United States

Environmental Protection

Agency

Office of Research and Development

Washington, DC 20460

EPA/600/R-99/030

March 1999

*

On assignment from the National Oceanic and Atmospheric Administration, U.S. Department of

Commerce

SCIENCE ALGORITHMS OF THE EPA MODELS-3

COMMUNITY MULTISCALE AIR QUALITY (CMAQ)

MODELING SYSTEM

Edited by:

D. W. BYUN*

 and J. K. S. CHING*

Atmospheric Modeling Division

National Exposure Research Laboratory

U.S. Environmental Protection Agency

Research Triangle Park, NC 27711
EPA/600/R-99/030

DISCLAIMER

The information in this document has been funded wholly or in part by the United States

Environmental Protection Agency. It has been subjected to the Agency’s peer and

administrative review, and has been approved for publication as an EPA document. Mention of

trade names or commercial products does not constitute endorsement nor recommendation for

use.
EPA/600/R-99/030

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CONTENTS

FOREWORD ............................................................... iii

ACRONYMS .............................................................. xiii

ACKNOWLEDGMENTS .................................................... xvi

EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-1

1.0 INTRODUCTION TO THE MODELS-3 FRAMEWORK AND THE COMMUNITY

MULTISCALE AIR QUALITY MODEL (CMAQ) ........................ 1-1

 (J. Ching and D. Byun)

1.1 The Models-3 Emissions, Meteorology, and the CMAQ Modeling Systems . . . 1-3

1.2 CMAQ Interface Processors ....................................... 1-5

1.3 The CMAQ Chemical Transport Model (CCTM) ....................... 1-7

1.4 Analysis of CMAQ Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-9

1.4.1 Process Analysis (Chapter 16) ................................ 1-9

1.4.2 Aggregation (Chapter 17) ................................... 1-10

1.5 Management of CMAQ Science Information Objects and Codes in Models-3 1-10

1.5.1 Program Control Processors (Chapter 15) ...................... 1-10

1.5.2 CMAQ Code Integration (Chapter 18) ........................ 1-11

1.6 Post Release Studies and Near-Future Plans . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11

1.6.1 CMAQ Evaluation Study ................................... 1-11

1.6.2 Testing Operational Configurations ........................... 1-11

1.6.3 Extensions and Science Additions ............................ 1-12

1.7 Opportunities and Encouragement for Long Term Extensions and Science

Community Involvement ......................................... 1-12

1.8 References .................................................... 1-14

2.0 MODELS-3 ARCHITECTURE: A UNIFYING FRAMEWORK FOR

ENVIRONMENTAL MODELING AND ASSESSMENT . . . . . . . . . . . . . . . . . . . . 2-1

(J. Novak and S. Leduc)

2.1 Introduction .................................................... 2-1

2.2 Overview of the Models-3 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.2.1 Dataset Manager .......................................... 2-1

2.2.2 Program Manager .......................................... 2-2

2.2.3 Study Planner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

2.2.4 Strategy Manager .......................................... 2-3

2.2.5 Tool Manager ............................................. 2-3

2.2.6 Science Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5

2.2.7 Model Builder ............................................ 2-5

2.2.8 Source Code Manager ...................................... 2-6

2.2.9 Framework Administrator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6
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2.3 Models-3 System Architecture ...................................... 2-6

2.4. Schedule and Future Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

2.5 References ..................................................... 2-9

3.0 DEVELOPING METEOROLOGICAL FIELDS ........................... 3-1

(T. Otte)

3.1 Credits and Disclaimers for Use of MM5 .............................. 3-1

3.2 Meteorology Model Pre-Processing .................................. 3-1

3.2.1 Defining the Simulation Domain (TERRAIN) . . . . . . . . . . . . . . . . . . . . 3-1

3.2.2 Processing the Meteorological Background Fields (DATAGRID) ..... 3-2

3.2.3 Objective Analysis (RAWINS) ................................ 3-2

3.2.4 Setting the Initial and Boundary Conditions (INTERP) . . . . . . . . . . . . . 3-3

3.3 The Meteorology Model (MM5) .................................... 3-3

3.3.1 Brief History .............................................. 3-3

3.3.2 Horizontal and Vertical Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4

3.3.3 Prognostic Equations ....................................... 3-4

3.3.4 Model Physics ............................................ 3-5

3.3.5 Nesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9

3.3.6 Four-Dimensional Data Assimilation ........................... 3-9

3.4 Meteorology Model Post-Processing ................................ 3-10

3.5 Changes to the MM5 System's Software for Models-3 .................. 3-10

3.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-11

4.0 EMISSION SUBSYSTEM .............................................. 4-1

(B. Benjey, J. Godowitch, and G. Gipson)

4.1 Emission Inventory Processors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1.1 Discussion ............................................... 4-1

4.1.2 General MEPPS Structure .................................. 4-10

4.2 The MEPPS Emission Processing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-11

4.2.1 The Inventory Data Analyzer (IDA) .......................... 4-13

4.2.2 The MEPPS Input Processor (INPRO) ........................ 4-15

4.2.3 Processing Procedure ...................................... 4-16

4.2.4 Modeled Emission Data .................................... 4-32

4.2.5 Chemical Speciation of Emission Data ........................ 4-77

4.2.6 Output Processor (OUTPRO) ............................... 4-91

4.3 Models-3 Emission Projection Processor (MEPRO) .................... 4-90

4.4 Emission Processing Interface ..................................... 4-93

4.4.1 Overview of Key Features of ECIP . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-93

4.4.2 Plume Rise of Point Source Emissions ......................... 4-95

4.4.3 Method for the Treatment of Initial Vertical Plume Spread ........ 4-98

4.4.4 Vertical Allocation of Plume Emissions ........................ 4-99

4.4.5 Generation of 3-D Emissions ................................ 4-99

4.5 Data Requirements .............................................. 4-99

4.6 Plans for Improvement ......................................... 4-101
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4.7 References ................................................... 4-102

5.0 FUNDAMENTALS OF ONE-ATMOSPHERE DYNAMICS FOR MULTISCALE

AIR QUALITY MODELING ...................................................................................... 5-1

(D. Byun)

5.1 Governing Equations and Approximations for the Atmosphere ........................ 5-1

5.1.1 Governing Equations in a Generalized Curvilinear Coordinate System 5-2

5.1.2 Assumptions of Atmospheric Dynamics ................................................ 5-5

5.2 Choice of Vertical Coordinate System for Air Quality Modeling .................... 5-10

5.3 Coupling of Meteorology and Air Quality ....................................................... 5-17

5.3.1 Meteorological Data for Air Quality Modeling .................................... 5-17

5.3.2 Off-line and On-line Modeling Paradigms ........................................... 5-17

5.4 Mass Conservation ........................................................................................... 5-21

5.4.1 Mass Consistency in Meteorological Data ........................................... 5-21

5.4.2 Techniques for Mass Conservation in Air Quality Models .................. 5-22

5.4.3 Temporal Interpolation of Meteorological Data .................................. 5-24

5.5 Conclusion ........................................................................................................ 5-25

5.6 References ........................................................................................................ 5-26

Appendix 5A. Tensor Primer and Derivation of the Continuity Equation in a 

Generalized Curvilinear Coordinate System ........................................ 5-29

6.0 GOVERNING EQUATIONS AND COMPUTATIONAL STRUCTURE OF THE

COMMUNITY MULTISCALE AIR QUALITY (CMAQ) CHEMICAL

TRANSPORT MODEL ............................................................................................... 6-1

(D. Byun, J. Young, and M.T. Odman)

6.1 Derivation of the Atmospheric Diffusion Equation ........................................... 6-2

6.2 Representation of Science Processes in CMAQ Modeling System .................... 6-8

6.2.1 Supporting Models and Interface Processors ......................................... 6-8

6.2.2 Modularity Concept of CMAQ .............................................................. 6-9

6.2.3 Description of Science Processes ......................................................... 6-14

6.3 Equivalent Model Formulations for Different Vertical Coordinates ................ 6-25

6.4 Nesting Techniques .......................................................................................... 6-27

6.5 Summary .......................................................................................................... 6-30

6.6 References ........................................................................................................ 6-30

Appendix 6A. Concentration Units Used for Air Quality Studies ............................... 6-33
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7.0 NUMERICAL TRANSPORT ALGORITHMS FOR THE COMMUNITY

MULTISCALE AIR QUALITY (CMAQ) CHEMICAL TRANSPORT MODEL 

IN GENERALIZED COORDINATES ....................................................................... 7-1

(D. Byun, J. Young, J. Pleim, M.T. Odman, and K. Alapaty)

7.1 Numerical Advection Algorithms ....................................................................... 7-2

7.1.1 Conservation Form Equation for Advection .......................................... 7-3

7.1.2 Classification of Advection Schemes ..................................................... 7-5

7.1.3 Description of Advection Schemes in CCTM ........................................ 7-5

7.1.4 Treatment of Boundary Conditions ...................................................... 7-10

7.1.5 Test of Algorithms with Idealized Linear Horizontal Flow Fields ....... 7-11

7.1.6 Vertical Advection ............................................................................... 7-14

7.1.7 Adjustment of Mass Conservation Error .............................................. 7-15

7.2 Vertical Mixing Algorithms .............................................................................. 7-17

7.2.1 Closure Problem ................................................................................... 7-18

7.2.2 Computing Vertical Mixing with the Eddy Diffusion Formulation: 

K-Theory .............................................................................................. 7-20

7.2.3 Flux Form Representation of Vertical Mixing ..................................... 7-30

7.3 Horizontal Mixing Algorithms .......................................................................... 7-42

7.4 Conclusions ...................................................................................................... 7-44

7.5 References ........................................................................................................ 7-45

Appendix 7A Numerical Solvers for Diffusion Equations .......................................... 7-50

8.0 GAS-PHASE CHEMISTRY ........................................................................................ 8-1

(G. Gipson and J. Young)

8.1 Background ........................................................................................................ 8-2

8.2 Chemical Mechanisms in the CMAQ System .................................................... 8-3

8.2.1 CB4 Mechanism ..................................................................................... 8-4

8.2.2 RADM2 Mechanism .............................................................................. 8-5

8.2.3 SAPRC-97 Mechanism .......................................................................... 8-6

8.2.4 Extended Mechanisms ........................................................................... 8-7

8.2.5 Changing or Adding Mechanisms in CMAQ ........................................ 8-10

8.3 Reaction Kinetics ............................................................................................. 8-11

8.3.1 Reaction Rates ..................................................................................... 8-11

8.3.2 Rate Constant Expressions ................................................................... 8-12

8.4 Mathematical Modeling ................................................................................... 8-14

8.4.1 Governing Equations ............................................................................ 8-15

8.4.2 SMVGEAR ........................................................................................... 8-17

8.4.3 QSSA Solver ......................................................................................... 8-21

8.4.4 Summary .............................................................................................. 8-24

8.5 References ........................................................................................................ 8-24

Appendix 8A Chemical Mechanisms Included in the CMAQ System ....................... 8-28
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9.0 PLUME-IN-GRID TREATMENT OF MAJOR POINT SOURCE 

EMISSIONS .................................................................................................................. 9-1

(N. Gillani and J. Godowitch)

9.1 Introduction ........................................................................................................ 9-1

9.2 Overview of the Conceptual Framework of the Plume-in-Grid Treatment .. 9-3

9.3 Formulation of the Plume-in-Grid Modeling Components ................................ 9-4

9.3.1 Description of the Plume Dynamics Model ........................................... 9-4

9.3.2 Formulation of the Plume-in-Grid Module .......................................... 9-11

9.4 Summary .......................................................................................................... 9-27

9.5 References ........................................................................................................ 9-27

10.0 THE AEROSOL PORTION OF MODELS-3 CMAQ ........................................... 10-1

(F. Binkowski)

10.1 Aerosol Dynamics ............................................................................................ 10-3

10.1.1 Modal Definitions ................................................................................. 10-3

10.1.2 New Particle Production by Nucleation ............................................... 10-4

10.1.3 Primary Emissions ................................................................................ 10-5

10.1.4 Numerical Solvers ................................................................................ 10-6

10.1.5 Mode Merging by Renaming ................................................................ 10-9

10.2 Aerosol Dry Deposition .................................................................................. 10-10

10.3 Cloud Processing of Aerosols ........................................................................ 10-10

10.4 Aerosol Chemistry .......................................................................................... 10-13

10.5 Visibility ......................................................................................................... 10-14

10.6 Summary ........................................................................................................ 10-16

10.7 References ...................................................................................................... 10-16

11.0 CLOUD DYNAMICS AND CHEMISTRY .............................................................. 11-1

(S. Roselle and F. Binkowski)

11.1 Background ...................................................................................................... 11-1

11.2 Model Description ............................................................................................ 11-1

11.2.1 Subgrid Convective Cloud Scheme ...................................................... 11-2

11.2.2 Resolved Cloud Scheme ....................................................................... 11-6

11.3 Conclusions ...................................................................................................... 11-7

11.4 References ........................................................................................................ 11-8

12.0 METEOROLOGY-CHEMISTRY INTERFACE PROCESSOR (MCIP) FOR

MODELS-3 COMMUNITY MULTISCALE AIR QUALITY (CMAQ) 

MODELING SYSTEM .............................................................................................. 12-1

(D. Byun, J. Pleim, R. Tang, and A. Bourgeois)

12.1 Introduction ...................................................................................................... 12-1

12.1.1 MCIP Functions ................................................................................... 12-3

12.1.2 MCIP’s Data Dependency ................................................................... 12-5

12.1.3 Computational Structure ...................................................................... 12-7

12.2 Data Types, Coordinates, and Grids ................................................................ 12-9
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12.2.1 Meteorological Data Types .................................................................. 12-9

12.2.2 Coordinates ........................................................................................ 12-13

12.2.3 Modification of Grid Structure ........................................................... 12-17

12.3 Estimation of Physical Parameters ................................................................. 12-22

12.3.1 PBL Parameters ................................................................................. 12-22

12.3.2 Dry Deposition Velocities .................................................................. 12-36

12.3.3 Cloud Parameters and Solar Radiation .............................................. 12-48

12.4 Meteorological Data for CCTM with Generalized Coordinate System ......... 12-51

12.4.1 Thermodynamic Variables: Pressure, Density and Entropy ............... 12-51

12.4.2 Vertical Jacobian and Layer Height ................................................... 12-53

12.4.3 Contravariant Velocity Components .................................................. 12-56

12.4.4 Mass Consistent Temporal Interpolation of Meteorological 

Parameters .......................................................................................... 12-59

12.4.5 Optional Conversion of Nonhydrostatic Data to Hydrostatic

Meteorological Data for MM5 ........................................................... 12-60

12.5 Operation of MCIP ........................................................................................ 12-62

12.5.1 MCIP Modules ................................................................................... 12-62

12.5.2 Building MCIP ................................................................................... 12-62

12.5.3 Executing MCIP ................................................................................. 12-64

12.5.4 Defining Grid and Domain for MCIP ................................................. 12-67

12.5.5 Extension of MCIP for Other Meteorological Models ....................... 12-71

12.6 Concluding Remarks ...................................................................................... 12-72

12.7 References ...................................................................................................... 12-73

Appendix 12A MCIP Output Data ............................................................................ 12-79

Appendix 12B Examples of Nest Domain Definitions for CMAQ system ............... 12-82

Appendix 12C Sample MCIP Configuration File ...................................................... 12-84

Appendix 12D Sample MCIP Run Script .................................................................. 12-86

13.0 THE INITIAL CONCENTRATION AND BOUNDARY CONDITION

PROCESSORS ........................................................................................................... 13-1

(G. Gipson)

13.1 Introduction ...................................................................................................... 13-1

13.2 Overview of the ICON and BCON Processors ................................................ 13-1

13.3 Input Sources .................................................................................................... 13-2

13.3.1 Time Invariant Concentration Profiles ................................................. 13-2

13.3.2 CCTM Concentration files ................................................................... 13-4

13.3.3 Tracer Species ...................................................................................... 13-4

13.4 Spatial Interpolation ......................................................................................... 13-6

13.4.1 Horizontal Interpolation ....................................................................... 13-6

13.4.2 Vertical Interpolation ........................................................................... 13-7

13.5 ICON/BCON Species Processing .................................................................... 13-8

13.6 Mechanism Conversions ................................................................................ 13-10

13.7 ICON/BCON Applications ............................................................................. 13-11
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13.8 References ...................................................................................................... 13-12

14.0 PHOTOLYSIS RATES FOR CMAQ ....................................................................... 14-1

(S. Roselle, K. Schere, J. Pleim, A. Hanna)

14.1 Background ...................................................................................................... 14-1

14.2 Preprocessor JPROC: Calculate Clear-sky Photolysis Rate Table .................. 14-2

14.3 Subroutine PHOT: Table Interpolation and Cloud Attenuation ....................... 14-3

14.4 Conclusions ...................................................................................................... 14-4

14.5 References ........................................................................................................ 14-5

15.0 PROGRAM CONTROL PROCESSING IN MODELS-3 ...................................... 15-1

(J. Young)

15.1 Domain Configuration ...................................................................................... 15-2

15.2 Input/Output Applications Programming Interface .......................................... 15-3

15.3 Other CCTM Configuration Control ................................................................ 15-3

15.3.1 CCTM Process Analysis ...................................................................... 15-3

15.3.2 CCTM Fixed Data ................................................................................ 15-4

15.4 Generalized Chemistry ..................................................................................... 15-5

15.4.1 Design ................................................................................................... 15-5

15.4.2 Operation .............................................................................................. 15-6

15.4.3 Supported Reaction Types ................................................................... 15-8

15.4.4 Mechanism Parsing Rules .................................................................... 15-9

15.4.5 Chemical Species Include Files ............................................................ 15-9

15.6 Conclusion ...................................................................................................... 15-15

15.7 References ...................................................................................................... 15-15

16.0 PROCESS ANALYSIS .............................................................................................. 16-1

(G. Gipson)

16.1 Integrated Process Rate Analysis .................................................................. 16-2

16.1.1 Computation of Integrated Process Rates ............................................ 16-2

16.1.2 Example IPR Analyses ......................................................................... 16-4

16.1.3 Implementation of IPR Analysis in the CMAQ System ....................... 16-5

16.1.4 Use of the PACP to set up an IPR Analysis ......................................... 16-6

16.2 Integrated Reaction Rate Analysis ................................................................... 16-8

16.2.1 Computation of Integrated Reaction Rates .......................................... 16-9

16.2.2 Example IRR Analyses ...................................................................... 16-10

16.2.3 Implementation of IRR Analysis the CMAQ system ........................ 16-12

16.2.4 Use of the PACP to set up an IRR Analysis ...................................... 16-13

16.3 Conclusion ...................................................................................................... 16-18

16.4 References ..................................................................................................... 16-19
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17.0 AN AGGREGATION AND EPISODE SELECTION SCHEME DESIGNED TO

SUPPORT MODELS-3 CMAQ ................................................................................ 17-1

(R. Cohn, B. Eder, and S. Leduc)

17.1 Introduction ...................................................................................................... 17-1

17.1.1 Background .......................................................................................... 17-1

17.1.2 Objectives ............................................................................................. 17-2

17.2 Summary of the Approach ............................................................................... 17-3

17.2.1 Basic Elements of the Methodology .................................................... 17-3

17.2.2 Rationale, Scope, and Limitations ........................................................ 17-4

17.2.3 Strategy ................................................................................................ 17-5

17.3 Cluster Analysis of Wind Fields ....................................................................... 17-7

17.3.1 Description of Wind Data .................................................................... 17-7

17.3.2 Basic Cluster Analysis Technique ........................................................ 17-7

17.3.3 Illustration of Cluster Analysis Results ................................................ 17-8

17.4 Evaluation of Alternative Aggregation Approaches ...................................... 17-10

17.4.1 Description of Alternative Approaches .............................................. 17-10

17.4.2 Description of Meteorological Data ................................................... 17-11

17.4.3 Analysis Methods ............................................................................... 17-11

17.4.4 Results ................................................................................................ 17-12

17.5 Refinement of the Sampling Approach .......................................................... 17-20

17.5.1 Determination of Appropriate Numbers of Strata and Events ........... 17-21

17.5.2 Selection of Stratified Sample of Events ............................................ 17-26

17.6 Application and Evaluation ............................................................................ 17-28

17.6.1 Application of the Aggregation Procedure ......................................... 17-28

17.6.2 Evaluation .......................................................................................... 17-30

17.7 Summary and Discussion ............................................................................... 17-31

17.8 References ...................................................................................................... 17-32

18.0 INTEGRATION OF SCIENCE CODES INTO MODELS-3 ................................. 18-1

(J. Young)

18.1 Introduction ...................................................................................................... 18-1

18.2 Classes and Modules ........................................................................................ 18-3

18.2.1 Operational Design ............................................................................... 18-3

18.2.2 CGRID ................................................................................................. 18-4

18.2.3 Class Driver .......................................................................................... 18-5

18.2.4 Synchronization Time Step ................................................................... 18-6

18.3 Input/Output Applications Programming Interface .......................................... 18-7

18.4 Code Configuration Management .................................................................... 18-9

18.4.1 The Need .............................................................................................. 18-9

18.4.2 The Tool ............................................................................................. 18-10

18.4.3 The Repository ................................................................................... 18-11

18.5 How a Model is Constructed .......................................................................... 18-13

18.5.1 Object Oriented Concepts .................................................................. 18-13

18.5.2 Global Name Table Data .................................................................... 18-13
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18.5.3 Build Template ................................................................................... 18-14

18.6 How a Model is Executed .............................................................................. 18-17

18.7 Using the Models-3 Framework ..................................................................... 18-17

18.8 Conformant Code ........................................................................................... 18-18

18.8.1 Thin Interface ..................................................................................... 18-18

18.8.2 Coding Rules ...................................................................................... 18-19

18.8.3 Science Process Code Template ........................................................ 18-20

18.8.4 Robustness and Computational Efficiency ........................................ 18-23

18.9 Conclusion ...................................................................................................... 18-24

18.10 References ...................................................................................................... 18-24
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FOREWORD

The Models-3 Community Multiscale Air Quality (CMAQ) modeling system has been developed

under the leadership of the Atmospheric Modeling Division of the EPA National Exposure

Research Laboratory in Research Triangle Park, NC. This new generation of modeling software

was under development for seven years and was made available in June 1998 without charge for

use by air quality regulators, policy makers, industry, and scientists to address multiscale,

multi-pollutant air quality concerns.

Models-3/CMAQ has a unique framework and science design that enables scientists and 

regulators to build their own modeling system to suit their needs. Users can access pre-installed

modeling systems provided by the EPA or can incorporate their own modeling systems to work

within the existing framework software.

This direct user involvement is key to the concept of a community modeling and analysis 

system. This approach to model development, application, and analysis leverages the

community's complementary talents and resources to set new standards for rapid incorporation of

better science into air quality model applications. The resulting comprehensive system forms the

foundation upon which the community, including governments, industry, academia, and other

stakeholders, can collaborate in the examination of issues and the subsequent development of

strategies that meet society challenges of environmental protection.

The release of Models-3/CMAQ is one of the many steps which we hope will unite the 

community under the common goal of advancing our knowledge and abilities to tackle critical

problems of the future in far more effective ways than have been attempted in the past. 

Scientifically sound modeling systems, developed and supported by the community, are one

method of achieving this goal.

The June 1998 release of the Models-3/CMAQ computer code was accompanied by a User

Manual [EPA-600/R-98/069(b)] to serve as a reference on how to use the software system. This

Science Document is the counterpart to the User Manual in that it presents the peer reviewed

scientific bases for the Models-3/CMAQ modeling systems. This document also includes

components such as interface processors, process analysis routines, and the present and planned

evaluation program. 

F. A. Schiermeier

March 1999
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ACKNOWLEDGMENTS

The development of the science components of the Models-3 Community Multiscale Air Quality

(CMAQ) system represents a major undertaking by a large team of dedicated atmospheric

scientists and a relatively long term effort beginning in the early 1990s, and covers a wide area of

subject material as embodied in each of the contributing chapters. Most of the EPA Models-3

CMAQ Science Team are from the Atmospheric Modeling Division (AMD) in the EPA Office of

Research And Development’s National Exposure Research Laboratory (NERL). With several

exceptions the contributing authors from this Division are on detail from the Air Resources

Laboratory of the National Oceanic and Atmospheric Administration (NOAA). The NOAAAMD effort is supported through Interagency Agreement (DW13937252) with the EPA. The

authors of each of the chapters of this document would like to collectively express their gratitude

to the other members of the NOAA Division. In particular we acknowledge the encouragement

and support of the Director of the Atmospheric Modeling Division, Francis Schiermeier. We are

indebted to Dr. Robin Dennis for his vision and role in ensuring the implementation of holisticone-atmosphere approach into CMAQ. We also recognize the technical help from our

colleagues Tom Pierce, Steven Howard, Alfreida Torian, and Gary Walter. 

We also want to recognize the contributions, helpful discussions, and assistance of many

members of the science community, either through formal agreements or through peer interest. 

For example, the work was supported by several cooperative agreements including:

• Atmospheric Modeling Research-Scientific and Computational (CR-822066: MCNC),

Principal Investigator (PI): Kenneth Galluppi Ed Bilicki, Steve Fine, Alison Eyth, and

Rohit Mathur;

• Research on Computational Framework in Generic Grids, Adaptive Grids, and Subgrid

Treatment of Air Quality Simulation (CR-822053: MCNC), PI: Talat Odman; with R.K.

Srivastava and D.S. McRae, North Carolina State University);

• Transport Algorithms for Air Quality Simulation Models (CR-822059, MCNC), PI: Talat

Odman;

• Emissions Modeling Research with High-Performance Computing (CR-822074: MCNC),

PI: Carlie Coats;

• Advanced Modeling of Meteorology in Support of Air Quality Models (CR-822628:

(MCNC), PI: Aijun Xiu, Kiran Alapaty, John N. McHenry, and Adel F. Hanna; with

Nelson L. Seaman and Aijun Deng, Pennsylvania State University; and John S. Kain,

National Severe Storms Laboratory);

• A Flexible and Efficient Methodology for Modeling Aerosol for Air Quality Models 

(CR-823634: MCNC), PI: Uma Shankar, Mark Read and Atanas Trayanov; with Anthony 

Wexler, University of Delaware; and John H. Seinfeld, California Institute of Technology;

• Develop Methods for Technology Transfer of Advanced Regional/Urban Air Quality 

Models (CR-822080: NCSU), PI: Alan Schula; and

• Plume-in-Grid development for a Multiscale Air Quality Modeling System (IAG

DW64937190, Tennessee Valley Authority (TVA)), PI’s: Robert E. Imhoff; with Noor

Gillani, Arastoo Biazar (now at Monash University, Australia), and Yu-Ling Lu,

University of Alabama, Huntsville).
EPA/600/R-99/030

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The following scientists also contributed under the formal contracts listed: Ruen Tang, Chris

Maxwell, and Hao Jin of the Technical Support Group, Dyntel Corp. (General Services

Administration IAG DW47937823); Tod Plessel, and Yan Ching Zhang of the Visualization

Laboratory, Lockheed Martin (EPA Contract 68-W7-0055); and Nick Moghari, Joe Susick, and

Dave Tivel of Science Applications International Corporation (SAIC) (EPA Contract 68-W1-

0055).

Significant contributions were made by: visiting scientists, including Sang-Mi Lee of the Seoul

National University, Korea, Chong Bum Lee of the KangWeon University, Korea, and Seiji

Sugata of the National Institute for Environmental Studies, Japan; UCAR’s post doctoral fellows

Yonghong Li, Qingyuan Song and Shoba Kondragunta; and Dr. Ingmar Akermann of Ford

Research in Aachen, Germany.

It is also a pleasure to thank the colleagues who engaged us in many seminal technical

discussions on various aspects of the CMAQ system. We recognize Professors Harvey Jeffries,

University of North Carolina at Chapel Hill; Ted Russell, Georgia Institute of Technology;

Nelson Seaman and Jack Kain, Pennsylvania State University; Dick McNider, University of

Alabama, Sonia Kreidenweis, Colorado State University, Panos Georgeopolus of Rutgers

University, and Itsushi Uno of Kyushu University, Japan. Many of our EPA colleagues provided

stimulating discussion or reviews of our effort including Gail Tonnesen, Deborah Luecken, Carey

Jang, John S. Irwin (NOAA) and Ed Edney. 

We are indebted to the following peer reviewers, who gave unselfishly of their time and whose

comments and suggestions were extremely valuable in improving the scientific aspects of the

report. Listed in alphabetical order, the reviewers are followed by the chapter(s) they reviewed: 

Jeff Brook, Atmospheric Environment Service, Canada (Ch. 17); David Chock, Ford Research

Laboratory (Ch. 2 and 7); Henry Hogo, Southern California Air Quality Modeling Division (Ch.

8 and 16); Sasha Madronich, National Center for Atmospheric Research (Ch. 14); Paulette

Middleton, RAND Environmental Science & Policy Center (Ch. 10); Ted Russell, Georgia

Institute of Technology (Ch. 18); Nelson Seaman, Pennsylvania State University (Ch. 3); 

Christian Seigneur, Atmospheric and Environmental Research, Inc. (Ch. 10 and 11); Trevor

Sholtz, ORTECH Corporation (Ch. 4); Saffet Tanrikulu, California Environmental Protection

Agency (Ch. 9 and 12); Robert Yarmartino, Earth Tech (Ch. 5 and 6); and Zion Wang (Ch. 13,

15, and 16).

Finally, the editors note with great appreciation, the efforts of Brian Eder, who facilitated many

of the activities and tasks involved in the preparation and completion of this document. We

thank the Raleigh, North Carolina, staff of SAIC, especially Alice Gilliland (now with NOAAAMD) and Andrea Verykoukis for their critical contributions in the area of technical editing,

assuring a high level of consistency and quality in all the chapters of this manuscript. We are

also grateful to Alice Gilliland for her invaluable work on the Executive Summary.
EPA/600/R-99/030

EXECUTIVE SUMMARY

THIS SCIENCE DOCUMENT PRESENTS THE PROCESSORS AND ALGORITHMS THAT

EMBODY THE INITIAL RELEASE OF THE MODELS-3 COMMUNITY MULTISCALE

AIR QUALITY (CMAQ) MODELING SYSTEM. CMAQ IS A MULTIPLE POLLUTANT

MODEL THAT CONTAINS NEW SCIENTIFIC APPROACHES TO AIR QUALITY

MODELING, WHICH REPRESENT THE CURRENT STATE OF SCIENCE. This CMAQ

Science Document is a living document that will be updated as the state of the science

progresses. The CMAQ Science Document provides a basis and point of reference for the state

of the science captured in the June1998 initial release of Models-3. Current and future efforts to

improve the Models-3 modeling system(s) will depart from the scientific reference points

presented in this document.

Models-3 is a flexible software system that provides a user-interface framework for CMAQ air

quality modeling applications and tools for analysis, management of model input/output, and

visualization of data. The Models-3 framework relies on two modeling systems to provide the

meteorological and emissions data needed for air quality modeling. With this data, the Models-3

CMAQ modeling system can be used for urban and regional scale air quality simulation of

tropospheric ozone, acid deposition, visibility, and particulate matter (PM2.5 and PM10). The

meteorological and emissions modeling systems that are provided with the current release of

Models-3 will be described in this document. However, CMAQ is designed as an open system

where alternative models can be used to generate the data.

This CMAQ Science Document contains chapters that address specific scientific and technical

issues involved in the development and application of the Models-3 CMAQ modeling system. 

The principal researchers for each model component or function authored the coinciding chapter

in this document. They serve as the points of contact for scientific questions regarding their

CMAQ air quality model components. For instructions on using the Models-3 framework and

using the MM5, MEPPS, and CMAQ modeling systems, refer to the Models-3 User Manual

(EPA/600/R-98/069b, National Exposure Research Laboratory, Research Triangle Park, NC)

and Tutorial (EPA/600/R-98/069c, National Exposure Research Laboratory, Research Triangle

Park, NC).

An overview of the MEPPS emissions, MM5 meteorology, and CMAQ air quality modeling

systems is provided in Chapter 1. Chapter 2 then introduces the Models-3 framework and

structure and explains how the framework’s user-interface is used with the MEPPS and CMAQ

modeling systems. More detailed discussions on the modeling systems are then discussed

separately in the following chapters. The amount of detail and the length of these discussions

vary depending on whether this information has already been provided elsewhere. Some

chapters provide a synopsis of the scientific components and refer to previously published

material on the subject, while other chapters provide extensive detail on new scientific

techniques that are not currently described in other publications.
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MM5 Meteorological data are essential for many processes simulated in the CMAQ chemical

transport model including transport, chemistry, and cloud processes. The Fifth-Generation Penn

State/NCAR Mesoscale Model (MM5) is the only meteorology model compatible with the initial

release of Models-3. MM5 is a complex, state-of-the-science community model, and it is

maintained by NCAR. MM5 is well documented by its primary developers in technical notes and

refereed journal articles. Chapter 3 briefly describes the scientific aspects of MM5, including

grid definitions, model physics, nesting, and four dimensional data assimilation. These

descriptions generally direct the user to more complete documentation about particular aspects of

MM5. To promote the flexibility of CMAQ, additional meteorology models will be compatible

with Models-3 future releases.

MEPPS Chapter 4 provides a description of the Models-3 Emission Processing and

Projection System (MEPPS) structure, its scientific approach, and the assumptions used in

modeling and processing emission data in the Models-3 framework. The chapter also discusses

data flow and quality control used with emission inventory and meteorological input data for

MEPPS. The description of the main Emission Processor addresses the basis of spatial and

temporal allocation procedures, and the methods and assumptions used in modeling mobile and

biogenic emissions and in the “lumping” of individual chemical species are also presented. This

chapter also explains the procedures used by the Models-3 Emission Projection Processor to

estimate emission data for use in modeling future air quality scenarios.

THE CMAQ CHEMICAL TRANSPORT MODEL (CCTM)

Fundamentals of One-Atmosphere Dynamics for Multiscale Air Quality Modeling

Chapter 5 provides information essential to the proper use of meteorological data in air

quality modeling systems. The chapter introduces a robust and fully compressible set of

governing equations for the atmosphere, which provides an integral view of atmospheric

modeling. The limitations of several simplifying assumptions on atmospheric dynamics

are presented, as are concepts of on-line and off-line coupling of meteorological and air

quality models. In addition, this chapter describes a procedure for conserving the mixing

ratio of trace species even in the case of meteorological data that are not mass consistent. 

In summary, Chapter 5 attempts to bridge the information gap between dynamic

meteorologists and air quality modelers by highlighting the implication of using different

meteorological coordinates and dynamic assumptions for air quality simulations.

Governing Equations and Computational Structure In Chapter 6, the governing

diffusion equation is derived in a generalized coordinate system, which is suitable for

multiscale atmospheric applications. CMAQ’s use of generalized coordinates for its

governing equations provides the flexibility to span multiple scales and to incorporate

meteorological data on different coordinates. The CMAQ system’s modularity concepts

and fractional time-step formulation, and CCTM’s key science processes are described. 

Chapter 6 also presents the dynamic formulations of several popular Eulerian air quality

models as emulated by the governing diffusion equations in the generalized coordinate

system.
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Numerical Transport Algorithms The transport processes in the atmosphere primarily

consist of advection and diffusion. In Chapter 7, CMAQ’s numerical algorithms for

advection and vertical and horizontal diffusion are discussed. To provide the CMAQ

system with multiscale capability, the transport processes, both advection and diffusion,

are formulated in conservation (i.e., flux) forms for the generalized coordinate system. 

Therefore, CMAQ’s numerical transport algorithms will function under a wide variety of

dynamical situations and concentration distribution characteristics. Users are encouraged

to experiment with their own algorithms to test different numerical schemes for air quality

simulations.

Gas Phase Chemistry Chapter 8 examines the way gas-phase chemistry is treated

in CMAQ. The CMAQ system currently includes two base chemical mechanisms,

RADM2 and CB4, while the incorporation of a third, the SAPRC97 mechanism, is

planned for the future. Chapter 8 describes each of these chemical mechanisms as well

as the manner in which the first two are linked to the aqueous chemistry and aerosol

formation processes. The chapter also discusses procedures for entering new chemical

mechanisms in the CMAQ system, the representation of reaction kinetics, the numerical

modeling of gas-phase chemistry, and the two numerical solvers included in CMAQ,

SMVGEAR and a variant of the QSSA method.

Plume-in-Grid Chapter 9 introduces the plume-in-grid (PinG) technique

developed for CMAQ. PinG is designed to treat more realistically the dynamic and

chemical processes impacting selected major point source pollutant plumes in CMAQ. 

The Plume Dynamics Model (PDM) simulates plume rise, horizontal and vertical plume

growth, and transport of each plume section during the subgrid scale phase. The PinG

module simulates the relevant physical and chemical processes during a subgrid scale

phase. This technique is in contrast to the traditional Eulerian grid modeling method of

instantly mixing the point source emissions into an entire grid cell volume. Chapter 9

describes the technical approach and model formulation of the relevant processes, and

discusses the capabilities and limitations of the initial version of the PinG approach.

The Aerosol Module One of CMAQ’s key strengths is that it is a multi-pollutant model

that fully addresses the criteria pollutants PM and ozone. Chapter 10 discusses the

aerosol module of CMAQ, which is designed to be an efficient and economical depiction

of aerosol dynamics in the atmosphere. This chapter discusses the techniques for

distributing particulates in three modes: coagulation, particle growth by the addition of

new mass, and particle formation. The aerosol module considers both PM2.5 and PM10

and includes estimates of the primary emissions of elemental and organic carbon, dust,

and other species not further specified. Secondary species considered are sulfate, nitrate,

ammonium, water and organic from precursors of anthropogenic and biogenic origin. 

Cloud Chemistry and Dynamics Chapter 11 discusses the role and functions of

clouds in CMAQ. Clouds are involved in aqueous chemical reactions, vertical mixing of

pollutants, and removal of aerosols by wet deposition, all of which affect the
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concentration of air pollutants. CMAQ’s cloud module performs several functions

related to cloud physics and chemistry, and it models three types of clouds: sub-grid

convective precipitating clouds, sub-grid non-precipitating clouds, and grid-resolved

clouds. The cloud module vertically redistributes pollutants for the sub-grid clouds,

calculates in-cloud and precipitation scavenging, performs aqueous chemistry, and

accumulates wet deposition amounts.

CMAQ INTERFACE PROCESSORS PREPARE INPUT DATA FROM SOURCES

INCLUDING THE EMISSION AND METEOROLOGICAL MODELING SYSTEMS FOR USE

IN THE CMAQ CTM. EACH OF THESE PROCESSORS, EACH OF WHICH HAVE

SPECIFIC FUNCTIONS, ARE DESCRIBED IN THE CHAPTERS MENTIONED BELOW. 

The interface processors that handle input data from the emissions and meteorological models are

essential because CMAQ is an open system in which meteorological and emissions data are

calculated separately (i.e., “off-line”), rather than during the chemical transport model

simulation. These interface processors also add extra quality control, so that inconsistencies

between input data and the CCTM are minimized.

ECIP In addition to describing the Models-3 MEPPS emission modeling system,

Chapter 4 discusses the Emission-Chemistry Interface Processor (ECIP). ECIP serves as

the key link between the MEPPS system and CCTM. ECIP’s primary function is to

generate hourly 3-D emission data files for CCTM from the individual emission file types

produced by the MEPPS. The key inputs for ECIP are the area emissions file, the stack

parameter and emission files for the point sources generated in MEPPS, and a set of

meteorological data files generated by the Meteorology-Chemistry Interface Processor

(MCIP) for the CCTM domain. All major point sources are subject to plume-rise and

initial vertical dispersion processes before being allocated to a particular vertical model

layer. 

MCIP Chapter 12 describes MCIP, which links meteorological models, such as MM5,

with the CCTM system to provide the complete set of meteorological data needed for air

quality simulation. To support CCTM’s multiscale generalized coordinate

implementation, MCIP provides appropriate dynamic meteorological parameters to allow

mass-consistent air quality computations. MCIP deals with issues related to data format

translation, conversion of parameter units, diagnostic estimations of parameters not

provided, extraction of data for appropriate window domains, and reconstruction of

meteorological data on different grid and layer structures. MCIP also relies on the

Landuse Processor (LUPROC) to provide landuse and vegetation information to define

surface characteristics to compute dry deposition and other PBL parameters. LUPROC

extracts information about landuse in the CMAQ domain from a landuse database and

converts it into the fractional landuse data used in MCIP. 

Initial and Boundary Conditions Initial conditions provide a simulation’s starting

point, while boundary conditions define influences from outside the domain.
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Chapter 13 describes the two interface processors that generate the concentration fields

for the initial and boundary conditions for CCTM. The chapter describes how the initial

condition (ICON) and boundary condition (BCON) processors can be used to generate

the concentration fields from either predefined default vertical profiles or from other

CMAQ simulation results when model nesting is being performed. This chapter also

discusses generating initial and boundary concentrations for special tracer species and

procedures for horizontal and vertical interpolation and conversions between chemical

mechanisms.

Photolysis Rate Processor Many chemical reactions in the atmosphere are initiated by

the photodissociation of numerous trace gases, including NO2, O3, and HCHO. Chapter

14 describes the photolysis rate processor (JPROC) that produces the photolysis rates

used in the CMAQ chemical transport simulation. JPROC predicts photolysis rates for

various altitudes, latitudes, and zenith angles. Currently, the radiative transfer algorithm

assumes clear-sky conditions (i.e., no clouds present), and CCTM then attenuates for

cloudiness.

As described above, each of the CMAQ interface processors incorporate raw data for CMAQ

and perform functions such as calculating parameters and interpolating or converting data. The

functions of the interface processors also include capabilities to handle raw data with various

resolutions or measurement units. Raw input data is currently specified in the source code for

JPROC, LUPROC, ICON, and BCON; however, the interface processors in future releases of

CMAQ will be modified to handle a more generalized set of input data.

PROGRAM CONTROL PROCESSORS, A SET OF PROGRAMS EMBEDDED IN THE

MODELS-3 FRAMEWORK, HANDLE SCIENCE INFORMATION OBJECTS SUCH AS GRID

AND LAYER SPECIFICATIONS, CHEMICAL MECHANISMS, AND MODEL

CONFIGURATIONS FOR REPEATED USE ACROSS SEVERAL PROCESS COMPONENTS

OF CMAQ.

Program Control Processing (PCP) Chapter 15 explains how PCP is used within

Models-3 to set up internal arrays, map species names, define global parameters, and

establish linkages among processors in the Models-3 CMAQ system. Specifications

needed for the CCTM simulation (e.g., grid and coordinate conditions and chemical

species names) are entered into the Models-3 system once by the graphical user

interfaces, and an object-oriented database accessible by all model components is

established. PCP utilizes this information in the object database and automatically

generates the required global FORTRAN include files. As a part of PCP, Models-3

CMAQ system employs a generalized chemistry mechanism processor (MP), also called

the “mechanism reader.” It greatly simplifies the task of implementing or altering gasphase chemistry mechanisms and provides the capability of easily and safely using

different mechanisms in the CMAQ system. 
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Integration of Science Codes into Models-3 One of the major objectives of the

Models-3 project was to develop a flexible, comprehensive air quality modeling system

with a modular coding structure that allows easy replacement of science process

components. Chapter 18 describes the modularity concepts, code management method,

and integration schemes of CMAQ science code with the Models-3 framework. The

CMAQ FORTRAN code was integrated into the Models-3 framework with the following

set of design, coding, and implementation standards: (1) modularity to allow easy

exchange of science process solvers, (2) a standard subroutine interface at the module

level, (3) restriction of coding practices, (4) the Models-3 I/O API

(http://www.iceis.mcnc.org/EDSS/ioapi/index.html/), which contains standardized file I/O

functions and a modeler-friendly interface built on top of self-describing netCDF

(http://www.unidata.ucar.edu/packages/netcdf/) files that are portable across most Unix

platforms.

MODELS-3 ALSO PROVIDES ANALYSIS ROUTINES FOR USE WITH CMAQ OUTPUT,

WHICH CAN BE USED TO PROVIDE PROCESS ANALYSIS RESULTS AND STATISTICAL

AGGREGATION TECHNIQUES.

Process Analysis Chapter 16 describes the implementation of process analysis

techniques in the CMAQ modeling system. These techniques can be used in CMAQ to

provide insights into how model predictions are obtained, which is particularly useful

when modeling nonlinear systems like atmospheric photochemistry. Two techniques are

available in the CMAQ system, integrated process rate (IPR) analysis and integrated

reaction rate (IRR) analysis. IPR analysis can be used to determine the relative

contributions of individual physical and chemical processes, and IRR analysis can help

elucidate important chemical pathways and identify key chemical characteristics.

Aggregation Chapter 17 discusses a statistical procedure called aggregation that is

applied to CMAQ’s outputs in order to derive the seasonal and annual estimates required

by assessment studies. Assessment studies require CMAQ-based distributional estimates

of ozone, acidic deposition, and PM2.5, as well as visibility, on seasonal and annual time

frames. Unfortunately, it is not financially feasible to execute CMAQ over such extended

time periods. Therefore, in practice CMAQ must be executed for a finite number of

episodes or “events,” which are selected to represent a variety of meteorological classes. 

The aggregation technique is used to incorporate these episode simulations into annual

and seasonal estimates. 

THE MODEL-3 CMAQ MODELING SYSTEM IS BEING FORMALLY EVALUATED TO

ASSESS THE PERFORMANCE OF CMAQ’S NEW DEVELOPMENTS IN AIR QUALITY

MODELING. THE EVALUATION WILL PROVIDE THE BASIS FOR UNDERSTANDING

THE STRENGTHS OR WEAKNESSES OF THE CURRENT STATE-OF-SCIENCE IN

CMAQ. With an evaluation of CMAQ simulations of 36, 12, and 4 km grid resolution,

CMAQ’s performance can be evaluated on both the regional and urban scales. This evaluation
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will include an initial comparison of relative performance against the RADM model and

diagnostic evaluation against databases from regional studies such as the 1995 Southern Oxidant

Study conducted in the vicinity of Nashville, TN and the 1995 NARSTO-NE study.

CMAQ can be configured for a wide range of applications, from scientific studies to regulatory

applications. While the scientific community can take advantage of CMAQ’s ability to create

alternative applications for research and development purposes, regulatory applications depend

upon a standardized, evaluated form of CMAQ for regulatory applications. The CMAQ

evaluation program will provide the scientific benchmark needed for this. 

FUTURE EXTENSIONS OF CMAQ INCLUDE NEAR-TERM EFFORTS TO PROVIDE A

NEW CHEMICAL MECHANISM AND EMISSION MODELING SYSTEM.

The SAPRC-97 gas phase mechanism will soon be incorporated into CMAQ, in addition

to the current CB-IV and RADM2 mechanisms available. The SAPRC mechanism will

be incorporated with a fixed subset of the approximately 100 organic species contained in

the semi-explicit version of the SAPRC mechanism.

The Sparse Matrix Operator Kernel Emissions modeling system (SMOKE)

(http://envpro.ncsc.org/products/) will also be incorporated into CMAQ in the near-term. 

The SMOKE model formulates emissions modeling in terms of sparse matrix operations

which require considerably less time to perform than current systems.

WE ENCOURAGE THE FULL PARTICIPATION AND INVOLVEMENT OF THE

SCIENTIFIC AND MODELING COMMUNITIES IN THE GROWTH AND USE OF MODELS3 CMAQ. THERE ARE MANY IDEAS AND PLANS FOR FUTURE DEVELOPMENTS OF

CMAQ, INCLUDING TOXIC POLLUTANT MODELING AND LINKAGES TO OTHER

MODELS.

Modeling atmospheric toxic pollutants A key opportunity for CMAQ is developing the

capability to model toxic pollutants. Models of airborne toxic pollutants are essential for

human exposure and risk assessments. They can also be used to assess the exchange of

toxic compounds between the atmosphere and sensitive ecosystems. With the ability to

simulate toxic pollutant processes in addition to the current photochemical oxidants and

particulates, it is planned to transport the CMAQ model to a finer than urban scale to link

with human exposure models. 

New linkages with global models It is hoped that information from the urban and

regional CMAQ applications and from global modeling applications can be bridged. 

CMAQ output, produced using state of the science techniques, can be used to benchmark

or examine the parametric basis of process formulations in global models. In addition,

global model output can be used to improve or enhance the initial and boundary

conditions for regional and urban scale CMAQ simulations. 
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Modeling ecosystems Efforts to combine environmental modeling techniques to

encompass an entire ecosystem is needed to address issues including (a) nutrient cycling

through the atmosphere, water bodies, and soil and (b) acidic wet and dry deposition into

sensitive ecosystems, including critical load analyses. With this ecosystem modeling

approach, air quality issues can be studied in combination with other aspects of

environmental health.

This is the Executive Summary of Science Algorithms of the EPA Models-3 Community

Multiscale Air Quality (CMAQ) Modeling System, edited by D. W. Byun and J. K. S.

Ching, 1999.