in operations. If implemented correctly, new research will lead to model 

advances from the private, military, and public application sectors of the R&D 

community. Testing of multiple model implementations can provide valuable 

insight on potential problems. 
Chapter 3: Model Capabilities Required to Meet User Needs 43

3.3.7 Documentation 

Public ATD models should have a range of documentation available: 

• User documentation with point-and-click details for the intended user; 

• Technical documentation so that other researchers or model developers can 

independently evaluate and test specific algorithms; and 

• Quality assurance and testing documentation. 

The code or modeling system should not be considered operational without these 

documentation components. 
44 Federal R&D Needs and Priorities for ATD Modeling
 45 

4.0 MODELING AND MEASUREMENT RESEARCH NEEDS

Chapter 3 examined ATD modeling system requirements (derived from user needs) and 

capabilities at the level of major functional components, such as the meteorological data 

inputs or the transport and diffusion code. This chapter identifies ATD modeling system 

capability gaps and R&D options to fill them by exploring modeling and measurement 

science and technology in greater technical depth. The objective is to identify the most 

promising scientific and technical opportunities to meet the ATD modeling needs. 

ATD models require knowledge of the local wind and turbulence fields at the scales of 

interest of the population at risk. Because these scales vary by incident and potential 

consequences, the domains of interest are case dependent and not usually known a priori. 

Thus, the modeler’s toolbox of capabilities and the skills to use them must cover a wide 

range. Nearby measurements are often not available, and conditions change rapidly, 

especially near the ground where people live and work and are most likely to encounter 

airborne hazardous materials. 

Advances in current ATD modeling are likely to come from improvements in 

meteorological model predictions and from measurements at the scales of interest. The 

former are closely related to better representations of atmospheric boundary layer (ABL) 

processes by improved parameterizations, initial conditions, boundary conditions, and 

representations in complex environments. As existing modeling and observing 

capabilities are improved, incorporating the realization that ATD processes are partly 

stochastic rather than entirely deterministic will enable better quantification of the 

uncertainties in the modeling process. The modeler must then learn how to communicate 

this uncertainty information to end users in ways that are relevant to the users’ decisions. 

Models and data must come together and complement one another. Techniques to 

localize and/or quantify source characteristics by fusing information from concentration 

sensors, ATD models, and other measurements are lacking or untested. To meet user 

requirements for timely modeling predictions, faster methods are needed to determine the 

quality of observed data, merge the acceptable data into modeling frameworks, and 

estimate concentrations rapidly across several scales of motion. Finally, to ensure the 

quality of the model estimates and provide the benchmark for improvements, the skill of 

the prediction and its robustness need to be assessed on a continuing basis. 

Section 4.1 explains the methodology applied by the JAG to prioritize the R&D 

requirements and opportunities presented here and in chapter 5. Section 4.2 focuses on 

modeling methods to address model deficiencies and unmet needs noted in Chapter 3. 

Section 4.3 does the same for measurement technology, including advanced sensor 

systems and methods for improving meteorological data inputs. Section 4.4 focuses on 

capability challenges related to the interface between data inputs and the ATD code. 

Understanding (and meeting) these interface challenges requires viewing them from both 

the data side and the modeling side (parameterization techniques, algorithm development, 

etc.). 
46 Federal R&D Needs and Priorities for ATD Modeling

4.1 R&D Prioritization Methodology 

The JAG considered the following factors and associated questions when prioritizing 

R&D needs and opportunities. 

• Time sensitivity. Is there a window of opportunity for achieving results? Does 

other R&D depend on this work (is it a prerequisite)? Is the user need that would 

be met a national priority of immediate concern, or is it a longer term improvement (longer term need)? The three values used for time sensitivity are 

immediate, near term, or longer term. 

• Short-term gains. Can the R&D results be ready for transition to operations 

within 2 years of initiating the R&D effort? For the research needs discussed by 

the JAG, short term gains were rated as either minimal, average, or high. 

• Overall level of effort (LOE). What are the total resources that the JAG 

members anticipate will be required relative to other R&D needs in this plan? 

Specific dollar amounts or ranges (i.e., quantitative cost estimates) were not 

considered. Instead, this factor includes the JAG’s qualitative estimate of the 

relative scale of labor, infrastructure, and procurement costs. Within the research 

needs discussed by the JAG, the LOE was designated as either low, moderate, or

high. 

• Lead time. Is this a long-lead effort; i.e., an effort that must be planned and 

initiated a relatively long time before an initial operational capability can be 

realized, or could a coordinated effort started quickly reap benefits soon? Lead 

times were rated as either short (within 2 years), average (more than 2 years but 

less than 7), or long (up to 10 years). 

• Ultimate potential for gain. What is the ultimate potential for gain relative to 

other research needs? Because all of the R&D needs selected by the JAG for this 

plan were considered above average in their ultimate benefits, the three ratings 

used were above average, high, or exceptional. 

Table 2 illustrates how a hypothetical R&D need might be rated using the prioritization 

factors. 

TABLE 2. Example of Prioritization Factor Ratings for a Hypothetical R&D Need 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

near term minimal moderate long exceptional 

• This need is not an immediate national concern, but it should be addressed soon. 

The time sensitivity rating is therefore near term. 

• This need is not expected to provide short-term gains because it has a long-lead 

time (between 7 and 10 years) before initial operating capability is likely to result. 

The JAG members therefore rated it minimal for short-term gains and long for 

lead time. 
Chapter 4: Modeling and Measurement Research Needs 47

• Directed and applied research will be required to tackle this research problem, but 

it is not comparable to the largest efforts considered by the JAG. Therefore, its 

overall level of effort was rated as moderate. 

• The ultimate potential for gain is great—among the highest of the efforts 

considered by the JAG. Therefore, its rating for ultimate potential gain is 

exceptional. 

Given its exceptional potential for ultimate gain and the long lead required, the R&D to 

address this hypothetical need should be started as soon as possible. A carefully 

coordinated R&D plan should be developed to control cost, ensure that the potential gain 

is realized, and provide for ongoing evaluation of the requirement and the R&D direction 

over time. 

As another example of the prioritization scheme, consider the kind of R&D effort that is 

sometimes referred to as “low-hanging fruit” because the benefits can be acquired 

relatively easily and quickly. An R&D effort of this kind might be rated as high for shortterm gain, low for overall level of effort, and short for the lead time required. The “lowhanging fruit” metaphor is typically applied to something of value but not essential to 

have immediately or of the greatest ultimate importance. Its time sensitivity would 

therefore probably be near term or longer term, and its ultimate potential for gain might 

be above average or high. 

4.2 Improving ATD Meteorological and Concentration Models 

4.2.1 The Meteorological Model Components of ATD Modeling Systems 

Predicting the concentration of airborne material at a given location and time after a 

release from a given source—the purpose of ATD modeling—cannot be isolated from 

predicting wind and turbulence, which is what a meteorological model does. The 

equations for conservation of mass (prediction of concentration given the source) of the 

airborne material are the same as for other scalar atmospheric variables, such as specific 

humidity and potential temperature. The sources and sinks (decay, chemical transformation, deposition) may vary by the material, but the movement of the material is 

controlled by the local wind and turbulence fields. 

Because the process of estimating the wind and turbulence in the areas of interest to ATD 

is largely independent of the source term release event, the ATD modeling process is 

usually divided into a meteorological model and a concentration model. The former 

represents the wind and turbulence; the latter represents the relationship between source 

and concentration at a location given the meteorological conditions. When identifying the 

R&D needed to address capability gaps in ATD modeling, the capabilities of the 

meteorological modeling component of the system must be included. 

For any given realization, an environmental prognostic model can depict only about two 

decades of distance scales. Therefore, as finer resolution is sought, the domain covered 

by a single realization must shrink. In four-dimensional atmospheric models, increasing 
48 Federal R&D Needs and Priorities for ATD Modeling

the resolution by halving the grid sizes leads to at least an order of magnitude increase in 

computational burden. Distributed processing has helped reduce this burden but cannot 

eliminate it. Increased computational capabilities have enabled nested prognostic models 

to be run at lateral grid spacings of hundreds of meters. Model nesting, however, raises 

issues of its own, which are discussed below. 

In the cascade of atmospheric energy toward smaller-scale motions, more information is 

required about issues at smaller scales. Instead of worrying about a few large processes, 

the modeler must contend with a multitude of small ones. Misrepresentation of actual 

processes with approximate expressions induces error and inhibits the quality of the 

model results. In Figure 7, moving from right to left represents the traditional top-down 

approach to modeling. The “bottom-up” processes of examining the flow from smaller 

scales, through physical modeling or simplified high-resolution numerical models 

(discussed in Appendix C) are represented in Figure 7 as progressing from left to right. In 

the range of scales from tens of meters to a few kilometers, the models do not adequately 

replicate atmospheric motions. 

FIGURE 7. Transport and diffusion scales and model grid sizes. GCM = global climate models. 

Courtesy of T. T. Yamada.

1 mm 1 cm 1 m 10 m 100 m 1 km 10 km 100 km 1000 km

Horizontal grid spacing

CFD Mesoscale

GCM

DNS LES

Building Urban Storm Fronts Synoptic

Physical Modeling

GAP

(open area)
Chapter 4: Modeling and Measurement Research Needs 49

4.2.2 R&D Needed to Improve ATD Modeling Components 

This section identifies R&D requirements based on the preceding discussions of ATD 

modeling concepts and current capabilities. The rationale for each requirement follows 

the statement of the requirement. The rationale is followed by a prioritization assessment 

of the R&D to meet the requirement, using the prioritization factors and methodology 

introduced in section 4.1. 

R&D Need: Bridge the gap from mesoscale to microscale/urban scale. 

Deterministic modeling at grid scales that would allow representation of transport and 

diffusion phenomena characteristic of urban regions or subregions (100 m to 1 km) does 

not seem feasible in the near term. The accumulation of error limits the applicability of a 

top-down approach beginning with a mesoscale meteorological model nested in a 

synoptic model. The complexity of the near-surface environment requires finer and finer 

detail about surface features and their temporal changes. Although part of the problem 

may be addressed by more accurate approximations of sub-grid processes, it is difficult to 

develop estimation techniques that are sensitive to every nuance of a complex, poorly 

quantified feedback system like the urban atmosphere. 

Top-down modeling is limited in its ability to represent ABL processes. An ABL process, 

such as three-dimensional, heterogeneous, anisotropic turbulence that scales with the 

boundary layer height Zi (about 1 to 2 km), cannot be represented with lateral grid scales 

of a few kilometers, regardless of the vertical grid spacing. In convective conditions, the 

lateral scales are about 1.5 times Zi. Consequently, there is a gap in capability between 

top-down modeling represented by mesoscale meteorological modeling, and bottom-up 

modeling represented by physical models and CFD models and LES. Unfortunately, the 

gap lies at the scales of phenomena that affect people. Considerable innovative thought is 

needed to bridge this modeling gap. 

Useful improvements in nesting or initialization approaches include better forecasts of 

boundary layer height and the wind speed and direction profile as a function of grid size. 

The data from 915 MHz wind profilers, now available in many locations around the 

country, can provide the ground-truth data. These data are particularly useful for ATD 

modeling where ABL processes are often dominant. If the information requirements and 

ground truth are well defined, remote sensing could aid in providing input data to models 

and in model performance evaluation. 

TABLE 3. Prioritization Factors for Bridging the Modeling Gap from Mesoscale to Microscale 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

near term average moderate average Exceptional 

Bridging the modeling gap is a long-term objective that will not be easily met. Success in 

bridging the two decades of length scales that are still poorly measured (the gap area in 

figure 7) depends on meeting the other R&D needs for ATD modeling identified in this 

section. The initial efforts to quantify the uncertainty in existing meteorological models, 

fine-scale models, and physical models can be used to identify areas where more 
50 Federal R&D Needs and Priorities for ATD Modeling

extensive research and model development are needed. Longer-term progress will depend 

on creative initiatives producing new, testable hypotheses about boundary-layer behavior 

and surface layer turbulence. This longer-term effort is essential to complete the 

modeling of the full spectrum of atmospheric processes at work in transport and 

diffusion. 

R&D Need: Improve characterizations of surface boundary conditions in 

model parameterizations and in input data sets (initial and boundary 

conditions). 

Accurate, well-resolved data on local surface conditions are critical to credible solutions 

of the equations describing ATD in the ABL. As noted in sections 3.2.2 and 4.2.3, key 

variables of interest are related to surface energy budgets and their spatial and temporal 

variations. Hence, information on surface type, surface cover and condition, surface 

temperature, surface moisture, and other characteristics is essential. In urban 

environments, the data must include accurate and up-to-date depictions of the buildings, 

as well as the often surprising amount of greenery and its effects on surface moisture and 

temperature. The three-dimensional distributions of surface characteristics and effects in 

urban environments are important because of the strongly three-dimensional nature of the 

wind, turbulence, and temperature fields in these areas. Remote sensing from aircraft and 

satellites will probably be the best solution (see section 4.3.2); however, ground-truth 

data and methods to test and correct the set of data intended as the initial conditions for 

ATD modeling will continue to be critical for improving accuracy and quantifying 

uncertainties (see section 3.2.3, ATD Input Processing). Further research is required to 

determine how accurate this description must be and the effects of scale on model 

performance. For CFD and laboratory (physical) models and perhaps for the next 

generation of high-resolution mesoscale models, the spatial resolution of the surfaceboundary conditions will probably be on the order of a few meters. 

Characterization of surface-boundary conditions will require collecting data over a wide 

range of known conditions in order to create a reliable ensemble of realizations for many 

possible circumstances. This effort must overcome a strong tendency in data collection 

experiments to look only locally in assessing surface energy characteristics and fluxes. 

Surface flux measurements are time-averaged values. As the averaging time increases, 

larger motion scales contribute to the measured fluxes. The height of the measurement 

above ground is important because of the upwind influences on the measurements. In 

stable boundary layers, near-ground stratification may separate the surface–air 

interactions from the processes used to measure them and from the sensors intended to 

measure them, (Mahrt, et al, 2001). Businger (1989) stated the issue clearly: “...for 

reliable flux measurements near the surface, we need to know the height of the 

convective boundary layer, the entrainment at the top of the boundary layer, and the 

mesoscale divergence and advection. We have hopes that, with remote sensing in the 

boundary layer, a significant portion of the required knowledge will be obtained.” 
Chapter 4: Modeling and Measurement Research Needs 51

TABLE 4. Prioritization Factors for Improving Characterization of Surface Conditions and Input 

Data Sets 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

near term average high average exceptional 

Inclusion of local surface heterogeneity in initial conditions for ATD modeling will be a 

challenging objective. In the short to near term, capabilities exist to describe surface 

morphology at high resolution using airborne and space-based platforms. These 

capabilities are valuable for routinely identifying morphology changes over time. 

Translating these surface morphology data into representations of surface energy and 

momentum budgets at high resolution in day and night conditions is the more daunting 

challenge. Because many complex processes contribute to these budgets across a range of 

time scales, the large uncertainties in estimates will be hard to reduce. It will probably be 

necessary to continue the R&D effort over the long term with modest improvements in 

capability being achieved along the way. 

Initial and lateral boundary conditions need to be more representative of the local 

environment. Progress will depend on the improved capability to characterize flows both 

laterally and vertically in the ABL through measurement at the scales of interest. 

R&D Need: Test and refine the physical basis for sub-grid-scale 

parameterizations. 

At each step in scale from the global to ABL modeling domains, approximations are 

made to account for physical processes that are too fine in scale to be resolved at the 

scale of the model. (Section 3.1 discusses the basics of model scales.) These 

approximations, called sub-grid-scale parameterizations, represent the unresolved 

processes using resolved grid values. They provide closure to the model (i.e., the model is 

closed when it has the same number of equations as independent variables). Because they 

are generalizations, sub-grid-scale parameterizations introduce sources of error. The 

cumulative effect is that the errors propagate (grow) as model output from one scale is 

passed as input to a model at a smaller scale. Well-designed models suppress the growth 

of accumulated errors, forcing them to dissipate; however, both theory and observation 

show that energy can transfer from small to larger scales. The consequences of such 

processes on a model realization are often suppressed or eliminated by the very 

techniques used to dissipate the errors from sub-grid-scale parameterization. Thus, two 

issues face the R&D community: 

• How large are the errors from sub-grid-scale parameterization? 

• How can these errors be reduced without interfering with accurate representation 

of real processes of energy transfer from one scale to another? 

While the problems have been recognized, very few attempts have been made to resolve 

them. Recent basic research suggests that a bottom-up approach—representing the finerscale processes at high resolution first and then generalizing to larger scales—allows the 

available data to be used effectively while improving the parameterizations that must be 
52 Federal R&D Needs and Priorities for ATD Modeling

included for closure. Physical models provide a mechanism for measuring at very fine 

scales under controlled conditions so that the parameterizations can be suggested by data 

or physical insight and tested independently. The consequences of averaging small scales 

to accommodate larger grid sizes can then be evaluated. High-resolution computational 

models can be (and have been) similarly used to understand and improve 

parameterizations. 

One suggested approach is to use grid-filtered equations rather than Reynolds stress 

models to assess the surface-layer energetics. The sub-filter-scale representations of these 

processes appear to have a wide range of stabilities (Sullivan et al. 2003). 

A second approach uses the characteristic that atmospheric processes near the surface 

tend to scale with the height above ground level. One-dimensional closures connecting 

the first level or two of the model to the surface processes can be tested against 

observations, as Poulos and Burns (2003) have done. Their work showed significant 

scatter—suggesting unpredictability—in the Louis surface parameterization for stable 

boundary layers. 

A third approach is to measure the atmosphere or concentrations at high resolution in all 

four dimensions. This will require instruments with greater capabilities than currently 

exist. As this cannot be done everywhere, selective experimentation will be required both 

routinely and in focused field campaigns. A common result of field programs, using new 

instrumentation, is the discovery of new phenomena and new insights into how the poorly 

resolved processes actually behave. 

The problem of parameterizations for sub-grid or sub-filter scales was also recognized by 

the 11th Prospectus Development Team of the U.S. Weather Research Program in its 

final report on meteorological research needs for improved air quality forecasting 

(Dabberdt et al. 2004). Efforts undertaken as part of the U.S. Weather Research Program 

should be closely coordinated with R&D on meteorological models to support ATD 

modeling systems, since the atmospheric processes to be dealt with are the same in both 

areas of application. 

TABLE 5. Prioritization factors for Testing and Refining the Physical Basis for Sub-Grid-scale 

Parameterizations 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

longer term average moderate average exceptional 

Improvements in models and model components will largely depend on addressing the 

other R&D needs in this report. Models, both meteorological and ATD, will improve 

incrementally with a moderate level of effort as the recommended actions are taken to 

quantify uncertainty, capture existing data, establish ATD test beds, and improve 

measurement technologies. The potential for gain in improving decision aids for users is 

substantial, but it will be achieved incrementally as the state of the science is advanced 

through methodical R&D. 
Chapter 4: Modeling and Measurement Research Needs 53

R&D Need. Characterize dispersion in complex environments. 

The phrase “complex terrain” is often taken to mean mountainous or hill-valley terrain, 

but it can apply to any terrain that affects the wind and thermal structure of the 

atmosphere in ways that make concepts and predictive models based on homogeneous 

conditions no longer appropriate. Because the interactions of terrain features with 

atmospheric phenomena are really the point, a better term than “complex terrain” is 

“complex environment.” For example, meteorologically speaking, coastal regions near 

large bodies of water are complex environments because the land–water interfaces often 

gives rise to land–sea breeze regimes. Similarly, dry regions adjacent to well-irrigated 

lands will generate localized wind fields. In mountainous areas, terrain steering, wind 

deflection, and irregular patterns of surface heating and vegetation will give rise to very 

complicated flow patterns. This is especially true at night when cold-air drainage into 

mountain valleys can produce significant jets of air moving above neighboring flatlands 

or basins at a time of day when the surface boundary-layer characteristics are particularly 

difficult to predict. In general, the common feature of all complex environments is the 

poorly understood impact of heterogeneous surface cover and surface energy budgets on 

local wind, turbulence, temperature, and moisture fields. 

Urban areas are a particular focus of this report because CBRN events can be expected to 

occur primarily in urban settings. The enhanced roughness and changes in thermal 

characteristics of the urban landscape alter the meteorological fields over the city. At 

night, the thermal characteristics often lead to a heat island over the city, which can 

generate its own flow fields under light synoptic wind conditions. Around the central 

business district, where tall and large structures are usually clustered, one can expect to 

find flow deflections, flow channeling along street canyons, preferred sites for 

recirculating flows, and other organized flow patterns, such as wake vortices, strong 

vertical mixing, and greatly enhanced turbulence. To improve our understanding of 

complex urban environments, efforts are required to compile adequate building 

morphology data and to conduct useful field measurements to aid in validating and better 

parameterizing ATD modeling systems for urban environments. Thermal remote-sensing 

data can aid in documenting urban surface energy budgets by providing accurate 

observations of the thermal variation of urban landscapes at spatial resolutions from 

several meters to a kilometer. 

Several urban ATD field studies have been conducted in the past 20 years, both in the 

United States and abroad. Despite their obvious importance, the number of these studies 

is small because of the expense and logistical problems in conducting field studies in 

actual urban areas. Our understanding of flow and dispersion in urban environments 

containing complex building clusters and street canyons is still inadequate. A major 

constraint has been the cost of making a sufficient number of in situ measurements at 

many locations over an extended period, especially measurements of the vertical profiles 

of key variables such as wind, turbulence, and temperature. Improvements in remotesensing systems may lessen this constraint considerably while providing more 

representative data. Future urban studies should make extensive use of remote-sensing 

systems, such as radars, light detection and ranging (lidar), and sound detection and 

ranging (sodar), to provide both meteorological and tracer concentration data. Aircraft 
54 Federal R&D Needs and Priorities for ATD Modeling

and satellite observations may become especially useful in understanding the processes 

controlling ATD over, around, and through cities. This understanding is crucial to 

improving predictive models. For an optimal fit between a remote-sensing system and an 

ATD model, both the model criteria and the phenomena to be observed must be 

thoroughly defined. 

In a sense, the stable boundary layer (SBL) provides another type of complex 

environment. When an SBL is present, deep convective plumes do not exist to dilute 

hazardous materials released near the surface by mixing with higher-elevation air. 

Because of the SBL’s strong surface stratification and weak or intermittent turbulence, 

the material remains concentrated near the surface for extended periods. SBLs occur 

almost nightly. Hazardous releases occurring at night (e.g., the fertilizer plant accident in 

Bhopal, India, in 1984) can expose large populations to concentrated doses of the hazard. 

Yet, SBL behavior is difficult to observe, generalize, and simulate because the weak, 

stratified turbulence of an SBL can be induced or maintained by a variety of processes, 

such as breaking gravity waves, Kelvin-Helmholtz instability, density current, or lowlevel jets (Banta et al. 2002). Recent high-quality field campaigns, such as CASES-99 

and VTMX, have explored SBLs. Numerical studies at several scales, such as the 

GABLS LES study (Beare et al. unpublished) and the GABLs single-column model study 

(Cuxart et al. unpublished), have attempted to improve SBL parameterizations by 

defining intermodel variability and uncertainty due to numerical parameterizations. 

SBLs are currently poorly parameterized in mesoscale models. The commonly used 

parameterizations (e.g., Louis 1979) frequently fail in two ways (Poulos and Burns 2003). 

First, the parameterizations often lead the model to predict too-rapid cooling of the 

surface, which suppresses turbulent mixing inappropriately. Second, they often maintain 

well-mixed layers that last too long and are too deep. In short, these parameterizations do 

not account for the intermittent sources of turbulent mixing mentioned above. Although 

the research community in boundary-layer meteorology is addressing the problem, 

substantial additional work is needed. 

TABLE 6. Prioritization Factors for Characterizing Dispersion in Complex Environments 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

immediate average high average high 

Initial efforts to quantify uncertainty in ATD modeling of complex environments will 

produce some near-term gains. Some efforts are already in progress, using existing tracer 

data from field experiments in cities and near missile ranges. One near-term activity 

should be to examine the existing archives of complex-terrain studies; however, field 

trials may not be sufficient to develop meaningful ensembles of realizations. Physical 

models of urban areas should be used to study uncertainty issues. Development of models 

for nocturnal transport and diffusion in cities can proceed from recent limited tests. For 

other environs, substantial R&D effort will be needed to understand and parameterize the 

nocturnal boundary layer in open, hilly, and mountainous terrain, as well as along 

coastlines. Significant advances in measurement technology may be needed to develop 

appropriate databases of meteorological and tracer data. 
Chapter 4: Modeling and Measurement Research Needs 55

R&D Need. Develop methods and technologies for improving ensemble 

construction and interpretation. 

Given the errors and uncertainties in initial or boundary conditions and in the model parts 

(e.g., numerical core, sub-grid-scale parameterizations), each run of a model produces a 

single realization in the ensemble of possible analyses (for a diagnostic model) or 

forecasts (for a prognostic model). As explained in section 3.2.2, an ensemble of possible 

realizations can be created by two basic approaches. The same model can be used to 

produce multiple realizations by perturbing initial conditions, using variable 

parameterization schemes, combining these two approaches, or using variable grid 

resolutions. A second approach is to use different models to produce the multiple 

realizations for the ensemble. 

Sometimes the consensus (least different) realization in an ensemble is taken as the most 

likely solution. For weather forecasting, Fritsch et. al. (2000) found that the consensus 

realization gives better skill scores in large-scale flows than do other approaches to 

selecting the best weather forecast in an ensemble produced from multiple models. 

Although the consensus approach is now widely used and accepted for weather 

forecasting, its applicability to ATD modeling and the phenomena that become important 

at finer scales has not been established. In large-scale and strongly forced flows like 

hurricanes or severe storms, the ensemble approach gave better skill scores by various 

measures; however, the suitability of various ensemble approaches has not been 

established for weakly forced or ABL flows. The consensus technique has only rarely 

been used to examine ABL flows over the variety of surface morphology, diurnal 

conditions, and climatic regimes routinely encountered for ATD modeling. The JAG 

could find only one instance of the use of consensus selection in an advanced ATD model 

(HPAC) to estimate concentrations. Even for this one ATD model, there are limited 

comparisons of predictions using the consensus realization with observations. The 

scientific bounds and applicability of this technique within ATD modeling need careful 

experimental and theoretical study. 

The complexity of near-surface flows and mixing suggests the variability may be large, 

and increased skill may be hard to demonstrate. Integration of local, near-surface 

measurements into larger-scale flow regimes has not been effective; the model and the 

observations often disagree about the distribution of mass (the pressure field) and 

momentum (wind and turbulence fields) because the scales of distributions contained in 

the model are much larger than the scales of the local observations. As local data become 

available at higher resolutions, the modeling approach of using large-scale forcing to 

drive local-scale models will have to bridge the scale gap. With sufficient local data, 

shorter-term, locally based predictions of wind and turbulence fields—and thus, 

predictions of concentration fields—will become feasible and reliable. 

The top-down and bottom–up approaches to assessing data representation and 

assimilating data into models are important across the spectrum of motions. The topdown approach is driven by nesting of models to approach smaller scales. The bottom-up 

approach is driven by local measurements to analyze, diagnose, and predict present and 
56 Federal R&D Needs and Priorities for ATD Modeling

future local conditions. At present, with a few exceptions, local data sources are too 

sparse. Remote measurement capabilities appear to be the best R&D pathway to meet this 

need to characterize the local wind fields for purposes of improved transport and 

diffusion prediction. Ensemble assimilation techniques must be developed for the local 

data utilization. 

As noted in section 3.2.2, substantial R&D on ensemble methods is also needed on the 

following topics: 

• The optimal number and types of ensemble methods to produce statistically 

significant improvements in results; 

• Advanced techniques for creating the individual realizations in the ensemble; 

• Development of techniques to link ensemble mesoscale meteorological prediction 

systems with ensemble ATD predictions and evaluate the overall uncertainty of 

the probabilistic results; and 

• Communication of probabilistic information from ensemble techniques to users 

through user-tailored decision aids. 

TABLE 7. Prioritization factors for Developing Methods and Technologies to Improve Ensemble 

Construction and Interpretation 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

immediate minimal high short exceptional 

The ability to quantify the uncertainty in ATD predictions will provide invaluable 

guidance on where R&D resources (funding and talent) should be invested to optimize 

the return. Because of its tremendous potential for gain in improving the quality of ATD 

prediction products if used to guide other R&D investments, the need for improved 

ensemble construction and interpretation is immediate. The overall level of effort, 

however, will be substantial. Many aspects, such as characterizing the ABL or 

quantifying uncertainty in data-sparse environments, are likely to require an extended 

period of R&D. Nevertheless, even incremental improvements in our capability to 

quantify uncertainty will produce moderate to substantial product improvements for users 

of consequence assessment systems. 

R&D Need. Develop and test techniques to better estimate wet and dry 

deposition and chemical interactions. 

As explained in section 3.2.5, removal or transformation processes and resuspension of 

previously deposited material are important factors for predicting concentrations 

downwind and are critical for assessing related impacts on the terrestrial and aquatic 

environments. Improvements in the parameterizations of the model physics for these 

processes, together with better empirical coefficients, are needed to improve ATD model 

predictions. 
Chapter 4: Modeling and Measurement Research Needs 57

Although both dry and wet deposition of airborne materials has been studied for decades, 

the airborne hazards of interest have generally been either radioactive products of nuclear 

testing or common air pollutants, such as sulfur, nitrogen, and mercury compounds. 

Removal or resuspension of other materials, especially small particles, is still difficult to 

predict. Wet deposition, whether by rainout or washout, is far more efficient than dry 

deposition, but it occurs only during periods of precipitation. Until more is known about 

these processes, especially as they occur in urban areas, improving modeling approaches 

to account for them will remain difficult. However, the importance is high because 

hazardous substances deposited in urban areas will find their way into run-off, 

wastewater, and aquatic ecosystems, with health and ecological effects that might be 

severe. 

Focused studies of the both wet and dry deposition processes are needed. Additional 

process-level understanding is important for developing successful simulations. In the 

case of dry deposition, the necessary measurements and modeling will be particularly 

difficult in urban areas because of the heterogeneity and general complexity of the 

surface. For wet deposition, it is known that urban areas modify the precipitation regimes 

downwind, but it is not yet known whether this translates into more rapid scavenging of 

hazardous materials contained in the air. Carefully designed experimental programs will 

be required to address these questions. In addition to improving the representations of the 

contributing processes in models, accurate prediction of deposition, transformation, and 

resuspension effects requires the capability to merge real-time information (e.g., radarderived values for rainfall rates and their spatial and temporal distributions) into the 

models. Prediction of exactly where precipitation will occur and at what rate requires 

characterization of many stochastic systems. At urban/local scales, predictions of where it 

will rain and at what rate are therefore likely to be best described in probabilistic terms. 

TABLE 8. Prioritization Factors for Develop and Test Techniques to Better Estimate Wet and Dry 

Deposition 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

near term average moderate Average high 

Deposition from the atmosphere constitutes the linkage between atmospheric 

concentrations and ecological (and other environmental) effects. There is accordingly a 

long history of research on both wet and dry deposition of various substances, usually for 

periods of an hour or more. However, substantial uncertainty exists in current 

representations. Wet deposition models require significantly improved cloud and 

precipitation models for time, location, and intensity, with due allowance for the 

sometimes dominating role of processes that cannot yet be addressed deterministically. 

Dry deposition formulation will necessarily need to take similarly stochastic factors into 

account, in this case related to the heterogeneity of the surface. The contributing 

processes have been studied principally for contexts other than atmospheric turbulence 

and diffusion. The results will certainly depend on the substance being deposited. 

This may be a difficult research effort, but the chances of success have improved with 

recent development of new measurement technologies. Use of remote-sensing methods 

(especially radar) and modern methods of chemical analysis could advance our 
58 Federal R&D Needs and Priorities for ATD Modeling

understanding of wet deposition and its temporal and spatial characteristics. Likewise, 

new methods for measuring dry deposition rates are now ready for exploitation, 

particularly modifications of well-known and long-proven eddy correlation techniques. 

4.2.3 Approaches for Model Improvement 

Physical Model Simulations of Transport and Diffusion 

Mathematical models of transport and diffusion must make substantial approximations 

for some of the fundamental fluid-dynamical processes involved, particularly those 

processes unresolved by the model. Numerical modeling can be supplemented and 

improved by using physical modeling (e.g., wind tunnels, water channels, or water tanks) 

to simulate the atmospheric conditions of interest. In these laboratory simulations, the 

primary variables can be controlled, and the time and expense are greatly reduced 

compared with full-scale field studies. 

A physical model must duplicate certain nondimensional parameters if it is to provide a 

realistic simulation of ABL processes (EPA 1981; Snyder 1972). Unfortunately, not all of 

these dimensionless quantities can be matched simultaneously to their full-scale 

(atmospheric) values. Research has been conducted to provide advice on which quantities 

are most important for simulating ATD of neutral and positive buoyancy gases (EPA 

1981) and of dense gases (Meroney 1986). 

Laboratory experiments (wind tunnel, water channel, or water tank) have been used to 

investigate a number of ATD problems, including transport around individual buildings 

and industrial structures, through clusters of buildings, and in street canyons. Some of 

these have investigated the dependence of concentration fluctuations on the initial size of 

the source. Water channels have been used to investigate stable and neutral boundary 

layer transport around isolated hills. Water-tank experiments have been used to 

investigate convective diffusion and plume rise from explosions. Results from such 

laboratory simulations are the basis for many of the model parameterizations in current 

use for these situations. 

Although laboratory simulations cannot fully replicate every characteristic of the fullscale condition of interest, they provide a cost-effective solution for exploratory research, 

confirmation of theoretical solutions, and construction of operational model 

parameterizations and estimation methods. Laboratory simulations are especially 

effective for investigating and characterizing the stochastic effects of ATD inherent in 

microscale flows around obstacles. 

High-Resolution Modeling of Turbulent Flow 

The fluid-modeling community has many years of experience in modeling turbulent flow 

regimes in a variety of circumstances. Special attention has been directed to turbulence 

near the interface of a fluid flowing past a fixed or movable surface, which is 

conceptually applicable to turbulence in atmospheric flows. 
Chapter 4: Modeling and Measurement Research Needs 59

Direct Numerical Simulation (DNS). For air flows less turbulent than the atmosphere, 

DNS of carefully described, idealized atmospheric boundary flows can resolve turbulent 

eddies down to the molecular scale. Consequently, approximations are not required for 

very small eddies, but these conditions are applicable to only a limited number of ATD 

scenarios. Nevertheless, for turbulent flows near the ground, DNS has demonstrated 

important properties of surface-layer flows. The DNS approach can be used to help 

quantify processes and improve parameterizations for larger spatial scales. 

Computational Fluid Dynamics (CFD). As noted in section 3.2.4, CFD codes, adapted 

from aerospace applications simulate mechanical turbulence in atmospheric flows around 

obstacles, particularly in urban settings. CFD grid spacings are several meters in the 

horizontal and vertical dimensions, so some representation of smaller scale processes is 

required. Inflow boundary conditions are often fixed in time, and larger scale motions are 

not usually included. In some cases, simple time variations can be imposed. In urban 

studies, CFD codes sometimes are embedded within prognostic mesoscale meteorological 

models, which provide time-varying boundary conditions for ATD simulations. In many 

cases, CFD codes do not account for the local sources or sinks of heat or their time 

variations. 

The computationally intensive CFD approach can be used to study features of the 

complex wind fields in urban environments such as those found in the MUST, URBAN 

2000, and Joint Urban 2003 field studies. An important feature of numerical simulations 

is that the external conditions can be controlled for many model runs, each of which has a 

slightly different initialization. The resulting ensemble of realizations can be used to 

obtain quantitative estimates of uncertainty in predicted concentration fields. Because 

CFD models have limited volume domains, their boundary conditions are often assumed 

rather than being calculated from larger scale simulations. 

Large Eddy Simulation (LES). Historically, mesoscale meteorological models have 

employed horizontal mesh sizes that were much greater than the depth of the ABL. The 

entire turbulent energy spectrum of the ABL was therefore well below the resolution of 

mesoscale models, and turbulence parameterization methods were needed. By contrast, 

an LES has a horizontal grid of 10 to 50 m and can resolve the larger, more energetic, 

turbulent eddies in the ABL. However, even an LES is unable to resolve the finer scales 

of turbulence, so a sub-grid-scale parameterization is needed to account for energy 

exchange between the resolved grid and the unresolved grid. Recent field experiments 

(Sullivan et al. 2003) and modeling studies (Chow and Street 2002; Juneja and Brasseur 

1999) have suggested several sub-grid-scale turbulence parameterizations that 

significantly improve the popular closures. 

The LES has become a common tool for investigating ATD because the statistical 

properties of LES results show many similarities to those of atmospheric turbulence, 

especially for unstable and neutral stability conditions. However, because the physics of 

some processes in the stable ABL are not well understood, LES is still being refined for 

stable conditions. 
60 Federal R&D Needs and Priorities for ATD Modeling

Lagrangian particle models using resolved LES wind fields are often used to characterize 

ATD of material from various sources. Substantially different realizations of 

concentrations in plumes from point sources at the same or different locations within the 

volume are commonly calculated. The statistically similar behavior of plume 

characteristics (mean and variability) as a function of stability and release height has been 

demonstrated (Weil 2004). As with physical models or CFD, the ability to control 

conditions provides the opportunity to compile ensembles of realizations for uncertainty 

estimation. 

Although the LES approach is commonly used, it is limited because the initial and 

boundary conditions are usually assumed rather than based on observational or modeled 

inputs. Since many LESs use cyclical boundary conditions, lateral motions typical of 

mesoscale phenomena cannot be included. 

R&D Need: Continue the development and use of physical modeling 

capabilities and high-resolution computational models (DNS, LES, and 

CFD) to simulate transport and diffusion in boundary layer and complex 

flow regimes and to assess components of uncertainty of concentrations 

and meteorological factors 

These modeling approaches are the foundation of small-scale modeling and attempts to 

link across the mesoscale–microscale modeling gap. They have many features that are in 

need of R&D efforts. The models provide a capability to specify the atmospheric state 

and develop ensembles of realizations. With concentration estimates, the approximate 

bounds to the inherent uncertainty of ensemble conditions can be assessed. We can gain 

significant insight and knowledge of the consequences of averaging concentrations, 

fluxes, and turbulence. Better parameterizations of scale-dependent processes can be 

developed. Quantified fields of concentrations, winds, and turbulence can be analyzed. 

These models will be used to test and evaluate consequences of high-resolution surface 

characterizations and boundary conditions. The models will be used to test new closure 

equations. Furthermore CFD- type modeling will continue to be used in complex 

geometries, so R&D on including energy budgets in the simulations is a crucial issue. 

TABLE 9. Prioritization Factors for Development and Use of Physical and High-Resolution 

Computational Models 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

near term Average moderate average exceptional 

A significant effort in small-scale ATD modeling (physical and computational) already 

exists. Physical models can provide an “observational” database for assessment of ATD 

model performance in complex conditions. Appropriate fields from high-resolution 

computational models are available or are reasonably easily regenerated. Analyses of 

existing data sets, such as those derived from Joint Urban 2003, should begin 

immediately to assess uncertainty issues as a function of scale. Substantial progress 
Chapter 4: Modeling and Measurement Research Needs 61

should come rather quickly where data exist. The effort must be sustained, as other R&D 

efforts progress, to help confirm new approaches and close the modeling gap. 

4.3 Improving Measurement Technologies 

Measurement of atmospheric properties and processes at and below the scales of interest 

is essential to improvements in ATD modeling. The primary area of concern of ATD is 

near the ground—in the surface layers of the atmosphere—where the hazard comes in 

contact with the ecosystem and its effects are felt. Meteorologically, the surface layer is 

connected to the large-scale flows through the ABL and is the most variable portion of 

the atmosphere. 

The ABL is unique in that it results from the interaction of the small-scale effects of 

surface properties with large-scale flow fields. Furthermore, the ABL responds to diurnal 

solar heating and radiational cooling processes, providing a three-dimensional turbulence 

structure whose height during the day is on the order of the boundary layer height and 

during the night is a sharply stratified two-dimensional turbulence with little vertical 

mixing. Measurement systems need to account for the wide variety of conditions and 

scaling lengths that arise even in open areas. 

Within urban areas, measurements become more difficult to make and then to understand 

because of the scales of buildings and the variations in vegetation and surface conditions. 

The increase in degrees of freedom challenges assumptions about relationships between 

measured quantities and can invalidate modeling assumptions. As the information in 

Table 1 (chapter 3) suggests, the ABL and the surface layer are poorly and sparsely 

sampled for ATD uses, both for tracer material (for the evaluation of ATD model 

performance) and for characterizing the environment (wind, temperature, humidity, and 

turbulence). 

The standard meteorological measurement methods for weather forecasting have not 

focused on observing the entities in atmospheric phenomena, such as eddies at different 

scales that cause the short-term fluctuations in airborne hazard concentration at a given 

point. Scanning lidar systems can now identify eddies and other fine-scale phenomena 

rather than simply measuring their effects on state variables at particular points and times. 

These technological advances in measurement open up entire new strategies for 

observing the atmospheric processes and phenomena that cause the variability in local air 

movements and, therefore, in hazard concentrations. These new observations will be able 

to feed the parameterizations in improved mesoscale models. They also may be able to 

provide the initial or boundary conditions for much more sophisticated and realistic 

representations of microscale phenomena in ATD codes. 

To quantify the uncertainties in ATD predictions, measurements of the distribution for a 

meteorological input parameter are necessary--not just a point observation of the 

parameter. For example, it would be preferable to have an observation-based estimate of 

the standard deviation in the wind direction during an increment of time, rather than a 

single point value for the wind direction. Existing quality standards for meteorological 
62 Federal R&D Needs and Priorities for ATD Modeling

observations used by NOAA/NWS, for example, aim for accurate point estimates and are 

not adequate for capturing this information about the temporal distribution of the 

parameter. 

R&D Need: Improve tracer materials and measurement technology. 

Field experiments are the preferred means for testing and improving transport and 

diffusion models. Controlled releases of inert gases or aerosols simulate the transport of 

active agents through the atmosphere. Measurements of concentrations of this tracer 

material over a given period at various locations provide “ground truth” for the 

concentration and its time history, defining the plume dimensions and the distribution of 

mass within the plume. Measurements of wind and turbulence properties and other 

atmospheric variables in the study area are taken at the same time. 

Previous dispersion experiments are useful mainly for studying the gross behavior of the 

effluent plume, as they provide reasonably good spatial location of the plume's path and 

footprint. The experiments were well suited for evaluating concentration prediction 

models that produced results with time and space scales similar to those of the sampling 

network. Tracer technology, however, has not kept pace with turbulence measurement 

technology. Improvements in tracer technology—both the tracer material and its 

measurement—are essential for assessing progress in ATD modeling. 

Tracer materials are an important part of field experiments. Their release characterizes 

the source terms of interest to ATD modeling. The measurement of tracer concentration 

as a function of distance and time from release defines the impact of ATD on a released 

material and thus the potential hazard zones. Selection of the tracer technique for an 

experiment can be a tedious task. 

Tracers must be inert—minimizing the potential health or environmental hazards. 

Aerosol tracers need to replicate the aerosols of interest. The tracer aerosol should closely 

replicate the hazard aerosol in size distribution, dielectric properties (for remote sensing), 

and affinity for moisture (since aerosols swell at unsaturated humidities). For gas or 

aerosol tracers, low cost per unit mass is desirable. Detectability at highly diluted 

concentrations (10-9 dilution) is also highly desirable. 

Instruments for detecting tracer concentration must have a rapid sampling rate, as 

variations in concentration for intervals shorter than the instrument response cannot 

otherwise be measured directly. If the response rate is not adequate, coarser 

parameterizations of fluxes and surface deposition are required, intermittency of 

concentrations cannot be determined, and short-period events of high concentrations 

cannot be identified. Instruments also need a large dynamic range of measurement to 

capture high and low concentration events as they occur and with equal precision. The 

devices should provide the measurement for real-time analysis. Low cost per sensor is 

needed to allow for a large array of sensors. Concentration detection by remote-sensing 

techniques is highly desirable for future studies because point measurements can never 

provide sufficient coverage near the ground and aloft. 
Chapter 4: Modeling and Measurement Research Needs 63

Few tracer system components—material or sensor—satisfy all these requirements. 

Trade-offs (except for safety) are usually required. At present, sulfur hexafluoride is the 

gaseous tracer of choice for urban and short-range studies, as in Joint Urban 2003, Urban 

2000, and Pentagon Shield. Rapid-response sulfur hexafluoride detectors are expensive 

and not readily available. Consequently, the measurement campaigns for these studies 

were highly labor intensive and logistically limited. Some long-range studies have used 

perfluorocarbons (e.g., the ANATEX experiments), but long integration times (about 12 

hours) were required to accrue enough material for analysis (Draxler 1991; Draxler et al. 

1991). 

Assessing and guiding future improvements in ATD modeling will depend on a 

concentrated effort to develop tracer materials and measurement technologies that meet 

these requirements. Adequate tracer studies must become routine so that ATD model 

performance can be regularly evaluated. Adequate studies are also needed to evaluate the 

sources of uncertainty in both measurements and modeling. 

TABLE 10. Prioritization Factors for Improving Tracer Materials and Measurement Technology 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

immediate High moderate short exceptional 

A concentrated effort to develop needed tracer technologies should begin immediately. It 

is impossible to routinely quantify uncertainty in ATD models in the field or in test beds 

without controlled tracer data. This effort is critical for the overall research objectives but 

may not be easily achieved because progress on development of fast-response sensors has 

been slow and remote-sensing capabilities will depend on the candidate tracer material. 

Having this capability is also essential to learning how best to communicate uncertainty 

to users. 

R&D Need: Improve boundary-layer atmospheric measurement 

capability. 

Atmospheric quantities other than hazard concentration, such as temperature, water 

vapor, and trace gases are also affected by atmospheric fluctuations. Short-term changes 

in wind speed and direction, which strongly affect the transport and diffusion of locally 

emitted contaminants, are of particular interest in the case of hazardous or toxic materials 

for which even short-term exposures to a threshold concentration may seriously affect 

human health and safety. 

Fluctuations in wind speed, wind direction, and atmospheric temperature are probably 

among the easiest data to obtain. Appropriate instrumentation has been available for 

decades and has been continuously improved. With the recent decrease in cost of threedimensional sonic anemometers (which offer the advantages of fast response, low 

threshold, and no moving parts), it is now possible to establish continuous, around-theclock measurement programs for wind and temperature fluctuations at most locations of 

interest. The accuracy of these instruments remains reasonably good for sampling rates 

up to 20 Hz. Data can be collected easily using inexpensive small computers. Data 

transfer and centralized data collection and storage are probably the main remaining 
64 Federal R&D Needs and Priorities for ATD Modeling

technical problems because of the amount of data that can be collected from even a 

modest-sized wind-sensor network in a relatively short time. 

Even with an ability to collect large amounts of data, data collection at discrete points 

may cause problems of data quality and data representation (see section 3.2.3) because of 

the spatial and temporal variability of the wind and its fluctuations. In principle, this issue 

could be handled by an instrument array that is dense both horizontally and vertically. 

While this solution is not feasible for routine instrument networks, it is feasible for 

specialized networks and field studies. The correlation length is a measure of the 

consistency of observations taken at one location with those at another location using 

common averaging times (e.g., 1 minute instead of 15 minutes). Dense networks provide 

an opportunity to measure correlation length and assess the density of measurements 

needed to characterize a location and its surroundings (urban or rural). Joint Urban 2003 

is a good example of a field experiment that employed a dense wind and turbulence 

network. 

Remote-sensing techniques offer the potential for technology solutions to ABL 

measurement needs. The major advantage of a remote-sensing method is that a line or 

volume of the atmosphere can be sampled at a known, designed (often rapid) rate without 

the sensor needing to be in direct physical contact with the spatial points or volumes 

being sampled. Remote-sensing systems produce observations either by active or passive 

means. In an active system, the system transmits a signal and records the direct or 

indirect interaction of that signal with environmental conditions of interest. In a weather 

radar system, for example, the reflections of a transmitted radio wave from a scatterer are 

captured. Passive sensors typically rely on the thermal properties of the ground or the 

atmosphere, without an emitted signal. A typical passive observing system detects the 

infrared energy naturally radiated from the environmental condition of interest. Both 

active and passive approaches to remote sensing typically require additional extensive 

processing of the received signal to obtain the desired information about the 

environmental condition of interest. 

Active systems such as radar, lidar, and sodar have been used to measure winds, 

temperature, and precipitation. Ground-based radar and sodar have been available to 

ATD modeling for many years to measure wind profiles, but they have not been 

networked for operational use. Implementation of clear-air radars, like the FAA Terminal 

Doppler Weather Radar system, provides wind fields at kilometer increments near major 

population centers. Efforts are underway to make similar use of the WSR-88D weather 

radar network. Recent availability of eye-safe Doppler lidar systems has permitted 

resolution of wind fields of urban and rural domains at resolutions measured in tens of 

meters. These systems generate volumetric data in tens of seconds. Improved and 

expanded lidar capabilities are being studied actively for both research and operational 

use. Airborne and satellite-based radar and lidar capabilities are becoming common. For 

directed-research programs, moisture and ozone are now commonly measured using 

remote-sensing systems on aircraft or satellites. The ATD R&D community must keep 

abreast of these developments as they relate to mesoscale through microscale 

applications. 
Chapter 4: Modeling and Measurement Research Needs 65

Satellite-based or airborne passive remote sensors can provide land cover information 

across a range of temporal and spatial scales. Passive remote sensing is well suited to 

gathering current data on local surface radiances, which can be interpreted into 

information about surface conditions. Thermal remote-sensing techniques can help 

document the surface energy budget by providing accurate observations of thermal 

changes across the landscape at spatial resolutions from several meters to a kilometer. 

Multispectral and hyperspectral imaging in fine bands within the same view may become 

a means of quantifying subtle but significant differences in surface conditions. Research 

must continue on methods of translating the sensed radiation signal into information on 

surface properties on a timely, reliable, and comprehensive basis. 

Remote-sensing systems that use sophisticated scanning techniques, such as push broom 

or framing techniques, have the potential to probe larger volumes of the atmosphere 

above a region than do local sensors; however, these systems have their limitations. Their 

view of the atmospheric volume can be degraded by precipitation or blocked by obstacles 

or clouds. They can be affected by unwanted reflections (clutter) or by scattering in the 

sensing medium. They may also have relatively coarse spatial resolution. Some systems 

(e.g., sodars) produce signals that can affect humans in the immediate area and, as a 

result, may be difficult to use in populated areas. Some remote-sensing systems, 

especially research-grade systems, are not well suited to autonomous operation. They 

require frequent or even continuous attention from skilled specialists. This requirement 

makes them expensive to operate. 

Remote-sensing systems generally have a high sampling rate, which means that very 

large quantities of raw data can be acquired in a short time. This rapid data acquisition 

poses potential storage and transfer problems. Interpreting the data is often not 

straightforward and may require considerable expertise and experience. For example, 

correct interpretation of the data is often complicated by the noise of turbulence effects, 

which generally must be removed by time-averaging to reveal the mean patterns. 

Ground-truth data over the sensing volume are needed to ensure that the remotely 

measured variables and derived parameters agree with conventional data sources. 

Finally, research is needed to improve the fundamental understanding of how models can 

incorporate a variety of remotely sensed data. 

TABLE 11. Prioritization Factors for Improving Boundary-Layer Measurement Technology 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

immediate high high short exceptional 

To realize short-term gains, further development of existing boundary-layer measurement 

capabilities should be accelerated and on-the-shelf improvements should be incorporated 

into existing measurement systems. New, low-cost instrumentation developments for 

measuring important boundary-layer variables and possibly fluxes, should be started by 

exploiting existing R&D mechanisms such as the Small Business Innovative Research 

(SBIR) and Small Business Technology Transfer (STTR) programs or agency-specific 

instrument development programs. New instrument development is often time consuming 

and expensive, so options to expedite the process should be explored through 
66 Federal R&D Needs and Priorities for ATD Modeling

laboratories, academia, and industry. The measurement improvements will enable other 

R&D needs to be met. 

4.4 Model–Data Interface Challenges 

In the end-to-end functional analysis of ATD modeling systems provided in section 3.2, 

there are a number of capability gaps that occur at the interface where data come into the 

modeling system. Such capability gaps could be viewed as either an input data problem 

(too few data, questionable data, not the right data, etc.) or a modeling problem (model 

representations not powerful enough to predict from the data given, etc.). Innovative 

approaches to filling them often amount to some combination of advanced input 

processing, as defined in section 3.2.3, and model improvements. Two recurring themes 

in these interface challenges are determining the impact of input data uncertainties on the 

uncertainty in modeling system predictions and issues of data quality and data 

representation, as defined in section 3.2.3. The JAG identified R&D needs for three areas 

of these model–data interface challenges: sensor fusion, data representation and data 

assimilation, and model performance evaluation. 

4.4.1 Source Characterization by Sensor Fusion 

A comprehensive assessment of source characterization technology—either current 

capabilities or R&D needs to meet application requirements—is beyond the scope of this 

report and beyond the charge of the JAG. However, the use of sensor fusion, as defined 

in section 3.2.1, to back-calculate to the estimated source term location, emission rate, 

and release duration is within the scope of this report. 

In certain hazard release scenarios, the first indication of a release will be alarms 

triggered by specific sensors at varying distances and directions from the exact location 

of release. The identification of the location and characteristics (emission rate, duration) 

of the source from these alarm data are often important objectives in response 

management. By combining data from networked sensors with predictive models, more 

can be learned about a release event than could be obtained from any individual sensor or 

predictive model alone. Sensor fusion modeling systems are being developed to 

backtrack from these initial sensor data to estimate the most probable source term 

location and emission characteristics. 

The process of integrating sensor data with predictive models is intended to result in 

adjusted predictions that better describe the release event than would model calculations 

made without the sensor data. The predictive models produce calculations based on 

previously available information about the source term release. Several predictive models 

may be run separately to produce a single prediction, or they can be used to create an 

ensemble of predictions. Some predictive models can estimate aspects of the uncertainty 

in their description of the event. Although the mathematical problem may not allow a 

single, definitive solution, even the capability to narrow the possible range of locations or 

characteristics can be valuable to response decisions. 
Chapter 4: Modeling and Measurement Research Needs 67

To explore the limits in characterizing the source term by coupling ATD modeling with 

monitoring data, Hanna, Chang, and Strimaitis (1990) analyzed data from the Project 

Prairie Grass diffusion experiment using Gaussian plume models. Two of the three 

models had been tuned to the release situation. The authors concluded that the source 

term emission rate could be estimated to no better than a factor of two, even with an 

advanced Gaussian plume model, a point source in ideal circumstances, and a nearsurface release having a known release height and a constant emission rate. This limit on 

emission rate estimation may represent an ultimate uncertainty that cannot be reduced, 

but further investigation is warranted. 

R&D Need: Improve and evaluate sensor fusion techniques. 

The R&D areas for improving sensor fusion techniques include the following: 

• Rapid interpretation of data streams from multiple sensors; 

• Increased detection confidence with reduced system-level false alarms; 

• Improved situational awareness for CBRN events; 

• Estimates of the most probable source terms; and 

• Refined model predictions of downwind hazards. 

Sensor fusion methodologies with the potential to provide these improvements include 

inverse dispersion modeling, Bayesian statistical methods, adjoint methods, artificial 

intelligence, neural networks, fuzzy logic, and others. Sensor fusion techniques should be 

developed that can use data from a wide range of detector types, as well as other relevant 

non-sensor data, such as intelligence and medical information. Existing mathematical and 

statistical concepts should be evaluated and incorporated as appropriate, but new or more 

advanced concepts may be required because of the wide range of unknowns and 

uncertainties involved. 

TABLE 12. Prioritization Factors for Improving and Evaluating Sensor Fusion Techniques 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

immediate high moderate moderate high 

Sensor fusion has been targeted as a high priority for the Department of Defense (DoD) 

and the Defense Threat Reduction Agency. Research plans for various single or multiple 

approaches have been developed, and implementation of them has begun across a wide 

range of atmospheric scales. Candidate approaches can be evaluated in a relatively short 

time frame. Since the success of this R&D effort depends on the ATD modeling 

capabilities, sensor fusion capabilities should improve as ATD models improve. 

However, uncertainty due to the nonlinearity of ATD may limit capability improvements. 

Opportunities exist to understand this uncertainty and feed that understanding back into 

the ATD model improvement and sensor fusion capabilities. These synergies and 

dependencies across individual R&D needs are typical of what the JAG found in many 

areas for which quantifying ATD modeling uncertainties is a major objective. 
68 Federal R&D Needs and Priorities for ATD Modeling

4.4.2 Data Representation and Data Assimilation 

Diverse sources of meteorological and agent-concentration data exist. The data are often 

taken at, or are representative of, varied time and space scales. Putting these data together 

in a coherent, physically realistic manner is the data assimilation process. As mentioned 

earlier, it may occur in one to four dimensions and cover a variety of scales. A key factor 

in data assimilation is how well the data fit the assumptions of the model: defined in 

section 3.2.3 as data representation. Prognostic models, for example, require initial 

conditions to describe the current state of the atmosphere in the volume being modeled 

and boundary conditions for that volume of atmosphere; namely, its inflow, outflow, and 

lower and upper sources or sinks of mass, energy, and momentum. The process of model 

initialization begins at global scales where radiosonde, satellite, aircraft, and other 

observational data sources are assimilated with previous forecasts for the new initial time 

to provide a comprehensive, updated, large-scale description of the atmosphere. As 

higher resolution grids are nested into subsets of the next larger scale, the initial and 

boundary conditions for the smaller grid must be provided to account for processes not 

represented at the larger scale. 

At the larger scales, data assimilation approaches are undergoing intensive research and 

development. In the past two decades, physics-based assimilation of observations has 

replaced interpolation approaches. For example, in nudging techniques, a tendency term 

is added to the differential equation for each explicit variable in a full-physics mesoscale 

model such as MM5. The tendency term is proportional to the difference between the 

model predictions and observations; the observations are weighted in space and time, 

depending on the data type. Another approach comes from Sakaki’s use of variational 

calculus, which opened the way to weighting observed data and constraints imposed by 

the equations of motion and conservation within the forecast model to produce threedimensional initializations. This approach, called 3-D VAR, has become a de facto 

standard for major forecast centers. Attempts to incorporate temporal variability into the 

basic 3-D VAR approach, called 4-D VAR, lead to a complex set of equations that can 

only be solved approximately. Producing a 4-D VAR analysis is almost as timeconsuming as the 72-hour forecast. Furthermore, it is closely coupled to the forecast 

model and its parameterizations. 

More recently, ensemble techniques are being explored to improve the data 

representation fit of initial conditions. As noted previously, ensembles can be composed 

of several runs using the same model with different initial conditions, several runs using 

different models, or a set of runs with the same model but using different 

parameterizations. Another approach being explored for developing an ensemble is to use 

principles of Kalman filtering. These techniques appear to be independent of the forecast 

model. Generally speaking, most of these techniques have not been well tested with the 

finely nested grids needed for ATD predictions. 

As grids are nested, typically in a ratio of 3:1, the meteorological data fields are usually 

interpolated to the finer grids without a physical constraint. Outside the ABL, this 

approach is reasonable. Inside the ABL, however, smaller scale processes that affect the 

turbulent state in the ABL are not represented. The usual assumption is that the finer-
Chapter 4: Modeling and Measurement Research Needs 69

scale lower boundary conditions will develop (in the model state) after an initial 

adjustment period. Lateral boundary conditions are likewise interpolated and, at the 

inflow, driven by the larger scale forecast fields. Unfortunately, because there is a severe 

lack of observational data at nesting scales, these assumptions are not tested or directly 

verifiable. The overall forecast quality is a surrogate indicator of the validity of the 

assumptions. In traditional measures of performance, which use indicators such as bias 

and root-mean-square error, there appears to be a practical limit to improving forecasts 

(i.e., reducing forecast errors) by reducing grid size (Mass et al. 2002). This limitation 

can largely be explained by the fact that, as higher resolution is added to the forecast, the 

traditional forecast statistics become increasingly affected by slight errors in the location 

or timing of the mesoscale features. Research is needed to develop measures of 

performance for high-resolution mesoscale model forecasts that better quantify their 

value for use with ATD models. 

Incorporating additional data on local winds and turbulence appears to have a positive 

effect on model performance at high resolution. As mentioned, the top-down approach of 

nesting appears to meet a practical limit near grid sizes of 5 km; however, the Army test 

ranges have had good results with operational modeling systems that use two-way grid 

interactions and an innermost domain resolution of about 1 km (Warner et al. 2004). For 

a wide range of weather conditions, models, and observational conditions, forecast 

models at that scale have large errors in wind-direction predictions—root mean square 

errors of about 40 to 60 degrees. Thus, inclusion of local observations in diagnostic and 

prognostic models of the wind field seems to be a reasonable and perhaps necessary 

approach to meeting user requirements for greater accuracy and useful information about 

predictive uncertainties. 

Two other aspects of ATD modeling system performance place additional challenges on 

data assimilation R&D to improve the prediction of concentrations for the end users 

(emergency managers, operations officers, and researchers). First, for initial and 

intermediate response to hazard releases, data QA/QC for model fit (data representation) 

and data assimilation need to be automated (i.e., handled by software-embedded 

algorithms), without requiring expert “tweaking” by the model user. Second, the remote 

measurement technologies discussed in section 4.3.2 provide input data that require new 

capabilities on the part of the ATD model code to assimilate the data. Some softwareembedded algorithms for data assimilation exist. However, as noted in section 3.2.3, 

assimilation of observations beyond t0 of a prognostic model is often restricted by the 

constraints necessary to perform the iterated computations. If the data to be assimilated 

diverge too far from the model’s predicted values for that space-time cell, the data are 

rejected. 

One of the emerging remote-sensing technologies, Doppler lidar, offers promise for 

providing high-resolution local wind fields in a variety of conditions. Following 

developments in deriving wind fields from Doppler radar data, Lin, Chai, and Sun (2001) 

used 4-D VAR with Doppler lidar data to construct three-dimensional wind fields 

characteristic of the convective boundary layer. As noted above, pure 4-D VAR is 

computationally time-consuming. Warner et al. (2002) used less restrictive constraints to 

permit a rapid analysis of 3-D wind fields obtained from scanning Doppler lidars. 
70 Federal R&D Needs and Priorities for ATD Modeling

Coupled with backscattered energy from airborne aerosols, almost real-time estimates of 

aerosol plume position, and short-term estimates of future paths (nowcasts) are feasible. 

Newsom and Banta (2004) and Calhoun et al. (2004) have suggested other approaches to 

assimilate lidar data at high resolution. Parallel development of lidar technology and data 

processing techniques should help advance the knowledge of smaller scale motions in 

real boundary layers. 

R&D Need: Improve and evaluate techniques for data QA/QC for model 

fit and data assimilation for both initial and boundary conditions. 

Data assimilation issues are closely tied to the scales of motion of interest, the availability 

of data representing those scales, and the techniques (models) used to link the data to the 

current state of the atmosphere at that scale. At present, data assimilation practices using 

variational or ensemble techniques exist for mesoscale operational models. These models 

are nested in global models but use finer resolution terrain conditions from surface, 

satellite, and/or aircraft regional observations. As finer scales are needed, assimilation 

approaches must adapt as surface and near-surface data become more important—an 

issue closely linked to improving characterization of surface-boundary conditions as 

discussed in section 4.2.2. At finer scales, assimilation becomes more temporally 

sensitive (perishable) and acceptant of observations appropriate to the model scale. The 

assimilation must allow representation of finer-scale dynamical processes (a need closely 

linked to bridging the mesoscale to microscale/urban scale gap, as discussed in section 

4.2.2). It must be able to accept data coming from emerging measurement technologies 

(closely linked to improving boundary-layer atmospheric measurement capabilities, as 

discussed in section 4.2.3. These improvements are particularly important for recognizing 

and incorporating into the model run the three-dimensional structure of the daytime and 

night-time boundary layers. 

Data QA/QC issues increase as remotely sensed and higher density in situ data are 

incorporated into the analyses. As much as possible, these issues should be addressed by 

onboard processing at the sensor, but errors due to data transmission, omissions, and 

losses must be identified before the data are used. Because volumetric remote sensing 

provides large sets of data to control and check, tests and filters for rapid and automated 

data QA/QC must be developed. Automated capabilities are also needed to ensure data 

representation by assessing the applicability of the data for the intended use. Including 

error bounds with observation data is an essential step toward understanding and 

quantifying the sources of uncertainty in model predictions that stem from factors outside 

the model itself. 

TABLE 13. Prioritization Factors for Improving and Evaluating techniques for Data QA/QC for 

Model Fit and Data Assimilation, for Both Initial and Boundary Conditions 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

immediate average moderate moderate high 
Chapter 4: Modeling and Measurement Research Needs 71

Substantial development has occurred in data QA/QC and data assimilation at the 

macroscale and the larger mesoscale. Applications of 4D VAR and ensemble techniques 

for meso–β and smaller scales can be initiated without much difficulty for testing these 

techniques where appropriate measurements exist. Full implementation of assimilation 

techniques will become beneficial when data become more plentiful and regular in test 

beds or large networks (i.e., when instrumentation is developed and in operation). As 

model scales become smaller and approach the urban scale, data assessment issues will 

become more location-specific, adding challenges to automation of the process and 

requiring faster execution times. Results of this R&D will link with the design and 

implementation of urban regional monitoring networks. 

4.4.3 Model Performance Evaluation Issues 

Model performance evaluation basically comes down to comparing a model’s predictions 

of concentration with the concentrations observed from field measurements. One can 

view the observed concentrations as a summation of three values: the ensemble average 

for the conditions present, the effects of measurement uncertainty, and the effects of 

unresolved processes (stochastic fluctuations). The modeled concentrations can be 

viewed as a summation of three values: the ensemble average for the conditions present, 

the effects of uncertainty in specifying the model inputs, and the effects of errors in 

model formulation (which may vary as conditions vary). 

The concept of natural variability acknowledges that the details of the stochastic 

concentration field resulting from transport and diffusion are difficult to predict. In this 

context, the difference between the ensemble average and any one observed 

concentration value (realization) is ascribed to natural variability. The ensemble is the 

ideal infinite population of all possible realizations meeting the (fixed) characteristics 

associated with the ensemble. In practice, one will only have a small sample from this 

ensemble. 

Measurement uncertainty in concentration values in most tracer experiments may be a 

small fraction of the measurement threshold. When this is true, the contribution of the 

measurement uncertainty to empirical determinations of the magnitude of natural 

variability can usually be deemed negligible. 

One method for performing an evaluation of modeling skill is to average separately the 

observations and the modeling results over a series of non-overlapping limited ranges of 

fixed conditions, which are called “regimes.” Averaging the observations provides an 

empirical estimate of what most of the current models are attempting to simulate; namely, 

the ensemble average. A comparison of the respective observed and modeled averages 

over a series of regimes provides an empirical estimate of the combined error associated 

with input uncertainty and formulation errors. 

This method for evaluating model skill is not perfect. Some models provide estimates of 

the average concentration for a volume of air (grid averages), whereas the observations 

represent what is seen for some point in the volume of air. The variance in observed 

concentration values due to natural variability can be on the order of the magnitude of the 
72 Federal R&D Needs and Priorities for ATD Modeling

regime averages. Hence, small sample sizes in the groups will lead to large uncertainties 

in the estimates of the ensemble averages. The variance in modeled concentration values 

due to input uncertainty can be quite large; small sample sizes in the groups will therefore 

lead to large uncertainties in the estimates of the deterministic error in each group. 

Finally, grouping data together for analysis requires large data sets, of which there are 

few. 

R&D Need. Develop physics-based evaluation metrics that recognize the 

fundamentally different sources for variations in observed and modelpredicted values of downwind hazard concentration. 

The most important concept expressed in the discussion above of modeling performance 

evaluation is that the observations and the modeling results come from different statistical 

populations whose means are (for an unbiased model) the same. The variance seen in the 

modeled values results from differences between estimates of ensemble averages and 

differences resulting from modeling errors. The variance in the observations results from 

differences in ensemble averages, differences arising from sampling uncertainties, and an 

additional variance, which is not represented in deterministic modeling, caused by 

stochastic variations between individual realizations. Because of these differences in the 

populations for which variances are being estimated, a thorough reassessment is needed 

of how transport and diffusion models are evaluated. The currently accepted model 

evaluation methods directly compare the observed and modeled concentration values (in 

contrast to comparison of regime averages), an approach that assumes the observations 

and the model estimates have the same sources of variance. As explained above, this 

assumption is erroneous. Viewed in this context, comparisons of observed and modeled 

frequency distributions of concentration values for transport and diffusion models are 

questionable, unless the models are attempting to estimate not only the variations to be 

expected in the ensemble average as conditions vary but also the effects of unresolved 

stochastic fluctuations. Thus, asking whether a deterministic model can match observed 

extreme values amounts to requiring the model to succeed at a task it is fundamentally 

incapable of doing, except by compensating for input uncertainties and formulation 

errors. Until now, model evaluations have focused on evaluating a model for how it is 

used rather than on the basis of what the physics in the model is capable of estimating. 

For example, models are now often evaluated as a characterization of extreme values—a 

task for which few, if any, models incorporate the necessary physics. 

Thus, the focus of model evaluation methods should be on assessing how well a model 

predicts those features of the concentration distribution (mean, variance, distribution) for 

which that model incorporates appropriate physics. While we cannot simulate exactly 

what is observed in time and space, we might (with suitable research) predict the average 

characteristics of the concentration distribution seen at each point (e.g., the mean, 

variance, and distribution). Of course, we only observe individual realizations, but if we 

properly predict the characteristics, the observed individual realizations will be within the 

predicted distribution of possible outcomes. If this approach to model evaluation is 

pursued, the evaluation methods can adapt to assess model performance as new model 

capabilities (e.g., probabilistic modeling) are developed. 
Chapter 4: Modeling and Measurement Research Needs 73

TABLE 14. Prioritization Factors for Developing Physics-based Model Evaluation Methods 

Time Sensitivity Short-Term Gain Overall LOE Lead Time Ultimate Gain Potential 

near term high low average exceptional 

Physics-based model evaluation methods must consider the internal, external, and 

stochastic components of model uncertainty. Models are evaluated unevenly, with 

different criteria applied by different developers. A reference standard or consensusbased methodology developed by an independent standards setting organization provides 

a standard by which developers and evaluators can uniformly evaluate modeling systems. 

This solution can be implemented rapidly by commissioning a standard-setting 

organization to develop and maintain (update) the standard. The sustaining activity by the 

organization will ensure the standard is maintained over time as experience is gained and 

innovation produces improvements. 

4.5 Summary of R&D Needs 

Table 15 is a compilation of the prioritization factors assigned to R&D needs in tables 3 

through 14. Although this summary table brings all the R&D needs together, the 

assignments of prioritization factors need to be interpreted through the explanations given 

in the paragraphs following each of the component tables. 
74 Federal R&D Needs and Priorities for ATD Modeling

TABLE 15. Summary Table of R&D Needs with Prioritization Factors R&D Need Time Sensitivity Short-Term Gain Overall Level of Effort Lead Time Ultimate GBridge the modeling gap near term average moderate average exceCharacterization of surface conditions & input data sets 

near term average high average exce

Test and refine physical basis for sub-grid-scale parameterizations 

longer term average moderate average exce

Characterize dispersion in complex environments 

immediate average high average 

Improve ensemble construction and interpretation 

immediate minimal high short exce

Techniques to better estimate wet and dry deposition 

near term average moderate average 

Physical and high-resolution computational models 

near term average moderate average exc

Improve tracer materials and measurement technology 

immediate high moderate short exc

Improve boundary-layer measurement technology 

immediate high high short exc

Improve and evaluate sensor fusion techniques 

immediate high moderate moderate 

Data QA/QC for model fit and data assimilation 

immediate average moderate moderate 

Develop physics-based model evaluation methods 

near term high low average exc
75 

5.0 AN R&D STRATEGY TO MEET USER NEEDS

5.1 The Goals for ATD Modeling R&D 

As the JAG considered user needs and the R&D to sustain continuous improvement of 

ATD modeling capability, a recurring theme was the necessity to quantify the uncertainty 

in ATD modeling predictions far more completely and accurately than is currently 

possible. Quantifying the uncertainty is essential for two reasons. 

First, ATD models must routinely characterize both the deterministic and stochastic 

contributions to the modeled effects. The stochastic contributions may in many instances 

override the deterministic processes. As emphasized in chapter 2 and throughout this 

report, the ATD modeling community must do better at interpreting for the end users of 

its products the implications of the uncertainty in predictions for the ways in which the 

predictions will be used. 

Second, effective progress in reducing the uncertainty (where possible) through 

continued R&D requires knowing how much there is and how much each factor 

contributes to the uncertainty in the product. As detailed throughout chapters 3 and 4, 

some sources of uncertainty in predictions can be reduced; others cannot (given the 

inherent nature of the processes involved or the limits of our science of them). Costeffective progress in reducing uncertainty requires quantifying and distinguishing these 

various contributions to the uncertainty in the product. 

The JAG identified two capstone goals for future R&D: routinely quantifying uncertainty

and interpreting the implications of this uncertainty to users. Supporting these capstone 

goals are six objectives (figure 8). Some of the objectives, such as multiple ATD test 

beds, correspond roughly to a single program element in the R&D plan. Other objectives, 

such as bridging the modeling gap, will be achieved through contributions from several 

R&D program elements. Overall, the JAG expects that a sustained and concerted effort of 

a decade or more will be required to attain all the objectives to the degree envisioned in 

this report. However, throughout that duration, all of the program elements will produce 

useful interim results. To reach the capstone goals, the elements of the overall strategy 

must be sustained, evaluated, and allowed to evolve as the knowledge base grows and the 

capabilities for ATD modeling improve. 

The synopsis below of each objective notes those program elements most directly related 

to obtaining the objective. Details of the program elements are discussed in sections 5.3 

through 5.7; next steps toward achieving these specific objectives and the capstone goals 

are recommended in chapter 6. 

Capture and Use Existing Data Sets. This objective identifies the need to assemble into 

a modern data format the available (open access) data sets from the many years of ATD 

experiments and model testing. These historical data are in various forms and storage 

media: punch card, paper, and electronic media. Oral histories from the participants will 

provide essential insights into the data quality, the understanding of objectives, and the 
76 Federal R&D Needs and Priorities for ATD Modeling

actual conduct of the field programs—all of which are in danger of being lost. These 

records of past ATD field experiments are the best extant source of concentration data 

currently available to quantify uncertainty in ATD models. The data from some of these 

experiments were not previously analyzed to the extent now technically feasible. The 

records from others are now worth studying for different objectives than before. Overall, 

they represent a resource that can quickly provide a useful base to begin assessing ATD 

model uncertainty. This objective can be achieved through the R&D program element 

described in section 5.3, Capture and Use Existing Data Sets. 

Model Evaluation Standards. Procedures for evaluating the performance of ATD 

modeling systems are not standardized across the Federal agencies or ATD model user 

communities. Further, the existing procedures may not fully deal with the complexities 

introduced by comparing calculations and observations having different inherent space 

and time averaging. Without common reference standards, model development and 

implementation tend to remain “stovepiped” within the developing agency, while other 

development efforts are discounted or go unused. An all-agency effort to establish and 

maintain a current set of criteria for evaluating model performance by a recognized 

independent standard development organization (SDO) will alleviate this problem. This 

objective can be achieved through the R&D program element described in section 5.4, 

Foster Evaluation Standards for Modeling Systems and Components. Other program 

FIGURE 8. Six R&D objectives need to be achieved to support the ultimate goals of 

quantifying uncertainty and communicating its implications to users. 

COORDINATED AGENCY SUPPORT & FUNDING

GOALS

MEASUREMENT 

MODEL EVAL 

CAPABILITIES

STANDARDS

ATD TEST BEDS

BRIDGE THE 

SCALE GAP

CAPTURE AND USE 

EXISTING DATA SETS

LOCAL/ REGIONAL 

MEASUREMENT SITING

Routinely Quantify Uncertainty

Interpret Uncertainty

OBJECTIVES

COORDINATED AGENCY SUPPORT & FUNDING

GOALS

MEASUREMENT 

MODEL EVAL 

CAPABILITIES

STANDARDS

ATD TEST BEDS

BRIDGE THE 

SCALE GAP

CAPTURE AND USE 

EXISTING DATA SETS

LOCAL/ REGIONAL 

MEASUREMENT SITING

Routinely Quantify Uncertainty

Interpret Uncertainty

OBJECTIVES
Chapter 5: An R&D Strategy to Meet User Needs 77

elements will help by contributing to the knowledge base needed to set standards and 

update them as needed. 

Bridge the Modeling Gap. Top-down modeling (from larger to smaller scales) and 

bottom-up modeling (from smaller to larger scales) do not merge across scales from 50 m 

to 5 km in realistic environments. This points to a fundamental lack of knowledge of how 

to model the processes at these scales. In all models, there is an element of turbulence 

carried in the smallest scales and the unresolved processes. Although there is perpetual 

optimism that higher-resolution models will give better results, current operational 

experience does not substantiate this optimism. 

The processes are primarily boundary-layer processes mixed with mesoscale phenomena 

and driven by the large synoptic flows. Specification of initial and boundary conditions 

for forecast models is difficult because of the complexity of the lower surface. Timing of 

flow features becomes more critical for ATD needs than for other forecast needs. 

Furthermore, smaller-scale features are typically lumped into sub-grid-scale processes 

and treated as a closure problem until some of the processes become effective in altering 

flows in significant ways. Feedback loops of small to large scale are poorly understood. 

In most ATD models, the crucial level of interest—within the first few meters above 

ground—is where the unresolved processes are dominant and mostly stochastic. 

The objective of bridging the modeling gap can be met through the combined and 

coordinated efforts from several R&D program elements. The major contributing 

elements will be coordinated programs for improved measurement technologies (section 

5.5), the multiple ATD test beds (section 5.6), and special studies and experiments 

(section 5.7). The additional analytical results obtained by capturing existing data sets 

(section 5.3) will provide early contributions toward this essential and difficult objective. 

Furthermore, the bottom-up approach of physical modeling and CFD and LES is critical 

to achieving his objective. Developments in theoretical constructs and understanding of 

basic processes within the gap will be keys to its closure. 

Improved Measurement Capabilities. Measurements are fundamental to advancing the 

realism of a science-based description or prediction. In ATD modeling, improving the 

capability to measure concentrations of tracers and atmospheric variables (e.g., wind 

velocity, turbulence, temperature) at the scales of ATD model use is essential to all the 

identified R&D needs leading to better ATD models. Quantifying the uncertainty in 

model variables requires in situ and/or remote measurements at the modeled scales. Most 

applications of ATD models are at much finer scales than are the available atmospheric 

data, especially in populated areas. To develop better parameterizations, measurements 

are needed to understand processes not resolved within models. 

Tracer measurement capabilities are needed to provide data for quantifying the 

uncertainty in ATD model predictions. They are also needed to build an experience base 

for users. Compared with meteorological model users, who routinely receive feedback on 

their forecasts, ATD model users rarely get real-time experience or feedback at present, 

except in emergency situations. Users need to build a base of experience with models and 
78 Federal R&D Needs and Priorities for ATD Modeling

develop local knowledge through feedback from real experiences. There is no training 

ground like real (not “canned”) hands-on experience. 

Two of the R&D strategy elements will make major contributions to this objective of 

improved measurement capabilities: the coordination of programs for improved 

measurement technologies (section 5.5) and the multiple ATD test beds (section 5.6). 

Supporting contributions will come from further analysis of existing data sets (section 

5.3), consensus evaluation standards (section 5.4), and special studies and experiments 

(section 5.7). 

Local/Regional Siting of Instrumentation. Each locality has a unique morphology. 

Many localities want to provide a network of instruments, within budget limitations, that 

will reasonably represent wind and turbulence fields needed for ATD concentration fields 

in emergency conditions. No one plan fits all these sites. Strategies are needed to make 

reliable recommendations through a combination of modeling exercises, optimization 

processes, and experience in other areas. Major contributions to this objective can come 

from analyzing existing data sets (section 5.3) and the multiple ATD test beds (section 

5.6). Parts of the coordinated programs for improved measurement technologies, 

particularly work on optimizing observational network design (section 5.5.2) will support 

this objective, as will aspects of the special studies and experiments (section 5.7). 

ATD Test Beds. Recently, fledgling infrastructures for routine ATD forecasting based on 

model and local information, such as NOAA’s DCNet, have developed in several urban 

areas. The JAG proposes a strategy of establishing test beds in appropriate areas across 

the country to address and test ATD models, model needs, and model capabilities on a 

continuing basis, just as weather forecasting operations and evaluations are conducted. 

By operating and performing evaluations continuously, by testing new ideas and 

instruments, and by interacting regularly with users, an ATD test bed becomes a crucible 

in which ATD modeling is made robust and refined from an art into a demonstrated and 

verified operational capability. 

Most ATD model studies in the past were defined field campaigns operated to maximize 

the probability of success. Within this context, benign, simple, and nontaxing weather 

conditions were preferred, although terrain conditions may have been complex. 

Controlled tracer releases were sampled and atmospheric measurements were made as 

densely as capabilities and resources permit. As accidental releases and terrorist incidents 

are not scheduled, little is known from these past studies about the performance of ATD 

models across the spectrum of daily conditions. The proposed test bed infrastructure will 

provide this coverage, while building on the results from past studies. 

The initial number of test bed installations should be limited so that operational 

procedures can be developed and refined without squandering scarce resources. Once the 

prototyping lessons have been learned, the set of installations could expand to cover more 

locations of priority interest, with each additional location chosen to represent different 

challenges from those already in place or being installed. 
Chapter 5: An R&D Strategy to Meet User Needs 79

The ATD test bed objective amply illustrates the interdependence among the program 

elements and the synergy across major applications for ATD modeling. Activities that 

support air quality forecasting can also employ the test beds and contribute ideas to 

improve test bed capabilities. Experience developed in working with the test beds will aid 

in siting instruments to optimize their utility. Measurement innovations, in turn, will feed 

and stimulate modeling innovations. Models will have access to better parameterizations 

to close knowledge gaps. New approaches to ATD modeling will develop as test bed 

capabilities for experimentation and evaluation improve. Baselines for characterizing 

uncertainty will develop. Improvements in models by reducing uncertainty will be more 

readily quantified using the test beds, and the performance of different modeling systems 

or different configurations of one system will be more easily compared and objectively 

evaluated. 

Multiple test beds are a major R&D program element, as well as a major objective. The 

extensive discussion in section 5.6 explains in greater detail why establishing and 

maintaining multiple test beds, with several in urban/city environments, is so important to 

the ultimate goals of quantifying uncertainty and interpreting its implications for users. In 

addition, the test bed objective will draw on other proposed program elements. Test beds 

need meteorological and tracer measurement capabilities to test siting strategies and to 

test and modify the instruments (section 5.5). Test beds should incorporate programs in 

which users and researchers work side by side in developing and using modeling systems 

and supporting tools (section 5.2). Standards are likely to evolve, based on test bed 

capabilities (section 5.4). 

5.2 The R&D Aspects of Interpreting Uncertainty Implications 

for Users 

This report addresses R&D needs, not training and outreach. However, some elements of 

training and outreach to users must be addressed because they impact the R&D process 

and outcomes. The most important of these elements concern the ability of users to 

understand how the limitations on model accuracy and precision—limits which the model 

researcher-developer quantifies in terms of the concept of uncertainty and the 

mathematics of probabilities—affect appropriate use of the model results. For example, 

users must have tools that incorporate probabilistic weather information with transport 

and diffusion applications and associated decision aids. As ensemble methodologies 

become more widely applied, traditional deterministic realizations will give way to 

statistical representations of plume evolution. It will be critical that the end user 

understand the difference between deterministic and probabilistic results and the 

subsequent effect on consequence management. 

As emphasized in chapter 2, users do not understand, and do not care to learn about, 

mathematically expressed statements of “uncertainty.” As important as those statements 

may be to the researcher-developer, they are difficult for most decision makers to 

understand and use constructively. They are even unlikely to be interpreted correctly by 

the person running the model in a real-life situation, unless that individual has an 

exceptional level of expertise and understanding. 
80 Federal R&D Needs and Priorities for ATD Modeling

In short, the ATD modeling system is not an operational tool for consequence assessment 

tasks until the end users know how to use it correctly. There is an experiential base on 

which to build, with both positive and negative cases from which to draw lessons. 

The efforts to learn how best to make uncertainty information useful to the users of ATD 

modeling predictions must take into account the three time frames for model use 

identified in chapter 2: first response (first hour), intermediate response/recovery, and 

long-term planning/recovery. Research is needed on how to give each user category 

results that those users can work with effectively. From the discussion in chapter 2, here 

are just a few preliminary and general distinctions that are likely to be important: 

• The most important information for the first responder is knowing what the 

hazard is and how to deal with it. Details of how the ATD modeler (whoever is 

running the model) got to the results and the mathematical uncertainties in the 

predictions are largely irrelevant. However, ways in which the uncertainties in the 

prediction could affect the response are important. These implications need to be 

communicated in terms relevant to immediate response decisions. 

• In the recovery phase, accountability for each step from the beginning to the end 

of the modeling activity is necessary. 

• Planners may want, and may be able to use in their larger planning, model 

predictions stated probabilistically. 

ATD test beds, an essential ingredient in the R&D strategy (section 5.6), provide the 

infrastructure where users, developers, and researchers can interact. Users are there to be 

trained to use the new products and information in their activities. Planners can even use 

test beds as an integral and essential part of scenario building and what-if analyses 

(gaming) for preparedness planning. The developers and researchers get to see how users 

respond, what they need, and where they have difficulty with products in development. 

These types of activities validate the usefulness of the ATD modeling activity. 

Independent, external consensus standards for evaluating modeling systems and tools 

(section 5.4) also play a major role in interpreting uncertainty for users. Such standards 

will: 

• Build user confidence in products; 

• Provide a science-based evaluation process for ATD products; and 

• Provide a mediation/facilitation role in dialogue between developers and users on 

what is needed to do the job. 

User training and the development of reach-back capability should be complementary 

activities. The effective integration of reach-back capability with tool dissemination to 

users is an intrinsic aspect of R&D, not just a post-R&D training issue. The National 

Atmospheric Release Advisory Center (NARAC) currently provides a national capability 

for training and reach-back in the context of incidents of national significance. Analogous 

training and reach-back support should be extended, perhaps through regional modeling 
Chapter 5: An R&D Strategy to Meet User Needs 81

centers, to incidents not at the nationally significant level, such as hazardous material 

spills. Reach-back capability for levels below incidents of national significance need 

ongoing coordination among existing infrastructure components, such as the NOAA/ 

NWS Weather Forecast Offices and regional ATD modeling centers. 

5.3 Capture and Use Existing Data Sets 

There are a number of existing high-quality ATD data sets from field research studies, 

which were conducted on terrain conditions ranging from very simple to very complex. 

The effects of hilly or mountainous terrain on winds and temperature have been studied 

for decades in the United States and abroad. Significant advances in understanding and 

predictive capability have evolved from this work. Many of the field studies sponsored by 

the U.S. Government used tracers, which makes the data potentially useful for testing and 

improving both meteorological models and ATD models. 

Many of these data sets, which were initially used to develop or test parameterizations for 

ATD models, can yield additional, valuable results on concentration uncertainty. They 

are the only current source of data for this purpose. New analyses, using tools developed 

since the studies were first done, can glean useful insights from the high-quality data sets. 

For instance, data sets that were treated with deterministic models when the studies were 

done can be re-evaluated with improved representations of the physical processes, 

including probabilistic models that incorporate stochastic representations of aleatory 

uncertainties. Many of the issues now recognized as important for meeting user needs—

such as short-term fluctuations in concentration and fuller quantification of 

uncertainties—were not major objectives at the time the studies were done. For example, 

in past experiments tracers were used to estimate time-averaged concentrations, not shortterm fluctuations. In some instances, the data sets were not thoroughly analyzed initially 

because of agency-funding limitations. Appendix A includes a partial annotated list of the 

past studies known to the JAG members to have data sets with substantial untapped 

potential for further analysis. 

One shortcoming of the data sets in their current condition is that they are housed in 

different laboratories and stored in different formats. The available data sets should be 

put into a format that allows the ATD research community to access and use them easily. 

For example, the Atmospheric Studies in Complex Terrain (ASCOT) program was 

sponsored by the Department of Energy (DOE) and conducted from the late 1970s to the 

early 1990s. The ASCOT data were stored at whichever DOE national laboratory was 

leading the field effort in that year. Data sets were collected at the Argonne, Lawrence 

Livermore, Los Alamos, and Pacific Northwest national laboratories, and probably 

elsewhere. Efforts are underway to collect and archive the ASCOT data sets. 

It will be far more cost-effective to archive and re-analyze these data sets than to repeat 

the underlying field studies at great expense. (In fact, data capture, reanalysis, and 

preparation of the data in accessible format could be a good opportunity for graduate 

student research and/or employment.) The data capture activities should include 

transferring the experimental data and associated metadata into an electronic format that 
82 Federal R&D Needs and Priorities for ATD Modeling

provides ease of access for analysts. For many of the studies listed in Appendix A, the 

effort should also include gleaning undocumented details and contextual information 

about the studies from the memories and expertise of the personnel who performed them. 

Several of the longer range transport studies are already incorporated in NOAA’s Data 

Archive and Tracer Experiments and Methodology (DATEM).1

 This archive may provide 

a framework for future archival activities. 

For several of these valuable data sets, work is already underway to perform additional 

analyses. Several simulations of the Prairie Grass field experiments have been done using 

more recent modeling approaches (Irwin et al. 2003; Hanna et al. 1990; Irwin 1984). The 

Metropolitan Tracer Experiment (METREX) data (Draxler 1985) have been re-examined 

in some DCNet modeling efforts. 

While some important cities and industrial facilities are located near mountains or in hilly 

terrain, much of the Nation's population and critical infrastructure are located near ocean 

coastlines or near large lakes. A modest number of field studies that collected both 

meteorological and tracer data within coastal zones have been conducted, usually for 

specific purposes such as testing and refining models of rocket effluent plumes. The data 

are often not as detailed as desired because it was difficult to make detailed 

measurements over large bodies of water, especially measurements aloft. Nevertheless, 

these data sets have value as a starting point for research on ATD in coastal urban 

environments. 

Reanalysis of these data sets can provide insights for designing test bed experiments 

(section 5.6), optimizing observational network design (section 5.5.2), and for special 

studies (section 5.7). This would allow more rapid progress toward improving ATD 

modeling system products. 

In terms of cost/benefit ratio, capitalizing on existing field data sets should be a top 

priority. With a modest level of effort, substantial short-term gains can be realized from 

these analyses in quantifying the uncertainty in existing ATD models and products. As 

discussed above, the strategy is time-sensitive with respect to providing input to the 

implementation of ATD test beds, planning for additional special studies, and capturing 

undocumented information about the past experiments while those who conducted them 

remain available. 

5.4 Foster Evaluation Standards for Modeling Systems and 

Components 

For this report, the term “performance evaluation” means ascertaining, as objectively as 

possible, how well a meteorological, ATD, or air quality modeling system is performing 

the tasks for which it was designed or used. The evaluation of model performance is 

essential to providing users with reliability measures and modelers with standard methods 

for assessing potential improvements. Various review groups have concluded that there is 

 

1

 For further information on DATEM, see the Internet homepage: http://www.arl.noaa.gov/datem/. 
Chapter 5: An R&D Strategy to Meet User Needs 83

a need to develop a common process for development of “consensus-based” evaluation 

procedures and metrics (OFCM 2002, NRC 2003, Dabberdt et al. 2004). Models simulate 

only a portion of the natural variability, thus their operational tasks are limited by the 

assumptions made in the construction of the model and the physical processes that are 

characterized. Differences between what is predicted and what is observed result from a 

combination of errors in model formulation (which can lead to systematic biases), 

propagation of measurement and input uncertainties (which can be amplified due to 

nonlinear effects), and the fact that nature contains more variability than do the models. 

The Standard Guide for the Statistical Evaluation of Atmospheric Dispersion Model 

Performance, ASTM D 6589 (2000), provides a framework for describing how 

observations and predictions differ. This standard employs the concept of ensembles, 

which in reality are imperfectly known. Ideally, one would compare the observed and 

predicted ensemble averages to objectively characterize any systematic bias in the 

model’s predictions. This approach has had some success for evaluation of short-range 

dispersion models by sorting observations into pseudo-ensembles, but much work 

remains to be done. 

Because uncertainties propagate forward in a prediction model, it is helpful to assess 

performance in a “front to back” sequence. For instance, the performance of the air 

quality/concentration model is dictated to a certain extent by any uncertainties in the 

characterization of the meteorology. The transport and diffusion of the emissions is based 

on the stated meteorological conditions. Often a portion of the emission specification 

(e.g., the dependence of surface spill evaporation on temperature and wind speed) is 

based on the representation of the meteorological conditions. Certain chemical processes 

are strongly influenced by the presence of and dynamics within clouds. 

After reviewing the state of practice, the JAG concluded that standardized “physicsbased” evaluation metrics are needed. Current skill scores and evaluation methods ignore 

the fact that the frequency distributions of the observations and predictions are inherently 

different; they have different sources of variation, as explained in ASTM D 6589 (2000). 

Thus, the JAG does not recommend attempting to catalogue and make use of current skill 

scores, as they have limited value, and then only if one realizes that seemingly correct 

predictions can be attained through a combination of offsetting errors. More sophisticated 

physics-based metrics would allow objective statistical tests to be made of whether 

differences in the results produced from various models should be deemed significant. 

From such statistical comparisons, measures of success can be developed. 

Different user needs will likely require different ways of evaluating model performance. 

Nevertheless, evaluation of all ATD models will have many common points of concern. 

The Federal agencies should agree on meeting this need for common model performance 

methods and metrics through a group under the aegis of an SDO. (An example is ASTM 

International, Committee D22, which has already developed ASTM D 6589.) 
84 Federal R&D Needs and Priorities for ATD Modeling

This approach to standards development is consistent with, and perhaps required by, 

Public Law 104-113.2

 In addition, other considerations favor development of ATD model 

performance standards by a standing committee of technical experts from a variety of 

disciplines. The SDOs have proven systems for developing technical standards. If 

information is needed, they can hold technical symposia to address specific concerns. If 

experts from various disciplines are needed, an SDO has a vast resource of experts within 

its technical committees for consultation. SDOs routinely review existing standards for 

needed updates. A standing SDO committee can provide continuity in methods 

development under the standard. Perhaps most important, development of a standard by 

an SDO carries the cachet necessary for public acceptance. 

With a view toward development of model evaluation methods, the JAG anticipates that 

the variety of user needs will provide fertile ground for development of a variety of 

performance metrics and methods tailored to specific user requirements. Furthermore, 

advances in modeling capabilities (e.g., models that provide a quantitative prediction of 

the distribution of possible individual outcomes) will place ever-increasing demands for 

development of new performance metrics tailored to specific user needs. In these 

dynamic conditions, a one-time definition of a set of performance metrics will not serve a 

useful purpose. A standing committee can provide continuity in standards development, 

acquire expertise over time, and leverage lessons learned to meet new demands over 

time. 

Further work is also needed on performance standards for instruments to detect and 

measure hazardous airborne species, particularly hazardous gases. Although essential to 

consequence assessment systems for those hazards, this need is beyond the scope of this 

JAG’s expertise and terms of reference. There are, however, other committees and 

working groups already established that can address these needs. 

5.5 Coordinate Programs for Improved Measurement 

Technologies 

Advances in all scientific endeavors come from the interaction of theory, model, and 

measurements. The measurement instruments to provide data for multiscale ATD 

modeling or for quantifying model uncertainties at the scales of interest to the users of 

ATD model predictions are scarce, and their use is far from routine. New and improved 

 

2

 On March 7, 1996, President Clinton signed into law The National Technology Transfer and 

Advancement Act of 1995. This law, codified as Public Law 104-113, serves to continue the policy changes 

initiated in the 1980s under Office of Management and Budget (OMB) Circular A-119, Federal 

Participation in the Development and Use of Voluntary Standards, which are effecting the transition of the 

Executive branch of the Federal Government from a developer of internal standards to a customer of 

external standards. Section 12 of Public Law 104-113, Standards Conformity, states that “... all Federal 

agencies and departments shall use technical standards (defined as ‘performance-based or design-specific 

technical specifications and related management systems practices’) that are developed and adopted by 

voluntary consensus standards bodies, using such technical standards as a means to carry out policy 

objectives or activities determined by the agencies and departments.” Public Law 104-113 further states 

that “... Federal agencies and departments shall consult with voluntary, private sector, consensus standards 

bodies, and shall ... participate with such bodies in the development of technical standards.” 
Chapter 5: An R&D Strategy to Meet User Needs 85

measurement techniques will add substantially to the development of theory and models 

appropriate for ATD user applications. Many parts of the modeling process need a variety 

of data types—for example, wind velocity, temperature, and flux data—so a variety of 

instrument types and capabilities are needed. 

The instrument development process often requires a long lead time: typically a decade 

or more from prototype development to a fielded system. However, to overcome barriers 

and shortcomings in ATD modeling and its use within the time frame set by national 

priorities, this development lead time must be shortened. In both the near term and longer 

term, the focus should be on developing instruments that, once their capabilities are 

demonstrated, can be used routinely in the ATD test beds and in special studies of ATD 

modeling issues. In the near term, the emphasis should be on incorporating and extending 

existing technologies. Development of a nationwide system, akin to the WSR-88D 

NEXRAD system, remains a vision for the future. 

The current trend toward compact, low-cost units closely networked into distributed 

systems is an appealing model for ATD instrumentation. An example is a distributed, 

compact phased-array Doppler radar system that is under development at the University 

of Massachusetts. Its key advantages are low cost, low maintenance, compact size, and 

connectivity. These features enable a network to be scaled to appropriate sizes and 

therefore widely applicable. 

New instrument development should focus on the R&D problems identified earlier in this 

report (e.g., chapter 3 and section 4.3): initial conditions, boundary conditions, closing 

the modeling gap from microscale to mesoscale, and improving tracer capabilities. Both 

remote and in situ measurement technologies are likely to be used. The remote 

technologies may employ active sensors (e.g., radar or lidar) or passive sensing (e.g., 

radiometric techniques) from platforms on the ground or aloft (carried on aircraft or 

satellites). One can expect that new system developments will use concurrent advances in 

digital signal processing to extract data at a high rate and with sufficient resolution. These 

systems will provide on-board processing and communicate information to the 

appropriate users—modelers, data assimilators, or emergency responders. 

5.5.1 Tracers 

Improved tracer capabilities are essential to progress in ATD modeling. Without an 

effective tracer technology for routine measurement of controlled or otherwise known 

releases, evaluation of ATD model concentrations—their uncertainty, their time averages, 

their variance, their intermittency—cannot be achieved. The task of identifying a suitable 

tracer, as discussed in section 4.3.1, is interwoven with the tasks of developing in situ and 

remote-sensing capabilities. Tracer selection must consider the measurement capability, 

as well as the measurement and diffusion properties of the hazards the tracer will 

represent. 
86 Federal R&D Needs and Priorities for ATD Modeling

5.5.2 Meteorological Data 

The meteorological data of principal interest are in the ABL—the least measured part of 

the ATD-modeled atmosphere. The ABL is essentially the connecting layer between the 

large scale flows above it and the land or water surface beneath it. It includes the finescale flows affected by surface conditions. 

Some near-term gains can be achieved by extending the capabilities of cutting-edge 

technologies to improve temporal and spatial resolution of wind field measurements. In 

particular, the capabilities of WSR-88D (NEXRAD) radars to measure winds and retrieve 

temperature profiles in clear air need to be explored. Doppler LIDAR systems are 

resolving boundary-layer wind fields and aerosol features within the modeling scale gap. 

Measurements of boundary-layer height and wind profiles above 100 m are currently 

available with 915 MHz radar wind profilers at widely scattered locations. Their vertical 

resolution may be increased by using new techniques under development at NOAA’s 

Environmental Technology Laboratory. However, the spatial variability of the wind 

profile is poorly captured by these systems. Adding mobility to eye-safe Doppler lidar 

measurements by placing them on aircraft, as suggested in a National Research Council 

report (NRC 2003), can expand the capability for area coverage, especially for 

emergency response to hazardous material ATD. Networking of smaller, more compact 

systems also promises greater capability. 

Measurement of the temperature distributions horizontally and vertically at meso-gamma 

scales to microscales is important for understanding ABL processes, especially in 

transitional and stable conditions. The temperature (density) structure governs the 

buoyancy of the atmosphere. These conditions are particularly important for ATD near 

the ground because density currents can control local flows even in simple terrain, while 

momentum and kinetic energy from a low-altitude jet (30–100 m AGL) may also be 

driving near-surface processes. Horizontal thermal gradients define the urban heat island 

and drive mesoscale and microscale circulations by differential heating (sea breezes, 

mountain breezes), which affects local transport and diffusion. 

The spatial variability of the temperature field would be a longer-term goal of a 

coordinated measurement program to support ATD modeling. Currently, most vertical 

temperature profile measurements are made in situ using balloons or kites to lift the 

sensors through the ABL. This process is labor-intensive and provides observations 

representative of only a small part of the atmosphere. Although current remote 

radiometric measurement techniques can produce local temperature profiles, they resolve 

the horizontal variability poorly, if at all. In most cases, the measurements using existing 

instrumentation have insufficient sampling rate and fidelity to compute heat flux profiles. 

High-resolution remote measurements of temperature profiles in the lowest 200 m of the 

atmosphere—comparable to in situ measurements on a meteorological tower—would 

provide major advantages for understanding processes at the surface and in near-surface 

layers. Raman scattering of selected electromagnetic frequencies is another promising 

remote-sensing technique still in early stages of development. 
Chapter 5: An R&D Strategy to Meet User Needs 87

For the meteorological component of ATD modeling, atmospheric moisture needs to be 

measured with precision comparable to that of temperature measurements. Differential 

absorption lidar systems have been used to measure water vapor along lines of sight and 

within scan volumes. This technology needs further exploration and, if warranted, 

development for ATD and other ABL-sensing applications. 

Measurement systems of the future need not be confined to the sensor technologies 

available today. Innovation in design and implementation of sensing systems must be 

actively encouraged and pursued. The atmosphere affects acoustic and electromagnetic 

propagation in ways that vary with the frequency of the wave phenomenon and the 

atmospheric conditions. Just as radar meteorology developed from the observation that 

clouds interfere with radar detection of aircraft, new sensing technologies for the future 

require critical thinking now about ways to acquire knowledge of atmospheric state 

variables from this “noise” in signals propagating through the atmosphere. From this 

perspective, a program for advancing measurement technology to support ATD modeling 

is highly dependent upon the sensor development community—in Federal laboratories, 

small business, industry, and academia—and is not confined to the meteorological 

community. 

5.5.3 Implementation 

Many Federal agencies and the National Science Foundation have existing programs to 

develop research instrumentation related to the atmospheric sciences. The DoD 

University Research Instrumentation Program provides for purchase of existing 

equipment or parts to make new sensor systems. Most agencies sponsor SBIR and STTR 

programs, which can provide substantial funding to move proof-of-concept and prototype 

systems toward commercial development. Coordinated efforts among the agencies are 

required to leverage their instrumentation opportunities to support ATD R&D. 

5.6 Establish Multiple ATD Test Beds 

For the purposes of this report, an ATD test bed is an infrastructure of atmospheric 

instruments including, as a minimum, an array of tower-mounted meteorological sensors 

capable of continuous observations. These observations should include not only 

measurements of the standard meteorological properties but also eddy fluxes of heat, 

moisture, and momentum. However, the JAG considers the type of test bed installation 

recommended in this report to be far more than a simple monitoring network. First, the 

test bed should provide a location and infrastructure to support the short-term deployment 

of other instrument systems, which could be ground-based, airborne, or satellite-based. 

Second an ATD test bed must have strategically located remote probing systems to yield 

wind speed and direction profiles, extending through the planetary boundary layer. Third, 

the test bed’s instrumentation should be tested and upgraded as developments dictate. 

Fourth, and most important, the observation data should be routinely scrutinized to 

improve the understanding of the wind, temperature, tracer, and turbulence fields (both 

horizontally and vertically) across and above the region encompassed by the test bed. 

Because of the importance that the JAG attaches to this element of the R&D strategy, a 
88 Federal R&D Needs and Priorities for ATD Modeling

full discussion of several aspects of test bed implementation and operations are included 

below. 

These basic requisites for an ATD test bed derive from the major reasons for 

implementing and sustaining a test bed infrastructure and operational capabilities. Section 

5.6.1 presents the reasons related to meeting R&D needs detailed in chapter 4. Additional 

reasons for the test bed strategy, presented in section 5.6.2, relate to the critical work of 

transitioning existing and forthcoming ATD products into operations, as discussed in 

section 3.3. 

While test beds are most often mentioned in connection with cities, an ATD test bed need 

not be limited to an urban environment. Section 5.6.3 presents the rationale for 

implementing a number (but not all) of the test beds in urban environments. Section 5.6.4 

compiles requirements for effective implementation of multiple test beds, consistent with 

the strategy proposed here. Finally, section 5.6.5 notes some key management issues that 

must be addressed, if this strategy is to achieve its potential gains cost-effectively. 

5.6.1 R&D to be Supported by ATD Test Beds 

As a tool for R&D, test beds enable benchmarking of options for ATD modeling systems 

and components in specific applications and conditions (the ATD environment of the test 

bed location). The data from the test bed, whether by itself or supplemented by shortduration intensive studies, can be used to test and refine predictive models. Results from 

test beds can be used in validating ATD modeling systems and components and as input 

to decisions on selecting, refining, and extending the modeling system and products to 

improve their suitability for specific consequence assessment needs. The experimental 

data from test beds also provide feedback to fundamental research on ATD processes and 

conceptual model improvement. 

As a permanently installed measurement system with supporting infrastructure and 

resources, a test bed can produce all-season, all-weather, 24-hour quantitative data on 

local environmental conditions. This capability is critical for broadening ATD modeling 

system performance evaluations beyond fair weather conditions and for establishing the 

historical base for ensemble forecasting. For R&D to improve ATD models, instrumentation is needed that can measure concentrations as a function of time and distance from 

release point (e.g., through use of tracers or surrogates), as well as measuring air transport 

and diffusion parameters. 

Short-term deployments of additional instrument systems at a test bed, to supplement its 

fixed monitoring infrastructure, are useful both for testing new instrument systems and 

for advancing the understanding of ATD processes and factors at that location. Shortterm deployments of remote-sensing systems, for example, can be used to update 

databases of surface-land interactions needed for microscale predictions even after the 

remote-sensing instruments are removed. Land cover, building location and spatial 

relationships, surface roughness, surface thermal variation, ultraviolet light intensity in 

open spaces, and urban canyon morphology, as well as other urban characteristics of 
Chapter 5: An R&D Strategy to Meet User Needs 89

importance to ATD models, can be derived from remotely sensed data and incorporated 

into the data infrastructure of the test bed. 

After review and discussion, the JAG concluded that the best way to get the data needed 

to feed better stochastic models is to have test beds where measurements can be made 

over time, under the full range of meteorological conditions that can occur at a site of 

interest. Test beds are the best way to produce the measurements and parameterizations 

needed to characterize uncertainties. They can support R&D on quantifying uncertainty, 

such as ensemble techniques and Monte Carlo simulations, as well as R&D on 

probabilistic models (models that incorporate stochastic representations of the physical 

processes controlling transport and diffusion). 

Because of the substantial level of effort and duration of investment needed to sustain not 

just one but multiple test beds, the benefits this strategy can provide relative to other field 

study approaches is worth consideration. Among the many merits of this strategy, the 

following are particularly significant: 

• Test beds are necessary to support the long-term measurement of atmospheric 

processes in urban airsheds and the archiving of the measurement data with 

associated ATD modeling results. 

• A well-instrumented, well-characterized test bed is an excellent tool for testing, 

evaluating, and incorporating measurement innovations in operational settings. 

An even better tool is to have multiple test beds, representing a range of complex 

environments (particularly urban environments), in which the strengths and 

limitations of various measurement innovations and approaches can be compared 

across environments. 

• Test beds are excellent tools for research on data QA/QC and data assimilation 

techniques. 

 The permanence of test beds makes them uniquely well suited for R&D to 

improve the assimilation of surface-based, satellite-based, and airborne 

remote-sensing data into the mesoscale meteorological models that provide 

input to ATD modeling systems. 

 The strategy proposed here is consistent with test bed work by NASA and by 

NOAA on better methods of assimilating satellite data into regional 

meteorological models (for example, the WRF and Eta models), which are 

often used to feed ATD models. 

• Test beds provide the infrastructure to encourage and support synergy and 

collaboration among developers working on different system components. (The 

DCNet installation provides a current example that should be encouraged and 

expanded.) 

• Test beds provide the infrastructure and reproducibility needed to develop and test 

methodologies for optimizing observational network design in general and design 

for urban areas in particular. (Section 5.5.3 contains further discussion of 

optimizing observational networks.) 
90 Federal R&D Needs and Priorities for ATD Modeling

• A permanent test bed can be used to conduct experiments on “sources of 

opportunity,” such as a benign but detectable emission from a point-source 

release. For urban environments, these opportunistic field studies can provide a 

cost-effective and pragmatic complement to planned experiments. 

For all of these R&D functions, one needs an instrumented test bed with sufficient 

density of observations to provide ground truth for comparison with model predictions. 

In addition to supporting R&D for ATD modeling, a test bed in an appropriate location 

can serve other R&D and operational objectives. Its monitoring infrastructure can 

improve local weather forecasts and nowcasts by characterizing the local variability of 

temperatures and precipitation. The monitoring network could be used, for example, to 

determine specific locations within the test bed region where freezing conditions are 

present, or whether an expected sea breeze front has propagated through the area. A test 

bed can also provide continuous support to air quality studies (essential nowadays in 

many urban areas) and provide assistance for emergency response when required. 

Thus, the test bed capabilities proposed here can serve purposes other than increased 

understanding of ATD processes. This multiple use aspect may serve to increase the 

available funding through cost sharing and to add to the political support for the project. 

The multiple functions for an ATD test bed—particularly test beds located in densely 

populated areas—are important for sustaining the long-term (10 years or more) public 

and political support for the installation, without which the ATD modeling R&D cannot 

be fully achieved. 

5.6.2 The Role of ATD Test Beds in Transitioning New Capability to Operations 

Sections 2.6 and 3.3 make the argument that developers of ATD modeling systems must 

begin taking more responsibility for the successful transition of forecasts and related 

products into operations. The task of development is not complete until the new 

capability has been proven useful in operations. The JAG members agreed that test beds 

are probably the best approach for (1) bringing users into the development process early 

and (2) providing productive, ongoing interfaces between fundamental research, model 

development for application, and operational use of developed products. Particularly 

when located in densely populated regions, test beds provide the following benefits, 

which complement and support these R&D-relevant objectives: 

• Test beds can accelerate user training in real environments. 

• Test beds encourage sustained, repeated input and feedback from users. 

• Test beds provide developers and governmental funding entities with a powerful 

tool for leveraging application support across diverse users and user communities. 

As an example, test problems can be run at a test bed with users directly involved. The 

lessons learned from the exercises can be used by researcher-developers to refine the 

output the model must provide, if it is to meet those users’ needs. The refined 

specifications for the output in turn define the modeling system’s input data 
Chapter 5: An R&D Strategy to Meet User Needs 91

requirements. At the same time, all of the interactions with users help to educate them 

about the modeling system’s output, including its measures of uncertainty and the 

implications of those measures for the users’ decisions. Thus, the involvement of users in 

test bed exercises provides a training ground for emergency response personnel who can 

return to their response/preparedness roles and use the more sophisticated predictive 

results effectively when the improved products become operational. 

5.6.3 Why Test Beds in Cities? 

Stated simply, urban test beds are needed because that is where the need for properly 

performing consequence assessment systems (as defined in chapter 1) is greatest. In our 

larger urban areas, the population is at risk from increasing levels of hazardous aerosols 

and gases of many kinds, including urban pollutants that affect air quality. Many of these 

urban regions are also at risk from point- or line-source releases of CBRN hazards, 

unintended or deliberate. Test beds established in several cities will provide the data to 

improve the currently limited capability to forecast the ATD of airborne substances in 

urban areas, regardless of the substance in question. 

As discussed in section 4.2.1, urban environments are complex with respect to factors 

affecting atmospheric transport. We need to develop and refine the capability to forecast 

the meteorological conditions of urban areas at the microscale relevant to reliable 

prediction of concentrations and exposure regimes downwind from source-term releases. 

The urgency of making progress in this area is intensified by the hard reality (discussed 

in chapters 3 and 4) that purely deterministic approaches fail to describe the range of 

possible outcomes necessary for emergency management decision making. This is 

especially true for environments characterized by deep street canyons, complex building 

morphology, and other land cover features that influence local wind conditions as much 

as, or more than, external meteorological conditions affect them. For the reasons 

discussed in section 4.2.4, coastal urban environments require special attention. 

Locating a test bed in an urban area (or in a region of coverage that includes an urban 

area) also has the potential to leverage diverse interests into sustained support for the test 

bed. A test bed infrastructure, as well as the data and studies produced using the test bed, 

can support emergency preparedness planning. Using the test bed for disaster planning 

and response exercises conducted by municipal or regional authorities increases the 

developer–user interactions. As noted in the previous section, these interactions feed back 

into R&D efforts to develop products that are better suited to meet evolving user needs. 

However, urban environments (or densely populated regions including suburban areas) 

are unlikely to provide coverage of all the environments of interest to all the consequence 

assessment communities described in chapter 2. Military operations, for example, or 

modeling of industrial accidents located away from population centers (such as 

transcontinental pipelines, nuclear power facilities, or military facilities) may be better 

served by an ATD test bed in a non-urban setting. (The special studies discussed in 

section 5.3 provide an option for covering some of these conditions.) The rationale for 

having a number of the ATD test beds in cities does not mean that all of them should be 

urban. 
92 Federal R&D Needs and Priorities for ATD Modeling

In summary, a number—and probably the majority—of the multiple test beds needed to 

implement this R&D strategy fully should be installed in high-priority urban regions. For 

a program of nationwide coverage sufficient for national interests, some of the test bed 

installations may not cover urban or even suburban regions. The siting decisions should 

reflect the objective of providing experimental coverage to test ATD modeling 

capabilities across the full range of environments in the Nation. 

5.6.4 Requirements for Effective Test Bed Implementation 

Test Bed Site Selection 

The proposed ATD test bed strategy includes implementing multiple test beds. The 

number of installations must be adequate to represent the Nation’s diverse environments, 

particularly urban environments in different meteorological regimes (air-land-water 

interactions and climatic patterns). For the strategy to be cost-effective, a limited set of 

ATD test bed sites must be carefully selected, representing the major climatic and 

meteorological regimes of the Nation. 

The previous section explained the importance of siting a number of ATD test beds in 

urban areas or metropolitan regions. Another siting objective should be to locate several 

of the test beds in coastal or lakeshore urban locations. Previous studies near ocean 

coastlines and large lakes have been limited. Thus, an initial task in the test bed strategy 

will be to weigh these objectives, along with other factors such as the ability to leverage 

costs with other users, to determine the number and optimal locations for ATD test beds. 

Baseline Capabilities of Each Testbed 

As discussed above, the ATD infrastructure should aim to support R&D, the transition of 

products to operations, and local forecasting and air quality monitoring in urban areas. To 

accomplish these aims, each test bed installation needs the following capabilities: 

• Operate and archive data continuously; 

• Continually test and evaluate existing analysis techniques; 

• Develop techniques for utilization of tracer sources of opportunity; 

• Perform periodic, controlled tracer experiments in both simple and complex 

meteorological conditions; and 

• Test and evaluate new measurement technologies and tracers. 

Each test bed in the program must have adequate base instrumentation. There should be a 

baseline of instrumentation common to all the ATD test beds, to ensure that comparisons 

can be made across the different environments they cover. The lessons learned from one 

test bed installation should be used to optimize the installation of those that follow. 
Chapter 5: An R&D Strategy to Meet User Needs 93

Involvement of User Communities 

In line with the arguments for user involvement made in section 5.6.2, potential user 

communities for the test bed should be involved in installation planning from its early 

stages. 

Long-Term Resource Commitment 

Perhaps the most difficult requirement to fulfill—and one that must be addressed as part 

of the site selection process—is the provision of sufficient long-term funding to sustain 

implementation and operations over an extended period. For some of the R&D needs 

specified in chapter 4, a decade or more will be required to achieve the longer term 

results that the JAG has envisioned. The decision to proceed with a test bed program 

should be with full recognition of the commitment necessary to reap the potential return 

on investment. Periodic review and evaluation of R&D projects must be built into the test 

bed management approach to ensure that progress is being made and that the envisioned 

outcomes are still supportable. 

Short-Term Gains 

At the same time that many substantial results will come only from long-term efforts 

sustained over years, there will be valuable interim results and products from the test 

beds. One short-term gain will be the evaluation and comparison of alternative ATD 

modeling systems (and system component options deployed within one overall modeling 

system) under comparable conditions. Testing across multiple, comparably instrumented 

sites is essential for the ultimate goal of compiling a set of models fully evaluated for a 

defined set of scenarios. Appropriate selection of a model from this evaluated set can 

then be made for any of a wide range of applications important to users. The near-term 

results from evaluation of existing modeling capabilities will establish criteria for 

deciding what further R&D should be done for modeling and measurement systems and 

methods. 

Partnering to Provide Sustained Support and Direction 

Stable, long-term resource allocations from a single Federal agency or program are 

difficult to sustain over multiple years of budget appropriations and administration 

priorities without strong partnerships from within and outside the Federal sector. 

Partnering is also essential to ensure that users’ needs at the local and regional levels 

continue to guide and inform each test bed’s projects. The test bed program at the Federal 

level should provide mechanisms to involve state and local public entities as partners 

with Federal funding agencies, as well as engaging the academic R&D community. 

Public-private partnerships with mutually consistent long-term objectives should be 

encouraged. 
94 Federal R&D Needs and Priorities for ATD Modeling

Coordination across Test Bed Installations 

The ATD test bed implementation program should require coordination across test bed 

installations to ensure that data are sharable and accessible. This coordination should 

include cross-installation activities and standards for data archiving, data access, and 

technical support to data users. There should also be coordination, where appropriate, 

with other atmospheric modeling efforts, including physical model facilities, modeling 

test beds for meteorological models, and air quality monitoring test beds for atmospheric 

chemistry and other air quality factors beyond transport and diffusion. 

5.6.5 Test Bed Management Issues 

This report does not recommend a particular management approach for ATD test beds. 

Implementing agencies and their partners will need to make decisions on a management 

structure based on a range of factors, many of which are beyond the scope of this report. 

However, there are some issues and concerns related to the program objectives presented 

in sections 5.6.1 through 5.6.4 that the selected management approach must be capable of 

handling. 

For the proposed ATD test bed implementation strategy to be effective, long-term 

planning and continuity of direction are essential. The strategy also requires channels for 

communicating upcoming possibilities for collaboration and leveraging among interested 

parties on studies, test and evaluation programs, and operational training and exercises. 

One approach that is worth considering is to have a single executive director for a test 

bed installation (e.g., a test city), who would have a scientific/technical policy board for 

advice. However, the executive director would retain decision authority and 

responsibility for a coherent, productive, and sustainable program covering research, 

development, testing, and evaluation. 

Program coordination and funding of test bed oversight functions by participating 

agencies could use any of several established mechanisms, such as competitive proposal 

solicitations. Existing contracting capabilities in the lead agency should be used as 

appropriate. 

5.7 Plan and Conduct Special Studies and Experiments 

In addition to a set of long-term test bed installations, special studies and experiments 

will be useful for particular purposes. These special studies would be field campaigns 

rather than sustained activities. A special study could be motivated by the requirement to 

address a specific, isolatable physical process, such as deposition or resuspension. It 

could be designed to explore transport and diffusion in a unique yet consequential 

environment, such as high-altitude droplet dispersion. Another rationale for a study could 

be to extend the utility of past field experiments (captured through the program element 

discussed in section 5.3) by relating historical data to current methodologies and 

instrument capabilities. A study could also be designed to extend recent test bed results to 

a broader range of conditions not covered by the set of operational test beds. 
Chapter 5: An R&D Strategy to Meet User Needs 95

One example of an important physical process to be studied on a special basis is the 

resuspension of hazardous materials once they have deposited on surfaces through 

settling, precipitation scavenging, or sorption to local materials. Deposited aerosol 

particles may be resuspended by the direct action of the wind or by abrasion of the 

surface (by other particles, foot traffic, vehicles, surface treatment, and so on). Deposited 

chemical species that sorb to local particles may also be resuspended. Because 

resuspension depends on a suite of complex factors, including the particle and surface 

morphology, specific chemistry, and local fluid mechanical effects, resuspension models 

tend to be highly parameterized. In the absence of surface disturbance, resuspension rates 

decrease with time, hence resuspension is of greatest concern in the following cases: 

• In the immediate short term after deposition; 

• For highly toxic materials, where small resuspension rates could pose grave 

concern; or 

• Where surfaces are likely to be disturbed, as during clean-up operations, normal 

urban operations, or large fluctuations in meteorological conditions such as a 

surface freeze followed by a quick thaw. 

When winds shift, resuspension can lead to a secondary plume and deposition pattern. 

Studies are needed to explore and quantify the following issues: (1) What is the effect of 

changing meteorological conditions on resuspension, including wind gusts and 

temperature effects? (2) How can we best model urban and vehicular resuspension? 

(3) For disturbed conditions, on what time scale can surfaces be considered sufficiently 

clean? (4) When do sorbed chemical species resuspend, and are “dust flux” models 

sufficient to describe these effects? Resuspension can be studied in field experiments 

through secondary collections of the released species, after initial plume passage. 

Special studies are also warranted for unique yet pertinent situations, such as high altitude 

releases, which have the potential to impact large populations. Field testing of a high 

altitude release would contribute to a better overall understanding of the risk and 

consequence associated with missile defense—an important national and homeland 

security topic. Previous studies of high altitude releases such as Cristal Mist (Diehl 1994; 

Kaman Sciences Corp. 1996) identified the importance of various high altitude processes, 

such as clear air turbulence, for predicting ATD and deposition. However, recent 

technological advances in remote-sensing and monitoring tools, notably lidar, could 

significantly improve upon earlier efforts. A carefully monitored and controlled 

meteorological study where stable droplets and/or particles are released at high altitude 

could be used to validate parameterizations of important processes associated with high 

altitude meteorology and dispersion. A possible field experiment would include the 

release of droplets/particles of different size (10 microns to 1 mm) and at different 

altitudes (above 1 km, above 10 km, above 20 km). The released droplets/particles could 

be carefully tracked and recorded as they settle and deposit over a large area. Depending 

on the altitude of release and droplet/particle size, the monitored area could be very 

large—potentially hundreds of square kilometers. 
96 Federal R&D Needs and Priorities for ATD Modeling

Repeating conditions of selected classic studies, but with new measurement technologies 

used alongside the old technologies, can increase the value of the old data sets by 

enabling cross-test comparability and longitudinal studies. Early field experiments were 

conducted within the then-available tracer and measurement technologies and with 

particular objectives. Results from these studies were used to develop, improve, or 

confirm ATD modeling assumptions and parameterizations. Recent significant advances 

in those capabilities offer the opportunity to revisit the classical field studies and evaluate 

and improve the parameterizations formulated in the classical arena. Reductions in 

uncertainty of models by the infusion of new technology can be quantified. Improved 

understanding of the earlier results may be possible. As an example, a large number of 

dispersion tests were conducted at Hanford, Washington, with zinc sulfide tracer 

particles, which have a high deposition rate. Comparative tests could enable data mining 

for the effects of deposition on the initial data and interpretation of field results (Doran 

and Horst 1985). In addition, certain meteorological parameters now understood to be 

crucial to transport and diffusion prediction, like surface heat flux and boundary-layer 

height, were not always documented in classic field studies. The results from the classic 

studies must be interpreted with estimates of these important parameters, introducing 

more variability and uncertainty than is necessary. If modern instrumentation and 

methods could be applied to the classic problems, a more complete set of measurements 

would result. This would provide a basis for improved characterization of transport and 

diffusion effects. 

With respect to coverage of special situations, a permanent test bed facility cannot be 

established for every set of conditions of interest. The gaps can be filled in with one-time 

experiments that provide a basis for interpolating and/or extrapolating from test bed 

results and old data sets. Special studies and experiments can also focus on the roles of 

particular physical processes to improve process-specific parameterizations. 

Test beds cannot cover all of the many challenges of ATD models. While urban domains 

are emphasized in this report, field campaigns in different circumstances will be needed 

to assess the multiscale properties of transport and diffusion. Some special studies may 

address terrain-driven and urban-driven flows, like the Department of Energy’s Vertical 

Transport and Mixing (VTMX) field study in 2000. Even after the initial study, the 

complex flows affecting lateral and vertical mixing in a stably stratified basin were not 

resolved. One conclusion of that study was the necessity for more temporally and 

spatially complete measurement of the sub-basin-scale motions near the ground and aloft. 

One purpose of test beds is for routine, day-by-day investigations of ATD modeling 

capability and evaluation. On occasion, special one-of-a-kind studies will be needed to 

address particularly difficult modeling performance issues. Within the framework of a 

test bed, additional instrumentation may supplement the test bed’s usual instrumentation 

suite to conduct a major field campaign. Such studies would be based on specific science 

objectives. 

Special studies can also be used outside the framework of the established test bed 

installations to address specific questions and issues. The greater understanding gained 

from a carefully designed special study could advance the theory and practice of 
Chapter 5: An R&D Strategy to Meet User Needs 97

atmospheric modeling. As an example, many individual field programs have examined 

components of the diurnal cycle for modeling mesoscale features. This field study 

produced a set of insightful science questions and issues, which are summarized in the 

following list, developed at the VTMX workshop in September 2002. 

Issue 1. The performance of mesoscale numerical models in describing the wind, 

temperature, and turbulent structure of the valley atmosphere was mixed. 

• Basic flow patterns and temperature fields were captured, but ... 

 the timing of important events (e.g., flow reversals) was often off by one or 

more hours, and 

 details of the flows over certain segments of the valley were in error (e.g., jet 

structure, flow strength). 

• The vertical temperature structure showed persistent biases, which are not yet 

understood. 

• The agreement between modeled and observed turbulent heat fluxes and turbulent 

kinetic energy was unsatisfactory. 

Issue 2. Aspects of radiational heating and cooling in valleys do not seem to be 

adequately accounted for in numerical models. For example, extreme cooling is observed 

in sinkholes. 

• Can this behavior be explained and modeled? 

• How relevant is this phenomenon to larger-scale valleys and basins? 

Issue 3. Wavelike features and ascending or descending layers of air were common along 

the side walls and may be common elsewhere. Their significance for vertical transport 

and mixing is unclear. 

• What is actually happening during these events? 

• What causes them? 

• Is there any hope that they can be modeled or predicted? 

• Does it matter? 

Issue 4. LES and DNS look promising, but can they deliver useful improvements in 

parameterizations for mesoscale models? 

• How can we generalize from highly idealized modeling conditions to the real 

world? 

• What insights into the basic physics of stable atmospheres can be gained? 

This list does not exhaust all of the questions and issues that special studies could 

address, but it does reflect current questions, results, and issues that have arisen in the 

context of the VTMX program and other recent field studies. 
98 Federal R&D Needs and Priorities for ATD Modeling
99 

6.0 RECOMMENDATIONS

The R&D elements presented in chapter 5 will require a robust, coordinated effort by the 

multiple Federal agencies engaged in research, development, or application of ATD 

modeling systems. No one agency holds all the capabilities needed to affect the 

recommended course of action. Shared responsibilities, shared vision, and shared 

resources are essential to success. Without the resource base and sustained direction that 

a well-coordinated Federal effort can provide, the R&D needs cannot be met within time 

horizons consistent with national policy priorities. 

In this chapter, the JAG presents nine recommendations, covering implementation efforts 

in seven areas: 

• Quantify model uncertainties and interpret their implications to users; 

• Capture and use existing data sets; 

• Implement ATD test beds, 

• Develop standards for evaluating modeling system performance; 

• Improve the spatial and temporal scale interactions between meteorological and 

ATD models; 

• Improve measurement technologies; and 

• Design and conduct special studies and experiments. 

Implementing these recommendations will require sustaining the effort over more than a 

decade. Some of the actions can be accomplished and will produce returns in the near 

term: within the next 2 years. Other actions will provide interim benefits at intervals 

along the way, even though the most significant benefits may require a decade or longer 

for realization. A paragraph on timing considerations follows each recommendation 

below. 

Many of the capability limitations identified in this report are systemic, resulting from a 

lack of coordinated effort across agency R&D programs aimed at understanding and 

modeling the fundamental drivers of ATD processes, such as boundary-layer processes. 

Coordinated efforts are also needed to develop capabilities to measure the spatial and 

temporal variability of the most changeable part of the atmosphere at the resolution 

required for ATD predictions. These limitations can be surmounted, but success will 

require commitment and coordination of resources and facilities. Particularly important 

are the human resources residing in dedicated teams with strong operational and science 

motivations. 
100 Federal R&D Needs and Priorities for ATD Modeling

6.1 Quantify Model Uncertainties and Interpret Their 

Implications to Users 

The JAG views the two recommendations in this section as together forming the keystone 

for delivering vastly more useful ATD modeling products as operational tools for the user 

communities described in Chapter 2. The remaining recommendations define enabling 

science and technology to achieve the full potential of these keystone recommendations. 

6.1.1 Improve ATD Modeling Systems to Routinely Quantify Uncertainties 

As explained in chapter 2, most of what users need from the ATD modeling system 

embedded in their consequence assessment system comes down to predicting airborne 

concentrations of hazards as a function of space and time. To deal with the uncertainties 

in predicting these concentrations, the ATD model developer has a growing array of 

probabilistic techniques available. Yet, the users do not want mathematical expressions of 

uncertainty or probability; they want answers on which to base decisions and take action. 

The usual practice has been to give users point estimates of concentration, whether those 

estimates were derived from deterministic representations of the physical ATD processes 

or incorporated modeling of stochastic processes. However, the uncertainties in the data 

inputs to the ATD model, the approximate nature of the model constructs and 

parameterizations, and the stochastic nature of atmospheric turbulence all lead to 

uncertainties in individual realizations of a model. Quantifying these uncertainties is 

essential for two reasons. First, reducing the uncertainty through continued R&D requires 

knowing how much there is and how much each factor contributes to the uncertainty in 

the product. Second, the ATD modeling community must do better at interpreting the 

implications of the uncertainty in model predictions for the users of ATD modeling 

products. 

Recommendation 1. ATD modeling systems should routinely quantify the uncertainties 

in their results. Implementation actions required for this recommendation include but are 

not limited to the following. 

• Develop robust techniques to assess probabilities of occurrence of concentrations 

above significant threshold levels. 

• Adapt (or develop) and verify measurement capabilities at or below the scales of 

interest for model predictions. 

• Test, verify, and validate model improvements, including probabilistic methods of 

process representation, parameterization, and data acceptance/assimilation. 

• Establish a shared data system with substantial sets of data to test and evaluate 

uncertainty quantification techniques. 

• Develop techniques to quantify and reduce uncertainty in predictions through 

improved model–data interfaces, including but not limited to sensor fusion, data 

assimilation, and evaluation criteria. 
Chapter 6: Recommendations 101

• Develop analysis techniques that are applicable in nonlinear regimes to display 

important sources of uncertainty. 

• Use the outcome from new techniques for estimating uncertainty to guide 

improvements in ATD models. 

• Develop and implement processes for uncertainty displays in data-sparse 

environments. 

Timing considerations for recommendation 1. Initial efforts to quantify uncertainty in 

current models, using existing databases, can begin immediately. At reasonable funding 

levels, 5 years will probably be required to conduct the necessary R&D on modeling 

methods and to improve measurement capabilities. Delays in starting or funding 

limitations will extend the time required. Some of the implementation actions listed for 

recommendation 1 will depend on progress in other actions in the list. For instance, 

techniques for estimating uncertainty that are applicable in nonlinear regimes will be 

needed before sources of uncertainty can be ranked in importance, as a guide to 

improving ATD models. 

6.1.2 Effective Communication of Model Uncertainties to Users 

To serve users better, the model development community must do two things differently. 

First, modeling systems must routinely quantify the various mathematical uncertainties in 

model results. Second, developers must find useful ways to communicate the practical 

import of these uncertainties to the users. For many of the applications discussed in 

chapter 2, users need to understand the impact on health and safety of concentration 

variability in space and time. For example, a model’s predicted probability distribution 

for the concentration in an area of interest (hazard area) during a specified time interval 

might be represented as the probability that a meaningful health or safety threshold 

concentration will be exceeded. This way of presenting model results may be practical 

and useful for some types of users once they understand it. Other types of users may need 

or prefer a different representation of results. 

Ongoing, sustained interaction between developers and users is the only way to 

determine which representations will work best for which users, while still adequately 

representing the uncertainty. For example, NASA has used product advisory boards, with 

members drawn from the state and local governments who are the customers (potential 

users) for remote-sensing data. The product advisory board participates throughout the 

product development process, commenting at critical milestones on the members’ 

perceptions of the value of the emerging product. 

Recommendation 2. The ATD modeling R&D community should work intensively and 

routinely with representative users to determine effective means of presenting 

information to users that incorporate the quantified uncertainties in model results. 

Implementation actions required for this recommendation include but are not limited to 

the following. 
102 Federal R&D Needs and Priorities for ATD Modeling

• Involve users in prototyping, testing, demonstration, and training sessions for 

probabilistic tools under development to determine which tools and 

representations are most useful and acceptable to specific user types and 

application contexts. 

• Incorporate the feedback and advice from users on the utility of representations of 

probability and/or measures of uncertainty into the ATD model R&D process. 

• As a requirement for the ATD test bed implementation proposed in 

recommendation 4, require planning and implementation with relevant user 

communities throughout the R&D programs conducted at a test bed facility. 

Timing considerations for recommendation 2. Although current capabilities to 

quantify the uncertainties in ATD predictions are limited, efforts to improve on current 

practices in interpreting uncertainty implications for users can and must start now. 

Those in the ATD model R&D community who work with users in demonstration or 

training exercises should take every opportunity to seek “best practices” and innovative 

approaches that help users understand how to accommodate prediction uncertainties in 

their decision processes. The dialogue among model researcher-developers, modelers 

(those who run the models operationally), and users of the model predictions can start 

immediately. 

6.2 Capture and Use Existing Data Sets 

An abundance of data from previous field experiments exists in various forms and 

formats. The richness of these experimental data has not been fully exploited, particularly 

given advances in analytical techniques and new objectives of analysis since the 

experiments were conducted. These data sets constitute the nation’s only current source 

of concentration data to quantify the uncertainty in ATD model predictions under actual 

atmospheric conditions. The studies were performed to achieve certain objectives, often 

related to air quality, such as estimating plume behavior in heterogeneous environments, 

transport and diffusion from elevated sources, or multi-state dispersion. They reflect a 

wide range of applications and learning conditions. However, few of these data sets have 

been analyzed using the probabilistic techniques now available. 

These data and important unrecorded knowledge about the experiments from which they 

were collected are in danger of being lost to the R&D community with the aging of the 

experiments’ designers and participants. Individual efforts to reanalyze these legacy data 

sets have occurred or are in progress. An immediate effort is needed to expand and 

coordinate the acquisition of the legacy data sets and supporting knowledge about the 

experiments, capture the data and supporting information in modern data files, and 

analyze the data using modern approaches and techniques. The OFCM, under FCMSSR 

instruction, could coordinate the multiagency effort needed. Appendix A describes a 

number of past experiments and demonstrations that this JAG views as important to 

capture and preserve. There may be others. Immediate action on these objectives is 

warranted because further work with these data sets using state-of-the-art and state-ofpractice methods will aid in formulating initial estimates of the stochastic uncertainty in 
Chapter 6: Recommendations 103

ATD processes, designing ATD test beds, planning experiments for the test beds, and 

designing special studies and experiments. 

A complementary objective to capturing and reanalyzing the legacy data sets is to 

increase their accessibility to the ATD modeling R&D community. For example, the 

OFCM could lead the effort to develop an XML standard for ATD-related data, taking 

into account any standards already developed or in progress for related types of data. 

Recommendation 3. OFCM should convene an interagency effort to develop guidelines 

for acquisition, archiving, and access of data from previous field and laboratory ATD 

experiments. Implementation actions required for this recommendation include but are 

not limited to the following. 

• Identify and communicate the reanalysis projects already completed or in 

progress. 

• Prioritize the experiments and demonstrations from which data sets should be 

captured and preserved in accessible format. 

• Adopt or adapt a data interchange standard as an archival format. 

Timing considerations for recommendation 3. Data capture and archiving for the 

priority experiments can be completed in 3 years. 

6.3 Implement ATD Test Beds 

Multifunctional ATD test beds will support a variety of research, development, and 

product-transition activities required to fill existing gaps in meeting user needs. Among 

the activities enabled by ATD test beds are the following. 

• Long-term measurement and archiving of measurement data on atmospheric 

processes and ATD modeling in urban airsheds; 

• Accelerating iterative rounds of user input and feedback to implement 

recommendation 2; 

• Accelerating user training in real environments (also essential to recommendation 

2); 

• Testing, evaluating, and incorporating measurement innovations; 

• Developing techniques for using sources of opportunity (benign atmospheric 

releases, other than planned tracer studies, which can be detected and traced as 

they are dispersed downwind); 

• Improving the assimilation of satellite-based and airborne remote-sensing data 

into the mesoscale meteorological models that provide input to ATD modeling 

systems; 

• Fostering a coordinated approach to model–data interaction issues including but 

not limited to sensor fusion, data assimilation, and evaluation criteria; 
104 Federal R&D Needs and Priorities for ATD Modeling

• Providing all-season, all-weather, 24-hour quantitative data on local 

environmental conditions; 

• Leveraging development efforts for users with diverse applications, such as 

weather nowcasts, air quality forecasts, public education and outreach, and 

transportation systems management; and 

• Developing and testing methodologies for optimizing observational network 

design in general and network designs for urban areas in particular. 

Because of the diversity of conditions across the United States and even among its major 

urban centers, the JAG assumes that multiple ATD test beds will be required. Urban 

locations are likely to predominate, but some nonurban settings will probably be 

appropriate as well. The sequencing and final number of test beds to be implemented and 

the appropriate types of locations for them should be initial tasks for an appropriately 

constituted body established by the stakeholder Federal agencies and referred to here as a 

joint (or multiagency) test bed authority. Coordination with parallel test bed development 

programs for atmospheric modeling goals (e.g., air quality and weather forecast 

modeling) is essential to ensure that the overall investment is cost-effective and efficient. 

Recommendation 4. Participating Federal agencies, through the FCMSSR, should 

establish a multiagency test bed authority to oversee multiple test beds for urban and 

complex environments in locations selected on the basis of national and R&D priorities. 

This joint test bed authority should have authority to undertake the following actions: 

• Assess the number of test beds required to meet research, development, and 

application transitioning needs for the Nation’s diverse environments. Implement 

those test beds consistent with these needs and overall resource constraints. 

• Ensure that test bed implementation plans provide for adequate base 

instrumentation to achieve the intended research, development, and application 

objectives of the test bed. 

• Incorporate user community feedback and advice on user training and technology 

transition activities at the test beds (supports recommendation 2). 

• Coordinate solicitations for competitive proposals to use the test bed 

infrastructure funded through interagency memoranda of agreement or other 

mechanisms. 

• Provide a point of contact for parties interested in using the test bed for 

experiments or projects supported by other means. 

• Enter into and encourage partnerships with state and local entities, as well as 

public-private partnerships, for support and utilization of the test beds, in 

particular for technology transition and user training projects. 

• Coordinate the ATD test bed implementation program with other atmospheric 

modeling test bed efforts. 
Chapter 6: Recommendations 105

• Establish and coordinate a program for sharing and archiving long-term 

measurement data across the ATD test beds, using the data interchange format or 

guidelines developed for recommendation 3. 

Timing considerations for recommendation 4. Implementing and acquiring results 

from the ATD test beds are evolutionary efforts lasting for a decade and more. 

Embryonic test beds exist and could be brought up to the level of capability envisioned in 

this report. Plans for others are developing and must be actively nurtured. 

6.4 Develop Standards for Evaluating Modeling System 

Performance 

Accepted standards for evaluating modeling system performance are essential. This need 

can be met efficiently by the proven processes used by established voluntary standards 

development organizations such as ASTM International. 

Recommendation 5. The OFCM should officially ask an existing standards development 

organization to establish and maintain a subcommittee to develop guidelines and 

standards for evaluating ATD modeling system performance. The Federal agencies 

involved in ATD research, development, or applications are encouraged to support this 

subcommittee. 

Timing considerations for recommendation 5. A working group could be established 

within an existing consensus standards organization within 6 months. Release of an initial 

set of performance standards will require 2 to 3 years. 

6.5 Improve the Spatial and Temporal Scale Interactions 

Between Meteorological and ATD Models 

As described in section 4.2, there are gaps in model nesting or initialization capability 

that seriously affect the interactions between models at different spatial and temporal 

scales. The capability gaps in connecting top-down modeling from NWP-based 

meteorological models with bottom-up modeling approaches, such as physical modeling, 

CFD, or LES, are at the very spatial and temporal scales that have major impacts on 

consequence assessment applications, particularly applications for densely populated 

environments. One approach is to test and further refine the physical basis for sub-gridscale parameterization in nested meteorological models. Other approaches to bridging 

this gap from the microscale or urban scale to mesoscale models should also be 

investigated. 

Another area for improvement is characterization of surface boundary conditions at the 

three-dimensional spatial scales relevant to ATD modeling requirements in urban 

environments. Remote sensing from airborne or satellite assets will probably provide the 

data (see sections 6.6.1 and 6.6.3), but algorithms to test and correct the observation data 

set will be needed to attain the desired accuracy in model predictions. 
106 Federal R&D Needs and Priorities for ATD Modeling

Recommendation 6. The R&D communities in meteorological and ATD modeling 

should work together to conduct the research, development, and testing needed to address 

difficulties in interfacing models at different scales and to improve the capability of urban 

scale ATD modeling systems to handle fine-scale surface-atmosphere boundary 

conditions. Implementation actions include but are not limited to the following. 

• Test and refine the physical basis for sub-grid-scale parameterizations. This will 

be the outcome of the bottom-up approach of using physical models and CFD and 

LES. 

• Explore innovative approaches for bridging the gap from microscale (urban scale) 

ATD features and events to mesoscale meteorological models. 

• Better characterize surface boundary conditions at urban scale, including methods 

for obtaining, maintaining, and using up-to-date land cover data. 

• Address the issues in model initialization, nesting, and data assimilation. This will 

be the outcome of the top-down approach. 

Timing considerations for recommendation 6. No specific timing considerations for 

this recommendation were identified. However, progress on bridging the gap from 

microscale ATD features and events to mesoscale meteorological model results used as 

initialization and boundary data in ATD modeling is essential to reducing the 

uncertainties in ATD predictions. 

6.6 Improve Measurement Technology 

6.6.1 Coordinated Measurements Technology Development Program 

Measurements provide the ground truth for models and theory, as well as the data for 

model initialization and bounding. For progress in theory and modeling and for 

understanding the uncertainty in model variables, measurements of concentrations and 

atmospheric variables must be made at or below the scales of interest. Test beds will not 

realize their full potential with point sensors alone. Capabilities to remotely sense 

meteorological fields of wind, turbulence, and moisture fluxes rapidly and volumetrically 

should be actively pursued and quickly incorporated into test beds for evaluation and 

utilization. Technology to rapidly sense induced tracers in situ and remotely is essential 

for test beds and for special experiments. Some sensing systems must be mobile, rather 

than fixed at one location. In emergency response applications, airborne or groundtransportable systems are highly desirable for plume tracking throughout the depth of the 

boundary layer and not just at ground level. 

Avenues already in place for instrumentation R&D can be leveraged for ATD 

instrumentation development. Many Federal agencies have small business innovative 

research (SBIR) and/or small business technology transfer (STTR) programs. These 

could be used as vehicles for a multiagency approach to enlisting the resources of the 

private sector and the university research community in innovative instrumentation 

technology R&D. The “bench scientists” in the Federal laboratories have creative ideas 
Chapter 6: Recommendations 107

and capabilities to develop new instrumentation providing these measurement 

capabilities. 

Recommendation 7. A coordinated Federal ATD R&D program should develop or 

improve evolutionary and revolutionary atmospheric sensors supporting ATD R&D and 

then transition the technology into tools for ATD model researchers, developers, and 

users. This coordinated program for measurement technology should tap the expertise 

from across the public (including Federal laboratories), private, and academic sectors. 

Implementation actions required for this recommendation include but are not limited to 

the following. 

• Identify potential tracer materials and determine the most useful combinations of 

tracer material and detection techniques to improve ATD models and quantify 

uncertainty. 

• Develop and test new atmospheric measurement technology, including those 

using remote-sensing techniques for volumetric measurements. 

• Coordinate measurement technology development with modeling R&D (see 

Recommendations 1 and 6) to address major model–data interface issues, 

including but not limited to sensor fusion, data assimilation, and evaluation 

criteria. 

Timing considerations for recommendation 7. Timing and priorities among the 

suggested implementation actions should reflect dependency relationships with other 

ATD R&D activities. For example, a reliable and cost-effective means for collecting 

large amounts of tracer data is essential for characterizing and quantifying the uncertainty 

in ATD predictions. Therefore, an early start, with high priority, should be given to 

selecting potential tracer materials. This selection should be completed within 2 years 

because it will dictate the direction of development for point and volumetric techniques 

for tracer measurement. Engineering development to lower cost and enhance mobility of 

sensor networks relative to existing systems could, if initiated soon, be completed within 

5 years. Development and test of new, innovative systems for meteorological 

measurements will be an ongoing process, continuing over a decade and more. 

6.6.2 Instrument Siting and Networking 

Guidelines for siting sensors for meteorological variables or processes have generally 

been developed for open environments. In the complexities of urban, coastal, or terrain 

environments, these guidelines can rarely be met. The existing criteria are suitable for 

their intended purpose of observations to support regional weather forecasting. However, 

in urban environments, there are multiple purposes that meteorological sensor systems 

can serve. The CFD modeling community may want time-sequenced data near buildings, 

as well as away from buildings, to discern circulation patterns. To ensure that consistent 

and reliable measurements are made at ATD test beds, performance guidelines need to be 

established for siting instrumentation in complex environments that take these multiple 

purposes into account. Given the rapid advance in instrumentation technologies and the 

variability from site to site in terrain and local meteorological characteristics (e.g., diurnal 
108 Federal R&D Needs and Priorities for ATD Modeling

wind patterns, air-land-water circulation and heat transfer patterns), traditional, 

deterministic design requirements for instrument siting are unlikely to work well for the 

intended purposes. Functional guidelines, in terms of desired performance from an 

instrumented site, are more appropriate. 

Recommendation 8. The OFCM should establish a working group, representing the 

Federal agencies involved in ATD research, development, or applications, to establish 

performance guidelines for ATD and meteorological instrumentation systems in complex 

environments. Implementation actions for this recommendation include but are not 

limited to developing and testing procedures for designing local observation networks 

and siting instrumentation in diverse, complex environments. 

Timing considerations for recommendation 8. A working group can be established 

through the OFCM in 3 months. Release of draft instrumentation performance guidelines 

will require about a year after that. Test of the guidelines will follow as required. 

6.7 Design and Conduct Special Studies and Experiments 

Many of the classic field transport and diffusion experiments upon which our modeling 

parameters are based date back to the 1950s to 1970s. Since then, significant advances 

have occurred in measurement technology, computational capabilities, and modeling 

algorithms. Among these advances are major reductions in the averaging times for 

meteorological or tracer sampling. Turbulence and its spatial variability are better 

characterized and captured in model representations. The importance of boundary-layer 

scaling has been demonstrated. 

Special field studies, replicating several of the classical studies, should be conducted with 

modern measurement technology. Such studies will improve the data foundation for 

model parameterization, assess the improvements in understanding gained from advances 

in science, and demonstrate the merits of new measurement technologies. Another reason 

for special studies is to provide an adjunct to the fixed test beds, allowing other 

environmental settings to be studied and to extend model testing and verification and 

validation (V&V) to these settings. A key ingredient for such studies is the ability to 

measure tracer concentrations remotely in four dimensions at high space-time resolution 

and meaningful concentrations. 

Like the design of the ATD test beds, design of special studies and experiments 

conducted at non–test bed sites should be informed by the results of the data capture and 

reanalysis efforts recommended in section 6.2 (see recommendation 4). These special 

studies will also prove more fruitful after some of the measurement technology 

improvements recommended in section 6.5 are available for use in them. Given these 

prerequisites and the high priority of proceeding with test bed implementation, special 

studies and experiments are intermediate-term to longer-term needs (3 to 7 years). 

Recommendation 9. The Federal agencies involved in ATD research, development, or 

applications should establish a working group to design and oversee the conduct of a 

series of classical experiments. The design and selection of these experiments should 
Chapter 6: Recommendations 109

reflect the information gained by capturing existing data (recommendation 3) and 

complement the infrastructure of ATD modeling system test beds (recommendation 4). 

Among experiments that the working group should consider are the following: 

• Characterize fundamental uncertainty (due to atmospheric turbulence) through 

highly instrumented testing under simplified conditions (a “Daughter of Prairie 

Grass” study but incorporating new technology). 

• A regional-scale study (covering about 5000 km2

) of the diurnal evolution of 

tracer transport and diffusion in the ABL in terrain-forced flows should be 

achieved within a decade. Such a study should use newly developed tracer 

technology to enable surface and airborne multidimensional remote sensing of 

concentration, even between urban structures. Surface and airborne networked 

Doppler lidar systems with overlapping coverage could be used to measure winds, 

turbulence, and stratified aerosol layers. The study should include low-altitude 

temperature and turbulence measurements for turbulence fluxes and a network of 

other measurement systems designed and deployed for the study location. 

Timing considerations for recommendation 9. At least 3 years will be needed before 

new experiments can be defined and designed, depending on progress in reanalyzing old 

data sets (recommendation 3) and improving measurement technologies 

(recommendations 7 and 8). 
110 Federal R&D Needs and Priorities for ATD Modeling
111 

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Appendices 

A-1 

APPENDIX A. HISTORICAL PERSPECTIVE

A.1 Historical Perspective—Meteorological and ATD Modeling 

When creating a roadmap for the future, it helps to understand the path that brought us to 

our present position. The earliest comprehensive effort by a Federal agency to predict the 

transport and diffusion of airborne particles for public safety purposes began in the 

1940s. As the nuclear age emerged, it became apparent that “radioactive fallout was an 

exceedingly complex issue, involving extremely long range transport through the air and 

affecting all aspects of the environment.”1

 

ATD modeling is generally classified into two subgroups: emergency-response and air 

quality predictions. Emergency-response forecasting focuses on situations where 

chemical, biological, or nuclear materials are unexpectedly emitted into the atmosphere 

and where the source is unknown or poorly described. Air quality forecasting focuses on 

the U.S. Environmental Protection Agency’s (EPA’s) criteria pollutants, such as ozone, 

particulate matter, nitrogen dioxide, carbon monoxide, sulfur dioxide, and lead. 

A.1.1 Emergency Response Modeling 

In 1948, the U.S. Weather Bureau—the predecessor of today’s National Weather Service 

(NWS) in the National Oceanic and Atmospheric Administration (NOAA), established 

the Special Projects Section. This office was the forerunner of the Air Resources 

Laboratory (ARL), now in NOAA’s Office of Oceanic and Atmospheric Research. The 

Special Projects Section was at first funded solely by the Department Of Defense (DOD). 

Later, it was funded jointly by the DOD and the Atomic Energy Commission (AEC), 

which was the predecessor of the Nuclear Regulatory Commission (NRC) and portions of 

the Department of Energy (DOE). The Special Projects Section conducted research 

related to the U.S. nuclear weapons and atomic energy programs. After a few years, it 

also provided operational services in support of these programs. 

Also in 1948, the U.S. Weather Bureau established and jointly funded with the AEC two 

research stations at Oak Ridge, Tennessee, and Idaho Falls, Idaho. The Oak Ridge 

research station was established to study the processes of atmospheric diffusion and 

understand the dispersion characteristics of the Oak Ridge area. Several other agencies 

subsequently contributed funds to develop these research facilities into what is now the 

Atmospheric Turbulence and Diffusion Division of NOAA/ARL. 

The AEC and the DOD began joint tests of nuclear weapons in 1951, in the southwest 

Great Basin, a desert region northwest of Las Vegas, Nevada. A majority of the early 

tests in this weapons program were atmospheric tests. They showed that a good 

understanding of the atmospheric environment was necessary to characterize the transport 

and fallout of airborne radioactive products of the test events. In 1956 the AEC 

 

1

 Excerpted from a historical sketch of the Air Resources Laboratory, on the Internet at www.arl.noaa.gov/ 

history.html. 
A-2 Federal R&D Needs and Priorities for ATD Modeling 

implemented an Interagency Agreement with the U.S. Weather Bureau to establish a 

Weather Bureau research station in Las Vegas: the predecessor to the NOAA/ARL 

Special Operations and Research Division (SORD). The primary function of this research 

station was to support AEC/DOD test operations by taking local surface and upper air 

weather observations, preparing weather and trajectory/fallout forecasts, and providing 

expert meteorological advice to event scientists. These functions continue today, as 

NOAA/ARL SORD supports DOE’s National Nuclear Security Administration (NNSA) 

at the Nevada Test Site (NTS). By the 1970s, the Field Research Division of ARL, as it is 

now known, had successfully participated in many experiments, which successfully 

pioneered the use of tracer technology and data analysis techniques. 

In 1973, the DOE Office of Biological and Environmental Research tasked the 

Atmospheric Sciences Group at Lawrence Livermore National Laboratory (LLNL) to 

investigate the feasibility of developing an end-to-end, fully integrated system to provide 

reliable and timely assessment advisories to emergency managers at DOE nuclear 

facilities in the event of an accidental release of radioactive material to the atmosphere. 

To characterize the source of the release, this system was designed to rely heavily on 

downwind measurements and analysis of isotopes. In 1972 the AEC recognized the need 

for real-time estimates of transport and diffusion (Knox et al. 1981). To meet this need, 

LLNL developed the Atmosphere Release Advisory Capability (ARAC), which includes 

an advanced, three-dimensional modeling system of pollutant dispersion and the 

communications capability to disseminate predictions from this modeling system to local 

officials (Dickerson and Orphan 1976; Lange 1978; Sherman 1978). A facility to exploit 

the ARAC, now known as the National Atmospheric Release Advisory Center 

(NARAC), was officially established in 1979 with funds from a number of agencies, 

including the DOE. Since its inception, NARAC has responded to a number of accidents 

including radiological releases at the Three Mile Island nuclear power station in 

Pennsylvania (Dickerson, Knox, and Orphan 1979) and at the Chernobyl nuclear power 

station in the USSR in 1986. In 1984, NARAC responded to the atmospheric release of a 

chemical hazard, methyl isocyanide, at a fertilizer manufacturing plant in Bhopal, India. 

Several of the DOE National Laboratories have conducted research in ATD modeling and 

related research areas. Sandia National Laboratory, for example, has a long of history of 

source term development for accidents at nuclear power plants. In 2002 the DOE 

Chemical and Biological National Security Program (CBNP) established the Local 

Integration of the NARAC with Cities (LINC) program. The CBNP, along with LINC, 

was transferred to the Department of Homeland Security (DHS) in 2003. The DHS used 

LINC during the TOPOFF2 exercise in 2003 to demonstrate a capability to provide local 

government agencies with advanced operational atmospheric plume predictions. NOAA 

developed a gas and chemical modeling system called CAMEO/ALOHA in 1992 to assist 

local fire departments in assessing the impacts of accidental releases of hazardous 

chemicals (NOAA and EPA 1992). (“CAMEO/ALOHA” stands for “Computer-Aided 

Management of Emergency Operations/Areal Locations of Hazardous Atmospheres.”). 

DOD was one of the first Federal agencies to fund the development, testing, and 

application of ATD models. Military interest in ATD modeling originated in the 1940s 

from a need to quantify the downwind hazards resulting from the use of chemical and 
Appendix A A-3

biological munitions, including accidental releases of chemical agents at U.S. Army 

storage depots. With the advent of liquid-fueled rockets in the 1950s, military 

requirements for ATD modeling expanded to include accidental releases during the 

transportation, storage, and handling of toxic liquid propellants. Prior to the 1970s, most 

of the empirical data on ATD came from field studies conducted by DOD organizations 

such as the Deseret Test Center, Dugway Proving Ground, and Air Force Cambridge 

Research Laboratory. These field studies included the landmark Prairie Grass (Barad 

1958; Haugen 1959) and Ocean Breeze/Dry Gulch (Haugen and Fuquay 1963) 

experiments. DOD continues to be one of the principal Federal sponsors of ATD field 

studies, with recent examples including the Mock Urban Setting Test (MUST) (Biltoft 

2001) and Joint Urban 2003, which was conducted in collaboration with DHS. During the 

past 50 years, the Army, Navy, Air Force, and several DOD agencies have developed a 

series of ATD models to meet specific military requirements. Capabilities from the three 

major DOD ATD modeling systems are currently being combined into a single Joint 

Effects Model (JEM) for operational use by all services. 

The mandate of the Defense Threat Reduction Agency (DTRA) is to safeguard America's 

interests from weapons of mass destruction (chemical, biological, radiological, nuclear, 

and high explosives) by controlling and reducing the threat and providing quality tools 

and services for the warfighter. DTRA was created in 1998 as the successor to the 

Defense Special Weapons Agency, which in turn succeeded the Defense Nuclear Agency 

(DNA) in 1996.2

 In 1996, DTRA developed the Hazard Prediction and Assessment 

Capability (HPAC) modeling system to calculate the effects of releases of biological, 

chemical, and nuclear agents. The HPAC modeling systems uses SCIPUFF, a Lagrangian 

puff model, to simulate transport and diffusion (Sykes et al. 1996). 

A.1.2 Air Quality Modeling 

In 1955, in response to a request from the Air Pollution Unit of the U.S. Public Health 

Service (a predecessor of part of the EPA), the U.S. Weather Bureau formed an air 

pollution unit under its Special Projects Section. It also detailed specialists to the Public 

Health Service to provide user-appropriate and scientifically credible air quality 

meteorological programs to support regulatory applications. Significant data collection 

and analysis efforts in the 1950s and 1960s led to a better understanding of air pollution 

episodes and the atmosphere’s controlling effect on air pollution (Heidorn 1978; 

Holzworth 1962). During this period, the Weather Bureau issued regional advisories of 

air pollution potential over the eastern United States (Niemeyer 1960; Boettger 1961) and 

municipal air quality agencies began to predict pollution on the local urban scale (e.g., 

Thuillier and Sandberg 1971). 

In 1965, President Johnson formed the Environmental Sciences Services Administration 

(ESSA) from two longstanding Department of Commerce agencies: the Coast and 

Geodetic Survey (established by President Jefferson in 1807) and the Weather Bureau 

(established by Congress in 1891). In 1970, President Nixon combined ESSA with seven 

 

2

 In 1948 DOD established the Armed forces Special Weapons Project. This effort led to formation of the 

Defense Atomic Support Agency in 1959, which became the DNA in 1971. 
A-4 Federal R&D Needs and Priorities for ATD Modeling 

other earth science programs to establish NOAA. By then, NOAA/ARL had five 

Divisions: Idaho Falls; Las Vegas; Oak Ridge; Washington, D.C.; and Research Triangle 

Park, North Carolina.3 Also in 1970, President Nixon established the EPA. As part of the 

Clean Air Act Amendments of 1970 and 1977, the EPA focused on setting air quality 

standards and controlling pollution at its sources. The NOAA group assigned to support 

the EPA, now known as the Atmospheric Sciences Modeling Division (ASMD) of 

NOAA/ARL, serves as the primary vehicle through which EPA supports research efforts 

in air pollution meteorology and atmospheric modeling. ASMD conducts research 

activities in-house and through contract and cooperative agreements for the National 

Exposure Research Laboratory and other EPA groups. ASMD also provides technical 

information, observational and forecasting support, and consulting on all meteorological 

aspects of the air pollution control programs mandated by the Clean Air Act to the EPA 

offices of Air Quality Planning and Standards, Research and Development, and Air and 

Radiation. It also supports EPA regional offices and various State and local agencies. 

During the 1970s and 1980s, air quality agencies prepared pollution predictions using 

objective statistical methods that required forecasts of atmospheric conditions as input 

(Aron and Aron 1978; Aron 1980; McCutchan and Schroeder 1973). In the 1980s, State 

Implementation Plans became a regulatory method to control air pollution at its sources 

by demonstrating how states would reduce emissions to meet the National Ambient Air 

Quality Standards. Numerical Eulerian grid models were used to develop State 

Implementation Plans by simulating historical air pollution episodes and demonstrating 

the effect of future emissions reductions. These models employed diagnostic wind field 

models to interpolate available meteorological observations to a three-dimensional grid 

(Collett and Oduyemi 1997). By the 1980s, the diagnostic models could be replaced with 

prognostic models (Chang et al. 1987). As prognostic real-time mesoscale meteorological 

models have matured, the air quality community has begun coupling them with air 

quality models, either keeping separate (offline) software for chemistry and meteorology 

(Vaughan et al. 2002; Hogrefe et al. 2001; Jakobs et al. 2001; McHenry et al. 2001) or 

using an integrated (online) approach (Grell et al. 2000). 

Air chemistry models describe the fate and transport of atmospheric chemical 

constituents in both the gas and the aerosol phases. They now track about 100 chemical 

species, interacting through mechanisms involving hundreds of chemical reactions. 

Because of the important role that aerosols play in radiative transfer, weather, and health 

impacts, most air quality models now include detailed descriptions of aerosol dynamics 

and calculate size-resolved aerosol composition, radiances, and photolysis rates 

interactively with the cloud and aerosol fields. With today’s computational power and 

efficiencies, air chemistry models can simulate pollution distributions in urban air sheds 

with spatial resolution of a few kilometers or they can cover the globe with horizontal 

grid spacing of less than 100 kilometers. These models are able to provide quantitative 

information on the distributions of many of the atmosphere’s key trace gases and 

aerosols. Air chemistry models have become an essential element in atmospheric 

 

3

 The ARL Cincinnati Office was moved to Research Triangle Park, North Carolina in 1969 specifically to 

provide support to the EPA. 
Appendix A A-5

chemistry studies. They also provide science-based input for decision makers locally, 

nationally, and globally. 

Although meteorological and chemical processes are strongly coupled, until recently the 

chemical processes in air quality modeling systems were usually treated off line from the 

meteorological model, as in EPA’s Community Multiscale Air Quality modeling system, 

whose output provided the transport function (Byun and Ching 1999). This type of 

system is usually termed a chemical transport model (CTM). In the newer online 

approach there is no CTM; the chemical processes are represented within the 

meteorological model. The online approach has a number of potential advantages for air 

quality forecasting, such as better characterization of the time-resolved dispersion of air 

pollutants. 

Within the context of mesoscale meteorological modeling, the Weather Research and 

Forecasting (WRF) model is being developed cooperatively by many government 

laboratories and universities led primarily by the NOAA, the National Center for 

Atmospheric Research (NCAR), DOD, and the Federal Aviation Administration. The 

WRF model is well suited to become the cornerstone for a next-generation air quality 

prediction system.4

 This model, currently under development, is nonhydrostatic, with 

several dynamic cores as well as many choices for physical parameterizations to 

represent processes that cannot be resolved by the model. This flexibility allows the 

model to be applied on many scales. A first version of an online WRF-based air quality 

prediction system for ozone prediction already exists (http://box.mmm.ucar.edu/ 

wrf/WG11); the chemical modules are based on the online MM5/chemistry model (Grell 

et al. 2000). The official future release of this model (planned for 2005) will include 

many additional chemical modules from other air quality prediction systems and a choice 

of offline coupling.

A.2 Field Studies 

This section lists and summarizes ATD field studies that could prove useful for 

supporting future ATD research initiatives. The list is not exhaustive; it represents those 

studies that the JAG members considered to be of greatest potential value for ongoing 

R&D efforts, as discussed in chapters 5 and 6. 

A.2.1 Point Source Dispersion Experiments 

Dispersion of Near-Surface Releases 

1. Round Hill was conducted in 1954/55 and 1957 using sulfur dioxide tracer (Cramer, 

Record, and Vaughan 1958). Ten-minute samples were measured for sulfur dioxide 

along three arcs (50, 100, and 200 m) downwind of a point source release. The release 

height for the 29 experiments in 1954/55 was 30 cm; the release height for the 10 

 

4

 Background information on the WRF model and the latest applications and development news can be 

found on the Internet at http://wrf-model.org/. 
A-6 Federal R&D Needs and Priorities for ATD Modeling 

experiments in 1957 was 50 cm. Receptor height was 2 m. Site roughness was greater 

than 10 cm. A unique feature of the 1957 experiments was that sampling was 

conducted for the first 0.5 min and 3-min of the 10-minute sampling periods. 

2. Project Prairie Grass was conducted in 1956 with sulfur dioxide tracer (Barad 1958; 

Haugen 1959). It included 68 ten-minute samples taken at 1.5 m intervals along five 

arcs (50, 100, 200, 400, and 800 m) downwind from a point source release of sulfur 

dioxide 46 cm above ground. The 20-minute releases were conducted during July and 

August of 1956, with an equal number of cases run during the daytime and nighttime. 

Sampling was done during the 10-minute period in the middle of the 20-minute 

release. Site roughness was 0.6 to 0.9 cm. 

3. Green Glow was conducted in 1959 with zinc sulfide tracer (Fuquay, Simpson, and 

Hinds 1964; Nickola 1977). Thirty-minute samples of zinc sulfide were taken along 

six arcs (200, 800, 1600, 3200, 12,800, and 25,600 m) downwind from a point source 

release 2.5 m above ground. Receptor height was 1.5 m. Site roughness was 3 cm. 

4. Hanford-30 was conducted in 1960–1961 with zinc sulfide (Fuquay, Simpson and 

Hinds 1964; Nickola 1977). Zinc sulfide samples were collected at 20 to 75 minutes 

along five arcs (200, 800, 1600, 3200, 12,800 m) downwind from a point source 

release 2.5 m above ground. Receptor height was 1.5 m. Site roughness was 3 cm. 

5. Dry Gulch ws conducted in 1961–1962 with zinc sulfide (Haugen and Fuquay 1963). 

Thirty-minute samples of zinc sulfide were collected along five arcs (853, 1500, 

2301, 4715, and 5665 m) downwind of a point source release 2 to 3 m above ground. 

Receptor height was 1.5 m. The terrain was sloping mesa cut by deep ravines; 

vegetation was mainly grasses with occasional brush and trees. 

6. Ocean Breeze was conducted in 1961–1962 with zinc sulfide (Haugen and Fuquay 

1963). Thirty-minute samples of zinc sulfide were collected along three arcs (1200, 

2400 and 4800 m) downwind of a point source release 2 to 3 m above ground. 

Receptor height was 1.5 m. The terrain was rolling sand dunes covered with dense 

palmetto and brushwood. 

7. Hanford-67 was conducted in 1963–1973 with zinc sulfide, fluorescein, rhodamine 

B, and krypton-85 tracers (Nickola 1977). Ten-minute and 30-minute samples of zinc 

sulfide, fluorescein (uranine), rhodamine B, and krypton 85 were collected along 

eight arcs (from 200 to 12800 m) downwind of a point source release, mostly at 2 m 

with several at 1 m. Receptor height was 1.5 m. Site roughness was 3 cm. There were 

23 experiments, most using dual tracers, including 14 with releases at both 2 and 26 

m. 

8. Mountain Iron was conducted in 1967 with zinc sulfide tracer (Hinds and Nickola 

1967; Hinds 1968). Several 5 minute, but mostly 30 minute samples of zinc sulfide 

tracer were collected along arcs ranging from 260 m to 11.4 km from a 2 m point 

source release. The experiment site was rugged rolling terrain near the central 

California coast. 
Appendix A A-7

9. Hanford-83 was conducted in 1983 with sulfur hexafluoride tracer (Doran and Horst 

1985). Thirty-minute samples were collected for zinc sulfide and sulfur hexafluoride, 

which were jointly released from a point source 2 m above ground. Six experiments 

were run, with sampling at 1.5 m above ground and along five arcs ranging from 100 

to 3200 m downwind. Site roughness was 3 cm. These experiments were conducted at 

the same site as the Green Glow, Hanford-30, and Hanford-67 experiments and had 

the objective of better characterizing the deposition properties of zinc sulfide. 

10. MADONA was conducted in 1992 with sulfur hexafluoride and propylene gas tracers 

(Cionco et al. 1999). The multination Meteorology and Diffusion over Non-Uniform 

Areas (MADONA) field study was conducted at Porton Down, Salisbury, Wiltshire, 

United Kingdom. MADONA combined high-resolution meteorological data 

collection with diffusion experiments using smoke, sulfur hexafluoride, and 

propylene gas during unstable, neutral, and stable atmospheric conditions. The 

objective was to obtain terrain-influenced meteorological fields, dispersion, and 

concentration fluctuation measurements using specialized sensors and tracer 

generators. Thirty-one days of meteorological data were collected during the period 

September 7 through October 7, 1992. Twenty-seven diffusion experiments were 

conducted from September 14–23, 1992. Puffs and plumes of smoke and sulfur 

hexafluoride were released simultaneously for most of the experiments. This welldocumented database is suitable for the evaluation and validation of short-range wind 

field and ATD models. The database was originally placed on CD-ROM in a 

structured way by the Chemical and Biological Defence Establishment, Porton Down. 

This database is now available from the Riso National Laboratory, Denmark, at 

<http://www/risoe.dk/vea-madona/ndescribtion.htm>. 

Dispersion of Elevated Releases–Rural, Simple Terrain

1. Hanford-67 was conducted in 1963-1973 with zinc sulfide, fluorescein, rhodamine 

B, and krypton-85 tracers (Nickola 1977). Ten-minute and 30-minute samples of zinc 

sulfide, fluorescein, rhodamine B, and krypton-85 were collected along eight arcs 

(from 200 to 12,800 m) downwind from a point source release. Receptor height was 

1.5 m. Site roughness was 3 cm. There were 46 releases at 26 m and 20 releases at 56 

m. There also were releases at 111 m, but no meteorological data are available for 

these cases. 

2. Cabauw was conducted in 1977–1978 using sulfur hexafluoride tracer (Nieuwstadt 

and van Duuren 1979). In a series of 15 experiments, sulfur hexafluoride tracer was 

released at either 800 m or 200 m, with sampling at 1.5 m above ground along a 

single arc that ranged downwind from 3 to 5 km (depending on wind direction). 

Sampling was for two consecutive 30 minute periods. Site roughness varied from 10 

to 20 cm, depending on wind direction. 

3. Kincaid was conducted in 1980–1981 using sulfur hexafluoride tracer (Bowne et al. 

1983). The sulfur hexafluoride tracer experiments conducted at Kincaid involved a 

release from a 183 m stack with a buoyant plume rise on the order of 200 m. There 

were 171 experiments conducted during April, May, and August of 1980 and May 
A-8 Federal R&D Needs and Priorities for ATD Modeling 

and June of 1981. Measurements were made of near-surface hourly concentrations 

and hourly meteorology. There were twelve roughly defined receptor arcs ranging 

from 0.5 to 50 km from the release. 

4. Teruel was conducted in 1985 with sulfur hexafluoride tracer (Sivertsen and Irwin 

1987, 1996). Ten experiments were conducted in which sulfur hexafluoride was 

released from the 343 m stack of the 1200 MW Teruel coal-fired electric power plant. 

Two consecutive 15-minute samples were collected 1.5 m above ground along three 

arcs at approximately 10, 24, and 48 km from the stack. The plant is located 600 m 

above sea level on the southern side of the Ebro valley, midway between Madrid and 

Barcelona. Site roughness was estimated to be about 30 cm. A key objective of these 

experiments was to characterize the decrease in the transport speed (and thus the 

increase in the transport time) as the plume flowed toward the coast and into the 

strong sea breeze. 

Dispersion of Elevated Releases–Rural, Complex Terrain

1. Cinder Cone Butte was conducted in 1980 using sulfur hexafluoride tracer (Snyder 

et al. 1985). During the autumn of 1980, 18 nighttime or early morning 8-hour tracer 

experiments were conducted at the 100-meter high hill at Cinder Cone Butte, which is 

near Boise, Idaho. The main tracer was sulfur hexafluoride; Freon 1381 was also used 

in ten experiments. Sampling was conducted with a network of approximately 100 

samplers located on the slopes of the hill. 

2. Hogback Ridge was conducted in 1982 using sulfur hexafluoride tracer and Freon 

1381 (Snyder et al. 1985). In October 1982, 11 nighttime or early morning 8-hour 

tracer experiments were conducted along an approximately 1.5-km section of the 

90 m high Hogback Ridge near Farmington, New Mexico. A network of 110 

samplers located on the slopes of the ridge collected samples of sulfur hexafluoride 

and Freon 1381. 

3. Tracy Power Plant was conducted in 1984 using sulfur hexafluoride tracer (Snyder 

et al. 1985). A feasibility study was conducted in November 1983, with a full-scale 

study in August 1984, at the Tracy Power Plant, which is located about 27 km east of 

Reno, Nevada. The site is located in the Truckee River Valley, with mountains 

surrounding the power plant on all sides. Peaks as high as 460 m above the stack base 

afforded opportunities for plume impaction in many directions. The power plant was 

maintained in warm standby condition as sulfur hexafluoride was injected in the base 

of the 91.4 m smokestack. The feasibility study consisted of 10 experiments during 

November 7–19, 1983, for a total of 90 hours of sampling at a network of 53 

samplers. The full-scale study consisted of 14 experiments during August 8–27, 1984, 

for a total of 128 hours of data collection at a network of 110 samplers, mainly during 

late evening or early morning hours. 

4. ASCOT Studies. Beginning with an exploratory field study in The Geysers 

geothermal region north of San Francisco, California, in 1979, the DOE funded a 

multi-year multi-organization study of ATD in complex terrain. The work included 
Appendix A A-9

both multiple tracers (sulfur hexafluoride, several perfluorocarbons, and other 

materials) and detailed micrometeorological measurements using both point and 

remote-sensing instruments. From 1979 to 1982, this work centered on valleys and 

basins astride the California coastal range. The emphasis then shifted for several 

years to a complex of simple individual valleys in the oil shale region north of Grand 

Junction, Colorado. Interest then shifted to the Front Range of the Rocky Mountains, 

near the DOE's Rocky Flats facility northwest of Denver. A field study was also 

performed in the ridge valley terrain of eastern Tennessee, which is typical of many 

areas near the Appalachian Mountains. Funding for ASCOT ended in the early 1990s. 

5. Model Validation Study. Because of tightened limits on human exposures to the 

products of both normal and abnormal rocket launches at the Cape Canaveral Air 

Station and Vandenberg Air Force Base ranges, the Rocket Exhaust Effluent 

Dispersion Model, which was used to predict concentrations from launches, was 

leading to too many weather-induced launch delays. In hopes of reducing the number 

of expensive launch delays while still protecting public health, the Air Force funded a 

series of transport and diffusion studies at the two sites in 1995 and 1996. To simulate 

the elevated releases expected from a rocket, a blimp was used to release sulfur 

hexafluoride tracer. Near-ground releases were also used. An extensive network of 

time-integrating samplers and a small network of mobile fast-response samplers 

mounted in vans were used to measure ground-level concentrations. Fast-response 

analyzers were also mounted in two Global Positioning System (GPS)–equipped light 

airplanes to measure concentrations aloft. Both launch ranges have extensive 

meteorological systems in place. These were supplemented by point and remotesensing instrument systems. Three 3-week seasonally spaced studies were performed 

at Cape Canaveral Air Station; one study was conducted at Vandenberg Air Force 

Base. Although the Cape Canaveral area is flat, the site is considered complex terrain 

because of the land–water contrasts and the wide range of land types. Vandenberg is 

in the midst of quite complex terrain. Both sites are subject to land–sea circulations. 

Point Source Releases in Urban Terrain 

1. St. Louis was conducted from 1963 to 1965 using sulfur dioxide tracer (McElroy and 

Pooler 1968). From the spring of 1963 to the spring of 1965, 26 daytime and 16 

evening experiments were conducted involving 1-hour releases of zinc sulfide from 

two site locations (Forest Park and a rooftop release from the Knights of Columbus 

Building). Sampling (total dose) was conducted typically along three arcs that ranged 

from 1 to 7 km from the release site, with a few cases having an arc at 15 km. The 

initial lateral dispersion was estimated at 50 to 60 m (length of a typical city block, 

160 m, divided by 4.3), with larger values when the wind was diagonally across the 

block. The initial vertical dispersion was estimated at 20 to 30 m. The authors 

concluded that dispersion from low-level sources in urban areas for downwind 

distances of less than 800 m is conjectural. 

2. Copenhagen was conducted in 1978–1979 using sulfur hexafluoride tracer (Gryning 

and Lyck 1984, 2002). This series of ten tracer experiments in the Copenhagen area 

was carried out under neutral and unstable atmospheric conditions. The sulfur 
A-10 Federal R&D Needs and Priorities for ATD Modeling 

hexafluoride tracer was released without buoyancy from a tower at a height of 115 m 

and sampled at 2–3 m above ground level on up to three crosswind arcs at 2–6 km 

from the point of release. Three consecutive 20-minute averaged tracer concentrations 

were measured, allowing for a total sampling time of 1 hour. The site was mainly 

residential, having a roughness length of 0.6 m. The meteorological measurements 

performed during the experiments included standard measurements at multiple 

heights on the tower of tracer release, as well as three-dimensional wind velocity 

fluctuations at the height of release. 

3. METREX. During all of 1983, fluorocarbon tracer was released at several locations 

around the Washington, D.C., beltway as part of the Metropolitan Experiment 

(Draxler 1985). The scale of this experiment, which was larger in both space and time 

than most urban studies, was intended to test dispersion models over the long term. 

Some supplementary meteorological data were collected in addition to the usual 

information from the sites around and within the Washington, D.C., metropolitan 

area. 

4. Indianapolis was conducted in 1985 using sulfur hexafluoride. (Murray and Bowne 

1988). Sulfur hexafluoride was released from an 84 m stack with buoyant plume rise. 

There were 170 experiments conducted during September and October of 1985, with 

measurements of near-surface hourly concentrations and hourly meteorology. 

Measurements were taken along twelve roughly defined arcs ranging from 0.2 to 12 

km from the release. 

5. Lillestrom was conducted in the town of Lillestrom (near Oslo), Norway, in 1987 

using sulfur hexafluoride tracer (Haugsbakk and Tonnesen 1989). The experiments 

took place in a flat residential area with buildings and trees ranging from 6 to 10 m in 

height. The surface roughness was about 0.5 m. Sulfur hexafluoride was released 

from a mast 36 m above the ground. Near-surface samples were collected along three 

arcs for two sequential 15-minute periods. The crosswind tracer concentration 

profiles were well determined for all trials, enabling a relatively accurate estimate of 

crosswind-integrated concentration. The temperature during the tracer experiments 

was low (approximately -20° C), and the ground was snow-covered. 

6. Kit Fox was conducted during late August and early September 1995. A "billboard" 

(flat plate) obstacle array was set up in the desert at Frenchman Flats at the Nevada 

Test Site. Local roughness was enhanced with a much larger array of much smaller 

obstacles. Carbon dioxide was used as the somewhat dense gaseous tracer, and an 

array of instruments provided horizontal and vertical meteorological measurements. 

The test is well documented and would make a good addition to a national archive. 

7. URBAN/VTMX was conducted in 2000 with sulfur hexafluoride tracer 

(documentation at <http://urban.llnl.gov/experiment.html> and 

<http://www.pnl.gov/vtmx/>). The URBAN 2000 experiment, which was conducted 

during October 2–25, 2000, consisted of six intensive observation periods with 

nighttime releases of sulfur hexafluoride in downtown Salt Lake City, Utah. During 

this same period, the Vertical Transport and Mixing (VTMX) meteorological field 
Appendix A A-11

measurement program took take place in the Salt Lake Valley. VTMX was designed 

to study the processes contributing to vertical transport and mixing of momentum, 

heat, and water vapor in the lowest few thousand feet of the atmosphere. The Salt 

Lake Valley was chosen as the study site because the surrounding mountains often 

contribute to the development of cold pools (i.e., conditions in which colder air is 

trapped in the valley while warmer air is found at higher elevations). Vertical 

transport and mixing processes in these conditions can be particularly difficult to 

describe. Flows over the mountains and out of the canyons, as well as winds 

generated by the temperature contrasts between the Great Salt Lake and the valley 

floor, may generate wind shear and atmospheric waves. These phenomena can in turn 

modify the vertical structure of the atmosphere. 

8. The Mock Urban Settings Test (MUST) was conducted during September 10–27, 

2001, at the Dugway Proving Ground in Utah using propylene as the tracer (Biltoft 

2001; Biltoft, Yee, and Jones 2002). A mock building array was created by placing 

shipping containers in a 10 x 12 regular aligned grid. Each shipping container was 

12.2 m wide, 2.42 m deep, and 2.54 m high. They were aligned with the long face 

perpendicular to the prevailing nighttime drainage winds and daytime upslope winds. 

The plan area density of the array was 13 percent and the height-to-width ratio was 

0.2, the latter indicative of the isolated roughness flow regime. Tracer gas puffs or 

plumes were released from positions within or immediately upwind of the MUST 

array. Tracer dispersion through the array was measured using fast-response 

photoionization detectors. A 32 m tower and several 6 m towers within the MUST 

array provided vertical sampling, while four sampling lines of photoionization 

detectors provided lateral dispersion information. Sixty-eight usable trial events, 

consisting of 63 continuous releases and 5 sets of puff releases, were completed 

during MUST, providing 16 hours of continuous release data and 4.75 hours of puff 

data for analysis. 

9. BUBBLE was conducted in 2002 using sulfur hexafluoride tracer (Gryning et al. 

2003). Between June 15 and July 12, 2002, a series of four experiments were 

conducted in Basel, Switzerland. Sulfur hexafluoride was released approximately 1.5 

m above the rooftops with rooftop sampling at 12 locations distributed close to the 

release and extending out to about 1.6 km. The mean building height in the area was 

15.1 meters, and the mean plan area density was 48 percent. Each experiment 

provided six 30-minute samples, starting at approximately 1400 LST. The aim was to 

perform the tracer experiments under Clara Wind conditions, a thermally-driven wind 

system that develops over Basel in the afternoon on cloud-free summer days and is 

characterized by persistent winds from the northwest. 

10. The Joint Urban 2003 Field Experiment was conducted in Oklahoma City, 

Oklahoma, during July 2003 (Halvorson et. al. 2004; <http://ju2003.pnl.gov/ 

study.html>). Its focus was characterizing the flow of sulfur hexafluoride tracer gas in 

an urban environment. More than 150 government, university, and private sector 

participants supported high-resolution atmospheric measurements and other 

instrumentation during the experiment. The field program consisted of six daytime 

and four nighttime intensive observation periods, each lasting approximately eight 
A-12 Federal R&D Needs and Priorities for ATD Modeling 

hours and typically including four puff releases and three 30-minute releases. The 

Joint Urban 2003 database (approximately 3 terabytes) is maintained by Dugway 

Proving Ground. 

A.2.2 Possible Urban Testbeds 

1. NOAA/ARL implemented a dispersion measurement testbed, DCNet, in the 

Washington, D.C., area to provide the best possible basis for dispersion computations 

needed for both planning and possible response. 

2. The Brookhaven National Laboratory’s Environmental Measurements Laboratory 

initiated the Urban Atmospheric Observatory in 2003 to establish a dense array of 

meteorological instrumentation, remote-sensing and satellite products, and model 

output, as well as radiation detection (gamma spectrometer) and aerosol 

measurements in a small area in the heart of downtown Manhattan in New York City. 

A.2.3 Long Range Transport Studies 

1. The Atlantic Coast Unique Regional Atmospheric Tracer Experiment 

(ACURATE-82/83), which was conducted in 1982 and 1983, consisted of measuring 

the krypton-85 air concentrations from emissions of the Savannah River Plant in 

South Carolina (Heffter, Schubert, and Meade 1984). For 19 months from March 9, 

1982, to September 30, 1983, 12- and 24-hour average air concentrations were 

collected at five locations along the United States east coast at distances of 300 to 

1000 km from the plant (Fayetteville, North Carolina, to Murray Hill, New Jersey). 

Measurements were made of hourly krypton-85 emissions from the plant (in curies) 

and of air concentrations (in picocuries per cubic meter). Ambient background 

concentration at each measurement station was subtracted from measured 

concentrations. Background varied by latitude, increasing to the north due to the 

prevalence of nuclear fuel reprocessing in the northern latitudes. 

2. The Across North America Tracer Experiment of 1987 (ANATEX-87) consisted 

of 66 perfluorocarbon tracer releases (33 each from two different locations) every 2.5 

days from January 5 to March 26, 1987 (Draxler and Heffter 1989). Air samples were 

collected for 24-hour periods continuing over 3 months (January 5 to March 29) at 75 

sites covering most of the eastern United States and southeastern Canada. 

Perfluorotrimethylcyclohexane was released from the site at Glasgow, Montana. 

Perfluorodimethylcyclohexane and perfluoromonomethylcyclohexane were released 

from the site at St. Cloud, Minnesota. Release units are recorded in grams; air 

concentrations were recorded in picograms per cubic meter. The two tracers released 

from the St. Cloud site were released at the same time and therefore do not provide 

any meteorologically independent data. 

3. The Cross Appalachian Tracer Experiment (CAPTEX-83) was conducted during 

September and October of 1983 (Ferber et al. 1986). It consisted of six 3-hour 

releases of perfluorocarbon tracer, four from Dayton, Ohio, and two from Sudbury, 

Ontario, Canada. Samples were collected at 84 sites 300 to 800 km from the source as 
Appendix A A-13

3- and 6-hour averages for about 48 hours after each release. One additional short (30 

minutes) tracer release from Dayton was not evident in the sampling data. 

4. The Idaho National Engineering Laboratory releases in 1974 (INEL74) consisted 

of about two months of krypton-85 releases, from February 27 to May 5, 1974, with 

continuous 12-hour sampling from February 27 to May 4, 1974, at 11 locations in a 

line about 1500 km downwind (Oklahoma City, Oklahoma, to Minneapolis, 

Minnesota). The same ambient background concentration (13.7 pCi/m3

) was 

subtracted from all stations (Ferber et al. 1977, Draxler 1982). 

5. The Oklahoma City 1980 experiment (OKC80) was a single release of two 

different perfluorocarbon tracers on July 8, 1980, over a 3-hour period (Ferber et al. 

1981). From July 8 to July 11, 1980, 3-hour samples were collected at 10 sites 100 

km downwind and at 35 sites 600 km downwind from the Oklahoma City release 

point. 

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Appendix A A-15

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A-18 Federal R&D Needs and Priorities for ATD Modeling 

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B-1 

APPENDIX B. FEDERAL CAPABILITIES AND RESEARCH AND 

DEVELOPMENT PROGRAMS

The dispersion modeling and consequence assessment capabilities of Federal departments 

and agencies are described below. Programs and activities of the Departments of 

Commerce, Defense, Energy, and Homeland Security are covered, as are programs in the 

National Aeronautics and Space Administration (NASA) and the U.S. Environmental 

Protection Agency (EPA). The information on programs presented here was current as of 

September 2004. Program details are subject to change. 

B.1 U.S. Department of Commerce, National Oceanic and 

Atmospheric Administration 

Within the context of atmospheric dispersion modeling, the National Oceanic and 

Atmospheric Administration (NOAA) is the principal supporting agency for atmospheric 

forecasts. NOAA provides meteorological and other products tailored for response 

applications. In partnership with the EPA, NOAA provides the dominant first-responders' 

dispersion model capability, the CAMEO/ALOHA modeling system, which is now in use 

by 10,000 to 20,000 emergency responders nationwide. NOAA provides dispersion 

forecasts based on the Nation's operational domestic mesoscale weather data and 

prediction models, both routinely (four times daily for selected sites) and on an aroundthe-clock on-demand basis. Through its 122 Weather Forecast Offices, NOAA provides 

weather and dispersion forecasts nationwide. Through its Realtime Environmental 

Applications and Display System (READY), it provides the dispersion forecasting 

capability that is a central element of the emergency systems of a large number of States 

and other emergency response organizations. 

NOAA provides forecasts of dispersion for international applications through its role as 

one of seven international sources recognized by the World Meteorological Organization. 

The other Regional Specialized Meteorology Centers for dispersion are located in 

Canada, Australia, Russia, England, France, and China. The NOAA modeling system is 

used by Australia and China. 

NOAA is a principal sponsor of the Weather Research and Forecasting (WRF) model. 

The WRF model is being developed as a collaborative effort among the Mesoscale and 

Microscale Meteorology Division of the National Center for Atmospheric Research 

(NCAR), the Environmental Modeling Center of the NOAA National Centers for 

Environmental Prediction (NCEP), the Forecast Research Division of the NOAA 

Forecast Systems Laboratory, the Air Force Weather Agency (AFWA) in the Department 

of Defense (DOD), the Federal Aviation Administration, the Center for the Analysis and 

Prediction of Storms at the University of Oklahoma, and other university-based scientists. 
B-2 Federal R&D Needs and Priorities for ATD Modeling

B.2 U.S. Department of Defense 

B.2.1 U.S. Northern Command 

The mission of the DOD is to provide the military forces needed to deter war and to 

protect the security of the United States. Within the DOD, the U.S. Northern Command 

(NORTHCOM) is the lead activity for homeland defense. 

NORTHCOM plans, organizes, and executes homeland defense and civil support 

missions. Specifically, it will conduct operations to deter, prevent, and defeat threats and 

aggression aimed at the United States, its territories, and interests within its assigned area 

of responsibility. As directed by the President or Secretary of Defense, NORTHCOM 

provides military assistance to civil authorities, including consequence management 

operations. 

Under NORTHCOM is the Joint Task Force for Civil Support (JTF-CS). The mission of 

JTF-CS is to provide command and control for DOD forces deployed in support of the 

Department of Homeland Security, Federal Emergency Management Agency 

(DHS/FEMA) to save lives, prevent injury, and provide temporary critical life support. 

DHS/FEMA is responsible for managing the consequences of a chemical, biological, 

radiological, nuclear or high-yield explosive (CBRNE) incident in the United States or its 

territories and possessions. As part of DOD's overall effort in support of the Terrorism 

Incident Annex added to the Federal Response Plan in 1997, JTF-CS is prepared to 

respond to requests for assistance from the lead Federal agency (LFA) following a 

CBRNE incident. When approved by the Secretary of Defense, JTF-CS supports the LFA 

in charge of consequence management—most likely DHS/FEMA. 

B.2.2. Defense Threat Reduction Agency 

The Defense Threat Reduction Agency (DTRA) safeguards America's interests from 

CBRNE used as weapons of mass destruction (WMD) by controlling and reducing the 

threat and providing quality tools and reach-back services for the warfighter and first 

responders in the event of a terrorist attack or hazardous material release. In an incident, 

DTRA will be asked by the LFA to participate in all National Special Security Events. 

DTRA Operational and Analytical Support 

DTRA provides operational and analytical support to the DOD and other organizations 

for critical WMD defense and response to related catastrophic events. Dispersion 

modeling and consequence assessment capabilities are an important element of this 

support. DTRA provides scientific and physics-based software tools that are easily 

deployed on a laptop personal computer. As part of its suite of consequence analysis 

tools, DTRA supports the user with on-demand meteorological data servers that provide 

real-time and forecasted four-dimensional weather, terrain, and land-use data from 

NOAA, U.S. Air Force and Navy, DTRA, Air Force Combat Climatology Center, and 

NCAR. DTRA supplies the research and development, training, and technical support 
Appendix B B-3

needed for automated software systems to accurately predict the effects of hazardous 

material releases into the atmosphere and their impact on civilian and military 

populations. The agency also supports emergency response for matters involving WMD 

events through the use of deployable Consequence Management Advisory Teams 

(CMATs), a 24-hour, 7-day-per week (24/7) continuous operations center, and technical 

reach-back support. Technical reach-back support, through the 24/7 Operations Center, 

provides DTRA assistance to user’s with immediate CBRNE response and consequence 

management needs. DTRA’s active development program is bringing new software tools 

to the user, providing decision support information consistent with operational concepts 

integrated vertically across all echelons in a Common Operating Picture environment. All 

these tools are available in a web-based, collaborative, net-meeting-type, geographical 

information system (GIS) environment for the use of operators, warfighters, and first 

responders. DTRA has a proven record of transitioning research and development 

software tools for operational use. 

DTRA Research and Development Programs By Topic Area 

Hazard Prediction and Assessment. DTRA’s Hazard Prediction and Assessment 

Capability (HPAC) program relies on various types of weather products to support 

transport and diffusion calculations and corresponding hazard predictions. Currently, 

DTRA provides three types of weather analyses in real time to its HPAC users: historical 

weather, observations, and forecasts. In addition to providing the HPAC user community 

with 24/7 operational meteorological data, the DTRA meteorology program is engaged in 

basic and applied atmospheric R&D to further improve its hazard assessment tools. 

Numeric Weather Prediction (NWP). DTRA provides NWP data to its customers from 

various models with grid resolutions from as coarse as 80 km to as fine as 1 km. For 

applications such as collateral effects from large nuclear strikes, coarse global-scale data 

suffice to characterize the fallout pattern over regional areas. At the other extreme, a 

chemical attack on a city or in a populated open area requires knowledge of the wind 

flow at resolutions corresponding to the local terrain: typically on the order of a few 

kilometers. Therefore, DTRA conducts basic research to improve NWP capabilities in 

general with particular emphasis on advances that can improve high-resolution modeling. 

Current research efforts funded by DTRA include expanding the capacity of mesoscale 

models to incorporate remotely sensed datasets such as radar and satellite-derived wind 

fields, the creation of high-resolution local analyses, and the potential use of emerging 

urban observational networks. Other projects are focused on improving land use 

parameterizations to provide better specifications of the urban canopy. Since DTRA 

makes use of data from numerous NWP modeling systems, research also is being 

conducted to determine biases associated with these systems and the appropriate 

measures of NWP forecast accuracy of these models at differing model resolutions. 

Weather Uncertainty. Meteorological predictions are inherently uncertain due to the 

stochastic nature of the atmosphere. Therefore, forecasts of meteorological quantities are 

incomplete if not accompanied by estimates of forecast precision. An accurate 

characterization of NWP forecast uncertainty is imperative when applied to atmospheric 
B-4 Federal R&D Needs and Priorities for ATD Modeling

transport, dispersion, and associated downwind hazard assessment. Due to the 

probabilistic framework of the HPAC toolset, DTRA is particularly interested in 

capturing the variability in predicted values of meteorological variables for inclusion in 

transport and diffusion calculations. 

Current research efforts designed at improving uncertainty estimates are focused 

primarily on the application of ensemble methodologies. In particular, DTRA-sponsored 

studies are currently investigating such issues as the minimum number of ensemble 

members needed to construct a statistically significant ensemble, real-time ensembling of 

DTRA’s operational NWP data sets, ensemble generation at the mesoscale, and the 

validation of ensemble members through real-time model performance statistics. Also, 

DTRA is sponsoring a university study directed toward improving the existing empirical 

uncertainty model within HPAC. 

Data Manipulation and Dissemination. Several developmental efforts to improve the 

dissemination of data to DTRA customers are ongoing, including the development of 

new architecture and software for the Meteorological Data Servers system. Once 

complete, these systems will provide state-of-the-art ingest, data manipulation, and server 

capabilities for DTRA’s user community. DTRA is also actively involved in the 

development of advanced methods to reduce meteorological data transfer times. This 

research is directed toward intelligent methods of “thinning” large high-resolution NWP 

data sets to reduce overall size and improve delivery to end users. 

B.2.3 U.S. Army 

The Army has the responsibility to provide fundamental knowledge of the atmospheric 

boundary layer (ABL) over land to all U.S. armed services. Army programs concerned 

with ATD include the Atmospheric Sciences R&D program within the Army Research 

Laboratory, the Chemical Stockpile Emergency Preparedness Program (CSEPP), and the 

Army Research, Development, Test, and Evaluation (RDT&E) Meteorology Program. 

Atmospheric Sciences R&D program. This program within the Army Research 

Laboratory is located at the Army Research Office (Research Triangle Park, North 

Carolina) and the Battlefield Environment Division (Adelphi, Maryland, and White 

Sands Missile Range, New Mexico). The program is broadly based to address the wide 

spectrum of physical conditions of the ABL and its influences on Army operations and 

systems. The program is divided into three general research areas: Atmospheric Sensing, 

Atmospheric Modeling, and Atmospheric Effects. 

• Atmospheric Sensing. ATD-related concerns in this area include rapid detection, 

identification, and quantification of chemical and biological agents, both gases 

and aerosols, and in situ characterization and volumetric remote sensing of the 

state of the environment. 

• Atmospheric Modeling. Within this area, the ARL focuses on understanding and 

modeling the diurnal dynamics of the ABL and on assimilation and fusion of 

volumetric measurement of atmospheric state variables at high resolution in 
Appendix B B-5

complex and urban domains. The effort results in the fusion of data with 

appropriate models to provide a real-time picture of the present state of the ABL 

and its likely development over short time periods, especially as they apply to 

ATD nowcasting. While working to reduce the uncertainty in data-fused model 

results, the Army Research Laboratory recognizes the need to communicate that 

uncertainty together with best estimates of expected ATD conditions in userfriendly products to decision makers at all echelons. 

• Atmospheric Effects. End users of ATD modeling systems are often most 

interested in quantifying the effects of the atmosphere and assessing its impacts 

on their systems, operations, and personnel. ATD-related concerns are to provide 

those parameters at the spatial and temporal scales needed to determine relevant 

effects such as visibility effects of aerosols and dosage effects on personnel. The 

research results are incorporated into the weather modeling and support functions 

of the integrated meteorological system deployed to support training and field 

operations. 

Chemical Stockpile Emergency Preparedness Program. The U.S. Army serves as 

DOD Executive Agent for the chemical weapons stockpile. Chemical weapons are 

stockpiled at eight locations: Aberdeen, Maryland; Lexington, Kentucky; Anniston, 

Alabama; Newport, Indiana; Pine Bluff, Arkansas; Pueblo, Colorado; Tooele, Utah; and 

Umatilla, Oregon. As a signatory to several international treaties, the United States has 

agreed to destroy its stockpile of chemical weapons. The Congress mandated in Public 

Law 99-145 that the Army provide maximum protection to the workers, general 

population, and the environment during the storage and destruction of these chemical 

weapons. 

CSEPP focuses on the protection of the general population in the unlikely event of an 

accident involving the chemical stockpile. At the national level, the program is jointly 

managed by the U.S. Army Chemical Materials Agency and FEMA. The Program Office 

is at Aberdeen Proving Ground, Maryland. CSEPP provides funding and technical 

assistance to ten states and 41 counties in the vicinity of the chemical stockpile. (Newport 

is located approximately six miles from the Illinois border and Umatilla is located 

approximately three miles from the Washington border). 

The D2-Puff model serves as the primary chemical hazard prediction tool in support of 

the US Army stockpile and non-stockpile programs. (The non-stockpile program handles 

demilitarization of chemical weapons or agents not included in the stockpile as defined 

by treaty and law, such as items uncovered from old ordnance disposal sites.) This 

segmented plume model is used daily in the planning for and potential response to 

accidents involving the chemical weapons stockpile. The primary stockpile chemical 

agents include the nerve agents sarin (designated GB in Army applications) and VX and 

the blister agent mustard (designated H in Army applications). The primary non-stockpile 

chemical agents include mustard, phosgene, and lewisite. The primary accidents of 

concern include spills, explosions, fires, and stack releases. 

The D2-Puff model incorporates several sources of real-time meteorological data, both 

on-post and off-post. Meteorological towers have been built specifically to support the 
B-6 Federal R&D Needs and Priorities for ATD Modeling

modeling system on the depots as well as in several nearby communities. In addition, the 

modeling system captures data from nearby NOAA National Weather Service (NWS) 

and university sites, as well as NOAA/NWS forecasts available via the Internet. With 

these data, the model continually generates wind fields and hypothetical chemical plume 

projections to support ongoing chemical weapons storage and demilitarization operations. 

The model typically updates every 15 minutes to account for temporal and spatial 

variability in the wind field due to the surrounding complex terrain. 

The modeling system connects Army depot operations centers with State and county 

emergency managers to allow rapid transmission of emergency management information. 

This information includes an automated communication system, a GIS, a model to 

support shelter-in-place strategies, and report summaries. 

The Army RDT&E Meteorology Program. The Army RDT&E Meteorology Program 

at Dugway Proving Ground, Utah, is responsible for providing operational 

meteorological support to U.S. Army RDT&E activities. The program is a user of ATD 

models, not a developer. However, as the principal DOD test center for chemical and 

biological defense systems, Dugway Proving Ground has a long history of ATD model 

R&D. It continues to provide technical assistance to other DOD agencies in ATD model 

R&D, including conducting field dispersion tests or experiments. Operational 

meteorological support of field tests involving releases of simulants of chemical and 

biological agents also requires that Dugway Proving Ground use ATD models in 

essentially the same ways that they are or could be used for homeland security 

applications. 

The Army RDT&E Program also sponsors applied R&D on mesoscale meteorological 

modeling. These efforts benefit ATD modeling because the accuracy of the gridded 

mesoscale model output used as inputs to ATD models is at least as important to the 

validity of CBRNE hazard assessments as the accuracy of the ATD models themselves. 

B.2.4 U.S. Air Force 

Air Force Weather resources are organized into a three-tiered structure to conduct 

worldwide operations and support homeland defense. The Air Force Weather Agency 

(AFWA) is the strategic-level center. It provides meteorological satellite processing, 

weather models (mesoscale NWP, cloud analysis/forecast, snow depth, surface 

temperature, and land-surface models), strategic-level weather products, and specialized 

support to Special Operations Forces and the intelligence community. The second tier 

consists of the Operational Weather Squadrons (OWSs), each of which supports a 

specific geographic area of responsibility. Each OWS provides forecasts, warnings, and 

advisories to a large number of Air Force and Army active duty, Reserve, and National 

Guard locations, which support numerous U.S. sites. The level of support is second only 

to NOAA/NWS. The OWSs provide homeland security support to NORTHCOM when 

tasked. The third tier consists of Combat Weather Teams, which provide mission-tailored 

support to local base and tactical units of the Air Force, Army, Special Operations 

Forces, and other specialized military units. The Combat Weather Teams also provide the 
Appendix B B-7

observations from their locations, which are disseminated to AFWA and, in most cases, 

also to the World Meteorological Organization for worldwide use. 

B.2.5 U.S. Navy 

The Navy's capability to provide operational support for homeland security is based on its 

expertise in conducting a Rapid Environmental Assessment and providing real-time 

environmental support, based on that assessment, for naval forces. The Navy's strengths 

in characterizing the environment through observations and modeling, together with its 

distributed facilities and effective network for communications and data exchange, are 

substantial contributions to the national requirement for the best meteorological and 

oceanographic support to the mission of homeland security. The Navy operates two 

primary Meteorology and Oceanography (METOC) production (modeling) centers, three 

regional METOC centers, and numerous facilities and detachments throughout the United 

States that work closely with NOAA operation centers. The Navy’s METOC community 

also owns seven military survey ships equipped with the latest oceanographic digital data 

collection systems to survey critical areas worldwide. 

B.3 U.S. Department of Energy 

B.3.1 Nuclear Incident Response Teams 

The Department of Energy (DOE) and the DHS are jointly responsible for the Nuclear 

Incident Response Team (NIRT) assets that would be used in response to a domestic 

nuclear release incident. A February 28, 2003, memorandum of agreement (MOA) 

between the two departments established a framework for DHS to use various DOE 

assets. The MOA delineates functions and responsibilities for the control, utilization, and 

exercise of, and the standards for, NIRT assets. NIRT assets, with the exception of the 

Radiological Assistance Program, which may continue to self-deploy under 

circumstances where self-deployment is currently authorized, will deploy at the direction 

of DHS for domestic events in connection with an actual or threatened terrorist attack, 

major disaster, or other emergency in the United States. NIRT assets include the: 

• Accident Response Group; 

• RAP; 

• Aerial Measuring System (AMS); 

• National Atmospheric Release Advisory Center (NARAC); 

• Federal Radiological Monitoring and Assessment Center (FRMAC); 

• Radiation Emergency Assistance Center/Training Site; and 

• Nuclear Emergency Support Team. 
B-8 Federal R&D Needs and Priorities for ATD Modeling

B.3.2 National Atmospheric Release Advisory Center 

NARAC, which is located at Lawrence Livermore National Laboratory (LLNL), provides 

atmospheric plume modeling tools and services for chemical, biological, radiological, 

and nuclear (CBRN) airborne hazards—both gases and particles. These capabilities 

employ real-time access to worldwide meteorological observations and forecasts via 

redundant communications links to resources of NOAA, the U.S. Navy, and the U.S. Air 

Force. NARAC can simulate downwind effects from a variety of scenarios, including 

fires, radiation dispersal device explosions, hazardous material (HAZMAT) spills, 

sprayers, nuclear power plant accidents, and nuclear detonations. A database of potential 

sources is maintained for input to NARAC models. The NARAC software tools include 

stand-alone local plume modeling tools for end user’s computers, and Internet web-based 

software to provide reach-back access to advanced modeling tools and expert analysis 

from the national center at LLNL. Initial automated, advanced three-dimensional 

predictions of plume exposure limits and protective action guidelines for emergency 

responders and managers are available in 5 to10 minutes. On-duty or on-call NARAC 

staff can follow up these initial products with more detailed analyses developed 

immediately, 24/7. NARAC continues to refine calculations using on-scene information, 

including measurements, until all airborne releases have stopped and the hazardous 

threats are mapped and the impacts are assessed. Model predictions include the threedimensional and time-varying effects of weather, land use, and terrain. NARAC provides 

a simple GIS for display of plume predictions with affected population counts and 

detailed maps. It can also export plume predictions to other standard GIS systems. 

NARAC supports the NIRT, the regional RAP teams, AMS, FRMAC, the DHS under the 

DOE–DHS MOA, and 40 DOE and DOD online sites. NARAC’s operational support to 

5 cities and 53 State and Federal organizations across the country has been successfully 

demonstrated under DHS and DOE oversight. 

B.4 U.S. Department of Homeland Security 

The capabilities of the DHS Emergency Preparedness and Response (DHS/EP&R) 

Directorate include an agency-wide GIS Service Center, which has evolved since 1994 

from the Mapping Analysis Center (MAC). The MAC had originally supported the 

FEMA Emergency Support Team and the Response and Recovery Directorate. At the 

GIS Service Center, the results of the various modeling software packages are 

incorporated with remote-sensing data and imported into multiplatform GIS software for 

subsequent analysis. Experts in the scientific and modeling community determine the 

parameters used to operate these highly complex and complicated programs. DHS/EP&R 

maintains Internet and Government intranet sites, as well as a continuity of operations 

site with a capability almost identical to the GIS Service Center. DHS/EP&R is in the 

process of installing secure classified communications. 
Appendix B B-9

B.5 National Aeronautics and Space Administration 

As an R&D agency, NASA has invested in three areas applicable to ATD: sensor 

technology, platform technology, and modeling and computing. 

Tropospheric chemistry is considered to be the next frontier of atmospheric chemistry, 

and understanding and predicting the global influence of natural and human-induced 

effects on tropospheric chemistry will be the next challenge for atmospheric research 

over the foreseeable future. NASA’s interest in trace gas species in the troposphere has 

driven investment in a number of active sensing techniques, e.g., differential absorption 

lidar. Experimental airborne prototypes are being developed and tested in various 

suborbital missions for tropospheric profiling of chemical species and may be adapted or 

used in homeland security applications. 

NASA is also investing in autonomous suborbital platforms that simultaneously enable in 

situ planetary exploration and improve the targeting capability of Earth observational 

systems. Investments in airspace improvements that provide unmanned aerial vehicles 

(UAVs) with access to the National Airspace System and with in-vehicle technology for 

safe robotic flight in populated areas can directly enable in situ observations of hazardous 

airborne material without endangering pilots. 

For many years, NASA has invested significantly in the development of data assimilation 

systems (DASs), especially global systems for medium-range weather forecasting and for 

climate, and in the study and understanding of forecast and modeling uncertainties. This 

data assimilation work now extends to the development of land-surface DASs and their 

integration into atmospheric simulation systems, to the development of an ocean DAS, 

and to collaboration with the National Weather Service to build a mesoscale atmospheric 

DAS. NASA’s data assimilation work is also being applied to the study of the Earth’s 

carbon cycle and to the study of global precipitation and of the Earth’s hydrologic cycle. 

While much of NASA’s work with data assimilation and with forecast and modeling 

uncertainty has been applied to spatial and temporal scales much larger than those 

relevant to ATD modeling, a great deal of the technology that has been developed and 

many of the lessons that have been learned can be transferred directly to smaller scales. 

The real challenge is to build a program where people from a varied range of disciplines 

can talk to each other. 

NASA has had extensive experience in using observation system experiments (OSEs) to 

evaluate the impact of various observations on global-scale atmospheric predictions and 

in using observation system simulation experiments (OSSEs) that first simulate the 

atmospheric observations and then predict the impact that these simulated observations 

would have on atmospheric predictions. These same OSE and OSSE techniques can be 

used to determine the impact that on-site data will have on ATD simulations and thereby 

help build ATD sensor networks. 

NASA has invested heavily in multi-model ensemble techniques to better understand 

model and observation uncertainty. Ensemble Kalman filters have been used to estimate 
B-10 Federal R&D Needs and Priorities for ATD Modeling

model spread and to thereby significantly reduce the number of ensemble members 

necessary, compared with those needed in a straightforward Monte Carlo approach. The 

agency has also carried out research on the use of the breeding vector to generate optimal 

forecast ensembles. All of these ensemble techniques can be fully adapted to the art of 

data assimilation for ATD modeling. 

B.6 U.S. Environmental Protection Agency 

As explained in appendix A, section A.1.2, the Atmospheric Sciences Modeling Division 

(ASMD), Air Resources Laboratory (ARL), NOAA, serves as the primary vehicle by 

which EPA funds its research efforts in air pollution meteorology and atmospheric 

modeling. ASMD conducts research activities in-house and through contract and 

cooperative agreements for the National Exposure Research Laboratory and other EPA 

groups. ASMD also provides technical information, observational and forecasting 

support, and consulting on all meteorological aspects of the air pollution control program 

to many EPA offices, especially the Office of Air Quality Planning and Standards. 

ASMD has identified five major research themes, summarized below, to guide its future 

research program development and resource planning efforts. 

B.6.1 New Directions to Criteria Pollutants and Air Toxics Modeling 

This research theme addresses the original and still primary area of research for which 

ASMD was created. The main research product is state-of-the-science modeling tools for 

assessment and mitigation of criteria pollutants and air toxics. Following the “one 

atmosphere” concept, the main tool for computer simulation of a multitude of air quality 

issues is currently the Community Model for Air Quality (CMAQ) system. While the 

CMAQ is adaptable to various meteorological models, ASMD has used only the 

Pennsylvania State University–NCAR Mesoscale Model (MM5). Future efforts will 

involve a gradual transition to the WRF model, which will be the next-generation 

mesoscale model for both research (replacing MM5) and operational forecasting 

(replacing the Eta and Rapid Update Cycle models). ASMD is becoming increasingly 

involved, especially in the chemistry component. WRF-Chem will be an "on-line" 

meteorology-chemistry model, representing a major step forward in the state of the 

science. 

Current research includes upgrades to the meteorology–chemistry interface program, 

upgrade linkages to the WRF model, initial testing of WRF-Chem, and installing the PXland surface planetary boundary layer (PBL) model into WRF-Chem. Upgrades are being 

made to the emissions processors to include wildfire, fugitive dust, and sea salt 

emissions. New gas-phase chemical mechanisms, readers, and solvers are being installed 

into CMAQ and tested. Research is being conducted to improve the condensed chemistry 

for long-term simulations. Updates are being made to the cloud dynamic processes and 

aqueous chemistry. A new version of CMAQ is scheduled for release in June 2004. 

Improvements are being made to the CMAQ photolysis rates and radiative transfer 

model, including the addition of feedbacks between aerosols and radiation. A sectional 
Appendix B B-11

model for treatment of aerosols is being added to CMAQ. The CMAQ plume-in-grid 

model is being extended to include aerosol chemistry. The CMAQ code is being 

optimized to reduce execution speed with testing on various platforms and compilers. 

Research is underway to include simulation of mercury chemistry and fate in CMAQ. 

The plume dispersion model AERMOD is being updated to include dry and wet 

deposition. Fluid and wind tunnel simulations are being conducted to investigate subgrid-scale phenomena, dispersion within convective boundary layers, and urban canyons. 

Computational fluid dynamics (CFD) modeling is being conducted in support of wind 

tunnel simulations of flow and dispersion around the World Trade Center site as part of 

the post-September 11, 2001, risk assessment for the New York City area. Research is 

being conducted to characterize sub-grid concentration distributions, including large eddy 

simulation with air chemistry and the probability distribution function of emissions. 

B.6.2 Air Quality and Global Climate Change 

From the air quality perspective, global climate change may make it more difficult in the 

future for the United States to achieve its air quality standards or goals at the regional and 

local level. Conversely, air pollution emanating from the United States, including 

methane, carbon dioxide, particles, and other constituents, may be exacerbating the rate 

of climate change. Climate change impacts act on long time scales (decades to centuries) 

and thus are difficult to detect. ASMD has established three research areas that address 

simulations of global air quality and the attendant effects of climate change: (1) 

assessment of intercontinental transport, (2) assessment of global climate change on 

regional/urban air quality, and (3) assessment of regional air quality on global climate 

change. 

One current research activity involves MM5 regional climate modeling simulations based 

on downscaled global climate model results for current and future years. Another activity 

is investigating emissions processing for current and future climate change conditions. 

B.6.3 Air Quality Forecasting 

A national real-time air quality forecast model will equip State and local air quality 

agencies with a tool for making accurate, multi-day predictions of air quality. The public 

can use these forecasts to reduce individual exposure to harmful levels of ozone and 

particulate matter during elevated pollutant episodes. The real-time modeling results can 

also be used for aiding decision makers in issuing air pollution advisories, for regulating 

controlled burns, and for helping the public to visualize air quality patterns. The goal of 

this emerging research program is to design, develop, and test models for real-time 

forecasting of airborne material. The goal of this research is to construct an operational 

national air quality forecasting system for ozone and particulate matter. This research 

theme supports EPA’s mission “to protect public health and welfare” and NOAA’s 

mission “to forecast changes in atmospheric conditions.” 
B-12 Federal R&D Needs and Priorities for ATD Modeling

Current research under this theme includes: (1) evaluation of summer ozone and fine 

particle simulation results for 2002 and 2003 for the northeastern United States, 

(2) collaboration and assistance to NOAA/NCEP in installing improvements and 

efficiencies in the operational modeling system for air quality forecasts, and (3) improved 

aerosol and radiation process treatment in the WRF-Chem air quality model to allow for 

meteorological and chemical feedbacks. 

B.6.4 Multimedia Modeling 

Many of the most difficult challenges facing the EPA span environmental media. Specific 

multimedia issues of concern include mercury, pesticides, hazardous waste, and excess 

nutrients. Effectively addressing these issues requires an improved understanding of 

cross-media processes. This research area will address the interaction of the atmosphere 

with adjoining media critical to nitrogen/nutrient cycling, acid deposition, and ozone 

formation and destruction, as well as the behavior of mercury and other toxic pollutants 

in the environment. Research activities include development of a multimedia nitrogen 

deposition model. 

B.6.5 Data Management and Analysis Research 

Many of the environmental issues currently being addressed will require simulations that 

demand significantly more computational resources and more complex model 

configurations than present activities require. As the scope of models increases and the 

models become more sophisticated, the amount of data they consume and generate will 

also increase. One area of ongoing research is to develop and test methods to address 

these data management issues. 

A second area of ongoing research is to develop and test methods for data analysis and 

visualization. The growing volumes of data collected and generated will increasingly 

strain our ability to analyze the data. Much data analysis still relies on a human being 

looking at the data or at summaries of the data. This approach will become increasingly 

impractical as data volumes increase. 

The ability of our modeling systems to replicate meteorological and chemical processes 

can only be determined through rigorous model evaluation. A third area of research is to 

develop and test improved methods for summarizing and characterizing model 

performance. 

Research being conducted within this theme includes development and testing of model 

evaluation metrics that assess the ability of regional-scale modeling of ozone and aerosols 

to replicate seasonal spatial and temporal trends. A comparison is planned to assess the 

performance of several regional-scale mercury models. Quality assurance analysis tools 

are being developed for assessment of MM5 meteorology and the emissions processed 

for use in CMAQ. 
C-1 

APPENDIX C. ATMOSPHERIC TRANSPORT AND DIFFUSION 

MODELING CONSIDERATIONS

C.1 Hierarchy Theory 

Most environmental models implicitly employ hierarchy theory in their construct, which 

in turn employs the concept of scale. Hierarchy theory is an extension of systems theory 

that attempts to analyze the effects of scale on the organization of complex systems. 

Simon (1973) was one of the first to argue for hierarchical systems in which each level 

communicates a small set of information or quantity of material to the next heigher 

(slower and coarser) level, and each level is formed from the interactions among a set of 

variables that share similar speeds (and geometries). O’Neill (1988) expanded this idea 

by shifting attention from the small-scale view to a multiscale view that recognized that 

processes could develop mutually re-enforcing relationships. Hierarchy theory has been 

used to separate the large and slow processes from the small and fast processes. Many 

have argued that environmental phenomena tend to have characteristic spatial and 

temporal scales (Simon and Ando 1961; Delcourt, Delcourt, and Webb 1983; Urban, 

O’Neill, and Shugart 1987). This hypothesis is supported empirically by the fact that 

many physical and ecological phenomena arrange themselves approximately along the 

45o

 line as depicted in figure C-1. 

FIGURE C-1. Depiction of various physical and ecological phenomena. Adapted from NASA 

Advisory Council 1988. 

Temporal Scale (Year)

Spatial Scale (km2)

Atmospheric

 turbulence

Atmospheric

 convection

Volcanic eruption

Earthquake

 cycle

Nutrient

 cycles

 Seasonal

vegetation

 cycles

 Soil

 moisture

variations

Synoptic

 weather

 systems

 Soil

erosion

Upper

ocean

mixing

 Global

weather

systems

Metallogenesis

 Soil

development

Ocean circulation

El Nino

Climate

Carbon dioxide

 variations

Extinction events

 Glacial

periods

Mountain building

Atmospheric

composition

Mantle convection

Plate tectonics

 Origin of

earth and life

Minute Day 1 Yr 102 Yr 104 Yr 106 Yr 109 Yr

1

10

102

103

104

Global

Local

Second
C-2 Federal R&D Needs and Priorities for ATD Modeling

C.2 Scale 

Depending on the horizontal scale of interest, different atmospheric processes take on 

greater or less significance. For the atmospheric processes shown in figure 2, the 

horizontal scale of motion seems to be the better quantity to use for classification 

(Orlanski 1975). These scales are all interconnected. Large-scale atmospheric processes 

(e.g., climatic and daily synoptic weather systems) drive smaller scale processes as 

energy is transferred from large to small scales. Conversely, small-scale processes can 

organize to develop large-scale systems: for example, convective storms developing from 

smaller disturbances. Many of the cases of interest in ATD occur in the troposphere, the 

portion of the atmosphere up to 15 km above the ground. However, there are cases when 

transport and diffusion within the upper atmosphere are important (e.g., protecting air 

Plumes

Roughness

Turbulence

Dust

 Devils

Thermals

Wakes

Tornadoes

Deep

 Convection

Short gravity

 waves

Thunder-

 storms

I.G.W.

C.A.T.

Urban Effects

Nocturnal

 low-level jet

Squall lines

Inertial waves

Cloud clusters

Mtn. & Lake

 breezes

Fronts

Hurri-

 canes

Baroclinic

 waves

Tidal

waves

Ultra long

waves

Standing

 waves

Micro γ Micro β Micro α Meso γ Meso β Meso α Macro β Macro α

1 Min

1 Hr

1 Day

1 Month

1 Year

Micro Scale

Meso Scale

Macro Scale

20 

M

200 

M

2 

Km

200 

Km

2,000 

Km

10,000 

Km

20

Km

FIGURE 2. Scale definitions and different atmospheric processes with characteristic time 

and horizontal scales (adapted from Orlanski 1975). C.A.T is Clear Air Turbulence, and 

I.G.W. is Inertial Gravity Waves.
Appendix C C-3

traffic from volcanic ash, tracking the path of materials from major explosions, or 

tracking the dispersion of materials originally constrained within the lower atmosphere 

but which slowly leak into the layers of air aloft). 

The atmospheric boundary layer (ABL) is defined for the purposes of this discussion as 

the lower part of the atmosphere, which is directly influenced by the presence of the 

earth’s surface during the diurnal cycle. This includes the diurnal evolution of solar 

heating and radiative cooling and the transitions between those states. For ATD, the 

three-dimensionality of the ABL is crucial to understanding the physics of its variability 

and, most important, for modeling the variability. With the exception of convective 

clouds, fronts, or terrain uplift, the troposphere above the ABL exhibits quasi-horizontal 

flow; vertical motions are slow and gradual. Within the ABL, the earth’s surface, through 

heating and cooling the air and friction over various roughness elements coupled with 

sources and sinks of moisture, produces three-dimensional turbulence and intermittent 

processes, which seldom reach truly steady-state conditions. An integral aspect of this 

diurnal variation is that some of the material originally constrained in the lower 

atmosphere “leaks” into the layers of air above it. This leakage is accelerated by the 

action of deep convection, whether or not accompanied by clouds. 

The upslope and downslope winds resulting from differential heating and cooling would 

not be explicitly treated in a model of ATD at the continental scale, but they would be of 

great concern if the scale were reduced to a local region. Mexico City is an example of a 

location where proper characterization of the interaction of local and mesoscale airflow 

circulation patterns is fundamental for proper characterization of transport and diffusion. 

Mexico City is located in a basin and is surrounded on most sides by hills and mountains. 

Observations and mesoscale analyses provide evidence that the local circulations are 

highly complex. A conceptual diagram depicting some of these processes is shown in 

figure C-3. Mexico City is not unique. Many major cities are located in valleys along 

major rivers, where upslope and downslope flows are common, or along shorelines of 

lakes and oceans, where land-sea breezes are common. 

The choice of horizontal scale plays an important role in the formulation and selection of 

an atmospheric model. According to hierarchy theory, describing effects at some scale 

(the scale of interest) requires at least three levels (scales) for both comprehensiveness 

and conciseness: (1) the next smaller scale, which provides information up to the scale of 

interest, (2) the scale of interest, which constrains processes at the next lower scale and 

provides information up to the next larger scale, and (3) the next larger scale, which 

constrains processes at the scale of interest. At each scale, a decision must be made as to 

which physical processes will be represented and how explicitly each selected process 

will be treated. For instance, at a fine scale the potential evapotranspiration depends on 

physical parameters such as temperature, vapor pressure deficit, wind speed, surface 

roughness, precipitation, and soil moisture status, as well as biological parameters such as 

stomatal conductance (Monteith 1965). At subcontinental scales, Thornthwaite and 

Mather (1955) show that the potential evapotranspiration can be predicted adequately 

using a monthly mean temperature and precipitation and latitude (to determine length of 

day). The nature of the process has not changed with scale, but the relative contribution 

of explanatory variables has. 
C-4 Federal R&D Needs and Priorities for ATD Modeling

FIGURE C-3. Conceptual diagram depicting some of the meteorological processes associated 

with pollutant transport within the Mexico City Basin. (Contributed by Jerome Fast of Pacific 

Northwest National Laboratory, operated by Battelle Memorial Institute for the U.S. Department of 

Energy. http://www.pnl.gov/atmos_sciences/as_meso5.html.) 

Aggregating a large number of processes or decomposing a complex system into a 

smaller number of levels is similar to approximating the solution of differential equations 

using a truncated Taylor series. The magnitude of the truncation errors depends on the 

nature of the processes (e.g., nonlinear interactions, feedbacks, and time delays), spatial 

heterogeneity, and the uncertainties of available model input and model parameterizations. Because of the feedbacks and interactions, it seems difficult to build a model that 

spans more than two orders of magnitude (“Carl Walters Rule of Thumb,” personal 

communication from Dr. Gerry Peterson). 

C.3 Predictability 

A composite of the spectrum of horizontal kinetic energy in the troposphere is shown in 

figure C-4. In its average state, the atmosphere has a large amount of energy in long 

wavelengths and decreasing energy as scales become smaller. The energy spectrum 

(energy per unit wave number interval) decreases as wave number increases (wavelength 

decreases). The energy spectrum shown in figure C-4 encompasses six decades. As 
Appendix C C-5

illustrated in figure C-2, the time scales of the motions compress and expand with spatial 

scale, so that in general, small-scale actions affect short time periods. 

Numerical weather prediction models have been successful over the years because the 

kinetic energy spectrum decreases toward the smallest scale processes and the 

atmosphere is essentially a weakly stratified fluid, exhibiting quasi-horizontal flow. As 

computational facilities exploded in capacity and capability, operational weather models 

have successfully transitioned from the larger scale synoptic flows with horizontal grid 

sizes of ~200 km to more detailed mesoscale models with horizontal grid sizes of ~20 km 

or less and representation of terrain influences, oceanic interactions, vertical motions, and 

larger scale cloud systems. Research and operational models at Army test ranges go to 

smaller grid lengths (~ 1 km) and include more and more processes of cloud physics, 

radiative transfer, and land surface interaction and texture. As the grid size is decreased, 

time steps are decreased, and more details of the small scale processes must be accounted 

for within the model. From a simple theoretical construct, Lorenz (1969) estimates the 

spatial scales for loss of predictability as a result of small errors in a uniformly turbulent 

two-dimensional atmosphere. His results, shown in figure C-5, indicate that the 

predictability is lost after an hour or so for motions on scales below 40 km. 

Wave Number (m-1)

Energy Spectral Amplitude (m3/s2)

Wave Length (km)

Chen and Wiin-Nielsen

Nastrom and Gage

Vinnichenko

Lilly and Petersen

Balsley and Carter

108

106

104

102

104 103 102 101

10-4 10-1 10-6 10-5

FIGURE C-4. Composite spectra of two-dimensional energy obtained from various sources. 

Adapted from Lilly 1985. 
C-6 Federal R&D Needs and Priorities for ATD Modeling

Wave Length (km)

Kinetic Energy Spectrum

40,000

20,00010,000

5000

2500

625

156

3912

5 days

1 day

5 hrs

1 hr

15 min

0

FIGURE C- 5. Results of a closure model calculation of the rate of loss of predictability of twodimensional flow where the kinetic energy spectrum is given by the upper (red) curve. The short 

(black) parabolic areas show the left edge of the unpredicted spectrum as it proceeds to larger 

scales. Adapted from Lilly 1985 and Lorenz 1969. 

Observations by Van der Hoven (1957) suggest that the spectrum of the intensity of 

horizontal wind fluctuations near the ground often shows a separation of scales, as in 

figure C-6. Large-scale motions of transient weather systems (highs and lows, fronts and 

storms) are at the left of the figure. A secondary peak representing diurnal processes 

occurs at about 12 hours. Processes of the order of an hour or so show little intensity 

compared with these longer processes or those with shorter periods (10 minutes, 1 

minute). Although there is some scientific debate about the persistent presence of this 

“mesoscale gap,” the data suggest that mesoscale models may be more effective in 

representing the larger scale processes than those at smaller scales. 

The energetics of ABL turbulence scales are relatively small compared to larger scale 

flows. This means that these flows have less structure and change more rapidly than do 

larger scale flows. The memory time of the flow is short and the correlation times and 

lengths of ABL motions are small. To maintain predictability, high-resolution models 

may need to be refreshed more often with changing local conditions—insolation, winds, 

and surface moisture—at the scales of interest. Historically, these processes have been 

approximated by parameterizations using forecast values of larger scale flows. Where 

complex terrain is a dominant factor, this conventional thinking remains to be well tested 

and can be best considered a first approximation to be used with caution until better 

understanding develops. In anticipation of the discussion to follow, it should be 

emphasized that dispersion over cities can display the characteristics of severe terrain 

complexity. 
Appendix C C-7

Period (hours)

Ene

rgy (m

2

/

s

2

)

5%

95%

Fiducial Limits

100 10 5 2 1 .5 .2 .1 .05 .02 .01 .005 .002.0001

1

2

3

4

5

6

FIGURE C-6. Horizontal wind-speed energy spectrum at Brookhaven National Laboratory at 

about 100 m height. Data analyzed were collected during the period from June 1955 through 

February 1956. The statistical significance of the major peaks and gaps of the spectrum is shown 

by the 5% and 95% confidence (fiducial) limits. Adapted from Van der Hoven 1957. 

As the scale of atmospheric motion becomes smaller, the effects of some processes 

become increasingly more difficult to treat explicitly or deterministically. Turbulence, the 

gustiness superimposed on the mean wind, can be visualized as consisting of irregular 

swirls of motion called eddies. Usually turbulence consists of many different size eddies 

superimposed on each other with different relative strengths. Compared with the other 

scales of meteorological motions, turbulence is on the small end of scale, as shown in 

figures C-1 and C-2. Phenomena such as turbulence with a spatial scale smaller than 

about 3 km and a time scale shorter than about 1 hour are classified as microscale. The 

small-scale phenomena associated with the microscale are so transient in nature that the 

deterministic description and forecasting of each individual eddy is virtually impossible 

(Stull 1988). 

Physical models—such as wind tunnels, flow channels, and convection tanks—have been 

used successfully to investigate stochastic effects embedded within local-scale flows. 

These physical models have the distinct advantage of controlling boundary and initial 

conditions, high-resolution measurements, and repeatability. Such control permits a large 

ensemble of realizations and the potential for measuring inherent uncertainty. Figure C-7 

illustrates how physical modeling results can be used to investigate the effect of temporal 

or spatial averaging. Vortex shedding from the corners of the building is apparent in the 

instantaneous pictures for both cases, but there is an obvious difference in the structure of 

the plumes. The tall building has a thin sinuous plume with much meandering, which is 

similar in some respects to the classical von Karman vortex street in the wake of a twodimensional cylinder. This structure is not as pronounced in the wake of the wide 

building, which appears to have a more random internal structure (Lee et al. 1988). 
C-8 Federal R&D Needs and Priorities for ATD Modeling

FIGURE C-7. A video image analysis of smoke concentration in wind tunnel flow fields (Lee et al. 

1988). The smoke source is centered on the lee side of the building at the surface. The plate 

illustrates the very large differences between instantaneous and time-averaged plumes for a tall 

building, H = 30 cm, and a wide building, H = W = 30 cm, where W is the cross-stream width of 

the building. Photographs were collected at 30 frames per second. The relation between the 

digitized smoke intensity and the vertically nondimensional integrated concentration, ψ, of the 

smoke particles was obtained from calibration experiments in which the smoke was replaced by a 

mixture of ethane and air. 

Most operational ATD models predict the ensemble mean transport and diffusion for the 

conditions specified. However, atmospheric releases are individual realizations from 

imperfectly defined ensembles. As illustrated in figure C-7, the within-ensemble variance 

of certain processes can be quite large. For instance, the first-order approximation of the 

lateral concentration profile of a plume is the often-assumed Gaussian shape. Inspection 

of tracer plumes reveals fluctuations superimposed on this Gaussian shape of the order of 

a factor of two. These fluctuations are not addressed or characterized in most operational 

transport and diffusion models (ASTM 2000). The approximation thus inherently 

constrains the predictability one can anticipate from such a model. 

At the microscale, the direction of transport cannot be treated deterministically (Irwin and 

Smith 1984; Weil, Sykes, and Venkatram 1992), which precludes the possibility of 

simulating the concentration time series as would be seen at some fixed receptor location. 

In the simplest of circumstances, the microscale transport direction of a plume can be 

defined to about 25 percent of the overall width of the plume, which typically is on the 

order of 20 degrees in width. As the winds become light and variable, the uncertainty in 

the transport direction increases. At the microscale, the transport direction is best viewed 

as a stochastic variable having a large variance. 
Appendix C C-9

Only rarely is the centerline of a plume a straight line. In reality, because the initial plume 

dimensions are small in comparison with the length scale of the turbulence, the plume of 

dispersing material waves back and forth in a serpentine fashion in both the vertical and 

horizontal dimensions, as shown in figure C-7. The resultant plume meander contributes 

to the time-integrated plume spread but complicates determination of the time history of 

concentration values at some fixed point relative to the release point. The plume 

meandering causes the plume to be present at a given point only intermittently. A 

frequently cited model used for characterizing the process just described is the Gifford 

(1959) fluctuating plume dispersion model. 

Further inspection of the crosswind profile of the instantaneous plume reveals that the 

vertical and lateral concentration profiles are not smooth bell-shaped curves but are 

“grassy-looking profiles, with many local deviations (greater and less) from the 

envisioned smooth bell-shaped profile.” Various models have been proposed to 

characterize the combined effects of random concentration fluctuations within a plume 

that in turn is randomly varying (meandering). Wilson, Robins, and Fackrell (1982) note 

that the variance of the concentration fluctuations is seen to be strongly dependent on 

height above the surface, which “...can pose difficulties in hazard assessment because 

variations in receptor height of only a few meters cause significant changes in the 

predicted variance and thus the probability of observing a specified concentration.” 

C.4 Model Selection and Application 

Part of the problem of model selection is knowing the horizontal scale of the various 

transport and diffusion processes of concern. Another part of the model selection problem 

is understanding which transformations and removal processes are of concern. Figure 

C-8, which depicts some of the major atmospheric processes typically addressed in 

transport and diffusion models, illustrates that not all processes are of interest at all 

scales. (Note that figure C-8 is not intended to provide guidance on when a process must 

be addressed.) For example, whether a model provides a means of characterizing buoyant 

plume rise is of no concern, even in the near field, unless the emissions are buoyant. 

Modeling systems that estimate the impacts of inert species typically focus on short 

transport distances where little dilution has occurred and concentration values are at their 

highest levels. Such models focus on characterization of diffusion, local flows, building 

effects, and initial source effects (e.g., buoyancy and explosive dispersal). Modeling 

systems that estimate impacts of chemical and radioactive species that form during 

transport are less concerned with microscale effects and are more concerned with 

mesoscale and macroscale processes. These models must address the consequences of 

variations in time and space of meteorological conditions that affect transport and 

diffusion. 

Transport and diffusion models are typically employed for two circumstances: (1) for the 

assessment of near-field impacts from one or more releases (e.g., involving transport 

distances of 5 km or less), and (2) for the assessment of long-range impacts from a 

radioactive or very toxic release (e.g., involving transport distances of 30 km or more). In 

the first instance, where the plume dimensions are small in comparison to the dominant 
C-10 Federal R&D Needs and Priorities for ATD Modeling

Micro γ Micro β Micro α Meso γ Meso β Meso α Macro β Macro α

Micro Scale

Meso Scale

Macro Scale

20 

M

200 

M

2 

Km

200 

Km

2,000 

Km

10,000 

Km

20

Km

Wet Depostion

Fine Particle Dry Deposition

Formation of Secondary Species and Decay

Land-Sea Breezes

Time/Space Varying Meteorology and Diffusion

Primary Emissions

Forest Fires (Buoyancy, Wind Circulation)

Positive Buoyany (e.g., Stacks, Vents)

Negative Buoyancy Effects

Urban Morphology (Turbulence, Diffusion, Winds)

Rural Diffusion(e.g., release 

height, initial volume)

Eddy Structure (peak to 

mean concentrations)

Street Canyon Effects

Building Wake Effects

Explosion/Blast Dispsersion

and Effects

Coarse Particle Gravitation Settling

Mountain-Valley Winds

FIGURE C-8. Depiction of varying horizontal scale transport and diffusion processes likely to be 

of most concern. 

turbulent eddy sizes, it will not be possible to simulate the actual transport explicitly. 

Turbulence and counter-gradient flows will make the near-field transport a highly 

stochastic process. For near-field impact assessment, the transport times are sufficiently 

short that the chemical/radioactive species being modeled can usually be treated as inert. 

In the second instance, the transport will still be uncertain but can be treated with more 

confidence. Counter-gradient flows are sufficiently short-lived that they can be neglected 

for longer-range transport problems. Whether the chemical or radioactive species can be 

treated as inert will depend on the species. As shown in Figure 8, many of the processes 

of interest for near-field impact assessment become of less importance for problems 

involving longer transport distances. 
Appendix C C-11

Most transport and diffusion models characterize emissions as coming from one of three 

source types: point, volume, or area. For any given application, the user must select the 

source type characterization that best represents those qualities of the release deemed to 

be most relevant to the questions being posed. Inevitably tradeoffs and compromises will 

be needed. Consider the situation depicted in figure C-9. This photograph of a pointsource emission was taken in early morning, when there was significant buoyant rise and 

stable atmospheric conditions. The rising gases are stabilizing at different heights where 

there are significant differences in the direction of the transport winds. Do we simulate 

this as one plume or two plumes with different plume rises? 

FIGURE C-9. Photo taken by Walt Lyons from his office window, which looks due south toward 

Denver, on a late spring day. (The year is uncertain but may have been 1997.) Contributed by 

Walter Lyons, President of FMA Research, Inc., Yucca Ridge Field Station, 46050 Weld County 

Road 13, Ft. Collins CO 80524. 

For each source and source type, decisions have to be made as to the rate of release, the 

temperature of the gases (if hotter or colder than ambient temperature), and the initial 

dilution volume at the release—all of which may vary in time. For instance, the initial 

dilution and rate of release of emissions are critical if concentration values are desired 

close in to the release point, but the initial dilution is of less concern at distances much 

beyond 10 km downwind of the release. The successful application of a model is thus one 

of knowing what questions are being posed, what capabilities are present in the models, 

and what the tradeoff consequences are as one tailors the application of the model to a 

particular situation. We can envision a model as a tool that can be used in a variety of 

ways. To apply a tool successfully takes wisdom (i.e., experiential knowledge as well as 

academic knowledge). A hammer, a stone chisel, and a suitable block of marble in the 

hands of an experienced sculptor can create a statue, but in the hands of one who is not a 

sculptor (or an apprentice who is just learning the trade), just a pile of smaller stones. 

Tennekes (1990) challenged the atmospheric modeling and measurement communities 

with three requirements: 
C-12 Federal R&D Needs and Priorities for ATD Modeling

• No observation is complete without an appropriately sampled estimate of the 

variance of the properties observed. 

• No forecast is complete without a preceding estimate of forecast skill. 

• No model calculation is complete without a calculation of its variance. 

Taking all of the above aspects under consideration, one can conclude that the 

appropriate choice of an ATD model depends on five selection parameters: 

• A definition (or redefinition) of the information to be gained or the decision to be 

made; 

• The selection of the scale of interest; 

• A knowledge of the physical processes that likely should be treated for the 

intended purpose; 

• An appreciation of the uncertainty associated with the tradeoffs made in the 

model’s construction; and 

• The limits of predictability associated with any modeling system for the scale of 

interest. 

C.5 General Model Types 

Models of environmental processes are approximate representations of reality. Each 

model involves a set of tradeoffs, taking into account objectives such as whether it will be 

used to aid understanding, to estimate changes that might occur, or to determine where 

areas might be affected if a release were to occur. There are six general model types: 

plume, segmented plume and puff, Lagrangian particle, box, Eulerian grid, and 

computational fluid dynamics (CFD). 

1. A plume model assumes that conditions are horizontally homogeneous (everywhere 

the same) and steady state as shown in figure C-10(B). As shown in figure C-10(A), 

plume models attempt to capture some essence of what is seen, but they make no 

claim to depict reality. Plume models are useful for quick estimates near a release, so 

long as the wind direction is relatively steady, the wind speed is greater than 1 to 3 

m/s, and the distances downwind from the release are on the order of 20 km or less. 

2. A segmented plume and puff model divides the emissions into a series of 

overlapping volumes (or puffs) so that one no longer need assume horizontal 

homogeneous conditions or require conditions to be steady state, as shown by the 

example in figure C-11. Developing the time- and space-varying meteorological 

conditions is resource intensive. These conditions include detailed terrain and land 

use data, meteorological observations from many locations within the domain, and a 

capability to model the local flows and circulations (either by dynamic or empirical 

models). Puff models have been used to study mesoscale transport and diffusion of 

species whose chemical or radioactive transformations can be represented using timedependent decay approximations. 
Appendix C C-13

(A)

(B)

FIGURE C-10. (A) Diffusion of smoke from a tall stack during a sunny afternoon. (B) Idealization 

that would be used in a Gaussian plume model to characterize the diffusion from such a tall 

stack. 

3. A Lagrangian particle model divides the emissions into thousands of tiny masses or 

particles that are individually tracked as they are stochastically transported 

downwind, as shown by the example in figure C-12. Each particle is “moved” at each 

time step by pseudo-velocities that take into account the three basic components of 

transport and diffusion: (1) the transport due to the mean wind, (2) the turbulent 

diffusion caused by the (seemingly) random fluctuations of wind components (both 

horizontal and vertical), and (3) the molecular diffusion (if not negligible). As shown 

by Hanna (1979), it is a plausible assumption to describe both Eulerian and 

Lagrangian wind vector fluctuations by a simple Markov velocity process 

(autocorrelation process of the first order). Particle models can be used to investigate 
C-14 Federal R&D Needs and Priorities for ATD Modeling

FIGURE C-11. HOTMAC/RAPTAD simulation of the transport and diffusion of pollutants over 

complex terrain. Contributed by Ted Yamada of YSA Corporation. Rt. 4, Box 81-A, Santa Fe, 

NM 87501. 

and depict transport and diffusion within complex wind regimes (e.g., between 

buildings or during a frontal passage).

4. A box model assumes the modeling domain is one large homogeneous volume (box). 

Emissions entering this volume are assumed to be uniformly and instantaneously 

mixed throughout the volume (figure C-13). The top of the box may rise to simulate 

the rise of the mixing depth after sunrise, and pollutants above this rising lid could 

then be entrained into the volume. The location of the box can be stationary (to 

simulate the air over a city), or it can move with the transport wind (to simulate the 

“aging” of an air mass). Box models have been used to study photochemical problems 

and to compare alternative chemical kinetics. 

5. An Eulerian grid model divides the world into a three-dimensional array of 

rectangular cells (grids) within each of which mixing is considered uniform and 

instantaneous. Grid models are used to simulate the formation of products through 

atmospheric chemistry and the removal of products by clouds and precipitation, all of 

which are usually sufficiently removed from the emissions of immediate concern that 

the “well-mixed” assumption in each cell is reasonable. Eulerian grid modeling 

systems have been used to study regional transport and fate of secondarily formed 

species (e.g., ozone, acid deposition, and fine particulate haze). Figure C-14 

illustrates the application of the Community Model of Air Quality (CMAQ) to 

simulate the effect of reducing nitrogen oxide (NOx) emissions by 50 percent. Results 

for ozone and particle matter (PM) with diameters less than 2.5 micrometers (PM2.5) 

are shown for the original base, the strategy simulation, and the difference (strategy 

minus base case) between the two for July 14, 1996, at 0100 GMT. Thus, negative 
Appendix C C-15

(A) (B) 

FIGURE C-12. (A) Typical plume trajectories calculated from RAMS output using only surface 

layer winds (trajectory A) which stays at 50 m altitude and the complete wind field (trajectory B) 

which rises to 1600 m altitude. (B, Top) Plan view of a simulated plume released from a 50 m 

high shoreline source into a weak lake breeze along the Lake Michigan shoreline. (B, Bottom) 

a perspective view of the plume from the southwest showing large quantities of the plume 

being transported vertically due to the strong upward motions in the lake breeze frontal zone. 

(Lyons et al. 1995; figures contributed by Walt Lyons.) 

differences indicate decreases in ozone and PM2.5 levels, whereas positive 

differences indicate increases in the pollutants. 

6. A CFD model is based on the three fundamental principles that govern the physical 

aspects of any fluid flow: 

• Mass is conserved. 

• Energy is conserved. 

• Newton's second law (the acceleration of an object is a function of the net force 

acting upon the object and the mass of the object). 
C-16 Federal R&D Needs and Priorities for ATD Modeling

FIGURE C-13. Schematic illustration of a photochemical box modeling domain (Schere 

and Demerjian, 1984).

These fundamental principles can be expressed in terms of mathematical equations, 

which in their most general form are usually partial differential equations. CFD is the 

science of determining a numerical solution to the governing equations of fluid flow 

while advancing the solution through space or time to obtain a numerical description 

of the complete flow field of interest. The governing equations for Newtonian fluid 

dynamics, the unsteady Navier-Stokes equations, have been known for over a 

century. However, the analytical investigation of reduced forms of these equations is 

still an active area of research, as is the problem of turbulent closure for the Reynolds 

averaged form of the equations. For non-Newtonian fluid dynamics, the theoretical 

development of chemically reacting flows and multiphase flows is at a less advanced 

stage. CFD has been used to study flows around airplane wings and rockets, air flow 

through engine parts, and the transport and diffusion of particles around and between 

hills and buildings (e.g., figure C-15). To date the data requirements, problem 

definition, and time required to generate results have limited the use of CFD models 

to studies of special situations. 
Appendix C C-17

FIGURE C-14. Illustration of the effect of reducing NOx emissions by 50 percent, as computed by 

the Models3/CMAQ modeling system. “PM2.5” stands for “particle matter with diameter less than 

2.5 μm. Source: Leduc, Schere, and Godowitch 2001. 

(A) (B) 

FIGURE C-15. Computational fluid dynamical simulation results for the adjustment of the wind 

profile to buildings associated with an urban domain (A) and a simulation of ambient carbon 

monoxide concentration values from street-level emissions (B). 
C-18 Federal R&D Needs and Priorities for ATD Modeling

C.6 Diffusion Characterizations 

Any contaminant released into the atmosphere is transported by a variety of processes 

that can be defined generically as either advection or molecular and eddy diffusion (often 

referred to as turbulent dispersion). Advection is the movement of contaminants as a 

result of the mean flow. Molecular diffusion is a redistribution of mass (or energy) within 

a gas by Brownian motion and tends towards uniformity in mass (or energy). Brownian 

motion is thermal energy and results in random molecular collisions. There is motion in 

all directions, but there is a tendency for the mass (or energy) to move from areas of high 

concentration to areas of low concentration. Molecular diffusion is rarely of significance 

in comparison with eddy diffusion. Eddy diffusion results from turbulent velocity 

deviations from the mean flow. Turbulent deviations are mechanically generated by 

friction drag and flow through and around obstacles. Turbulent deviations can be 

enhanced or suppressed thermally by buoyancy forces arising from relative differences in 

temperature in air layers next to the ground or other surfaces. In this discussion, the term 

“diffusion” encompasses both molecular and eddy diffusion. 

When reading the ATD literature, one can easily become confused by different usage of 

the terms “diffusion” and “dispersion.” “Diffusion” is often used without mentioning 

whether it is meant to include both molecular and eddy diffusion. To further complicate 

matters, some texts use the term “dispersion” to refer to the combined effects of eddy 

diffusion and advection, whereas other texts use the term “dispersion” in the sense of 

“turbulent dispersion” but without the qualifier “turbulent.” For the sake of clarity, this 

discussion avoids use of the term “dispersion.” It uses “diffusion” to refer to the 

combined effects of molecular and eddy diffusion. 

There are two other considerations to be recognized in characterizations of eddy 

diffusion. First, there is an implicit averaging time assumed, which is associated with the 

definition of the “mean flow” of the atmosphere. In atmospheric transport and diffusion 

models, the mean flow is typically defined at 1-hour intervals, but this choice is not 

mandated. If the mean flow is defined at 5-minute intervals, then a portion of what would 

have been characterized as eddy diffusion for 1-hour intervals will instead necessarily be 

characterized by the time and space variations in the 5-minute mean flow. Second, not all 

deviations from the mean flow are random; hence, not all eddy diffusion is random. The 

thermal eddies of a convective boundary layer transport mass from the surface to the top 

of the convective boundary layer in an organized manner. This convective eddy transport 

could be thought of as advection, but since it occurs in less than 1 hour and cannot be 

simulated deterministically, it is typically viewed as a component of eddy diffusion. 

C.6.1. Empirical Characterizations of Diffusion 

There are many instances when field-data observations of smoke and tracer diffusion 

have been organized into empirical schemes for the characterization of diffusion. 

Typically, these schemes rely on the empirical observation that the vertical and lateral 

crosswind concentration distribution appears to be similar to a Gaussian shape. Hence, 

these schemes provide characterizations of the vertical and lateral Gaussian parameters 
Appendix C C-19

(as a function of travel time or distance of transport, stability, and roughness length of the 

surface). The well-known Pasquill-Gifford diffusion parameters (Pasquill 1961; Gifford 

1961) have been used extensively in many of the popular transport and diffusion models. 

The Pasquill-Gifford diffusion parameters were derived from various experiments over 

rural terrain. The lateral diffusion parameters have an implied averaging time of 3 

minutes and a roughness length of 3 cm for transport distances less than 1 km. For 

transport greater than 10 km, the lateral diffusion parameters still have an implied 

averaging time of 3 minutes but a roughness length of 30 cm. The vertical diffusion 

parameters are likely appropriate for all averaging times up to 30 minutes and have the 

same variation in roughness as the lateral diffusion. Field studies conducted in the 1960s 

in St. Louis, Missouri, by McElroy and Pooler (1968) included observations of a tracer 

released and sampled for 1-hour periods. Their results have been used to develop a 

characterization of diffusion for urban environs. There are many such studies (Randerson 

1985); however, the important consideration in the use of any of these schemes is to 

recognize that they may be relevant only for the circumstances under which the data were 

collected. 

C.6.2 Similarity Theory and PBL Parameterizations 

Useful schemes for the characterization of diffusion have been fashioned though an 

analysis of field studies of diffusion that incorporate known important variables and 

governing “scaling” parameters. Such models are called similarity models because they 

imply “similar” behavior of the atmosphere from one place or time to another, if one 

assumes that certain scaling parameters are held constant. The important scaling 

parameters in surface-layer similarity models of diffusion are L, u*, and zo, where L is 

the Monin-Obkhov length, u* is the friction velocity, and zo is the surface roughness 

length. Examples of surface-layer similarity models of diffusion are provided by Briggs 

and McDonald (1978); Horst, Doran, and Nickola (1979); and Briggs (1982). The surface 

layer is nominally the lower of |L| or Zi/10, where Zi is the mixing height. Scaling 

parameters for convective diffusion models are L, Zi, and w*, where w* is the convective 

velocity scale. Examples of convective diffusion models are Weil and Furth (1981), 

Misra (1982), and Venkatram (1983). 

C.6.3 Statistical Characterizations of Diffusion 

Taylor’s (1921) theory for homogeneous and stationary turbulence has served as the basis 

for several statistical models of atmospheric diffusion. These models imply that the 

spread is linear with time in the near field, and proportional to the square-root of time in 

the far field. The transition from near field to far field is typically specified in terms of a 

Lagrangian time scale. Examples of statistical theory for the characterization of the 

vertical or lateral extent of a release as it is transported downwind are Draxler (1976) and 

Venkatram, Strimaitis, and DiCristofaro (1984). The Monte Carlo particle trajectory 

models of diffusion are statistical characterizations of diffusion. 
C-20 Federal R&D Needs and Priorities for ATD Modeling

C.6.4 First- and Second-Order Closure Assumptions 

Gradient transport (K-theory) models are derived from the continuity equation with the 

turbulent fluxes of concentration (C) assumed to be proportional to the mean gradient of 

C, with K being the constant of proportionality. This is called a “first-order closure” 

assumption because it retains the prognostic equations for only the zero-order mean 

variables and parameterizes the turbulent fluxes. K-theory models are typically used with 

grid models, where the emphasis is often on atmospheric chemistry and regional 

transport. An important consideration is that gradient transport models of diffusion have 

implicit time and space scales. The mean wind components represent averages over some 

time scale and space scale. Velocity fluctuations with time and space scales less than 

those implicit in the mean wind components are considered turbulence. Therefore, they 

are implicitly included in the proportionality constant K. However, as discussed by 

Taylor (1921), the rate of diffusion of a plume depends on the plume size. This limits the 

applicability of K-theory models of diffusion to instances where the size of the plume is 

greater than the size of the dominant turbulent eddies so that all of the turbulence implicit 

in K is taking part in the diffusion. The vertical diffusion of point sources can be modeled 

using K-theory for sources near the ground, where the turbulent eddies are sure to have 

scales less than the thickness of the plume. However, K-theory can be used to model 

elevated releases only when the vertical extent is spread out over several hundred meters 

(Hanna, Briggs, and Hosker 1982). 

There are “higher-order” closure assumptions (e.g., second-order) where the prognostic 

equations are retained for both the mean and the flux variables and the third moments are 

parameterized. Second-order closure has been used to develop plume models (Sykes 

Lewellen, and Parker 1986) and puff diffusion models (Sykes and Henn 1995). 

Appendix C References 

ASTM. 2000. Standard Guide for the Statistical Evaluation of Atmospheric Dispersion 

Model Performance, D 6589-00, Annual Book of Standards Volume 11.03, 

ASTM International, West Conshohocken, PA 19428 (http://www.astm.org/). 

Briggs, G.A.. 1982. Similarity forms for ground-source surface-layer diffusion. 

Boundary-Layer Meteorology, 23: 489–502. 

Briggs, G.A., and K.R. McDonald. 1978. Prairie Grass revisited. Optimum indicators of 

vertical spread. Pp. 209–220 in Proceedings of the 9th International Technical 

Meeting on Air Pollution Modeling and Its Application, No. 103. NATO/CCMS. 

Delcourt, H.R., P.A. Delcourt, and T. Webb. 1983. Dynamic plant ecology: The spectrum 

of vegetation change in space and time. Quaternary Science Reviews, 1: 153–175. 

Draxler, R.R. 1976. Determination of atmospheric diffusion parameters. Atmospheric 

Environment, 10: 99–105. 
Appendix C C-21

Gifford, F.A. 1959. Statistical properties of a fluctuating plume dispersion model. 

Advances in Geophysics, 6: 117–138. 

Gifford, F.A. 1961. Use of routine meteorological observations for estimating the 

atmospheric dispersion. Nuclear Safety, 2(4): 47–57. 

Hanna, S.R. 1979. Some statistics of Lagrangian and Eulerian wind fluctuations. Journal 

of Applied Meteorology, 18: 518–525. 

Hanna, S.A., G.A. Briggs, and R.P. Hosker. 1982. Handbook on Atmospheric Diffusion. 

NTIS DE82002045 (DOE/TIC-11223). 108 pp. 

Horst, T.W., J.C. Doran, and P.W. Nickola. 1979. Evaluation of empirical atmospheric 

diffusion data. NRC Final Report No. B2086. Richland, WA: Battelle Pacific 

Northwest Laboratories. 137 pp. 

Irwin, J.S. and M.E. Smith. 1984. Potentially useful additions to the Rural Model 

Performance Evaluation. Bulletin of the American Meteorological Society, 65: 

559–568. 

Lee, J.T., D.L. Call, R.E. Lawson Jr., W.E. Clements, and D.E. Hoard. 1988. A Video 

Image Analysis System for Concentration Measurements and Flow Visualization 

in Building Wakes. Progress Report LA-UR-88-3853. November 1988. Los 

Alamos National Laboratory, Los Alamos, NM. 67 pp. 

LeDuc, S.K, Schere, K.L., and Godowitch, J.M., (2001): Models3/CMAQ applications 

which illustrate capability and functionality. Pp. 737–738 in S.-E. Gryning and 

F.A. Schiermeier (eds.), Air Pollution Modeling and Its Application XIV. New 

York: Plenum Press. 

Lilly, D.K. 1985. Theoretical predictability of small-scale motions. In Gill, R. Benzi, and 

G. Parisi, eds. Turbulence and Predictability in Geophysical Fluid Dynamics and 

Climate Prediction. Proceedings of the International School of Physics, Enrico 

Fermi, Course LXXXVIII M. Amsterdam: Elsevier. 449 pp. 

Lorenz, E.N. 1969. The predictability of a flow which possesses many scales of motion. 

Tellus, 21: 289–307. 

Lyons, W.A., R.A. Pielke, C.J. Tremback, R.L. Wlako, D.A. Moon, and C.S. Keen. 1995. 

Modeling impacts of mesoscale vertical motions upon coastal zone air pollution 

dispersion. Atmospheric Environment, 29(2): 283–301. 

McElroy, J.L., and F. Pooler. 1968. St. Louis Dispersion Study. Volume I: 

Instrumentation, Procedures and Data Tabulations; Volume II: Analysis. 

Publication Number AP-53. National Air Pollution Control Administration, U.S. 

Department of Health, Education and Welfare, Arlington, VA. 352 pp. (vol. I) and 

51 pp. (vol. II). 
C-22 Federal R&D Needs and Priorities for ATD Modeling

Misra, P.K., 1982. Dispersion of nonbuoyant particles inside a convective boundary 

layer. Atmospheric Environment, 16: 239–243. 

Monteith, J.L. 1965. Evaporation and the environment. Pp. 205–234 in The Movement of 

Water in Living Organisms, 19th Symposium of the Society of Experimental 

Biology. Swansea and Cambridge, MA. 

NASA Advisory Council. 1988. Earth System Science: A Closer View. National 

Aeronautics and Space Administration, Earth System Science Committee, 

Washington, D.C. 205 pp. 

O’Neill, R.V., (1988): Hierarchy theory and global change. Pp. 29–45 in T. Rosswall, 

R.G. Woodmansee, and P.G. Risser (eds.), Scales and Global Change. New York: 

John Wiley & Sons. 

Orlanski, I. 1975. A rational subdivision of scales for atmospheric processes. Bulletin of 

the American Meteorological Society, 56(5): 527–530. 

Pasquill, F. 1961. The estimation of the dispersion of windborne material. Meteorological 

Magazine, 90: 33–49. 

Randerson, D. (ed.). 1984. Atmospheric Science and Power Production. NTIS 

DE84005177 (DOE/TIC-27601). 850 pp. 

Schere, K.L. and Demerjian, K.L., (1984): User’s guide for the photochemical box model 

(PBM). EPA-600/8-24-022a. U.S. Environmental Protection Agency, Office of 

Research and Development, Research Triangle Park, NC 27711, 119 pages 

Simon, H.A. 1973. The organization of complex systems. Pp. 3–27 in H.H. Pattee (ed.), 

Hierarchy Theory: The Challenge of Complex Systems, New York: George 

Braziller. 

Simon, H.A., and A. Ando. 1961. Aggregation of variables in dynamic systems. 

Econometrica, 29: 111–138. 

Stull, R.B. 1988. An Introduction to Boundary-Layer Meteorology. Dordrectht: Kluwer 

Academic. 

Sykes, R. I. and D.S. Henn. 1995. Representation of velocity gradient effects in a 

Gaussian puff model. Journal of Applied Meteorology, 34:2715–2723. 

Sykes, R.I., W.S. Lewellen, and S.F. Parker. 1986. A Guassian plume model of 

atmospheric dispersion based on second-order closure. Journal of Climate and 

Applied Meteorology, 25: 322–331. 

Taylor, G.I. 1921. Diffusion by continuous movements. Proceedings of the London 

Mathematical Society, 20: 196–212. 
Appendix C C-23

Tennekes, H. (1990). Summary by the chair on the session on Resolution and 

Predictability. Reported in W. H. Hooke, D.L. Albrittion, R.E. Carbone, T. GalChen, C.W. Kreitsburg, C.G. Little, and H. Tennekes. Report of the Critical 

Review Panel - Lower Tropospheric Profiling Symposium: Needs and 

Technologies. Bulletin of the American Meteorological Society 71: 680–690. 

Thornthwaite, C.W., and J.R. Mather. 1955. The water balance. Volume 8, No. 1, of 

Publications in Climatology, Drexel Institute of Technology [Philadelphia], 

Laboratory of Climatology, Centerton, New Jersey. 104 pps. 

Urban, D.L., R.V. O’Neill, and H.H. Shugart. 1987. Landscape ecology. BioScience, 37: 

119–127. 

Van der Hoven, I. 1957. Power spectrum of horizontal wind speed in the frequency range 

from 0.0007 to 900 cycles per hour. Journal of Meteorology, 14: 160–164. 

Venkatram, A. 1983. On dispersion in the convective boundary layer. Atmospheric 

Environment, 17: 529–533. 

Venkatram, A., E. Strimaitis, and D. DiCristofaro. 1984. A semi-empirical model to 

estimate vertical dispersion of elevated releases in stable boundary layer. 

Atmospheric Environment, 18: 923–928. 

Weil, J.C., and Furth, W.F., (1981): A simplified numerical model of dispersion from 

elevated sources in the convective boundary layer. Pp. 76–77 in Proceedings of 

the 5th Symposium on Turbulence, Diffusion and Air Pollution. Boston: American 

Meteorological Society. 

Weil, J.C., R.I. Sykes, and A. Venkatram. 1992. Evaluating air quality models: review 

and outlook. Journal of Applied Meteorology, 31: 1121–1145. 

Wilson, D.J., A.G. Robins, and J.E. Fackrell. 1982. Predicting the spatial distribution of 

concentration fluctuations from a ground level source. Atmospheric Environment. 

16: 497–504. 

D-1 

APPENDIX D. ACRONYM LIST

24/7 24 hours per day, 7 days per week 

ABL atmospheric boundary layer 

ACARS Aircraft Communications Addressing and Reporting System 

AEC Atomic Energy Commission 

AFWA Air Force Weather Agency 

ALOHA Areal Locations of Hazardous Atmospheres [an ATD model] 

AMS Aerial Measuring System [a NIRT asset] 

ARAC Atmospheric Release Advisory Capability 

ARL Air Resources Laboratory [of NOAA] 

ASMD Atmospheric Sciences Modeling Division [of NOAA/ARL] 

ATD atmospheric transport and diffusion 

CAMEO Computer-Aided Management of Emergency Operations [software system] 

CBNP Chemical and Biological National Security Program 

CBRN chemical, biological, radiological, or nuclear 

CBRNE chemical, biological, radiological, nuclear or high-yield explosive 

CFD computational fluid dynamics 

CD-ROM compact disk–read-only memory 

CMAQ Community Model for Air Quality 

CMAT Consequence Management Advisory Team 

CSEPP Chemical Stockpile Emergency Preparedness Program 

CTM chemical transport model 

DAS data assimilation system 

DOE Department of Energy 

DHS Department of Homeland Security 

DNA Defense Nuclear Agency 

DNS direct numerical simulation 

DOD Department of Defense 

DTRA Defense Threat Reduction Agency 

EPA U.S. Environmental Protection Agency 

EP&R Emergency Preparedness and Response [Directorate of DHS] 

ESSA Environmental Sciences Services Administration 

FCMSSR Federal Committee for Meteorological Services and Supporting Research 

FEMA Federal Emergency Management Agency 

FRMAC Federal Radiological Monitoring and Assessment Center 

GMU George Mason University 

GIS geographical information system 

GPS Global Positioning System 

HAZMAT hazardous material 

HMRD Hazardous Materials Response and Assessment Division [of NOAA] 
D-2 Federal R&D Needs and Priorities for ATD Modeling

HPAC Hazard Prediction and Assessment Capability 

JAG Joint Action Group; unless otherwise qualified, the JAG/ATD(R&DP), which is the 

author of record of this R&D plan. 

JAG/ATD(R&DP) Joint Action Group for Atmospheric Transport and Diffusion Modeling (R&D Plan) 

JAG/SEATD Joint Action Group for Selection and Evaluation of Atmospheric Transport and Diffusion 

Models 

JEM Joint Effects Model 

JTF-CS Joint Task Force for Civil Support 

LES large eddy simulation 

LFA lead Federal agency 

LINC Local Integration of NARAC with Cities 

LLNL Lawrence Livermore National Laboratory 

LOE level of effort 

MAC Mapping Analysis Center 

MADONA Meteorology and Diffusion over Non-Uniform Areas [field study] 

METOC Meteorology and Oceanography [U.S. Navy] 

METREX Metropolitan Tracer Experiment 

MOA memorandum of agreement 

MUST Mock Urban Settings Test 

NARAC National Atmospheric Release Advisory Center 

NASA National Aeronautics and Space Administration 

NCAR National Center for Atmospheric Research 

NCEP National Centers for Environmental Prediction 

NIRT Nuclear Incident Response Team 

NNSA National Nuclear Security Administration 

NOAA National Oceanic and Atmospheric Administration 

NORTHCOM U.S. Northern Command [of DOD] 

NRC Nuclear Regulatory Commission 

NTS Nevada Test Site 

NWP numeric weather prediction 

NWS National Weather Service of the National Oceanic and Atmospheric Administration 

OFCM Office of the Federal Coordinator for Meteorological Services and Supporting Research 

OSE observation system experiment 

OSSE observation system simulation experiment 

OWS Operational Weather Squadron 

QA/QC quality acceptance and quality control 

R&D research and development 

RAP Radiological Assistance Program 

RDT&E research, development, test, and evaluation 

READY Realtime Environmental Applications and Display System 

SBIR Small Business Innovative Research 

SBL stable boundary layer 
Appendix D D-3

SDO standard development organization 

SORD Special Operations and Research Division [of NOAA/ARL] 

STTR Small Business Technology Transfer [program] 

UAV unmanned aerial vehicle 

VTMX Vertical Transport and Mixing [field program] 

WMD weapons of mass destruction 

WRF Weather Research and Forecasting [model] 

WSR-88D Weather Service Radar 1988, Doppler (also Doppler Weather Radar; NEXRAD) 

Appendix E E-1

APPENDIX E. LIST OF JAG/ATD(R&DP) PARTICIPANTS

E.1 Members 

Dr. Walter D. Bach, Jr., Cochairperson

Program Manager, Environmental Sciences 

Division, Army Research Office 

AMSRD-ARL-RO-EN 

P.O. Box 12211 

Research Triangle Park, NC 27709-2211 

Phone: 919-549-4247 

Fax: 919-549-4310 

DSN: 832-4247 

Email: walter.d.bach@us.army.mil

Mr. James A. McNitt, Executive 

Secretary 

Office of the Federal Coordinator for 

Meteorology (Science and Technology 

Corporation) 

8455 Colesville Road, Suite 1500 

Silver Spring, MD 20910 

Phone: 301-427-2002, Ext. 15 

Fax: 301-427-2007 

DSN: 851-1460 

Email: james.mcnitt@noaa.gov

Ms. Nancy Suski, Cochairperson 

Director, Emergency Preparedness and 

Response Portfolio 

Science and Technology Directorate 

Department of Homeland Security 

Washington, DC 20528 

Phone: 202-254-5743 

Fax: 202-772-9720 

Email: nancy.suski@dhs.gov

CDR Stephanie W. Hamilton, USN 

Meteorology Program Manager 

Consequence Assessment Branch (TDOC) 

Defense Threat Reduction Agency 

8725 John J. Kingman Road 

MSC 6201 

Fort Belvoir, VA 22060-6201 

Tel: 703-325-1933, DSN 221-1933 

Fax: 703-325-0398 

Email: stephanie.hamilton@dtra.mil
E-2 Federal R&D Needs and Priorities for ATD Modeling

Mr. Bruce Hicks 

Director, Air Resources Laboratory 

National Oceanic and Atmospheric 

Administration (R/ARL) 

1315 East-West Highway 

Silver Spring, MD 20910 

DOC/NOAA/ARL 

Phone: 301-713-0684 Ext. 136 

Fax: 301-713-0119 

Email: bruce.hicks@noaa.gov

Mr. Jim Bowers 

Chief, Meteorology Division 

West Desert Test Center 

Dugway Proving Ground 

Dugway, UT 84022-5000 

Phone: 435-831-5101 

Fax: 435-831-5289 

Email: jbowers@dpg.army.mil

Ms. Debbie Payton

Hazardous Materials Program Manager for 

Fate and Transport Modeling 

Office of Response and Restoration 

National Ocean Service 

National Oceanic and Atmospheric 

Administration (N/ORR-1) 

7600 Sand Point Way, N.E. 

Seattle, WA 98115-0070 

Phone: 206-526-6320 

Fax: 206-526-6329 

Email: debbie.payton@noaa.gov

Mr. Rickey C. Petty 

Program Manager 

Environmental Meteorology Component of 

the Atmospheric Science Program

Department of Energy, SC-74 

19901 Germantown Road

Germantown, MD 20874-1290 

Phone: 301-903-5548 

Fax: 301903-8519 

Email Rick.Petty@Science.Doe.Gov

Dr. Paula M. Davidson

Environmental Hazards Planning Lead 

Office of Science and Technology 

National Weather Service 

National Oceanic and Atmospheric 

Administration (W/OST12) 

1315 East-West Highway, Rm. 11117 

Silver Spring, MD 20910 

Phone: 301-713-3557 x171

Fax: 301-713-1253 

Email: paula.davidson@noaa.gov

Mr. John S. Irwin 

(October 2003 through October 2004)

Meteorologist NOAA Air Policy Support 

Branch, Office of Air Quality Planning and 

Standards, Environmental Protection 

Agency (OAQPS/D 243-01) 

Research Triangle Park, NC 27711 
Appendix E E-3

Mr. John Perry 

GIS/Remote Sensing Coordinator 

FEMA Response, Emergency Preparedness 

and Response Directorate 

Department of Homeland Security 

Federal Center Plaza, M-27 

500 C Street, SW 

Washington, DC 20472 

Phone: 202-646-2817 

Fax: 202-646-7595 

Email: johnj.perry@dhs.gov

Ms. Jocelyn Mitchell 

Senior Level Technical Advisor 

Office of Research 

Nuclear Regulatory Commission 

Mail Stop T9C34 

Washington,, DC 20555 

Phone: 301-415-5289 

Fax: 301-415-5385 

Email: jam@nrc.gov

Mr. Stephen D. Ambrose

(February 2004 – December 2004) 

Program Manager for Homeland Security 

Applied Sciences Program 

Office of Science 

National Aeronautics and Space 

Administration 

300 E. Street, SW 

Washington, DC 20546 

Phone: 202-358-0851 

Fax: 202-358-3098 

Email: sambrose@nasa.gov 

Dr. Tsengdar Lee 

(October 2003 – January 2004) 

Headquarters, Code YS 

National Aeronautics and Space 

Administration 

300 E. Street, SW 

Washington, DC 20546 

Phone: 202-358-0860 

Fax: 202-358-2770 

Email: tlee@hq.nasa.gov
E-4 Federal R&D Needs and Priorities for ATD Modeling

E.2 Alternates 

Mr. Mark W. Miller

CAMEO Program Manager 

Office of Response and Restoration 

National Ocean Service 

National Oceanic and Atmospheric 

Administration (N/ORR-1) 

7600 Sand Point Way, N.E. 

Seattle, WA 98115-0070 

Phone: 206-526-6272 

Fax: 206-526-6329 

Email: mark.w.miller@noaa.gov

Maj Brian Beitler, USAF 

(October 2003 through June 2004) 

Advanced Weather Officer 

Consequence Assessment Branch 

Defense Threat Reduction Agency 

6801 Telegraph Road 

Alexandria, VA 22310-3398 

Dr. Rayford P. Hosker, Jr. 

Director, Atmospheric Turbulence and 

Diffusion Division 

Air Resources Laboratory 

National Oceanic and Atmospheric 

Administration (R/ARL1) 

456 S. Illinois Avenue 

P.O. Box 2456 

Oak Ridge, TN 37830 

Phone: 865-576-1248 

Fax: 865-576-1327 

Email: Ray.Hosker@noaa.gov

Mr. Peter Lunn 

Program Director 

Atmospheric Science Program 

Department of Energy (SC-74) 

19901 Germantown Road 

Germantown, MD 20874-1290 

Phone: 301-903-4819 

Fax: 301-903-8519 

Email: peter.lunn@science.doe.gov 

Mr. Walter Schalk 

Special Operations and Research Division 

Air Resources Laboratory 

National Oceanic and Atmospheric 

Administration (R/ARL7) 

P. O. Box 94227 

Las Vegas, NV 89193-4227 

Phone: 702-295-1262 

Fax: 702-295-3068 

Email: schalk@nv.doe.gov

Dr. Gayle Sugiyama

Homeland Security Projects Leader 

National Atmospheric Release Advisory 

Center (NARAC) 

Lawrence Livermore National Laboratory 

7000 East Avenue, L-103 

Livermore, CA 94551 

Phone: 925-422-7266 

Fax: 925-422-5844 

Email: sugiyama@llnl.gov
Appendix E E-5

Mr. Chris Doyle 

Deputy Director, Emergency Preparedness 

and Response Portfolio 

Science and Technology Directorate 

Department of Homeland Security 

Washington, DC 20528 

Phone: 202-772-9864 

Email: christopher.doyle@dhs.gov

Dr. H. Mark Helfand 

Global Modeling and Assimilation Office 

National Aeronautics and Space 

Administration Goddard Space Flight 

Center, Code 900.3 

Greenbelt, MD 20771 

Phone: 301-614-6158 

Fax: 301-614-9257 

Email: mark.helfand@nasa.gov

Ms. Teresa Lustig 

CB Countermeasures 

Science and Technology Directorate 

Department of Homeland Security 

Washington, DC 20528 

Phone: 202-254-5766 

Email: teresa.lustig@dhs.gov

Dr. Bruce A. Davis 

(April 2004 through September 2004) 

Applications Engineering Division 

National Aeronautics and Space 

Administration Stennis Space Center 

Building 1100, Mail Code MA20 

John C. Stennis Space Center, MS 39529 

Phone: 228-688-1921 

Email: bruce.a.davis@dhs.gov

Dr. Bruce A. Davis 

(October 2004 through December 2004) 

Director, Interagency Modeling and 

Atmospheric Assessment Center (IMAAC) 

Science and Technology Directorate 

Department of Homeland Security 

Phone: 202 254 5893 

Email: Bruce.A.Davis@dhs.gov

Dr. Steve McGuire 

MS T-4A43 

Nuclear Regulatory Commission 

11545 Rockville Pike 

Rockville, MD 20852-2738 

Phone: 301-415-6204 

Fax: 301-816-5151 

Email: sam2@nrc.gov
E-6 Federal R&D Needs and Priorities for ATD Modeling

E.3 Technical Advisors 

Dr. John Hannan 

Defense Threat Reduction Agency 

Northrop Grumman IT 

Defense Enterprise Solutions

6940 South Kings Hwy, Ste 210

Alexandria, VA 22310 

Phone:703-971-3103 X103 

Fax:703-971-4654 

Email john.hannan@ngc.com

Mr. John Pace

Environmental Hazards Modeling Program, 

Joint Science and Technology Office, 

Chem-Bio Defense Program. 

Physical Sciences Division, 

Chem-Bio Directorate. 

Defense Threat Reduction Agency 

8725 John J. Kingman Road 

MSC 6201 

Fort Belvoir, VA 22060-6201 

Tel: 703-325-7404, DSN 221-7404 

Fax: 703-325-2136 

Email: john.pace@dtra.mil

 
Appendix E E-7

E.4 Subject-Matter Experts 

Mr. John S. Irwin 

(November 2004 through December 

2004)

Consulting Meteorologist 

1900 Pony Run Road 

Raleigh, NC 27615-7415 

Phone: 919-872-7654 

Cell: 919-210-8723 

Email: jsirwinetal@nc.rr.com

Dr. Nelson Seaman

Environmental Modeling Center, National 

Centers for Environmental Prediction 

National Weather Service 

National Oceanic and Atmospheric 

Administration (W/NP2) 

Phone: 301-763-8000 Ext. 7222 

Fax: 301-763-8545. 

Email: nelson.seaman@noaa.gov

Dr. Robert Addis

Manager, Atmospheric Technologies, 

Savannah River National Laboratory 

Building 773-A 

Aiken, SC 29808 

Phone: 803-725-3325 

Email: robert.addis@srs.gov Phone: 803-

725-3325 

Email: robert.addis@srs.gov

Dr. J. Christopher Doran 

Staff Scientist 

Pacific Northwest National Laboratory 

P.O. Box 999, K9-30 

Richland, WA 99352 

Phone: (509) 372-6149 

Fax: (509) 372-6168 

Email: christopher.doran@pnl.gov

Dr. Michael Brown 

Los Alamos National Laboratory 

Energy and Environmental Analysis 

Group 

Drop Point 19S, SM-30 

Bikini Atoll Road 

Group D-4, MS F604 

Los Alamos, NM 87545 

Phone: 505-667-1788 

Fax: 505-665-5125 

Email: mbrown@lanl.gov

Dr. Jerry K. Allwine 

Fundamental Sciences Directorate 

Pacific Northwest National Laboratory 

P.O. Box 999, K9-30 

Richland, WA 99352 

Phone: 509-375-6741 

Fax: 509-372-6168 

Email: jerry.allwine@pnl.gov
E-8 Federal R&D Needs and Priorities for ATD Modeling

Dr. Robert Banta 

Environmental Technology Laboratory 

National Oceanic and Atmospheric 

Administration (R/ETL) 

325 Broadway 

Boulder, CO 80305 

Phone: 303-497-5535 

Fax: 303-497-5318 

Email: Robert.Banta@noaa.gov

Dr Alan Huber 

National Environmental Research 

Laboratory 

US EPA (Mail Code E243-03) 

Research Triangle Park, NC 27711 

Phone: 919-541-1338 

Fax: 919-541-0905 

Email: huber.alan@epa.gov

Dr. Sue Grimmond 

Atmospheric Science Program 

Geography, Student Building 104 

701 E. Kirkwood Ave. 

Indiana University, IN 47405-7100 

Phone: 812-855-7971 

Fax: 812-855-1661 

Email: grimmon@indiana.edu

Dr. David Neff 

Wind Engineering and Fluids Laboratory 

Engineering Research Center 

Colorado State University 

Fort Collins, CO 80523-1320 

Phone: 970-491-8576 

Fax: 970-491-8576 

Email: david.neff@colostate.edu

Dr. William J. Coirier 

Manager, Defense Applications 

CFD Research Corporation 

215 Wynn Dr 

Huntsville, AL 35805 

Phone: 256-726-4927 

Fax: 256-726-4806 

Email: wjc@cfdrc.com

Dr. Steve Hanna 

Hanna Consultants 

7 Crescent Ave. 

Kennebunkport, ME 04046 

Phone: 207 967 4478 

Fax: 207 967 5696 

Email: hannaconsult@adelphia.net

Dr. Edward H. Barker 

(January 2004) 

Section Head, Data Assimilation Section 

Naval Research Laboratory 

7 Grace Hopper Ave. 

Monterey, CA 93943 

Dr. Ted Yamada 

Yamada Science and Art Corp. 

13 Heiwa 

Santa Fe, NM 87501 

Phone: 505-989-7351 

Fax: 505-989-7965 

Email: ysausa@ysasoft.com
Appendix E E-9

Dr. Tom Warner 

Lead Scientist for DOD and Homeland 

Security Applications 

Research Applications Laboratory 

National Center for Atmospheric Research 

PO Box 3000 

Boulder, CO 80307-3000 

Phone: 303.497.8411 

Fax: 303.497.8401 

Email: warner@ucar.edu

LTjg Jereme Altendorf, USCG 

HAZMAT Response Policy Officer 

Office of Response 

United States Coast Guard 

2100 Second Street, SW, 

Washington, DC 20593 

Battalion Chief Randy R. Hansen 

Seattle Fire Department 

301 2nd Avenue South 

Seattle, WA 98104 

Phone: 206-465-3643 

Fax: 206-283-2870 

Email: randy.hansen@seattle.gov

Mr. John Murray 

Federal Emergency Management Agency 

Emergency Preparedness and Response 

Directorate, Response Division 

Department of Homeland Security 

Federal Center Plaza, M-27 

500 C Street, SW 

Washington, DC 20472 

Phone: (202) 282-8131 

Email: John.Murray@hq.dhs.gov

Mr. Ryan Nash 

HAZMAT Technician 

Seattle Fire Department 

301 2nd Avenue South 

Seattle, WA 98104 

Email: Ryan.Nash@seattle.gov

Mr. Seamus Mooney 

Office of Domestic Preparedness

Maryland Emergency Management Agency 

5401 Rue Saint Lo Drive 

Reisterstown, MD 21136 
E-10 Federal R&D Needs and Priorities for ATD Modeling

Mr. Al Valerioti 

Director of Training 

Waterbury Fire Department 

235 Grand St 

Waterbury, CT 06702 

Phone: 203-232-4940 

Email: alwfd@aol.com

Mr. Rich Evans 

HAZMAT Technician 

Eastside HAZMAT Team 

Bellevue Fire Department 

Bellevue, WA 

Email: revans@ci.bellevue.wa.us

Mr. John Fox 

HAZMAT Technician 

Eastside HAZMAT Team 

Bellevue Fire Department 

Bellevue, WA 

Email: jfox@foxinternet.net
JOINT ACTION GROUP FOR ATMOSPHERIC TRANSPORT AND DIFFUSION 

MODELING (RESEARCH AND DEVELOPMENT PLAN) (JAG/ATD(R&DP)) 

Dr. Walter D. Bach, Jr., Cochairperson 

Department of Defense 

Ms. Nancy Suski, Cochairperson 

Department of Homeland Security 

Mr. Bruce Hicks 

Department of Commerce 

Ms. Debbie Payton 

Department of Commerce 

Dr. Paula M. Davidson 

Department of Commerce 

CDR Stephanie W. Hamilton, USN 

Department of Defense 

Mr. James F. Bowers 

Department of Defense 

Mr. Rickey C. Petty 

Department of Energy 

Mr. John J. Perry 

Department of Homeland Security 

Mr. John S. Irwin 

NOAA Air Policy Support Branch 

Environmental Protection Agency 

Mr. Stephen D. Ambrose 

National Aeronautics and Space Administration 

Ms. Jocelyn Mitchell 

Nuclear Regulatory Commission 

Mr. James McNitt, Executive Secretary 

Office of the Federal Coordinator for 

Meteorological Services and Supporting Research
Two images of the same release. In (a) we see a photograph of

an instant during a point source release of smoke within a wind

tunnel (view is taken looking down on the plume), where large and

small swirls have distorted the plume into serpentine twists and turns.

In (b) we see a time-average photographic exposure of the smoke

release, where the time-average of the individual chaotic swirls

are seen to have the “traditional” Gaussian plume shape used in

ATD plume dispersion models. (Photographs are courtesy of

U.S. EPA/NOAA Fluid Modeling Facility).

Model of World Trade Center site, Manhattan, New York City in the

U.S. EPA/NOAA Fluid Modeling Facility.