August 16, 2005 

Final Report for the Western Regional Air 

Partnership (WRAP) Regional Modeling 

Center (RMC) for the Project Period March 1, 

2004, through February 28, 2005 

WGA Contract Number: 30203 

Prepared for: 

Western Regional Air Partnership 

Western Governors’ Association 

1515 Cleveland Place, Suite 200 

Denver, CO 80202 

Prepared by: 

Gail Tonnesen, Zion Wang, Mohammad Omary, Chao-Jung Chien

Bourns College of Engineering Center for Environmental Research and 

Technology (CE-CERT), University of California, Riverside 

1084 Columbia Avenue, Riverside, CA 92507 

http://pah.cert.ucr.edu/aqm/308/

Ralph Morris, Gerry Mansell, Susan Kemball-Cook, Greg Yarwood 

ENVIRON International Corporation 

101 Rowland Way, Suite 220, Novato, CA 94945 

http://www.environcorp.com 

Zac Adelman, Andy Holland, Kim Hanisak 

UNC-Chapel Hill, Carolina Environmental Program 

Bank of America Plaza, CB# 6116, 137 E. Franklin Street, Chapel Hill, NC 27599-6116 

http://cf.unc.edu/cep/empd
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 ii 

Executive Summary 

This report describes work completed for project year 2004 (March 1, 2004, through February 

28, 2005) at the Regional Modeling Center (RMC), which comprises staff from the University of 

California, the University of North Carolina, and ENVIRON International Corporation. Funding 

for this work was provided by the Western Governors’ Association’s Western Regional Air 

Partnership (WRAP). This was the fourth year of a continuing project to analyze and model 

regional haze and visibility in the western United States. Listed below are the titles and purposes 

of the tasks that were included in the 2004 RMC work plan, which was developed in coordination with WRAP technical staff and the WRAP Modeling Forum. 

• Task 0.5, 2002 Ammonia Emissions Inventory for WRAP Region: To review current ammonia 

emissions generation techniques and develop a GIS-based ammonia emissions model. 

• Task 1, Project Administration: To manage the WRAP RMC activities, participate in WRAP 

conference calls, attend WRAP meetings, and prepare project status reports. 

• Task 2, Test, Improve, Quality Control, Obtain External Peer Review, and Finalize 36-km 

and 12-km MM5 Simulations for Eventual Use in CMAQ: To perform MM5 modeling for 

2002 on the 36-km Inter-RPO continental U.S. grid and a 12-km western U.S. WRAP grid. 

• Task 3, 2002 Base Year Emissions Modeling, Processing, and Analysis: To revise the interim 

2002 inventory by integrating missing emissions sources; to assimilate the results of applying 

the new analysis tools and QA plan for improving the emissions modeling process; and to 

integrate the final 2002 emissions inventories into a base 2002 emissions data set. 

• Task 4, Air Quality Model Evaluation for 2002 Annual Simulation: To test the 2002 base year 

air quality modeling performed with CMAQ, including a preliminary simulation using the 

2002 interim emissions inventory followed by several iterations with bug fixes or updates. 

• Task 5, Preparation and Reporting of Geographic Source Apportionment Results: To 

implement, test, and apply Tagged Species Source Attribution PM algorithms in CMAQ. 

• Task 6, Further Analysis of Model Performance in Regard to the Contribution of Natural 

Emissions to Visibility Impairment: To perform modeling without anthropogenic emissions to 

help elucidate natural background visibility levels. 

• Task 7, Evaluation and Comparison of Alternative Models: To analyze alternative models to 

CMAQ for 2002 modeling. 

• Task 9, Testing and Further Improvements to the Windblown Dust Emissions Modeling 

Methodology: To further refine and test the WRAP windblown dust model. 

• Task 10, Continued Improvement to Model Evaluation Software: To continue developing 

model evaluation software for meteorology, emissions, and air quality modeling. 

• Task 11, Sensitivity Studies Designed to Evaluate Uncertainties in Fire Emissions: To 

perform fire sensitivity simulations as requested by the Fire Emissions Joint Forum. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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• Task 12, Preliminary Meteorological, Emissions, and Air Quality Modeling Activities for 

Alaska: To perform MM5 modeling of Alaska and preliminary dispersion modeling using a 

Lagrangian puff model. 

• Task 13, Training Courses for the WRAP States and Tribes: To conduct training activities as 

needed to transfer datasets and technology to WRAP member tribes and states. 

Section 1 of this report provides project background information. Sections 2 through 14 discuss 

the tasks in detail, and include lists of project deliverables as well as links to where they can be 

downloaded or viewed on the project web site, at http://pah.cert.ucr.edu/aqm/308/. Section 15 

gives concise summaries of all the tasks. 

The overall goal for 2004 was to develop, quality assure, test, and refine all model input data 

required to perform simulations of visibility for calendar year 2002. Visibility was modeled on 

both a continental 36-km domain and a higher-resolution 12-km domain for the WRAP region. 

A primary objective was to validate the modeling scenarios so that they can be used in subsequent analysis to support the development of State and Tribe Implementation Plans, which are 

due in December 2007. Most of these activities were performed under Tasks 1-4, 7, and 10. 

Another major activity (Task 12) was the development of modeling scenarios to analyze visibility in Alaska. These data were developed independently of the conterminous WRAP region 

because Alaska is so far removed from the other WRAP states and because it has much lower 

emissions densities and longer transport distances. Alaska modeling required separate simulations that used a polar version of the meteorology model and that employed the CALPUFF 

model, which is more appropriate than grid-based models for studying visibility in Alaska. 

The 2004 project year also included development of tools and model simulations to analyze the 

sources of haze. This activity was performed under Tasks 5 and 7. The CMAQ model was initially used to analyze source apportionment—that is, to identify particular emissions source 

categories and regions that are responsible for visibility impairment at individual receptor sites. 

Because of mass conservation errors in the modeling system, the source apportionment results in 

CMAQ were more qualitative than quantitative. We also evaluated the CAMx model’s source 

apportionment tools, and plan to use CAMx in future source apportionment work. 

Several model simulations were performed to assess CMAQ’s sensitivity to changes in the fire 

emissions data (Task 11). Some key data needed for the fire sensitivity studies (e.g., detailed 

information on small fires) were not available, so the task definition was modified during 2004; 

the small fire sensitivity analysis and other plume rise analyses will now be completed in 2005. 

In 2005-2006, the RMC will finish developing future visibility scenarios for 2018 to be used in 

additional modeling to support development of SIPs/TIPs. The ultimate goal is to transfer these 

data sets to the states and tribes so that they can carry out additional modeling analyses for their 

areas. During 2004 there were extensive discussions about what combination of training and 

technical support would be most beneficial for technology transfer. It was agreed that the most 

efficient approach would be to develop a standardized version of all data sets, computer software, and computer hardware, and that instructions for model setup and use would be provided 

on the project web site. Additionally, training classes will be offered on an “as needed” basis. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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Contents 

Executive Summary...................................................................................................................... ii

Tables ............................................................................................................................................ xi

Figures......................................................................................................................................... xiv

Abbreviations ........................................................................................................................... xxiv

1. Introduction............................................................................................................................ 1

1.1 Background.................................................................................................................... 1 

1.1.1 Need for regional haze modeling ..................................................................... 1 

1.1.2 Role of the Western Regional Air Partnership................................................. 2 

1.1.3 WRAP strategic plan ........................................................................................ 3 

1.1.4 Organization of the Regional Modeling Center ............................................... 4 

1.2 Overview of RMC 2004 Activities................................................................................ 5 

2. Task 0.5: 2002 Ammonia Emissions Inventory for WRAP Region .................................. 7

2.1 Introduction ................................................................................................................... 7 

2.2 Emissions Inventory Development................................................................................ 9 

2.2.1 Emission factors ............................................................................................. 10 

2.2.2 Activity data ................................................................................................... 13 

2.2.3 Temporal allocation........................................................................................ 14 

2.2.4 Spatial allocation ............................................................................................ 15 

2.2.5 Land use and environmental data................................................................... 16 

2.2.6 GIS-based modeling ....................................................................................... 19 

2.3 2002 Ammonia Emissions inventory .......................................................................... 19 

2.3.1 Livestock operations....................................................................................... 22 

2.3.2 Fertilizer application....................................................................................... 23 

2.3.3 Native soils ..................................................................................................... 25 

2.3.4 Domestic sources............................................................................................ 26 

2.3.5 Wild animals................................................................................................... 27 

2.3.6 Comparison with CMU emission estimates ................................................... 28 

2.4 Summary and Recommendations ................................................................................ 29 

2.4.1 Summary ........................................................................................................ 30 

2.4.2 Recommendations .......................................................................................... 30 

2.5 Status of Task 0.5 Deliverables ................................................................................... 31 

3. Task 1: Project Administration.......................................................................................... 33
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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3.1 Project Administration................................................................................................. 33 

3.2 Computer Systems Administration.............................................................................. 33 

3.3 Status of Task 1 Deliverables ...................................................................................... 35 

4. Task 2: Test, Improve, Quality Control, Obtain External Peer Review, and 

Finalize 36-km and 12-km MM5 Simulations for Eventual Use in CMAQ ................... 36

4.1 Introduction ................................................................................................................. 36 

4.1.1 Task history .................................................................................................... 36 

4.1.2 Summary of approach .................................................................................... 38 

4.2 Additional 36-km MM5 Sensitivity Tests ................................................................... 39 

4.3 Analysis Procedures .................................................................................................... 40 

4.4 MM5 Sensitivity Test Results ..................................................................................... 41 

4.4.1 Cumulus parameterization sensitivity test...................................................... 41 

4.4.2 LSM/PBL sensitivity test ............................................................................... 45 

4.4.3 FDDA sensitivity test ..................................................................................... 49 

4.4.4 Tests in other seasons ..................................................................................... 54 

4.4.5 Selection of best-performing run.................................................................... 61 

4.5 Summary of Results for the WRAP Region, and Performance in Other 

Subdomains ................................................................................................................. 62 

4.5.1 Summary of WRAP region results................................................................. 62 

4.5.2 Tests in other subdomains .............................................................................. 67 

4.6 Additional 12-km MM5 Sensitivity Tests ................................................................... 70 

4.7 Evaluation of the Final WRAP 2002 MM5 36/12-km Annual Run............................ 72 

4.7.1 METSTAT Surface Evaluation ......................................................................... 73 

4.7.2 Precipitation Evaluation .................................................................................... 80 

4.7.3 Upper-Air Evaluation........................................................................................ 83 

4.7.4 12-km METSTAT Surface Performance Evaluation ........................................ 87 

4.7.5 12-km Precipitation Performance Evaluation ................................................... 91 

4.8 Summary...................................................................................................................... 94 

4.9 Status of Task 2 Deliverables ...................................................................................... 96 

5. Task 3: 2002 Base Year Emissions Modeling, Processing, and Analysis........................ 98

5.1 Introduction ................................................................................................................. 98 

5.2 WRAP 2002 Emissions Inventories .......................................................................... 100 

5.2.1 Stationary area sources................................................................................. 103 

5.2.2 Road dust sources......................................................................................... 104 

5.2.3 Windblown dust ........................................................................................... 105 

5.2.4 Anthropogenic fugitive dust sources............................................................ 105 

5.2.5 Agricultural NH3 sources ............................................................................. 106 
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5.2.6 On-road mobile sources................................................................................ 107 

5.2.7 Nonroad mobile sources............................................................................... 108 

5.2.8 Stationary point sources ............................................................................... 109 

5.2.9 Offshore sources........................................................................................... 110 

5.2.10 Fire sources................................................................................................... 112 

5.2.11 Biogenic sources........................................................................................... 116 

5.2.12 Inventories summary for the final preliminary 2002 simulations ................ 117 

5.3 WRAP 2002 Ancillary Emissions Inputs .................................................................. 118 

5.3.1 Temporal allocation data .............................................................................. 119 

5.3.2 Spatial allocation data .................................................................................. 120 

5.3.3 Chemical speciation data.............................................................................. 121 

5.3.4 Meteorology data.......................................................................................... 121 

5.3.5 Other emissions input data ........................................................................... 121 

5.4 Description of FY 2004 Emissions Modeling Simulations ....................................... 122 

5.4.1 Emissions simulation Pre02a_36.................................................................. 123 

5.4.2 Emissions simulation Pre02b_36 ................................................................. 124 

5.4.3 Emissions simulation Pre02c_36.................................................................. 124 

5.4.4 Emissions simulation Pre02c_12.................................................................. 125 

5.4.5 Emissions simulation Pre02c_PinG ............................................................. 125 

5.4.6 Emissions simulation Pre02c_36s01............................................................ 125 

5.4.7 Emissions simulation Pre02d_36 ................................................................. 126 

5.4.8 Emissions simulation Pre02e_36.................................................................. 127 

5.4.9 Emissions simulation Pre02f_36 .................................................................. 128 

5.4.10 Emissions simulation Pre02d_12 ................................................................. 128 

5.5 RMC Emissions Modeling Deliverables and QA/QC Products............................... 129 

5.5.1 SMOKE time requirements and disk use ..................................................... 130 

5.5.2 RMC version control .................................................................................... 132 

5.5.3 Quality assurance products........................................................................... 133 

5.6 WRAP Preliminary 2002 Emissions Progress........................................................... 134 

5.7 Emissions Sensitivities .............................................................................................. 152 

5.8 Problems Encountered and Corrections .................................................................... 153 

5.8.1 Stationary area sources................................................................................. 153 

5.8.2 Nonroad mobile sources............................................................................... 154 

5.8.3 Road dust sources......................................................................................... 157 

5.8.4 On-road mobile sources................................................................................ 157 

5.8.5 Point sources................................................................................................. 158 

5.8.6 Biogenic sources........................................................................................... 160 

5.8.7 SMOKE ancillary input files........................................................................ 160 
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5.9 Outstanding Emissions Issues ................................................................................... 161 

5.10 Emissions Data Technology Transfer........................................................................ 162 

5.11 Next Steps.................................................................................................................. 163 

5.12 Status of Task 3 Deliverables .................................................................................... 163 

6. Task 4: Air Quality Model Evaluation for 2002 Annual Simulation............................ 165

6.1 Operational Evaluation Approach ............................................................................. 165 

6.1.1 Performance evaluation tools ....................................................................... 166 

6.1.2 Subdomains analyzed................................................................................... 167 

6.1.3 Model performance goals and criteria.......................................................... 167 

6.1.4 Performance time periods............................................................................. 168 

6.1.5 Performance measures.................................................................................. 169 

6.2 Operational Evaluation of CMAQ Base Case D (pre02d) in the WRAP States ....... 169 

6.2.1 Sulfate (SO4)................................................................................................. 169 

6.2.2 Nitrate (NO3) ................................................................................................ 177 

6.2.3 Ammonium (NH4)........................................................................................ 184 

6.2.4 Organic carbon (OC) .................................................................................... 186 

6.2.5 Elemental carbon (EC) ................................................................................. 191 

6.2.6 Other fine PM (soil) and coarse mass (CM)................................................. 196 

6.3 Evaluation of CMAQ at Class I Areas for the Best-20% and Worst-20% Days....... 201 

6.3.1 Procedures for projecting visibility improvements ...................................... 201 

6.3.2 Evaluation for best/worst-20% days............................................................. 202 

6.4 Conclusions of CMAQ 2002 pre02d 36-km and 12-km Base Case Model 

Performance Evaluation ............................................................................................ 212 

6.5 Status of Task 4 Deliverables .................................................................................... 215 

7. Task 5: Preparation and Reporting of Geographic Source Apportionment 

Results................................................................................................................................. 216

7.1 Overview ................................................................................................................... 216 

7.2 Description of TSSA Method .................................................................................... 218 

7.2.1 Initialization of tagged species ..................................................................... 218 

7.2.2 Definition of source regions ......................................................................... 219 

7.2.3 Definition of source categories..................................................................... 222 

7.2.4 Updating of tracers in CMAQ science algorithms ....................................... 223 

7.2.5 Postprocessing of TSSA results ................................................................... 225 

7.2.6 Uncertainties in the TSSA results ................................................................ 226 

7.3 Source Attribution Modeling Results ........................................................................ 227 

7.4 Status of Task 5 Deliverables .................................................................................... 230 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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8. Task 6: Further Analysis of Model Performance in Regard to the Contribution 

of Natural Emissions to Visibility Impairment............................................................... 232

8.1 Background................................................................................................................ 232 

8.2 Work Performed from March 2004 through February 2005 ..................................... 233 

8.3 Next Steps.................................................................................................................. 233 

8.4 Status of Task 6 Deliverables .................................................................................... 234 

9. Task 7: Evaluation and Comparison of Alternative Models ......................................... 235

9.1 Advantages in Operating Multiple Models ............................................................... 235 

9.2 Description of the CAMx Modeling System............................................................. 236 

9.2.1 Overview ...................................................................................................... 236 

9.2.2 PM Source Apportionment Technology (PSAT) ......................................... 238 

9.3 Approach for Testing and Evaluation of Alternative Models ................................... 239 

9.4 Comparative Evaluation of the CAMx and CMAQ Models ..................................... 240 

9.4.1 Evaluation for Sulfate (SO4)......................................................................... 240 

9.4.2 Evaluation for Nitrate (NO3) ........................................................................ 241 

9.4.3 Evaluation for organic carbon (OC) and elemental carbon (EC)................. 246 

9.4.4 Evaluation for other PM2.5 (soil) and coarse mass (CM) ............................. 246 

9.5 Comparison of the PSAT and TSSA PM Source Attribution ................................... 249 

9.5.1 CAMx PSAT PM Source Attribution Configuration ................................... 249 

9.5.2 Differences in TSSA and PSAT Configurations.......................................... 251 

9.5.3 Source Attribution Modeling Results........................................................... 251 

9.5.4 Conclusions on PM Source Attribution........................................................ 254 

9.6 Status of Task 7 Deliverables .................................................................................... 261 

10. Task 9: Testing and Further Improvements to the Windblown Dust Emissions 

Modeling Methodology...................................................................................................... 262

10.1 Introduction ............................................................................................................... 262 

10.2 Summary of Phase I Methodology and Phase II Literature Review ......................... 262 

10.2.1 Summary of Phase I methodology ............................................................... 262 

10.2.2 Review of recent literature ........................................................................... 264 

10.3 Phase II Windblown Dust Emission Estimation Methodology ................................. 268 

10.3.1 Friction velocities ......................................................................................... 268 

10.3.2 Threshold friction velocities......................................................................... 268 

10.3.3 Surface roughness lengths ............................................................................ 269 

10.3.4 Emission fluxes ............................................................................................ 269 

10.3.5 Reservoir characteristics............................................................................... 269 

10.3.6 Soil disturbance ............................................................................................ 271 

10.3.7 Data sources ................................................................................................. 271 
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10.3.8 Agricultural land adjustments ...................................................................... 273 

10.4 Model Results for 2002 ............................................................................................. 274 

10.4.1 Scenario a ..................................................................................................... 274 

10.4.2 Scenario b..................................................................................................... 277 

10.4.3 Scenario c ..................................................................................................... 279 

10.4.4 Scenario d..................................................................................................... 281 

10.4.5 Summary of results and recommendations .................................................. 283 

10.5 Model Performance Evaluation ................................................................................. 287 

10.5.1 Evaluation, Part 1: Comparisons of windblown dust emissions with 

the occurrence of enhanced “dust” at IMPROVE monitors......................... 288 

10.5.2 Evaluation, Part 2: Enhancements to CMAQ to separately track dust......... 289 

10.6 Summary.................................................................................................................... 289 

10.6.1 Results .......................................................................................................... 290 

10.6.2 Recommendations ........................................................................................ 290 

10.7 Status of Task 9 Deliverables .................................................................................... 291 

11. Task 10: Continued Improvement to Model Evaluation Software............................... 292

11.1 Work Performed from March 2004 through February 2005 ..................................... 292 

11.2 Status of Task 10 Deliverables .................................................................................. 293 

12. Task 11: Sensitivity Studies Designed to Evaluate Uncertainties in Fire 

Emissions ............................................................................................................................ 294

12.1 Scenario 1(a).............................................................................................................. 295 

12.2 Scenario 1(b).............................................................................................................. 296 

12.3 Scenario 2 .................................................................................................................. 296 

12.4 Scenario 3 .................................................................................................................. 297 

12.5 Scenario 4 .................................................................................................................. 297 

12.6 Scenario 5 .................................................................................................................. 298 

12.7 Status of Task 11 Deliverables .................................................................................. 299 

13. Task 12: Preliminary Meteorological, Emissions, and Air Quality Modeling 

Activities for Alaska .......................................................................................................... 311

13.1 Introduction ............................................................................................................... 311 

13.2 Meteorological Modeling Approach ......................................................................... 313 

13.2.1 Overview of MM5........................................................................................ 313 

13.2.2 The meteorology of Alaska .......................................................................... 314 

13.2.3 MM5 configuration ...................................................................................... 315 

13.2.4 Procedure to simulate the year 2002 ............................................................ 324 

13.2.5 Evaluation procedures for the 2002 annual run............................................ 324 

13.2.6 CALMET modeling ..................................................................................... 329 
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13.3 Emissions Modeling Approach ................................................................................. 329 

13.4 Air Quality Modeling Approach................................................................................ 330 

13.5 Status of Task 12 Deliverables .................................................................................. 330 

14. Task 13: Training Courses for the WRAP States and Tribes ....................................... 331

14.1 Work Performed from March 2004 through February 2005 ..................................... 331 

14.2 Status of Task 13 Deliverables .................................................................................. 332 

15. Summary of Work for March 1, 2004, through February 28, 2005 ............................. 333

15.1 Task 0.5: 2002 Ammonia Emissions Inventory for WRAP Region.......................... 333 

15.2 Task 1: Project Administration.................................................................................. 334 

15.3 Task 2: Test, Improve, Quality Control, Obtain External Peer Review, and 

Finalize 36-km and 12-km MM5 Simulations for Eventual Use in CMAQ ............. 334 

15.4 Task 3: 2002 Base Year Emissions Modeling, Processing, and Analysis................. 335 

15.5 Task 4: Air Quality Model Evaluation for 2002 Annual Simulation ........................ 336 

15.6 Task 5: Preparation and Reporting of Geographic Source Apportionment 

Results ....................................................................................................................... 337 

15.7 Task 6: Further Analysis of Model Performance in Regard to the Contribution 

of Natural Emissions to Visibility Impairment.......................................................... 338 

15.8 Task 7: Evaluation and Comparison of Alternative Models ..................................... 338 

15.9 Task 9: Testing and Further Improvements to the Windblown Dust Emissions 

Modeling Methodology ............................................................................................. 338 

15.10 Task 10: Continued Improvement to Model Evaluation Software............................ 339 

15.11 Task 11: Sensitivity Studies Designed to Evaluate Uncertainties in Fire 

Emissions................................................................................................................... 340

15.12 Task 12: Preliminary Meteorological, Emissions, and Air Quality Modeling 

Activities for Alaska.................................................................................................. 340 

15.13 Task 13: Training Courses for the WRAP States and Tribes .................................... 341 

References.................................................................................................................................. 342

Appendices A through E: Five Appendices to Section 5, “Task 3: 2002 Base Year 

Emissions Modeling, Processing, and Analysis”............................................................. 349

Appendix F: Appendix to Section 11, “Task 10: Continued Improvement to Model 

Evaluation Software” ........................................................................................................ 350
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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Tables 

Table 1-1. WRAP 2003 Strategic Plan (WGA, 2003).....................................................................4 

Table 2-1. Ammonia emission source categories included in the WRAP NH3 inventory. ............9 

Table 2-2. Livestock emission factors (kg/animal-yr)...................................................................10 

Table 2-3. Fertilizer emission factors. ...........................................................................................10 

Table 2-4. Emission factors for native soils...................................................................................11 

Table 2-5. Emission factors for domestic ammonia sources. ........................................................12 

Table 2-6. Emission factors for wild animal ammonia sources.....................................................12 

Table 2-7. Monthly livestock allocation factors. ...........................................................................14 

Table 2-8. NLCD land cover classification codes. ........................................................................17 

Table 2-9. Annual 2002 NH3 emissions by state (tons).................................................................20 

Table 2-10. Status of the Task 0.5 deliverables.............................................................................32 

Table 3-1. Status of the Task 1 deliverables..................................................................................35 

Table 4-1. MM5 configurations from original (Run 0) and modified (Run 5) MM5 runs............37 

Table 4-2. Summary of additional MM5 sensitivity tests..............................................................39 

Table 4-3. METSTAT subdomain abbreviations...........................................................................40 

Table 4-4. Surface model performance evaluation statistics summary. ........................................67 

Table 4-5. Summary of cumulus sensitivity tests on the 12-km grid. ...........................................70 

Table 4-6. Final MM5 configuration for 2002 annual run for 36-km and 12-km grids. ...............96 

Table 4-7. Status of the Task 2 deliverables..................................................................................97 

Table 5-1. Major emissions modeling tasks completed in project year 2004................................99 

Table 5-2. Emissions inventory categories included in the preliminary 2002 simulations .........102 

Table 5-3. Preliminary 2002 stationary-area-source emissions inventory summary with 

references for inventories used in all simulations..................................................................104 

Table 5-4. Preliminary 2002 road dust emissions inventory summary with references for 

inventories used in all simulations.........................................................................................105

Table 5-5. SCCs removed from the area-source inventories and replaced with VISTAS or 

Canadian inventory data to represent explicit fugitive dust emissions..................................105 

Table 5-6. Preliminary 2002 fugitive dust emissions inventory summary with references 

for inventories used in all simulations. ..................................................................................106 

Table 5-7. Preliminary 2002 on-road mobile-source emissions inventory summary with 

references for inventories used in all simulations..................................................................107 

Table 5-8. Preliminary 2002 nonroad mobile-source emissions inventory summary with 

references for inventories used in all simulations..................................................................108 
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Table 5-9. Preliminary 2002 stationary-point-source emissions inventory summary with 

references for inventories used in all simulations..................................................................110 

Table 5-10. Preliminary 2002 offshore-point-source emissions inventory summary with 

references for inventories used in all simulations..................................................................111 

Table 5-11. Preliminary 2002 offshore-mobile-source emissions inventory summary with 

references for inventories used in all simulations..................................................................112 

Table 5-12. Preliminary WRAP 2002 agricultural fire emissions inventory summary with 

references for inventories used in all simulations..................................................................113 

Table 5-13. Preliminary WRAP 2002 prescribed-fire emissions inventory summary with 

references for inventories used in all simulations..................................................................114 

Table 5-14. Preliminary 2002 wildfire emissions inventory summary with references for 

inventories used in all simulations.........................................................................................115

Table 5-15. Preliminary 2002 other fire emissions inventory summary with references for 

inventories used in all simulations.........................................................................................116

Table 5-16. Preliminary 2002 biogenic emissions inventory summary with references for 

inventories used in all simulations.........................................................................................117

Table 5-17. Summary of final preliminary 2002 emissions inventories compiled during 

2004 and used for simulations Pre02d_36 and Pre02d_12....................................................117 

Table 5-18. Mapping of ESRI/CIESIN to EPA99 codes for 12-km Mexican spatial 

surrogates. ..............................................................................................................................120 

Table 5-19. Web sites and Bugzilla ticket numbers for preliminary 2002 emissions 

simulations. ............................................................................................................................129 

Table 5-20. SMOKE time and disk use statistics for simulation Pre02c_36 km.........................131 

Table 5-21. CVS revision tags for preliminary 2002 modeling...................................................133 

Table 5-22. Summary of emissions updates between the Pre02c and Pre02d inventories..........134 

Table 5-23. Comparison among the three Pre02 simulations of annual U.S. pollutant totals 

in tons/year for all source categories combined.....................................................................137 

Table 5-24. Comparison among the three Pre02 simulations of annual WRAP-region 

pollutant totals in tons/year for all source categories combined............................................137 

Table 5-25. Pre02b U.S. pollutant totals by source category.......................................................139 

Table 5-26. Pre02c U.S. pollutant totals by source category.......................................................139 

Table 5-27. Pre02d U.S. pollutant totals by source category.......................................................139 

Table 5-28. Pre02b WRAP-region pollutant totals by source category.......................................140 

Table 5-29. Pre02c WRAP-region pollutant totals by source category.......................................140 

Table 5-30. Pre02d WRAP-region pollutant totals by source category.......................................140 

Table 5-31. Description and profile assignments for new WRAP 2002 nonroad mobile 

SCCs. .....................................................................................................................................155 

Table 5-32. Status of the Task 3 deliverables..............................................................................164 

Table 6-1. Model performance goals/criteria used to help interpret modeling results. ...............168 
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Table 6-2. Status of the Task 4 deliverables................................................................................215

Table 7-1. Combinations of source categories and geographic regions that are typically 

included in a TSSA simulation. .............................................................................................223 

Table 7-2. Status of the Task 5 deliverables................................................................................231

Table 8-1. Status of the Task 6 deliverables................................................................................234

Table 9-1. Definitions of the geographic source regions used in the TSSA and PSAT 

source attribution modeling. ..................................................................................................250 

Table 9-1. Status of the Task 7 deliverables................................................................................261

Table 10-1. Summary of surface characteristics for application of the Phase II dust model. .....269 

Table 10-2. Number of days after precipitation event to re-initiate wind erosion for rainfall 

amounts (constant) ≥2 in........................................................................................................270 

Table 10-3. Number of days after precipitation event to re-initiate wind erosion for rainfall 

amounts (constant) <2 in........................................................................................................270 

Table 10-4. Percentage of each land use type for the U.S. portion of the modeling domain. .....271 

Table 10-5. STATSGO soil texture and soil group codes. ..........................................................272 

Table 10-6. Status of the Task 9 deliverables..............................................................................291 

Table 11-1. Status of the Task 10 deliverables............................................................................293 

Table 12-1. Summary of new air quality model simulations used to evaluate effects of fire 

emissions................................................................................................................................295 

Table 12-2. Status of the Task 11 deliverables............................................................................300 

Table 13-1. Summary of MM5 configuration for sensitivity tests. .............................................317 

Table 13-2. Physics options selected for the 2002 WRAP winter Alaska MM5 simulation.......322 

Table 13-3. Physics options selected for the 2002 WRAP summer Alaska MM5 simulation. ...322 

Table 13-4. FDDA analysis nudging coefficients (s-1). ...............................................................323 

Table 13-5. Status of the Task 12 deliverables............................................................................330 

Table 14-1. Status of the Task 13 deliverables............................................................................332 
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 xiv 

Figures 

Figure 1-1. Regional Planning Organizations engaged in regional haze modeling.........................2 

Figure 1-2. Locations of Class I areas in the WRAP states.............................................................3 

Figure 2-1. RPO Unified Continental 36-km Modeling Grid domain.............................................8 

Figure 2-2. NLCD data for the conterminous United States. ........................................................17 

Figure 2-3. Mean soil pH for the conterminous United States. .....................................................18 

Figure 2-4. 2002 annual NH3 emissions by source category and state in tons per year (tpy). ......22 

Figure 2-5. Annual livestock NH3 emissions by animal type for WRAP states............................23 

Figure 2-6. Annual fertilizer NH3 emissions by fertilizer type for WRAP states..........................24 

Figure 2-7. Annual fertilizer NH3 emissions: dataset a0 is with pH effects, dataset a1 is 

without them. ...........................................................................................................................25 

Figure 2-8. Annual native soil NH3 emissions by land use type for WRAP states. ......................26 

Figure 2-9. Annual domestic-source NH3 emissions by source type for WRAP states. ...............27 

Figure 2-10. Annual wild animal NH3 emissions by animal type for WRAP states. ....................28 

Figure 2-11. Comparison of total 2002 annual ammonia emissions between the CMU and 

WRAP models. ........................................................................................................................29 

Figure 4-1. Definition of METSTAT subdomains. .......................................................................41 

Figure 4-2a. Wind soccer plot for cumulus parameterization sensitivity test................................43 

Figure 4-2b. Temperature soccer plot for cumulus parameterization sensitivity test....................43 

Figure 4-2c. Humidity soccer plot for cumulus parameterization sensitivity test. ........................44 

Figure 4-3. Precipitation comparison for the cumulus parameterization sensitivity test. Run 

2a (top left): Betts-Miller; Run 2b (bottom left): Grell; Run 5 (bottom right): KainFritsch II. Observed is shown at top right................................................................................45 

Figure 4-4a. Wind soccer plot for LSM/PBL parameterization sensitivity test. ...........................47 

Figure 4-4b. Temperature soccer plot for LSM/PBL parameterization sensitivity test.................47 

Figure 4-4c. Humidity soccer plot for LSM/PBL parameterization sensitivity test......................48 

Figure 4-5. Precipitation comparison for the LSM/PBL parameterization sensitivity test. 

Run 2a (top left): PX/ACM; Run 3b (bottom left): 5-layer/M-MRF; Run 1ba (bottom 

right): NOAH/M-MRF. Observed is shown at top right..........................................................49 

Figure 4-6a. Wind speed soccer plot for FDDA configuration sensitivity test. ............................50 

Figure 4-6b. Wind speed/direction soccer plot for FDDA configuration sensitivity test..............51 

Figure 4-6c. Temperature soccer plot for FDDA configuration sensitivity test............................51 

Figure 4-6d. Humidity soccer plot for FDDA configuration sensitivity test.................................52 

Figure 4-7a. Wind soccer plot for NOAH/M-MRF nudging sensitivity test.................................53 

Figure 4-7b. Temperature soccer plot for NOAH/M-MRF nudging sensitivity test. ....................53 
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 xv 

Figure 4-7c. Humidity soccer plot for NOAH/M-MRF nudging sensitivity test. .........................54 

Figure 4-8a. Wind soccer plot for Run 2ae seasonal test...............................................................55 

Figure 4-8b. Wind soccer plot for Run 1bb seasonal test..............................................................56 

Figure 4-8c. Temperature soccer plot for Run 2ae seasonal test. ..................................................56 

Figure 4-8d. Temperature soccer plot for Run 1bb seasonal test. .................................................57 

Figure 4-8e. Humidity soccer plot for Run 2ae seasonal test. .......................................................57 

Figure 4-8f. Humidity soccer plot for Run 1bb seasonal test. .......................................................58 

Figure 4-9a. CPC observed precipitation for the five-day modeled segment in each season........59 

Figure 4-9b. Seasonal precipitation test for Run 2ae (PX). ...........................................................60 

Figure 4-9c. Seasonal precipitation test for Run 1bb (M-MRF)....................................................61 

Figure 4-10a. Wind speed soccer plot for comparison of original (Run 0), interim (Run 5), 

and final (Run 2ae) 2002 MM5 runs........................................................................................63 

Figure 4-10b. Wind direction soccer plot for comparison of original (Run 0), interim (Run 

5), and final (Run 2ae) 2002 MM5 runs. .................................................................................63 

Figure 4-10c. Temperature soccer plot for comparison of original (Run 0), interim (Run 5), 

and final (Run 2ae) 2002 MM5 runs........................................................................................64 

Figure 4-10d. Humidity soccer plot for comparison of original (Run 0), interim (Run 5), 

and final (Run 2ae) 2002 MM5 runs........................................................................................64 

Figure 4-11a. July 1-5, 2002, MM5 predicted versus observed surface temperature time 

series for Run 0. .......................................................................................................................65 

Figure 4-11b. July 1-5, 2002, MM5 predicted versus observed surface temperature time 

series for Run 2ae.....................................................................................................................65 

Figure 4-12. Precipitation comparison for July 2002 for original (Run 0), interim (Run 5), 

and final (Run 2ae) MM5 runs. Observed is shown at top right..............................................66 

Figure 4-13a. Wind soccer plot for comparison of original (Run 0), interim (Run 5), and 

final (Run 2ae) 2002 MM5 runs for subdomains outside WRAP. ..........................................68 

Figure 4-13b. Temperature soccer plot for comparison of original (Run 0), interim (Run 5), 

and final (Run 2ae) 2002 MM5 runs for subdomains outside WRAP.....................................69 

Figure 4-13c. Humidity soccer plot for comparison of original (Run 0), interim (Run 5), 

and final (Run 2ae) 2002 MM5 runs for subdomains outside WRAP.....................................69 

Figure 4-14a. Wind soccer plot for cumulus sensitivity test on 12-km grid..................................71 

Figure 4-14b. Temperature soccer plot for cumulus sensitivity test on 12-km grid......................71 

Figure 4-14c. Humidity soccer plot for cumulus sensitivity test on 12-km grid. ..........................72 

Figure 4-15. 36-km surface wind performance soccer plot for January for western U.S. 

(top) and central and eastern U.S. (bottom).............................................................................74 

Figure 4-16. 36-km surface temperature performance soccer plot for January for western 

U.S. (top) and central and eastern U.S. (bottom).....................................................................75 

Figure 4-17. 36-km surface humidity performance soccer plot for January for western U.S. 

(top) and central and eastern U.S. (bottom).............................................................................76 
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Figure 4-18. 36-km surface wind performance soccer plot for July for western U.S. (top) 

and central and eastern U.S. (bottom)......................................................................................77 

Figure 4-19. 36-km surface temperature performance soccer plot for July for western U.S. 

(top) and central and eastern U.S. (bottom).............................................................................78 

Figure 4-20. 36-km humidity performance soccer plot for July for western U.S. (top) and 

central and eastern U.S. (bottom). ...........................................................................................79

Figure 4-21. January observed and modeled precipitation. ...........................................................81 

Figure 4-22. July observed and modeled precipitation..................................................................82 

Figure 4-23. July 2, 2002, 12Z Sounding for Spokane, WA. Upper left-hand panel: 

CENRAP; upper right-hand panel: VISTAS; lower left-hand panel: WRAP_0; lower 

right-hand panel: new WRAP..................................................................................................85 

Figure 4-24. July 16, 2002, 00Z Sounding for Midland, TX. Upper left-hand panel: 

CENRAP; upper right-hand panel: VISTAS; lower left-hand panel: WRAP_0; lower 

right-hand panel: new WRAP..................................................................................................86 

Figure 4-25. 12/36-km surface temperature performance soccer plot for January for the 

WRAP region...........................................................................................................................87 

Figure 4-26. 12/36-km surface wind performance soccer plot for January for the WRAP 

region. ......................................................................................................................................88 

Figure 4-27. 12/36-km surface humidity performance soccer plot for January for the 

WRAP region...........................................................................................................................88 

Figure 4-28. 12/36-km surface temperature performance soccer plot for July for the 

WRAP region...........................................................................................................................89 

Figure 4-29. 12/36-km surface wind performance soccer plot for July for the WRAP 

region. ......................................................................................................................................89 

Figure 4-30. 12/36-km surface humidity performance soccer plot for July for the WRAP 

region. ......................................................................................................................................90 

Figure 4-31. Annual cycle in 12-km MM5 precipitation. (a) January CPC observed 

precipitation. (b) January MM5 predicted total precipitation. (c) March CPC observed 

precipitation. (d) March MM5 predicted total precipitation. (e) July CPC observed 

precipitation. (f) July MM5 predicted total precipitation. (figure continued on next 

page).........................................................................................................................................93 

Figure 4-31 (cont’d.). Annual cycle in 12-km MM5 precipitation. (g) October CPC 

observed precipitation. (h) October MM5 predicted total precipitation. (i) December 

CPC observed precipitation. (j) December MM5 predicted total precipitation.......................94 

Figure 5-1. Project year 2004 emissions processing schedule.......................................................99 

Figure 5-2. Example of WRAP ocean-going shipping CO emissions in the 36-km 

modeling domain. ..................................................................................................................112 

Figure 5-3. RPO Unified Continental 36-km Modeling Grid domain.........................................122 

Figure 5-4. WRAP nested 12-km modeling domain. ..................................................................123 

Figure 5-5. Simulation Pre02c_36 timing statistics by source and SMOKE program for the 

programs that took the longest to run. (AR: stationary area, RD: road dust, NR: nonroad mobile, 
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 xvii 

PT: point, MB: WRAP on-road mobile, MB-VMT: non-WRAP on-road mobile, OS: offshore point, 

BG: biogenic, AGF: agricultural fires, RXF: prescribed fires, WF: wildfires) .........................................131 

Figure 5-6. Simulation Pre02c_36 disk usage by source and SMOKE program for the 

programs that required the greatest amount of disk space. (AR: stationary area, RD: road dust, 

NR: nonroad mobile, PT: point, MB: WRAP on-road mobile, MB-VMT: non-WRAP on-road mobile, 

OS: offshore point, BG: biogenic, AGF: agricultural fires, RXF: prescribed fires, WF: wildfires)...............132 

Figure 5-7. U.S. total emissions comparison of three annual Pre02 simulations. .......................138 

Figure 5-8. WRAP-region total emissions comparison of three annual Pre02 simulations. .......138 

Figure 5-9. Pre02b_36 total domain annual emissions summary................................................141 

Figure 5-10. Pre02c_36 total domain annual emissions summary. .............................................141 

Figure 5-11. Pre02d_36 total domain annual emissions summary..............................................142 

Figure 5-12. Pre02b total U.S. annual emissions pie charts. .......................................................143 

Figure 5-13. Pre02c total U.S. annual emissions pie charts.........................................................144 

Figure 5-14. Pre02d total U.S. annual emissions pie charts. .......................................................145 

Figure 5-15. Pre02c WRAP annual emissions pie charts. ...........................................................146 

Figure 5-16. Pre02d WRAP annual emissions pie charts............................................................147 

Figure 5-17. Pre02d annual CO source contributions by WRAP state........................................148 

Figure 5-18. Pre02d annual NOx source contributions by WRAP state. .....................................148 

Figure 5-19. Pre02d annual VOC source contributions by WRAP state.....................................149 

Figure 5-20. Pre02d annual NH3 source contributions by WRAP state. .....................................149 

Figure 5-21. Pre02d annual SO2 source contributions by WRAP state.......................................150 

Figure 5-22. Pre02d annual PM2.5 source contributions by WRAP state. ...................................150 

Figure 5-23. Pre02d annual PM10 source contributions by WRAP state.....................................151 

Figure 5-24. Pre02d annual PMc source contributions by WRAP state......................................151 

Figure 6-1a. CMAQ 2002 pre02d base case simulation SO4 model performance in the 

WRAP states for January 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks. (NADP measures 

wet SO4 deposition, whereas the other three networks measure ambient air SO4

concentrations.)......................................................................................................................171 

Figure 6-1b. CMAQ 2002 pre02d base case simulation SO4 model performance in the 

WRAP states for April 2002 using the IMPROVE (top left), CASTNet (top right), STN 

(bottom left), and NADP (bottom right) monitoring networks. (NADP measures wet 

SO4 deposition, whereas the other three networks measure ambient air SO4

concentrations.)......................................................................................................................172 

Figure 6-1c. CMAQ 2002 pre02d base case simulation SO4 model performance in the 

WRAP states for July 2002 using the IMPROVE (top left), CASTNet (top right), STN 

(bottom left), and NADP (bottom right) monitoring networks. (NADP measures wet 

SO4 deposition, whereas the other three networks measure ambient air SO4

concentrations.)......................................................................................................................173 
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Figure 6-1d. CMAQ 2002 pre02d base case simulation SO4 model performance in the 

WRAP states for October 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks. (NADP measures 

wet SO4 deposition, whereas the other three networks measure ambient air SO4

concentrations.)......................................................................................................................174 

Figure 6-2. Monthly fractional bias (top) and fractional gross error (bottom) for the CMAQ 

2002 pre02d 36-km and 12-km base case SO4 model performance across IMPROVE 

sites in the WRAP states........................................................................................................175 

Figure 6-3. Monthly fractional bias (top) and fractional gross error (bottom) for the CMAQ 

2002 pre02d base case SO4 model performance as a function of observed 

concentration..........................................................................................................................176 

Figure 6-4a. CMAQ 2002 pre02d base case simulation NO3 model performance in the 

WRAP states for January 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks.....................................178 

Figure 6-4b. CMAQ 2002 pre02d base case simulation NO3 model performance in the 

WRAP states for April 2002 using the IMPROVE (top left), CASTNet (top right), STN 

(bottom left), and NADP (bottom right) monitoring networks..............................................179 

Figure 6-4c. CMAQ 2002 pre02d base case simulation NO3 model performance in the 

WRAP states for July 2002 using the IMPROVE (top left), CASTNet (top right), STN 

(bottom left), and NADP (bottom right) monitoring networks..............................................180 

Figure 6-4d. CMAQ 2002 pre02d base case simulation NO3 model performance in the 

WRAP states for October 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks.....................................181 

Figure 6-5 Monthly fractional bias (top) and fractional gross error (bottom) for the CMAQ 

2002 pre02d 36-km and 12-km base case NO3 model performance across IMPROVE 

sites in the WRAP states........................................................................................................182 

Figure 6-6. Monthly fractional bias (top) and fractional gross error (bottom) for the CMAQ 

2002 pre02d 36-km and 12-km base case NO3 model performance as a function of 

observed concentration. .........................................................................................................183 

Figure 6-7. Monthly fractional bias (top) and fractional gross error (bottom) for the CMAQ 

2002 pre02d base case NH4 model performance as a function of observed 

concentration..........................................................................................................................185 

Figure 6-8a. CMAQ 2002 pre02d base case simulation OC model performance in the 

WRAP states for January (top) and April (bottom) using the IMPROVE (left) and STN 

(right) monitoring networks...................................................................................................187 

Figure 6-8b. CMAQ 2002 pre02d base case simulation OC model performance in the 

WRAP states for July (top) and October (bottom) using the IMPROVE (left) and STN 

(right) monitoring networks...................................................................................................188 

Figure 6-9. Monthly fractional bias (top) and fractional gross error (bottom) for the CMAQ 

2002 pre02d base case OC model performance across IMPROVE sites in the WRAP 

states.......................................................................................................................................189 
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Figure 6-10. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case OC model performance as a function of observed 

concentration..........................................................................................................................190 

Figure 6-11a. CMAQ 2002 pre02d base case simulation EC model performance in the 

WRAP states for January (top) and April (bottom) using the IMPROVE (left) and STN 

(right) monitoring networks...................................................................................................192 

Figure 6-11b. CMAQ 2002 pre02d base case simulation EC model performance in the 

WRAP states for July (top) and October (bottom) using the IMPROVE (left) and STN 

(right) monitoring networks...................................................................................................193 

Figure 6-12. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case EC model performance across IMPROVE sites in the 

WRAP states. .........................................................................................................................194 

Figure 6-13. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case EC model performance as a function of observed 

concentration..........................................................................................................................195 

Figure 6-14. CMAQ 2002 pre02d base case simulation “other fine PM” (soil) model 

performance in the WRAP states for January (top left), April (top right), July (bottom 

left), and October (bottom right) using the IMPROVE monitoring network. .......................197 

Figure 6-15. CMAQ 2002 pre02d base case simulation CM model performance in the 

WRAP states for January (top left), April (top right), July (bottom left), and October 

(bottom right) using the IMPROVE monitoring network......................................................198 

Figure 6-16. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case “other fine PM” (soil) model performance across the 

IMPROVE sites in the WRAP states.....................................................................................199 

Figure 6-17. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case CM model performance across the IMPROVE sites in 

the WRAP states. ...................................................................................................................200 

Figure 6-18a. Comparison of predicted and observed average extinction for the worst-20% 

visibility days in 2002 at IMPROVE monitors for SO4, NO3, OC, EC, CM, and soil. .........205 

Figure 6-18b. Comparison of predicted and observed average extinction for the best-20% 

visibility days in 2002 at IMPROVE monitors for SO4, NO3, OC, EC, CM, and soil. .........206 

Figure 6-19a. Comparison of predicted (right) and observed (left) average extinction for the 

worst-20% visibility days in 2002 at the Grand Canyon (top) and Chiricahua (bottom) 

Class I areas. ..........................................................................................................................207 

Figure 6-19b. Comparison of predicted (right) and observed (left) average extinction for 

the worst-20% visibility days in 2002 at the Bandelier (top) and Rocky Mountain 

National Park (bottom) Class I areas. ....................................................................................208 

Figure 6-19c. Comparison of predicted (right) and observed (left) average extinction for the 

worst-20% visibility days in 2002 at the Yellowstone (top) and Glacier (bottom) Class I 

areas. ......................................................................................................................................209 
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Figure 6-19d. Comparison of predicted (right) and observed (left) average extinction for 

the worst-20% visibility days in 2002 at the Mount Rainier (top) and Kalmiopsis 

(bottom) Class I areas. ...........................................................................................................210 

Figure 6-19e. Comparison of predicted (right) and observed (left) average extinction for the 

worst-20% visibility days in 2002 at the Point Reyes (top) and San Gorgonio (bottom) 

Class I areas. ..........................................................................................................................211 

Figure 6-20. Comparison of predicted (right) and observed (left) average extinction for the 

best-20% visibility days in 2002 at the Grand Canyon (top) and Yellowstone (bottom) 

Class I areas. ..........................................................................................................................212 

Figure 6-21. Comparison of CMAQ 2002 36-km pre02d and Base Case C wet SO4

deposition model performance in the WRAP states for May (top left), June (top right), 

July (bottom left) and September (bottom right). ..................................................................214 

Figure 7-1. Three-dimensional plot of aerosol nitrate attributed to California mobile-source 

emissions on January 14, 2002. .............................................................................................219 

Figure 7-2a. Source area mapping file as used in the CMAQ TSSA algorithm, with each 

source region distinguished by a unique numeric code. ........................................................221 

Figure 7 2b. Source area mapping file as used in CAMx PSAT showing the boundaries for 

each state/region.....................................................................................................................222 

Figure 7-3. Flowchart of the TSSA implementation in CMAQ’s CCTM. ..................................224 

Figure 7-4. Example bar plot produced by UCR postprocessing software showing the 

largest 20 contributors to aerosol nitrate at a receptor site in the Grand Canyon. The xaxis labels are explained in Table 7-1....................................................................................226 

Figure 7-5. CMAQ source attribution results at the Ft. Peck IMPROVE site on July 2, 

2002: (a) modeled and measured SO4 concentrations; (b) TSSA results using 

proportional mass renormalization; (c) TSSA results with lost mass represented as 

“other.”...................................................................................................................................229 

Figure 7-6. Source attribution results for SO4 at the Grand Canyon IMPROVE site on July 

2, 2002: (a) CMAQ TSSA results; (b) CAMx PSAT results.................................................230 

Figure 9-1a. Comparison of CMAQ (red) and CAMx (blue) SO4 model performance at 

sites across the U.S. for July 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and SEARCH (bottom right) monitoring networks. ...............................242 

Figure 9-1b. Comparison of CMAQ (red) and CAMx (blue) SO4 model performance at 

sites across the U.S. for February 2002 using the IMPROVE (top left), CASTNet (top 

right), STN (bottom left), and SEARCH (bottom right) monitoring networks. ....................243 

Figure 9-2a. Comparison of CMAQ (red) and CAMx (blue) NO3 model performance at 

sites across the U.S. for July 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and SEARCH (bottom right) monitoring networks. ...............................244 

Figure 9-2b. Comparison of CMAQ (red) and CAMx (blue) NO3 model performance at 

sites across the U.S. for February 2002 using the IMPROVE (top left), CASTNet (top 

right), STN (bottom left), and SEARCH (bottom right) monitoring networks. ....................245 
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Figure 9-3. Comparison of CMAQ (red) and CAMx (blue) OC (left) and EC (right) model 

performance at sites across the U.S. for July 2002 (top) and February 2002 (bottom) 

using the IMPROVE monitoring network. ............................................................................247 

Figure 9-4. Comparison of CMAQ (red) and CAMx (blue) soil (left) and CM (right) model 

performance at sites across the U.S. for July 2002 (top) and February 2002 (bottom) 

using the IMPROVE monitoring network. ............................................................................248 

Figure 9-5. Geographic source regions used in the PSAT and TSSA PM source attribution 

modeling. ...............................................................................................................................250 

Figure 9-6. Locations of IMPROVE monitoring sites in the western U.S. where the TSSA 

and PSAT PM source attribution approaches were compared...............................................252 

Figure 9-7. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Grand Canyon National Park on July 1, 2002 (day 182).......................................................255 

Figure 9-8. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Grand Canyon National Park on July 7, 2002 (day 188).......................................................256 

Figure 9-9. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Grand Canyon National Park on February 1, 2002 (day 32). ................................................257 

Figure 9-10. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Fort Peck, Montana on July 4, 2002 (day 185)......................................................................258 

Figure 9-11. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Rocky Mountain National Park, CO, on July 1, 2002 (day 182)...........................................259 

Figure 9-12. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Salmon, ID, on July 1, 2002 (day 182)..................................................................................260 

Figure 10-1. Comparison between (1) the Marticorena et al. (1997) modeled relationship of 

threshold friction velocity and aerodynamic roughness length and (2) wind tunnel data 

from Gillette et al. (1980, 1982), Gillette (1988), and Nickling and Gillies (1989).] ...........266 

Figure 10-2. The emission flux as a function of friction velocity predicted by the Alfaro 

and Gomes (2001) model constrained by the four geometric-mean-diameter soil classes 

of Alfaro et al. (2003). ...........................................................................................................267 

Figure 10-3. Spatial distribution of total 2002 annual PMc dust emissions for Scenario a.........275 

Figure 10-4. Distribution of total annual 2002 PM10 dust emissions by land use type and 

state for Scenario a.................................................................................................................276 

Figure 10-5. Spatial distribution of total 2002 annual PMc dust emissions for Scenario b. .......277 

Figure 10-6. Distribution of total annual 2002 PM10 dust emissions by land use type and 

state for Scenario b.................................................................................................................278 

Figure 10-7. Spatial distribution of total 2002 annual PMc dust emissions for Scenario c.........279 

Figure 10-8. Distribution of total annual 2002 PM10 dust emissions by land use type and 

state for Scenario c.................................................................................................................280 

Figure 10-9. Spatial distribution of total 2002 annual PMc dust emissions for Scenario d. .......281 

Figure 10-10. Distribution of total annual 2002 PM10 dust emissions by land use type and 

state for Scenario d.................................................................................................................282 

Figure 10-11. Distribution of total annual 2002 PM10 dust emissions by scenario and state......283 
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Figure 10-12. Monthly distribution of total 2002 PM10 dust emissions for each scenario 

across the entire WRAP domain............................................................................................284 

Figure 10-13. Distribution of total 2002 PM10 dust emissions by land use type for each 

scenario for all WRAP states combined. ...............................................................................285 

Figure 10-14. Spatial distribution of annual 2002 PMc dust emissions for Scenario b...............286 

Figure 10-15. Spatial distribution of 2002 PMc dust emissions by season for Scenario b..........287 

Figure 12-1. (a) The July total fire emissions of carbon monoxide, used as an indicator of 

fire size and location; (b) the effect of combined wild fire, agricultural burning, and 

prescribed burning fire emissions on visibility, calculated as the July average. ...................301 

Figure 12-2. Two example plots of results showing effects of fire emissions on extinction 

coefficient at the Grand Canyon IMPROVE site. Top panel is for anthropogenic 

emissions; bottom panel is for natural emissions. .................................................................302 

Figure 12-3. Monthly average for January through June (as labeled in each plot) showing 

effect of total fire emissions calculated as the difference of the Pre02f case with both 

natural and anthropogenic fire emissions minus the Pre02b case with no fire emissions. ....303 

Figure 12-4. Monthly averages for July through December (as labeled in each plot) 

showing effect of total fire emissions calculated as the difference of the Pre02f case 

with both natural and anthropogenic fire emissions minus the Pre02 case with no fire 

emissions................................................................................................................................304 

Figure 12-5. Left panels show change in carbon monoxide emissions. Right panels shows 

the change in visibility from all aerosol species, as monthly average deciviews, for each 

fire sensitivity case: (b) July wild fires; (d); November prescribed burning; (f) 

November agricultural burning..............................................................................................305 

Figure 12-6. Seasonal total change in carbon monoxide emissions for the Optimal Smoke 

Management emissions compared to the Base Smoke Management emissions....................306 

Figure 12-7. Seasonal average change in deciviews showing effect of Optimal Smoke 

Management emissions compared to Base Smoke Management emissions. ........................307 

Figure 12-8. Monthly average for January through June (as labeled in each plot) showing 

effect of Optimal Smoke Management compared to Base Smoke Management 

emissions................................................................................................................................308 

Figure 12-9. Monthly average for July through December (as labeled in each plot) showing 

effect of Optimal Smoke Management compared to Base Smoke Management 

emissions................................................................................................................................309 

Figure 12-10. (a) The July total natural fire emissions of carbon monoxide. Panels (b) 

through (d) show the effect of fire sensitivity simulations on visibility, calculated as 

deciviews and averaged for the month of July, as the difference of each fire sensitivity 

case minus base case (i.e., Pre02b) for (b) the natural fire sensitivity case; (c) the 

anthropogenic fire emissions, and (d) the combined natural and anthropogenic fire 

emissions................................................................................................................................310 

Figure 13-1. Locations of the four Class I areas in Alaska and of the two biggest Alaskan 

cities, Anchorage and Fairbanks............................................................................................312 
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 xxiii 

Figure 13-2. Spatial coverage of the Alaska Grid with 45-km grid point spacing (D01) and 

the nested 15-km grid (D02)..................................................................................................316 

Figure 13-3. Wind soccer plot for winter and summer Alaska sensitivity tests. .........................319 

Figure 13-4. Temperature soccer plot for winter and summer Alaska sensitivity tests...............320 

Figure 13-5. Humidity soccer plot for winter and summer Alaska sensitivity tests....................321 

Figure 13-6. Stations contributing data used for observational nudging.....................................324 

Figure 13-7. Observed (black) and modeled (red) temperature and dew point soundings for 

a sample weather station (BRW) in the Alaska domain for July 3, 2002..............................326 

Figure 13-8. NCAR ds472 surface observing network stations in the 45-km Alaska domain....327 

Figure 13-9. Example of METSTAT graphics from a WRAP Alaska January 2002 

sensitivity test.........................................................................................................................328 
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 xxiv 

Abbreviations 

ACM ....................Asymmetric Convective Model 

ADEC...................Alaska Department of Environmental Conservation 

AIM......................Aerosol Inorganic Module 

AML.....................ARC Macro Language 

AoH......................Attribution of Haze 

AQS......................Air Quality System 

ARDB ..................Acid Rain Database 

BART...................Best Available Retrofit Technology 

BC ........................boundary condition 

BEIS.....................Biogenic Emissions Inventory System 

BELD ...................Biogenic Emissions Landcover Database 

BM .......................Betts-Miller 

BRAVO................Big Bend Regional Aerosol and Visibility Observations Study 

CAA .....................Clean Air Act 

CAFO...................confined animal feed operations 

CALMET .............California Meteorological Model 

CALPUFF............California Puff Model 

CAMx ..................Comprehensive Air quality Model with extensions 

CAPE ...................convective available potential energy 

CARB...................California Air Resources Board 

CASTNet..............Clean Air Status and Trends Network 

CB-IV...................Carbon Bond IV (also referred to in the modeling community as CBIV, CB4) 

CCM2...................Community Climate Model 2 

CCTM ..................CMAQ Chemical Transport Model 

CEM ....................continuous emissions monitoring 

CENRAP..............Central Regional Air Planning Association 

CEP ......................Carolina Environmental Program 

CM .......................coarse mass (a term used in the context of the IMPROVE network; see also 

PMc) 

CMAQ..................Community Multiscale Air Quality 

CMAS ..................Community Modeling and Analysis System 

CMC.....................Chemical Mechanism Compiler 

CMU ....................Carnegie Mellon University 

CNG .....................compressed natural gas

CPC......................Climate Prediction Center 

CVS......................Concurrent Versions System

CY02....................calendar year 2002 

DDM ....................decoupled direct method 

DEAD ..................Dust Entrainment and Deposition 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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dV.........................deciview 

EC ........................elemental carbon 

EDAS ...................Eta Data Assimilation System 

EEA......................European Environment Agency 

EGAS ...................Economic Growth Analysis System 

EGU .....................electric generating utility 

EPA......................U.S. Environmental Protection Agency 

ESSC....................Earth System Science Center 

FAQ......................frequently asked question 

FDDA...................four-dimensional data assimilation 

FEJF .....................Fire Emissions Joint Forum 

FIPS......................Federal Implementation Standards 

GCVTC................Grand Canyon Visibility Transport Commission 

GIS .......................geographic information system 

GUI ......................graphical user interface 

IC..........................initial condition 

IDNR....................Iowa Department of Natural Resources 

IEH.......................Implicit-Explicit Hybrid 

IMPROVE............Interagency Monitoring of Protected Visual Environments 

IOA ......................index of agreement 

IPR .......................Integrated Process Rates 

IRR.......................Integrated Reaction Rates 

KF ........................Kain-Fritsch 

LADCO................Lake Michigan Air Directors Consortium 

LAC......................light-absorbing carbon 

lat-lon ...................latitude-longitude 

LPG......................liquefied petroleum gas 

LSM .....................land-surface model 

LULC ...................land use/land cover 

M4........................Mechanism 4 

MADM.................Multicomponent Aerosol Dynamics Model 

MADRID .............Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution 

MANE-VU...........Mid-Atlantic/Northeast Visibility Union 

MM5 ....................Fifth-Generation Penn State/NCAR Mesoscale Model 

M-MRF ................modified Medium-Range Forecast 

MPE .....................model performance evaluation 

MRF .....................Medium-Range Forecast 

MRLC ..................Multi-Resolution Land Characterization 

MRPO ..................Midwest Regional Planning Organization 

NADP...................National Atmospheric Deposition Program 

NASS ...................National Agricultural Statistics Service 

NCAR ..................National Center for Atmospheric Research 

NCEP ...................National Centers for Environmental Prediction 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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NEI.......................National Emissions Inventory (NEI96 = 1996 version; NEI99 = 1999 version) 

NLCD...................National Land Cover Database 

NNRP...................NCAR/NCEP Reanalysis Project 

NWS.....................National Weather Service 

OC........................organic carbon 

ORD .....................Office of Research and Development 

OSAT ...................Ozone Source Apportionment Technology 

PAMS...................Photochemical Assessment Monitoring Station 

PAVE ...................Program for the Analysis and Visualization of Environmental data 

PBL ......................planetary boundary layer 

PDM.....................Plume Dynamics Model 

PinG .....................plume-in-grid 

PM........................particulate matter 

PM10 .....................particulate matter ≤10 μm in diameter

PM2.5 ....................particulate matter ≤2.5 μm in diameter (referred to in this report as “fine 

particulate matter”)

PMc......................coarse particulate matter: PM that ranges from 2.5 to10 μm in diameter (note 

that PMc is also referred to as CM in the context of the IMPROVE network)

PPM......................Piecewise Parabolic Method

PSAT....................PM Source Apportionment Technology

PX ........................Pleim-Xiu 

QA........................quality assurance 

QAPP ...................quality assurance project plan 

QC........................quality control 

QSSA ...................Quasi Steady State Approximation 

RADM..................Regional Acid Deposition Model 

RAMS ..................Regional Atmospheric Modeling System 

REMSAD.............Regional Modeling System for Aerosols and Deposition 

RHR .....................Regional Haze Rule 

RMC.....................Regional Modeling Center 

RMSE...................root mean squared error 

RPO......................Regional Planning Organization 

RRF......................relative reduction factor 

RRTM ..................Rapid Radiative Transfer Model 

Rx.........................prescribed (used with “wildfires”) 

SAPRC.................Statewide Air Pollution Research Center 

SCC......................source classification code 

SEARCH..............Southeastern Aerosol Research and Characterization (monitoring network) 

SEARCH_H.........SEARCH monitoring network collecting hourly data (vs. daily [24-hour] data) 

SIP........................State Implementation Plans 

SMOKE................Sparse Matrix Operator Kernel Emissions 

SMVGEAR..........Sparse-Matrix Vectorized Gear 

SOA......................secondary organic aerosols 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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SST.......................sea-surface temperature 

STATSGO............State Soil Geographic 

STN......................Speciated Trends Network 

TIP........................Tribal Implementation Plan 

TM........................thematic mapper 

TSD......................Technical Support Document 

TSSA....................Tagged Species Source Apportionment 

UAF......................University of Alaska Fairbanks 

UCR .....................University of California, Riverside 

UNC .....................University of North Carolina 

USDA...................U.S. Department of Agriculture 

USGS ...................U.S. Geological Survey 

VISTAS................Visibility Improvement – State and Tribal Associations of the Southeast 

VMT.....................vehicle miles traveled 

VOC .....................volatile organic compound(s) 

VRSM ..................Variable Size-Resolution Model 

WEQ ....................Wind Erosion Equation 

WRAP..................Western Regional Air Partnership 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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1. Introduction 

The Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) is responsible 

for performing air quality modeling simulations for the WRAP region’s states and tribes in order 

to provide analytical results used in developing implementation plans under the U.S. Environmental Protection Agency (EPA) Regional Haze Rule. Responsibilities of the RMC include 

• meteorological modeling; 

• emissions processing and modeling; 

• air quality and visibility modeling simulations; 

• analysis, display, and reporting of modeling results; and 

• storage and quality assurance of the modeling input and output files. 

This document is a final report that discusses the activities the WRAP RMC performed from 

March 1, 2004, through February 28, 2005. 

1.1 Background 

1.1.1 Need for regional haze modeling 

The Clean Air Act (CAA) establishes special goals for visibility in many national parks, 

wilderness areas, and international parks. Through the 1977 amendments to the Clean Air Act, 

Congress set a national goal for visibility as “the prevention of any future, and the remedying of 

any existing, impairment of visibility in mandatory Class I Federal areas which impairment 

results from manmade air pollution” (40 CFR 51.300). States are required to develop State 

Implementation Plans (SIPs) to attain visibility standards; also, tribes may opt to assume responsibility for visibility programs under 40 CFR Part 49 by developing Tribal Implementation Plans 

(TIPs). The goal of the Regional Haze Rule (RHR) is to achieve natural visibility conditions at 

156 Federally mandated Class I areas by 2064. To achieve this goal, the RHR has set up milestone years of 2018, 2028, 2038, 2048, 2058, and 2064 to monitor progress toward natural visibility conditions. CAA Section 308 (§308) requires that the first visibility SIP/TIP be submitted 

to EPA by 2007-2008 to demonstrate progress toward natural visibility conditions in 2018 using 

the 2000-2004 five-year baseline. 

Regional haze is linked to fine particulate matter (PM2.5, which is particulate matter ≤2.5 μm in 

diameter), for which EPA has a new standard. PM2.5 SIPs/TIPs are to be submitted three years 

after designation of PM2.5 nonattainment areas. EPA performed the final designations of the 

PM2.5 nonattainment areas in December 2004, so the PM2.5 SIPs/TIPs are due by December 

2007. As regional haze is intricately linked to PM2.5, the PM2.5 and regional haze SIPs/TIPs are in 

the process of being aligned so that they would both be due by approximately December 2007 

(January 2008). EPA designated 8-h ozone nonattainment areas in April 2004, which makes 8-h 

ozone SIPs/TIPs also due in 2007. States and tribes may use the integrated one-atmosphere 
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WRAP RMC base-year modeling as the regional component of the 8-h ozone attainment 

activities as well as for PM2.5 and regional haze. Those decisions will be based on schedule 

constraints, model performance, appropriateness of the episodes, feasibility, and need. 

1.1.2 Role of the Western Regional Air Partnership 

WRAP is one of five Regional Planning Organizations (RPOs) (Figure 1-1) that are responsible 

for coordinating development of SIPs and TIPs in selected areas of the Unites States to address 

the requirements of the RHR. The WRAP region is composed of states and tribal lands located 

within the boundaries of Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, New 

Mexico, North Dakota, Oregon, South Dakota, Utah, Washington and Wyoming; it includes the 

Class I areas indicated in Figure 1-2. WRAP is a regional partnership of states, tribes, Federal 

agencies, stakeholders, and citizen groups that was established to initiate and coordinate activities associated with the management of regional haze and other air quality issues within the 

WRAP states; their role includes providing technical and policy tools to help states and tribes. 

The members of WRAP committees and forums (http://wrapair.org/about/orgchart.htm) consist 

of representatives from the affected stakeholder groups identified above. Committee and forum 

members are urged to communicate freely with their peers and professional associations so that 

they can adequately represent the views of that segment of the public. Each committee and forum 

is expected to translate technical materials into a form accessible to the public. More information 

about the WRAP organizational structure and activities is available at 

http://wrapair.org/facts/forumdescribe.html.

Figure 1-1. Regional Planning Organizations engaged in regional haze modeling. 
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Figure 1-2. Locations of Class I areas in the WRAP states. 

1.1.3 WRAP strategic plan 

On September 29, 2003, WRAP released their strategic plan for the years 2003-2008 (WGA, 

2003): http://wrapair.org/WRAP/meetings/031014board/Tab_4_Strategic_Plan_Final.pdf. Prior 

to 2003, WRAP’s activities focused on developing the Section 309 (§309) SIPs/TIPs that nine 

WRAP states may elect to opt into and thereby adopt the recommendations of the Grand Canyon 

Visibility Transport Commission (GCVTC). These §309 SIPs/TIPs address visibility at the 16 

Class I areas on the Colorado Plateau (see Figure 1-2). In 2003, WRAP’s efforts turned to performing the technical analyses needed to develop the §308 SIPs/TIPs that address all 116 Class I 

areas in the WRAP region. The WRAP 2003-2008 strategic plan is broken down into two phases 

(Table 1-1). It will be reviewed and revised in 2005. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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Table 1-1. WRAP 2003 Strategic Plan (WGA, 2003). 

Phase I 2003-2005 Phase II 2005-2007 

Purpose: Dry run for Phase II. Hedge against 

earlier due dates. 

Refine and apply Phase I approaches 

for SIP/TIP purposes. 

Scale: Regional. Regional and subregional. 

Apportionment: 1996 & 2002 source contributions. 

Areas each plan to address.

2002 source contributions. Reduction 

obligations. 

Strategies: Identify options, screen. Perform cost/benefit, select, design. 

Communications: Public education. Public acceptance. 

Major state/tribe 

submittals: 

2002 emissions inventories. Modeling run specifications. 

1.1.4 Organization of the Regional Modeling Center 

The WRAP RMC is composed of researchers from the University of California, Riverside 

(UCR), ENVIRON International Corporation, and the University of North Carolina’s Carolina 

Environmental Program (UNC-CEP). Dr. Gail Tonnesen of UCR is the Project Manager and 

Principal Investigator for the RMC. Mr. Ralph Morris and Mr. Zac Adelman lead the RMC 

efforts at ENVIRON and UNC-CEP, respectively. We are performing meteorological, emissions, 

and air quality modeling to support WRAP’s efforts to comply with the requirements of the 

RHR. UCR hosts the RMC’s computer center, which includes numerous Linux computer platforms and RAID disk storage systems. In the past, training of states, tribes, and others has been 

performed at UCR’s facilities. 

The primary modeling tools used by the RMC include the Fifth-Generation Pennsylvania State 

University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) 

meteorological modeling system, the Sparse Matrix Operator Kernel Emissions (SMOKE) 

emissions modeling system, and the Models-3 Community Multiscale Air Quality (CMAQ) air 

quality modeling system. We have also performed some sensitivity tests using the Comprehensive Air Quality Model with extensions (CAMx) and Regional Modeling System for Aerosols 

and Deposition (REMSAD) air quality models. In addition, there are plans to apply MM5 and 

the California Meteorological Model/California Puff Model (CALMET/CALPUFF) modeling 

system to Alaska. 

During 2002 and 2003, the RMC focused on performing the technical analysis needed to develop 

the §309 SIPs/TIPs for those states and tribes that elect to pursue the §309 process. We set up the 

SMOKE emissions and Models-3/CMAQ modeling system for 1996 for the western states using 

36-km-resolution MM5 data, and performed the technical analysis needed for the §309 SIPs and 

TIPs. More details can be found in the Section 309 Technical Support Document (TSD) (WRAP, 

2003) located at http://wrapair.org/309/031215Final309TSD.pdf. 

In 2003, the RMC then turned its efforts to setting up the modeling tools needed to address the 

technical requirements of the §308 SIPs/TIPs. This included setting up the MM5, SMOKE, and 
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CMAQ modeling systems for calendar year 2002 (CY02) using a 36-km-resolution grid for the 

continental United States and a 12-km-resolution grid for the WRAP states. During 2004 and 

early 2005, we continued setting up the modeling infrastructure for CY02 to address the 

requirements of the WRAP strategic plan. 

The RMC notes and understands the importance of the guidance contained in the WRAP Tribal 

Template (http://wrapair.org/WRAP/reports/Gen-Tribe-Temp.pdf). Work products from the 

RMC are reviewed by the WRAP Modeling Forum and any interested stakeholders. 

1.2 Overview of RMC 2004 Activities 

From March 2004 through February 2005, the RMC activities continued to focus on developing 

the modeling databases needed to address the technical requirements of the §308 SIPs/TIPs. This 

included not only setting up the emissions, meteorological, and air quality modeling for CY02, 

but also implementing a particulate matter (PM) source apportionment capability to obtain a 

preliminary assessment of source culpability to regional haze at Class I areas. Our work efforts 

also included separate meteorological and dispersion modeling for Alaska, a WRAP state with 

four Class I areas whose size and remoteness from the other states make it inefficient to include 

with the other states’ modeling domain. 

At the end of 2003, all 2003 RMC work was completed except for developing the new WRAP 

ammonia emissions model, for which the data collection efforts were not completed. This 

remaining work was rolled into the WRAP 2004 RMC work effort. The 2004 work plan 

(http://www.cert.ucr.edu/aqm/308/reports/RMC_2004_Workplan_Final_

Version_03_01_04.pdf) was divided into 13 new tasks plus a task for the ammonia emissions 

work (Task 0.5). Note that Task 8 is not covered in this report because it did not receive funding 

for 2004. 

• Task 0.5—2002 Ammonia Emissions Inventory for WRAP Region: This task was initiated 

in 2003 to develop a new GIS-based ammonia emissions model that uses high-resolution 

land cover data, new ammonia emission factors, and MM5 meteorological data to 

generate gridded, day-specific ammonia emissions. 

• Task 1—Project Administration: Project management activities, including progress 

reports, interim and final reports, conference calls, and day-to-day management, were 

performed under this task. It also covered the purchase of computing hardware. 

• Task 2—Test, Improve, Quality Control, Obtain External Peer Review, and Finalize 36-

km and 12-km MM5 Simulations for Eventual Use in CMAQ: The MM5 meteorological 

modeling for CY02 for the continental U.S. at 36 km and the western U.S. at 12 km was 

performed under this task. This included sensitivity modeling to identify the optimal 

MM5 configuration, development of an MM5 modeling protocol, peer review of the 

MM5 approach, and the 2002 annual MM5 modeling. 

• Task 3—2002 Base Year Emissions Modeling, Processing, and Analysis: This task 

covered setting up and performing the 2002 SMOKE emissions modeling at 36- and 12-

km resolution. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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• Task 4—Air Quality Model Evaluation for 2002 Annual Simulation: Under this task the 

RMC performed the CMAQ 2002 base case simulation and model performance 

evaluation. 

• Task 5—Preparation and Reporting of Geographic Source Apportionment Results:

Implementation of the Tagged Species Source Apportionment (TSSA) algorithms in 

CMAQ was performed under this task, along with the evaluation and application of the 

TSSA as part of the Attribution of Haze (AoH) study. 

• Task 6—Further Analysis of Model Performance in Regard to the Contribution of Natural Emissions to Visibility Impairment: The use of models to aid in the definition of 

natural conditions was investigated under this task. 

• Task 7—Evaluation and Comparison of Alternative Models: This task investigated and 

evaluated the benefits of applying alternative models to CMAQ. 

• Task 8—Improvement of WRAP Spatial, Chemical Speciation, and Temporal Allocation 

Profiles: The purpose of this task was to improve several components of the emissions 

inventory. However, it was not funded during 2004. 

• Task 9—Testing and Further Improvements to the Windblown Dust Emissions Modeling 

Methodology: This task refined the WRAP windblown dust model and applied it to 2002, 

and included a model performance evaluation. 

• Task 10—Continued Improvement to Model Evaluation Software: Updates and enhancements to the UCR model evaluation software were performed under this task. 

• Task 11—Sensitivity Studies Designed to Evaluate Uncertainties in Fire Emissions: The 

analysis of the sensitivity of PM and visibility to modeling assumptions made for wildfires, prescribed burns, and agricultural burning was accomplished under this task. 

• Task 12—Preliminary Meteorological, Emissions, and Air Quality Modeling Activities 

for Alaska: Meteorological and dispersion modeling for Alaska were evaluated under this 

task. 

• Task 13—Training Courses for the WRAP States and Tribes: Any training requested was 

to be performed under this task. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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2. Task 0.5: 2002 Ammonia Emissions Inventory for WRAP 

Region 

2.1 Introduction 

Ammonia (as ammonium nitrate and ammonium sulfate) plays an important role in the formation 

of particulate matter. Recent advances in understanding the health impacts of particulate pollution and the important role ammonia (NH3) emissions play in the formation of secondary PM has 

spawned a great deal of new research into these emissions. Major sources of NH3 emissions 

include livestock operations, fertilizer use, waste management, mobile sources, industrial point 

sources, and various biological sources such as human respiration, wild animals, and soil microbial processes. For each of these source categories there remain large uncertainties in the magnitude of emissions, the diurnal and seasonal variations, and the spatial distribution. Uncertainty in 

NH3 emissions is a key source of uncertainty in modeling the formation of sulfate and nitrate 

aerosols. Thus, development of improved NH3 emissions inventories is essential for modeling 

the formation of fine PM and regional haze, and for developing effective plans to mitigate 

visibility impairment at national parks, forests, and wilderness areas. 

The understanding of ammonia emissions has significantly improved since the development of 

the 1996 National Emissions Inventory (NEI) that was used in the WRAP visibility modeling to 

meet CAA §309 requirements. WRAP funded the RMC to develop an improved NH3 emissions 

inventory for the WRAP states and tribes to use in CAA §308 modeling. The development of 

this improved inventory involved a literature review of recent research in NH3 emissions, the 

incorporation of environmental factors that influence the magnitude and temporal variation of 

ammonia emission, and the development of a geographic information system (GIS)-based 

modeling system for efficient generation of a gridded modeling inventory of ammonia emissions 

for CY02. We developed the inventory at a spatial resolution of 36 km on the RPO Unified Continental 36-km Modeling Grid domain (abbreviated in this report as “RPO Unified domain”), 

which is shown in Figure 2-1. This modeling grid is common to all five RPOs, with all emissions 

gridded to 36-km2

 cells for subsequent air quality modeling simulations. The source categories 

considered in the emissions inventory include the four major contributors of ammonia on a 

regional scale: livestock operations, fertilizer usage, domestic sources, and native soils. At the 

request of the WRAP Emissions Forum, we also included ammonia emissions from wild animals 

in the inventory. 

As part of developing the improved inventory, we reviewed recent literature concerning ammonia emission factors for various source categories. Particular attention was given to identifying 

current research focusing on the temporal variation of ammonia emissions and the effects of 

environmental factors. The results of this literature review are documented in Chitjian and 

Mansell (2003a). The review identified a number of environmental factors affecting ammonia 

emission factors and temporal allocation of annual emission estimates; these factors include soil 

pH and meteorological data. Data on the effects were obtained from various sources, and the 

effects were incorporated into the modeling system used in creating the improved ammonia 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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emissions inventory. Chitjian and Mansell (2003b) present a detailed discussion of the environmental factors incorporated into the ammonia emissions modeling system, and their impacts on 

the resulting ammonia emissions are discussed in Mansell (2004a). 

Figure 2-1. RPO Unified Continental 36-km Modeling Grid domain. 

We developed a GIS-based modeling system using the latest version of ESRI’s GIS, ArcGIS, 

and the ARC Macro Language (AML). The model incorporates the improved estimation algorithms just discussed, and includes a user-friendly graphical user interface (GUI) to facilitate 

implementation. It also allows easy modification of activity data, emission factors, and associations between the ammonia emission source categories’ land use/land cover (LULC) characteristics. We populated the modeling system with all the data needed to produce an ammonia inventory for the U.S. portion of the RPO Unified domain for 2002 at a 36-km spatial resolution. The 

development of the GIS-based model is documented in Chitjian and Mansell (2003b). 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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2.2 Emissions Inventory Development 

The development pathway of the 2002 ammonia emissions inventory is summarized below. 

(Chitjian and Mansell [2003a,b] and Mansell [2004a] provide greater detail.) Since WRAP 

forums other than the Air Quality Modeling Forum will be developing ammonia emissions 

inventories for mobile sources, industrial point sources, and fire sources, we considered only 

selected major and minor sources of ammonia emissions for our inventory: livestock operations, 

fertilizer usage, domestic sources, native soils, and wild animals. Table 2-1 shows the source 

categories and subcategories included in the inventory. 

Table 2-1. Ammonia emission source categories included in the WRAP 

NH3 inventory. 

Major Source 

Category Subcategories Major Source Category Subcategories 

Beef cattle Human respiration 

Dairy cattle Human perspiration 

Swine Infant cloth diapers 

Poultry Infant disposable diapers 

Horses Cats 

Livestock 

operations 

Sheep 

Domestic 

sources 

Dogs 

Anhydrous ammonia Urban 

Aqueous ammonia Barren/desert 

Nitrogen solutions Deciduous forest 

Urea Evergreen forest 

Ammonium nitrate Mixed forest 

Ammonium sulfate Shrubland 

Ammonium thiosulfate Grassland 

Other straight nitrogen Fallow 

Ammonium phosphates Urban/recreational grasses 

N-P-K 

Native soils 

Wetlands 

Calcium ammonium nitrate Black bears 

Fertilizer 

usage 

Potassium nitrate Grizzly bears 

Elk 

Wild animals 

Deer 
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2.2.1 Emission factors 

2.2.1.1 Livestock operations 

Ammonia emissions from livestock were developed using county-level head counts and emission 

factors based on a literature review performed by Chinkin et al. (2003). Our inventory includes 

emissions for beef and dairy cattle, poultry, swine, sheep, and horses. The approach we used 

does not treat the individual processes leading to ammonia emissions from various management 

practices, which has been the subject of recent research in the emissions inventory development 

community. Rather, we used emission factors based on a “whole animal” approach. The factors 

used in developing the WRAP inventory are presented in Table 2-2. 

Table 2-2. Livestock emission factors (kg/animal-yr). 

Source 

Category 

Emission 

Factor 

Source 

Category 

Emission 

Factor 

Beef cattle 25.0 Swine 7.0 

Dairy cattle 9.0 Horses 12.2 

Poultry 0.1 Sheep 3.4 

2.2.1.2 Fertilizer application 

Ammonia emission estimates from fertilizer application were developed using emission factors 

from the European Environment Agency (EEA, 2002) as recommended in Chitjian and Mansell 

(2003b). Emission factors for fertilizer application are presented in Table 2-3. Fertilizer emission 

factors are adjusted as a function of soil pH. Based on research conducted by Potter et al. (2001), 

the emission factors are scaled by applying a scalar, a, to each factor, where a is calculated using 

the following relationship: 

a = (0.3125 · pH) - 1.01 

Note that soil pH scalars were not applied to urea emission factors, as research has indicated that 

urea emissions are not affected by initial soil pH. Data for soil pH were derived from the State 

Soil Geographic (STATSGO) database from the U.S. Department of Agriculture (USDA, 1994). 

The soil pH data source and processing are discussed in more detail in Mansell (2004a). 

Table 2-3. Fertilizer emission factors. 

Fertilizer Type %N Fertilizer Type %N 

Anhydrous ammonia 4.0 Calcium ammonium nitrate 2.0 

Aqueous ammonia 2.4 Ammonium thiosulfate 2.4 

Nitrogen solutions 8.0 Other straight nitrogen 2.4 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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Fertilizer Type %N Fertilizer Type %N 

Urea 15.0 Ammonium phosphates 5.0 

Ammonium nitrate 2.0 N-P-K 2.0 

Ammonium sulfate 10.0 Potassium nitrate 2.4 

2.2.1.3 Native soils 

Natural soils can be both a source and a sink of ammonia emissions, depending on the ambient 

NH3 concentrations, climatic conditions, and the conditions of the soils. While there are a number of researchers considering this issue, ammonia emissions from natural soils remain highly 

uncertain. For our inventory, ammonia emissions from natural soils were estimated based on 

emission factors developed or recommended by Battye et al. (2003) and Chinkin et al. (2003) 

(Table 2-4). Land use data used for the inventory were developed from the National Land Cover 

Database (http://edcwww.cr.usgs.gov/pub/edcuser/vogel/states/). A more complete description of 

the LULC data we used can be found in Section 2.2.5.1 and in Mansell (2004a). Emissions from 

agricultural lands are assumed to be included in the fertilizer application estimates. 

Table 2-4. Emission factors for native soils. 

Land Use Type Emission Factor 

(kg/km2-yr) Land Use Type Emission Factor (kg/km2-yr) 

Urban 10 Shrubland 400 

Barren/desert land 10 Grassland 400 

Deciduous forest 174 Fallow 205 

Evergreen forest 54 Urban/recreational grasses 400 

Mixed forest 114 Wetlands 400 

Potter et al. (2001) estimated ammonia emissions from native soils based on several environmental variables, including monthly rainfall, surface air temperature, solar radiation, soil texture, 

land cover type, and vegetative type. The model first calculates the available mineral nitrogen 

substrate for ammonia emissions and then modifies this value by applying scalars for soil surface 

temperature (T, in K), pH, and soil moisture content (M, in g/g). The scalars are of the form 

 (1/{1 + 10[0.09018 + 2729.92/(273.16+T) – (c · pH)]}) · (1-M), 

where c is a constant that determines the sensitivity to pH. The authors used c=1.3 (consistent 

with measurements they had made), and c=10 to produce results with minimal pH effects. 

Ammonia emissions were calculated for seven nonagricultural soil types. Emission factors 

derived from the model range from 6.5 kg/km2

-yr for evergreen needle leaf forests, using a 

moderate pH effect, to 206 kg/km2

-yr for mixed forests, using a minimal pH effect. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 12 

The emission factors presented in Table 2-4 have been modified for temperature and pH effects 

using the scalars described above. We used a value of c=1.3. Soil pH data were derived from the 

STATSGO database (USDA, 1994). Soil temperature and soil moisture content were taken from 

the meteorological data used in the development of the ammonia emissions inventory, as 

discussed in Section 2.2.5.3. 

2.2.1.4 Domestic sources 

Ammonia emissions from domestic source considered in the current inventory include human 

respiration and perspiration, disposable and cloth diapers, and domestic pets (cats and dogs). 

Table 2-5 presents the emission factors recommended by Chitjian and Mansell (2003b) and 

Chitjian et al. (2000). 

Table 2-5. Emission factors for domestic ammonia sources. 

Source Emission Factor Unit 

Cats 0.348 lb N/cat/yr 

Dogs 2.17 lb N/dog/yr 

Human perspiration 0.55 lb NH3/person/yr 

Human respiration 0.0035 lb NH3/person/yr 

Cloth diapers 6.9 lb NH3/infant/yr 

Disposable diapers 0.36 lb NH3/infant/yr 

2.2.1.5 Wild animals 

Ammonia emissions from wild animals considered in the current inventory include emissions 

from grizzly bears, black bears, elk, and deer. The emission factors presented in Table 2-6 were 

obtained from the Carnegie Mellon University (CMU) Ammonia Model (Strader et al., 2004). 

Table 2-6. Emission factors for wild animal ammonia sources.

Source Emission Factor Unit 

Black bears 0.378 kg NH3/animal/yr 

Grizzly bears 0.378 kg NH3/animal/yr 

Elk 1.707 kg NH3/animalyr 

Deer 0.378 kg NH3/animal/yr 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 13 

2.2.2 Activity data 

Complete listings of county-level activity data for all source categories considered in the ammonia inventory can be found in Mansell (2004b). Summaries are given below. 

2.2.2.1 Livestock operations 

Activity data for livestock ammonia emissions were based on total county-level animal head 

counts. We obtained animal head counts from the National Agricultural Statistics Service’s 

(NASS) county livestock files (NASS, 2003). 

2.2.2.2 Fertilizer application 

Activity data for fertilizer ammonia emissions were based on county-level fertilizer usage data. 

Originally, we sought to obtain these data from a number of sources, including the Association 

of American Plant Food Control Officers (AAPFCO, 2003), the USDA agricultural census 

(2001), and the county crop files (NASS, 2003). However, these data did not provide the monthly or seasonal variation required for temporal allocation of the resulting emission estimates. As 

an alternative, fertilizer sales data obtained from the most recent version of the CMU Ammonia 

Model (Strader et al., 2004) were extracted and reformatted for use in our inventory. These data 

were available for calendar year 2002 on a monthly basis for each county in the conterminous 

U.S. The primary source of these data was the AAPFCO, with monthly variation based on crop 

planting times and fertilizer application times and rates as documented in Strader et al. (2004). 

2.2.2.3 Native soils 

The total area of each land use type from the LULC data used for this task provided the activity 

data for estimating soil emissions. These LULC data were developed from the National Land 

Cover Database (http://edcwww.cr.usgs.gov/pub/edcuser/vogel/states/). 

2.2.2.4 Domestic sources 

Activity data for domestic sources were based on the most recent U.S. Census (2000), and per 

capita pet ratios based on recommendations of Dickson et al. (1991). 

2.2.2.5 Wild animals 

As noted in Section 2.1, wild animal ammonia emissions were included in the inventory at the 

request of the WRAP Emissions Forum. Because this source category was added after the data 

collection efforts were completed, activity data for wild animals were obtained from the latest 

version of the CMU Ammonia Model (Strader et al., 2004). Note that these data were used “as 

is” (i.e., no additional quality assurance of the data was performed). 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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2.2.3 Temporal allocation 

2.2.3.1 Livestock operations 

A review of current literature revealed a lack in consistency of results quantifying temporal 

variations in ammonia emissions from livestock (Chitjian and Mansell, 2003a). However, most 

of the studies cited concluded that ammonia emissions from livestock display both a seasonal 

and a diurnal variation consistent, in general, with increased ammonia emissions associated with 

warmer temperatures. 

Chinkin et al. (2003) recommend seasonal allocation factors derived from those proposed by 

Gilliland et al. (2002), which are based on inverse modeling results. The factors were adjusted to 

reflect the current recommended emission factors from EPA’s Office of Research and Development (ORD) (U.S. EPA, 2002), which were not available at the time Gilliland and coworkers 

performed their modeling. The adjusted factors are shown in Table 2-7. Inspection of this table 

indicates a 3- to 4-fold increase in emissions during the warmest months. Also note that the minimum emissions occur during the late fall, as opposed to the coldest months; the minimum in fall 

is explained by the relatively dry conditions at that time of year. 

Table 2-7. Monthly livestock allocation factors. 

Month Temporal 

Allocation Factor Month Temporal Allocation Factor 

January 67 July 183 

February 75 August 154 

March 75 September 115 

April 82 October 73 

May 126 November 51 

June 164 December 51 

A discussion of the diurnal variation of livestock ammonia emissions is presented in Chitjian and 

Mansell (2003b). In general, the literature reports an increase in daytime emissions over nighttime emissions. Russell and Cass (1986) developed a theoretical equation to predict diurnal 

emission variations as a function of meteorological data. This equation relates hourly ammonia 

emission rates to temperature and wind speed, as follows: 

Ei ∝ [2.36( Ti

-273)/10] Vi A

where 

Ei = ammonia emission rate at hour i from animal waste decomposition 

A = daily total emission rate for ammonia from animal waste = ∑ Ei 

Ti = ambient temperature in Kelvin at hour i
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 15 

Vi = wind speed in m/s at hour i (a minimum wind speed of 0.1 m/s was assumed) 

For our inventory, the model of Russell and Cass was used to provide the diurnal variation of 

livestock ammonia emissions. This approach is consistent with first-principle assumptions. 

Although the seasonal and diurnal variations presented here are both empirically based, they are 

consistent with theory and measurements showing increased ammonia volatilization rates with 

increased temperature and wind speed. The monthly allocation factors shown in Table 2-7 were 

used to allocate annual emission estimates to each month of the year. 

2.2.3.2 Fertilizer application 

Emissions from fertilizer application were temporally allocated monthly based on the monthly 

activity data (see Section 2.2.2.2). Diurnal variations in fertilizer emissions are expected, as 

temperature and wind speed affect ammonia production and volatilization. Our inventory used 

the equations developed by Russell and Cass (1986) to temporally allocate daily emissions to 

each hour of the day as a function of temperature and wind speed, as with livestock emissions. 

Chitjian and Mansell (2003b) provide a more detailed discussion of the temporal variation of 

fertilizer ammonia emissions. 

2.2.3.3 Native soils 

Temporal allocation of native soil ammonia emissions was calculated using the emission factor 

scalars described in Section 2.2.1.3, which are temporally resolved. 

2.2.3.4 Domestic sources 

The ammonia emissions from domestic sources were assumed to be temporally invariant. Thus, 

daily emissions can be estimated as the annual estimates divided by 365. 

2.2.3.5 Wild animals 

The ammonia emissions from wild animals were assumed to be temporally invariant. Thus, daily 

emissions can be estimated as the annual estimates divided by 365. 

2.2.4 Spatial allocation 

2.2.4.1 Livestock operations 

Ideally, for spatial allocation of livestock ammonia emissions, the locations of large confined 

animal feed operations (CAFOs) should be used to geocode each CAFO within the modeling 

domain. In addition, grazing livestock must be spatially allocated from the county level to 

modeling grid cells using land use surrogates. For our inventory, geocoding of large CAFOs was 

not practical for the WRAP regional modeling domain, given the task schedule and budget constraints. Therefore, all livestock ammonia emissions were spatially allocated using surrogates 

based on the grassland and pasture land use categories (codes 71 and 81 in Table 2-8, which is 

discussed in Section 2.2.5.1). 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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2.2.4.2 Fertilizer application 

Ammonia emissions from fertilizer application are spatially allocated as a function of land use 

types. We developed spatial allocation factors based on the agricultural land classes available in 

the LULC data used for this task. 

2.2.4.3 Native soils 

Native soil emissions are spatially allocated based on the land area for each land use category 

from the LULC data used for this task. 

2.2.4.4 Domestic sources 

Domestic animal ammonia emissions were spatially allocated by population density based on the 

2000 U.S. Census. 

2.2.4.5 Wild animals 

Wild animal ammonia emissions were spatially allocated to forestland, shrubland, and grassland 

as determined by the LULC data used for this task. 

2.2.5 Land use and environmental data 

The land use and environmental data utilized in developing the WRAP ammonia emissions 

inventory are discussed below. 

2.2.5.1 Land use/land cover 

The default LULC data used in creating the inventory was based on the National Land Cover 

Database (NLCD). The NLCD was developed as part of a cooperative project between the U.S. 

Geological Survey (USGS) and EPA to produce a consistent land cover data layer for the entire 

conterminous U.S. based on 30-m Landsat thematic mapper (TM) data. The NLCD was developed from TM data acquired from the Multi-Resolution Land Characterization (MRLC) Consortium, a partnership of Federal agencies that produce or use land cover data. The partners include 

USGS (the National Mapping, Biological Resources, and Water Resources Divisions), EPA, the 

U.S. Forest Service, and the National Oceanic and Atmospheric Administration. 

NLCD datasets are available as flat generic raster image files that are easily imported into a GIS 

(e.g., ARC/Info) and are provided in an Albers Conic Equal Area projection at a spatial resolution of 30 m. They can be found at http://edcwww.cr.usgs.gov/pub/edcuser/vogel/states/, along 

with a discussion on how they were developed. The land cover characteristics are defined in 

terms of the 21 separate categories given in Table 2-8. A sample display of the NLCD data is 

presented in Figure 2-2. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 17 

Table 2-8. NLCD land cover classification codes. 

Code Description Code Description 

11 Open water 51 Shrubland 

12 Perennial ice/snow 61 Orchards/vineyards/other 

21 Low-intensity residential 71 Grassland/herbaceous 

22 High-intensity residential 81 Pasture/hay 

23 Commercial/industrial/transportation 82 Row crops 

31 Bare rock/sand/clay 83 Small grains 

32 Quarries/strip mines/gravel pits 84 Fallow 

33 Transitional 85 Urban/recreational grasses 

41 Deciduous forest 91 Woody wetlands 

42 Evergreen forest 92 Emergent herbaceous wetlands 

43 Mixed forest 

Figure 2-2. NLCD data for the conterminous United States. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 18 

2.2.5.2 Soil pH 

The STATSGO database developed by USDA’s Natural Resources Conservation Service was 

used to specify the soil pH necessary for developing our emissions inventory. These soils data 

are available as geospatial coverages and associated attribute tables for each state in the U.S., and 

require considerable effort to calculate and extract the relevant parameters. Detailed documentation of the database and data structures can be found in USDA (1994). 

To reduce the resource-intensive data processing required with the complete STATSGO database, processed soils datasets were obtained from the Earth System Science Center (ESSC) at 

Pennsylvania State University via anonymous ftp at http://www.essc.psu.edu/soil_info/. The 

datasets archived by the ESSC include a single GIS coverage of soil pH for the conterminous 

U.S. (Figure 2-3). 

Figure 2-3. Mean soil pH for the conterminous United States. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 19 

2.2.5.3 Meteorological data 

Meteorological data were derived from MM5 simulations for 2002 on the RPO Unified domain. 

Data required as input to the GIS-based ammonia emissions modeling system include wind 

speeds, ambient temperatures, soil temperatures, and soil moisture. These data were extracted 

from the MM5 output files and reformatted for use in the GIS-based modeling system. 

2.2.6 GIS-based modeling 

We developed a GIS-based modeling system to generate a gridded ammonia emissions inventory 

that includes the improvements identified for this project. The county-level activity data were 

used in conjunction with the appropriate emission factors and environmental factors described 

above to generate the gridded inventory on the RPO Unified domain. The inventory is temporally resolved hourly. A detailed discussion of the design and implementation of the modeling 

system is presented in Chitjian and Mansell (2003b). 

As noted earlier, the GIS-based modeling system was developed using AML and the latest version of ArcGIS. The model incorporates our improved estimation algorithms, includes a userfriendly GUI, and allows easy modification of activity data, emission factors, and associations 

between ammonia emission source categories’ LULC characteristics. The modeling system was 

populated with all the necessary data to produce an ammonia inventory for the U.S. portion of 

the RPO Unified domain for 2002 at a 36-km spatial resolution. See Chitjian and Mansell 

(2003b) for further information. 

As discussed in Mansell (2004c), the modeling system applies gridding surrogates to the countylevel emission estimates on an annual basis (or monthly for fertilizer application emissions). 

Effects of soil pH on the emission factors for fertilizer application are applied to the annual gridded ammonia emission estimates. Other environmental factors are incorporated in the temporal 

allocation modules, since these factors impact the diurnal variation of emissions through gridded, 

hourly temperatures and wind speeds. An exception applies for the emissions from native soils: 

for this source category, the effects of soil conditions (pH and moisture) and meteorological data 

are both incorporated in the emissions estimates during the temporal allocation process. 

2.3 2002 Ammonia Emissions inventory 

As stated earlier, the 2002 ammonia emissions inventory developed for this project includes the 

source categories of livestock operations, fertilizer usage, domestic sources, native soils, and 

wild animals; see Table 2-1 for the subcategories under each of these. Below we present the 

inventory in terms of the complete inventory and also by major source category. A comparison 

with the results of the CMU Ammonia Model is also presented. A more detailed discussion of 

the 2002 ammonia emissions inventory can be found in Mansell (2004a). 

Table 2-9 shows the annual ammonia emissions estimates for calendar year 2002 by state for 

each of the five major source categories above. With the exception of the soil pH adjustments to 

fertilizer emission factors, the results presented here for fertilizer usage, livestock operations, and 

native soil emissions do not reflect the effects of environmental factors, as explained before. In 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 20 

general, the ammonia inventory is dominated by livestock operations, fertilizer usage, and native 

soil emissions, as expected. This trend varies by state, with largely agricultural regions representing the majority of the emissions from these source categories. Although native soil ammonia 

emissions are highly uncertain, the variation in emissions from this category across states reflects 

the variation in land use types within each state. The magnitude of domestic-source emissions 

are reflective of the population in each state, based on the 2000 U.S. Census data used as activity 

data. Ammonia emissions from wild animals are based on activity data and emission factors 

extracted from the CMU Ammonia Model. The wild animal data were not scrutinized in detail, 

and clearly there are some omissions and/or inconsistencies in the datasets, as discussed in 

Section 2.3.5. 

Figure 2-4 gives a graphical representation of the data in Table 2-9. 

Table 2-9. Annual 2002 NH3 emissions by state (tons). 

State Livestock

Operations

Fertilizer 

Usage 

Native 

Soils 

Domestic 

Sources 

Wild 

Animals TOTAL 

AL 34,306 5,562 15,793 2,259 7,500 65,419

AZ 10,331 12,109 102,837 2,553 1,798 129,628

AR 45,396 38,744 14,578 1,365 4,023 104,107

CA 97,721 78,052 102,447 16,704 302 295,225

CO 34,133 18,848 74,967 2,127 5,586 135,661

CT 1,802 796 1,937 1,668 375 6,577

DE 4,607 888 531 387 110 6,523

DC 0 0 9 272 0 281

FL 20,562 3,300 28,667 6,632 6 59,167

GA 38,408 6,468 18,229 4,132 5,203 72,440

ID 30,082 31,442 51,230 660 3,425 116,839

IL 48,391 120,817 6,361 6,137 3,699 185,405

IN 38,690 46,642 4,418 3,055 2,123 94,928

IA 159,638 120,694 6,729 1,472 1,500 290,032

KS 69,753 100,340 44,151 1,351 748 216,344

KY 34,476 26,858 12,066 2,049 2,184 77,633

LA 9,326 15,273 18,973 2,246 5,001 50,820

ME 2,321 924 10,449 645 1,299 15,638

MD 11,049 4,294 2,753 2,604 974 21,673

MA 1,487 405 3,059 3,079 304 8,334

MI 25,964 26,409 22,820 4,954 8,874 89,021

MN 86,787 114,864 26,827 2,460 4,587 235,525

MS 26,361 14,228 13,630 1,469 7,498 63,186

MO 62,834 61,084 16,543 2,798 3,820 147,079

MT 24,926 31,376 102,946 454 4,024 163,727

NE 80,050 91,584 48,356 859 1,255 222,105

NV 4,415 1,395 106,982 988 92 113,872
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 21 

State Livestock

Operations

Fertilizer 

Usage 

Native 

Soils 

Domestic 

Sources 

Wild 

Animals TOTAL 

NH 967 56 3,150 619 335 5,128

NJ 1,268 808 2,425 4,101 931 9,532

NM 21,397 5,599 112,468 913 1,414 141,791

NY 33,157 4,471 14,724 9,289 3,673 65,315

NC 101,463 6,771 17,005 4,071 5,517 134,827

ND 21,540 95,026 32,021 323 1,141 150,051

OH 34,443 55,453 7,192 5,632 2,049 104,769

OK 72,221 37,626 35,297 1,740 1,652 148,535

OR 17,325 22,988 55,624 1,691 3,361 100,989

PA 46,840 8,924 13,934 6,014 5,894 81,605

RI 112 56 450 507 52 1,176

SC 10,754 3,864 10,081 2,025 4,249 30,973

SD 50,786 66,051 43,705 386 1,316 162,244

TN 29,369 14,680 12,330 2,861 3,704 62,944

TX 146,410 83,349 175,959 10,502 18,012 434,233

UT 17,458 2,091 62,973 1,150 1,623 85,295

VT 6,529 624 3,087 309 635 11,183

VA 26,787 8,352 12,851 3,501 4,021 55,512

WA 18,349 17,300 28,024 2,911 2,186 68,771

WV 6,449 1,331 9,465 911 3,790 21,947

WI 73,031 28,807 16,352 2,674 6,932 127,795

WY 15,159 11,037 93,013 246 2,944 122,400

TOTAL 1,755,633 1,448,659 1,620,414 137,752 151,744 5,114,202
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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Annual NH3 Emissions (tpy) - WRAP NH3 Model

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

AL

AZ

ARCA

CO

CT

DE

DC

FL

GA

ID

IL

IN

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NENV

NH

NJ

NM

NY

NCND

OH

OK

OR

PA

RI

SCSD

TN

TX

UT

VT

VA

WAWV

WI

WY

State

NH3 (tpy)

Fertilizer Livestock

Domestic Wild_Animals

Native_Soils

Figure 2-4. 2002 annual NH3 emissions by source category and state in tons per year (tpy). 

2.3.1 Livestock operations 

Figure 2-5 presents the 2002 annual livestock emissions by animal type for each the WRAP 

states. Beef cattle, dairy cattle, and swine contribute approximately 87% of the total livestock 

emissions across all 13 states. The majority of dairy cattle emissions are found in California. 

Beef cattle emissions are more evenly distributed across the WRAP region, with the exception of 

Arizona and Nevada; South Dakota shows the highest amount of ammonia emissions from beef 

cattle. The remaining source categories (swine, poultry, horses, and sheep) are minor contributors of livestock ammonia emissions across the WRAP region. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 23 

2002 Annual Livestock NH3 Emissions

0

10000

20000

30000

40000

50000

60000

70000

Arizona

California

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

State

NH3 (tpy)

Beef Cattle Dairy Cattle Swine

Poultry Horses Sheep

Figure 2-5. Annual livestock NH3 emissions by animal type for WRAP states. 

Tabulated livestock ammonia emissions by state and animal type are presented in Mansell 

(2004a), as is the spatial distribution of total annual livestock ammonia emissions. Both the 

county-level emissions and the gridded emission estimates generated using the display modules 

of the GIS-based modeling system are given in the draft report for this task (Mansell, 2004a). 

2.3.2 Fertilizer application 

Figure 2-6 shows the 2002 annual fertilizer application emissions by fertilizer type for the 

WRAP states. The majority of fertilizer ammonia emissions are found in California and the 

Dakotas. The distribution among the various fertilizer types can also be seen. Note that these 

emission summaries include the dependence of emission factors on soil pH. As expected, states 

with large agricultural regions dominate the fertilizer-usage ammonia emissions. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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2002 Annual Fertilixer NH3 Emissions

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

State

NH3 (tpy)

Anhydrous Ammonia

Aqua Ammonia

Nitrogen Solutions

Urea

Ammonium Nitrate

Ammonium Sulfate

Ammonia Thiosulfate

Ammonium Phosphates

Calcium Ammonium Nitrate

Miscellaneous

Figure 2-6. Annual fertilizer NH3 emissions by fertilizer type for WRAP states. 

Tabulated fertilizer-usage ammonia emissions by state and fertilizer type can be found in 

Mansell (2004a), as can the spatial distribution of total monthly fertilizer-usage ammonia 

emissions. 

To assess the impact of the adjustments made to account for soil pH, we estimated the emissions 

for this source category based on unadjusted emission factors. Figure 2-7 displays the annual 

fertilizer ammonia emissions both with emission factor adjustments (dataset a0) and without 

them (dataset a1). The difference between these two cases is relatively minor at the state level. 

The differences are both positive and negative, as the soil pH varies regionally. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 25 

Comparison of Annual Fertilizer NH3 Emissions - Datasets a1 vs a0

0

15,000

30,000

45,000

60,000

75,000

90,000

105,000

120,000

135,000

150,000

AL

AZ

ARCA

CO

CT

DE

DC

FL

GA

ID

IL

IN

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NENV

NH

NJ

NM

NY

NCND

OH

OK

OR

PA

RI

SCSD

TN

TX

UT

VT

VA

WAWV

WI

WY

State

NH3 (tpy)

Fertilizer (a1) Fertilizer (a0)

Figure 2-7. Annual fertilizer NH3 emissions: dataset a0 is with pH effects, 

dataset a1 is without them. 

2.3.3 Native soils 

Figure 2-8 presents the 2002 annual native soil ammonia emissions by land use type for the 

WRAP states. Note that these emission summaries do not reflect the dependence of soil emissions on environmental factors; as discussed in Mansell (2004a), these factors are incorporated 

during the temporal allocation procedures in the modeling system. The variation seen in soil 

emissions across the WRAP region reflects the land use characteristics of each state. These 

emissions are dominated by shrubland and grassland, which reflects the characteristics of the 

land use/cover types in the western U.S. and their relatively high emission factors. The spatial 

distribution of total annual native soil ammonia emissions is presented in the draft report for this 

task (Mansell, 2004a). 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 26 

2002 Annual Native Soil NH3 Emissions

0

20000

40000

60000

80000

100000

120000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

State

NH3 (tpy)

Urban

Barren

Deciduous Forest

Evergreen Forest

Mixed Forest

Shrubland

Grassland

Fallow

Urban Grass

Wetlands

Figure 2-8. Annual native soil NH3 emissions by land use type for WRAP states. 

2.3.4 Domestic sources 

Figure 2-9 gives the 2002 annual ammonia emissions from domestic sources for the 13-state 

WRAP region. The magnitude of domestic-source emissions reflects the population in each state, 

based on the 2000 U.S. Census data used as activity data. Emissions from California dominate 

the contribution to the inventory from this source category, reflecting the state’s higher population. Displays of the spatial distribution of total annual domestic-source ammonia emissions 

are presented in Mansell (2004a). 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 27 

2002 Anual Domestic Source NH3 Emissions

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

State

NH3 (tpy)

Respiration Perspiration

Cloth Diapers Disposable Diapers

Cats Dogs

Figure 2-9. Annual domestic-source NH3 emissions by source type for WRAP states. 

2.3.5 Wild animals 

Figure 2-10 presents the 2002 annual ammonia emissions from wild animals for the WRAP 

states. Domainwide, results are dominated by the deer emissions estimates, consistent with the 

large deer populations in the activity data. While elk populations are considerably smaller than 

those of other animals in most states (based on the CMU data), the emission factor for elk is 

much higher, resulting in a significant contribution by elk to the wild animal source category 

emissions. However, as noted previously, the activity data and emission factors were taken from 

the CMU Ammonia Model “as is.” The emission results highlight some of the deficiencies in 

those datasets. For example, although the figure shows that elk dominate the emissions, in reality 

a significant contribution from deer should be seen in several of the states. This is not reflected in 

the CMU activity datasets; for California, for instance, the data show no deer populations. The 

spatial distribution of total annual wild animal ammonia emissions is presented in the draft task 

report (Mansell, 2004a). A review of the displays in that report also highlights dataset problems 

for Florida and Nevada. As this source category is a relatively minor contributor to the overall 

ammonia emissions inventory, no corrective action was taken to rectify these problems. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 28 

2002 Annual WIld Animal NH3 Emissions

0

1000

2000

3000

4000

5000

6000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

State

NH3 (tpy)

Black bears Grizzly bears

Elk Deer

Figure 2-10. Annual wild animal NH3 emissions by animal type for WRAP states. 

2.3.6 Comparison with CMU emission estimates 

We compared the ammonia emissions inventory we developed against results obtained from the 

CMU Ammonia Model for the source categories considered. This comparison served as check on 

the reasonableness and consistency of our inventory, as well as an assessment of the various 

environmental factors incorporated in the modeling system. The CMU model was applied for all 

states in the conterminous U.S. to obtain monthly and annual ammonia emission estimates for 

the five major source categories estimated for the WRAP. 

Figure 2-11 compares the total 2002 annual ammonia emissions, by state, between the CMU 

model and the WRAP inventory. In general, the overall state-level emission totals were comparable for most states. For some states, the CMU emission estimates were higher than those from 

the WRAP model, while for others the WRAP model estimates were the higher ones. Certainly, 

differences in the estimation methodologies, activity data, and emission factors contributed to 

these differences in ammonia emissions. For example, the CMU model treats livestock emissions 

using a processed-based approach, while our system considers this source category using a 

“whole animal” approach. Other differences and similarities between the two inventories are 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 29 

described in Mansell (2004a) for each major source category. Considering the emissions uncertainties associated with some source categories (e.g., native soils), overall the two inventories 

compared quite favorably. A more complete comparison between the WRAP and CMU 

ammonia emissions inventories is presented and discussed in Mansell (2004a). 

Comparison of Annual Total NH3 Emissions - CMU Model vs WRAP NH3 Model

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

AL

AZ

ARCA

CO

CT

DE

DC

FL

GA

ID

IL

IN

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NENV

NH

NJ

NM

NY

NCND

OH

OK

OR

PA

RI

SCSD

TN

TX

UT

VT

VA

WAWV

WI

WY

State

NH3 (tpy)

CMU WRAP

Figure 2-11. Comparison of total 2002 annual ammonia emissions between the 

CMU and WRAP models. 

2.4 Summary and Recommendations 

The results of the WRAP ammonia emissions inventory improvement task are summarized 

below. Also included here are recommendations regarding improvements in data quality and 

enhancements to the ammonia inventory and emissions modeling system. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 30 

2.4.1 Summary 

We created a gridded modeling ammonia emissions inventory for the following major source 

categories: livestock operations, fertilizer application, native soils, domestic sources, and wild 

animals (see Table 2-1 for a breakdown). We developed the inventory using the improvements 

and estimation methodologies described above and documented in detail in Chitjian and Mansell 

(2003a,b). The improvements, based on a literature survey of recent research in ammonia emissions inventory development, were associated with the effects of environmental parameters on 

the emission factors, and temporal and spatial allocation of ammonia emissions for several 

source categories. A GIS-based ammonia emissions modeling system was developed and applied 

for calendar year 2002 to generate a gridded, hourly emissions inventory for the WRAP modeling domain at a spatial resolution of 36 km. The data sources and environmental factors used in 

the inventory improvements are presented in more detail in the draft task report (Mansell, 

2004a). 

The 2002 ammonia emissions inventory for the WRAP domain is dominated, on a regional scale, 

by fertilizer application and livestock operation emissions. This result is entirely consistent with 

the current understanding of ammonia emissions within the air quality and emissions modeling 

community. Ammonia emission from native soils, while a major component of the overall 

inventory, remains highly uncertain. The uncertainty arises mainly from unresolved issues in the 

research community regarding whether soils act as a source or a sink of ammonia. Ammonia 

emissions from domestic sources, while only a small contributor to the total regional ammonia 

inventory, can be a major source of emissions on smaller, urban scales. The wild animal ammonia emissions, although only a small portion of the inventory, were included at the request of the 

WRAP Emissions Forum. 

A comparison of the WRAP ammonia inventory with similar results obtained from the CMU 

Ammonia Model was summarized above and is discussed in greater detail in Mansell (2004a). 

Based on state-level comparisons, the results of this task are seen to be consistent with existing 

ammonia emissions inventories. Evaluation of the differences and similarities, and further investigation of the two emissions inventories for detailed source categories at the county level, may 

provide useful insights into potential enhancements to the inventory and the emissions modeling 

system. 

2.4.2 Recommendations 

We make the following recommendations with respect to improving data quality and refining the 

ammonia inventory and emissions modeling system: 

• The quality and detail of activity data collected for the study should be improved. In 

particular, livestock head counts for large CAFOs should be obtained and reconciled with 

the county-level activity data. This would allow improved spatial allocation of these large 

CAFO sources as point emissions sources. 

• The current inventory could be improved further by obtaining detailed information on the 

specific types of livestock. For example, the current inventory considered beef cattle and 

dairy cattle in aggregate (i.e., independent of the specific processes occurring at the farm 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 31 

level and using a single “whole animal” emission factor.) Given the recent trend toward a 

process-based emission estimation methodology, emission factors for the different stages 

of an animal’s lifetime are available and can be used, given the appropriate activity data. 

Similarly, the WRAP model treated poultry as a composite, rather than separately for 

broilers, layers, pullets, etc., for which emission factors are available. 

• The temporal allocation of the inventory would benefit from the collection of activity 

data on a monthly basis for livestock operations, as is done for fertilizer usage data. 

• Ammonia emissions from native soils should be evaluated to see whether implementing 

some of the modeling techniques currently undergoing research and development with 

respect to ammonia emission and deposition would improve their estimation. 

• The existing inventory was generated at a spatial resolution of 36 km, for two reasons: 

the air quality modeling will be conducted at this resolution, and the meteorological data 

necessary for applying the various environmental factors used for this task were available 

only at 36-km resolution at the time the inventory was developed. Higher spatial 

resolution of meteorological data would allow development of emissions data at a higher 

resolution, thus improving the overall spatial allocation of various emission source 

categories. The GIS-based modeling system is already capable of handling modeling 

domains of arbitrary spatial resolution. Results from running the ammonia model using 

the latest 12-km MM5 modeling results will be documented in the final report for this 

task. 

• While domestic-source ammonia seems to be a relatively small component of the inventory, refinement of these emissions estimates using 2002 population estimates would 

provide additional improvements in the overall emissions inventory. 

• The activity data used in developing the wild animal emissions should be reviewed and 

updated to address the various problems and omissions we identified. 

• Some additional ammonia emission sources could be added to the current inventory. The 

ammonia emissions modeling system we developed is designed to treat essentially any 

source category, provided the activity and emission factor data are available. 

• With respect to source classification of anthropogenic versus natural, all source 

categories estimated by the ammonia model except native soils and wild animal 

emissions should be considered anthropogenic sources of ammonia emissions. 

2.5 Status of Task 0.5 Deliverables 

Table 2-10 gives the status of each Task 0.5 deliverable listed in the 2004 WRAP RMC work 

plan, plus any additional deliverables agreed upon after the work plan was prepared. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 32 

Table 2-10. Status of the Task 0.5 deliverables.

Deliverable Status 

Draft final task report Delivered August 26, 2004 

Final task report Expected completion: March 18, 2005 

User’s guide and modeling system Expected completion: March 18, 2005 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 33 

3. Task 1: Project Administration 

3.1 Project Administration 

Project administration activities included communicating with WRAP members and other 

WRAP contractors; participating in monthly conference calls with the RMC project management 

team and with the WRAP Air Quality Modeling Forum; attending or participating by teleconference in meetings with several of the other WRAP forums; preparing project reports and a 

quality assurance project plan (QAPP); and maintaining and updating the project web page and 

project mailing lists. 

During 2004, the RMC team participated in the following meetings: 

• Air Quality Modeling Forum Meeting, January, Phoenix, AZ 

• RPO Modeling Discussion Group Meeting, May, Denver, CO 

• Attribution of Haze Meeting, July, Denver, CO 

• Emissions Forum Conference Call, August 27 

• Fire Emissions Joint Forum Meeting (by phone), September 9, Worley, ID 

• Attribution of Haze Meeting, September, Salt Lake City, UT 

• Attribution of Haze Meeting, November, Las Vegas, NV 

• Fire Emissions Joint Forum Meeting, December, Las Vegas, NV 

Because the RMC contract in 2004 included projects under the direction of the WRAP’s Air 

Quality Modeling Forum, Fire Emissions Joint Forum, Dust Emissions Joint Forum, and Emissions Forum, there were frequent conference calls to coordinate and report progress on the 

various subtasks within the work plan. 

3.2 Computer Systems Administration 

Computer systems administration included maintenance, updates, expansion, and optimization of 

the computing systems. All emissions, meteorology, and air quality modeling was performed on 

RMC computer equipment located at UCR; staff from UNC-CEP and ENVIRON have accounts 

on these computer systems. Much of the computer hardware used for the WRAP modeling was 

purchased in 2001 or early 2002, and is no longer under warranty. As a result, considerable effort 

was required to maintain and repair the computing systems. 

We also experimented with new CPUs, network equipment, and system configurations to optimize the computer system performance during 2004. UCR had previously ported CMAQ to 

Linux, implemented a parallel version of CMAQ in Linux, and ported CMAQ to the 64-bit 

Opteron CPUs. During 2004 we attempted to port CMAQ to the Apple G5/PowerPC CPU (a 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 34 

new 64-bit CPU that was priced competitively with Athlon and Intel CPUs). We were motivated 

to complete the port because we felt that the Apple/G5 systems would have better performance 

and be more reliable than PC systems. After considerable effort invested in porting the code, 

however, we were unable to achieve good performance of CMAQ on the G5/PowerPC platform. 

We still believe it would be worthwhile to consider operating CMAQ on the G5 in the long term, 

but the effort needed to optimize CMAQ for the G5/PowerPC platform will require greater 

resources than were available to the RMC in 2004 for this activity. The CMAS Center at UNCCEP is currently working on this project. Given that the RMC is already heavily invested in 

PC/Linux systems, we plan to complete the project with PC/Linux hardware; however, we 

recommend that states and tribes who are purchasing new hardware consider the G5 systems if 

the CMAS Center is able to optimize CMAQ and SMOKE for that platform. 

Within the PC/Linux platforms there are a variety of CPU choices. We believe that the Athlon 

CPUs are the most cost effective, and we purchased new test systems for the Athlon CPU to 

perform benchmarks for comparison to the more expensive Opteron systems. Benchmarks on the 

36-km domain showed that the cheaper Athlon system’s performance was very similar to that of 

the Opteron system. 

Increased computational and input/output load from running multiple model simulations and 

increasing domain size have continued to increase the network traffic associated with the modeling effort. During 2004 we purchased a new high-speed network switch to reduce problems 

resulting from network congestion. We also continued to experiment with system configurations 

to attempt to reduce network congestion and improve system I/O performance. 

As of February 2005, we continue to experience problems with CMAQ jobs crashing in simulations using multiple CPUs. Usually this occurs when a CMAQ job fails to start after the completion of a previous day’s simulation. This is caused by communication failure between CPUs 

on different machines. Jobs using one or two CPUs run without difficulty, but jobs using four or 

more CPUs will fail to restart on average about 10% of the time. This requires considerable 

effort to monitor and manually restart CMAQ jobs when they fail to start. This problem may 

occur only on the UCR cluster, perhaps because of the large number of CPUs and/or the amount 

of disk storage in the cluster, and the resulting heavy load on net communications. Other groups 

have not reported this problem, so it is likely that states or tribes will not experience it, but it is a 

possibility worth noting here. 

We expect that computer hardware will need to be upgraded and expanded before doing large 

numbers of emissions sensitivity simulations during 2005. Hardware needs will depend on the 

number of model simulations to be performed and whether we run only the 36-km model or both 

the 36-km and 12-km models. We believe that the RMC’s current computing facilities are 

sufficient to handle the 36-km modeling and a limited number of 12-km simulations. Additional 

CPUs will be needed if we perform a large number of 12-km simulations. We will need 

additional data storage for modeling control strategies during 2005. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 35 

3.3 Status of Task 1 Deliverables 

Table 3-1 gives the status of each Task 1 deliverable listed in the 2004 WRAP RMC work plan, 

plus any additional deliverables agreed upon after the work plan was prepared. 

Table 3-1. Status of the Task 1 deliverables. 

Deliverable Status 

Biweekly Modeling Forum (MF) 

conference call 

Ongoing. Switched to a monthly MF call and a monthly RMC 

project management call in October 2004. 

Quality Assurance Project Plan 

(QAPP) 

Completed first draft in September 2004. Revised version 

completed in December 2004. 

Monthly project reports Combined into a single interim report for January through 

September 2004. Monthly reports began in August 2004; see 

http://pah.cert.ucr.edu/aqm/308/progress_reports04.shtml. 

Periodic web site updates Web site has been periodically updated with reports, PowerPoint files, QA products, and evaluation results; see 

http://pah.cert.ucr.edu/aqm/308/.

MM5, emissions, and CMAQ data files Datasets are available via portable disk drives. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 36 

4. Task 2: Test, Improve, Quality Control, Obtain External 

Peer Review, and Finalize 36-km and 12-km MM5 

Simulations for Eventual Use in CMAQ 

To support CMAQ visibility modeling for the §308 SIPs/TIPs, the RMC has carried out MM5 

simulations for the entire year of 2002 on grids with resolutions of 36 km and 12 km. In this 

section, we first describe the process through which the optimal MM5 model configuration was 

determined. We then give an overview of the performance of the final WRAP 2002 annual 

36-km and 12-km simulations. 

4.1 Introduction 

4.1.1 Task history 

In the fall of 2003, we made an initial MM5 run for the year 2002 on the RPO Unified Continental 36-km Modeling Grid domain. The model configuration for this run was based on a prior 

2002 MM5 application undertaken by the Iowa Department of Natural Resources (IDNR) 

(Matthew Johnson, IDNR, personal communication, 2003), which was in turn set according to 

the optimal MM5 physics options that resulted from an in-depth sensitivity project carried out by 

IDNR and the Lake Michigan Air Directors Consortium (LADCO). While the IDNR simulations 

used MM5 version 3.5, the applications carried out for the WRAP used the latest version of the 

model (v3.6.1) available at the end of 2003. Additional modifications to the physics configuration and application methodology were made for the WRAP simulation based on the latest information from EPA, IDNR, LADCO, and others. First, we opted to use the Reisner 2 mixed-phase 

cloud microphysics package (Reisner et al., 1998), according to suggestions from EPA/ORD. 

Second, the INTERPPX option, which allows for continuous soil moisture initialization from one 

run segment to the next, was not used, based on poor performance reported by IDNR and 

LADCO. 

The initial 2002 36-km WRAP simulation (referred to hereafter as Run 0) results showed that 

MM5 performed better in the central and eastern U.S. than in the West, and generally performed 

better in winter than in summer (Morris et al., 2004a; Kemball-Cook et al., 2004). In the western 

U.S., the amplitude of the diurnal temperature cycle was persistently underestimated during the 

summer, especially in the Southwest. In the desert Southwest, the humidity was greatly overestimated during the summer as well, and there was a pronounced cold bias. Some of these problems 

appeared to be linked to the excessive simulated precipitation generated by MM5 during the 

summer, especially in the Southwest. This can have serious repercussions for CMAQ modeling, 

since too much rain can “wash out” pollutants, while the too cool, humid, and cloudy environment may lead to incorrect pollutant chemistry and aerosol thermodynamics. Temperature and 

humidity problems overshadowed the surface wind performance, which was not particularly 

good, but was likely affected by smaller-scale topographic influences that are not well represented even at the finer 12-km resolution. Wind performance improved quickly with height 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 37 

above the surface, suggesting that regional transport speeds/directions were reasonably 

represented. 

In April 2004, we undertook initial sensitivity tests for a five-day period in July 2002 for the 

RPO Unified domain in an attempt to find alternative MM5 configurations that would improve 

the poor summertime performance at 36 km. Furthermore, in-depth analyses were conducted to 

compare our performance against 36-km MM5 runs made for the Visibility Improvement – State 

and Tribal Associations of the Southeast (VISTAS) RPO, and against operational Eta Data 

Assimilation System (EDAS) fields. From these analyses, we found that the use of the KainFritsch II scheme (as in the VISTAS 2002 MM5 modeling) improved, but did not entirely solve, 

the precipitation overprediction problem. We also found that removal of soil moisture nudging 

improved temperature and humidity performance for the short summertime tests. The evaluation 

of the EDAS fields used in the MM5 four-dimensional data assimilation (FDDA) revealed that 

EDAS did not exhibit the summertime cold, wet bias in the Southeast, so the bias was not 

introduced by the FDDA. From these initial tests, we identified a new model configuration for 

the 36-km run (referred to as Run 5) for new annual 2002 simulations, and updated the WRAP 

MM5 modeling protocol to reflect this (see Table 4-1). (“Analysis FDDA” and “observational 

FDDA” are defined in Section 4.2.)

Table 4-1. MM5 configurations from original (Run 0) and modified (Run 5) MM5 runs.* 

Run 

ID LSM PBL Cumulus Microphysics 

 Analysis FDDA 

 3-D Surface 

Obs 

FDDA 

Soil Moisture 

Nudging 

Run 0 PX ACM KF Reisner 2 W/T/H W/T/H None Yes 

Run 5 PX ACM KF II Reisner 2 W/T/H W/T/H None No 

*Abbreviations used in this table: LSM = land-surface model; PBL = planetary boundary layer; FDDA = fourdimensional data assimilation; obs = observational; PX = Pleim-Xiu; ACM = Asymmetric Convective Model; 

KF = Kain-Fritsch; W/T/H = wind/temperature/humidity. 

Although the configuration identified in the new MM5 modeling protocol did improve humidity, 

temperature, and precipitation performance over the short July test period (especially where 

improvement was needed most), the desert Southwest continued to exhibit unsatisfactory levels 

of precipitation and humidity. As stated above, these issues will likely play crucial roles in 

CMAQ performance. 

These initial MM5 sensitivity simulations attempted to identify a better-performing 36-km MM5 

simulation that could be used to simulate all of 2002 but retain the Pleim-Xiu (PX) land-surface 

model (LSM) and the Asymmetric Convective Model (ACM) planetary boundary layer (PBL) 

options. When results of the Run 5 model performance were presented at the May 24-25, 2004, 

national RPO modeling meeting in Denver, CO (Emery et al., 2004), there were still concerns 

about the 2002 MM5 model performance in the western U.S. Thus, WRAP requested that the 

RMC perform further MM5 sensitivity tests to identify a better-performing configuration, including investigating (1) alternative LSM/PBL configurations to PX/ACM and (2) the possibility 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 38 

of using different MM5 configurations for different times of year. After the performance issues 

at 36-km resolution had been addressed, we could then turn our attention to MM5’s performance 

on the WRAP nested 12-km grid.

4.1.2 Summary of approach 

During the summer of 2004, we carried out additional 36-km MM5 test simulations in an attempt 

to further improve MM5 performance on that grid. The latest available version of MM5 (v3.6.2) 

was used. Since the most severe problems for the initial WRAP 2002 MM5 annual simulation 

were the wet bias and precipitation overestimation in summer, we first investigated these issues 

for the five-day July test period (July 1-5, 2002) on the 36-km grid to maximize computational 

efficiency. We tested four physics options: (1) the cumulus parameterization; (2) the LSMs; 

(3) the PBL models; and (4) FDDA.

The first group of tests focused on the cumulus parameterization, which is the major MM5 component responsible for the overstated summertime rainfall. Most of the summer rainfall in the 

U.S. is due to convective clouds; stratiform rain produced by midlatitude cyclones is a smaller 

fraction of the total rainfall in summer than in winter. We examined the performance of the 

Kain-Fritsch II, Betts-Miller, and Grell cumulus schemes.

Next, we conducted a series of tests using different combinations of PBL and LSM options. In 

other MM5 applications around the country, including in California, we have achieved good 

model performance using the historically standard five-layer slab model as well as the NOAH 

LSM. Our choices for PBL models include the Eta scheme, the ACM, the Blackadar scheme, and 

our “modified” Medium-Range Forecast (MRF) model (M-MRF). The standard MRF available 

with MM5 is known to contain deficiencies that lead to overestimates of mixing depth and 

diurnal phase lags in wind speed due to overestimates of surface momentum flux. Our simple 

modifications have been shown to fix these problems (ATMET, 2003). 

In the past, we have avoided performing MM5 sensitivity tests that altered the Pleim-Xiu/ACM 

LSM/PBL configuration because it is recommended by EPA and it has been shown by other 

RPOs (e.g., VISTAS) to provide the best overall meteorological model performance and best 

CMAQ performance (Olerud and Sims, 2003; Morris et al., 2004b). Also, it allows the use of the 

Pleim-Xiu dry deposition scheme in CMAQ, which is technically superior and performs better 

than the alternative (Wesely scheme). However, because most of the MM5 Pleim-Xiu tests to 

date have focused on the eastern U.S., and given the importance of the LSM/PBL scheme to 

MM5 performance, we tested alternative LSM/PBL schemes to see if they would provide better 

performance in the western United States. 

Once the cumulus and LSM/PBL schemes had been selected, we performed a series of experiments to determine the best FDDA configuration. Finally, because the above sensitivity tests 

were done for July only, we ran 5-d segments in winter, spring, and fall to assess whether the 

performance showed improvement in seasons other than summer. Once we had an optimal 

configuration, we went back and tested it against the original MM5 run from the initial WRAP 

2002 36-km modeling (Table 4-1, Run 0), and also against our interim Run 5 configuration from 

Table 4-1. We then examined the non-WRAP subdomains to determine how the new set of 

physics options affected MM5’s performance in the eastern U.S. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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After settling on a configuration for the 36-km annual run, we turned our attention to MM5’s 

performance on the WRAP nested 12-km grid. Further sensitivity tests at this resolution were 

undertaken, allowing for the possibility that the 36-km physics options might differ from those 

selected for the 12-km run. Sections 4.2 through 4.6 provide details on each step of the approach 

just discussed. 

4.2 Additional 36-km MM5 Sensitivity Tests 

Table 4-2 shows the additional 36-km MM5 sensitivity tests discussed above that were used to 

identify a better MM5 configuration for simulating meteorology in the western U.S. These sensitivity tests differed in the choice of LSM, PBL scheme, cumulus parameterization, explicit 

cloud microphysics, and type and level of FDDA. Below are the various schemes tested in this 

study. No soil moisture nudging was performed for any of the runs.

• LSMs: NOAH; Pleim-Xiu (PX); five-layer slab 

• PBL Schemes: Eta; modified MRF (M-MRF); Asymmetric Convective Model (ACM); 

Blackadar 

• Cumulus Schemes: Betts-Miller (BM); Grell; Kain-Fritsch II (KF II)

• Cloud Microphysics Schemes: simple ice; Reisner 2 

• FDDA: W = wind; T = temperature; H= humidity. “Analysis FDDA” refers to analysis 

(grid) nudging; “observational FDDA” indicates that FDDA is used to nudge the model 

solution toward observational data at individual measurement sites. 

Table 4-2. Summary of additional MM5 sensitivity tests. 

Run ID LSM PBL Cumulus Microphysics 

 Analysis FDDA 

 3-D Surface 

Obs 

FDDA 

Run 1a NOAH Eta Betts-Miller Simple ice W/T/H W/T/H None 

Run 1ba NOAH M-MRF Betts-Miller Reisner 2 W/T/H W/T/H None 

Run 1bb NOAH M-MRF Betts-Miller Reisner 2 W/T/H W/T/H W 

Run 2a Pleim-Xiu ACM Betts-Miller Reisner 2 W/T/H W/T/H None 

Run 2aa Pleim-Xiu ACM Betts-Miller Reisner 2 W/T/H W None 

Run 2ac Pleim-Xiu ACM Betts-Miller Reisner 2 W/T/H W/T/H W/T/H 

Run 2ad Pleim-Xiu ACM Betts-Miller Reisner 2 W/T/H W W 

Run 2ae Pleim-Xiu ACM Betts-Miller Reisner 2 W/T/H W W 

Run 2b Pleim-Xiu ACM Grell Reisner 2 W/T/H W/T/H None 

Run 3a 5-layer Blackadar Betts-Miller Simple ice W/T/H W/T/H None 

Run 3b 5-layer M-MRF Betts-Miller Reisner 2 W/T/H W/T/H None 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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4.3 Analysis Procedures 

We conducted the MM5 sensitivity test evaluation by examining the surface performance statistics using the METSTAT evaluation software package developed by ENVIRON, and by comparing the output MM5 total (convective + large-scale) precipitation fields with gridded observed 

precipitation amounts. Observed precipitation data came from the Climate Prediction Center 

(CPC) gridded precipitation amount dataset, available from the National Weather Service (NWS) 

CPC web site at http://www.cpc.ncep.noaa.gov/products/precip/realtime/retro.shtml. 

The CPC daily precipitation amounts are on a grid that covers the U.S. mainland at a resolution 

of 0.25° x 0.25°, and are ramped down to zero immediately offshore. The advantage of this field 

is that it has a reasonably high resolution, which is especially important when it comes to resolving the effects of orography on precipitation over the western U.S. We calculated the performance statistics for surface wind, temperature, and humidity using METSTAT with the NCAR 

ds472 surface observations dataset. 

Our analysis is organized around a type of scatterplot called “soccer plots,” developed by 

ENVIRON, that display average performance statistics for each subdomain for the modeled time 

period. Subdomain definitions are shown in Figure 4-1; Table 4-3 lists the METSTAT subdomain numbers shown in Figure 4-1 and the corresponding abbreviations used in the discussions below. Soccer plots usually plot average bias versus error for a meteorological variable. 

Model performance benchmarks or goals also plotted on the soccer plot are used to show 

whether the performance of the variable meets the goal. In the following subsections, soccer 

plots are shown for wind speed bias versus wind speed root mean squared error (RMSE), temperature bias versus temperature error, and humidity bias versus humidity error. In each plot, a 

solid line indicates the benchmark. The benchmarks, developed by Emery and Tai (2001), were 

based upon the evaluation of more than 30 MM5 and Regional Atmospheric Modeling System 

(RAMS) meteorological simulations in support of air quality applications performed in the last 

few years, as reported by Tesche et al. (2002). A data point that falls inside the box represents a 

model run that meets the performance benchmark. Perfect model performance is indicated by a 

data point at (0,0); the closer a data point is to the origin, the better the model’s performance. We 

emphasize that the benchmarks are not used as acceptance/rejection criteria of the MM5 model 

simulation. Rather, they put the MM5 model performance into perspective and allow the 

identification of potential problems in the MM5 fields. 

Table 4-3. METSTAT subdomain abbreviations. 

METSTAT 

Subdomain 

Name 

Abbreviation

METSTAT 

Subdomain 

Name 

Abbreviation 

1 PacNW 7 GreatLakes 

2 SW 8 OhioVal 

3 North 9 SE 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 41 

METSTAT 

Subdomain 

Name 

Abbreviation

METSTAT 

Subdomain 

Name 

Abbreviation 

4 DesertSW 10 NE 

5 CenrapN 11 MidAtl 

6 CenrapS 

Figure 4-1. Definition of METSTAT subdomains. 

4.4 MM5 Sensitivity Test Results 

4.4.1 Cumulus parameterization sensitivity test 

We began by testing MM5’s sensitivity to the choice of cumulus parameterization. The original 

2002 MM5 run (Run 0) used the Kain-Fritsch scheme. Because results from VISTAS 36-km 

MM5 modeling showed improved surface fields relative to Run 0 using Kain-Fritsch II, KF II 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 42 

was the first scheme we tested (Run 5). Next, we compared runs that used the Grell (Run 2b) and 

Betts-Miller (Run2a) parameterizations to Run 5. All of these runs were made using the PX/ACM 

LSM/PBL schemes, the Reisner 2 cloud microphysics scheme, W/T/H analysis nudging at the 

surface and aloft, and no FDDA nudging to observations (“obs nudging”).

Figure 4-2a summarizes the wind performance for the July 2002 test period. All three cumulus 

schemes show a low wind speed bias. For the SW, PacNW, and North subdomains, the Grell 

scheme has a smaller error than the other schemes, and is similar in bias. In the DesertSW subdomain, the Betts-Miller scheme slightly outperforms the others in both bias and error, but all 

three schemes are far from the benchmark for DesertSW.

Figure 4-2b shows the results for temperature. All three schemes show a general cold bias. The 

Betts-Miller scheme has the smallest overall bias, and does slightly better than the Grell scheme 

for error. The KF II scheme has the smallest error, except in the PacNW subdomain, in which it 

is an outlier. The Betts-Miller scheme has the best overall temperature performance. It has the 

smallest bias, and although it has a slightly larger error than the KF II scheme for DesertSW, 

SW, and North, it does significantly better than KF II in the Pacific Northwest. 

The humidity performance is shown in Figure 4-2c. Here again, the Betts-Miller scheme shows 

the best overall performance. For the SW and PacNW subdomains, the performance of the three 

schemes is comparable, but the Betts-Miller scheme is clearly better for the North and DesertSW 

subdomains. 

Figure 4-3 is a plot of observed versus MM5 precipitation for the modeled time period. All three 

runs are too wet across the northern United States. Kain-Fritsch II has a strong wet bias, particularly in the SW and DesertSW subdomains, and also underpredicts the precipitation maximum 

over Texas. Both the Grell and Betts-Miller schemes produce a maximum over Texas, although 

they translate it to the southwest of the observed location. Over the southwestern U.S., the BettsMiller precipitation field is closer to the observations than that from Grell, which generates too 

much rain in this region. Based on the improvement in performance over the Southwest and 

Texas, we concluded that the Betts-Miller scheme produced the best simulation of precipitation 

for this time period. 

Overall, of the three cumulus packages tested, Betts-Miller was the best-performing scheme for 

temperature, humidity, and precipitation. We therefore selected this scheme and used it in all 

subsequent runs as we continued with further sensitivity tests. 
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 43 

WRAP 36 km July Wind Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Wind Speed Bias (m/s)

WindSpeed RMSE (m/s)

Benchmark Run 2a: Betts-Miller Run 2b: Grell Run 5: Kain-Fritsch II

Cumulus Parameterization Sensitivity Test

DesertSW 

PacNW

SW

North

Figure 4-2a. Wind soccer plot for cumulus parameterization sensitivity test. 

WRAP 36 km July Temperature Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Temperature Bias (K)

Temeprature Error (K)

Benchmark Run 2a: Betts-Miller Run 2b: Grell Run 5: Kain-Fritsch II

Cumulus Parameterization Sensitivity Test

DesertSW 

PacNW

SW

North

Figure 4-2b. Temperature soccer plot for cumulus parameterization sensitivity test. 
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WRAP 36 km Humidity Performance Comparison

0

1

2

3

4

5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark Run 2a:Betts-Miller Run 2b: Grell Run 5: Kain-Fritsch II

Cumulus Parameterization Sensitivity Test

DesertSW 

PacNW

SW

North

Figure 4-2c. Humidity soccer plot for cumulus parameterization sensitivity test. 
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Figure 4-3. Precipitation comparison for the cumulus parameterization sensitivity test. 

Run 2a (top left): Betts-Miller; Run 2b (bottom left): Grell; Run 5 (bottom right): 

Kain-Fritsch II. Observed is shown at top right. 

4.4.2 LSM/PBL sensitivity test 

Next, we tested the LSM and PBL parameterizations. All of the runs described in this section 

used the Betts-Miller cumulus parameterization, W/T/H analysis nudging at the surface and aloft, 

and no obs nudging. We began by comparing the NOAH LSM (Run 1ba) with the five-layer 

model (Run 3b). In both runs, the modified MRF (M-MRF) PBL scheme and the Reisner 2 cloud 

microphysics scheme were used. 

Figure 4-4a shows the wind performance for the Runs 1ba and 3b LSM sensitivity test.* Both the 

five-layer and NOAH runs show a low wind speed bias, with the bias more pronounced in the 

 

*

Note that Figures 4-4a through 4-4c include results for more than just these two runs. The other runs are discussed 

later in the text. The same is true for Figure 4-5. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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five-layer case. For both runs, the SW and PacNW subdomains lie within the benchmark, while 

North and DesertSW do not. The NOAH run performs better for all subdomains except for the 

DesertSW. For DesertSW, the NOAH run has a slightly smaller bias, but a larger error. 

The LSM temperature comparison is shown in Figure 4-4b. Both runs have a cold bias, but the 

bias is remarkably pronounced for the NOAH run in the PacNW and North subdomains. The 

error is smaller for all subdomains in the five-layer run, as is the bias in PacNW and North. For 

the SW and DesertSW subdomains, the NOAH run has a smaller bias, but larger error. Overall, 

the five-layer model temperature performance was an improvement over that of the NOAH run. 

Figure 4-4c shows the humidity performance for these runs. There is an overall wet bias, except 

in the SW subdomain. For humidity, the NOAH run meets the performance goal, while the fivelayer run lies entirely outside the benchmark. The NOAH run performs better in the North and 

PacNW than in the SW and DesertSW subdomains. 

The precipitation performance is shown in Figure 4-5. Run 1ba does slightly better here, producing less rain over the desert Southwest states than does run 3b; Run 1ba is therefore closer to the 

observed rainfall field than Run 3b.

Overall, the NOAH model outperformed the five-layer model. The five-layer model introduces a 

large positive humidity bias in the DesertSW and SW subdomains, which is one of the main 

problems from the original Run 0 that we were trying to ameliorate in this study. Having eliminated the five-layer LSM model from further consideration, we then compared the NOAH/MMRF combination (Run 1ba) with the run that used the PX/ACM configuration (Run 2a), also in 

Figures 4-4 and 4-5. 

Figure 4-4a displays the wind comparison for the NOAH/M-MRF and PX/ACM runs. The two 

runs show equivalent performance for error, and the NOAH/M-MRF run has a smaller overall 

bias. For temperature (Figure 4-4b), neither run meets the performance benchmark, but the 

PX/ACM run is a clear improvement in both bias and error over the NOAH/M-MRF run. The 

cold bias is far more pronounced in the NOAH/M-MRF run for the PacNW and North subdomains. 

For humidity (Figure 4-4c) there is not much difference between the two runs, except in 

DesertSW, where PX/ACM has a higher bias and error. For precipitation, the PX/ACM run 

shows some slight improvement over the NOAH/M-MRF case over the southwestern U.S. The 

intensity of the maximum over Texas is less well simulated in the PX/ACM run, however. 

Performance is roughly equivalent elsewhere. 

Overall, there was no single factor that made it easy to select one LSM/PBL scheme over the 

other. As there was no significant improvement in going to the NOAH/M-MRF configuration, 

we decided to use the PX/ACM configuration because it allows the use of the Pleim-Xiu dry 

deposition algorithm in subsequent CMAQ modeling, is endorsed by EPA, and allows consistency with the other RPOs.
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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WRAP 36 km July Wind Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Wind Speed Bias (m/s)

WindSpeed RMSE (m/s)

Benchmark Run 1ba: NOAH-MRF Run 3b: 5 lyr-MRF Run 2a: PX-ACM Run 1bb: NOAH-MRF, OBS Nudged

LSM/PBL Sensitivity Test

DesertSW 

PacNW

SW

North

Figure 4-4a. Wind soccer plot for LSM/PBL parameterization sensitivity test. 

WRAP 36 km July Temperature Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Temperature Bias (K)

Temeprature Error (K)

Benchmark Run 1ba: NOAH-MRF Run 3b: 5 lyr-MRF Run 2a: PX-ACM 1bb: NOAH-MRF, OBS Nudged

LSM/PBL Sensitivity Test

DesertSW 

PacNW

SW

North

Figure 4-4b. Temperature soccer plot for LSM/PBL parameterization sensitivity test. 
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WRAP 36 km Humidity Performance Comparison

0

1

2

3

4

5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark Run 1ba: NOAH-MRF Run 3b: 5 lyr-MRF

Run 2a: PX-ACM Run 1bb: NOAH-MRF, OBS Nudged

LSM/PBL Sensitivity Test

DesertSW 

PacNW

SW

North

Figure 4-4c. Humidity soccer plot for LSM/PBL parameterization sensitivity test. 
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Figure 4-5. Precipitation comparison for the LSM/PBL parameterization sensitivity test. 

Run 2a (top left): PX/ACM; Run 3b (bottom left): 5-layer/M-MRF; Run 1ba (bottom 

right): NOAH/M-MRF. Observed is shown at top right. 

4.4.3 FDDA sensitivity test 

Having selected a cumulus parameterization and an LSM/PBL combination, we turned to the 

FDDA configuration. The runs discussed in this section (2aa, 2ac, 2ad, and 2ae) differ only in 

their FDDA (see Table 4-2 for the run specifications); all use the PX/ACM, Betts-Miller, and 

Reisner 2 schemes and W/T/H analysis nudging aloft. 

Figure 4-6a compares the wind performance for the runs with different FDDA configurations. 

For all runs, all subdomains except PacNW (2a and 2aa) and SW (2a) lie outside the benchmark. 

There is low wind speed bias for all runs and all subdomains. Run 2a shows the smallest bias, 

while 2ac has the lowest overall error. It is difficult to say which runs exhibit the best wind 

performance based on this figure. Figure 4-6b shows the wind direction error versus the wind 

speed RMSE. In this figure, the effects of obs nudging on the surface wind field are apparent. 
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Runs that had obs nudging (2ac, 2ad, and 2ae) lie together closer to the benchmark than the runs 

that were not obs nudged (2aa). 

For temperature (Figure 4-6c), Run 2ae clearly shows the best performance. All subdomains but 

one are within the benchmark; Run 2ae also has the smallest bias and error. For humidity (Figure 

4-6d), performance is comparable among the runs, and all subdomains except DesertSW lie 

within the benchmark. Differences in the precipitation fields for all the FDDA sensitivity runs 

were small (not shown) and did not help to distinguish among the runs in terms of performance. 

On the basis of its superior temperature performance and wind performance, which were similar 

to those of the best-performing runs, Run 2ae was determined to have the best FDDA 

configuration. 

WRAP 36 km July Wind Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Wind Speed Bias (m/s)

WindSpeed RMSE (m/s)

Benchmark Run 2a Run 2aa Run 2ac Run 2ad Run 2ae

FDDA Sensitivity Test

DesertSW 

PacNW

SW

North

Figure 4-6a. Wind speed soccer plot for FDDA configuration sensitivity test. 
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WRAP 36 km July Wind Performance Comparison

0

0.5

1

1.5

2

2.5

3

0 20 40 60

Wind Direction Error (Degrees)

WindSpeed RMSE (m/s)

Benchmark Run 2a Run 2aa Run 2ac Run 2ad Run 2ae

DesertSW 

PacNW

SW

North

Figure 4-6b. Wind speed/direction soccer plot for FDDA configuration sensitivity test. 

WRAP 36 km July Temperature Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Temperature Bias (K)

Temeprature Error (K)

Benchmark Run 2a Run 2aa Run 2ac Run 2ad Run 2ae

FDDA Sensitivity Test

DesertSW 

PacNW

SW

North

Figure 4-6c. Temperature soccer plot for FDDA configuration sensitivity test. 
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WRAP 36 km Humidity Performance Comparison

0

1

2

3

4

5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark Run 2a Run 2aa Run 2ac Run 2ad Run 2ae

FDDA Sensitivity Test

DesertSW 

PacNW

SW

North

Figure 4-6d. Humidity soccer plot for FDDA configuration sensitivity test. 

Next, to allow for the possibility that nudging the NOAH/M-MRF case might cause significant 

improvement, we made another run (Run 1bb), which was identical to Run 2ae except that the 

NOAH/M-MRF LSM/PBL scheme was used instead of PX/ACM. The results are shown in Figures 4-7a through 4-7c, with the more weakly nudged Run 1ba included to show the effects of 

nudging. For wind speed bias and error, Run 1bb (NOAH) is a slight improvement over Run 2ae 

(PX), except for the SW subdomain, for which NOAH shows marked improvement over PX. For 

temperature, Run 2ae (PX) had a smaller bias and error than the other runs (NOAH). For humidity and precipitation, there is no run that clearly performed better than the others. We selected 

Run 2ae (PX, with W/T/H analysis nudging at the surface and W observational nudging) as the 

best-performing run overall for July 2002, because the temperature performance is deemed to be 

more important than the wind performance, given that the terrain in the western U.S. may prevent good agreement between modeled and observed surface winds at 36-km resolution. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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WRAP 36 km July Wind Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Wind Speed Bias (m/s)

WindSpeed RMSE (m/s)

Benchmark Run 1ba Run 1bb Run 2ae

DesertSW 

PacNW

SW

North

 

Figure 4-7a. Wind soccer plot for NOAH/M-MRF nudging sensitivity test. 

WRAP 36 km July Temperature Performance Comparison

0

0.5

1

1.5

2

2.5

3

3.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Temperature Bias (K)

Temeprature Error (K)

Benchmark Run 1ba 1bb Run 2ae

DesertSW 

PacNW

SW

North

Figure 4-7b. Temperature soccer plot for NOAH/M-MRF nudging sensitivity test. 
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WRAP 36 km Humidity Performance Comparison

0

1

2

3

4

5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark Run 1ba Run 1bb Run 2ae

DesertSW 

PacNW

SW

North

Figure 4-7c. Humidity soccer plot for NOAH/M-MRF nudging sensitivity test. 

4.4.4 Tests in other seasons 

Having determined a better MM5 configuration based on the July sensitivity tests, we then 

moved to other parts of the year, leaving open the possibility that we may use a different MM5 

configuration for different times of year for the final year-long 2002 simulation. The two bestperforming runs overall for July (Runs 2ae and 1bb) were run for 5-d periods in January, April, 

and October, representing the winter, spring, and fall seasons. 

Figures 4-8a through 4-8f show the performance of Runs 2ae and 1bb during the four 5-d runs 

representing the four seasons. We begin by comparing the wind performance of the two runs for 

all four seasons (Figures 4-8a,b). For January, performance in the PacNW, SW, and DesertSW is 

similar, while Run 1bb shows significant improvement in the North subdomain over Run 2ae. In 

both April and October, Run 1bb has generally lower bias and error, with the four subdomains 

clustered closer to the benchmark than in Run 2ae. In July, Run 1bb has a smaller bias, while 

Run 2ae has a smaller error. This is true for all subdomains except PacNW, for which Run 2ae 

has a smaller bias than Run 1bb. Overall, then, Run 1bb performs better for wind throughout the 

year, with the points clustered closer to the benchmark. Note, however, that all points lie outside 

the benchmark for both simulations except for January PacNW and North for Run 1bb. 

For temperature (Figures 4-8c,d) in January, Run 2ae has a smaller bias and error for North and 

DesertSW. For SW, performance is similar, and for PacNW, Run 1bb has a smaller bias and 

error. In April, Run 1bb does better; in Run 2ae, PacNW and North have a strong cold bias and 

lie far outside the benchmark. In July and October, Run 2ae performs better, with most of the 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 55 

subdomains situated within the benchmark, while in Run 1bb all subdomains lie in a halo around 

the benchmark. 

For humidity (Figures 4-8e,f), performance between the two runs is comparable. The precipitation performance (not shown) was similar enough that differences in the precipitation fields were 

not useful in choosing between the two runs. 

Figures 4-9a through 4-9c compare the observed precipitation (Figure 4-9a; for the U.S. only) 

versus estimated precipitation for Runs 2ae and 1bb (Figures 4-9b,c) for the four 5-d seasonal 

periods. There is good agreement between the observed and estimated precipitation in January. 

Both MM5 runs overstate the extent of the precipitation in April and July, but agree with the 

observations fairly well in October. The precipitation performance for Runs 2ae at 1bb is nearly 

identical. 

WRAP 36 km Run 2ae Wind Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Wind Speed Bias (m/s)

WindSpeed RMSE (m/s)

Benchmark Jan Apr Oct Jul

DesertSW 

PacNW

SW

North

Figure 4-8a. Wind soccer plot for Run 2ae seasonal test. 
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WRAP 36 km Run 1bb Seasonal Wind Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Wind Speed Bias (m/s)

WindSpeed RMSE (m/s)

Benchmark Jul Jan Apr Oct

DesertSW 

PacNW

SW

North

 

Figure 4-8b. Wind soccer plot for Run 1bb seasonal test. 

WRAP 36 km Run 2ae Temperature Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Temperature Bias (K)

Temeprature Error (K)

Benchmark Jul Jan Apr Oct

DesertSW 

PacNW

SW

North

Figure 4-8c. Temperature soccer plot for Run 2ae seasonal test. 
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WRAP 36 km Run 1bb Seasonal Temperature Performance 

Comparison

0

0.5

1

1.5

2

2.5

3

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Temperature Bias (K)

Temeprature Error (K)

Benchmark Jul Jan Apr Oct

DesertSW 

PacNW

SW

North

Figure 4-8d. Temperature soccer plot for Run 1bb seasonal test. 

WRAP 36 km Run 2ae Humidity Performance Comparison

0

1

2

3

4

5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark Jul Jan Apr Oct

DesertSW 

PacNW

SW

North

Figure 4-8e. Humidity soccer plot for Run 2ae seasonal test. 
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WRAP 36 km Run 1bb Seasonal Humidity Performance Comparison

0

1

2

3

4

5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark Jul Jan Apr Oct

DesertSW 

PacNW

SW

North

 

Figure 4-8f. Humidity soccer plot for Run 1bb seasonal test. 
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Figure 4-9a. CPC observed precipitation for the five-day modeled segment in each season. 
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 60 

Figure 4-9b. Seasonal precipitation test for Run 2ae (PX). 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 61 

Figure 4-9c. Seasonal precipitation test for Run 1bb (M-MRF). 

4.4.5 Selection of best-performing run 

The regional haze events critical for WRAP CMAQ modeling tend to occur in the summer. We 

therefore selected Run 2ae, which gave better temperature performance during the summer, as 

the best-performing run overall. In the July simulation, the winds and humidity were comparable 

between Runs 2ae and 1bb. The performance improvement in Run 1bb in January and April was 

insufficient reason to switch the LSM/PBL scheme in winter/spring because the improvement 

brought only one additional subdomain point into the benchmark, and there was no improvement 

in temperature or humidity. We therefore chose to stay with PX/ACM throughout the year to get 

the best summer simulation and to allow the use of the Pleim-Xiu dry deposition algorithm in 

CMAQ throughout the year. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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4.5 Summary of Results for the WRAP Region, and Performance in 

Other Subdomains 

4.5.1 Summary of WRAP region results 

A graphical representation of the differences between the initial MM5 run (Run 0) (Morris et al., 

2004a; Kemball-Cook et al., 2004), the interim run (Run 5) (Emery et al., 2004), and the final 

run (Run 2ae) is shown in Figures 4-10 through 4-12. Figure 4-10a shows the wind bias and error 

for all three runs. For the DesertSW subdomain, there is improvement in both bias and error as 

we progress from Run 0 through Run 2ae. For the other three subdomains, as we move from Run 

0 to Run 5, there is a reduction in error and an increase in the low wind speed bias. All points but 

one (Run 0, PacNW) lie outside the benchmark. Figure 4-10b, which shows wind direction bias 

and gross error, indicates that Run 2ae also produces improvements in the wind direction field 

relative to Runs 0 and 5. 

For temperature (Figure 4-10c), all subdomains but DesertSW now lie within the benchmarks for 

Run 2ae, and that run is clearly the best-performing run for both error and bias in all subdomains. 

The most noticeable change in going from Run 0 to Run 2ae is that DesertSW is no longer an 

outlier, but now lies close to the benchmark for error, and within it for bias. 

For humidity (Figure 4-10d), all subdomains now lie within the benchmark. Again, DesertSW 

has gone from being an outlier, whose wet bias left it far wide of the benchmark, to falling within 

the benchmark. For North, going from Run 0 to Run 5 and from Run 5 to Run 2ae brings the 

subdomain inside the benchmark. For SW, Run 2ae is an improvement in error but not in bias. 
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 63 

WRAP 36 km Run 0, Run 5, and Run 2ae Wind Performance 

Comparison

0

0.5

1

1.5

2

2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Wind Speed Bias (m/s)

WindSpeed RMSE (m/s)

Benchmark Run 0 Run 5 Run 2ae

DesertSW 

PacNW

SW

North

Figure 4-10a. Wind speed soccer plot for comparison of original (Run 0), 

interim (Run 5), and final (Run 2ae) 2002 MM5 runs. 

WRAP 36 km Run 0, Run 5, and Run 2ae Wind Direction 

Performance Comparison

0

10

20

30

40

50

-15 -10 -5 0 5 10 15

Wind Direction Bias (m/s)

Wind Direction Gross Error (m/s)

Benchmark Run 0 Run 5 Run 2ae

DesertSW 

PacNW

SW

North

Figure 4-10b. Wind direction soccer plot for comparison of original (Run 0), 

interim (Run 5), and final (Run 2ae) 2002 MM5 runs. 
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 64 

WRAP 36 km Run 0, Run 5, and Run 2ae Temperature Performance 

Comparison

0

0.5

1

1.5

2

2.5

3

3.5

4

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Temperature Bias (K)

Temeprature Error (K)

Benchmark Run 0 Run 5 Run 2ae

DesertSW 

PacNW

SW

North

Figure 4-10c. Temperature soccer plot for comparison of original (Run 0), 

interim (Run 5), and final (Run 2ae) 2002 MM5 runs. 

WRAP 36 km Run 0, Run 5, and Run 2ae Humidity Performance 

Comparison

0

1

2

3

4

5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark Run 0 Run 5 Run 2ae

DesertSW 

PacNW

SW

North

Figure 4-10d. Humidity soccer plot for comparison of original (Run 0), 

interim (Run 5), and final (Run 2ae) 2002 MM5 runs. 
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 65 

Figures 4-11a and 4-11b are time series of predicted (MM5) and observed temperature for Run 0 

and Run 2ae. In Run 0, the amplitude of the predicted diurnal temperature cycle is smaller than 

in the observations. This was attributed to the presence of excess water at the surface, indicated 

by the wet bias in DesertSW (Figure 4-10d) and the excessive precipitation seen in Run 0 in 

Figure 4-12. Since water has a higher specific heat than soil, the presence of excess water at the 

surface tends to damp the amplitude of daily temperature rise and fall. There is a marked 

improvement in Run 2ae; the amplitude of the diurnal cycle more nearly matches that of the 

observations, and the substantial cold bias of Run 0 has been replaced by a slight warm bias. 

Observed/Predicted Temperature

285

290

295

300

305

310

315

 7/ 1 7/ 2 7/ 3 7/ 4 7/ 5

K

ObsTemp PrdTemp 

 

Figure 4-11a. July 1-5, 2002, MM5 predicted versus observed surface temperature 

time series for Run 0. 

Observed/Predicted Temperature

285

290

295

300

305

310

315

 7/ 1 7/ 2 7/ 3 7/ 4 7/ 5

K

ObsTemp PrdTemp 

Figure 4-11b. July 1-5, 2002, MM5 predicted versus observed surface temperature 

time series for Run 2ae.

The precipitation field for Run 2ae is notable for its improvement over Runs 0 and 5 (Figure 

4-12). The general overprediction of precipitation is less severe in Run 2ae than in the other two 

runs. The WRAP subdomains in particular see rainfall amounts from Run 2ae that are closer to 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 66 

observed; this is reflected in the humidity soccer plot (Figure 4-10d), which shows a reduction of 

the wet bias in the DesertSW seen in Runs 0 and 5. The observed precipitation maximum over 

Texas is simulated reasonably well in Run 2ae, but is underpredicted in Runs 0 and 5. These 

precipitation patterns suggest that the Betts-Miller scheme is simulating the buildup and release 

of convective available potential energy (CAPE) more accurately than the Kain-Fritsch schemes, 

which seem to be discharging CAPE too soon and causing more widespread, less intense rainfall. 

Figure 4-12. Precipitation comparison for July 2002 for original (Run 0), 

interim (Run 5), and final (Run 2ae) MM5 runs. Observed is shown at top right. 

Table 4-4 summarizes the change in surface model performance evaluation (MPE) statistics, 

moving from the original WRAP 2002 annual run (Run 0) through the interim version (Run 5) to 

the final version (Run 2ae) in the western subdomains. There is significant improvement from 

Run 0 to Run 2ae. Of particular note are the increases in the humidity indexes of agreement 

(IOAs) in SW and DesertSW. These two subdomains had a substantial deficiency in the original 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 67 

run, with a large wet bias. Note that the wet bias in the DesertSW subdomain has been more than 

halved in going from Run 0 to Run 2ae. The gross humidity error is also greatly reduced in these 

two subdomains. PacNW and SW also show improvement in humidity gross error, but show a 

slight degradation in bias performance. Overall, the new Run 2ae model configuration resulted in 

a greatly improved simulation of humidity over the western U.S. 

Table 4-4. Surface model performance evaluation statistics summary. 

DesertSW Runs North Runs PacNW Runs SW Runs 

Statistic Bench

mark 0 5 2ae 0 5 2ae 0 5 2ae 0 5 2ae 

Wind spd bias <±0.5 -1.47 -1.30 -1.22 -0.90 -0.85 -0.94 -0.34 -0.35 -0.59 -0.82 -0.77 -0.94

Wind spd RMSE <2.0 2.69 2.51 2.21 2.32 2.2 1.88 1.97 2.05 1.67 2.16 1.98 1.85 

Wind spd IOA ≥0.6 0.55 0.57 0.70 0.72 0.73 0.83 0.67 0.65 0.77 0.68 0.70 0.77 

Wind dir bias <±10 0.05 4.79 3.13 -3.22 6.23 2.19 -3.75 9.23 5.18 -11.85 -2.28 -2.23

Wind dir gross 

error 

<30 50.5 50.3 32.8 42.2 43.4 28.3 45.9 46.6 34.2 49.3 47.3 34.2 

Temp bias <±0.5 -2.07 -0.4 0.2 -0.8 -0.26 0.22 -0.56 -1.28 -0.10 -0.64 -0.39 -0.26

Temp gross error <2 3.42 2.19 2.07 2.32 2.09 1.77 2.14 2.8 1.86 2.37 2.72 2.02 

Temp IOA ≥0.8 0.89 0.96 0.96 0.94 0.95 0.97 0.94 0.89 0.96 0.95 0.99 0.97 

Humidity bias <±1 2.73 1.90 1.00 1.08 0.75 0.40 -0.23 -0.41 -0.30 -0.28 -0.24 -0.47

Humidity gross 

error 

<2 3.14 2.40 1.91 1.80 1.60 1.30 0.92 0.96 0.88 1.72 1.41 1.42 

Humidity IOA ≥0.6 0.65 0.75 0.80 0.73 0.79 0.83 0.70 0.69 0.72 0.62 0.73 0.77 

There is also a reduction in the cold bias that was apparent in Run 0. In DesertSW, the cold bias 

that was originally –2.1°C drops to 0.2°C in Run 2ae. Reductions in the cold bias are also seen in 

the other subdomains. Likewise, temperature error is reduced in all subdomains, although not 

enough to bring it entirely within the performance benchmark. Corresponding improvements are 

seen in the temperature IOA. Finally, the wind speed and wind direction bias and error generally 

exhibit improved model performance from initial Run 0 to final Run 2ae. 

4.5.2 Tests in other subdomains 

The tests described above focused on the western U.S., and the final configuration was selected 

on the basis of those results. Optimizing the MM5 configuration for the western U.S. may cause 

degradation of performance in other parts of the country, however. To investigate this, we 

compared model performance in the non-WRAP subdomains for Run 0, Run 5, and Run 2ae. 

Example results are presented for several of the subdomains; all results are available in the task 

interim report (http://pah.cert.ucr.edu/aqm/308/docs.shtml).

Figure 4-13a displays the wind performance comparison. Run 2ae is clearly the best of the three 

runs here, with a lower error than Run 0 or Run 5 for all subdomains. For MidAtl, CenrapN, and 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 68 

NE, Run 2ae also has the smallest bias (note that the point for CenrapN coincides with the point 

for MidAtl for Run 2ae). For the CenrapS subdomain, Run 2ae has a smaller bias than Run 0, but 

a slightly larger bias than Run 5. For the OhioVal subdomain, the bias for Run 0 and Run 2ae is 

identical, and is slightly larger than that of Run 5. 

For temperature (Figure 4-13b), Run 2ae has lower or equal error for all subdomains, and a 

smaller bias for MidAtl and CenrapS. For the other subdomains, the bias in 2ae is slightly larger 

than in Run 5, but still falls well within the benchmark. All points show a warm bias in Run 2ae, 

which is a shift from Run 0, for which all subdomains have a cold bias. Run 5 shows the 

subdomains equally split between warm and cold biases. 

For humidity (Figure 4-13c), Run 2ae shows improvement over Runs 0 and 5 for all subdomains, 

lowering or eliminating the wet bias seen in Runs 0 and 5. Changes in error are less pronounced, 

but where they occur, Run 2ae has a smaller error. 

July 1-5, 2002 36 km: Run 0, Run 5, and Run 2ae Wind Performance 

Comparison

0

0.5

1

1.5

2

2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Wind Speed Bias (m/s)

WindSpeed RMSE (m/s)

Benchmark Run 0 Run 5 Run 2ae

cenrapN 

cenrapS

SE

midAtl

NE

OhioVal

 x

 o

Figure 4-13a. Wind soccer plot for comparison of original (Run 0), interim (Run 5), 

and final (Run 2ae) 2002 MM5 runs for subdomains outside WRAP. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 69 

July 1-5, 2002 36 km: Run 0, Run 5, and Run 2ae Temperature 

Performance Comparison

0

0.5

1

1.5

2

2.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Temperature Bias (K)

Temeprature Error (K)

Benchmark Run 0 Run 5 Run 2ae

cenrapN 

cenrapS

SE

midAtl

NE

OhioVal

 x

 o

Figure 4-13b. Temperature soccer plot for comparison of original (Run 0), interim (Run 5), 

and final (Run 2ae) 2002 MM5 runs for subdomains outside WRAP. 

July 1-5, 2002, 36 km: Run 0, Run 5, and Run 2ae Humidity 

Performance Comparison

0

1

2

3

4

5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark Run 0 Run 5 Run 2ae

cenrapN 

cenrapS

SE

midAtl

NE

OhioVal

 x

 o

Figure 4-13c. Humidity soccer plot for comparison of original (Run 0), interim (Run 5), 

and final (Run 2ae) 2002 MM5 runs for subdomains outside WRAP. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 70 

4.6 Additional 12-km MM5 Sensitivity Tests 

Having settled on a better MM5 configuration for the 36-km grid, we turned our attention to the 

WRAP 12-km grid. Reviewers of the MM5 sensitivity study report titled “MM5 Sensitivity 

Studies to Identify a More Optimal MM5 Configuration for Simulating Meteorology in the 

Western U.S.” (Kemball-Cook et al., 2004) raised concerns about the use of the Betts-Miller 

scheme at 12-km resolution. We therefore ran several tests to evaluate the effect of different 

cumulus parameterizations on the 12-km domain using the final “Run 2ae” configuration. At the 

12-km scale, the problem of cumulus parameterization is not well posed, as there is no clear 

spectral gap between the resolved grid-scale process and the scale of the parameterized process 

(Arakawa and Chen, 1987). Molinari (1993) suggests that parameterization of cumulus convection for grid resolutions of 2 to 20 km cannot be addressed with either a fully explicit method or 

a hybrid parameterization approach. However, our ultimate goal of doing visibility modeling 

with CMAQ required us to be pragmatic and configure MM5 in a physically reasonable way that 

produces the most accurate representation of the 2002 meteorology. Our plan for the 12-km-grid 

cumulus scheme, therefore, was to conduct several sensitivity tests to determine which cumulus 

scheme (or alternatively, no cumulus scheme at all) gives the best performance in terms of 

rainfall and surface temperature, humidity, and wind. The sensitivity tests were conducted for the 

modeling period July 1-5, 2002, and are summarized in Table 4-5. The runs are identically configured except for the choice of cumulus parameterization, and, in the case of Run BM0a, the 

surface obs nudging and surface analysis nudging. 

Table 4-5. Summary of cumulus sensitivity tests on the 12-km grid. 

Run 

ID 

36-km 

Cumulus 

12-km 

Cumulus 

 Analysis FDDA 

 3-D Surface 

Obs 

FDDA 

BM Betts-Miller Betts-Miller W/T/H W/T/H W 

BM0 Betts-Miller None W/T/H W/T/H W 

BM0a Betts-Miller None W/T/H W W 

KF Kain-Fritsch Kain-Fritsch W/T/H W/T/H W 

KFII Kain-Fritsch II Kain-Fritsch II W/T/H W/T/H W 

Figure 4-14a shows the soccer plot for the wind performance on the 12-km grid for the above 

five MM5 runs. The BM and BM0 runs have the smallest biases in all four subdomains, and 

slightly larger errors. BM0a represents a degradation in performance relative to BM and BM0. 

For wind, then, the BM and BM0 runs give the best performance, and are too similar to one 

another to say which of the two is preferable. For temperature (Figure 4-14b), the KF run is a 

clear outlier. KFII and the BM simulations give approximately equivalent performance in terms 

of temperature. For humidity (Figure 4-14c), the BM and BM0 runs are a clear improvement 

over KF and KFII. Run BM0a offers a slight improvement over BM and BM0 for the DesertSW 

and North subdomains. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 71 

WRAP 12 km July Wind Performance Comparison

0

0.5

1

1.5

2

2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Wind Speed Bias (m/s)

WindSpeed RMSE (m/s)

Benchmark BM BM0 KF KFII BM0a

Cumulus SensitivityTest

DesertSW 

PacNW

SW

North

Figure 4-14a. Wind soccer plot for cumulus sensitivity test on 12-km grid. 

WRAP 12 km July Temperature Performance Comparison

0

0.5

1

1.5

2

2.5

3

3.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Temperature Bias (K)

Temeprature Error (K)

Benchmark BM BM0 KF KFII BM0a

Cumulus Sensitivity Test

DesertSW 

PacNW

SW

North

Figure 4-14b. Temperature soccer plot for cumulus sensitivity test on 12-km grid. 
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 72 

WRAP 12 km Humidity Performance Comparison

0

1

2

3

4

5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark BM BM0 KF KFII BM0a

Cumulus Sensitivity Test

DesertSW 

PacNW

SW

North

 

Figure 4-14c. Humidity soccer plot for cumulus sensitivity test on 12-km grid. 

On balance, the BM and BM0 runs produced the best performance out of all the simulations 

tested on the12-km grid, with BM0a producing slightly degraded but comparable performance 

relative to BM and BM0. Based on peer reviewer comments on the document “MM5 Sensitivity 

Studies to Identify a More Optimal MM5 Configuration for Simulating Meteorology in the 

Western U.S.,” we chose BM0a. The reason for this choice is the concern raised by the reviewers 

regarding surface analysis nudging of temperature and humidity. While this type of nudging can 

improve the surface statistics (making the run look better on the soccer plot), it can cause a 

degradation in the aloft simulation by adversely affecting the modeled stability. 

The BM0a configuration was then used for the annual 2002 MM5 simulation to support WRAP’s 

regional haze modeling. This final MM5 simulation was conducted using the RPO Unified 

domain 36-km grid combined with the WRAP nested 12-km grid (this type of nested-grid 

simulation is referred to from here forward as a “36/12-km” simulation). The WRAP modeling 

protocol (ENVIRON and UCR, 2004) was updated to reflect the new model configuration. 

4.7 Evaluation of the Final WRAP 2002 MM5 36/12-km Annual Run 

After rerunning the MM5 36/12-km 2002 annual simulation, we compared the 36-km run’s 

performance with that of the initial WRAP_0 run (referred to as Run 0 above) as well as with 

two additional 36-km continental-scale annual runs performed by the CENRAP and VISTAS 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 73 

RPOs. We evaluated the performance of each model run in replicating the evolution of observed 

winds, temperature, humidity, precipitation, and boundary layer morphology to the extent that 

resources and data availability allowed. This served as an assessment of the reliability of the new 

WRAP run’s 36-km meteorological fields in adequately characterizing the state of the atmosphere and for serving as (1) boundary conditions for the 12-km regional-scale WRAP domain 

MM5 run and (2) the meteorological driver for the CMAQ 36-km continental-scale run. Finally, 

we evaluated the final 12-km WRAP run and assessed its suitability for providing meteorological 

conditions for the proposed 12-km CMAQ application. This work is discussed in the WRAP 

MM5 final report available at http://pah.cert.ucr.edu/aqm/308/mm5_reports04.shtml. 

4.7.1 METSTAT Surface Evaluation 

The starting point for the analysis of the new 2002 36-km MM5 runs was an assessment of the 

surface statistics for wind, temperature, and humidity using METSTAT. Soccer plots were 

generated and evaluated for each month of 2002. For brevity, we include only January (Figures 

4-15 through 4-17) and July (Figures 4-18 through 4-20) in this report. MM5's performance was 

qualitatively different during summer and winter, with the spring and fall seasons serving as 

transitional periods between winter and summer. January and July were found to be 

representative of MM5’s performance during the winter and summer seasons. The model’s 

spring statistical performance was similar to that of fall. The main strengths and weaknesses of 

the 2002 MM5 runs are captured in the January and July plots. For each of those months, we 

show a soccer plot for the subdomains in the western U.S. (the WRAP region) and a second 

soccer plot for the central and eastern U.S. This is done to reduce the amount of data shown on 

one plot to a manageable level, and also because MM5’s performance was qualitatively different 

between the western U.S. and the rest of the country. A detailed analysis is given in the final 

report (link listed above). Here, we list the major results of the analysis. 

Summary of the Annual Cycle in MM5 Surface Performance: 

• For temperature and humidity, the best-performing run overall was VISTAS; it had no 

outliers and had no serious performance problems at any particular time or subdomain. 

• For wind, the best-performing run was the new WRAP run. There was not much 

difference in wind performance among the other three runs (WRAP_0, CENRAP, 

VISTAS). 

• For wind, there is not much variation in performance in the east or the west over the 

course of the annual cycle, although the wind direction error is smaller in winter than in 

summer. The wind direction RMSE does not vary significantly over the course of the 

year for any of the four runs. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 74 

Figure 4-15. 36-km surface wind performance soccer plot for January for western U.S. 

(top) and central and eastern U.S. (bottom) 

CENRAP / VISTAS / WRAP January Wind Performance 

Comparison Over the Western U.S.

0

10

20

30

40

50

60

70

80

0 0.5 1 1.5 2 2.5 3

Wind Speed RMSE (m/s)

Wind Direction Error (m/s)

Benchmark VISTAS OldWRAP CENRAP WRAP

DesertSW 

PacNW

SW

North

CENRAP / VISTAS / WRAP January Wind Performance Comparison 

Over the Central and Eastern U.S.

0

10

20

30

40

50

60

70

80

0 0.5 1 1.5 2 2.5 3

Wind Speed RMSE (m/s)

Wind Direction Error (m/s)

Benchmark VISTAS OldWRAP CENRAP WRAP

CENRAP N

CENRAP S

Ohio Val

SE

NE 

MidAtl
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 75 

CENRAP / VISTAS / WRAP January Temperature Performance 

Comparison Over Western U.S.

0

0.5

1

1.5

2

2.5

3

3.5

-2 -1.5 -1 -0.5 0 0.5 1

Temperature Bias (K)

Temperature Error (K)

Benchmark VISTAS CENRAP WRAP OldWRAP

DesertSW 

PacNW

SW

North

CENRAP / VISTAS / WRAP January Temperature Performance 

Comparison over Central and Eastern U.S.

0

0.5

1

1.5

2

2.5

3

3.5

-2 -1.5 -1 -0.5 0 0.5 1

Temperature Bias (K)

Temperature Error (K)

Benchmark VISTAS OldWRAP CENRAP WRAP

CENRAP N

Figure 4-16. 36-km surface temperature performance soccer plot for January for western 

U.S. (top) and central and eastern U.S. (bottom).
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 76 

Figure 4-17. 36-km surface humidity performance soccer plot for January for western U.S. 

(top) and central and eastern U.S. (bottom).

CENRAP / VISTAS / WRAPJanuary Humidity Performance 

Comparison Over the Western U.S.

0

0.5

1

1.5

2

2.5

3

-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark VISTAS WRAP CENRAP OldWRAP

DesertSW 

PacNW

SW

North

CENRAP / VISTAS / WRAPJanuary Humidity Performance 

Comparison Over the Central and Eastern U.S.

0

0.5

1

1.5

2

2.5

3

-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark VISTAS OldWRAP CENRAP WRAP

CENRAP N

CENRAP S

Ohio Val

SE

NE 

MidAtl
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 77 

Figure 4-18. 36-km surface wind performance soccer plot for July for western U.S. (top) 

and central and eastern U.S. (bottom).

CENRAP / VISTAS / WRAP July Wind Performance 

Comparison Over the Western U.S.

0

10

20

30

40

50

60

70

80

0 0.5 1 1.5 2 2.5 3

Wind Speed RMSE (m/s)

Wind Dierction Error (m/s)

Benchmark VISTAS OldWRAP CENRAP WRAP

DesertSW 

PacNW

SW

North

CENRAP / VISTAS / WRAP July Wind Performance Comparison 

Over the Central and Eastern U.S.

0

10

20

30

40

50

60

70

80

0 0.5 1 1.5 2 2.5 3

Wind Speed RMSE (m/s)

Wind Dierction Error (m/s)

Benchmark VISTAS OldWRAP CENRAP WRAP

CENRAP N

CENRAP S

Ohio Val

SE

NE 

MidAtl
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 78 

Figure 4-19. 36-km surface temperature performance soccer plot for July for western U.S. 

(top) and central and eastern U.S. (bottom).

CENRAP / VISTAS / WRAP July Temperature Performance 

Comparison Over the Western U.S.

0

0.5

1

1.5

2

2.5

3

3.5

-2 -1.5 -1 -0.5 0 0.5 1

Temperature Bias (K)

Temeprature Error (K)

Benchmark VISTAS WRAP CENRAP OldWRAP

DesertSW 

PacNW

SW

North

CENRAP / VISTAS / WRAP July Temperature Performance 

Comparison Over the Central and Eastern U.S.

0

0.5

1

1.5

2

2.5

3

3.5

-2 -1.5 -1 -0.5 0 0.5 1

Temperature Bias (K)

Temeprature Error (K)

Benchmark VISTAS WRAP CENRAP OldWRAP 

CENRAP N

CENRAP S

Ohio Val

SE

NE 

MidAtl
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 79 

Figure 4-20. 36-km humidity performance soccer plot for July for western U.S. (top) and 

central and eastern U.S. (bottom).

CENRAP / VISTAS / WRAP July Humidity Performance 

Comparison Over the Western U.S.

0

0.5

1

1.5

2

2.5

3

-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark VISTAS WRAP CENRAP OldWRAP

DesertSW 

PacNW

SW

North

CENRAP / VISTAS / WRAP July Humidity Performance Comparison 

Over the Central and Eastern U.S.

0

0.5

1

1.5

2

2.5

3

-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark VISTAS OldWRAP CENRAP WRAP

CENRAP N

CENRAP S

Ohio Val

SE

NE 

MidAtl
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 80 

Summary of the Annual Cycle in MM5 Surface Performance (continued):

• For temperature in the west, all runs except the nudged WRAP_0 run lie outside the 

benchmark (except for the SW and PacificNW subdomains, which lie barely within the 

benchmark in December). Performance is much better in the east than in the west. 

• The new WRAP run had a less accurate surface temperature simulation than WRAP_0, 

which used analysis nudging of surface temperature. 

• For temperature in the east, all or nearly all of the subdomains lie within the benchmark 

during most months. 

• Humidity performance was generally within the benchmarks for all regions for all runs 

except the WRAP_0 run. 

• For humidity, in the west, performance is best in winter and deteriorates as summer 

approaches. The culprit is the humidity error, not the bias, except for the WRAP_0 run 

outliers in July in the North and DesertSW subdomains. 

• Except for SW, all western subdomains have a wet bias in July. This is likely related to 

excess simulated precipitation. 

• For eastern subdomains, humidity performance is comparable to western subdomains, as 

was true for wind. 

• The new WRAP run showed significant improvement in July humidity performance in 

the west relative to the WRAP_0 run, but does worse in the east. We can attribute both 

of these changes in performance to reduction in rainfall and therefore surface moisture 

that we get in going from the Kain-Fritsch cumulus scheme to the Betts-Miller scheme 

4.7.2 Precipitation Evaluation 

Next, we evaluated the precipitation fields in the VISTAS, CENRAP, WRAP_0, and new 

WRAP 2002 36-km MM5 runs. We examined the annual cycle in the monthly rainfall totals in 

the MM5 runs and compare them to observed monthly rainfall totals (Figures 4-21 and 4-22). 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 81 

Figure 4-21. January observed and modeled precipitation.
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 82 

Figure 4-22. July observed and modeled precipitation. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 83 

In summary, differences among the four 36-km MM5 runs in terms of precipitation performance 

were relatively small. All four runs were able to reproduce many of the major stratiform (i.e., 

resolved at grid-scale) precipitation features, and all four had difficulty with convective 

precipitation. This is to be expected, given that convection is a subgrid-scale phenomenon 

whose parameterization is poorly understood. The biggest difference among the four simulations 

came in July; in the western U.S., the overestimation of precipitation was smallest in the new 

WRAP run, but the new WRAP run also overpredicted convective rainfall in the southeastern 

United States. These changes are clearly related to the use of a different type of cumulus scheme 

in the new WRAP run than in the other three runs. For the purposes of WRAP visibility 

modeling, the new WRAP run is clearly an improvement over the WRAP_0 run, and has more 

accurate precipitation fields in the western U.S. in both summer and winter. 

4.7.3 Upper-Air Evaluation 

To assess whether MM5 is simulating the vertical structure of the atmosphere with reasonable 

accuracy in the four 36-km runs, we compared model temperature and dew point soundings with 

those from a limited number of radiosonde stations. We focused on the months of January and 

July, as these two months were at the extremes of good and poor model performance at the 

surface. 

For the months of January and July, a radiosonde station was selected from each subdomain, and 

the 00Z and 12Z observed and modeled soundings from each day were compared for the 

CENRAP, VISTAS, WRAP_0, and WRAP runs. 12Z and 00Z soundings were analyzed in order 

to examine differences between stable (nocturnal) and unstable (daytime) conditions (00Z and 

12Z are 4pm PST/7pm EST and 4am PST/7am PST, respectively). In the following discussion, 

we summarize the general features of the upper-air soundings found in the extended analysis of 

January and July. By looking at two months for a limited number of stations, we had a 

manageable amount of sounding data to analyze, but we caution that it would be unwise to place 

too much confidence in generalizing these results from two months of data from 11 stations to 

the entire MM5 domain and the entire year. We summarize the results of the upper-air analysis 

below:

• In both January and July, the CENRAP and VISTAS runs were better able to simulate 

PBL temperature inversions than were the two WRAP runs. Figure 4-23 shows the 12Z 

sounding for Spokane, WA (OTX) for July 2, 2002. The CENRAP and VISTAS runs 

both underestimate the strength of the inversion, but the problem is worse in the 

WRAP_0 and new WRAP runs. 

• CENRAP and VISTAS dew point temperature profiles were generally closer than that of 

WRAP_0 to the observed profile, and these two runs were better able to handle extreme 

excursions in temperature and dew point profiles in the lower troposphere. 

• The new WRAP run tended to have a more accurate dew point profile than the WRAP_0 

run at 12Z, and a temperature profile that was less accurate near the surface, which is 

reasonable given the lack of surface temperature analysis nudging the new WRAP run. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 84 

• Because of the analysis nudging of surface temperature, the WRAP_0 surface temperature was generally closer than CENRAP or VISTAS to the observed surface temperature, but profiles above surface were less accurate in WRAP_0 than in CENRAP or 

VISTAS. 

• For July soundings with a deep, convecting boundary layer, CENRAP and VISTAS 

frequently better reproduced the observed temperature inversion at the top of the PBL, 

while the WRAP_0 run showed a smoother decrease of temperature with increasing 

altitude. This problem is somewhat ameliorated in the new WRAP run. Figure 4-24 

shows the observed and simulated soundings for 00Z on July 16 at Midland, TX. The 

observed sounding shows a deep, dry adiabatic convecting layer with an inversion at 

700 mb. Both the CENRAP and VISTAS runs simulate an inversion just above 700 mb, 

although it is weaker than the observed inversion. The WRAP_0 temperature sounding 

shows temperature decreasing smoothly with increasing altitude, with no inversion. The 

new WRAP run still does not manage to produce an inversion, but does a better job of 

approximating the temperature profile immediately above the observed inversion. 

• The new WRAP run has a more accurate surface pressure than any of the other three 

runs. This improvement in the surface pressure in the new WRAP run seems to be a 

general feature of this run, and was noted in both summer and winter soundings. It is 

unclear what caused this improvement. 

• During winter, there was not much difference between the new and old WRAP runs. 

What differences there were occurred near the surface. The WRAP_0 run tended to have 

a more accurate temperature profile near the surface, due to its surface analysis nudging. 

Because cumulus convection is not as active during the winter, the surface temperature 

differences predominated in winter. There was generally little change in the dew point 

profile in winter. 

• In summer, there were larger differences between the new and old WRAP runs stemming 

from the difference in parameterization of cumulus convection. Although there was a 

deterioration in the temperature profile near the surface in the new WRAP run (due to 

turning off surface temperature nudging), the dew point profile was often more accurate 

in the new WRAP run 

• Overall, the upper-air profiles in the CENRAP and VISTAS runs were very similar to 

one another, and both of these runs had some marked differences with the two WRAP 

runs (i.e., success at simulating low-level nocturnal inversions or the PBL top inversion). 

It is possible that the differences between the two WRAP runs and the other two runs 

may be attributed to the different explicit moisture schemes. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 85 

Figure 4-23. July 2, 2002, 12Z Sounding for Spokane, WA. 

Upper left-hand panel: CENRAP; upper right-hand panel: VISTAS; 

lower left-hand panel: WRAP_0; lower right-hand panel: new WRAP. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 86 

Figure 4-24. July 16, 2002, 00Z Sounding for Midland, TX. 

Upper left-hand panel: CENRAP; upper right-hand panel: VISTAS; 

lower left-hand panel: WRAP_0; lower right-hand panel: new WRAP. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 87 

4.7.4 12-km METSTAT Surface Performance Evaluation 

Next, we assessed the performance of the 2002 12-km MM5 run. The evaluation of the MM5 

surface meteorological variables using METSTAT is presented first, followed by a comparison 

of the CPC observed and MM5 predicted precipitation patterns. 

As in the analysis of the 36-km run, soccer plots were generated and evaluated for each month of 

the year 2002 for the 12-km grid. On these soccer plots, we also include for reference the data 

from the 36-km run, so that we can see the effects on performance of running at higher resolution 

and with different cumulus parameterization (none, in the case of the 12-km run). As in the 

36-km run, MM5’s performance was qualitatively different during summer and winter, with the 

spring and fall seasons serving as transitional periods between winter and summer. January and 

July were found to be representative of MM5’s performance during the winter and summer 

seasons. For brevity, we include only January and July in this report. The main strengths and 

weaknesses of the 2002 12-km MM5 run are captured in the January plots (Figures 4-25 through 

4-27) and July plots (Figures 4-28 through 4-30). For both months, we show a soccer plot for the 

subdomains in the western U.S. (the 12-km WRAP region). The subdomain definitions are the 

same as those used in the 36-km run. 

WRAP 12/36 km January Temperature Performance Comparison

0

0.5

1

1.5

2

2.5

3

3.5

-2 -1.5 -1 -0.5 0 0.5 1

Temperature Bias (K)

Temeprature Error (K)

Benchmark WRAP 12 km WRAP 36 km

DesertSW 

PacNW

SW

North

Figure 4-25. 12/36-km surface temperature performance soccer plot for January for the 

WRAP region.
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WRAP 12/36 km January Wind Performance Comparison

0

10

20

30

40

50

60

70

80

0 0.5 1 1.5 2 2.5 3

Wind Speed RMSE (m/s)

Wind Direction Error (m/s)

Benchmark WRAP 12 km WRAP 36 km

DesertSW 

PacNW

SW

North

Figure 4-26. 12/36-km surface wind performance soccer plot for January for the 

WRAP region.

WRAP 12/36 km January 

Humidity Performance Comparison

0

1

2

3

-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark WRAP 12 km WRAP 36 km

DesertSW 

PacNW

SW

North

Figure 4-27. 12/36-km surface humidity performance soccer plot for January for the 

WRAP region.
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 89 

WRAP 12/36 km July Temperature Performance Comparison

0

0.5

1

1.5

2

2.5

3

3.5

-2 -1.5 -1 -0.5 0 0.5 1

Temperature Bias (K)

Temeprature Error (K)

Benchmark WRAP 12 km WRAP 36 km

DesertSW 

PacNW

SW

North

Figure 4-28. 12/36-km surface temperature performance soccer plot for July for the 

WRAP region.

WRAP 12/36 km July Wind Performance Comparison

0

10

20

30

40

50

60

70

80

0 0.5 1 1.5 2 2.5 3

Wind Speed RMSE (m/s)

Wind Direction Error (m/s)

Benchmark WRAP 12 km WRAP 36 km

DesertSW 

PacNW

SW

North

Figure 4-29. 12/36-km surface wind performance soccer plot for July for the WRAP region.
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 90 

WRAP 12/36 km July Humidity Performance Comparison

0

1

2

3

-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark WRAP12 km WRAP 36 km

DesertSW 

PacNW

SW

North

Figure 4-30. 12/36-km surface humidity performance soccer plot for July for the 

WRAP region.

We summarize the results of the 12-km analysis below: 

• January: For temperature (Figure 4-25), all subdomains fall outside the benchmark for 

both the 36-km and 12-km runs. For the North, SW, and DesertSW subdomains, the 12-

km run offers a small improvement in performance, but not enough to move the 

subdomains significantly closer to the benchmark. 

• January: For all four subdomains, the 12-km wind and humidity performance (Figures 

4-26 and 4-27) falls within the benchmark and is a slight improvement over the 36-km 

performance. 

• July: The soccer plot for temperature (Figure 4-28) shows that the bias for the 

PacificNW, North, and DesertSW subdomains is now within the benchmark. Although 

the temperature error is not within the benchmark for these three subdomains, it has been 

reduced relative to the error in January. This is a significant improvement in 

performance relative to the WRAP_0 36-km and 12-km runs, and is a result of the new 

WRAP run configuration’s optimization for improvement of the July humidity and 

temperature performance. 

• July: Very small improvements are seen in the July 2002 wind speed and wind direction 

model performance using the 12-km over the 36-km grid (Figure 4-29). 

• July: For humidity (Figure 4-30), all subdomains fall within the benchmark. This is in 

marked contrast to the WRAP_0 run (not shown), where a strong wet biases occurred in 
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 91 

the North and DesertSW subdomains due to excessive convective rainfall in those 

regions. 

The METSTAT surface analysis shows that the 12-km run is within or near performance benchmarks for wind and humidity over the annual cycle of 2002, but the temperature results fall 

outside the bias benchmark. It is possible that this is due to terrain resolution effects. The new 

12-km WRAP is a significant improvement in terms of its surface performance relative to the 

WRAP_0 12 km run. 

4.7.5 12-km Precipitation Performance Evaluation 

In this section, we evaluate the precipitation performance of the 2002 12-km MM5 run. The 

original WRAP_0 run had a strong positive precipitation bias over the WRAP region on both the 

36-km and 12-km grids. In tandem with the 36-km sensitivity tests described in Kemball-Cook 

et al. (2004b), a second series of sensitivity tests was performed on the 12-km grid to identify a 

more optimal configuration for that domain. The best choice for cumulus scheme turned out to 

be no cumulus parameterization, as discussed in Section 2. Figures 4-31a through 4-31j show a 

comparison of the observed CPC precipitation and the MM5-predicted precipitation over the 

course of the new WRAP 2002 12-km simulation. 

In January (Figures 4-31a and 4-31b), the agreement between the overall predicted and observed 

pattern is reasonably good. MM5 picks up the precipitation maxima over the mountain ranges in 

the Pacific Northwest, although rainfall amounts are too high over both ranges. There is excessive precipitation in the CenrapN and North regions, and MM5 underpredicts the rainfall over 

the central California coast. In March (Figure 4-31c and 4-31d), the model again overpredicts 

the rainfall over the mountain ranges of the Pacific Northwest. Aside from this, however, MM5 

shows impressive skill. 

As we move from winter to summer and convective rainfall becomes more important, MM5’s 

forecast skill deteriorates. Figure 4-31e and Figure 4-31f show the observed and modeled precipitation for July. Although MM5 does a reasonable job with the SW and PacNW subdomains, 

where little or no rain falls, there is a general overprediction of rainfall. This is consistent with 

the wet bias seen in the surface humidity soccer plot for July (Figure 4-30) for all subdomains 

except SW. In general, the model does a good job with the overall precipitation pattern, but 

individual maxima are overpredicted. The model is running with no convective parameterization. This means that in order for convection to occur, the entire 12-km grid column must 

saturate and be unstable. In the real world, convective updrafts tend to be smaller than 12 km 

across. This may mean that it is relatively difficult for modeled convection to be initiated, and it 

becomes unrealistically intense when it does occur because an unphysical amount of instability 

has been allowed to build up in the convecting grid cell. 

As fall arrives, and the partitioning of rainfall moves toward an increase in the stratiform component, MM5’s performance improves. Figures 4-31g and 4-31h show the observed and modeled 

precipitation for October. In October, the model underestimates precipitation in the banded 

features over the CenrapS region, but otherwise agrees reasonably well with observations. 
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 92 

MM5’s December performance (Figures 4-31i and 4-31j) is similar to that of January. The 

model simulates the overall pattern of precipitation over the Pacific Northwest, but overestimates 

the intensity of the maxima. As in January, precipitation over the North and CenrapN subdomains is overestimated. Otherwise, the model does a good job of simulating the December 

precipitation field. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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(a) (b)

(c) (d)

(e) (f)

Figure 4-31. Annual cycle in 12-km MM5 precipitation. (a) January CPC observed 

precipitation. (b) January MM5 predicted total precipitation. (c) March CPC observed 

precipitation. (d) March MM5 predicted total precipitation. (e) July CPC observed 

precipitation. (f) July MM5 predicted total precipitation. (figure continued on next page) 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 94 

(g) (h)

(i) (j)

Figure 4-31 (cont’d.). Annual cycle in 12-km MM5 precipitation. (g) October CPC 

observed precipitation. (h) October MM5 predicted total precipitation. (i) December CPC 

observed precipitation. (j) December MM5 predicted total precipitation. 

In summary, MM5 predicts the precipitation on the 12-km grid with reasonable skill over most 

of the annual cycle. The performance is better in winter than in summer. Throughout the year, 

the model tends to overpredict precipitation maxima, but does a good job of simulating the 

overall precipitation pattern. The new WRAP run exhibits better skill than the WRAP_0 run, 

particularly in July. The modeled rainfall is still excessive in July, but the severity of the overprediction and the corresponding biases in July surface temperature and humidity have lessened. 

4.8 Summary 

In this task, we conducted a series of sensitivity tests and arrived at a revised 36-km MM5 configuration that resulted in significant improvements in the precipitation, temperature, humidity, 

and wind fields, all of which are likely to help produce more accurate CMAQ results. At both 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 95 

12-km and 36-km resolutions, the new WRAP 2002 MM5 simulation produced results that are 

generally within the range of meteorological model results that have been used in the past for air 

quality applications. The new 36-km and 12-km runs represent a significant improvement in 

performance over the original WRAP_0 run. They show an improvement in the modeled precipitation fields, particularly in the summer in the Southwest. With a reduction in the overprediction of summer convective rainfall in both the 36- and 12-km runs, wet biases in the surface 

humidity and cold biases in the surface temperature have been reduced in the North and 

DesertSW subdomains in the summer months. Although the new WRAP run was optimized for 

summer performance over the WRAP region, the winter performance in the west did not deteriorate significantly. There was a loss of accuracy, particularly in temperature and humidity in the 

east, but this region is not the focus of WRAP. To summarize, the new WRAP 36/12-km run: 

• Saw its surface wind performance improve significantly throughout the year relative to 

the WRAP_0 run due to observational nudging of surface winds. 

• Showed significant improvement in summer rainfall and surface humidity performance 

relative to the WRAP_0 run in the WRAP region. 

• Did worse than the original WRAP run for humidity performance in the east (at 36-km 

resolution). We can attribute both of these changes in performance to a reduction in the 

areal coverage in rainfall and an increase in convective rainfall in active cells (and 

therefore surface moisture) that occur when going from the KF scheme to the BettsMiller scheme. 

• Showed improved temperature performance in summer in the west, and slightly worse 

performance in winter relative to the WRAP_0 run. 

• Showed a small overall degradation in performance in the east. Some of this was the 

result of eliminating the surface analysis nudging of temperature and moisture that was 

done in WRAP_0. 

Our comparison of the two 36-km WRAP runs (WRAP_0 and new WRAP) to the CENRAP and 

VISTAS 36-km continental-scale annual runs showed: 

• Overall, VISTAS performed best in the simulation of surface temperature and humidity. 

It had no outliers and no serious performance problems at any particular time or subdomain. It is unclear whether this is due to the explicit moisture physics or convection 

schemes or to the interaction between them, or to effects of differences in FDDA. 

• The new WRAP run performed best for surface winds throughout the year. 

• For precipitation, the four runs were similar in terms of performance if the whole 36-km 

domain and the entire year were considered. Over the WRAP region, however, the new 

WRAP run performed best, with the smallest overprediction of convective rain of all 

runs. The overprediction of convective precipitation was most severe in the WRAP_0 

run. 

• For upper-air structure, CENRAP and VISTAS performed best and were similar to one 

another. We attribute this to the use of the Reisner 2 scheme, as the simulation of, for 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 96 

example, the PBL inversion was relatively insensitive to the change of convection 

scheme in the WRAP runs. 

• In all four 36-km runs, MM5 performed better in winter than in summer. 

• Although WRAP_0 had the best surface statistics for temperature, its upper-air performance was worst of all four runs, which suggests that analysis nudging of surface temperature and humidity is counterproductive. The new WRAP run, which did not have 

analysis nudging of surface temperature and humidity, had larger errors in its temperature 

structure in the lowest levels of the atmosphere, but had a more realistic dew point profile 

and a smaller surface pressure bias than any of the four runs. 

Based on the upper-air soundings, the most serious problem is the difficulty MM5 has in 

establishing the observed PBL structure. MM5 has trouble getting the PBL depth right, 

particularly in the stable nocturnal case. Also, the model’s difficulty in simulating the observed 

fine structure of the dew point temperature profile and the overall level of saturation in the lower 

troposphere is cause for concern. It is important that the model produce cloud decks at the 

correct height. Errors in humidity and cloud prediction will have a negative impact on the 

accuracy of downwelling solar radiation, cause errors in the temperature profile and the surface 

fluxes, affecting chemistry, and making it difficult for the PM model to perform properly. 

We conclude, based on the results of this study, that the 36-km and 12-km WRAP MM5 runs 

exhibit reasonably good performance and are certainly within the bounds of other meteorological 

databases used for prior air quality modeling efforts. It is therefore reasonable to proceed with 

their use as inputs for future CMAQ visibility modeling. Table 4-6 shows the final MM5 

configuration that produced the results that will be used as input for the regional particulate 

matter and visibility modeling. 

Table 4-6. Final MM5 configuration for 2002 annual run for 36-km and 12-km grids. 

Run ID LSM PBL Cumulus Microphysics 

 Analysis FDDA 

 3-D Surface 

Obs 

FDDA 

36-km Grid Pleim-Xiu ACM Betts-Miller Reisner 2 W/T/H W W 

12-km Grid Pleim-Xiu ACM None Reisner 2 W/T/H W W 

4.9 Status of Task 2 Deliverables 

Table 4-7 gives the status of each Task 2 deliverable listed in the 2004 WRAP RMC work plan, 

plus any additional deliverables agreed upon after the work plan was prepared. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 97 

Table 4-7. Status of the Task 2 deliverables. 

Deliverable Status 

2002 MM5 modeling 

protocol 

Revised December 10, 2004; see 

http://pah.cert.ucr.edu/aqm/308/reports/mm5/WRAP_modeling_protocol_

revsd.pdf

2002 preliminary MM5 

modeling report 

Completed March 2004; see 

http://pah.cert.ucr.edu/aqm/308/reports/2002_MM5_2003WRAP.pdf

2002 MM5 modeling 

sensitivity report 

Revised December 10, 2004; see 

http://pah.cert.ucr.edu/aqm/308/reports/mm5/MM5SensitivityRevRep_De

c_10_2004.pdf

Response to peer-review 

comments on WRAP 

2002 MM5 modeling 

Completed December 17, 2004; see 

http://pah.cert.ucr.edu/aqm/308/reports/mm5/Response_to_Comments.pdf

2002 MM5 36/12-km 

distribution disks 

Generated January 2005 

2002 MM5 36/12-km 

CMAQ MCIP input files 

Generated January 2005 

2002 MM5 36/12-km 

CAMx input files 

Generated January 2005 

2002 MM5 36/12-km 

final evaluation report 

Completed February 28, 2005; see 

http://pah.cert.ucr.edu/aqm/308/mm5_reports04.shtml
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 98 

5. Task 3: 2002 Base Year Emissions Modeling, Processing, 

and Analysis 

5.1 Introduction 

From March 1, 2004, through February 28, 2005 (referred to in this report as project year 2004), 

the RMC emissions modeling efforts focused on (1) improving the emissions modeling 

infrastructure at the RMC; (2) developing a preliminary annual 2002 base case emissions 

simulation over the RPO Unified Continental 36-km Modeling Grid domain; (3) developing and 

evaluating emissions on the WRAP nested 12-km-resolution modeling domain; and (4) simulating various emissions perturbations for CMAQ sensitivity simulations. The RMC performed 

most of this emissions modeling with SMOKE version 2.0 (CEP, 2003). UCR and ENVIRON 

continued work begun in 2003 on the development of stand-alone emissions models for fugitive 

windblown dust (Mansell et al., 2004a) and agricultural ammonia emissions (Mansell, 2004a), 

respectively, providing emissions from these models to be merged with the emissions generated 

by SMOKE. UNC-CEP and UCR conducted all of the SMOKE modeling and quality assurance 

(QA), emissions modeling quality control (QC), and documentation. 

During the first quarter of 2004, we finished implementing a comprehensive QA/QC protocol for 

emissions modeling to ensure that all RMC emissions modeling is well-documented, reproducible, and free of avoidable errors, such as double-counted or mistakenly dropped emissions data 

(Adelman, 2004). In January 2004, we completed a preliminary 36-km emissions simulation 

with SMOKE for January and July 2002 as a test of the emissions modeling protocol and new 

versions of the SMOKE scripts; this is referred to as preliminary 2002 simulation case A, or 

Pre02a_36 (Adelman and Omary, 2004). This simulation served as a prototype for emissions 

simulation Pre02b_36 (Holland and Adelman, 2004), the first annual CMAQ-ready emissions 

simulation for 2002 conducted by the RMC. With the delivery of 2002 WRAP fire emissions 

inventories by Air Sciences Inc. for prescribed fires and wildfires in April 2004, we created 

emissions simulation Pre02c_36 (Adelman and Holland, 2004) by merging these fire data with 

the typical-year agricultural fire emissions (version July 31, 2002) and emissions simulation 

Pre02b_36. In the fall of 2004, actual 2002 inventories for the WRAP, VISTAS, and Central 

Regional Air Planning Association (CENRAP) RPOs became available, along with revised 

prescribed and wildfire inventories for the WRAP states, a 2000 Canadian inventory, and results 

from the windblown dust and agricultural NH3 emissions models mentioned above. The RMC 

combined these major data updates with other minor modifications to create the final preliminary 

36-km 2002 simulation, Pre02d_36. After the annual 2002 meteorology data for the WRAP 12-

km modeling domain became available in January 2005, we were able to complete the annual 

preliminary 12-km 2002 simulation, Pre02d_12, in February as the final simulation of project 

year 2004. Technical details, problems encountered and their resolutions, QA products, and 

qualitative summaries for emissions simulations Pre02a_36, Pre02b_36, and Pre02c_36 are 

available in the respective technical reports just cited. The same kinds of information for 

emissions simulations Pre02d_36 and Pre02d_12 are available later in this section. 
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 99 

Along with developing the above preliminary versions of the 2002 annual base case emissions 

simulation, we also created test emissions on the WRAP nested 12-km modeling domain 

(Pre02c_12), preliminary plume-in-grid (PinG) emissions for the top NOx and SO2 sources in the 

WRAP region (Pre02c_PinG), two one-month emission control scenarios for the nonroad diesel 

retrofit program (Pre02c_36s01), and two sensitivity simulations for testing the effects of natural 

versus anthropogenic fire emissions (Pre02e_36 and Pre02f_36). In addition, we integrated 

numerous data updates into the WRAP modeling from March 2004 through February 2005. 

Figure 5-1 is a timeline of all of the emissions simulations completed during project year 2004.

Simulation ID 

11/03 12/03 

1/04 2/04 3/04 4/04 5/04 6/04 7/04 8/04 9/04 

10/04 11/04 12/04 

1/05 2/05 

Pre02a_36 

Pre02b_36 

Pre02c_36 

Pre02c_12 

Pre02c_Ping 

Pre02c_36s01 

Pre02e_36 

Pre02f_36 

Pre02d_36 

Pre02d_12 

Figure 5-1. Project year 2004 emissions processing schedule. 

Table 5-1 lists the major emissions processing tasks we completed from March 2004 through 

February 2005 and indicates how they fit into the WRAP RMC 2004 work plan. 

Table 5-1. Major emissions modeling tasks completed in project year 2004. 

Simulation ID Work Plan 

Task # 

Completion 

Date Description 

Pre02a_36 3-1 01/2004 Winter/summer 2002 test 36-km simulation 

Pre02b_36 3-2 05/2004 Preliminary annual 2002 36-km simulation 

Pre02c_36 3-3 06/2004 Enhancement to simulation Pre02b_36 with the addition 

of WRAP prescribed, agricultural, and wildfires 

Pre02c_12 3-4 07/2004* Simulation to generate CMAQ emissions for the WRAP 

12-km nested domain* 
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 100 

Simulation ID Work Plan 

Task # 

Completion 

Date Description 

Pre02c_PinG 3-5 07/2004 Simulation to generate plume-in-grid 36-km emissions 

for CMAQ; a one-month (January 2002) test dataset 

Pre02c_36s01 3-3 9/2004 Nonroad mobile-source diesel retrofit sensitivity 36-km 

simulation 

Pre02e_36 3-3 9/2004 Enhancement to simulation Pre02b_36 with the addition 

of natural fire events only 

Pre02f_36 3-3 9/2004 Enhancement to simulation Pre02b_36 with the addition 

of agricultural fires and revised 2002 prescribed and 

wildfires 

Pre02d_36 3-3 1/2005 Final preliminary annual 2002 36-km simulation 

Pre02d_12 3-4 2/2005 Preliminary annual 2002 12-km simulation 

*Meteorology data were not yet available at the time of the simulation; only non-meteorology-dependent 

emissions were completed. 

The rest of Section 5 summarizes the results of each emissions case and how the data progressed 

from one simulation to the next. We first discuss all of the emissions inventories used in 

developing the preliminary 2002 modeling covered in this report (Section 5.2). Section 5.3 

highlights the sources of and major updates to the SMOKE ancillary data (spatial surrogates, 

temporal profiles, etc.) used in these emissions simulations. Section 5.4 gives details on all of the 

emissions modeling simulations listed in Table 5-1. Section 5.5 describes the emissions quality 

control system that the RMC used to document and track the different modeling tasks. Next, we 

provide a summary of the 2004 deliverables from the RMC emissions modeling team, including 

the model-ready emissions files and QA products associated with each deliverable (Section 5.6). 

The progress of the WRAP 2002 annual base case emissions modeling is discussed in Section 

5.7, which also includes summaries of the differences and qualitative comparisons among the 

various preliminary 2002 emissions cases. Section 5.8 presents technical details of the emissions 

sensitivities built from the preliminary base case 2002 simulations. A concise summary of the 

problems encountered in the 2002 emissions simulations and their corrections is then provided 

(Section 5.9), followed by a list of the outstanding issues to be addressed during the next round 

of modeling in 2005 (Section 5.10). Section 5.11 describes emissions data technology transfer 

activities that the RMC performed during this reporting period. We finish Section 5 with a 

discussion of the directions that we anticipate the RMC emissions modeling team will take 

through 2005 and into 2006 (Section 5.12). 

5.2 WRAP 2002 Emissions Inventories 

From December 2003 to December 2004, we built the 2002 WRAP emissions inventory database 

for the preliminary 2002 modeling with data from various inventory development contractors 

that are used by the WRAP Air Quality Modeling Forum and by other RPOs, the U.S. EPA, and 

Environment Canada. This section presents details about the inventories received, including the 

sources and formats of the files, how we applied them, and any problems we addressed or 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 101 

modifications we made to integrate them into the preliminary 2002 modeling. While some of the 

information presented in this section is redundant of similar text in the final reports for simulations Pre02a_36, Pre02b_36, and Pre02c_36, we have included it because the purpose of this 

section is to present a unified summary of all of the emissions inventories we modeled from 

March 2004 through February 2005. Simulation Pre02c_36 was the last simulation we completed 

that used “placeholder” U.S. 2002 inventories; all of the non-WRAP U.S. inventories in 

simulation Pre02c_36 were either U.S. 1999 National Emissions Inventory (NEI99) or NEI96 

inventories that the RMC grew to 2002 using U.S. EPA projection factors. For additional details 

about all of the emissions inventories used through simulation Pre02c_36, please refer to the 

final reports for the specific simulation. For simulation Pre02d_36, we transitioned to using 

actual 2002 inventories either developed explicitly for the RPOs or provided by the U.S. EPA as 

the preliminary NEI2002. The final inventory updates in project year 2004 occurred for 

simulation Pre02d_36. All subsequent simulations were either fire sensitivities based on 

simulation Pre02b_36 (Pre02e_36 and Pre02f_36) or a nested simulation (Pre02d_12) and did 

not integrate any new inventory data. In lieu of individual final reports for simulations 

Pre02d_36, Pre02e_36, Pre02f_36, and Pre02d_12, we are documenting these simulations 

collectively in this project report. The inventories used for Pre02d_36 represent the last set of 

inventory files received in 2004; the documentation for these data and the simulations that used 

them is included in this report. 

Table A-1 in Appendix A gives details about the inventory files received after the completion of 

simulation Pre02c_36, which represent the final set of 2002 inventories used during project year 

2004. The information in Table A-1 includes exact emissions inventory file names, the source 

agency of the files with the delivery date to the RMC, the number of records in the files, the 

spatial and temporal coverage of the files, and the pollutants contained in the files. The inventory 

files used in the Pre02d_36, Pre02e_36, Pre02f_36, and Pre02d_12 modeling are a combination 

of files created by WRAP inventory contractors, files created by contractors for other RPOs, the 

NEI2002 provided by the U.S. EPA, and inventories supplied by the U.S. EPA in cooperation 

with Environment Canada and the Mexican government. The files prepared for the WRAP, 

VISTAS, and CENRAP are actual 2002 inventories for each respective region, and the NEI2002 

covers the areas of the U.S. domain not included in the RPO inventories. The Canadian data are 

a preliminary 2000 province-level inventory distributed by the U.S. EPA, and the Mexican data 

are the same 1999 dataset that the WRAP RMC used throughout all of the preliminary 2002 

modeling. Additional details about these data are provided in Sections 5.2.1 through 5.2.11. First, 

we provide brief background information on the primary emissions categories used in SMOKE 

modeling. 

Emissions inventories are typically divided into area, mobile, point, and biogenic source categories. These divisions stem from differing methods for preparing the inventories, different characteristics and attributes of the categories, and how the emissions are processed through models. 

Generally, emissions inventories are divided into the following source categories, which we refer 

to later in the section as “SMOKE processing categories.” 

• Stationary area: Sources that are treated as being spread over a spatial extent (usually a 

county or air district) and that are not movable (as compared to nonroad mobile and onroad mobile sources). Because it is not possible to collect the emissions at each point of 

emission, they are estimated over larger regions. Examples of stationary area sources are 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 102 

residential heating and architectural coatings. Numerous sources, such as dry cleaning 

facilities, may be treated either as stationary area sources or as point sources. 

• Mobile sources: Vehicular sources that travel on roadways. These sources can be computed either as being spread over a spatial extent or as being assigned to a line location 

(called a link). Data in on-road inventories can be either emissions or activity data. 

Activity data consist of vehicle miles traveled (VMT) and, optionally, vehicle speed. 

Activity data are used when SMOKE will be computing emission factors via another 

model, such as MOBILE6. Examples of on-road mobile sources include light-duty 

gasoline vehicles and heavy-duty diesel vehicles. 

• Point sources: These are sources that are identified by point locations, typically because 

they are regulated and their locations are available in regulatory reports. Point sources are 

often further subdivided into electric generating utilities (EGUs) and non-EGU sources, 

particularly in criteria inventories in which EGUs are a primary source of NOx and SO2. 

Examples of non-EGU point sources include chemical manufacturers and furniture 

refinishers. Point sources are included in both criteria and toxics inventories. 

• Biogenic land use data: Biogenic land use data characterize the types of vegetation that 

exist in either county-total or grid cell values. The biogenic land use data in North 

America are available using two different sets of land use categories: the Biogenic 

Emissions Landcover Database (BELD) version 2 (BELD2), and the BELD version 3 

(BELD3) (CEP, 2004b). 

To initiate the preliminary 2002 modeling, we refined the emissions source categories from the 

standard definitions listed above to include more explicit emissions sectors. The advantage of 

using more detailed definitions of the source categories is that it leads to more flexibility in 

designing control strategies, substituting new inventory or profile data into the modeling, and 

managing the input and output data from SMOKE. The major drawback to defining more emissions source categories is the increased level of complexity that results from having a larger 

number of input datasets. Table 5-2 summarizes the entire group of source sectors that composed 

each of the major emissions simulations completed for the preliminary 2002 WRAP emissions 

modeling. The rest of Section 5.2 describes all of these various emissions sectors in detail. Each 

of the emissions sectors is described in terms of the SMOKE processing category, the year 

covered by the inventory, and the source(s) of the data. Additional details about the inventories 

are also provided, including any modifications that we made to prepare them for input into 

SMOKE. 

Table 5-2. Emissions inventory categories included in the preliminary 2002 simulations. 

Emissions Sector Pre02a Pre02b Pre02c Pre02d Pre02e Pre02f 

Stationary Area 9 9 9 9 9 9

Road Dust 9 9 9 9 9 9

Windblown Dust 9 

Fugitive Dust 9 9 9 9* 9 9

Agricultural and Natural NH3 9 9 9 9* 9 9
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 103 

Emissions Sector Pre02a Pre02b Pre02c Pre02d Pre02e Pre02f 

On-road Mobile 9 9 9 9 9 9

Nonroad Mobile 9 9 9 9 9 9

Offshore Mobile 9 

Offshore Point 9 9 9 9 9 9

Stationary Point 9 9 9 9 9 9

WRAP Agricultural Fires 9 9 9 9

WRAP Prescribed (Rx) Fires 9 

WRAP Anthropogenic Rx Fires 9 9

WRAP Natural Rx Fires 9 9 9

WRAP Wildfires 9 9 9 9

VISTAS Fires 9 

Biogenic 9 9 9 9 9 9

*

These sources are represented explicitly as separate categories in simulation Pre02d.

5.2.1 Stationary area sources 

We initialized the preliminary 2002 stationary-area-source emissions modeling up through 

simulation Pre02c_36 by combining an inventory developed by E.H. Pechan and Associates 

specifically for the WRAP modeling with the NEI96 (U.S. EPA, 2003a) “grown” to 2002 by the 

RMC. The annual WRAP inventory represented actual 2002 emissions and contained data for 

both the WRAP and CENRAP states (Pechan, 2003). To cover the rest of the U.S., we grew the 

NEI96 inventory to 2002 using Economic Growth Analysis System (EGAS) 4.0 growth factors 

(Pechan, 2001). We used the EPA 1995 Clear Skies inventory for Canada (U.S. EPA., 2003b) 

and the 1999 Big Bend Regional Aerosol and Visibility Observational Study (BRAVO) 

inventory for Mexico (Kuhns et al., 2003) to cover the non-U.S. inventories. In September and 

November 2004, we made the final round of inventory updates for the year, resulting in 

simulations Pre02d_36 and Pre02d_12. Starting in September, we created simulation Pre02d_36 

by replacing the grown NEI96 data with updated inventories that Alpine Geophysics began 

distributing in May 2004: an actual 2002 area-source inventory for the VISTAS region (Alpine, 

2004), the preliminary NEI2002 inventory for the rest of the U.S. (U.S. EPA, 2004b), and areasource fire data for the CENRAP and VISTAS states (Alpine, 2004). In November the U.S. EPA 

posted a preliminary 2000 area-source inventory for Canada through the Emissions Factors and 

Inventory Group (EFIG) web site (U.S. EPA, 2004c). We replaced the Canadian area-source 

inventories with these new data as the final area-source inventory update for simulation Pre02d. 

Table 5-3 summarizes the U.S. stationary-area-source inventories used for all simulations in the 

preliminary 2002 modeling. The highlighted cells in the table illustrate the major inventory 

update just described that occurred during the preliminary 2002 modeling for simulations 

Pre02d_36 and Pre02d_12. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 104 

Table 5-3. Preliminary 2002 stationary-area-source emissions inventory summary 

with references for inventories used in all simulations. 

SMOKE Processing Category: Stationary area 

Inventory Years: 2002 (U.S.), 1995 or 2000 (Canada), 1999 (Mexico) 

Temporal Coverage: Annual 

Pollutants: VOC, NOx, CO, SO2, PM10, PM2.5, NH3

Simulation WRAP CENRAP MRPO* VISTAS MANE-VU* Mexico Canada 

Pre02a_36 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02b_36 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02c_36/12 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02c_PinG Same as Pre02c but with top 100 NOx and SO2 point sources treated with plume-in-grid 

Pre02c_36s01 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02d_36/12 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA, 

2004b 

Alpine, 

2004 

U.S. EPA, 

2004b 

Kuhns et 

al., 2003 

U.S. EPA, 

2004c 

Pre02e_36 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02f_36 Pechan, 

2003

Pechan, 

2003 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

*MRPO = Midwest Regional Planning Organization; MANE-VU = Mid-Atlantic/Northeast Visibility Union 

5.2.2 Road dust sources 

To create the U.S. road dust emissions inventory for the preliminary 2002 modeling, we linearly 

interpolated between the 1996 and 2018 seasonal road dust inventories created by ENVIRON in 

late 2003 (Pollack et al., 2004). As a derivative of the 1996 and 2018 inventories, the 2002 road 

dust inventory uses the same methodology to account for the transportable fraction reductions as 

was used in developing the inventories for the WRAP §309 modeling performed in 2001-2003 

(Pollack et al., 2004). The seasonal U.S. road dust inventory covers the entire country and 

includes PM2.5 and PM10 emissions for both paved and unpaved roads. We extracted the source 

classification codes (SCCs) for paved and unpaved road dust emissions (2294000000 and 

2296000000) from the Canada and Mexico area-source inventories to create separate road dust 

inventories for these sections of the modeling domain. Table 5-4 summarizes the road dust 

inventories used for the preliminary 2002 modeling. The road dust inventories did not change 

throughout all of the preliminary 2002 WRAP modeling. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 105 

Table 5-4. Preliminary 2002 road dust emissions inventory summary with references 

for inventories used in all simulations. 

SMOKE Processing Category: Stationary area 

Inventory Year: 2002 (US), 1995 (Canada), 1999 (Mexico) 

Temporal Coverage: Seasonal (U.S.), annual (Canada, Mexico) 

Pollutants: PM10, PM2.5

Simulation WRAP CENRAP MRPO VISTAS MANE-VU Mexico Canada 

All 

Simulations 

Pollack et 

al., 2004 

Pollack et 

al., 2004 

Pollack et 

al., 2004 

Pollack et 

al., 2004 

Pollack et 

al., 2004 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

5.2.3 Windblown dust 

Pre02d_36 was the first preliminary 2002 simulation completed by the RMC that contained 

emissions from the WRAP windblown dust model (Mansell, 2004a). Refer to Section 10 of this 

report for the technical details of the windblown dust emissions used in simulations Pre02d_36 

and Pre02d_12. 

5.2.4 Anthropogenic fugitive dust sources 

For all of the preliminary 2002 simulations before Pre02d_36 and Pre02d_12, anthropogenic 

fugitive dust (referred to herein as just “fugitive dust”) emissions were included in the areasource inventory; they did not have transport fractions (which indicate the portion that is 

transported out of a source area) applied to them. To create the fugitive dust inventory for the 

Pre02d modeling, we removed the area sources represented by the SCCs in Table 5-5 and 

replaced them with data from the U.S. inventory prepared by Alpine Geophysics for VISTAS 

modeling (Alpine, 2004). The VISTAS inventory uses county-based transport fractions based on 

county land cover data. For Canada, we extracted the SCCs listed in Table 5-5 from the 2000 

Canadian inventory (U.S. EPA, 2004c) for use in simulations Pre02d_36 and Pre02d_12. There 

are no fugitive dust emissions in the BRAVO 1999 inventory for Mexico. Table 5-6 summarizes 

the fugitive dust inventories used for the preliminary 2002 modeling. The highlighted cells in 

the table indicate the major inventory update that occurred during the preliminary 2002 modeling 

for simulations Pre02d_36 and Pre02d_12. 

Table 5-5. SCCs removed from the area-source inventories and replaced with VISTAS 

or Canadian inventory data to represent explicit fugitive dust emissions. 

SCC Description Canada*

2275085000 Aircraft;Unpaved Airstrips;Total N 

2311010000 Construction:General Building Construction;Total Y 

2311010070 Construction:General Building Construction;Vehicle Traffic N 

2311020000 Construction:Heavy Construction;Total Y 

2311030000 Construction: Road Construction;Total Y 

2325000000 Mining and Quarrying: All Processes;Total Y 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 106 

2801000003 Agriculture Production - Crops;Agriculture - Crops;Tilling Y 

2801000005 Agriculture Production - Crops;Agriculture - Crops;Harvesting N 

2801000008 Agriculture Production - Crops;Agriculture - Crops;Transport Y 

2805001000 Agriculture Production - Livestock;Beef Cattle Feedlots;Dust 

Kicked-up by Hooves 

Y 

* Y = SCC present in Canadian inventory, N = not present. 

Table 5-6. Preliminary 2002 fugitive dust emissions inventory summary with references 

for inventories used in all simulations. 

SMOKE Processing Category: Stationary area 

Inventory Year: 2002 (US), 1995 (Canada), 1999 (Mexico) 

Temporal Coverage: Seasonal (U.S.), annual (Canada, Mexico) 

Pollutants: PM10, PM2.5

Simulation WRAP CENRAP MRPO VISTAS MANE-VU Mexico Canada 

Pre02a_36 in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

not 

available 

in area 

inventory 

Pre02b_36 in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

not 

available 

in area 

inventory 

Pre02c_36/12 in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

not 

available 

in area 

inventory 

Pre02c_PinG Same as Pre02c but with top 100 NOx and SO2 point sources treated with plume-in-grid 

Pre02c_36s01 in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

not 

available 

in area 

inventory 

Pre02d_36/12 Alpine, 

2004 

Alpine, 

2004 

Alpine, 

2004 

Alpine, 

2004 

Alpine, 

2004 

not 

available 

U.S. EPA, 

2004c 

Pre02e_36 in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

not 

available 

in area 

inventory 

Pre02f_36 in area 

inventory

in area 

inventory 

in area 

inventory 

in area 

inventory 

in area 

inventory 

not 

available 

in area 

inventory 

5.2.5 Agricultural NH3 sources 

Pre02d_36 was the first preliminary 2002 simulation completed by the RMC that contained 

explicit emissions from the WRAP agricultural NH3 emissions model (Mansell, 2004b). Refer to 

Section 2 of this report for the technical details of the model used to prepare NH3 emissions for 

simulations Pre02d_36 and Pre02d_12. To limit redundancy in the emissions modeling, we 

removed several SCCs from the U.S. area-source inventory that were represented explicitly in 

the NH3 emissions model. As only U.S. emissions are represented by the NH3 model, we 

retained these SCCs in the area-source inventories for Mexico and Canada. Table B-1 in 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 107 

Appendix B lists the SCCs and descriptions of the agricultural NH3 emissions sources that we 

removed from the U.S. area-source inventory. 

5.2.6 On-road mobile sources 

To model the U.S. on-road mobile sources for all preliminary 2002 simulations except 

Pre02d_36 and Pre02d_12, we combined precomputed mobile-source emissions with mobilesource activity data from the NEI99 (U.S. EPA, 2004a). ENVIRON created SMOKE emissions 

inputs for the WRAP states by running stand-alone versions of MOBILE6 and PART5 (Pollack 

et al., 2004). These seasonal 2003 inventories treated California separately from the rest of the 

WRAP region because of state-specific controls and technologies for on-road mobile sources that 

apply only to California. For both the California and “Other-WRAP” inventories, these files 

contain prespeciated PM2.5 inventories in addition to the gas-phase pollutants and PM10

(particulate matter ≤10 μm in diameter). Because these inventories contain precomputed 

emissions (daily emissions by source by county), we modeled them as area sources. We modeled 

the rest of the U.S. as mobile sources with MOBILE6 in SMOKE using activity data from the 

annual NEI99. We used the EPA 1995 Clear Skies on-road mobile inventory for Canada (U.S. 

EPA., 2003b) and the BRAVO 1999 on-road mobile inventory for Mexico (Kuhns et al., 2003) 

to cover the non-U.S. inventories. For simulations Pre02d_36 and Pre02d_12 we replaced the 

non-WRAP U.S. activities with the preliminary NEI2002 (U.S. EPA, 2004b), and the Canadian 

inventory with the 2000 inventory that became available in November 2004 (U.S. EPA, 2004c). 

Table 5-7 summarizes the U.S. on-road mobile-source inventories that we used for all 

simulations in the preliminary 2002 modeling. The highlighted cells in the table illustrate the 

major inventory update that occurred for simulations Pre02d_36 and Pre02d_12. 

Table 5-7. Preliminary 2002 on-road mobile-source emissions inventory summary with 

references for inventories used in all simulations. 

SMOKE Processing Category: Stationary area (WRAP, Canada, Mexico), mobile (rest of U.S) 

Inventory Year: 2003 (WRAP), 2002 (rest of U.S.), 1995 or 2000 (Canada), 1999 (Mexico) 

Temporal Coverage: Seasonal (WRAP), annual (rest of U.S., Canada, Mexico) 

Pollutants: VOC, NOx, CO, NH3, SO2, SO4_2.5, PMC_PRE, EC2.5, OC2.5, OTHER2.5, 

VMT, speed

Simulation WRAP CENRAP MRPO VISTAS MANE-VU Mexico Canada 

Pre02a_36 Pollack et 

al., 2004 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA,

2004a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

Pre02b_36 Pollack et 

al., 2004 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA,

2004a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

Pre02c_36/12 Pollack et 

al., 2004 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA,

2004a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

Pre02c_PinG Same as Pre02c but with top 100 NOx and SO2 point sources treated with plume-in-grid 

Pre02c_36s01 Pollack et 

al., 2004 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA,

2004a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

Pre02d_36/12 Pollack et 

al., 2004 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

Kuhns et al., 

2003 

U.S. EPA, 

2004c 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 108 

SMOKE Processing Category: Stationary area (WRAP, Canada, Mexico), mobile (rest of U.S) 

Inventory Year: 2003 (WRAP), 2002 (rest of U.S.), 1995 or 2000 (Canada), 1999 (Mexico) 

Temporal Coverage: Seasonal (WRAP), annual (rest of U.S., Canada, Mexico) 

Pollutants: VOC, NOx, CO, NH3, SO2, SO4_2.5, PMC_PRE, EC2.5, OC2.5, OTHER2.5, 

VMT, speed

Simulation WRAP CENRAP MRPO VISTAS MANE-VU Mexico Canada 

Pre02e_36 Pollack et 

al., 2004 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

Pre02f_36 Pollack et 

al., 2004

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

U.S. EPA, 

2004a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

5.2.7 Nonroad mobile sources 

To create the nonroad mobile-source inventory for all preliminary 2002 simulations, the RMC 

combined seasonal 2003 inventories for the WRAP states with the U.S. NEI, the Canadian 

National Pollutant Release Inventory, and data extracted from the Mexico area-source inventory. 

For modeling the WRAP region, we used an inventory developed by ENVIRON. Using 

NONROAD2003, ENVIRON created seasonal emissions inventories for the WRAP states only 

(Pollack et al., 2004). Like the on-road mobile-source emissions, the WRAP nonroad mobilesource inventories contain prespeciated PM2.5 inventories in addition to the gas-phase pollutants 

and PM10. For the simulations up through Pre02c_36, to model the rest of the U.S. we grew the 

NEI96 inventory (U.S. EPA., 2003a) to 2002 using EGAS 4.0 growth factors. We used the EPA 

1995 Clear Skies on-road mobile-source data for Canada (U.S. EPA., 2003b) and the BRAVO 

1999 on-road mobile inventory for Mexico (Kuhns et al., 2003) to cover the non-U.S. 

inventories. For simulations Pre02d_36 and Pre02d_12 we replaced the non-WRAP U.S. 

inventories with the preliminary NEI2002 (U.S. EPA, 2004b) and the Canadian inventory with 

the updated 2000 inventory that became available in November 2004 (U.S. EPA, 2004c). Table 

5-8 summarizes the U.S. nonroad mobile-source inventories that we used for all simulations in 

the preliminary 2002 modeling. The highlighted cells in the table illustrate the major inventory 

update that we performed for simulations Pre02d_36 and Pre02d_12. 

Table 5-8. Preliminary 2002 nonroad mobile-source emissions inventory summary with 

references for inventories used in all simulations. 

SMOKE Processing Category: Stationary area

Inventory Year: 2003 (WRAP), 2002 (rest of U.S.), 1995 or 2000 (Canada), 1999 (Mexico) 

Temporal Coverage: Seasonal (WRAP), annual (rest of U.S., Canada, Mexico) 

Pollutants: VOC, NOx, CO, NH3, SO2, SO4_2.5, PMC_PRE, EC2.5, OC2.5, OTHER2.5

Simulation WRAP CENRAP MRPO VISTAS MANE-VU Mexico Canada 

Pre02a_36 Pollack et 

al., 2004 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA,

2003a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

Pre02b_36 Pollack et 

al., 2004 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA,

2003a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

Pre02c_36/12 Pollack et 

al., 2004 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA,

2003a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 109 

SMOKE Processing Category: Stationary area

Inventory Year: 2003 (WRAP), 2002 (rest of U.S.), 1995 or 2000 (Canada), 1999 (Mexico) 

Temporal Coverage: Seasonal (WRAP), annual (rest of U.S., Canada, Mexico) 

Pollutants: VOC, NOx, CO, NH3, SO2, SO4_2.5, PMC_PRE, EC2.5, OC2.5, OTHER2.5

Simulation WRAP CENRAP MRPO VISTAS MANE-VU Mexico Canada 

Pre02c_PinG Same as Pre02c but with top 100 NOx and SO2 point sources treated with plume-in-grid 

Pre02c_36s01 Pollack et 

al., 2004 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA,

2003a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

Pre02d_36/12 Pollack et 

al., 2004 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

Kuhns et al., 

2003 

U.S. EPA, 

2004c 

Pre02e_36 Pollack et 

al., 2004 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

Pre02f_36 Pollack et 

al., 2004

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

U.S. EPA, 

2003a 

Kuhns et al., 

2003 

U.S. EPA, 

2003b 

5.2.8 Stationary point sources 

We initialized the preliminary 2002 stationary-point-source emissions modeling by combining an 

actual 2002 inventory developed by E.H. Pechan and Associates specifically for the WRAP 

modeling with the NEI96 grown to 2002 by the RMC. For all simulations except Pre02d_36 and 

Pre02d_12, we used the entire annual WRAP inventory containing data for both the WRAP and 

CENRAP states (Pechan, 2003), and for the rest of the U.S. we grew the NEI96 inventory (U.S. 

EPA., 2003a) to 2002 using EGAS 4.0 growth factors (Pechan, 2001). We used version 1 of the 

EPA 1995 Clear Skies inventory for Canada (U.S. EPA, 2003b) and the BRAVO 1999 inventory 

for Mexico (Kuhns et al., 2003) to cover the non-U.S. inventories. The major update to the 

stationary-point-source inventory for simulations Pre02d_36 and Pre02d_12 was replacing all of 

the non-WRAP U.S. inventories with NEI2002 (U.S. EPA, 2004b) data. We removed the records 

for the CENRAP states from the 2002 WRAP inventory generated by Pechan and replaced these 

data with the preliminary NEI2002. Table 5-9 summarizes the stationary-point-source 

inventories that we used for all simulations in the preliminary 2002 modeling. The highlighted 

cells in the table illustrate the major inventory update that occurred for simulations Pre02d_36 

and Pre02d_12. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 110 

Table 5-9. Preliminary 2002 stationary-point-source emissions inventory summary with 

references for inventories used in all simulations. 

SMOKE Processing Category: Point 

Inventory Year: 2002 (US), 1999 (Mexico) 

Temporal Coverage: Annual 

Pollutants: VOC, NOx, CO, SO2, PM10, PM2.5, NH3

Simulation WRAP CENRAP MRPO VISTAS MANE-VU Mexico Canada 

Pre02a_36 Pechan, 

2003 

Pechan, 

2003 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02b_36 Pechan, 

2003 

Pechan, 

2003 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02c_36/12 Pechan, 

2003 

Pechan, 

2003 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02c_PinG Same as Pre02c but with top 100 NOx and SO2 point sources treated with plume-in-grid 

Pre02c_36s01 Pechan, 

2003 

Pechan, 

2003 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02d_36/12 Pechan, 

2003 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02e_36 Pechan, 

2003 

Pechan, 

2003 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Pre02f_36 Pechan, 

2003

Pechan, 

2003 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

(U.S. EPA., 

2003a) 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

5.2.9 Offshore sources 

5.2.9.1 Offshore point sources 

The RMC combined a 1996 emissions inventory for the Gulf of Mexico with 2002 data for 

California to create an offshore-point-source inventory for the preliminary 2002 modeling. We 

obtained the Gulf of Mexico data from the EPA Clear Skies database (U.S. EPA, 2003b). The 

California data we extracted from a spreadsheet provided by the California Air Resources Board 

(CARB) (Michael Benjamin, CARB, personal communication, February 11, 2004). Table 5-10 

summarizes the U.S. offshore-point-source inventories we used. The offshore-point-source 

inventories did not change throughout all of the preliminary 2002 WRAP modeling. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 111 

Table 5-10. Preliminary 2002 offshore-point-source emissions inventory 

summary with references for inventories used in all simulations. 

SMOKE Processing Category: Point 

Inventory Year: 1996 (Gulf of Mexico), 2002 (CA) 

Temporal Coverage: Annual 

Pollutants: VOC, NOx, CO, SO2, PM10, PM2.5, NH3

Simulation Gulf of Mexico California 

All simulations US. EPA, 2003b Michael Benjamin, CARB, personal 

communication, February 11, 2004 

5.2.9.2 Offshore mobile sources 

During the QA of simulation Pre02c, we determined that a portion of the shipping emissions in 

the WRAP states was not being spatially allocated correctly. SCC 2280002000 in the nonroad 

inventory represents both in-port and ocean-going shipping emissions (Pollack et al., 2004). The 

ocean-going portion of this SCC accounts for shipping traffic outside of ports and within 25 

miles of landfall. The “Marine Ports” spatial surrogate that we used to spatially allocate this 

source placed all of the emissions in ports and did not account for the ocean-going portion of the 

data. To reconcile this issue, UNC-CEP and ENVIRON split out the ocean-going emissions from 

SCC 2280002000 to a new source that we represented by SCC 2280002001. 

After this correction, the original shipping SCC represented only in-port emissions and was 

spatially allocated using the “Marine Ports” surrogate. The new SCC represents the ocean-going 

emissions and is spatially allocated with a custom spatial surrogate created by UNC-CEP 

especially for the WRAP modeling. We created this new surrogate (“Ocean-going Shipping”) 

manually using information provided by ENVIRON about the locations of the ports with emissions represented in the WRAP nonroad mobile-source inventory. We allocated the emissions to 

cells within 25 miles of the ports in the WRAP nonroad mobile-source inventories that contained 

emissions. We used the data from the summer season nonroad mobile-source inventory to 

represent the annual ocean-going shipping emissions. For those counties that contained shipping 

emissions but did not have any ports, we allocated the emissions within 25 miles of land along 

the entire coast of the county. We introduced the offshore-mobile-source emissions into 

simulations Pre02d_36 and Pre02d_12. Table 5-11 summarizes the U.S. offshore-mobile-source 

inventory used for these two simulations. Figure 5-2 is an example of the ocean-going shipping 

emissions in the WRAP region of the 36-km modeling domain. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 112 

Table 5-11. Preliminary 2002 offshore-mobile-source emissions inventory 

summary with references for inventories used in all simulations. 

SMOKE Processing Category: Stationary area 

Inventory Year: 2002 

Temporal Coverage: Annual 

Pollutants: VOC, NOx, CO, SO2, PM10, PM2.5, NH3

Simulation U.S. Pacific Coast 

Pre02d_36/12 Pollack et al., 2004

Figure 5-2. Example of WRAP ocean-going shipping CO emissions 

in the 36-km modeling domain. 

5.2.10 Fire sources 

The WRAP Fire Emissions Joint Forum (FEJF) contractors provided fire inventories as SMOKE 

point-source files for the 13-state WRAP region. Prior to the Pre02d_36 simulation, we modeled 

U.S. and Canadian fires outside of the WRAP domain in the area-source inventory; the Mexican 

inventories do not contain any fire data. One of the inventory updates for simulation Pre02d_36 

included adding point-source fires for the VISTAS states (Alpine, 2004). The preliminary 2002 

WRAP fire inventories included two separate fire categories: prescribed (“Rx”) burning, and 

wildfires. In September 2004 we received refined fire inventories from Air Sciences Inc. (the 

WRAP fire emissions inventory contractor) that split the prescribed fire inventory into natural 

and anthropogenic fires and included a new 2002 wildfire inventory (Randall, 2004a). All 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 113 

WRAP fire inventories include daily emissions data and hourly, precomputed plume rise 

information. To represent agricultural burning in the preliminary 2002 modeling, we used the 

2018 inventory that was originally developed during the RMC’s §309 modeling and included 

base smoke management controls on the fire sources (Air Sciences, 2004). The 2002 prescribed 

and wildfire inventories were created specifically for the preliminary and final 2002 modeling 

(Randall, 2004a). The fire inventory contractor delivered the files as annual inventories with 

three files per fire category: (1) PTINV inventory files with latitude-longitude (lat-lon) locations 

for the fires; (2) PTDAY files with daily emissions totals for each fire; and (3) PTHOUR files 

with hourly plume rise characteristics that included for each fire the fraction of emissions 

allocated to model layer 1, the top of the plume in meters, and the bottom of the plume in meters. 

Additional details about each of the fire inventories used in the preliminary 2002 emissions 

modeling follow. 

5.2.10.1 Agricultural fires 

The RMC used the 2018 base smoke management agricultural burning inventory (version July 

31, 2002) to represent agricultural fires in the WRAP region for the preliminary 2002 modeling. 

The agricultural fires are the one source that did not change throughout all of the preliminary 

2002 modeling. Table 5-12 summarizes the agricultural fire inventory we used. 

Table 5-12. Preliminary WRAP 2002 agricultural fire emissions inventory 

summary with references for inventories used in all simulations. 

SMOKE Processing Category: Point with precomputed plume rise 

Inventory Year: 2018 

Temporal Coverage: Daily emissions, hourly plume rise 

Pollutants: VOC, NOx, CO, SO2, PM10, PM2.5, NH3

Simulation Agricultural fires in the WRAP states 

Pre02a_36 Air Sciences, 2004 

Pre02b_36 not used 

Pre02c_36/12 Air Sciences, 2004 

Pre02c_PinG Not applicable

Pre02c_36s01 Air Sciences, 2004 

Pre02d_36/12 Air Sciences, 2004 

Pre02e_36 not used 

Pre02f_36 Air Sciences, 2004 

5.2.10.2 Prescribed fires 

In May 2003, Air Sciences Inc. provided actual 2002 prescribed-fire inventories for the WRAP 

region to include in the preliminary 2002 modeling (Randall, 2004a). After splitting the annual 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 114 

inventory file into monthly files,*

 the RMC included these data in emissions simulation 

Pre02c_36. In September 2004 Air Sciences delivered to us a refined set of 2002 prescribed-fire 

inventories for the WRAP region that distinguished between natural prescribed fire events and 

anthropogenic ones (Randall, 2004b). Due to the large number of events in the naturalprescribed-fire inventory, we once again split the annual inventory into monthly files to avoid 

memory problems with SMOKE; the anthropogenic prescribed fires we were able to model as an 

annual inventory. The RMC included both of the refined prescribed-fire inventories in 

simulations Pre02d_36, Pre02d_12, and Pre02f_36, while in simulation Pre02e_36 we included 

only the natural-prescribed-fire inventory. Simulations Pre02e_36 and Pre02f_36, derived from 

the Pre02b_36 simulation, were designed for evaluating the effects of the new WRAP fire 

inventories and the effects of natural versus anthropogenic fire emissions on modeled haze in the 

WRAP region. To see the results of these emissions sensitivities on air quality model result, 

please refer to Section 12 of this report. Table 5-13 summarizes the prescribed-fire inventories 

that the RMC used in the different preliminary 2002 emissions simulations. 

Table 5-13. Preliminary WRAP 2002 prescribed-fire emissions inventory 

summary with references for inventories used in all simulations. 

SMOKE Processing Category: Point with precomputed plume rise 

Inventory Year: 2002 

Temporal Coverage: Daily emissions, hourly plume rise 

Pollutants: VOC, NOx, CO, SO2, PM10, PM2.5, NH3

Simulation Total Prescribed Fires 

in the WRAP States 

Natural Prescribed Fires 

in the WRAP States 

Anthropogenic Prescribed 

Fires in the WRAP States 

Pre02a_36 not used not used not used 

Pre02b_36 not used not used not used 

Pre02c_36/12 Randall, 2004a not uses not used 

Pre02c_PinG Not applicable Not applicable Not applicable 

Pre02c_36s01 Randal, 2004a not used not used 

Pre02d_36/12 not used Randall, 2004b Randall, 2004b 

Pre02e_36 not used Randall, 2004b not used 

Pre02f_36 not used Randall, 2004b Randall, 2004b 

5.2.10.3 Wildfires 

In May 2004 Air Sciences Inc. provided actual 2002 wildfire inventories for the WRAP region to 

include in the preliminary 2002 modeling (Randall, 2004a). We included these wildfire 

emissions in the simulations based on the Pre02c inventories. Then in September, Air Sciences 

delivered a refined 2002 WRAP-region wildfire inventory to us (Randall, 2004b). We included 

this refined inventory in simulations Pre02d_36, Pre02d_12, Pre02e_36, and Pre02f_36. Table 

 

*

 We split the files because the large number of sources in the annual inventory caused SMOKE to run out of 

memory. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 115 

5-14 summarizes the wildfire inventories that the RMC used in the various preliminary 2002 

emissions simulations. 

Table 5-14. Preliminary 2002 wildfire emissions inventory summary 

with references for inventories used in all simulations. 

SMOKE Processing Category: Point with precomputed plume rise 

Inventory Year: 2002 

Temporal Coverage: Daily emissions, hourly plume rise 

Pollutants: VOC, NOx, CO, SO2, PM10, PM2.5, NH3

Simulation Wildfires in the WRAP states 

Pre02a_36 not used 

Pre02b_36 not used 

Pre02c_36/12 Randall, 2004a 

Pre02c_PinG Not applicable

Pre02c_36s01 Randall, 2004a 

Pre02d_36/12 Randall, 2004b 

Pre02e_36 Randall, 2004b 

Pre02f_36 Randall, 2004b 

5.2.10.4 Other fire sources 

In the May 2004 release of VISTAS actual 2002 inventories, Alpine Geophysics provided pointsource and area-source fires for the VISTAS and CENRAP regions. The RMC adopted these 

files for the WRAP, integrating them in the preliminary 2002 modeling starting with simulation 

Pre02d_36. The VISTAS fire inventories consist of both a point-source inventory for the large 

wildfires and an area-source inventory for the agricultural fires, solid waste burning, prescribed 

fires, and small wildfires. The VISTAS point-source fire inventory is structured like the WRAP 

point-source fire inventories, consisting of daily emissions and hourly precomputed plume rise 

information. The CENRAP fire inventory treats agricultural fires, prescribed fires, and wildfires, 

regardless of size, as stationary area sources. Other U.S. fire sources, such as municipal waste 

burning, yard waste burning, and structure fires, are contained in the area-source inventories for 

all regions of the country, including the WRAP states. Canadian fires are all contained in the 

area-source inventory and include agricultural fires, wildfires, municipal and residential waste 

burning, and structure fires. Mexican fires are also contained in the area-source inventory and 

include agricultural fires, municipal and residential waste burning, and structure fires. Table 

5-15 summarizes the fire inventories that we used in the preliminary 2002 emissions simulations 

in addition to the WRAP agricultural, prescribed, and wildfire inventories discussed above. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 116 

Table 5-15. Preliminary 2002 other fire emissions inventory summary with references 

for inventories used in all simulations. 

SMOKE Processing Category: Point with precomputed plume rise (VISTAS); stationary area (entire domain) 

Inventory Year: 2002 (US), 1995/2000 (Canada), 1999 (Mexico) 

Temporal Coverage: Daily emissions (VISTAS), hourly plume rise (VISTAS), annual (entire 

domain) 

Pollutants: VOC, NOx, CO, SO2, PM10, PM2.5, NH3

Simulation WRAP CENRAP MRPO VISTAS MANE-VU Mexico Canada 

Pre02a_36 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

Kuhns et 

al., 2003 

U.S. EPA., 

2003b 

Pre02b_36 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

Kuhns et 

al., 2003 

U.S. EPA., 

2003b 

Pre02c_36/12 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

Kuhns et 

al., 2003 

U.S. EPA., 

2003b 

Pre02c_PinG Same as Pre02c but with top 100 NOx and SO2 point sources treated with plume-in-grid 

Pre02c_36s01 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

Kuhns et 

al., 2003 

U.S. EPA., 

2003b 

Pre02d_36/12 Pechan, 

2003 

Alpine, 

2004; 

U.S. EPA, 

2003a 

U.S. EPA, 

2004b 

Alpine, 

2004 

U.S. EPA, 

2004b 

Kuhns et 

al., 2003 

U.S. EPA., 

2004c 

Pre02e_36 Pechan, 

2003 

Pechan, 

2003 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

Kuhns et 

al., 2003 

U.S. EPA., 

2003b 

Pre02f_36 Pechan, 

2003

Pechan, 

2003 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

U.S. EPA., 

2003a 

Kuhns et 

al., 2003 

U.S. EPA., 

2003b 

5.2.11 Biogenic sources 

While most of the emissions inventory data came either directly from WRAP inventory contractors or from EPA, the RMC benefited from previous work done using the RPO Unified domain 

by acquiring gridded biogenic land use files developed by Alpine Geophysics for VISTAS 

modeling (Alpine, 2004). The VISTAS gridded land use data came to us in the form of binary 

input files for running the Biogenic Emissions Inventory System, version 3 (BEIS3) in SMOKE; 

they cover the entire RPO Unified domain, including Mexico and Canada. The first set of BEIS3 

biogenic emission factors that we used through simulation Pre02c_36 was version 0.97 and came 

from EPA (2004d). Starting in simulation Pre02d_36 we used an updated set of emissions 

factors, version 0.98, also provided by EPA (2004d). The differences in these files relate to 

emissions factor changes for fir and spruce trees. Table 5-16 summarizes the biogenic emissions 

inputs that we used in the preliminary 2002 emissions simulations. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 117 

Table 5-16. Preliminary 2002 biogenic emissions inventory summary 

with references for inventories used in all simulations. 

SMOKE Processing Category: Biogenic 

Inventory Year: 2002 

Temporal Coverage: n/a 

Pollutants: n/a

Simulation Gridded Land Use BELD Emissions Factors 

Pre02a_36 Alpine, 2004 v0.97 (U.S. EPA, 2004d) 

Pre02b_36 Alpine, 2004 v0.97 (U.S. EPA, 2004d) 

Pre02c_36/12 Alpine, 2004 v0.97 (U.S. EPA, 2004d) 

Pre02c_PinG Not applicable Not applicable 

Pre02c_36s01 Alpine, 2004 v0.97 (U.S. EPA, 2004d) 

Pre02d_36/12 Alpine, 2004 v0.98 (U.S. EPA, 2004d) 

Pre02e_36 Alpine, 2004 v0.97 (U.S. EPA, 2004d) 

Pre02f_36 Alpine, 2004 v0.97 (U.S. EPA, 2004d) 

5.2.12 Inventories summary for the final preliminary 2002 simulations 

Table 5-17 summarizes all of the inventories used in the final iterations of the preliminary 2002 

WRAP modeling performed in 2004: simulations Pre02d_36 and Pre02d_12.*

 This table includes 

the inventory categories modeled, the inventories’ spatial and temporal coverages, and the 

sources of the data. The cells in Table 5-17 provide references for the documentation for each 

inventory. The color codes correspond to the temporal coverages (see key at bottom of table). 

The spatial coverages of the inventories are provided in terms of the RPO or country represented 

in each inventory. The table includes inventory coverage for all five of the U.S. RPOs and for 

Mexico and Canada. 

Table 5-17. Summary of final preliminary 2002 emissions inventories compiled during 2004 

and used for simulations Pre02d_36 and Pre02d_12. 

Inventory WRAP CENRAP MRPO VISTAS MANE-VU Mexico Canada 

Stationary 

area 

Pechan, 

2003 

Pechan, 

2003 

U.S. EPA, 

2004b 

Alpine, 

2004 

U.S. EPA, 

2004b 

Kuhns et 

al., 2003 

U.S. EPA, 

2004c 

Road dust Pollack et al., 

2004 

Pollack et 

al., 2004 

Pollack et 

al., 2004 

Pollack et 

al., 2004 

Pollack et al., 

2004 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Windblown 

dust 

Mansell, 

2004a 

Mansell, 

2004a 

Mansell, 

2004a 

Mansell, 

2004a 

Mansell, 

2004a 

Not 

available 

U.S. EPA, 

2004c 

Fugitive dust Alpine, 2004 Alpine, 2004 Alpine, 

2004 

Alpine, 

2004 Alpine, 2004 Not available 

U.S. EPA, 

2004c 

 

*The same kind of table for simulations Pre02a, Pre02b, and Pre02c was given in “2004 Interim Report for the 

Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)”; see Table 5-14 in that report.
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 118 

Inventory WRAP CENRAP MRPO VISTAS MANE-VU Mexico Canada 

Agricultural 

NH3 

Mansell, 

2004b 

Mansell, 

2004b 

Mansell, 

2004b 

Mansell, 

2004b 

Mansell, 

2004b 

Kuhns et 

al., 2003 

U.S. EPA, 

2004c 

On-road 

mobile 

Pollack et al., 

2004 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

U.S. EPA, 

2004b 

Kuhns et 

al., 2003 

U.S. EPA, 

2004c 

On-road 

mobile (CA) 

Pollack et al., 

2004 

Not 

applicable 

Not 

applicable 

Not 

applicable

Not 

applicable 

Not 

applicable 

Not 

applicable

Nonroad 

mobile 

Pollack et al., 

2004 

U.S. EPA, 

2004b 

U.S. EPA,

2004b 

U.S. EPA,

2004b 

U.S. EPA, 

2004b 

Kuhns et 

al., 2003 

U.S. EPA, 

2004c 

Stationary 

point 

Pechan, 

2003 

U.S. EPA, 

2004b 

U.S. EPA,

2004b 

U.S. EPA,

2004b 

U.S. EPA, 

2004b 

Kuhns et 

al., 2003 

U.S. EPA, 

2003b 

Offshore 

point 

CARB, 

2004 

U.S. EPA, 

2003b 

Not 

available 

Not 

available 

Not 

available 

Not 

available 

Not 

available 

Offshore 

mobile 

Pollack et al., 

2004 

Not 

available 

Not 

available 

Not 

available 

Not 

available 

Not 

available 

Not 

available 

WRAP 

agricultural 

fires 

Randall, 

2004b 

Not 

applicable 

Not 

applicable 

Not 

applicable

Not 

applicable 

Not 

applicable 

Not 

applicable

WRAP 

anthropogenic Rx fires 

Randall, 

2004b, 

Not 

applicable 

Not 

applicable 

Not 

applicable

Not 

applicable 

Not 

applicable 

Not 

applicable

WRAP 

natural Rx 

fires 

Randall, 

2004b 

Not 

applicable 

Not 

applicable 

Not 

applicable

Not 

applicable 

Not 

applicable 

Not 

applicable

WRAP 

wildfires 

Randall, 

2004b 

Not 

applicable 

Not 

applicable 

Not 

applicable

Not 

applicable 

Not 

applicable 

Not 

applicable

Other fire 

sources 

Not 

applicable 

Alpine, 

2004; 

U.S. EPA, 

2003a 

U.S. EPA, 

2004b 

Alpine, 

2004 

U.S. EPA, 

2004b 

Kuhns et 

al., 2003 

U.S. EPA, 

2004c 

Biogenic Alpine, 

2004 

Alpine, 

2004 

Alpine, 

2004 

Alpine, 

2004 

Alpine, 

2004 

Alpine, 

2004 

Alpine, 

2004 

 Annual inventory Seasonal inventory Daily meteorology-dependent emissions 

5.3 WRAP 2002 Ancillary Emissions Inputs 

In addition to emissions inventories, SMOKE requires various kinds of supporting information, 

such as data that describe how to distribute the emissions in space and time, data that enable the 

partitioning of emitted pollutants to different chemical parameterizations, and meteorology data 

for modeling certain source categories. We refer to these supporting data as SMOKE ancillary 

inputs. Examples of other ancillary information include tables of default stationary-point-source 

stack parameters; numeric codes for indexing countries, states, and counties; and detailed 

descriptions of the modeled sources by SCC. For all ancillary information other than the 

meteorology data, the SMOKE directory structure stores these data in a general data directory 

aliased by the environment variable GE_DAT. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 119 

Except for the temporal profile/cross-reference files and the spatial surrogates/cross-reference 

files, all of the nonmeteorology ancillary emissions input files that we used for the preliminary 

2002 modeling originated from the SMOKE version 2 distribution (sources for the temporal and 

spatial ancillary files are discussed below). We initiated the preliminary 2002 modeling with 

nonmeteorology ancillary SMOKE inputs from the §309 modeling that the RMC performed in 

2002-2003. Throughout the preliminary 2002 modeling we made minor updates to these data and 

added new inventory-specific information as they were delivered with the new inventories. We 

also generated an initial set of 2002 36-km and 12-km meteorology data that we used in all of the 

preliminary 2002 emissions modeling to model plume rise from stationary point sources and 

fires, biogenic emissions, and on-road mobile activities; these data are described in detail in 

Section 4 of this report. The rest of Section 1.3 highlights the sources of and major updates to the 

SMOKE ancillary data used in the preliminary 2002 modeling and is organized by each major 

class of ancillary data. Tables A-2 and A-3 in Appendix A summarize the ASCII and binary 

ancillary input files used in the Pre02d_36 modeling. For detailed listings of the ancillary inputs 

used in Pre02a_36, Pre02b_36, and Pre02c_36, please refer to the final reports for those 

simulations. 

5.3.1 Temporal allocation data 

SMOKE uses temporal profiles and SCC cross-reference files to apply monthly, weekly, and 

daily temporal variability to annual and daily emissions inventories. Temporal profiles are a 

group of scalars that describe the fraction of emissions allocated to each specific temporal unit 

(i.e., month of the year, day of the week, or hour of the day). An accompanying temporal crossreference file associates monthly, weekly, and diurnal profiles (indexed with a numeric code) to 

SCCs. Temporal profile and cross-reference files combine to form the SMOKE temporal 

allocation data and contain profiles for most inventory sources; for sources that do not have 

entries in these files, default profiles are applied. The default temporal profiles are flat—in other 

words, they do not vary across months, days, or hours. We initiated the preliminary 2002 

emissions modeling with temporal data developed during the WRAP §309 modeling (Houyoux 

et al., 2003). 

Throughout the preliminary 2002 modeling, the RMC integrated new inventories into the WRAP 

modeling that required the addition of new profiles and cross-references to the base temporal 

data. Adelman and Omary (2004) document the addition of temporal profiles to model the new 

SCCs contained in the WRAP on-road mobile-source inventory. Adelman and Holland (2004) 

describe the addition of diurnal profiles for the new WRAP fire sources. For the Pre02d 

modeling, we added month-specific stationary-point-source profiles provided by Alpine 

Geophysics (2004) for VISTAS modeling that are based on continuous emissions monitoring 

(CEM) data. We also added cross-references for the new 2002 fire SCCs that apply the fire 

profiles developed for modeling simulation Pre02c (Adelman and Holland, 2004). The temporal 

allocation data files used in the final round of preliminary 2002 36-km emissions modeling 

(simulation Pre02d_36) are documented in Table A-2. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 120 

5.3.2 Spatial allocation data 

SMOKE uses spatial surrogates and SCC cross-reference files to allocate county-level emissions 

inventories to model grid cells. GIS-calculated fractional land use values define the percentage 

of a grid cell that is covered by standard sets of land use categories. For example, spatial 

surrogates can define a grid cell as being 50% urban, 10% forest, and 40% agricultural. In 

addition to land use categories, spatial surrogates can also be defined by demographic or 

industrial units, such as population or commercial area. Similar to the temporal allocation data, 

an accompanying spatial cross-reference file associates the spatial surrogates (indexed with a 

numeric code) to SCCs. The U.S. EPA distributes GIS Shapefiles, unified spatial surrogates, and 

spatial cross-referencing data for modeling the U.S., Canada, and Mexico. We used the unified 

surrogates and cross-references files to build the spatial allocation dataset used to model the 

preliminary 2002 emissions. 

The RMC initiated the preliminary 2002 emissions modeling with spatial surrogates distributed 

by the U.S. EPA. Adelman and Omary (2004) describe the integration of the “old” EPA 

surrogates into simulation Pre02a_36, and Holland and Adelman (2004) describe the application 

of the same surrogates for simulation Pre02b_36. With the delivery of the VISTAS inventory 

and ancillary data from Alpine Geophysics in May 2004, we obtained a copy of the “new” EPA 

surrogates for the RPO Unified 36-km modeling domain. We transitioned to using these data in 

simulation Pre02c_36 (Adelman and Holland, 2004). The “new” EPA surrogates (U.S. EPA, 

2004) use 65 surrogate categories as opposed to the 15 categories contained in the “old” 

surrogate dataset. UNC-CEP developed spatial surrogates for Canada on the RPO Unified 36-km 

domain and mapped the standard ESRI Shapefile codes to those contained in the EPA “new” 

surrogates. For modeling Mexico, we used Shapefiles developed for the BRAVO modeling to 

create surrogates for Mexico on the RPO Unified 36-km domain (U.S. EPA, 2004e). The 

Mexico surrogates use the 15 land use categories contained in the EPA “old” surrogates. For the 

12-km modeling, we applied the EPA Shapefiles in the “new” surrogate database to create 

spatial surrogates for the WRAP 12-km modeling domain. We used the same Shapefiles for 

Canada and Mexico that we had applied in the 36-km modeling. A minor difference between the 

36-km and 12-km modeling is that we mapped the ESRI/CIESIN Shapefile codes for Mexico to 

the U.S. EPA “new” surrogate codes (EPA99); Table 5-18 documents the mapping of spatial 

surrogate codes used by the RMC for the 12-km preliminary 2002 simulations (Pre02c_12 and 

Pre02d_12). The spatial allocation data files used in the final round of preliminary 2002 36-km 

emissions modeling (simulation Pre02d_36) are documented in Table A-2. 

Table 5-18. Mapping of ESRI/CIESIN to EPA99 codes for 12-km 

Mexican spatial surrogates. 

ESRI 

Code Description Maps to... 

EPA99 

Code Description 

1 Agriculture Æ 310 Total agriculture 

2 Airports Æ 700 Airport area 

3 Land area Æ 340 Land 

4 Housing Æ 110 Housing 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 121 

ESRI 

Code Description Maps to... 

EPA99 

Code Description 

7 Major highways Æ 240 Total road miles 

8 Population Æ 100 Population 

9 Ports Æ 800 Marine ports 

10 Railroads Æ 260 Total railroad miles 

5.3.3 Chemical speciation data 

SMOKE uses chemical profiles and SCC cross-reference files to map inventory pollutants to the 

various chemical parameterizations required by air quality models. For the preliminary 2002 

modeling, we used Carbon Bond IV gas-phase chemistry and the third-generation CMAQ 

aerosol model chemistry parameterizations (aero3). We modeled the emissions inventories 

specifically for these chemistry mechanisms, adding SCCs to the base chemical cross-reference 

file for new sources in the WRAP inventories. Adelman and Holland (2004) describe the 

addition of new VOC and PM2.5 profiles and cross-reference information to model the WRAP 

fire sources. Adelman and Omary (2004) describe the addition of chemical cross-reference 

codes to accommodate the new on-road mobile-source SCCs contained in the 2002 WRAP 

inventories. The changes that we made to the base chemical speciation data used for the 

preliminary 2002 modeling, which originated from the SMOKE version 2.0 distribution, were 

relatively minor. The chemical allocation data files used in simulation Pre02d_36 are 

documented in Table A-2. 

5.3.4 Meteorology data 

SMOKE uses meteorology data to model specific processes for stationary point sources and 

fires, biogenic emissions, and on-road mobile activities. The SMOKE/BEIS3.12 integration uses 

temperature and radiation data to calculate biogenic emissions. SMOKE uses temperature and 

atmospheric stability variables to calculate plume rise from point sources and the vertical layer 

structure of the meteorology to allocate the fire inventories with pre-computed plume rise to the 

appropriate model layers. The SMOKE/MOBILE6 integration uses temperatures and humidity 

data to adjust mobile-source emissions factors. For additional details on how hourly meteorology 

data are used by SMOKE, please refer to CEP (2004b). The RMC generated the meteorology 

data used for the 36-km and 12-km emissions modeling runs. For documentation about these 

data, refer to Section 4 of this report. The binary meteorology input data files used in simulation 

Pre02d_36 are documented in Table A-3. 

5.3.5 Other emissions input data

Table A-2 lists all of the other nonmeteorology ancillary input data used in simulation 

Pre02d_36. For the most part, these additional support files did not change from their original 

forms in the SMOKE version 2.0 distribution. We made minor modifications to these supporting 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 122 

emissions input files as the preliminary 2002 emissions modeling progressed, such as adding 

SCC descriptions for new sources in the inventory and adding descriptions of the spatial 

surrogates used for the three countries in the modeling domain. 

5.4 Description of FY 2004 Emissions Modeling Simulations 

As discussed earlier, emissions modeling performed by the RMC in project year 2004 focused 

primarily on developing the WRAP 2002 base case emissions simulation. From March 2004 to 

February 2005, we completed four preliminary 2002 (Pre02) emissions simulations on the RPO 

Unified 36-km domain (Figure 5-3): Pre02a_36, Pre02b_36, Pre02c_36, and Pre02d_36. We also 

finished one full simulation on the WRAP 12-km domain (Figure 5-4), Pre02d_12; two fire 

sensitivity simulations on the 36-km domain, Pre02e_36 and Pre02f_36; and several test simulations for assessing the RMC’s capabilities for performing plume-in-grid modeling (Pre02_PinG), 

emissions reduction sensitivities (Pre02c_36s01), and nested modeling (Pre02c_12). 

Grid 

Specification Value 

Columns 148 

Rows 112 

Layers 19 

X-origin -2,736,000 m 

Y-origin -2,088,000 m 

X-center -97 degrees 

Y-center 40 degrees 

X-cell 36,000 m 

Y-cell 36,000 m 

Vertical Top 10,000 m 

Vertical 

Layers (σ) 

1.0, 0.995, 0.99, 0.985, 

0.98, 0.97, 0.96, 0.95, 

0.94, 0.92, 0.9, 0.88, 

0.86, 0.82, 0.77, 0.7, 0.6, 

0.45, 0.25, 0.0 

Figure 5-3. RPO Unified Continental 36-km Modeling Grid domain. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 123 

Grid 

Specification Value 

Columns 207 

Rows 186 

Layers 19 

X-origin -2,376,000 m 

Y-origin -936,000 m 

X-center -97 degrees 

Y-center 40 degrees 

X-cell 12,000 m 

Y-cell 12,000 m 

Vertical Top 10,000 m 

Vertical 

Layers (σ) 

1.0, 0.995, 0.99, 0.985, 

0.98, 0.97, 0.96, 0.95, 

0.94, 0.92, 0.9, 0.88, 

0.86, 0.82, 0.77, 0.7, 0.6, 

0.45, 0.25, 0.0 

Figure 5-4. WRAP nested 12-km modeling domain. 

We used these Pre02 simulations to test the SMOKE scripts, quality assure the preliminary 2002 

emissions inventories generated by the WRAP Air Quality Modeling Forum inventory 

contractors, refine the definition of the emissions sectors, and provide preliminary results for 

conducting emissions sensitivity simulations with CMAQ. Table 5-1 in Section 5.1 and Table 

5-2 in the introductory paragraphs of Section 5.2 summarize key information on all of the 

preliminary emissions simulations covered in this section. 

5.4.1 Emissions simulation Pre02a_36 

The RMC initiated the 2004 emissions modeling tasks by completing preliminary 2002 simulation case A (Pre02a_36). A six-month simulation of the winter (December, January, February) 

and summer (June, July, August) months of 2002, Pre02a_36 carried over from the 2003 WRAP 

RMC work plan (Tonnesen et al., 2004) and served to resolve any major problems with the emissions modeling system and with the preliminary 2002 emissions and meteorology datasets. It 

also acted as a test bed for the WRAP emissions modeling QA protocol (Adelman, 2004). Table 

5-2 lists the emissions source categories included in simulation Pre02a_36. 

With the completion of simulation Pre02a_36, we established a version control system for emissions datasets, modeling inputs, SMOKE scripts and executables, and important pieces of documentation for the 2002 emissions modeling. Details about the emissions inventories used in 

simulation Pre02a_36 are included in the 2003 emissions modeling final report (Adelman and 

Omary, 2004). The quality assurance web site for simulation Pre02a_36 is 

http://pah.cert.ucr.edu/aqm/308/Pre02a_36.shtml. After successfully evaluating the emissions 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 124 

modeling results for Pre02a_36, we moved on to the first annual emissions simulation using the 

2002 meteorology and emissions inventories for the RMC in 2004: preliminary 2002 simulation 

case B (Pre02b_36). 

5.4.2 Emissions simulation Pre02b_36 

Compared with Pre02a_36, simulation Pre02b_36 integrated a few minor changes to the input 

data as well as revisions to the emissions modeling QA protocol. We modified the SMOKE 

scripts following the Pre02a_36 simulation in order to modularize the system further, add errorchecking loops, and break up the input and output directories by source category. We also added 

more in-line documentation to the scripts and cleaned up extraneous lines of source code to make 

the SMOKE user interface at the RMC more transparent. In addition to correcting some inventory-related issues discovered during the QA of Pre02a_36, we added offshore-point-source 

emissions in the Gulf of Mexico and off the coast of California, and removed the WRAP agricultural fires from the simulation in order to have an emissions simulation with no fires in the 

WRAP region to use in fire emissions sensitivities. Table 5-2 lists the emissions source categories included in simulation Pre02b_36. On the ancillary input side of the modeling, we upgraded the spatial surrogates used in Pre02a_36 with new EPA spatial surrogates for the U.S. and 

Canada. We continued to use the surrogates from Pre02a_36 to spatially allocate the Mexican 

emissions. Details about all of the changes made between simulations Pre02a_36 and Pre02b_36 

are included in the emissions simulation Pre02b_36 final report (Holland and Adelman, 2004). 

The QA web site for simulation Pre02b_36 is http://pah.cert.ucr.edu/aqm/308/Pre02b_36.shtml. 

During the final phase of the Pre02b_36 modeling, we received the 2002 WRAP fire inventories 

from the FEJF inventory contractor. The next major update to the RMC emissions inventory 

included the 2002 fires in simulation Pre02b_36, which resulted in the creation of the next 

preliminary 2002 annual simulation: Pre02c_36. 

5.4.3 Emissions simulation Pre02c_36 

After receiving the 2002 WRAP fire inventories from the FEJF inventory contractor in April 

2004, we preprocessed the files and modeled them through SMOKE for inclusion in emissions 

simulation Pre02c_36. This simulation differed from Pre02b_36 only in the addition of the 2002 

prescribed fire and wildfire and 2018 agricultural fire (version 07/31/02) inventories. Table 5-2 

lists the emissions source categories included in simulation Pre02c_36. Details about all of the 

changes made between simulations Pre02b_36 and Pre02c_36 are included in the emissions 

simulation Pre02c_36 final report (Adelman and Holland, 2004). The QA web site for simulation 

Pre02c_36 is http://pah.cert.ucr.edu/aqm/308/Pre02c_36.shtml. 

Before conducting the next annual preliminary simulation in the series (Pre02d_36), we 

completed several other, short-term simulations based on simulation Pre02c_36: (1) a simulation 

for testing nested grid modeling, (2) a simulation for testing PinG treatment of point sources, and 

(3) an emissions sensitivity study targeting a specific source category (nonroad mobile diesel 

engines). These are discussed in Sections 5.4.4 through 5.4.6. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 125 

5.4.4 Emissions simulation Pre02c_12 

We began work on the emissions for the WRAP nested 12-km modeling domain (Figure 5-4 

above) in July 2004. UNC-CEP prepared spatial surrogates using the EPA unified 12-km 

surrogates (EPA99) for the U.S. and Canada (U.S. EPA, 2004) and ESRI/CIESIN Shapefiles for 

Mexico (ESRI, 2002; CIESIN, 2004). To facilitate the cross-referencing of the spatial surrogate 

codes to specific SCCs, we mapped the ESRI/CIESIN codes to the EPA99 codes being used for 

the U.S. and Canada, as explained in Section 5.3.2 (see Table 5-18 above). With the WRAP 12-

km meteorology data not available until late 2004, we modeled only those source categories that 

did not require meteorology information to derive emissions estimates (area, nonroad mobile, onroad mobile for the WRAP states, and road dust). Once we completed the processing and QA of 

the non-meteorology-dependent emissions sectors, we archived the SMOKE scripts and 

executables in the RMC version control system and deleted the output files to conserve disk 

resources. We never used the results from emissions simulation Pre02c_12 for air quality 

modeling; we developed this simulation to test the 12-km spatial surrogates and prepare the 

scripts for the annual 12-km simulation. When the 12-km meteorology data became available, 

we modeled the entire emissions simulation, including non meteorology-dependent data, using 

the best available inventories at that time. A description of the full annual preliminary 2002 12-

km simulation (Pre02d_12) is described below in Section 5.4.10. The QA web site for the 

Pre02c_12 12-km-resolution emissions modeling is 

http://pah.cert.ucr.edu/aqm/308/qa_pre02b12.shtml. 

5.4.5 Emissions simulation Pre02c_PinG 

A parallel activity to the Pre02c_36 12-km-resolution emissions modeling was the creation of a 

one-month plume-in-grid (PinG) emissions simulation for testing SMOKE emissions with the 

CMAQ Plume Dynamics Model (PDM). We used SMOKE to select the top 100 NOx and top 

100 SO2 stationary point sources in the WRAP states from emissions simulation Pre02c to 

receive the PinG treatment. Due to a shortcoming in the way SMOKE selects sources for the 

PinG treatment, we had to split the stationary-point-source inventories into two components: the 

WRAP states and the non-WRAP portion of the domain. After dividing the inventory, we 

applied the PinG selection criteria to the WRAP inventory and prepared CMAQ-ready PinG 

emissions. SMOKE selected 144 unique sources to receive the PinG treatment in the WRAP 

region. The reason SMOKE selected 144 sources and not 200 (top 100 NOx and top 100 SO2) 

was that many of these sources are redundant (i.e., the same source exists in both lists). The 

annual PinG emissions simulation was never completed as the status of the CMAQ PinG test 

simulation. We were waiting on the results of this test simulation before proceeding with the rest 

of the modeling. 

5.4.6 Emissions simulation Pre02c_36s01 

In September 2004 we completed a two-and-a-half-month emissions sensitivity with simulation 

Pre02c that targeted nonroad mobile diesel engines in three WRAP states. Working with the 

WRAP Economic Analysis Forum, we developed a preliminary set of control factors for 

simulating the effects of a diesel retrofit emission control program on agricultural, mining, and 

construction nonroad mobile sources in Arizona, North Dakota, and Montana. If the Economic 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 126 

Analysis Forum determines that air quality modeling results using Pre02c_36s01 emissions lend 

compelling evidence to the effectiveness of these types of controls, then we would revisit the 

simulation, targeting with greater precision the sources and pollutants that the actual controls 

would affect. Additional information on simulation Pre02c_36s01 is provided in Section 5.8. 

5.4.7 Emissions simulation Pre02d_36 

Simulation Pre02d_36 was the final preliminary annual 36-km 2002 simulation of the 2004 

project year. To prepare for it, we made several improvements to the Pre02c emissions, such as 

substituting new inventories for the non-WRAP states and Canada, and updating the temporal 

and chemical allocation profiles used in SMOKE. Details about all of the changes made between 

simulations Pre02c_36 and Pre02d_36 are given in this subsection. 

As we were completing emissions simulation Pre02c_36, we also began collecting updated 2002 

inventory data to include in simulation Pre02d_36. We ultimately compiled new inventory data 

for each sector for every region of the 36-km modeling domain, except for Mexico, to create 

Pre02d_36. In addition to integrating newly available inventory data into the WRAP emissions 

modeling, we also corrected a few errors that we discovered during the modeling and QA of 

simulation Pre02c_36. The data collection effort for Pre02d_36 began in May 2004 with the 

release of the VISTAS 2002 emissions inventory by Alpine Geophysics, and finished in 

November 2004 with the release of a preliminary 2000 national Canadian inventory by the U.S. 

EPA. The only source categories that did not need reprocessing for simulation Pre02d_36 were 

the offshore point sources and the agricultural fire emissions. Due to inventory updates, 

refinements, or additions, the RMC reprocessed every other source category beginning in 

November 2004 and finishing the modeling and QA of simulation Pre02c_36 in January 2005. 

The emissions modeling tasks we performed for the Pre02d_36 simulation consisted of the 

following components: 

• Receive and preprocess the 2002 VISTAS and preliminary 2002 U.S. NEI inventories 

from Alpine Geophysics. 

o Split the area-source inventories by emissions source sector and spatial region to 

conform with the WRAP RMC emissions inventory configuration (Section 5.2). 

We split these inventories into the following components: stationary area, fugitive 

dust, and road dust for the VISTAS, CENRAP, and “other U.S.” regions of the 

modeling domain. 

o Remove the fugitive dust sources that are contained in the dust inventory prepared 

by Alpine (which uses county-based transport fractions) from the U.S. area-source 

inventories. Table 5-5 lists the dust sources that we removed from these 

inventories. 

o Remove the agricultural and natural NH3 emissions sources from the U.S .areasource inventories that are represented in the WRAP process-based NH3

emissions model. Table B-1 lists the NH3 emissions sources that we removed 

from these inventories. 

• Receive and preprocess the revised 2002 wildfire and prescribed fire emissions 

inventories from Air Sciences Inc. that distinguish between natural and anthropogenic 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 127 

fires. This involved splitting the annual natural prescribed fire inventory into monthly 

files. 

• Integrate the 2018 base smoke management agricultural burning area-source 

inventory received in July 2002 during the §309 modeling. 

• Integrate the gridded output from the WRAP windblown dust model. 

• Integrate the gridded output from the ENVIRON NH3 emissions model. 

• Ensure correct temporal and spatial coverage of the inventories. 

• Run SMOKE for the annual simulation. 

• Perform QA on the annual period results. 

• Deliver the emissions for use in CMAQ modeling. 

More information on these components of the Pre02d_36 simulation is given in various sections 

of this report. In Sections 5.2 and 5.3, respectively, we described the SMOKE inventory files 

and SMOKE ancillary files used. In Sections 5.5 and 5.6, we provide summaries and descriptions 

that illustrate the application of the WRAP QA protocol to the emissions modeling for this task; 

the documentation and QA web site for simulation Pre02d_36 are accessible at 

http://pah.cert.ucr.edu/aqm/308/Pre02d_36.shtml. Section 5.7 discusses the differences between 

simulation Pre02d_36 and the two previously completed annual simulations, Pre02b_36 and 

Pre02c_36. In Section 5.9 we describe the problems and issues that arose during the modeling of 

this simulation, and make recommendations about what can be done in the next round of 2002 

emissions modeling to improve the data collection, modeling, and QA efforts. Appendix A 

summarizes the input and output data (Tables A-1 through A-3) and the SMOKE configuration 

options (Table A-4) for simulation Pre02d_36. Appendix C contains emissions density maps for 

the significant pollutants from all the major emissions source categories modeled in this 

simulation. Appendix D contains bar charts that qualitatively compare simulations Pre02c_36 

and Pre02d_36, illustrating how the inventory updates that occurred between these two 

simulations affect the emissions totals in each region of the modeling domain. 

5.4.8 Emissions simulation Pre02e_36 

The RMC designed the annual emissions simulation Pre02e_36 to study the effects of natural 

versus anthropogenic fire emissions. With the delivery of the revised 2002 fire inventories by 

Air Sciences in September 2004, we designed this simulation to evaluate the effects of the new 

natural prescribed and wildfire inventories on the modeling. We created simulation Pre02e_36 

by adding the natural prescribed and wildfire inventories to simulation Pre02b_36, the base 

simulation that contained no fires at all; a comparison between Pre02b_36 and Pre02e_36 

therefore illustrates how the natural fire inventories affect the modeling. As simulation 

Pre02b_36 was extensively quality assured and documented in Holland and Adelman (2004) and 

we changed it to create Pre02e_36 only by adding the revised fire inventories, we did not have to 

perform much additional QA on simulation Pre02e_36. The fire inventories that distinguish 

Pre02e_36 from Pre02b_36 are documented and quality assured in Section 5.2.10. Section 5.7 

discusses the differences between simulation Pre02e_36 and the other annual simulations 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 128 

completed in 2004. Section 5.8 also summarizes the emissions sensitivities completed during this 

calendar year, including simulations Pre02e_36 and Pre02f_36. 

5.4.9 Emissions simulation Pre02f_36 

We designed the annual emissions simulation Pre02f_36 to study the effects of the revised 2002 

fire emissions inventories and to study the effects of natural fire emissions versus all fire 

emissions. When we received the revised 2002 fire inventories in September 2004, we designed 

this simulation for comparison with simulations Pre02b_36, Pre02c_36, and Pre02e_36. We 

created simulation Pre02f_36 by adding the base smoke management agricultural fires, natural 

and anthropogenic prescribed fires, and revised wildfire inventories to simulation Pre02b_36, the 

base simulation that contained no fires at all. A comparison between Pre02b_36 and Pre02f_36 

therefore illustrates how adding all of the 2002 fire inventories affects the air quality modeling 

results. Comparing simulations Pre02c_36 and Pre02f_36 shows the effects of the old versus 

new fire inventories. Finally, comparing simulations Pre02e_36 and Pre02f_36 illustrates the 

effects of natural fire inventories only versus all fire inventories. As simulation Pre02b_36 was 

extensively quality assured and documented in Holland and Adelman (2004) and we changed it 

to create Pre02f_36 only by adding the above revised fire inventories, we did not have to 

perform much additional QA on simulation Pre02f_36. The fire inventories that distinguish 

Pre02f_36 from Pre02b_36 are documented and quality assured in Section 5.2.10. Section 5.7 

discusses the differences between simulation Pre02f_36 and the other annual simulations 

completed in 2004. Section 5.8 also summarizes the emissions sensitivities completed during this 

calendar year, including simulations Pre02e_36 and Pre02f_36. 

5.4.10 Emissions simulation Pre02d_12 

The RMC completed the entire 12-km annual meteorology data set in January 2005, allowing us 

to begin the modeling and QA of all the 12-km emissions. We initiated the 12-km emissions 

modeling with simulation Pre02c_12 in July 2004, debugging the SMOKE scripts and checking 

the initial results of the fine-grid simulation (see Section 5.4.4). We completed the full 12-km 

emissions simulation, Pre02d_12, by the end of February 2005. To create spatial surrogates for 

this simulation, we used the EPA 65-category “new” 12-km surrogates (U.S. EPA, 2004e) (see 

Section 5.3.2). We applied the same ESRI/CIESIN Shapefiles that we used for the Pre02d_36 

simulation, simply applying them to the finer-scale 12-km grid. The inventory QA that we 

applied to simulation Pre02d_36 also applied to the 12-km nested grid; basic QA of the 12-km 

emissions involved confirming that we had allocated the data to the correct model grid cells. The 

same input and output data and SMOKE configuration options for simulation Pre02d_36 

summarized in Appendix A also applied to simulation Pre02d_12. We prepared the 12km Pre02d 

emissions for use in a nested simulation that is based off the 36km Pre02d simulation. Appendix 

C contains emissions density maps by county for the significant pollutants from all of the major 

emissions source categories modeled in Simulation Pre02d_12. These maps are not based on a 

model grid resolution but merely show county level emissions normalized by the county land 

area. Appendix D contains bar charts comparing the inventories from simulations Pre02c and 

Pre02d, illustrating the effects of the inventory updates that occurred between these two 

simulations. Similar to the density maps, these results are model grid-independent as they are 

based on state totals only and are applicable to all model grid resolutions. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 129 

5.5 RMC Emissions Modeling Deliverables and QA/QC Products 

Quality assurance in the context of emissions modeling refers to ensuring that the results of the 

simulations are of the highest quality possible with respect to the data. Quality control, on the 

other hand, refers to ensuring that the model simulations completed successfully and without 

errors. QA focuses on the quality of the data; QC focuses on the process of modeling, archiving, 

and documenting the data. Some of RMC products, like the emissions spatial plots and the 

simulation web sites, are used for both QA and QC. Other products, such as the SMOKE settings 

document are used for QC only. This section describes both the QA and QC products that the 

RMC used during this project year to ensure high quality model results and that the modeling 

software performed as we expected. 

Table 5-19 lists the RMC web site locations and the Bugzilla ticket numbers associated with all 

of the emissions simulations processed from March 2004 through February 2005 (see Section 

5.4). Bugzilla is an on-line bug tracking system that we use as an alternative to conventional email for tracking and archiving correspondence among the emissions modeling team members. 

To access the database containing the Bugzilla tickets, visit http://bugz.unc.edu and use the 

“Enter a bug #” box to search for the ticket numbers listed in Table 5-19. The web sites listed in 

that table contain links to documentation and QA/QC graphics for each of the simulations. Those 

reports and QA/QC graphics, plus the CMAQ-ready emissions they document, constitute the 

deliverables for Task 3 in the WRAP RMC 2004 work plan. Section 5.6 summarizes the 

deliverables and QA/QC products generated by the RMC during this reporting period. 

Table 5-19. Web sites and Bugzilla ticket numbers for preliminary 2002 

emissions simulations. 

Simulation 

ID 

Web Site for Links to Documentation and QA Graphics Bugzilla Ticket # 

Pre02a_36 http://pah.cert.ucr.edu/aqm/308/

Pre02a_36.shtml

805, 858, 859 

Pre02b_36 http://pah.cert.ucr.edu/aqm/308/

Pre02b_36.shtml

905, 924, 925, 929, 930, 933, 939, 941-944, 

949, 952-958, 960, 967, 968, 973-977, 979, 

981, 982, 985, 989, 991, 994 

Pre02c_36 http://pah.cert.ucr.edu/aqm/308/

Pre02c_36.shtml

992, 999, 1007, 1010 

Pre02b_12 http://pah.cert.ucr.edu/aqm/308/

Pre02b_12.shtml

1067 

Pre02c_PinG None 1068 

Pre02c_36s01 None 1356 

Pre02d_36 http://pah.cert.ucr.edu/aqm/308/

Pre02d_36.shtml

1104, 1146, 1179, 1184, 1185, 1259, 1260, 

1340, 1364, 1411, 1412, 1431, 1454, 1482 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 130 

Simulation 

ID 

Web Site for Links to Documentation and QA Graphics Bugzilla Ticket # 

Pre02e_36 None None 

Pre02f_36 None None 

Pre02d_12 http://pah.cert.ucr.edu/aqm/308/

Pre02d_12.shtml 

1512 

The following subsections summarizes the deliverables and QA products we generated during 

project year 2004. It also includes a summary of the SMOKE time and disk requirements for 

annual simulations on the RMC compute servers (using simulation Pre02c_36 as an example), 

and documents the location of the data on the servers and the version control system we used. 

We generated all of the SMOKE modeling results for these simulations on the RMC computing 

cluster. The input files, SMOKE executables, SMOKE scripts, and model output are all available 

through the RMC. We documented simulations Pre02a through Pre02c with comprehensive final 

reports (Adelman and Omary, 2004; Holland and Adelman, 2004; Adelman and Holland, 2004); 

these include descriptions of the work performed to complete the simulations, comparisons to the 

previous simulation, and QA products documenting the input files and model configurations 

used for each simulation. We have documented the final preliminary 2002 simulation, Pre02d, in 

this report. The web sites documenting the QA procedures performed on these simulations are 

listed in Table 5-19 above. 

5.5.1 SMOKE time requirements and disk use 

Managing the disk use and time requirements of the SMOKE simulations is as large a component of the emissions quality control as is the assurance of quality results. With large-spatialscale, annual simulations like those described above, the length of time required for completing 

the simulations and the amount of disk space required to store all of the input, intermediate, and 

final output files are both massive. Table 5-20 summarizes the run times and disk use requirements for each emissions component of simulation Pre02c_36. The statistics in Table 5-20 are 

approximations. The disk use estimates account for the intermediate and output files only; the 

actual disk requirements for the 36-km Pre02c simulation are slightly larger than those shown. 

The totals show that simulation Pre02c_36 required over 1.6 terabytes of disk storage and took 

almost 400 total hours to complete. Figures 5-5 and 5-6 are graphical summaries of the time 

requirements and disk use by source category and SMOKE program. Only the SMOKE programs that required the greatest amount of disk space and took the longest to run are displayed in 

these figures. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 131 

Table 5-20. SMOKE time and disk use statistics for simulation Pre02c_36 km. 

Source 

Category 

Disk Use 

(MB) 

Disk Use

(%) 

Run Time

(Hours) 

Run Time 

(%) 

Stationary area 25,540 1.6 8.54 2.1 

Road dust 3,776 0.2 1.33 0.3 

Nonroad mobile 20,675 1.3 5.12 1.3 

On-road mobile 29,150 1.8 80.55 20.1 

Point 431,028 26.4 82.75 20.7 

Offshore 181,743 11.1 9.07 2.3 

Ag fire 219,489 13.4 55.03 13.8 

Rx fire 243,713 14.9 71.48 17.9 

Wildfire 251,263 15.4 20.97 5.2 

Biogenic 29,503 1.8 4.08 1.0 

Merge 199,655 12.2 60.83 15.2 

Total 1,635,535 100.0 399.75 100.0 

AR

RD

NR

PT

MB

MB-VMT

OS

BG

AGF

RXF

WF

Merge

SMKINVEN

EMISFAC

TMPBEIS3

TEMPORAL

LAYPOINT

SMKMERGE

MRGGRID

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Minutes

Figure 5-5. Simulation Pre02c_36 timing statistics by source and SMOKE program for the 

programs that took the longest to run. (AR: stationary area, RD: road dust, NR: nonroad mobile, 

PT: point, MB: WRAP on-road mobile, MB-VMT: non-WRAP on-road mobile, OS: offshore point, 

BG: biogenic, AGF: agricultural fires, RXF: prescribed fires, WF: wildfires)
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 132 

AR

RD

NR

PT

MB

MB-VMT

OS

BG

AGF

RXF

WF

Merge

SMKINVEN

NORMBEIS3

TEMPORAL

LAYPOINT

SMKMERGE MRGGRID

0

50000

100000

150000

200000

250000

Megabytes

Figure 5-6. Simulation Pre02c_36 disk usage by source and SMOKE program for the 

programs that required the greatest amount of disk space. (AR: stationary area, RD: road dust, 

NR: nonroad mobile, PT: point, MB: WRAP on-road mobile, MB-VMT: non-WRAP on-road mobile, 

OS: offshore point, BG: biogenic, AGF: agricultural fires, RXF: prescribed fires, WF: wildfires)

5.5.2 RMC version control 

To facilitate reproducibility of the RMC emissions modeling results and to provide enhanced QC 

of the modeling, we first implemented the WRAP emissions modeling QA protocol (Adelman, 

2004) during simulation Pre02a_36. The protocol defines strict procedures for data and simulation management, administering the SMOKE modeling system and input/output data, and documenting all modeling procedures. As specified in the QA protocol, we used the Concurrent Versions System (CVS) to provide version control for the simulations completed in 2004. Version 

control is a system for archiving the scripts, configuration files, and input files so that a simulation can be reproduced in the future. CVS uses a system of revision tags, or text identifiers, to 

label both ASCII and binary files. As the RMC adds new files or updates existing files to the 

modeling system, a CVS command is invoked to tag the files and associate them with the applicable simulation. As the simulation is completed and the QA finalized, a final CVS tag is added 

to the files that can be used to recall all of the scripts, executables, and inputs that generated the 

emissions simulation. Table 5-21 lists the CVS revision tags used for version control during the 

preliminary 2002 modeling. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 133 

Table 5-21. CVS revision tags for preliminary 2002 modeling. 

Simulation ID CVS Revision Tag 

Pre02a_36 Pre02a_36_Final 

Pre02b_36 Pre02b_36_Final 

Pre02c_36 Pre02c_36_Final 

Pre02b_12 Pre02c_12_Final 

Pre02c_PinG Pre02c_36_Ping_Final 

Pre02c_36s01 Pre02c_36s01_Final 

Pre02d_36 Pre02d_36_Final 

Pre02e_36 Pre02e_36_Final* 

Pre02f_36 Pre02f_36_Final* 

Pre02d_12 Pre02d_12_Final 

*Assigns directory only. 

To invoke the CVS revision tags on the RMC compute servers, the CVSROOT environment 

variable must be set to point to the location of the preliminary 2002 modeling archives. The 

CVSROOT location on the RMC machines is /home/aqm3/edss2/archive. Once CVSROOT is 

defined, the installation for any of the simulations listed in Table 5-21, including the run scripts, 

executables, and all input files, can be recalled from the archive by issuing the command “cvs 

checkout –r ‘CVS Revision Tag’”. For example, to check out the files needed to reproduce the 

Pre02b_36 simulation, you would invoke the command “cvs checkout –r Pre02b_36_Final” from 

the command line anywhere on the RMC servers. 

5.5.3 Quality assurance products 

In addition to version control, the RMC emissions modeling QA protocol also defines a series of 

QA products that must be created to document every simulation that produces emissions input 

for CMAQ. Before delivering the results of an emissions simulation to the RMC air quality 

modeling team, the RMC emissions modelers generate and scrutinize tabular and graphical summaries that summarize the simulation and provide insight into its quality. The emissions QA protocol contains a checklist of QA procedures that must be completed before the simulation results 

are ready to be given to the air quality modelers. The checklist provides guidance for ensuring 

that each step of the SMOKE modeling process is checked and documented. The final reports for 

simulations Pre02a through Pre02c contain completed QA checklists documenting that each 

simulation was thoroughly reviewed. The QA checklist for simulation Pre02d is contained in 

Appendix E. For the many QA procedures in the checklist that do not produce tangible products 

such as emissions plots or tabulated information, the checklist itself is the documentation 

substantiating completion of the QA procedures. Other, more tangible QA products that we 

generate include spatial plots of the daily maximum emissions for each source sector and 

pollutant, daily and annual time-series plots of the emissions, linear regressions comparing 

annual inventory summaries against reports from each SMOKE process (e.g., inventory import, 

speciation, spatial allocation), and daily vertical emissions profiles of the point-source 

simulations. Refer to the links in Table 5-19 to see these QA products for each preliminary 2002 

simulation. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 134 

5.6 WRAP Preliminary 2002 Emissions Progress 

As noted in Section 5.3, the preliminary 2002 emissions modeling performed by the RMC in 

project year 2004 was an iterative process whereby each simulation was built from the previous 

one. We used the six-month simulation Pre02a to test the emissions QA protocol, the SMOKE 

run scripts, and the preliminary 2002 emissions inventories and meteorology. After making 

adjustments to both the modeling protocol and the input data, we moved on to the first annual 

simulation of the year, Pre02b. With the delivery of the 2002 WRAP fire inventories, we then 

created simulation Pre02c by adding these new data on top of simulation Pre02b. Adding 

updated 2002 fire data to the emissions from simulation Pre02b_36 resulted in simulations 

Pre02e_36 and Pre02f_36. To create simulations Pre02d_36 and Pre02d_12, we combined 

entirely new inventories for the non-WRAP states, revised Canadian inventory data, gridded 

emissions data from the WRAP windblown dust model and the ENVIRON NH3 emissions 

model, and the revised 2002 fire inventories. While the final reports on the first three simulations 

mentioned above detail the modeling and QA performed for each, they do not include 

comparisons between the simulations to demonstrate how the preliminary 2002 base emissions 

simulation progressed through project year 2004. This section contains comparisons of 

simulations Pre02b, Pre02c, and Pre02d to illustrate how the inventory changes that we made 

affected the overall distribution of emissions among the major source categories and across the 

various regions of the modeling domain. For details about the exact inventory changes that 

occurred between these three simulations, refer to Section 5.4. 

As we used Pre02a simply as a short-time-period test for the annual simulation Pre02b, the 

changes to the inventory inputs between these two simulations were minor, so a comparison between the inventories used for these two cases is unnecessary. The changes to the Pre02b 

inventory that resulted in simulation Pre02c included the addition of 2002 wildfire and 

prescribed fire inventories and the 2018 base smoke management agricultural fire inventory. The 

Pre02d inventory represented several updates to all regions of the modeling domain. Table 5-22 

summarizes the updates by source category that occurred between the Pre02c and Pre02d 

inventories. 

Table 5-22. Summary of emissions updates between the Pre02c and Pre02d inventories. 

Source Category Pre02d Update 

Stationary Area • Upgraded the NEI96 grown to 2002 with the preliminary NEI2002 for the nonWRAP states 

• Removed the NH3 SCCs represented by the ENVIRON NH3 model from all 

U.S. inventories 

• Removed the fugitive dust SCCs represented from all U.S. inventories 

• Added the VISTAS and CENRAP area-source fire inventories; removed these 

fire sources from the non-WRAP stationary-area-source inventory 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 135 

Source Category Pre02d Update 

Nonroad Mobile • Upgraded the NEI96 grown to 2002 with the preliminary NEI2002 for the nonWRAP states 

• Removed the refueling SCCs represented by MOBILE6 from the WRAP 

inventory 

• Split the shipping SCC into in-port and ocean-going sources; allocated the 

ocean-going sources to marine grid cells within 25 miles of the coast 

On-road Mobile • Upgraded NEI99 to NEI2002 for non-WRAP states 

Stationary Point • Replaced CENRAP records in the WRAP inventory with NEI2002 records 

• Added the VISTAS hourly CEM inventory and temporal profiles for modeling 

sources in the VISTAS region 

Fugitive Dust • Added an explicit fugitive dust category that uses an inventory with countybased transport fractions 

Road Dust • No change 

Windblown Dust • Added gridded emissions from the WRAP windblown dust model for U.S. 

sources 

Agricultural and 

Natural NH3 

• Added gridded emissions from the ENVIRON NH3 model for U.S. sources; 

these replaced emissions for SCCs in the stationary area inventory 

Prescribed Fire • Replaced the WRAP 2002 prescribed fire inventory with revised inventories 

that differentiate between natural and anthropogenic events 

Agricultural Fire • No change 

Wildfire • Upgraded to the revised 2002 inventory for the WRAP states 

• Added the 2002 VISTAS fire inventory 

Offshore Point • No change 

Offshore Mobile • Add an explicit offshore-mobile-source category derived from nonroad mobile 

shipping SCCs 

Biogenic • Upgraded the white spruce and fir emissions factors based on revised U.S. 

EPA data 

Canada • Upgraded all of the nonpoint inventory to the preliminary 2000 National 

Canadian Inventory 

Mexico • No change 

Tables 5-23 and 5-24 are cursory summaries of the annual total emissions by pollutant between 

the Pre02b, Pre02c, and Pre02d inventories for the entire U.S. and the WRAP states, 

respectively, for all modeled source categories combined. Figures 5-7 and 5-8 illustrate the U.S. 

domainwide trends indicated in these tables: (1) the total non-NH3 and non-PM10 emissions 

slightly increased between the Pre02b and Pre02c inventories and then decreased with the 

Pre02d inventory; and (2) the total NH3 and PM10 emissions increased with each new inventory. 

Tables 5-25 through 5-27 expand upon Table 5-23 by showing the total annual U.S. emissions 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 136 

for the Pre02b, Pre02c, and Pre02d inventory pollutants by source category. Tables 5-28 through 

5-30 expand upon Table 5-24 in the same way for the total annual WRAP-region emissions. 

Figures 5-9 through 5-16 illustrate the differences among the Pre02b, Pre02c, and Pre02d 

inventories. Figures 5-9 through 5-11 show the total emissions differences among the three 

inventories in each of the major regions of the 36-km modeling domain. These figures illustrate 

the interregional differences in magnitude of the total emissions among the five RPOs, Canada, 

and Mexico. A comparison between Figures 5-9 and 5-10 demonstrates that only the WRAP 

emissions changed between these two simulations, the result of adding fire emissions in the 

WRAP region. Comparing Figures 5-10 and 5-11 shows the overall reduction in emissions for 

most pollutants in the U.S. and Canada. This comparison also illustrates the effect of the 

windblown dust emissions in the increase in PM10 emissions between the Pre02c and Pre02d 

inventories. Figures 5-12 through 5-14 are pie chart summaries of the total U.S. emissions for the 

Pre02b, Pre02c, and Pre02d inventories. Figures 5-15 and 5-16 are pie chart summaries of the 

total WRAP-domain emissions for the Pre02c and Pre02d inventories. These emissions pie 

charts show how the contribution of each of the major emissions components to the total 

emissions changed among the various inventories. The effects of adding the first set of 2002 fire 

emissions are most evident in comparing the CO and PM pie charts in Figures 5-12 and 5-13. A 

comparison of Figures 5-13 and 5-14 shows the effects of the NH3 model’s emissions in the NH3

pie chart, and the effects of adding the windblown dust and other explicit dust categories in the 

three PM pie charts. Figures 5-15 and 5-16 illustrate the source category contributions to the total 

emissions for the WRAP states only for the Pre02c and Pre02d inventories. These figures 

magnify the effects of the WRAP fire inventories relative to the total U.S. pie charts. 

Figures 5-17 through 5-24 expand on the WRAP-region pie charts in Figure 5-16 by showing the 

contribution of each of the emissions source categories to the total annual emissions in each of 

the WRAP states in simulation Pre02d for the major inventory pollutants (CO, VOC, NOx, SO2, 

NH3, PM2.5, PM10, and coarse particulate matter [PMc]). For the pollutants that are related to 

land area or population—such as VOC emissions, which are dominated by the biogenic sources, 

and NOx emissions, which are dominated by the mobile- and area-source sectors—California 

tends to be the largest emissions source state in the WRAP region. Oregon is the largest emitter 

of CO, PM10, and PM2.5 in the WRAP region, due to an active wildfire year in 2002. North 

Dakota is the largest SO2 emitter in the WRAP states because of large contributions from the 

area- and point-source sectors. California, Montana, and South Dakota are the largest emitters of 

NH3 in the WRAP region, attributable to the high amount of agricultural activity in these states 

captured in the new agricultural and natural NH3 inventory. Although Oregon was the largest 

emitter of PM10 and PM2.5 in the WRAP region during 2002, when the wildfire signal from PM2.5

is removed by looking at PMc, Oregon drops to the fifth-largest emitter of PMc behind 

California, North Dakota, South Dakota, and Colorado. Outside of Oregon, the PMc emissions 

levels are affected primarily by the fugitive dust and road dust sectors. 

The inventory comparisons presented here also apply to the 12-km (Pre02c_12 and Pre02d_12), 

PinG (Pre02c_PinG), and fire sensitivity (Pre02e_36 and Pre02f_36) simulations. All of the 

inventories used in the preliminary 2002 emissions modeling are included in these analyses. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 137 

Table 5-23. Comparison among the three Pre02 simulations of annual 

U.S. pollutant totals in tons/year for all source categories combined. 

Pollutant Pre02b Pre02c Pre02d 

CO 115,323,289 128,369,633 69,281,420 

NOx 26,155,742 26,444,753 16,546,150 

VOC 74,773,640 75,399,946 68,584,807 

NH3 5,094,587 5,156,807 5,487,759 

SO2 19,512,966 19,590,788 15,250,166 

PM10 12,271,953 13,542,065 17,099,895 

PM2.5 4,206,658 5,298,888 5,225,398 

PMc 8,065,560 8,243,442 6,645,623 

Table 5-24. Comparison among the three Pre02 simulations of annual 

WRAP-region pollutant totals in tons/year for all source categories combined* 

Pollutant Pre02b Pre02c Pre02d 

CO 21,468,363 34,514,706 34,488,024

NOx 4,248,803 4,537,814 4,532,715

VOC 24,134,792 24,761,099 24,686,023

NH3 1,038,013 1,100,233 1,823,284

SO2 1,247,963 1,325,785 1,324,388

PM10 2,903,972 4,174,085 4,922,535

PM2.5 942,716 2,034,946 1,924,883

PMc 1,961,521 2,139,403 1,688,846

*Does not include offshore point sources. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 138 

0.00E+00

2.00E+07

4.00E+07

6.00E+07

8.00E+07

1.00E+08

1.20E+08

1.40E+08

 CO NOX VOC NH3 SO2 PM10 PM2_5 PMC

(tons/yr)

Pre02b

Pre02c

Pre02d

Figure 5-7. U.S. total emissions comparison of three annual Pre02 simulations. 

0.00E+00

5.00E+06

1.00E+07

1.50E+07

2.00E+07

2.50E+07

3.00E+07

3.50E+07

4.00E+07

 CO NOX VOC NH3 SO2 PM10 PM2_5 PMC

(tons/yr)

Pre02b

Pre02c

Pre02d

Figure 5-8. WRAP-region total emissions comparison of three annual Pre02 simulations. 
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 139 

Table 5-25. Pre02b U.S. pollutant totals by source category 

Category CO NOx VOC NH3 SO2 PM10 PM2.5 PMc 

Area 8788585 2133791 8743374 4547733 1328943 7381849 2351239 5030603

Biogenic 6335679 968676 54484314 0 0 0 0 0

On-road 69371953 8078587 5868161 251904 273748 225877 170672 55205

Nonroad 25336215 5320217 3166860 12032 942749 471442 423330 48382

Road Dust 0 0 0 0 0 2917582 507809 2409776

Point 5458565 9515094 2462456 282919 16967419 1275162 753570 521592

Offshore 32293 139377 48474 0 107 40 39 2

Total 115323289 26155742 74773640 5094587 19512966 12271953 4206658 8065560

Table 5-26. Pre02c U.S. pollutant totals by source category 

Category CO NOx VOC NH3 SO2 PM10 PM2.5 PMc 

Area 8788585 2133791 8743374 4547733 1328943 7381849 2351239 5030603

Biogenic 6335679 968676 54484314 0 0 0 0 0

On-road 69371953 8078587 5868161 251904 273748 225877 170672 55205

Nonroad 25336215 5320217 3166860 12032 942749 471442 423330 48382

Road Dust 0 0 0 0 0 2917582 507809 2409776

Point 5458565 9515094 2462456 282919 16967419 1275162 753570 521592

Rx Fire 753945 19826 37702 3523 5436 73922 64302 9620

Ag Fire 217083 10129 20353 4379 1353 22083 20954 1129

Wildfire 12075316 259056 568251 54318 71032 1174106 1006973 167133

Offshore 32293 139377 48474 0 107 40 39 2

Total 128369633 26444753 75399946 5156807 19590788 13542065 5298888 8243442

Table 5-27. Pre02d U.S. pollutant totals by source category 

Category CO NOx VOC NH3 SO2 PM10 PM2.5 PMc 

Area 8997365 1588240 8053704 155713 1228561 1434640 1280075 154557

Biogenic 6335679 968676 54484314 0 0 0 0 0

On-road 14077418 1565522 1205058 51452 39573 57953 45619 12334

Nonroad 22400629 4316894 2508503 6562 509894 410306 358358 51949

Road Dust 0 0 0 0 0 3011884 526178 2485710

Windblown Dust 0 0 0 0 0 5228817 0 0

Ammonia 0 0 0 5114196 0 0 0 0

Fugitive Dust 0 0 0 0 0 4243739 841450 3402280

Point 4418380 7683523 1666043 98185 13395672 1455641 1093396 362237

Anthro Rx Fire 363499 7799 17106 1636 2139 35344 30313 5032

Natural Rx Fire 363759 6935 13001 1212 1902 25343 22080 3263

Ag Fire 217083 10129 20353 4379 1353 22084 20955 1129

Wildfire 12075316 259056 568251 54318 71032 1174106 1006973 167133

Offshore 32293 139377 48474 107 40 39 2 0

Total 69281420 16546150 68584807 5487759 15250166 17099895 5225398 6645623
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 140 

Table 5-28. Pre02b WRAP-region pollutant totals by source category 

Category CO NOx VOC NH3 SO2 PM10 PM2.5 PMc 

Area 1448071 453881 1741918 961371 153465 2117539 640254 1477275

Biogenic 2662325 288738 20640180 0 0 0 0 0

On-road 12630468 1399267 1097751 45603 32312 53796 42621 11175

Nonroad 4059991 1101916 422651 4537 155124 75230 69735 5767

Road Dust 0 0 0 0 0 470112 82714 387403

Point 667508 1005001 232292 26501 907063 187296 107393 79901

Total 21468363 4248803 24134792 1038013 1247963 2903972 942716 1961521

Table 5-29. Pre02c WRAP-region pollutant totals by source category 

Category CO NOx VOC NH3 SO2 PM10 PM2.5 PMc 

Area 1448071 453881 1741918 961371 153465 2117539 640254 1477275

Biogenic 2662325 288738 20640180 0 0 0 0 0

On-road 12630468 1399267 1097751 45603 32312 53796 42621 11175

Nonroad 4059991 1101916 422651 4537 155124 75230 69735 5767

Road Dust 0 0 0 0 0 470112 82714 387403

Point 667508 1005001 232292 26501 907063 187296 107393 79901

Rx Fire 753945 19826 37702 3523 5436 73922 64302 9620

Ag Fire 217083 10129 20353 4379 1353 22084 20955 1129

Wildfire 12075316 259056 568251 54318 71032 1174106 1006973 167133

Total 34514706 4537814 24761099 1100233 1325785 4174085 2034946 2139403

Table 5-30. Pre02d WRAP-region pollutant totals by source category 

Category CO NOx VOC NH3 SO2 PM10 PM2.5 PMc 

Area 1448071 453881 1674437 12479 153465 320940 289328 31607

Biogenic 2662325 288738 20640180 0 0 0 0 0

On-road 12630468 1399267 1097751 45603 32312 53796 42621 11175

Nonroad 4059996 1101910 422651 4537 155123 85159 79089 6070

Road Dust 0 0 0 0 0 490176 87032 403144

Windblown Dust 0 0 0 0 0 1308787 287933 1020854

Ammonia 0 0 0 1672618 0 0 0 0

Fugitive Dust 0 0 0 0 0 1219504 239098 980394

Point 667508 1005001 232292 26501 907063 187296 107393 79901

Anthro Rx Fire 363499 7799 17106 1636 2139 35344 30313 5032

Natural Rx Fire 363759 6935 13001 1212 1902 25343 22080 3263

Ag Fire 217083 10129 20353 4379 1353 22084 20955 1129

Wildfire 12075316 259056 568251 54318 71032 1174106 1006973 167133

Total 34488024 4532715 24686023 1823284 1324388 4922535 2212816 2709700
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 141 

WRAP

CENRAP

VISTAS

MWRPO

MANE-VU

Canada

Mexico

NH3

PM2.5

PM10

SO2

NOx

VOCCO

0.00E+00

5.00E+06

1.00E+07

1.50E+07

2.00E+07

2.50E+07

3.00E+07

3.50E+07

(tons/yr)

Figure 5-9. Pre02b_36 total domain annual emissions summary. 

WRAP

CENRAP

VISTAS

MWRPO

MANE-VU

Canada

Mexico

NH3

PM2.5

PM10

SO2

NOx

VOCCO

0.00E+00

5.00E+06

1.00E+07

1.50E+07

2.00E+07

2.50E+07

3.00E+07

3.50E+07

(tons/yr)

Figure 5-10. Pre02c_36 total domain annual emissions summary. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 142 

WRAP

CENRAP

VISTAS

MWRPO

MANE-VU

Canada

Mexico

NH3

PM2.5

PM10

SO2

NOx

VOCCO

0.00E+00

5.00E+06

1.00E+07

1.50E+07

2.00E+07

2.50E+07

3.00E+07

3.50E+07

(tons/yr)

Figure 5-11. Pre02d_36 total domain annual emissions summary. 
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 143 

Pie Chart Legend 

 Stationary Area 

 Biogenic 

 On-Road Mobile 

 Nonroad Mobile 

 Road Dust 

 Point 

 Offshore 

CO NOx

VOC SO2 NH3

PMc PM10 PM2.5

Figure 5-12. Pre02b total U.S. annual emissions pie charts. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 144 

Pie Chart Legend 

 Stationary Area 

 Biogenic 

 On-Road Mobile 

 Nonroad Mobile 

 Road Dust 

 Point 

 WRAP Rx Fire 

 WRAP Ag Fire 

 WRAP Wildfire 

 Offshore 

CO NOx

VOC SO2 NH3

PMc PM10 PM2.5

Figure 5-13. Pre02c total U.S. annual emissions pie charts. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 145 

Pie Chart Legend 

 Stationary Area 

 NH3 Model 

 Biogenic 

 On-Road Mobile 

 Nonroad Mobile 

 Road Dust 

 Fugitive Dust 

 Windblown Dust 

 Point 

 Offshore 

 WRAP Nat Rx Fire 

 WRAP Anthro Rx Fire 

 WRAP Ag Fire 

 WRAP Wildfire 

CO NOx

VOC SO2 NH3

PMc PM10 PM2.5

Figure 5-14. Pre02d total U.S. annual emissions pie charts. 
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 146 

Pie Chart Legend 

 Stationary Area 

 Biogenic 

 On-Road Mobile 

 Nonroad Mobile 

 Road Dust 

 Point 

 WRAP Rx Fire 

 WRAP Ag Fire 

 WRAP Wildfire 

 Offshore 

CO NOx

VOC SO2 NH3

PMc PM10 PM2.5

Figure 5-15. Pre02c WRAP annual emissions pie charts. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 147 

Pie Chart Legend 

 Stationary Area 

 NH3 Model 

 Biogenic 

 On-Road Mobile 

 Nonroad Mobile 

 Road Dust 

 Fugitive Dust 

 Windblown Dust 

 Point 

 Offshore 

 WRAP Nat Rx Fire 

 WRAP Anthro Rx Fire

 WRAP Ag Fire 

 WRAP Wildfire 

CO NOx 

VOC SO2 NH3

PMc PM10 PM2.5

Figure 5-16. Pre02d WRAP annual emissions pie charts. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 148 

 Area 

Biogenic 

 On-Road 

 Nonroad 

 Point 

 Nat Rx Fire 

 Anthro Rx Fire 

 Ag Fire 

Wildfire 

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

8000000

9000000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

(Tons/Yr)

Figure 5-17. Pre02d annual CO source contributions by WRAP state. 

0

200000

400000

600000

800000

1000000

1200000

1400000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

(Tons/Yr)

Figure 5-18. Pre02d annual NOx source contributions by WRAP state. 
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 149 

 Area 

Biogenic 

 On-Road 

 Nonroad 

 Point 

 Nat Rx Fire 

 Anthro Rx Fire 

 Ag Fire 

Wildfire 

 NH3 Model 

0

1000000

2000000

3000000

4000000

5000000

6000000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

(Tons/Yr)

Figure 5-19. Pre02d annual VOC source contributions by WRAP state. 

0

50000

100000

150000

200000

250000

300000

350000

400000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

(Tons/Yr)

Figure 5-20. Pre02d annual NH3 source contributions by WRAP state. 
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 150 

 Area 

 On-Road 

 Nonroad 

 Point 

 Nat Rx Fire 

 Anthro Rx Fire 

 Ag Fire 

Wildfire 

 Road Dust 

 Fugit Dust 

 Windblown Dust 

0

50000

100000

150000

200000

250000

300000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

(Tons/Yr)

Figure 5-21. Pre02d annual SO2 source contributions by WRAP state. 

0

100000

200000

300000

400000

500000

600000

700000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

(Tons/Yr)

Figure 5-22. Pre02d annual PM2.5 source contributions by WRAP state. 
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 151 

 Area 

 On-Road 

 Nonroad 

 Point 

 Nat Rx Fire 

 Anthro Rx Fire 

 Ag Fire 

Wildfire 

 Road Dust 

 Fugit Dust 

 Windblown Dust 

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

(Tons/Yr)

Figure 5-23. Pre02d annual PM10 source contributions by WRAP state. 

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

500000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

(Tons/Yr)

Figure 5-24. Pre02d annual PMc source contributions by WRAP state. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 152 

5.7 Emissions Sensitivities 

As most of the RMC’s efforts during project year 2004 were focused on developing and refining 

the 2002 emissions base case, the number of emissions sensitivities performed during the 

reporting period was limited. 

• A group of sensitivities for testing the effects of different configurations of the fire 

inventories (Task 11) was carried over from the 2003 work plan and is covered in detail 

in Section 12 of this report. 

• Although simulation Pre02c is considered an extension of the preliminary 2002 base case 

modeling rather than an emissions sensitivity, Pre02c could be evaluated as an emissions 

sensitivity of the WRAP fire inventories by comparing it with Pre02b. Comparisons 

between the two simulations are provided in Section 5.7. 

• Using the revised 2002 WRAP fire emissions inventories, we designed two sensitivities 

to test the effects of these new fire inventories on the air quality modeling. We designed 

simulation Pre02e_36 to evaluate of the effects of natural versus anthropogenic fires and 

simulation Pre02f_36 to study the overall effects of the new fire inventories on the 

modeling. Additional details about simulations Pre02e_36 and Pre02f_36 are found in 

Sections 5.4.8 and 5.4.9, respectively. 

• Although several emissions sensitivities were outlined in the 2004 RMC work plan (for 

evaluating different configurations of the agricultural NH3 emissions and windblown dust 

models, plus a series of other unspecified sensitivities), we were able to complete only 

one emission control sensitivity that was derived from the Pre02c_36 simulation: 

Pre02c_36s01, which was introduced in Section 5.4.6 and is discussed in more detail 

below. 

Simulation Pre02c_36s01 addressed nonroad mobile diesel controls. Working with the WRAP 

Economic Analysis Forum, we implemented a control scenario to evaluate the effects of the 

diesel retrofit program on nonroad diesel emissions in Arizona, North Dakota, and Montana. The 

Economic Analysis Forum provided us with control factors by Federal Implementation Standards (FIPS) code and SCC. We used this information to generate a SMOKE input file to apply 

the diesel retrofit program controls to VOC, CO, and PM emissions in the applicable counties in 

those three states. As the WRAP nonroad mobile-source inventory is not detailed enough for us 

to apply the controls to the exact sources that were targeted, we decided to use a more 

conservative approach that would control more simulated emissions than the actual controls 

would impact. This conservative approach overstates the effects of the control program by 

affecting more emissions sources and pollutants than is realistic. The Economic Analysis Forum 

contractor that provided the control factors agreed that the conservative approach was reasonable 

because of the relatively limited amount of emissions that the controls target in the model (Tom 

Timbario, Emissions Advantage, personal communication, October 13, 2004). Emissions 

Advantage and the RMC agreed that if the conservative approach showed significant results 

when used in an air quality modeling simulation, we would then revisit the Pre02c_36s01 

sensitivity simulation and refine the controls to be more realistic. Analysis of the modeling 

results are available in the Economic Analysis Framework Test Application report (BBC, 2004) 
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5.8 Problems Encountered and Corrections 

This section summarizes the emissions modeling problems we encountered during the preliminary 2002 modeling and the corrections we made. Subsections on the various emissions components present details about each major issue, including an explanation of the issue, how it was 

discovered, and (if necessary) the corrective actions implemented. If the issue did require corrective action, we also explain how we confirmed the fix. 

5.8.1 Stationary area sources 

• Area Issue #1 

♦ Question: Why did we observe large differences between winter and summer areasource CO emissions in the eastern U.S. and Canada? Spatial plots of the emissions 

showed large differences in magnitude (>100%) across vast spatial extents in some 

areas of the modeling domain. 

♦ Answer: The differences in CO emissions are due to (1) seasonal activity trends and 

(2) different approaches for spatially allocating residential wood smoke and 

prescribed-fire emissions. 

• As those wood combustion sources are the largest sources of CO in the stationaryarea-source inventory, the highly seasonal temporal profiles applied to them result 

in large differences in the emissions throughout the year. 

• The reason behind the spatial patterns of the emissions differences is related to 

how the emissions from those combustion sources are spatially allocated. 

Canada: Due to the lack of high-resolution spatial surrogates for Canada, the 

area-source emissions are spatially allocated at the province level, as opposed to 

the county level in the U.S. The large-magnitude wood smoke emissions are 

spread over a wide spatial extent and appear to impact major areas of Canada in 

the modeling domain when spatial plots of the emissions are viewed. 

United States: In the eastern U.S., the prescribed-fire emissions exhibit wide 

spatial impacts as opposed to the western U.S. because in the 2002 WRAP 

inventory, fires are treated as point sources and are concentrated to single grid 

cells in the WRAP states. Treated as area sources in the inventory for the eastern 

U.S., the fire emissions appear to have larger spatial impacts because they are 

being fractionally allocated to the area of entire counties. 

• Area Issue # 2 

♦ Question: Why were there no NH3 emissions from area sources in the eastern U.S.? 

Spatial plots of the emissions showed NH3 emissions everywhere in the domain 

except the eastern U.S.

♦ Answer: The NH3 column in the NEI96 inventory that we used for the preliminary 

2002 modeling was corrupted, resulting in the loss of some data. After reinstalling the 

inventory on the RMC machines, we regenerated the simulation with SMOKE. The 
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spatial plots created from this new simulation showed complete coverage for all areasource pollutants. 

• Area Issue # 3 

♦ Question: After integrating the NH3 model emissions into simulations Pre02d_36, 

why were there still NH3 emissions from the modeled sources in the VISTAS areasource inventory? 

♦ Answer: To keep from double-counting emissions, we removed the SCCs covered by 

the NH3 emissions model from the U.S. area-source inventories. The first round of 

the removal missed the sources in the VISTAS inventory. We discovered during QA 

of simulation Pre02d_36 that the sources we were supposed to have removed were 

still in the VISTAS inventory. We removed the sources and reran the area-source 

simulation. 

5.8.2 Nonroad mobile sources 

• Nonroad Issue # 1 

♦ Question: Why did the spatial plots of CO, NOx, and SO2 show emissions in water 

cells off the coasts of Canada? 

♦ Answer: The Canadian nonroad mobile-source inventory contains shipping emissions 

that are spatially allocated in shipping lanes off the coasts of Canada. 

• Nonroad Issue #2 

♦ Question: Why do time-series plots of the domain-total area-source emissions show 

that the emissions for the month of January are lower than in December and 

February? 

♦ Answer: We expect all three winter months to have the same emissions magnitude. In 

this case, however, December and February are the same but January is lower. For 

some reason the WRAP area-source emissions did not get imported correctly for the 

January simulation and we lost these emissions from the results. A simple rerun of 

SMOKE for the month of January resolved the issue. The final time-series plot shows 

that the emissions magnitudes for December, January, and February are the same.

• Nonroad issue #3 

♦ Question: Why did the WRAP nonroad mobile-source emissions exhibit less diurnal 

variability than the rest of the domain? 

♦ Answer: Animations of the emissions data showed strong diurnal variability in the 

nonroad mobile-source emissions everywhere but in the WRAP states. Diagnostic 

analysis revealed that because of a new lumped SCC contained in the WRAP 

inventory, we were assigning the default uniform temporal profiles to the WRAP 
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nonroad emissions. These default profiles are flat, meaning that they assign the 

emissions uniformly across all months, days, and hours. These flat profiles caused the 

WRAP nonroad mobile-source emissions to exhibit less diurnal variability relative to 

the rest of the domain. Since the WRAP inventory contractor created these SCCs 

specifically for the 2002 WRAP modeling, we did not have cross-references for them 

in the default SMOKE ancillary input files. To correct this problem, we added crossreferences for these new SCCs to the temporal, speciation, and spatial cross-reference 

files. We also added descriptions of these new WRAP nonroad SCCs to the SMOKE 

SCC description file. Table 5-31 lists the descriptions and the temporal, speciation, 

and spatial cross-reference codes applied to these new SCCs. After rerunning SMOKE 

with the corrected cross-reference codes, animations of the nonroad mobile-source 

emissions showed that the diurnal variability in these emissions in the WRAP states 

was more similar to that in the rest of the domain.

Table 5-31. Description and profile assignments for new WRAP 2002 nonroad mobile SCCs.* 

SCC Description TREF

code†

SREF 

code†

GREF 

code†

2200001000 Mobile Sources; Off-highway Vehicles; Gasoline+CNG+LPG; 

Recreational Equipment; Total 26 VOC: 1186 

PM2.5: 35700 1 

2200002000 Mobile Sources; Off-highway Vehicles; Gasoline+CNG+LPG; 

Construction and Mining Equipment; Total 26 VOC: 1186 

PM2.5: 35700 1 

2200003000 Mobile Sources; Off-highway Vehicles; Gasoline+CNG+LPG; 

Industrial Equipment; Total General Industrial 26 VOC: 1186 

PM2.5: 35700 1 

2200004000 Mobile Sources; Off-highway Vehicles; Gasoline+CNG+LPG; 

Lawn and Garden Equipment; Total 26 VOC: 1186 

PM2.5: 35700 2 

2200005000 Mobile Sources; Off-highway Vehicles; Gasoline+CNG+LPG; 

Agricultural Equipment; Total 26 VOC: 1186 

PM2.5: 35700 6 

2200006000 Mobile Sources; Off-highway Vehicles; Gasoline+CNG+LPG; 

Commercial Equipment; Total 26 VOC: 1186 

PM2.5: 35700 1 

2200007000 Mobile Sources; Off-highway Vehicles; Gasoline+CNG+LPG; 

Logging Equipment; Total 26 VOC: 1186 

PM2.5: 35700 14 

2200008000 Mobile Sources; Off-highway Vehicles; Gasoline+CNG+LPG; 

Aircraft; Total 26 VOC: 1186 

PM2.5: 35700 10 

2200008005 

Mobile Sources; Off-highway Vehicles; Gasoline+CNG+LPG; 

Airport Ground Support Equipment; Airport Ground Support 

Equipment 

26 VOC: 1186 

PM2.5: 35700 10 

2200009000 Mobile Sources; Off-highway Vehicles; Gasoline+CNG 

+LPG; Underground Mining Equipment; Total 26 VOC: 1186 PM2.5: 35700 1 

2200010000 Mobile Sources; Off-highway Vehicles; Gasoline+CNG 

+LPG; Industrial Equipment; Total Oil Field Equipment 26 VOC: 1186 PM2.5: 35700 1 

*New abbreviations used in this table: CNG = compressed natural gas; LPG = liquefied petroleum gas. 

†

 TREF, SREF, and GREF are SMOKE cross-reference input files for temporal allocation, chemical speciation, and 

spatial allocation, respectively. 
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• Nonroad Issue #4

♦ Question: Why do the time-series plots of the nonroad mobile-source emissions show 

monthly as opposed to the expected seasonal variability? 

♦ Answer: Similar to the area-source emissions, the nonroad emissions data exhibit 

strong seasonal temporal patterns. The time-series plots of the nonroad emissions 

showed more monthly variation than we expected. Diagnostic analyses revealed that 

because the time-series plots are derived from domainwide emissions totals (U.S. + 

Canada + Mexico), conflicting definitions of the seasons between the U.S. and Canada affect the temporal patterns in the aggregate emissions totals. In the U.S., the 

winter temporal profiles encompass the months January, February, and March; spring 

includes April, May, and, June; etc. In Canada, the winter temporal profiles encompass the months December, January, February; spring comprises March, April, May; 

etc. Buried in the time-series plots are two different patterns of seasonal variability, 

one for the U.S. and one for Canada. Because these definitions are out of phase, they 

give the aggregate time-series plots the appearance of monthly variation. We did not 

do anything to correct this issue. Two schools of thought exist on what months constitute each season. These two schools of thought are captured in the U.S. versus 

Canadian temporal profiles. 

• Nonroad Issue #5

♦ Question: Shipping emissions in the WRAP nonroad inventory are all allocated to 

ports, but the inventory represents both in-port and ocean-going emissions. 

♦ Answer: UNC-CEP and ENVIRON split the shipping SCC in the nonroad inventory 

into in-port and ocean-going sources. We used the original SCC to represent the inport component of the data and modeled them as emissions occurring at the ports. 

ENVIRON provided guidance on how to split the inventory to obtain the ocean-going 

component of the data. We allocated these emissions to cells within 25 miles of the 

ports or landfall. The Pacific coast of the WRAP domain now correctly represents 

the shipping emissions in the nonroad mobile-source inventory. 

• Nonroad Issue #6

♦ Question: Why were on-road mobile-source refueling emissions counted in both the 

nonroad and MOBILE6-derived inventories? 

♦ Answer: The developer of the WRAP on-road mobile-source emissions inventory 

alerted us to the fact that refueling emissions are contained in the on-road mobilesource inventory that they derived with MOBILE6. Investigation of the nonroad 

inventory revealed that we are counting refueling emissions in that inventory as well. 

Looking at the processes that the RMC is modeling with MOBILE6 for the nonWRAP states also reveals that we are modeling the refueling emissions in the on-road 

inventory there. To correct this double-counting issue, we removed the refueling 

SCCs from the WRAP nonroad inventory and turned off the refueling emissions 
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process in the non-WRAP on-road mobile simulations. Because of the form in which 

we received the mobile-source inventory data from the WRAP inventory developers, 

we are forced to be inconsistent in the way we model refueling emissions in the 

WRAP and non-WRAP states. For the WRAP states, we are using precomputed 

MOBILE6 emissions that include refueling emissions and a nonroad inventory that 

does not include refueling emissions. For the non-WRAP states we are running 

MOBILE6 to calculate on-road mobile emissions without computing refueling 

emissions, and using a nonroad mobile inventory that does contain refueling 

emissions. A more consistent approach would be either to run MOBILE6 for the 

entire domain and represent refueling emissions in the on-road mobile inventory or to 

use the precomputed refueling emissions in the nonroad mobile inventory for the 

entire domain. 

5.8.3 Road dust sources 

The RMC encountered no major QA issues during the road dust emissions modeling. 

5.8.4 On-road mobile sources 

• On-road Issue #1

♦ Question: What was causing MOBILE6 to crash on the RMC computers? 

♦ Answer: We discovered that we can run MOBILE6 only on a dedicated node because 

of memory limitations. MOBILE6 requires too much system memory to run on both 

processors of a node. We now run MOBILE6 on dedicated RMC machines. 

• On-road Issue #2

♦ Question: Why do the MOBILE6 emissions files sporadically contain bad values and 

infinities? 

♦ Answer: We are speculating that networking traffic corrupts the write functions of 

some SMOKE programs. We think that the system is the culprit and not SMOKE. 

Rerunning the modeling period where the bad values occurred corrects the problem, 

thus identifying the state of the system as the variable affecting the model results. 

When we find files containing bad values, we rerun SMOKE to correct the problem. 

• On-road Issue #3

♦ Question: Why do the on-road mobile-source time-series plots show weekly and 

monthly variability? There does not appear to be any uniformity in the temporal 

patterns. 

♦ Answer: Unlike the stationary-area- or nonroad mobile-source emissions, on-road 

mobile-source emissions are derived using hourly temperatures. The influence of the 

hourly meteorology causes emissions differences on an hourly basis. 
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• On-road Issue #4

♦ Question: Why were PM emissions double-counted in the states outside of the WRAP 

domain? 

♦ Answer: Analyses of the spatial plots of the on-road mobile-source emissions 

revealed that PM emissions existed in both the VMT simulations and the precomputed emissions simulations in the non-WRAP states. The redundancy indicated that 

the emissions were being double-counted during the final merge of the files. We 

removed the PM contribution from the precomputed emissions and reran the simulations. Now PM is being calculated only by MOBILE6 for the non-WRAP states. 

• On-road Issue #5

♦ Question: Why do the spatial plots of the on-road mobile-source emissions show that 

there is no SO2 from mobile sources in Canada, nor NH3 from mobile sources in 

Mexico? 

♦ Answer: The Canadian and Mexican emissions inventories do not contain on-road 

mobile-source emissions for SO2 or NH3, respectively. 

5.8.5 Point sources 

5.8.5.1 Stationary point sources 

• Point Issue #1

♦ Question: Why did the first PinG simulation drop all of the emissions in the WRAP 

states? 

♦ Answer: Spatial plots of vertically summed emissions showed that there were no 

stationary-point-source emissions in the WRAP states following the PinG SMOKE 

simulation. Because we wanted to select only PinG sources in the WRAP states, we 

attempted to define the WRAP region as a subdomain of the entire modeling region. 

SMOKE was not able to handle the definition of the subdomain and ended up dropping all emissions in the WRAP states. The solution to the problem was to split the 

point-source inventory between the WRAP and non-WRAP states. We then applied 

the PinG selection criteria to the WRAP inventory and merged it with the non-WRAP 

point-source inventory later. The vertically summed spatial plots derived from this 

new simulation revealed that only a select subset of cells in the WRAP states are 

affected by the PinG simulation. 

• Point Issue #2

♦ Question: Why were there stationary point NH3 sources in the CENRAP states that 

are 50% larger than similar sources in other parts of the modeling domain? 
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♦ Answer: An error in the NEI96 propagated through to the WRAP 2002 stationarypoint-source inventory, which was based on the NEI96. The NEI2002 released by the 

EPA in 2004 corrected these errors. We removed the CENRAP point sources from 

the WRAP inventory and replaced these data with the NEI2002. 

5.8.5.2 Fire sources 

• Fire Issue #1

♦ Question: Why was SMOKE having a memory fault when we tried to import the 

prescribed-fire inventory? 

♦ Answer: The prescribed-fire emissions inventory is too large (too many sources) to 

process as an annual inventory. We broke the inventory into monthly segments and it 

processed through SMOKE with no problems. 

• Fire Issue #2

♦ Question: What do the vertical profile plots for the prescribed and wildfire emissions 

show large emissions spikes in layer 15 on some days? 

♦ Answer: Some prescribed and wildfire emissions reach to the top of the model (layer 

19). When the emissions layers are capped at layer 15, all of the emissions that are 

allocated above layer 15 are collapsed into the top emissions layer by SMOKE. We 

reconfigured SMOKE to extend the emissions layers for prescribed and wildfires to 

layer 19. Regenerated vertical profile plots then showed a smooth falloff of the 

emissions with height. 

• Fire Issue #3

♦ Question: Why was SMOKE unable to recognize the time zone “AKT” in the 2002 

fire inventories? 

♦ Answer: The 2002 fire inventories contained data for Alaska that are tagged with the 

time zone code “AKT”. SMOKE is not configured to run using the AKT time zone, 

so it crashed when it read this field in the inventory files. As Alaska is outside of the 

WRAP 36-km modeling domain, we changed the time zone of the Alaska emissions 

to “PST” to allow SMOKE to run through to completion. The Alaska emissions are 

not being used in this application of the preliminary 2002 modeling, so this modification to the time zone for these sources is irrelevant. 

• Fire Issue #4

♦ Question: Why did the new prescribed fire and wildfire sources have default temporal 

and speciation profiles applied, when we created profiles specifically for these 

sources during the §309 modeling in 2002? 
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♦ Answer: The new 2002 fire inventories contained SCCs that we had never modeled. 

We needed to add these SCCs to the temporal and speciation cross-reference files to 

apply the correct temporal and chemical profiles to these new sources. After adding 

the correct cross-references to these sources to the SMOKE input files, we reran the 

natural and anthropogenic fire emissions and the revised wildfire emissions through 

SMOKE. QA on the new simulation revealed that the correct profiles are now being 

applied to these sources. 

5.8.5.3 Offshore sources 

• Offshore Issue #1 

♦ Question: Why did the first version of the off-shore emissions inventory for California contain sources on-shore?

♦ Answer: Spatial plots of the off-shore emissions revealed several sources on-shore in 

California. We adjusted the lat-lon coordinates for these sources to place them in their 

intended locations off the coast. 

5.8.6 Biogenic sources 

The RMC encountered no major QA issues during the biogenic emissions modeling. 

5.8.7 SMOKE ancillary input files 

5.8.7.1 Spatial surrogates 

• Surrogate Issue #1

♦ Question: When we implemented the new spatial cross-reference file from EPA, why 

did SMOKE indicate that it could not find surrogates for Dade County, FL? 

♦ Answer: The EPA spatial cross-reference files use an updated FIPS code for MiamiDade County, FL. We updated all of the inventory files for Florida to be consistent 

with the new coding scheme. By changing the FIPS code for Dade County from 

12025 to 12086 in the appropriate inventory files, we made the inventories 

compatible with the new cross-reference files. 

• Surrogate Issue #2

♦ Question: Why were there a large number of SCCs from the WRAP inventory missing from the new EPA spatial cross-reference file? 

♦ Answer: The WRAP inventory contained more recent information than the EPA 

surrogates file. Switching to the new EPA surrogates in April 2004 and obtaining the 

new cross-reference file for these surrogates corrected most of these missing SCC 

assignments. 
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5.8.7.2 Temporal profiles 

The RMC encountered no major QA issues with the temporal profiles. 

5.8.7.3 Speciation profiles 

The RMC encountered no major QA issues with the speciation profiles. 

5.9 Outstanding Emissions Issues 

This section outlines two sets of issues with the preliminary 2002 modeling. The first set below 

are those that needed to be resolved after simulation Pre02c. These were listed in the WRAP 

2004 interim report, but had not yet been resolved at that time. As discussed below, we corrected 

many of the issues from the Pre02c simulation in simulation Pre02d .The second set are issues 

that came to light during the Pre02d modeling and that are yet to be resolved. They will need to 

be corrected in the next round of 2002 modeling. 

• Pre02c emissions issues (now corrected): 

♦ We did not include windblown dust emissions in simulation Pre02c. 

 Emissions from the WRAP windblown dust model were added in simulation 

Pre02d. 

♦ No transport fractions were applied to the fugitive dust emissions sources within the 

stationary-area-source inventory. 

 Fugitive dust sources were treated explicitly in simulation Pre02d using an inventory that contains transport fraction corrections based county land-cover data. 

♦ Placeholder 2002 emissions inventories were used for the non-WRAP U.S. states. 

(We were waiting for actual 2002 emissions inventories from the other RPOs.) 

 In simulation Pre02d we replaced the placeholder inventories with the VISTAS 

2002 inventories and the NEI2002. 

♦ We did not include output from the ENVIRON NH3 emissions model in simulation 

Pre02c. 

 We added emissions from the NH3 model in simulation Pre02d. 

♦ We used an outdated Canadian emissions inventory. 

 In simulation Pre02d we replaced all of the nonpoint Canadian inventories with 

the 2000 National Canadian Inventory. 

♦ There were no 12-km meteorology data to use for finishing the emissions simulations 

on the nested WRAP domain. 

 Annual meteorology data for the 12-km WRAP modeling domain became available in February 2005, allowing us to complete the 12-km emissions simulation. 
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♦ We did not include offshore-mobile-source (shipping) emissions in simulation 

Pre02c. 

 For simulation Pre02d, we split the shipping emissions in the nonroad inventory 

into in-port and ocean-going sources. The ocean-going emissions only are a 

fraction of the shipping emissions, but there is still work required to add 

emissions from the commercial shipping lanes and in international waters. 

• Pre02d emissions issues (still to be corrected): 

♦ We need to replace the placeholder NEI2002 inventories with actual 2002 inventories 

from the other RPOs as they become available. The MANE-VU, CENRAP, and 

Midwest RPOs all have 2002 inventories that we will include in the next iteration of 

the 2002 emissions base case simulation. 

♦ No in-flight aircraft emissions were included. 

♦ We treated Mexican and Canadian emissions with U.S. rather than local holidays. 

♦ We need to replace the 1999 Mexican emissions inventory with the 2000 inventory 

that will be available in Spring 2005. 

♦ We need to replace the Gulf of Mexico offshore-point-source emissions inventory 

with the 2002 Minerals Management area- and point-source inventories. 

♦ Emissions from many natural sources are missing, such as geogenic, volcanic, and 

sea salt emissions. 

♦ We need to replace the preliminary Canadian 2000 inventories with the final version 

of these data. 

♦ We need to upgrade the RMC systems to SMOKE version 2.1. 

5.10 Emissions Data Technology Transfer 

As part of an outreach program, the RMC prepared a number of SMOKE reports and gridded 

emissions data in ASCII format and sent them to Cassie Archuleta of Air Resource Specialists, 

Inc. These reports included annual totals by state for area, mobile, nonroad mobile, point, road 

dust, and offshore sources from scenario Pre02b, and daily reports for the fire and biogenic 

sources from scenario Pre02c. 

Also for Cassie Archuleta, we summed and extracted yearly totals for PMFINE and PMC from 

netCDF files, and prepared ASCII gridded emissions files for area, point, road dust, and 

windblown dust sources and total emissions. We also sent her files of NH3 data from the 

ammonia emissions model developed by ENVIRON, as gridded data and “by county” data for 

total NH3 and by NH3 category. 

Annual averages for EXT, DECV, OC, and EC were calculated and sent to Air Resource 

Specialists, Inc., as ASCII gridded values for scenarios Pre02b, Pre02c, Pre02e, and Pre02f. 
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We sent SMOKE programs and scripts to the Park Services in Colorado and helped them install 

and run these scripts for scenario Pre02d. The Pre02 inventory and meteorology data along with 

some SMOKE outputs were also sent to them. Other SMOKE reports were sent to the Utah 

Division of Air Quality. 

5.11 Next Steps 

As stated at the beginning of Section 5, the primary goal of the RMC emissions modeling team 

in project year 2004 was to develop a comprehensive 2002 base case emissions inventory for the 

WRAP. Through the modeling of the various preliminary base case simulations discussed above, 

we refined and quality-assured the WRAP 2002 emissions inventories. The next step in the 

emissions simulation task is to conduct the final (as opposed to preliminary) 2002 base case 

simulation. We anticipate starting on this simulation after the RMC finishes preparing the final 

36-km and 12-km meteorology data in early 2005. The first step in simulating the final 2002 

base case will be to upgrade the RMC modeling system to SMOKE version 2.1 (CEP, 2004a). 

Then, as the data become available, we will reprocess the meteorology-dependent emissions 

categories, integrate any new inventory data that become available, and address any 

issues/problems discovered during the QA process for simulation Pre02d. In addition to the new 

meteorology, we expect new Mexican inventories, additional U.S. RPO 2002 inventories, and 

updates to the WRAP windblown dust emissions to become available before the final 2002 base 

case simulation is completed. The final 2002 emissions modeling will be completed for both the 

continental 36-km domain and the nested WRAP 12-km domain. Depending on the results of the 

PinG emissions sensitivity that is pending with the RMC, the final 2002 emissions may also 

include some PinG emissions for the largest WRAP point sources. 

After completing the final 2002 base case simulation, we will work on creating a future-year, 

2018 base case emissions simulation. After finalizing the 2018 emissions, we will begin 

conducting emissions sensitivities to evaluate natural sources of haze in the WRAP region. 

Throughout all of the modeling that the RMC emissions modeling team will complete in 2005, 

we will continue to refine the WRAP emissions modeling QA protocol and streamline the 

process of running annual emissions simulations, completing the quality assurance, and 

transmitting the data to the air quality modeling team for input to CMAQ or CAMx. 

5.12 Status of Task 3 Deliverables 

Table 5-32 gives the status of each Task 3 deliverable listed in the 2004 WRAP RMC work plan, 

plus any additional deliverables agreed upon after the work plan was prepared. 
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 164 

Table 5-32. Status of the Task 3 deliverables. 

Deliverable Status 

2002 36-km winter/summer emissions 

simulation 

Completed in January 2004: simulation Pre02a_36 

2002 36-km annual emissions simulation Completed in May 2004: simulation Pre02b_36 

Periodic major updates to the annual 2002 

simulation 

Completed in June 2004: simulation Pre02c_36 

Completed in December 2004: simulation Pre02d_36 

Completed in September 2004: simulation Pre02e_36 

Completed in September 2004: simulation Pre02f_36 

2002 12-km emissions simulation Completed meteorology-independent emissions in 

August 2004: simulation Pre02c_12 

Completed in February 2005: simulation Pre02d_12 

Emissions for PinG case Completed one-month preliminary PinG emissions 

simulation in August 2004: simulation Pre02c_36_PinG 
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6. Task 4: Air Quality Model Evaluation for 2002 Annual 

Simulation 

In this section we present the operational model performance evaluation of the preliminary 2002 

Base D (pre02d) CMAQ base case annual simulation. The pre02d scenario is the fourth and 

final preliminary 2002 base case emissions scenario generated as part of the WRAP 2002 

modeling using preliminary and projected estimates of 2002 emissions (see Section 5).*

 We 

applied CMAQ for the 2002 calendar year on the 36-km continental RPO Unified domain and 

the 12-km WRAP modeling domain. For meteorological input, the pre02d CMAQ base case 

simulation used results from the final WRAP 2002 MM5 simulation discussed in Section 4. The 

CMAQ pre02d model performance evaluation presented in this section focuses on the 

performance for PM components within the WRAP subregion, although model performance 

across the entire continental U.S. is also presented. The CMAQ 2002 pre02d 36- and 12-km 

simulations were evaluated using gas-phase and particulate matter species and wet deposition 

from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, the 

Clean Air Status and Trends Network (CASTNet), the Speciated Trends Network (STN), and the 

National Atmospheric Deposition Program (NADP) network, as well as the Southeastern 

Aerosol Research and Characterization 24-hour (SEARCH) and hourly (SEARCH_H) 

observations networks in the southeastern United States. Many displays of CMAQ model 

performance have been generated for the pre02d base case simulation and are contained on the 

project web site: 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#pre02d

Section 6 summarizes the operational evaluation of the CMAQ pre02d base case simulation for 

PM species only in the WRAP subregion. The reader is invited to peruse the project web site for 

additional model performance information. 

6.1 Operational Evaluation Approach 

As the 2002 CMAQ pre02d simulation is an interim simulation using preliminary emissions 

inputs, the model performance evaluation is neither comprehensive nor exhaustive. Rather, it is 

designed to test the 2002 modeling approach and to identify any obvious flaws in the modeling 

system and model inputs so that any corrections needed can be applied to the subsequent revised 

2002 CMAQ base case simulation to be conducted in 2005. The WRAP 2002 CMAQ modeling 

database is intended be used to develop the visibility SIPs and TIPs due in December 2007 (see 

Section 1.1.1). Accordingly, our operational evaluation focuses on the six components of PM 

that are used to generated visibility estimates using the IMPROVE aerosol extinction equation 

(these include four of the major components of PM2.5): 

 

*

Future 2002 emissions modeling performed in the next project year will be based on final 2002 emissions 

inventories. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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• Sulfate (SO4) 

• Particulate nitrate (NO3) 

• Organic carbon (OC) 

• Elemental carbon (EC) or light-absorbing carbon (LAC) 

• Other inorganic fine PM (soil) 

• Coarse mass (CM) 

The IMPROVE aerosol extinction equation (Malm et al., 2000) assumes that SO4 and NO3 are 

completely neutralized by ammonium (NH4). This is partly because the standard IMPROVE 

measurements do not include NH4. Thus, the evaluation below only briefly discusses NH4 model 

performance using derived NH4 “observations” for the IMPROVE network by assuming NH4

completely neutralizes the SO4 and NO3 measurements. 

6.1.1 Performance evaluation tools 

One of the challenges in evaluating an annual PM-ozone model simulation is the synthesis and 

interpretation of many graphical and tabular displays of model performance into a few concise 

and descriptive displays that identify the most salient features of model performance. As part of 

the WRAP evaluation of the 2002 36- and 12-km CMAQ pre02d base case simulations, we used 

several analysis tools that automatically generated numerous graphical and statistical summaries 

used to gauge model performance, taking advantage of their various descriptive and complementary characteristics: 

• UCR Analysis Tools: The UCR analysis tools were used extensively in the WRAP §309 

modeling for 1996, and are run on a Linux platform separately for each monitoring 

network. Graphics are automatically generated using gnuplot, and the software generates 

the following: 

♦ Tabular statistical measures 

♦ Time-series plots 

♦ Scatterplots for three stratifications: (1) all sites in the selected subregion and selected 

time period (allsite_allday); (2) all days in the selected time period for each site 

(allday_onesite); and (3) all sites within the selected subregion for each day 

(allsite_oneday). 

• ENVIRON Analysis Tools: ENVIRON also has model evaluation analysis tools similar to 

the UCR tools. Because they were redundant, they were not used in the WRAP study. 

However, as part of the WRAP §309 SIP analysis, ENVIRON developed specialized 

evaluation tools that interface with the UCR tools to analyze visibility model performance for the best-20% and worst-20% visibility days that are used in visibility 

projections. 

• Bugle Plots: Dr. James Boylan of the Georgia Department of Natural Resources has 

developed a technique for displaying model bias and error with model performance goals 
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and criteria as a function of observed concentration that allows the performance goals to 

expand as the concentrations decrease. 

The analysis tools generated thousands of statistical measures and graphical displays of model 

performance that cannot all be displayed in this report. We went through the plots and measures 

using slide shows, looking for indications of potentially faulty inputs or model formulation 

problems that should be corrected for the revised 2002 CMAQ simulations and pulling out key 

descriptive displays for this report. 

Note that model performance statistics are calculated separately for each of the monitoring 

networks. Different PM measurement technology can produce different measurement values 

even when measuring the same air parcel. Thus, we did not mix measurements from different 

networks when calculating model performance metrics. 

6.1.2 Subdomains analyzed 

Using the monitoring networks listed in the first paragraph of Section 6, we analyzed PM model 

performance for the preliminary 2002 CMAQ 36-km and 12-km pre02d base case simulations 

for just the WRAP subregion. Note that because the SEARCH network monitors reside only in 

the southeastern U.S., the model performance for the SEARCH and SEARCH_H networks is not 

included in this report. 

6.1.3 Model performance goals and criteria 

The issue of model performance goals for PM species is an area of ongoing research and debate. 

For ozone modeling, EPA has established performance goals for 1-h ozone normalized mean 

bias and gross error of ≤ ±15% and ≤35%, respectively (U.S. EPA, 1991). EPA’s draft fine PM 

modeling guidance notes that performance goals for ozone should be viewed as upper bounds of 

model performance that PM models may not be able to always achieve, and that we should 

demand better model performance for PM components that make up a larger fraction of the PM 

mass than those that are minor contributors (U.S. EPA, 2001). Measuring PM species is not as 

precise as monitoring ozone. In fact, the differences in measurement techniques for some species 

likely exceed the more stringent performance goals, such as those for ozone. For example, recent 

comparisons of the PM species measurements using the IMPROVE and STN measurement 

technologies found differences from approximately ±20% (SO4) to ±50% (EC) (Solomon et al., 

2004). 

For the preliminary 2002 CMAQ pre02d base case modeling, we adopted three levels of model 

performance goals or criteria for bias and gross error (Table 6-1). They start with the most 

stringent ozone bias/error performance goals of ±15%/35%, which are followed by ±30%/50% 

bias/error performance goals that we believe a good-performing PM model should achieve, and 

they conclude with ±60%/75% bias/error criteria that, if equaled or exceeded, suggest potential 

problems with the model simulation. Note that we are not suggesting that these performance 

goals/criteria be generally adopted or that they are the most appropriate to use. Rather, we are 

using them just to frame and put the PM model performance into context and to facilitate model 

performance intercomparison across episodes, species, models, and sensitivity tests. 
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Table 6-1. Model performance goals/criteria used to help interpret modeling results. 

Fractional 

Bias 

Fractional 

Error Comment 

≤ ±15% ≤35% 

If these ozone model performance goals are met, PM model 

performance would be considered good. Note that for many PM 

species, measurement uncertainties may exceed these goals. 

≤ ±30% ≤50% Proposed PM model performance goals that we would hope 

each PM species could meet. 

≤ ±60% ≤75% If these proposed PM criteria are exceeded, that indicates 

potential fundamental problems with the modeling system. 

As noted in EPA’s draft fine PM modeling guidance, less abundant PM species should have less 

stringent performance goals. Accordingly, we are also using performance goals that are a continuous function of average observed concentrations, as proposed by Dr. James Boylan at the 

Georgia Department of Natural Resources (Boylan, 2004), which have the following features: 

• Asymptotically approaching proposed performance goals/criteria (i.e., the ±30%/50% 

and ±60%/75% bias/error levels listed in Table 6-1) when the mean of the observed 

concentrations is greater than 2.5 μg/m3

. 

• Approaching 200% error and ±200% bias when the mean of the observed concentrations 

approaches zero. 

On “bugle plots,” bias and error are plotted as a function of average observed concentrations. As 

the mean observed concentration approaches zero, the bias performance goals and criteria flare 

out to ±200%, creating a horn shape (hence the plot name). Dr. Boylan has defined three zones 

of model performance: Zone 1 meets the ±30%/50% bias/error performance goals and is 

considered “good” model performance; Zone 2 lies between the ±30%/50% performance goals 

and ±60%/75% performance criteria and is an area where concern for model performance is 

raised; and Zone 3 lies above the ±60%/75% performance criteria and is an area of questionable 

model performance. 

Note that Dr. Boylan’s chosen observed concentration (2.5 μg/m3

) where the performance 

goals/criteria begin to flare toward ±200% may be more appropriate for the eastern U.S., where 

concentrations are higher and visibility impairment greater; it may be too high for the western 

U.S., which has lower concentrations. For now we are using his chosen concentration in our 

evaluation, but the issue should be re-examined for the western states. 

6.1.4 Performance time periods 

In this 2002 CMAQ pre02d evaluation, model performance evaluation statistics and graphical 

displays were generated for monthly time periods using the native averaging times of each 

monitoring network (i.e., 24 hours for IMPROVE and STN; one week for CASTNet and NADP). 

We did not attempt to evaluate the model using longer-term averages (e.g., seasonal and annual 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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averages). The monthly performance evaluation does provide some indications of seasonal trends 

in performance. We could have used longer-term averaging in the model evaluation by converting the predicted and observed short-term concentrations into longer-term averages, but using 

that procedure can mask some performance problems and may introduce compensating errors, 

with overpredictions compensating for underpredictions. For example, in the WRAP 1996 

multimodel evaluation we saw that the three models (CMAQ, REMSAD, and CAMx) exhibited 

low sulfate bias for annual averages, but that was due to a winter overestimation bias being 

compensated for by a summer underestimation bias. We therefore avoided using longer-term 

averages so that we did not defeat one of the purposes of the CMAQ 2002 pre02d base case 

evaluation: to uncover flaws and problems that should be corrected in revised CMAQ base cases. 

6.1.5 Performance measures 

The UCR model performance analysis tools generate hundreds of graphical displays over 20 

statistical measures of model performance for each species, network, subdomain, and time period 

comparison (see http://pah.cert.ucr.edu/aqm/308/cmaq.shtml). As we reviewed the CMAQ 

performance, we found that the fractional bias and fractional gross error, along with graphical 

displays including scatter- and time-series plots, contain the best descriptive power for 

evaluating PM species. We also focus much of our discussion on the performance for four 

months that represent the different seasons: January, April, July, and October (although 

fractional bias and error are presented for all 12 months of 2002). 

6.2 Operational Evaluation of CMAQ Base Case D (pre02d) in the 

WRAP States 

In the following discussions we use selected monthly scatterplots, time-series plots, and model 

performance statistical measures in an operational evaluation of the model for PM species. As 

noted above, we focus on the six main components of PM that are used to project visibility: SO4, 

NO3, OC, EC, soil, and CM. We also briefly examine NH4 model performance. 

6.2.1 Sulfate (SO4) 

Figure 6-1 displays scatterplots of predicted and observed sulfate concentrations for IMPROVE, 

CASTNet, STN, and NADP sites in the WRAP states for January, April, July, and October 2002. 

In January, the CMAQ 36-km simulation exhibits SO4 fractional bias levels of 42%, 28%, 

and -7% across monitors in the IMPROVE, CASTNet, and STN networks, respectively, in the 

western U.S. When a 12-km grid is used, the fractional bias across the same three networks is 

much higher: 65%, 63%, and 60%, respectively. The use of the higher-resolution grid results in 

higher SO4 concentrations and a degradation in the SO4 model performance. The January 

performance for wet SO4 deposition is also degraded when the 12-km grid is used, from a 10% 

fractional bias using a 36-km grid (not shown) to a 69% bias using a 12-km grid (Figure 6-1a). 

The fractional error for January SO4 performance degrades by approximately 20 percentage 

points when going from the 36-km to 12-km grid (e.g., IMPROVE SO4 January fractional error 

increases from ~60% to ~80%). 
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Better SO4 performance is exhibited across the WRAP IMPROVE sites in April, with a 

relatively low bias of -6% and 7% and error 39% and 43% using the 36-km and 12-km grid, 

respectively (Figure 6-1b). The CASTNet bias (-24% at 36-km and -1% at 12-km) and STN bias 

(-14% at 36-km and 13% at 12-km) suggest fairly good model performance using both grid 

resolutions. The fractional errors for April SO4 concentrations are also fairly low (34% to 43%) 

using both grid resolutions. The April SO4 concentrations are higher using a 12-km grid than a 

36-km grid, but because the model SO4 bias straddles zero, the model performance between the 

36-km and 12-km grid resolutions is comparable. Wet SO4 deposition in April across the 

western U.S. indicates a slight underprediction bias of -16% using the 36-km grid and slight 

overprediction bias of 26% using the 12-km grid, with comparable errors of 93% and 86%. 

The model also estimates higher SO4 levels in July using the higher resolution grid, with 

fractional bias across the IMPROVE, CASTNet, and STN networks of 4%, -1%, and -23% using 

a 36-km grid, increasing to 14%, 23%, and -9% using a 12-km grid. With the exception of the 

STN network, there is a slight degradation in July SO4 model performance when the 12-km grid 

is used. However, the July SO4 model performance is still fairly good. The July wet SO4

deposition bias increases from -38% using the 36-km grid to 28% using the 12-km grid, with 

both simulations exhibiting ~100% error. 

In October 2002, use of the 12-km grid slightly degrades model performance across the more 

rural IMPROVE and CASTNet monitors in the western U.S. but improves it across the STN 

monitors, with fractional bias levels across the three networks increasing from 13%, 3%, 

and -19% using a 36-km grid to 26%, 21%, and 13% using a 12-km grid. Wet SO4 deposition 

using a 36-km grid exhibits low bias (6%) but high error (84%); results for this metric using the 

12-km grid are not available. 

The time series of monthly fractional bias and gross error for SO4 at sites in the WRAP states for 

the 2002 CMAQ pre02d 36-km and 12-km simulations (Figure 6-2) confirm the results from the 

scatterplots for the four months; SO4 is overestimated in the winter and underestimated in the 

summer, and the fractional bias levels using the 12-km grid are always higher than when using a 

36-km grid. With the exception of June 2002, the SO4 model performance using the IMPROVE 

network exhibits better bias (i.e., closer to zero) and lower error when using a 36-km grid versus 

using a 12-km grid, with the differences in the model performance using the two grid resolutions 

greater in winter than in summer. 

Figure 6-3 displays the SO4 bugle plots for the 2002 CMAQ pre02d 12-km simulation and the 

IMPROVE, CASTNet, and STN networks. The monthly average fractional bias for SO4

concentrations in the western U.S. mostly resides in the Zone 1 area of “good” model 

performance (bias/error of less than ±30%/50%), although there are four network-month 

combinations that reside in the Zone 2 area between the performance goals and criteria, which is 

an area of concern. SO4 performance never exceeds the performance criteria. The poorer winter 

SO4 model performance occurs under lower SO4 concentrations, so the bias and error points 

reside in the horn of the bugle plot, where goals and criteria are relaxed. As noted previously, 

the relaxing of performance goals at around 2 μg/m3

 may be appropriate for the eastern U.S., but 

more stringent goals/criteria should be defined at these levels for the western U.S. due to the 

lower concentrations present in the west. 
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Figure 6-1a. CMAQ 2002 pre02d base case simulation SO4 model performance in the 

WRAP states for January 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks. (NADP measures wet 

SO4 deposition, whereas the other three networks measure ambient air SO4

concentrations.) 
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Figure 6-1b. CMAQ 2002 pre02d base case simulation SO4 model performance in the 

WRAP states for April 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks. (NADP measures wet 

SO4 deposition, whereas the other three networks measure ambient air SO4

concentrations.). 
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Figure 6-1c. CMAQ 2002 pre02d base case simulation SO4 model performance in the 

WRAP states for July 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks. (NADP measures wet 

SO4 deposition, whereas the other three networks measure ambient air SO4

concentrations.) 
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Figure 6-1d. CMAQ 2002 pre02d base case simulation SO4 model performance in the 

WRAP states for October 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks. (NADP measures wet 

SO4 deposition, whereas the other three networks measure ambient air SO4

concentrations.) 
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Figure 6-2. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d 36-km and 12-km base case SO4 model performance across 

IMPROVE sites in the WRAP states. 

Monthly Fractional Bias, IMPROVE-SO4

-40

-20

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Month

FB (%)

12k 36k

Monthly Fractional Gross Error, IMPROVE-SO4

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12

Month

FE (%)

12k 36k
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Figure 6-3. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case SO4 model performance as a function of 

observed concentration. 
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6.2.2 Nitrate (NO3) 

The plots of nitrate (NO3) model performance are shown in Figures 6-4 through 6-6. Except for 

the STN network, which exhibits an average NO3 underestimation tendency throughout the year, 

the CMAQ pre02d base case simulation tends to overestimate NO3 in the winter and underestimate it in the summer. The 12-km base case tends to estimate slightly higher NO3 levels that 

exacerbate the NO3 overestimation tendency in the winter, but improve the underestimation 

tendency in the summer. As seen in the bugle plots (Figure 6-6), the winter overestimation 

generally occurs at concentration levels twice that of the summer underestimation and causes the 

fractional bias and error points for the IMPROVE and CASTNet networks to reside in the Zone 3 

area of serious model performance concern. At the more urban STN sites, the NO3 underprediction bias occurs throughout the year and is more pronounced in the summer, when its performance also resides in the Zone 3 area of model performance concern (See Figures 6-4c and 6-6). 

The NADP wet NO3 deposition bias is higher using the 12-km than the 36-km grid, with 36-km 

values for January and July being -11% and -73% and corresponding values using the 12-km 

grid being 21% and 43%. 
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Figure 6-4a. CMAQ 2002 pre02d base case simulation NO3 model performance in the 

WRAP states for January 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks. 
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Figure 6-4b. CMAQ 2002 pre02d base case simulation NO3 model performance in the 

WRAP states for April 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks. 
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Figure 6-4c. CMAQ 2002 pre02d base case simulation NO3 model performance in the 

WRAP states for July 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks. 
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Figure 6-4d. CMAQ 2002 pre02d base case simulation NO3 model performance in the 

WRAP states for October 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and NADP (bottom right) monitoring networks. 
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Figure 6-5 Monthly fractional bias (top) and fractional gross error (bottom) for the CMAQ 

2002 pre02d 36-km and 12-km base case NO3 model performance across IMPROVE sites 

in the WRAP states. 

Monthly Fractional Bias, IMPROVE-NO3

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

FB (%)

12k 36k

Monthly Fractional Gross Error, IMPROVE-NO3

0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12

Month

FE (%)

12k 36k
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Figure 6-6. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d 36-km and 12-km base case NO3 model performance as a function of 

observed concentration. 
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6.2.3 Ammonium (NH4) 

The IMPROVE network does not directly measure NH4. Consequently, we use “derived” NH4

concentrations in the IMPROVE model performance evaluation whereby the “observed” NH4 is 

assumed to be the level of NH4 needed to completely neutralize SO4 and NO3. During the winter 

this is likely an adequate assumption; during the warmer months, however, this may overstate 

NH4 concentrations. The NH4 evaluation should thus be considered qualitative, so only bugle 

plots are presented for NH4 (Figure 6-7). The bugle plots suggest that NH4 is overestimated, 

with the overestimation severe enough that the fractional bias and error exceed the model 

performance goal and the fractional bias exceeds the model performance criterion for CASTNet 

for several months. 
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Figure 6-7. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case NH4 model performance as a function of observed 

concentration. 
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6.2.4 Organic carbon (OC) 

The CMAQ 2002 pre02d base case simulation exhibits very different OC model performance 

across the IMPROVE and STN networks in the WRAP states (Figures 6-8 through 6-10). 

Whereas OC is overestimated across the IMPROVE sites, it is underestimated across the STN 

sites. We believe this is due to (1) higher OC concentrations at the more urban STN sites versus 

the more rural IMPROVE sites; and (2) different OC measurement technologies, whereby the 

STN measurement technology is believed to understate OC and overstate EC. 

During April 2002, a very high observed OC value occurs (~115 μg/m3

), which was traced to an 

observation on April 5 at Bryce Canyon. Unusually high EC (~17 μg/m3

) was also observed at 

this site on this day, suggesting that biomass burning may have contributed (e.g., wildfire or 

prescribed burn). 

The time series of monthly fractional bias and error for the IMPROVE network are shown in 

Figure 6-9 and suggest a winter overestimation bias and summer underestimation bias for OC in 

the western US. Unlike SO4 and NO3, where the 12-km modeling results showed higher concentrations than those obtained using the 36-km grid, for OC the 36-km bias values are substantially 

larger than those obtained using the 12-km grid. In the summer, when the model underestimates, 

this results in improved OC model performance using a 36-km grid, whereas in the winter, when 

the model overestimates, OC model performance is worse using a 36-km grid. For example, in 

January and July the OC bias using the 36-km grid is 35% and 8%; the corresponding values 

using the 12-km grid are 25% and -28%. The bugle plots using the 12-km grid are shown in 

Figure 6-10 and suggest that the summer OC underestimation is a performance area of concern, 

whereas the winter overestimation occurs under conditions of lower OC levels so may not be as 

much of a concern. 
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Figure 6-8a. CMAQ 2002 pre02d base case simulation OC model performance in the 

WRAP states for January (top) and April (bottom) using the IMPROVE (left) and 

STN (right) monitoring networks. 
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Figure 6-8b. CMAQ 2002 pre02d base case simulation OC model performance in the 

WRAP states for July (top) and October (bottom) using the IMPROVE (left) and 

STN (right) monitoring networks. 
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Figure 6-9. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case OC model performance across 

IMPROVE sites in the WRAP states. 

Monthly Fractional Bias, IMPROVE-OC

-40

-30

-20

-10

0

10

20

30

40

50

1 2 3 4 5 6 7 8 9 10 11 12

Month

FB (%)

12k 36k

Monthly Fractional Gross Error, IMPROVE-OC

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12

Month

FE (%)

12k 36k
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Figure 6-10. Monthly fractional bias (top) and fractional gross error (bottom) for 

the CMAQ 2002 pre02d base case OC model performance as a function of 

observed concentration. 
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6.2.5 Elemental carbon (EC) 

The EC performance (Figures 6-11 through 6-13) across the IMPROVE network exhibits fairly 

low bias, without as strong a seasonal variation as was exhibited by the other species. For the 

STN network, EC tends to be overestimated during most months, although the bias is still low 

relative to the other species. As seen in the scatterplots and bugle plots, EC measurements at the 

IMPROVE sites tend to be fairly low, except when there are incursions of plumes from fires. As 

seen for OC, but in contrast to SO4 and NO3, the CMAQ 12-km EC estimates are lower than 

their 36-km counterparts. For example, during the first six months of 2002 the EC fractional bias 

for the IMPROVE monitoring sites is approximately –20% using the 36-km grid and approximately –30% using the 12-km grid (Figure 6-12). The bugle plots show that the EC concentrations are typically very low across the IMPROVE network, so that the model performance meets 

the expanding performance goal in the horn of the bugle plot (Figure 6-13). 
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Figure 6-11a. CMAQ 2002 pre02d base case simulation EC model performance in the 

WRAP states for January (top) and April (bottom) using the IMPROVE (left) and 

STN (right) monitoring networks. 
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Figure 6-11b. CMAQ 2002 pre02d base case simulation EC model performance in the 

WRAP states for July (top) and October (bottom) using the IMPROVE (left) and 

STN (right) monitoring networks. 
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Figure 6-12. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case EC model performance across IMPROVE sites in the 

WRAP states. 

Monthly Fractional Bias, IMPROVE-EC

-50

-40

-30

-20

-10

0

10

20

30

1 2 3 4 5 6 7 8 9 10 11 12

Month

FB (%)

12k 36k

Monthly Fractional Gross Error, IMPROVE-EC

0

10

20

30

40

50

60

70

80

90

1 2 3 4 5 6 7 8 9 10 11 12

Month

FE (%)

12k 36k
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Figure 6-13. Monthly fractional bias (top) and fractional gross error (bottom) for 

the CMAQ 2002 pre02d base case EC model performance as a function of 

observed concentration. 
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6.2.6 Other fine PM (soil) and coarse mass (CM) 

Figures 6-14 and 6-15 display scatterplots of predicted and observed other fine PM (soil) and 

coarse mass (CM), respectively, across the IMPROVE network in the western U.S. for the 

CMAQ 2002 36-km and 12-km pre02d base case simulations. The time series of monthly bias 

and error for soil and CM are given in Figures 6-16 and 6-17, respectively. Soil is overestimated 

in January by approximately 100%, with the bias dropping almost linearly to an underestimation 

bias of -33% (36-km) and -24% (12-km) in April. The soil bias is then <±20% from May to 

September, when both the 36-km and 12-km base cases exhibit near zero bias. The soil bias then 

increases linearly from zero in September to ~80% in December. Unlike the other species, for 

soil the 36-km and 12-km performance statistics track each other very closely in the winter, early 

spring, and late fall, and in the summer the 12-km results are lower than when a 36-km grid is 

used, resulting in slightly worse performance with the 36-km grid than the 12-km grid. 

The performance for CM, on the other hand, exhibits a severe underprediction bias throughout 

the year that is worse (exceeding -100%) in summer than in winter. The January-December CM 

fractional bias is approximately -40%; it then decreases as one moves toward summer, with the 

lowest fractional bias of approximately -120% (12-km) and -130% (36-km) occurring in JuneJuly. The reasons for the CM underestimation bias are unclear but are likely due to missing dust 

sources and subgrid-scale local dust impacts that are not simulated by the model. 
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Figure 6-14. CMAQ 2002 pre02d base case simulation “other fine PM” (soil) model 

performance in the WRAP states for January (top left), April (top right), July (bottom 

left), and October (bottom right) using the IMPROVE monitoring network. 
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Figure 6-15. CMAQ 2002 pre02d base case simulation CM model performance in the 

WRAP states for January (top left), April (top right), July (bottom left), and October 

(bottom right) using the IMPROVE monitoring network. 
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Figure 6-16. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case “other fine PM” (soil) model performance across the 

IMPROVE sites in the WRAP states. 

Monthly Fractional Bias, IMPROVE-SOIL

-60

-40

-20

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Figure 6-17. Monthly fractional bias (top) and fractional gross error (bottom) for the 

CMAQ 2002 pre02d base case CM model performance across the IMPROVE sites 

in the WRAP states. 

Monthly Fractional Bias, IMPROVE-CM

-140

-120

-100

-80

-60

-40

-20

0

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FB (%)

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6.3 Evaluation of CMAQ at Class I Areas for the Best-20% and 

Worst-20% Days 

The 2007/2008 RHR SIP/TIP will be the first progress SIP/TIP that must demonstrate progress 

toward achieving natural conditions at Federally mandated Class I areas by 2064. The 2000-2004 

five-year baseline is used as the starting point for the visibility projections, with the first futureyear progress demonstration year being 2018. For the 2007/2008 SIP/TIP, visibility is projected 

at Class I areas for the worst-20% visibility days and the best-20% visibility days to achieve the 

following goals: 

• demonstrate progress toward natural conditions in 2064 for the worst-20% visibility days, 

and 

• demonstrate no worsening in visibility conditions for the best-20% days. 

6.3.1 Procedures for projecting visibility improvements 

Visibility is projected using modeling results in a relative fashion to scale the observed components of light extinction for the worst/best-20% days (U.S. EPA, 2001). The species-dependent 

and Class-I-area-dependent scaling factors are referred to as relative reduction factors (RRFs). 

Although the exact procedures for projecting visibility are undergoing analysis and refinement 

and will depend on the amount of modeling results available, the steps that would be taken using 

2002 annual CMAQ modeling results to project current-year to 2018 future-year visibility 

conditions would likely be as follows (U.S. EPA, 2001): 

1. Identify the worst-20% and best-20% observed visibility days at each Class I area during 

each year within the 2000-2004 baseline. 

2. Calculate the average CMAQ estimated concentrations of SO4, NO3, OC, EC, soil, and 

CM for the worst-20% days and the best-20% days (separately) for the 2002 “typical year 

base case” and the 2018 emissions scenario, then calculate the species- and Class-I-areadependent RRFs as the ratio of average future to current year modeling results 

(2018/2002) across the observed worst-20% or best-20% days from 2002. 

3. Apply the model-derived RRFs for the worst-20% days to the current observed concentrations at each Class I area for each of the worst-20% days from the years 2000-2004 to 

obtain projected 2018 PM species concentrations for the worst-20% days from 2000-

2004. 

4. Calculate visibility extinction from the 2018 projected concentrations for each of the 

observed worst-20% days from 2000-2004 using the reconstructed mass extinction 

equation and monthly-average Class-I-area-specific relative humidity adjustment factors. 

5. Convert the 2018 projected daily average total visibility extinction for each of the worst20% days from 2000-2004 to deciview (dV) and average across each year to obtain the 

five average 2018 deciviews for 2000-2004. 

6. Average the five annual worst-20% 2018 deciviews to obtain the five-year-average 2018 

deciview. 
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7. Repeat steps 3 through 6 for the best-20% days. 

8. Compare the 2018 projected five-year average deciviews with the visibility progress goal 

for each Class I area. 

The key modeling components used to project future-year visibility are the Class-I-area- and 

species-specific RRFs that are based on the model-estimated average concentrations for the 

worst-20% and best-20% observed visibility days. Consequently, the CMAQ model performance 

for the worst-20% and best-20% days is very important. 

6.3.2 Evaluation for best/worst-20% days 

In this section we provide examples of the CMAQ model performance for extinction on the best20% and worst-20% visibility days during 2002. Note that in this evaluation we used results 

from the previous CMAQ Base Case C rather than pre02d results. Base Case C is one iteration 

previous to the pre02d scenario, but since Base Case C produced model performance similar to 

pre02d’s (see http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#pre02d) our evaluation results provide 

insights to the CMAQ pre02d performance. 

The evaluation was conducted by comparing the predicted and observed average extinction for 

each component of light extinction in the IMPROVE aerosol mass extinction equation using the 

Class-I-area-specific monthly relative humidity adjustment factors. The extinction is averaged 

across the worst-20% and best-20% observed visibility days during 2002. 

6.3.2.1 Performance across IMPROVE sites for average of worst-20% days 

Figure 6-18 compares the predicted and observed extinction averaged for the worst-20% days at 

western U.S. IMPROVE sites for the six components of light extinction due to SO4, NO3, OC, 

EC, soil, and CM. The model estimates the observed average extinction due to SO4 on the worst20% days fairly well, although there is an underestimation tendency when the observed 

extinction due to SO4 is above ~15 Mm-1. 

At most of the IMPROVE sites the model exhibits little bias in the extinction due to NO3 at western U.S. IMPROVE sites for the worst-20% days, although there is a lot of scatter. However, 

there do appear to be three sites where the model underestimates the average extinction due to 

NO3 on the worst-20% days by a factor of 2 to 3 (Figure 6-18a, top right). 

With the exception of one very large overestimation outlier, the extinctions due to OC and EC on 

the worst-20% days appear to be reproduced reasonably well (Figure 6-18a, middle). The one 

outlier is the Kalmiopsis site, which was highly influenced by fires during August 2002. 

Extinction due to CM is understated by the model for the worst-20% days. However, both the 

model and observations indicate that CM is a minor contributor to the total extinction on the 

worst-20% days. There do appear to be three predicted and observed CM extinction points for 

the average of the worst-20% days that stand out from the rest (Figure 6-18a, bottom left): (1) a 

point lying above the 1:1 line at around 10 Mm-1 predicted/observed value, which is Kalmiopsis; 

(2) a point with a 10 Mm-1 predicted and 20 Mm-1 observed, which is Phoenix; and (3) a point 

with about 2 Mm-1 predicted and 17 Mm-1 observed, which is Saguaro National Park. 
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With the exception of the 40 Mm-1 point that is Kalmiopsis, the model and observations agree 

that soil is also a minor contributor to the extinction budget at IMPROVE monitors in the 

western U.S. during the worst-20% days. 

6.3.2.2 Performance across IMPROVE sites for average of best-20% days 

On the best-20% days (Figure 6-18b), the agreement between the predicted and observed average 

extinction across the six species is not as good as it was for the worst-20% days. Extinction due 

to SO4, NO3, and OC is overstated for the average of the best-20% days. For around a half-dozen 

IMPROVE monitors, extinction due to OC, EC, and soil is greatly overstated on the average of 

the best-20% days, whereas extinction due to CM is generally understated at most monitors on 

the best-20% days. 

6.3.2.3 Example predicted and observed Class I area extinction budgets for 

worst-20% and best-20% days 

Figure 6-19 displays comparisons of predicted and observed stacked bar charts of total extinction 

averaged across the worst-20% days at several of the IMPROVE monitors at Class I areas in the 

western U.S. These stacked bar charts are shown without including Rayleigh background, which 

would add a constant 10 Mm-1 to all plots if it were included. A comparison of the average 

predicted and observed extinction for the worst-20% days at Grand Canyon National Park 

(Figure 6-19a, top) reveals fairly good agreement with the total extinction. Although extinction 

due to SO4 and NO3 is slightly underestimated, this is compensated for by slightly overstated 

extinction due to OC. However, the extinction due to CM is greatly understated at the Grand 

Canyon for the worst-20% days. 

Results for the average of the worst-20% days at Chiricahua National Monument in southeastern 

Arizona are not as good, with the observed total extinction of approximately 35 Mm-1

 underestimated by approximately a factor of 3 (Figure 6-19a, bottom). Every component of extinction 

for the worst-20% days is underestimated at Chiricahua, with the underestimation of extinction 

due to CM being over an order magnitude lower. We suspect that issues associated with the 

Mexican inventory, missing fugitive dust sources (e.g., windblown dust), and local impacts are 

contributing to the poor extinction performance at Chiricahua for the worst-20% days. 

At Bandelier National Monument in north central New Mexico (Figure 6-19b, top), the total 

extinction on the worst-20% days is underestimated by approximately 20% due to an 

understatement of the extinction that is due to SO4, NO3, EC, soil, and especially CM. Extinction 

due to OC is overstated at Bandelier for the worst-20% days. 

For Rocky Mountain National Park the total extinction for the worst-20% days is underestimated 

by approximately 35% (Figure 6-19b, bottom). Again, extinction due to SO4, NO3, EC, soil, and 

CM is understated, with the underestimation of the extinction due to CM being about a factor of 

10. The extinction due to OC is reproduced quite well by the CMAQ Base Case C simulation at 

Rocky Mountain National Park for the worst-20% days. 

At Yellowstone National Park (Figure 6-19c, top), the model overstates the total extinction on 

the worst-20% days by approximately 60% due to an overstatement of the extinction caused by 

OC and EC, where extinction due to these two components is overstated by a factor of 2 and 3, 
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respectively. Extinction due to soil is also overstated, although soil is a minor contributor to total 

extinction for the worst-20% days at Yellowstone. As seen at the other Class I areas, extinction 

due to CM is understated by the model. 

At Glacier National Park in northwestern Montana, total extinction on the worst-20% days is 

understated by approximately 30%, with underestimation of the extinction due to SO4, EC, and 

CM contributing the most to this bias (Figure 6-19c, bottom). 

At Mount Rainier National Park (Figure 6-19d, top) the amount of extinction due to NO3 is 

overestimated by the model by almost a factor of 5, resulting in a net overestimation bias for 

total extinction of 65%. Extinction due to OC and soil is also overstated, whereas extinction due 

to SO4 and CM is understated at Mount Rainier for the worst-20% days. 

The overstated OC and EC impacts at Kalmiopsis (Figure 6-19d, bottom) due to the August 2002 

fires in southwestern Oregon are clearly evident in the extinction budgets for the worst-20% days 

at this site. The observed total extinction (187 Mm-1) is overestimated by a factor of 3 by the 

model (581 Mm-1). Even though Kalmiopsis has the highest observed total extinction of any site 

examined in Figures 6-19a-e, the model still greatly overstates it. 

The final two IMPROVE sites with predicted and observed extinction comparisons for the worst20% days are Point Reyes and San Gorgonio in California (Figure 6-19e). The high NO3

contributions in both the observed and estimated extinction budgets are clearly evident. At San 

Gorgonio the model estimates the extinction due to NO3 reasonably well, overstates the 

extinction due to EC and OC, and understates it due to CM, resulting in a good agreement in 

total extinction for the worst-20% days (within 10%). At Point Reyes, however, the model 

underestimates extinction due to SO4 and NO3 by approximately a factor of 2, resulting in an 

understatement of total extinction by 33%. 

Finally, Figure 6-20 presents two fairly typical examples of the CMAQ Base Case C extinction 

performance for the best-20% days. The model overstates total extinction at Grand Canyon and 

Yellowstone on the best-20% days by approximately a factor of 2, with overstated extinction due 

to SO4, NO3, and OC contributing to this bias. Clearly the model has difficulty in simulating the 

clearest-visibility days. 
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Figure 6-18a. Comparison of predicted and observed average extinction for the worst-20% 

visibility days in 2002 at IMPROVE monitors for SO4, NO3, OC, EC, CM, and soil. 

bNO3 (US)

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Figure 6-18b. Comparison of predicted and observed average extinction for the best-20% 

visibility days in 2002 at IMPROVE monitors for SO4, NO3, OC, EC, CM, and soil. 

bSO4 (US)

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Figure 6-19a. Comparison of predicted (right) and observed (left) average extinction 

for the worst-20% visibility days in 2002 at the Grand 

Canyon (top) and Chiricahua (bottom) Class I areas. 

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Grand Canyon NP, AZ 

Chiricahua NM, AZ 
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Figure 6-19b. Comparison of predicted (right) and observed (left) average extinction 

for the worst-20% visibility days in 2002 at the Bandelier (top) and Rocky Mountain 

National Park (bottom) Class I areas. 

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Bandelier NM, NM 

Rocky Mtn. NP, CO 
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Figure 6-19c. Comparison of predicted (right) and observed (left) average extinction for 

the worst-20% visibility days in 2002 at the Yellowstone (top) 

and Glacier (bottom) Class I areas. 

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Yellowstone NP, WY 

Glacier NP, MT 
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Figure 6-19d. Comparison of predicted (right) and observed (left) average extinction 

for the worst-20% visibility days in 2002 at the Mount 

Rainier (top) and Kalmiopsis (bottom) Class I areas. 

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Mount Rainier NP, WA

Kalmiopsis, OR
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Figure 6-19e. Comparison of predicted (right) and observed (left) average extinction 

for the worst-20% visibility days in 2002 at the Point Reyes (top) 

and San Gorgonio (bottom) Class I areas. 

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Point Reyes, CA 

San Gorgonio, CA
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Figure 6-20. Comparison of predicted (right) and observed (left) average extinction for 

the best-20% visibility days in 2002 at the Grand Canyon 

(top) and Yellowstone (bottom) Class I areas. 

6.4 Conclusions of CMAQ 2002 pre02d 36-km and 12-km Base Case 

Model Performance Evaluation 

Performance for the CMAQ 2002 pre02d 36-km and 12-km base case simulation for sulfate 

across the western states is reasonably good, albeit with a summer underestimation and winter 

overestimation bias; the biases in the spring and fall are near zero. The summer SO4 underestimation bias affects the model performance for visibility for the worst-20% days. One of the 

biggest changes between the previous CMAQ Base Case C simulation and the new CMAQ 

0

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Grand Canyon NP, AZ
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pre02d base case was the inclusion of the new MM5 meteorological modeling results (see 

Section 4). WRAP expended extensive effort finding a more optimal MM5 configuration for 

simulating meteorological conditions in the western U.S. In particular, a summer overestimation 

of the precipitation in the southwestern U.S. led to overstated wet deposition and understated 

concentrations in previous CMAQ base case simulations. Figure 6-21 compares the SO4 wet 

deposition across NADP sites in the WRAP states for the previous Base Case C and current 

pre02 2002 36-km CMAQ simulations. Using the older MM5 meteorology, wet SO4 deposition 

is overstated by Base Case C during these four months by 36%, 61%, 55%, and 57%. However, 

using the new MM5 meteorology, wet SO4 deposition is understated for the same four months by 

smaller amounts: -25%, -15%, -38% and -34%; also, in each month this deposition exhibits 

lower bias and the same or lower error. The CMAQ 2002 pre02d 12-km simulation produced 

higher SO4 concentrations than the 36-km simulation. Comparisons of 36-km and 12-km wet 

SO4 deposition were not available. 

Nitrate performance exhibits a large winter overestimation and summer underestimation bias, 

with large errors throughout the year. During the summer the model and observations both show 

low NO3 values, so the NO3 underestimation does not drastically affect visibility model performance for the worst-20% days. However, the winter NO3 overestimation bias can affect 

performance the best-20% days. The 12-km base case produced higher NO3 concentrations than 

the 36-km base case, resulting in slightly improved model performance in the summer and 

slightly degraded model performance in the winter when a 12-km grid is used. 

Organic carbon at the IMPROVE sites has a positive bias in the winter and near zero (36-km) or 

negative (12-km) bias in the summer. There were large differences in the CMAQ OC performance using the 36-km and 12-km grids, with the 12-km modeling results estimating lower OC 

than did the 36-km results. On at least one documented case of large fire impacts (August 2002 

in southwestern Oregon), the model greatly overstated the amount of both OC and EC at the 

monitors. Other than that, EC bias across the IMPROVE sites was relatively low, with an 

approximately -20% (36-km) and -30% (12-km) bias during the nonsummer months and bias 

<±20% during the summer. 

Coarse mass was understated throughout the year, with the understatement greatest during the 

summer, late spring, and early fall, exceeding -100% for some months. Other fine PM (soil) was 

overstated in the winter (60% to 100%) and understated in the summer (0% to -40%). 
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 214 

Figure 6-21. Comparison of CMAQ 2002 36-km pre02d and Base Case C wet SO4 deposition 

model performance in the WRAP states for May (top left), June (top right), 

July (bottom left) and September (bottom right). 
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 215 

6.5 Status of Task 4 Deliverables 

The table below gives the status of each Task 4 deliverable listed in the 2004 WRAP RMC work 

plan, plus any additional deliverables agreed upon after the work plan was prepared. 

Table 6-2. Status of the Task 4 deliverables. 

Deliverable Status 

CMAQ pre02b (Base B) Base Case Performance 

Evaluation 

Posted January 2004: 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#pre02b

Comparison of CMAQ V4.3 with CMAQ V4.4beta 

model performance 

Posted June 2004: 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#compare

CMAQ pre02c (Base C) Base Case Performance 

Evaluation 

Posted July 2004: 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#pre02c

Comparison of CMAQ V4.4beta with final CMAQ 

V4.4 (October 2004 release) 

Posted November 2004: 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#beta

CMAQ pre02d (Base D) Base Case Performance 

Evaluation 

Posted January and April 2005: 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#pre02d
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7. Task 5: Preparation and Reporting of Geographic Source 

Apportionment Results 

7.1 Overview 

A primary motivation for using air quality models is the attempt to identify the particular emissions sources that contribute to pollutant exposure at a particular receptor site. This source 

attribution information can then be used to develop the most effective emissions reduction 

strategy for attaining air quality goals at that site. 

A variety of modeling and data analysis methods have been used to perform source attribution 

(also sometimes referred to as “source apportionment”). Model sensitivity simulations have been 

used in which a “base case” model simulation is performed and then a particular source is 

“zeroed out” of the emissions. The importance of that source is assessed by evaluating the 

change in pollutants at the receptor site, calculated as pollutant concentration in the sensitivity 

case minus that in the base case. This approach is known as a “brute force” sensitivity because a 

separate model simulation is required for each sensitivity. 

Although sensitivity approaches are widely used and provide valuable information, they also 

have some disadvantages. For small sources, the model used may lack sufficient numerical 

precision to represent the effect of the source at a receptor site. More importantly, the chemical 

reactions of some precursor species have a nonlinear dependence on ambient species concentrations and are subject to positive and negative feedback effects when a model input parameter is 

changed. Thus, the change in pollutant concentration at a receptor site in the sensitivity case 

might not accurately reflect the contribution of that source to the receptor in the base case. The 

concern regarding nonlinearity primarily affects NOx, O3, and nitrate, with a smaller effect on 

organic aerosols and sulfate. (Nonlinearity is not a concern for nonreactive or slowly reacting 

species.) Another limitation of brute force sensitivity approaches is that very large numbers of 

sensitivity simulations must be completed to develop source attribution for all of the emissions 

source categories and source regions of interest. More efficient sensitivity approaches, e.g., the 

decouple direct method (DDM), are available for gas-phase sensitivities and are being developed 

for aerosols. 

Mass-tracking approaches such as a tagged-species algorithm represent a second approach for 

performing source attribution. Mass-tracking approaches do not attempt to predict how the 

pollutant concentrations will change at a receptor site; instead, they attempt to identify the mass 

contributions of each source to the pollutant at the receptor site in the base case model 

simulation. Thus, these mass-tracking approaches for performing source attribution have the 

potential to evaluate the contribution or “responsibility” for all sources in a single model 

simulation. These approaches have not been widely used in air quality modeling because of the 

complexity and difficulty of implementing a mass-tracking algorithm. A tagged species approach 

using tracers to track the contributions of particular emissions sources is probably the most easily 

implemented type of mass tracking approach. EPA has previously used tagged-species methods 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 217 

to do source attribution for sulfate in regional acid deposition modeling. ENVIRON’s CAMx 

model uses a source attribution algorithm for ozone (OSAT). ENVIRON has recently 

implemented a particulate source attribution algorithm in CAMx, known as PM Source 

Apportionment Technology (PSAT). It is still uncertain whether sensitivity approaches or source 

attribution approaches are more useful for attributing responsibility to emissions sources. It is 

likely that a combination of both approaches will be most useful for developing optimal emission 

control strategies. The key difference between the approaches is that source attribution estimates 

the contribution of a source to pollutants at a receptor, while sensitivity simulations estimate the 

change in pollutants at receptor when an individual source is changed by a particular amount. 

During 2003 and 2004, UCR developed a new algorithm in CMAQ to assess source attribution. 

The Tagged Species Source Apportionment (TSSA) algorithm uses a system of tracers or 

“tagged species” to track the chemical transformations, transport, and removal of emissions from 

particular source categories or source locations. This algorithm has been implemented and tested 

for NOx-NOz-nitrate chemistry, sulfate, and ammonium. The algorithm was initially implemented in CMAQ version 4.2.2 using the Sparse-Matrix Vectorized Gear (SMVGEAR) gas-phase 

chemistry solver. During 2004 we implemented the TSSA algorithm in a beta release of 

CMAQ 4.4 and adapted it for use with a more computationally efficient Quasi Steady State 

Approximation (QSSA) gas-phase chemistry solver in CMAQ. The algorithm is written in 

Fortran 90 and supports multiprocessor usage. The results of this algorithm were first demonstrated at the WRAP Technical Workshop in January 28-29, 2004, using the RMC’s preliminary 

2002 modeling with CMAQ 4.2.2. In this section we describe the implementation of the 

algorithm and summarize results for modeling with the beta CMAQ 4.4 using the Pre02c 2002 

simulation discussed in Section 5. 

During the testing of the TSSA algorithm in CMAQ 4.4beta, we discovered significant mass 

conservation errors in the algorithm. These errors were likely the result of a bug that affected 

mass conservation in CMAQ 4.4beta. However, it is also possible that the vertical advection 

algorithm in CMAQ introduces errors in mass conservation in the TSSA algorithm. During 2004 

we experimented with several different methods to handle problems with mass conservation in 

CMAQ. Nonetheless, large fractions of mass of PM species could not be attributed to the source 

categories. Additional development and testing of the TSSA algorithm are required. This effort is 

currently underway with separate funding from EPA using a new release of CMAQ 4.4 in which 

the mass conservation error was corrected. 

Although there are concerns regarding the absolute mass attributions in the CMAQ TSSA 

results, comparisons with brute force sensitivity tests and alternate models have made us more 

confident that the relative mass attributions are correct. As noted above, a similar source 

attribution algorithm has been implemented in the CAMx model by ENVIRON. The CAMx 

PSAT was released in 2004 and uses a system of tracers similar to the system used in the TSSA 

algorithm to attribute particulate mass at receptor sites to emissions source categories or source 

regions. To compare CMAQ TSSA results with CAMx PSAT results, we performed CAMx 

PSAT simulations for two months: February and July, 2002. Results for both models are 

described in Section 7.3. The CAMx and CMAQ results are not directly comparable because 

updated versions of the emissions and meteorology data were used in the CAMx PSAT 

simulation. However, we would expect the results to be generally similar, so the CAMx results 

provide a useful test to evaluate the usefulness of the CMAQ results. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 218 

In summary, the CAMx PSAT results are generally consistent with the relative source 

attributions in CMAQ. However, the PSAT much more accurately accounts for the total mass of 

PM at receptor sites. In the CMAQ results large fractions of mass were either not attributed to 

emissions sources at some receptor sites or were attributed to a combination of emissions 

categories, such as fires and area sources, that were not individually tracked. Although work is in 

progress to develop a new version of the TSSA algorithm in CMAQ, we do not expect this to be 

completed in time for additional WRAP source attribution studies in 2005. We therefore plan to 

complete additional source attribution simulations in 2005 using the CAMx model with PSAT. 

7.2 Description of TSSA Method 

7.2.1 Initialization of tagged species 

The CMAQ TSSA methodology uses tagged species to track the temporal evolution of emissions, chemical transformations, transport, and dispersion of mass across the model’s spatial 

domain. Tracers are defined for a subset of key chemical species and for certain predefined 

subregions and emissions source categories. In addition, tracers are included to represent mass 

contributions from the initial conditions (ICs) and from transport into the model domain from the 

boundary conditions (BCs). The tracers are all initialized with zero values, except for the IC 

tracer, which is identical to the initial species concentrations. Then, the tracer concentrations are 

updated at each model time step over the entire model simulation period. 

For each species, the “bulk concentration” is defined to be the model-predicted concentration of 

the species for each grid cell. Thus, a three-dimensional (3-D) array is required to represent the 

concentration of the bulk species. For the TSSA method, the source attribution requires the addition of one additional 3-D array for each source category or source region that is tracked by the 

algorithm. For example, if source attribution is performed for three emissions categories (e.g., 

point, mobile, and biogenic) and 10 different source regions (e.g., 10 different state or tribal 

areas), a total of 32 new 3-D tracers must be added to represent the 30 combinations of source 

category and region plus the ICs and BCs. By definition, the sum of the tracers must always be 

identical to the bulk species concentration because the tracers are defined to represent the sources 

that compose the bulk species, as shown in Equation 7-1: 

= ∑ + +

M N

j

BC

j

IC

j

m n

j Cbulk C C C

,

1,1

, (7-1) 

where j represents the chemical species, m represents emissions source categories, and n represents geographical source regions. 

The 3-D concentration fields for each tracer are output at the same time interval (typically hourly) that is used for the CMAQ bulk concentration output files. Thus, tracer output files provide 

three-dimensional fields showing the time-varying contribution of each tagged source (where a 

“tagged source” is defined by the use of a set of tagged species to identify the contributions from 

that source) in all spatial regions throughout the model domain. For example, Figure 7-1 shows 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 219 

the 3-D concentration field for a tracer for aerosol nitrate attributed to California mobile-source 

emissions at 24:00 UTC on January 14, 2002. 

Figure 7-1. Three-dimensional plot of aerosol nitrate attributed to California 

mobile-source emissions on January 14, 2002. 

Currently, the tagged species implemented in CMAQ include the family of nitrogen model 

species, including NOX (the “reactive N” family), HNO3, PAN, RNO3, ANO3I, and ANO3J. 

The reactive NOX species is defined as the sum of NO, NO2, NO3, 2*N2O5, and HONO. We 

note that this definition of “big NOX” differs from the usual definition of NOx as the sum of NO 

and NO2. We use the expanded definition of big NOX because these short-lived, reactive forms 

of nitrogen are in fairly rapid chemical equilibrium, and we achieve a substantial computation 

cost reduction by using a single family to represent the sum of reactive N. 

7.2.2 Definition of source regions 

Tracers are defined for selected, predefined geographical regions. Source regions are defined by 

assigning a unique numeric code to the model grid cells that compose each source area. The 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 220 

source region can be defined to be a single grid cell or any number of grid cells up to the total 

number of cells used in the spatial domain. Typically, a source region is composed of a group of 

contiguous cells, but a source region could also be composed of noncontiguous cells. Figure 7-2a 

illustrates the TSSA regions for each state within the WRAP 36-km modeling domain. In this 

figure, California is assigned the number 1, Nevada is assigned 2, etc. The entire source regions 

of Canada and Mexico are assigned the numeric codes of 91 and 92, respectively. All U.S. states 

outside of the WRAP region are grouped together. The boundaries of California, Oregon, and 

Washington were extended westward so that offshore shipping would be included in the source 

attribution for those states. The map in Figure 7.2a is truncated, so portions of Canada, Mexico, 

and the easternmost U.S. states are not shown, but the full model domain is included in the 

algorithm. 

The state and national boundaries can be only coarsely represented in this map because the 

resolution is limited by the 36-km grid used in the modeling. We assigned each grid cell to 

whichever state accounted for the largest area of the grid cell. Thus, emissions sources near 

political boundaries might be incorrectly located in an adjacent state. 

A 12-km grid definition would allow better representation of political boundaries. Alternatively, 

the digital map could easily be modified to change the source region definitions by simply 

editing the numeric values in the ASCII text file that contains the source region definitions. For 

example, if a border cell is located primarily in California but includes a large point source 

located in Nevada, that grid cell could easily be reassigned to Nevada by changing the cell’s 

numeric code. The ASCII file can also be edited to define new source regions as needed. 

Figure 7.2b shows the boundaries of the source regions used in the CAMx PSAT model. It 

appears that the boundaries are not always correctly represented, possibly because of the map 

projection used. These should be adjusted in future simulations. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 221 

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8 8 8 8 8 8 8 8 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66

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66 66 8 8 8 8 8 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 79 66 66 66 66 66 66

66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66

66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66

66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66

92 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66

92 92 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66

92 92 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 0

92 92 92 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 0 0

92 92 92 66 66 66 66 66 92 92 92 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 0 0

92 92 92 92 92 66 66 92 92 92 92 92 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 0 0 0 0 0 0 0 0 0 66 66 0 0 0 0

92 92 92 92 92 92 92 92 92 92 92 92 92 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

92 92 92 92 92 92 92 92 92 92 92 92 92 66 66 66 66 66 66 66 66 66 66 66 66 66 66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

92 92 92 92 92 92 92 92 92 92 92 92 92 92 66 66 66 66 66 66 66 66 66 66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 66 66 66 66 66 66 66 66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 66 66 66 66 66 66 66 66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 66 66 66 66 66 66 66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Figure 7-2a. Source area mapping file as used in the CMAQ TSSA algorithm, with each 

source region distinguished by a unique numeric code. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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-2000 -1500 -1000 -500 0 500 1000 1500 2000

-1500

-1000

-500

0

500

1000

1

2

3

4

5

6

7 8

9

10

11 12

13

14

15

Figure 7 2b. Source area mapping file as used in CAMx PSAT showing the 

boundaries for each state/region. 

7.2.3 Definition of source categories 

The source categories include initial and boundary conditions; individual types of emissions 

sources, such as motor vehicles, point sources, area sources, and fires; and “other” sources, 

which include all sources other than those that are explicitly defined. Lumping all unassigned 

sources together in the “other” category allows the total mass of tracers to be identical to the bulk 

species concentration, as required by Equation 7-1. 

In the SMOKE/CMAQ modeling system, all emissions are merged into a single emissions file 

that is read by CMAQ. However, all information regarding the source categories is lost in this 

merging step. The TSSA method therefore requires that emissions files be read by CMAQ for 

each individual emissions source category that is included among the tagged sources. Because of 

the file structure used in CMAQ, this can require a vast increase in disk storage cost for the 

emissions, because sparse data storage is not employed in the CMAQ emissions files. 

Table 7-1 gives details on the tagged-sources naming scheme that is used in the algorithm and 

displayed in some of the plots showing model simulation results. Source category codes in 

column 2 of the table are combined with two-letter state abbreviations to create identifiers for 

particular source categories within a given state. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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Table 7-1. Combinations of source categories and geographic regions 

that are typically included in a TSSA simulation. 

Types Source 

Category† Notes 

ICON ICON Initial concentration 

BCON BCON Boundary concentration 

MV_* Mobile sources of any state 

BG_* Biogenic sources of any state 

RD_* Road dust of any state 

NR_* Nonroad dust of any state 

PN_* Point sources without SO2 of any state 

PS_* Point sources with SO2 of any state 

AR_* Area sources of any state 

WF_* Wildfire sources of any state 

AG_* Agricultural fire sources of any state 

RX_* Prescribed fire sources of any state 

MX_* Mexico fire 

ET_* Total emission of any state 

Emissions 

*_WRAP Any type of emission of WRAP domain 

Others OTHERS Any sources other than those listed above 

†

The asterisks can be the two-letter state code for any state in the WRAP region 

(or for Canada [CN] or Mexico [MX]), or all states in the WRAP region. 

7.2.4 Updating of tracers in CMAQ science algorithms 

The CMAQ Chemical Transport Model (CCTM) is a three-dimensional, regional-scale, nonhydrostatic air quality model based on the mass conservation equation to simulate transport, transformation, dry and wet deposition, and aerosol formation of pollutants. The governing equation 

can be rewritten in generalized coordinates where the turbulent flux terms are expressed with 

eddy diffusion theory (further described in Byun and Ching [1999]). Through operator splitting, 

or the use of fractional time steps, modularity is achieved in CCTM and computationally efficient algorithms are applied to solve each science process (e.g., advection, chemistry). The TSSA 

routine is implemented into each science process to account for the change in species concentration and update the tracer species accordingly. Figure 7-3 illustrates the TSSA implementation 

in CCTM science processors. The TSSA routine checks for mass conservation at each advection 

time step and adjusts mass (renormalizes) if needed. The program will halt if large errors are 

detected during the mass conservation check. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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driver.F: 

read tssa configuration, ptssa_init ...

do n = 1, nsteps

tssa_couple

tssa_decouple

write tssa output

end do

sciproc.F

Xadv 

Yadv

Yadv

Xadv

Zadv

Tssa Adjadv

Hdiff

Tssa Decouple

Vdiff

Tssa Cldproc

Chem

Tssa Aero

Tssa Couple

Hppm 

Tssa adv 

update

vppm 

Tssa hdiff

update

Tssa vdiff

update

smvgear Tssa chem

update

TSSA mass 

normalization

driver.F: 

read tssa configuration, ptssa_init ...

do n = 1, nsteps

tssa_couple

tssa_decouple

write tssa output

end do

sciproc.F

Xadv 

Yadv

Yadv

Xadv

Zadv

Tssa Adjadv

Hdiff

Tssa Decouple

Vdiff

Tssa Cldproc

Chem

Tssa Aero

Tssa Couple

Hppm 

Tssa adv 

update

vppm 

Tssa hdiff

update

Tssa vdiff

update

smvgear Tssa chem

update

TSSA mass 

normalization

Figure 7-3. Flowchart of the TSSA implementation in CMAQ’s CCTM. 

The TSSA initialization stage takes place at the beginning of the model simulation. The tagged 

species are initialized with concentrations from the initial conditions input file. The TSSA algorithm also updates the tagged species at the boundary with concentrations from the boundary 

conditions input file. 

The equation below is the mass continuity equation that defines the processes that are solved for 

in the CMAQ model. We added new computer code to CMAQ to track the changes in tagged 

species at each time step for each of the processes represented in the equation. 

 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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The terms in this equation are summarized below: 

a. Time rate of change in species concentration g. Off-diagonal vertical eddy diffusion 

b. Horizontal advection h. Production or loss from chemical reactions 

c. Vertical advection i. Emissions 

d. Horizontal eddy diffusion (diagonal term) j. Cloud mixing and aqueous-phase chemical 

production or loss 

e. Vertical eddy diffusion (diagonal term) k. Aerosol process 

f. Off-diagonal horizontal eddy diffusion l. Plume-in-grid process 

7.2.5 Postprocessing of TSSA results 

The tracer output files can be immediately visualized using the Program for the Analysis and 

Visualization of Environmental data (PAVE) and can also be converted into the format required 

by the Vis5D visualization software. This is useful for visualizing the transport of pollutants 

from a source across the model domain. However, the result of most interest is the source 

attribution at a particular receptor site. UCR has developed postprocessing software to read the 

3-D TSSA output file and extract data for a list of IMPROVE sites, and create bar plots and 

ASCII files showing the 20 largest contributors at each site. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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The source attribution can be calculated as the attribution averaged over a 24-h period (to match 

the temporal resolution specified in the IMPROVE monitoring protocol) or for some other averaging period. In particular, UCR has developed software to sort and identify for each receptor 

site the average source attribution for the best-20% and worst-20% visibility days, where the 

worst and best can be defined using either ambient IMPROVE data or model predictions. 

Figure 7-4 shows an example bar plot created using the UCR postprocessing software. UCR also 

provides the ASCII data to Air Resource Specialists, another WRAP contractor, to develop other 

types of plots for displaying these results. 

Figure 7-4. Example bar plot produced by UCR postprocessing software showing the 

largest 20 contributors to aerosol nitrate at a receptor site in the Grand Canyon. 

The x-axis labels are explained in Table 7-1. 

7.2.6 Uncertainties in the TSSA results 

While we have made considerable progress in developing and testing the TSSA algorithm in 

CMAQ, further work is needed. Most significantly, the algorithm should be implemented in the 

final release of CMAQ 4.4. The beta release contained a mass conservation error, which also 

introduced errors into the tracers used in the TSSA method. Although we experimented with a 

number of approaches for renormalizing to minimize the effects of mass errors, there are cases in 

which this introduces error terms into the TSSA algorithm that may accumulate over time. It is 

difficult to quantify the effect of these mass errors. In the current TSSA results for the 2002 

modeling, we in some cases accumulated errors in the “other” category. The result of this 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 227 

approach was that attributions to “other” were overestimated and attributions to mass transport at 

the domain boundary were probably underestimated. However, we believe that we obtained 

useful information from these model results by evaluating the relative contributions of the 

various source and emissions categories at each receptor site. 

7.3 Source Attribution Modeling Results 

During 2004 we completed three iterations of the source attribution modeling. Results were 

presented at WRAP Attribution of Haze Meetings in July, September, and November. These 

results represented three different approaches used in the CMAQ TSSA algorithm to account for 

mass conservation errors. Specifically, we tried two different algorithms to force mass 

conservation in the TSSA tagged species by renormalizing the sum of the tagged species to be 

identical to the bulk species concentration at each step in the CMAQ model: 

• We first used a proportional renormalization scheme in which lost mass was added to 

each tagged species proportionally to the mass of each tag within a given grid cell. This 

method failed in grid cells with very low concentrations. 

• We then used an equal-weighting scheme where lost mass was added equally to all 

tagged species within a given grid cell. This scheme produced unrealistic source 

attributions, for example, when mass was added to tracers for the boundary conditions. 

We also experimented with several variations and hybrids of these two schemes, but no approach 

gave satisfying results. In the end, we simply allocated lost mass to an artificial tag labeled 

“other” and tracked this separately. The relative source attribution results were similar for each 

of these approaches, so we used the results in a relative sense, i.e., to rank the importance of each 

emissions source at the receptor sites. Here we describe example plots for the results that we 

presented at the September 2004 Attribution of Haze meeting. Complete results are on the RMC 

web page (see http://www.cert.ucr.edu/aqm/308/cmaq.shtml#source) and have also been further 

processed and reported by Air Resource Specialists. 

Figure 7-5a shows an SO4 concentration time series at the Ft. Peck IMPROVE site for part of 

July 2002. On July 2, the first day of the time-series, the model-simulated SO4 was 0.5 μg/m3

. 

Figures 7-5b and 7-5c show the CMAQ TSSA results for this site on July 2 using the proportional mass renormalization scheme (Figure 7-5b) and the method in which lost mass was 

attributed to an “other” category (Figure 7-5c). This site is representative of the sites that had 

larger disagreements between the TSSA algorithms, in which even the relative ranking differed. 

In Figure 7-5b, boundary conditions (bcon) were ranked as the largest contributor, although this 

primarily represents the accumulation of mass error terms. Among the other source categories, 

point sources in the eastern U.S. and in North Dakota were ranked as the largest contributors, 

followed by smaller contributions from point sources in Canada and Washington and mobile 

sources in North Dakota and Washington. In Figure 7-5c, mass error terms were accumulated in 

the “other” tag, and point sources in North Dakota were ranked as the largest contributors, 

followed by contributions from point sources in the eastern U.S. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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Due to the uncertainty of the results from the CMAQ TSSA algorithm, we also used the CAMx 

PSAT model to evaluate source attribution for February and July of 2002. Figure 7-6a shows 

CMAQ TSSA results for SO4 at the Grand Canyon IMPROVE site on July 2, 2002. Point 

sources from Nevada were the single largest contributor, followed by smaller contributions from 

point sources in Arizona and Mexico. Figure 7-6b shows CAMx PSAT results for SO4 for the 

same site and date. Point sources from Nevada were again the single largest contributor, 

followed by smaller contributions from point sources in Mexico and Arizona. The similarity in 

the relative rankings and in the magnitudes of the sources at the Grand Canyon site increased our 

confidence in the results of the CMAQ TSSA simulations. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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(a)

(b)

(c)

Figure 7-5. CMAQ source attribution results at the Ft. Peck IMPROVE site on 

July 2, 2002: (a) modeled and measured SO4 concentrations; (b) TSSA results using 

proportional mass renormalization; (c) TSSA results with lost mass represented as “other.” 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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24-hr average contributions to SO4 at GRCA on 2002 182

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

All other

Fir_COPts_UT

Mob_NV

Mob_CA

ANR_NM

Mob_Mex

Mob_AZ

Fir_UT

Pts_Est

Pts_NM

Pts_CA

ANR_CA

ANR_AZ

ANR_Mex

ANR_NV

BC

Fir_AZ

Pts_AZ

Pts_Mex

Pts_NV

PSAT Tracer

ug/m3

24-hr average contributions to SO4 at GRCA on 2002 182

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

All other

Fir_COPts_UT

Mob_NV

Mob_CA

ANR_NM

Mob_Mex

Mob_AZ

Fir_UT

Pts_Est

Pts_NM

Pts_CA

ANR_CA

ANR_AZ

ANR_Mex

ANR_NV

BC

Fir_AZ

Pts_AZ

Pts_Mex

Pts_NV

PSAT Tracer

ug/m3

Figure 7-6. Source attribution results for SO4 at the Grand Canyon IMPROVE 

site on July 2, 2002: (a) CMAQ TSSA results; (b) CAMx PSAT results. 

7.4 Status of Task 5 Deliverables 

Table 7-2 gives the status of each Task 5 deliverable listed in the 2004 WRAP RMC work plan, 

plus any additional deliverables agreed upon after the work plan was prepared. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 231 

Table 7-2. Status of the Task 5 deliverables. 

Deliverable Status 

Source attribution report using 

emissions and 

meteorology data 

available in early 

May 

Because of problems with mass conservation in the CMAQ source code, we 

performed several iterations of CMAQ source attribution modeling. The final 

model simulations were completed in October 2004. Analysis products (bar charts 

showing source attribution at receptor sites) are available on the RMC web site, 

and ASCII results were provided to Air Resource Specialists for additional 

processing during September-October 2004. Plots for each receptor site are 

available here: http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#source

Additional source attribution simulations with CAMx PSAT were completed for 

two months, February and July, to evaluate the CMAQ results. The CAMx PSAT 

source attribution results are available at 

http://pah.cert.ucr.edu/aqm/308/meetings/March_2005/03-08_09-05.SF_CA/ by 

clicking on Alternative_Model_Mar8-9_2005_MF_Meeting.ppt

Simulation interim results and final results were presented as PowerPoint files at 

three meetings of the Attribution of Haze work group (below) and at the WRAP 

Modeling Forum Meeting. See http://pah.cert.ucr.edu/aqm/308/meetings.shtml. 

• Attribution of Haze Meeting, July 21-22, 2004, Denver, CO 

• Attribution of Haze Meeting, November 18-19, 2004 Las Vegas, NV: 

UCR_TSSA_results_Nov_2004 (ppt) 

• Attribution of Haze Meeting, September 22, 2004 Salt Lake City, UT: 

UCR TSSA Tracer Results (ppt files) 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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8. Task 6: Further Analysis of Model Performance in Regard 

to the Contribution of Natural Emissions to Visibility 

Impairment 

In addressing the effects of regional haze on visibility, estimating reasonable progress goals and 

formulating the associated control strategies require an understanding of how two types of emissions sources contribute to haze: natural sources, and non-U.S. background sources. To develop 

this understanding, it is important to identify the natural emissions sources that impact regional 

haze. Combining guidance provided by EPA, analyses performed by other RPOs, publications in 

the primary literature, and the results of RMC modeling sensitivities, we have worked on identifying and quantifying the natural and non-U.S. background haze emissions sources in the 

WRAP region. This section summarizes the work completed from March 2004 through February 

2005 to further our understanding of these sources of haze and their potential impacts on model 

performance evaluations and control strategy development in the WRAP states. 

8.1 Background 

One of the first phases of developing regional haze control strategies involves identifying the 

various sources of haze. During 2003, we began reviewing work by other groups and performing 

modeling sensitivities to identify and quantify the contributions of different sources of haze to 

visibility impairment in the WRAP region. We performed brute-force emissions sensitivities to 

study the effects of biogenic and wildfire emissions on visibility in the WRAP states. These 

simulations, in which we eliminated all anthropogenic emissions and included only biogenic and 

wildfire emissions, were insightful but somewhat incomplete in that the biogenic model did not 

include emissions estimates for some important sources, such as lightning NOx. Also, the boundary conditions in these simulations did not differentiate between naturally derived and anthropogenic sources of haze, which is important for drawing conclusions about the influence of background haze on regional visibility. In addition to performing cursory model simulations, we have 

leveraged regional haze guidance from EPA and research completed by other RPOs to provide 

direction in identifying the research needs for addressing visibility issues in the WRAP region. 

The Draft Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule 

(U.S. EPA, 2001) helps regional haze planning efforts by establishing default approaches for estimating the natural visibility conditions in the Class I areas. Through descriptions on how EPA 

interprets baseline haze and assesses visibility improvements, the guidance provides insight on 

what to consider when trying to identify natural and background sources of haze. Work by the 

VISTAS RPO (Tombach, 2004; Kumar, 2004; Brewer, 2004) has pointed out problems and 

recommended possible refinements to the EPA approach for calculating baseline and natural 

conditions, identified the importance of background or transported haze to the model domain 

boundary, and suggested definitions of controllable and uncontrollable haze sources. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 233 

8.2 Work Performed from March 2004 through February 2005 

While the information sources cited above treat haze sources in general, they do not provide 

explicit direction concerning which emissions are classified as natural emissions. During the past 

project year, the primary focus of our research for Task 6 was to clearly define the distinction 

between natural and anthropogenic emissions sources contributing to visibility impairment in the 

WRAP region. The RMC initiated the 2004 work with a draft memorandum identifying all 

potential sources of natural emissions in the WRAP region (Tonnesen, 2004). A comprehensive 

literature review based on this memo identified sources of emission factors published in the 

primary literature and in technical reports in the past decade (CEP, 2004b). These documents 

outline potential deficiencies in the current inventories and provide a basis for enhancing the 

WRAP emissions inventories to explicitly treat natural emissions sources in the future. To prepare for the development of refined WRAP emissions inventories for addressing the issue of 

natural haze sources, the WRAP Emissions Forum worked on characterizing natural versus 

anthropogenic emissions for various emissions sectors. 

The RMC and several WRAP forums completed work in 2004 to identify the components of 

emissions sources that contribute to haze. The Fire Emissions Joint Forum worked to define 

natural versus anthropogenic fires in their existing inventories (Randall, 2004b), and in September 2004 the RMC received a revised set of 2002 WRAP fire inventories applying this distinction for prescribed-fire emissions. (Wildfire emissions are defined as natural, and agricultural 

burning emissions are defined as manmade; prescribed-fire emissions can be either.) After 

modeling these new fire inventories in SMOKE, we conducted a CMAQ sensitivity to evaluate 

the impact of the natural fires on modeled visibility. We discuss the emissions preparation for 

these simulations in Section 5.4. The Dust Emissions Joint Forum is currently working on characterizing natural versus anthropogenic dust sources and will provide guidance during the next 

project year on how to develop refined fugitive and windblown dust inventories that make this 

distinction. Exploratory work by the RMC has looked at adding sea salt emissions, geogenic and 

volcanic emissions, and lightning NOx emissions to the WRAP 2002 modeling. In addition to 

emissions inventory refinements, we have included boundary conditions derived from a global 

model (GEOS-CHEM) to better capture the temporal and spatial variation in the pollutants 

transported to the boundary of the WRAP modeling domain. 

To enhance the representation of the background sources of haze, we integrated seasonally averaged 1° x 1° grid cell GEOS-CHEM (Park et al., 2003) modeled boundary conditions representing calendar year 2001 into the preliminary 2002 simulations. We used 2001 GEOS-CHEM 

results because they were readily available during the time frame in which we performed this 

modeling; we anticipate replacing them with 2002 data when they become available. These temporally and spatially resolved boundary conditions better capture seasonal variations in pollutant 

transport impacting the United States. 

8.3 Next Steps 

Upcoming work will focus on finalizing the definition of what sources constitute natural emissions sources and on integrating refinements to the WRAP inventories for distinguishing 

between natural and anthropogenic sources. During the next project year we anticipate receiving 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 234 

fugitive and windblown dust inventories that are split to identify the natural components of these 

emissions. We will also work on integrating missing natural emissions sources into the 2002 

modeling, such as lightning NOx, sea salt, and geogenic emissions. With the creation of these 

refined and new emissions inventories, the RMC will continue the simulation and analysis of air 

quality model sensitivities to assess the contribution of natural emissions to visibility 

impairment. 

8.4 Status of Task 6 Deliverables 

Table 8-1 gives the status of each Task 6 deliverable listed in the 2004 WRAP RMC work plan, 

plus any additional deliverables agreed upon after the work plan was prepared. 

Table 8-1. Status of the Task 6 deliverables. 

Deliverable Status 

Natural emissions inventory Partially complete. Natural versus manmade fire inventories 

became available in September 2004; still need emissions for 

other categories (e.g., dust, marine, geogenic). 

CMAQ simulation with natural 

emissions 

Partially complete. Simulations are underway to study the 

effects of the natural versus manmade fires; will complete 

when other natural emissions sources are modeled (see above). 

Report describing natural emissions 

research and CMAQ simulations 

A literature review of natural emissions sources was completed in August 2004; will complete this deliverable in 2005. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 235 

9. Task 7: Evaluation and Comparison of Alternative Models 

Under this task, the RMC was directed to apply an alternative air quality model and compare its 

results to those from CMAQ. The Comprehensive Air quality Model with extensions (CAMx) 

(ENVIRON, 2004) was selected as the alternative model. CAMx is currently being applied as 

the primary model by MRPO and is also being used by VISTAS as a secondary model to 

corroborate and serve as a diagnostic tool for the CMAQ results. CENRAP is currently applying 

the CMAQ and CAMx models in parallel and has not yet decided whether one will be a primary 

and the other a secondary model. Thus, it may be important for WRAP to familiarize themselves 

with CAMx in order to interpret other RPOs’ modeling procedures and results. 

Under Task 7 the RMC applied CAMx for February and July 2002 on the 36-km grid, and 

compared CAMx’s performance with that of CMAQ. In addition, the CAMx PM Source 

Apportionment Technology (PSAT) was configured in a way that is similar to CMAQ’s TSSA 

source attribution algorithm (see Section 7) and the two PM source attribution schemes were 

compared for February and July 2002. 

9.1 Advantages in Operating Multiple Models 

Although CMAQ is the primary air quality modeling system being used by WRAP, there are 

many advantages in applying and evaluating alternative models. All models have uncertainties 

and limitations. By applying multiple models, we obtain more confidence in the results and also 

obtain an estimate of the uncertainties in the modeling. During the CY02 work efforts, WRAP 

applied and evaluated the CMAQ, REMSAD, and CAMx models for 1996. The three models 

generally exhibited similar performance, both good and bad. CAMx was operated in both a twosection mode and a multisection mode whereby secondary PM (e.g., sulfate and nitrate) was 

allowed to grow into the coarse (i.e., PM2.5-10) mode; CMAQ, on the other hand, assumes that all 

of the secondary PM is in the fine mode. The CAMx model estimated that a vast majority of the 

secondary PM was in the fine mode, a finding that strengthened the credibility of CMAQ’s 

assumption and therefore the confidence in its results. Thus, WRAP has already received some 

benefits from applying multiple models. 

EPA’s guidance on model selection for PM2.5 SIPs/TIPs and regional haze reasonable progress 

demonstrations does not identify a preferred photochemical grid modeling system, recognizing 

that at present there is “no single model which has been extensively tested and shown to be 

clearly superior or easier to use than several alternatives” (U.S. EPA, 2001, pg. 169). The agency 

recommends that models used for PM2.5 SIPs/TIPs or regional haze reasonable progress demonstrations should meet the requirements for alternative models. The CMAQ, CMAQ-MADRID 

(Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution), CMAQ-AIM (Aerosol 

Inorganic Module), and CAMx modeling systems all meet these requirements. 

We believe that there may be significant value in including multiple modeling systems in the 

WRAP modeling analysis. Our testing and comparisons of the CMAQ and CAMx models for 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 236 

WRAP, VISTAS, and other recent PM2.5/regional haze applications demonstrate that the models 

are capable of producing results of comparable accuracy and reliability, and having results from 

CAMx as well as CMAQ has many benefits. For example: 

• Diagnosis: To serve as an efficient diagnostic tool for addressing model performance 

issues that may arise in the establishment of the annual 2002 base case. CMAQ and 

CAMx both include process analysis that can help diagnose model performance. CAMx’s 

suite of diagnostic probing tools and its flexi-nesting algorithms make it an attractive tool 

for assisting in the diagnosis of model performance. 

• Model Evaluation Corroboration: To provide corroboration of the base case model 

performance evaluation exercises to be performed with the two models, and help identify 

any compensatory errors in the modeling systems. 

• Emission Control Response Corroboration: To provide corroboration of the response of a 

modeling system to generic and specific future-year emissions changes on modeled gasphase and particulate aerosol concentrations and resultant regional haze impacts. 

• Quantification of Model Uncertainty: To provide one estimate of the range of uncertainty 

in the annual and episodic base case simulations, and in the estimates of PM2.5 and visibility reductions associated with future emissions-change scenarios. 

• Alternative Science: CAMx and CMAQ contain alternative science algorithms that may 

elucidate model performance issues with one model or the other or provide an alternative 

approach for simulating aerosols. 

• Consistency with Other RPOs: MRPO may end up using CAMx for their regional haze 

modeling. The CENRAP states are currently using both CMAQ and CAMx. As sources 

in the MRPO and CENRAP regions likely influence visibility at Class I areas in the 

WRAP region, and vice versa, having results from both models would be useful for 

reconciling any differences. 

• Backup Contingency: To provide a “backstop” model in the event that unforeseen difficulties with the primary model occur. 

The benefits of employing a pair of complementary state-of-the-science air quality models are 

thus quite significant and well worth the extra effort. Note that these two particular models can 

be applied without performing any additional meteorological or emissions modeling, because the 

same MM5 output (through MCIP2.3 and MM5CAMx) and SMOKE output and CMAQ IC/BC 

files (through CMAQ-to-CAMx emissions and IC/BC converters) can be used to operate both 

CMAQ and CAMx. 

9.2 Description of the CAMx Modeling System 

9.2.1 Overview 

The CAMx modeling system is a publicly available (http://www.camx.com), three-dimensional, 

multiscale photochemical-aerosol grid modeling system that ENVIRON developed and is 
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maintaining. CAMx was developed with all new code during the late 1990s using modern and 

modular coding practices. This has made the model an ideal platform for extension to treat a 

variety of air quality issues, including ozone, PM, visibility, acid deposition, and mercury and 

other air toxics. The flexible CAMx framework has also made it a convenient and robust host 

model for the implementation of a variety of mass balance and sensitivity analysis techniques, 

including process analysis (Integrated Reaction Rates [IRR] and Integrated Process Rates [IPR]), 

the decoupled direct method (DDM), and the Ozone Source Apportionment Technology 

(OSAT). Designed originally to address multiscale ozone issues from urban to regional scales, 

CAMx has been widely used in recent years by a variety of regulatory agencies for 1-h and 8-h 

ozone and PM10 SIP/TIP modeling studies, as well as by several RPOs for regional haze 

modeling. Key attributes of the CAMx system include the following: 

• Two-way grid nesting that supports multiple levels of fully interactive grid nesting (e.g., 

36/12/4/1.33 km); 

• Carbon Bond IV (CB-IV) or Statewide Air Pollution Research Center (SAPRC-99) 

chemical mechanisms; 

• Two chemical solvers, the CAMx Chemical Mechanism Compiler (CMC) Fast Solver or 

the highly accurate Implicit-Explicit Hybrid (IEH) solver; 

• Multiple numerical algorithms for horizontal transport, including the Piecewise Parabolic 

Method (PPM), Bott, and Smolarkiewicz advection solvers; 

• A sub-grid-scale plume-in-grid algorithm to treat the near-source plume dynamics and 

chemistry from large NOx point-source plumes; 

• The ability to interface with a variety of meteorological models, including the MM5 and 

RAMS prognostic hydrostatic meteorological models and the CALMET diagnostic 

meteorological model (others are also compatible); 

• The OSAT ozone attribution technique, which identifies the ozone contribution due to 

geographic source regions and source categories (e.g., mobile, point, biogenic); 

• The DDM sensitivity method, which is implemented for emissions and ICs/BCs to obtain 

first-order sensitivity coefficients for all gas-phase species; and 

• Treatment of PM using an empirical aerosol thermodynamics algorithm. 

Culminating extensive model development efforts at ENVIRON and other participating groups, 

the CAMx (ver 4.11s) code was released in autumn 2004 as a truly “one-atmosphere” model that 

rigorously integrates the gas-phase ozone chemistry with the simulation of primary and secondary fine and coarse particulate aerosols. This extension of CAMx to treat PM involved the addition of several science modules to represent important physical processes for aerosols. Noteworthy among these are the following: 

• Two separate treatments of PM. The Mechanism 4 (M4) “one-atmosphere” treatment 

uses two size sections and science modules comparable to CMAQ’s (e.g., the Regional 

Acid Deposition Model [RADM] aqueous-phase chemistry and ISORROPIA equili-
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brium). The multisection “full-science” approach uses aerosol modules developed at 

Carnegie Mellon University (CMU). 

• Size distribution is represented using the Multicomponent Aerosol Dynamics Model 

(MADM), which uses a sectional approach to represent the aerosol particle size distribution (Pilinis et al., 2000). MADM treats the effects of condensation/evaporation, 

coagulation, and nucleation upon the particle size distribution. 

• Inorganic aerosol thermodynamics can be represented using the ISORROPIA (Nenes et 

al., 1998, 1999) equilibrium approach within MADM, or a fully dynamic or hybrid 

approach can be used. 

• Secondary organic aerosol thermodynamics are represented using the semivolatile 

scheme of Strader and coworkers (1999). 

• Aqueous-phase chemical reactions are modeled using either the RADM module (as 

CMAQ does) or the Variable Size-Resolution Model (VRSM) of Fahey and Pandis 

(2001), which automatically determines whether water droplets can be represented by a 

single “bulk” droplet-size mode or whether it is necessary to use fine and coarse dropletsize modes to account for the different pH effects on sulfate formation. 

CAMx (ver 4.11s) provides two key options to users interested in simulating PM. For CPUefficient annual PM modeling applications, CAMx can be run using M4 with only two size 

sections (fine and coarse) and the efficient RADM bulk aqueous-phase module (as is used in 

CMAQ). Alternatively, more rigorous aerosol simulations (perhaps for shorter episodes) can be 

addressed using the version that treats N size sections (N is typically 10) and the rigorous, but 

computationally intensive, CMU multisection aqueous-phase chemistry module. 

9.2.2 PM Source Apportionment Technology (PSAT) 

PSAT has been developed for CAMx to provide geographic and source-category-specific PM 

source attribution (Yarwood et al., 2004). PM source attribution information from PSAT is 

useful for: 

1. understanding model performance and thereby improving model inputs/formulation, 

2. performing culpability assessments to identify sources that contribute significantly to PM 

pollution, and 

3. designing the most effective and cost-effective PM control strategies.

Source attribution for primary PM is relatively simple to obtain from any air pollution model, 

because source-receptor relationships are essentially linear for primary pollutants. Gaussian 

steady-state models and Lagrangian puff models have been used extensively to model primary 

PM pollution from specific sources, which provides source attribution. The Gaussian and 

Lagrangian approaches work for primary PM because the models can assume that emissions 

from separate sources do not interact. This assumption breaks down for secondary PM pollutants 

(e.g., sulfate, nitrate, ammonium, secondary organic aerosol), so puff models may dramatically 

simplify the chemistry (to eliminate interactions between sources) when they are applied to 
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secondary PM. Eulerian photochemical grid models are better suited to modeling secondary 

pollutants because they account for chemical interactions between sources. However, these 

models do not naturally provide source attribution because the impact of all sources has been 

combined in the total pollutant concentration. PSAT has been developed to retain the advantage 

of using a grid model to describe the chemistry of secondary PM formation and also provide 

source attribution. 

Like the CMAQ TSSA algorithm discussed in Section 7, the CAMx PSAT source attribution 

uses reactive tracers (or tagged species), which are extra species added to a grid model to track 

pollutants from specific sources. For example, a standard grid model calculates concentrations 

for a species X that has many sources, and the concentration of X is the total concentration due to 

all sources. A reactive tracer (xi) is assigned to each source (i) with the intention that the sum of 

the reactive tracers will equal total concentration (X = ∑ xi). The challenge is to develop 

numerical algorithms for solving the reactive tracer concentrations that ensure this equality is 

maintained. Depending upon the formulation of the tracer algorithms, it may be possible to 

model tracers for a single source of interest and omit tracers for all other sources, or it may be 

necessary to include tracers for all sources (as is the case for PSAT). Reactive tracers can potentially provide true source attribution (X = ∑ xi); however, the numerical value of the source 

attribution will depend upon assumptions within the reactive tracer formulation. In particular, for 

any process that is nonlinear in species concentrations (e.g., chemistry), there is no unique way 

to assign the total concentration change to the reactive tracers. The researchers at ENVIRON and 

UCR have separately and independently implemented the PSAT and TSSA PM source 

attribution approaches into CAMx and CMAQ, respectively. A comparison and evaluation of 

these two PM source attribution methods is warranted. 

MRPO is using the CAMx PSAT to address their requirements to identify whether Best Available Retrofit Technology (BART)-eligible sources contribute to visibility impairment at Class I 

areas (see http://www.ladco.org/tech/photo/photochemical.html). The MRPO BART PSAT 

application demonstrates the usefulness of these techniques for satisfying regulatory modeling 

requirements. 

9.3 Approach for Testing and Evaluation of Alternative Models 

CAMx was set up on the same RPO Unified Continental 36-km Modeling Grid domain used for 

CMAQ modeling. The CAMx modeling was conducted for February and July 2002 using the 

latest 2002 MM5 simulations that are discussed in Section 4. We used the MM5CAMx 

processor to generate the CAMx-ready meteorological inputs from the WRAP final 2002 36-km 

MM5 simulation. The CAMx ICs/BCs and emissions were generated from the CMAQ inputs 

using the CMAQ-to-CAMx IC/BC and emissions processors. The CAMx model simulations 

used a 15-day spin-up period that started on January 16 and June 15 for the February and July 

2002 runs, respectively. Although approximately 45 days were simulated for each of the 

monthly applications, only the last ~30 days were analyzed in the model performance evaluation. 

CAMx was applied using the 2002 pre02d Base D base case emissions and evaluated against 

measurements; the model performance was compared with CMAQ’s. Since one of the objectives 

of this work was comparison of the CAMx PSAT and CMAQ TSSA PM source attribution 

approaches, a beta version of the CAMx Version 4.20 (V4.20beta) that includes the PSAT 
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algorithm was used in the analysis. This version of the model is going through its final testing 

and evaluation and will soon be made available on the CAMx website (www.camx.com), and is 

currently publicly available on request. 

9.4 Comparative Evaluation of the CAMx and CMAQ Models 

The CMAQ and CAMx models were evaluated using speciated PM measurements from four 

separate air quality monitoring networks: 

• IMPROVE 

• CASTNet; 

• STN 

• SEARCH 

Because some of these networks use different averaging times (IMPROVE and STN collect 24-

hour samples, whereas CASTNet collects weekly samples) and measurement technologies, the 

two models were evaluated separately for each network. Note that differences in measurement 

technologies can result in differences in measured speciated concentrations comparable to model 

performance objectives. For example, a comparison of the IMPROVE and STN measurement 

technologies using collocated samples found differences in sulfate (SO4) and organic carbon 

(OC) as high as 20% and 50%, respectively (Solomon et al., 2004). 

The comparative evaluation of the CMAQ and CAMx models for February and July 2002 was 

conducted for all monitors in the United States (U.S.) and for five subregions: 

• Monitors in the WRAP Western states 

• Monitors in the CENRAP Central states 

• Monitors in the MRPO Midwestern states 

• Monitors in the VISTAS Southeastern states 

• Monitors in the MANE-VU Northeastern states 

In the discussion below we present results for the entire United States. 

9.4.1 Evaluation for Sulfate (SO4) 

Figure 9-1 compares the CMAQ and CAMx sulfate (SO4) model performance at sites across the 

U.S. from the IMPROVE, CASTNet, STN, and SEARCH monitoring networks. Sulfate model 

performance for July 2002 for the two models (Figure 9-1a) is generally quite good, with 

fractional bias <±10% and fractional error of 30-40%. The exception to this is the SEARCH 

network in the Southeastern U.S., where both models exhibit a slight overestimation tendency of 

17% (CMAQ) and 25% (CAMx) and errors of approximately 50%. 
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For February 2002 (Figure 9-1b), both models generally exhibit a slight SO4 overestimation tendency, with the CAMx overestimation tendency being greater than CMAQ’s. For example, for 

the IMPROVE monitors in February 2002, CMAQ and CAMx produce fractional bias values of 

26% and 41% and fraction error values of 49% and 56%, respectively. 

9.4.2 Evaluation for Nitrate (NO3) 

Comparisons of the CMAQ and CAMx nitrate (NO3) model performance for February and July 

2002 are shown in Figure 9-2. NO3 model performance for both models is fairly poor, exhibiting 

summer underestimation and winter overestimation tendencies. The summer NO3 underestimation is more severe in CMAQ than in CAMx, whereas the winter NO3 overestimation is more 

severe with CAMx than CMAQ. The winter overestimation tendency is of more concern, given 

that NO3 can be is a higher fraction of the fine particulate in the winter on some days. 
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Figure 9-1a. Comparison of CMAQ (red) and CAMx (blue) SO4 model performance at 

sites across the U.S. for July 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and SEARCH (bottom right) monitoring networks. 
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Figure 9-1b. Comparison of CMAQ (red) and CAMx (blue) SO4 model performance at 

sites across the U.S. for February 2002 using the IMPROVE (top left), CASTNet (top 

right), STN (bottom left), and SEARCH (bottom right) monitoring networks. 
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Figure 9-2a. Comparison of CMAQ (red) and CAMx (blue) NO3 model performance at 

sites across the U.S. for July 2002 using the IMPROVE (top left), CASTNet (top right), 

STN (bottom left), and SEARCH (bottom right) monitoring networks. 
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Figure 9-2b. Comparison of CMAQ (red) and CAMx (blue) NO3 model performance at 

sites across the U.S. for February 2002 using the IMPROVE (top left), CASTNet (top 

right), STN (bottom left), and SEARCH (bottom right) monitoring networks. 
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9.4.3 Evaluation for organic carbon (OC) and elemental carbon (EC) 

Figure 9-3 compares the CMAQ and CAMx OC and EC model performance at IMPROVE sites 

across the U.S. for February and July 2002. Both models exhibit a similar fractional bias 

underestimation for OC for July 2002 of -34% and -39% and fractional errors of 83% and 63% 

for CMAQ and CAMx, respectively. OC performance for the two models in February 2002 is 

also very similar except the bias is toward overestimation, with fractional bias values of 30% and 

33% and fraction errors of 63% and 65% for CMAQ and CAMx, respectively. EC model 

performance for July 2002 differs between the two models, with CMAQ exhibiting a -16% 

underestimation and CAMx exhibiting a +16% overestimation fractional bias, and the CAMx 

fractional error (57%) being slightly lower than that for CMAQ (68%). For February 2002, 

however, CMAQ exhibits a lower EC fractional bias of -4% whereas the CAMx value is 47%, 

and the CMAQ fractional error (60%) is slightly lower than is seen for CAMx (69%). 

9.4.4 Evaluation for other PM2.5 (soil) and coarse mass (CM) 

The soil and CM model performance across the U.S. IMPROVE monitors for CMAQ and CAMx 

for February and July 2002 are shown in Figure 9-4. The soil species is overestimated by both 

CMAQ and CAMx for July (17% and 62%) and for February (102% and 136%). This overestimation is expected, as the modeled species mapped to the IMPROVE soil measurement contains 

other unidentified compounds, whereas the IMPROVE soil measurement is built up from the 

elements. In fact, the usual application of the CAMx model is to model the fine and coarse 

crustal PM separately from the other unidentified PM components so that the crustal PM can be 

compared directly against the IMPROVE soil measurement. When CAMx treats dust emissions 

as separate crustal species, much better soil performance is seen. However, since the CAMx 

emissions for this application were generated from the CMAQ emissions using the CMAQ-toCAMx processor and CMAQ does not separately treat the soil species, CAMx could not be 

configured with the separate fine and coarse crustal species. 
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Figure 9-3. Comparison of CMAQ (red) and CAMx (blue) OC (left) and EC (right) model 

performance at sites across the U.S. for July 2002 (top) and February 2002 (bottom) 

using the IMPROVE monitoring network. 
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Figure 9-4. Comparison of CMAQ (red) and CAMx (blue) soil (left) and CM (right) model 

performance at sites across the U.S. for July 2002 (top) and February 2002 (bottom) 

using the IMPROVE monitoring network. 
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9.5 Comparison of the PSAT and TSSA PM Source Attribution 

In Section 7, results from the TSSA PM source attribution algorithm implemented in CMAQ 

were described and presented. In this section we compare those source attribution results with 

results from the CAMx PSAT scheme. 

Currently CMAQ TSSA is capable of providing source attribution for SO4, NO3, and primary 

PM particle species and related gaseous compounds (e.g., SO2 and NOx). The CAMx PSAT is 

capable of providing PM source attribution for the following families of species: 

• SO4 

• NO3 

• NH4 

• Primary PM, which includes EC, primary organic carbon (POC), PMFINE, and PMc 

• SOA (secondary organic aerosols) 

• Hg (mercury) 

For the WRAP February and July 2002 PSAT application, only the SO4 family of PM source 

attribution was specified. 

9.5.1 CAMx PSAT PM Source Attribution Configuration 

CAMx PSAT was applied for February and July 2002 using a configuration similar to the one 

used in the CMAQ TSSA source attribution modeling. 

Both TSSA and PSAT obtain PM source attribution for user-defined source groups. Source 

groups typically consist of geographic source regions combined with source categories—for 

example, elevated-point-source emissions in Arizona. Figure 9-5 displays the geographic source 

regions used in the TSSA and PSAT source attribution modeling. These regions are defined in 

Table 9-1. 
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-2000 -1500 -1000 -500 0 500 1000 1500 2000

-1500

-1000

-500

0

500

1000

1

2

3

4

5

6

7 8

9

10

11 12

13

14

15

Figure 9-5. Geographic source regions used in the PSAT and TSSA PM source 

attribution modeling. 

Table 9-1. Definitions of the geographic source regions used in the TSSA and PSAT 

source attribution modeling. 

Region Definition Region Definition Region Definition 

1 California (CA) 6 Utah (UT) 11 Montana (MT) 

2 Nevada (NV) 7 Arizona (AZ) 12 North Dakota (ND) 

3 Oregon (OR) 8 New Mexico (NM) 13 South Dakota (SD) 

4 Washington (WA) 9 Colorado (CO) 14 Eastern States (East) 

5 Idaho (ID) 10 Wyoming (WY) 15 Mexico/Canada/Ocean (Mex) 

For the CAMx PSAT source attribution modeling, the emissions inventory was split into the 

following major source categories: 

• Biogenic sources (Bio) 

• On-road mobile sources (Mob) 

• Point sources (Pts) 

• Fires (Fir) 

• Area plus nonroad mobile sources (ANR) 
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Thus, the CAMx PSAT application was configured with 15 geographic regions and five source 

categories. Adding in the initial conditions (ICs) and boundary conditions (BCs) that are always 

included in the CAMx PSAT source attribution, this results in a total of 77 source groups that 

tracked PM source attribution in PSAT (15 x 5 + 2 = 77). 

9.5.2 Differences in TSSA and PSAT Configurations 

The PM source attribution algorithms in PSAT and TSSA were developed independently from 

one other, so they would be expected to produce similar, but not exactly the same, results. In 

addition, there were some differences in the way the two schemes were configured that will also 

produce results differences. 

One of the biggest differences between the PSAT and TSSA source attribution schemes is that in 

PSAT all emissions in the domain must be accounted for by the source groups. In TSSA, on the 

other hand, only a portion of the emissions inventory can be tagged, emission sources that are not 

tagged are collected in an “Other” category. This “Other” category also includes mass 

conservation errors and mass adjustments designed to ensure mass consistency in the CMAQ 

model. After the completion of the CMAQ TSSA modeling using CMAQ Version 4.4beta in 

2004, a mass conservation error was discovered in the CMAQ vertical transport algorithm; it was 

subsequently corrected in the final version of CMAQ Version 4.4. The mass conservation error, 

along with the CMAQ mass adjustment, can accumulate in the TSSA “Other” category, resulting 

in its being a large contributor to PM source attribution. As CAMx does not have similar mass 

conservation errors or the mass adjustment operator, all PM is accounted for and allocated to the 

source groups. 

Two differences in the way the TSSA and PSAT source attribution schemes were configured for 

the source attribution tests were as follows: 

• TSSA modeled on-road and nonroad mobile sources as one source group (mv), whereas 

PSAT modeled on-road mobile sources (mob) and area plus nonroad sources (ANR) as 

separate source groups. 

• TSSA did not track separate source attribution for fires (fir) and ICs/BCs, whereas CAMx 

did. Thus, TSSA accumulated any SO4 source attribution due to fires and BC in the 

“Other” category. 

9.5.3 Source Attribution Modeling Results 

We present TSSA and PSAT source attribution modeling results for a few IMPROVE sites/Class 

I areas and days from the February and July 2002 modeling periods. These Class I areas and 

days were selected to illustrate the similarities and differences between the TSSA and PSAT PM 

source attribution approaches. Figure 9-6 shows the locations of the Class I areas (actually, 

IMPROVE and IMPROVE protocol sites) where PM source attribution was obtained. Example 

results are presented below for the following sites: 

• GRCA – Grand Canyon National Park in northwestern Arizona 
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• FOPE – Fort Peck in northeastern Montana 

• RMHQ – Rocky Mountain National Pak in central Colorado 

• SALM – Salmon in eastern-central Idaho 

-2000 -1750 -1500 -1250 -1000 -750 -500 -250 0

-750

-500

-250

0

250

500

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GUMO1

ORPI1

CHIR1

SAGU1 SAWE1 SAAN1

GICL1

QUVA1

SACR1

AGTI1

WHIT1

PHOE1TONT1

BOAP1

BALD1

JOSH1TR

SIAN1

SAGO1

SAGA1

IKBA1

HILL1

RAFA1

WIMO1

PEFO1

SYCA1

DOLA1ME1

BAND1

GRCA2

MEAD1

SAPE1

GRCA1HS INGA1HS GRCA1 INGA1

ELLI1

PINN1

SEQU1

DEVA1

CHER1

WHPE1

MEVE1

KAIS1

ZION1 BRCA1

WEMI1

YOSE1

GRSA1

HOOV1

CARE1 CAPI1

TALL1

CANY1

CEBL1

ARCH1

BLIS1 SOLA1

GRBA1

WHRI1

WHCL1

ROMO1 RMHQ1

LOPE1

STPE1 MOZI1

LAVO1

WAUB1

TRIN1

BRLA1 NOPL1

REDW1

HALS1

LABE1

JARB1

WALT1

KALM1 CRLA1

NIOB1

BRID1

CRMO1

BLMO1 WICA1

SCOV1

BADL1

SAWT1

THSI1

CLPE1

YELL21

THBA1

NOAB1

HECA1

SALM1

STAR1

MOHO1

NOCH1

COGO1CORI1

SULA1

WHPA1

MORA1

GAMO1

THRO1

MONT1

SNPA1

PUSO1

ULBE1

SPOK1

CABI1

OLYM1

FLAT1

PASA1

FOPE1MELA1

GLAC1

LOST1

NOCA1

Figure 9-6. Locations of IMPROVE monitoring sites in the western U.S. where 

the TSSA and PSAT PM source attribution approaches were compared. 

Figures 9-7 through 9-9 display three example comparisons of TSSA and PSAT SO4 source 

contributions to sulfate at GRCA for July 1, July 7, and February 1, 2002. July 1, 2002 (Figure 

9-7) was the worst visibility day at GRCA during 2002. Both TSSA and PSAT agree that point 

sources from Nevada (Pts_NV) are the largest contributor to SO4 at GRCA on this day, although 

the PSAT Pts_NV SO4 contribution is over twice that of TSSA. The TSSA second largest 

contributing category is “Other” (described in Section 9.5.2). TSSA and PSAT agree that point 

sources from Arizona and “Mexico” (i.e., Mexico, Canada, and Ocean) are the next most 

important contributors, but then the rankings of the source contributions begin to deviate from 
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each other. PSAT estimates that fires from Arizona and boundary conditions (BC) are the next 

most important categories, whereas in TSSA these contributions are assumed to reside in the 

“Other” category. Both source attribution schemes estimate that mobile sources from NV, AZ, 

and CA make contributions in the TSSA mv category (on-road plus nonroad mobile sources) and 

the PSAT ANR category (area plus nonroad mobile); because on-road mobile sources use lowsulfur gasoline, most of the SO2 emissions in the TSSA mobile sources mv category come from 

the nonroad mobile-source sector, suggesting that the TSSA mv and PSAT ANR contributions 

are being driven by the same source category and so are consistent. 

July 7, 2002, was the 15th worst visibility day at the Grand Canyon in 2002. The SO4 contributions from TSSA and PSAT (Figure 9-8) exhibit more differences than were seen for July 1. 

Whereas PSAT estimates that point sources from Nevada (Pts_NV) are the largest contributors 

to SO4 at GRCA on this day, the TSSA “Other” category is by far the largest contributor (over 3 

times larger than the Pts_NV contribution). This “Other” category consists of untagged sources, 

such as BCs and possibly fires, and mass errors/corrections in CMAQ. Based on this category’s 

dominant contribution, the mass conservation errors in CMAQ must have accumulated to a large 

amount on this day. Although the rankings are a little different between the two source 

attribution methods, they agree that point sources and nonroad sources from AZ, NV, CA, and 

Mex are the major contributors to SO4 at GRCA on July 7, 2002. 

February 1, 2002, was the cleanest visibility day at the Grand Canyon during 2002. The TSSA 

and PSAT SO4 contributions are shown in Figure 9-9. Both source attribution techniques agree 

that point sources from Utah are the largest contributor to SO4 at GRCA on this clean day. 

Whereas PSAT estimates that BCs are the next largest contributor, TSSA estimates that the 

“Other” category is next largest. In fact, looking at the cleanest days at other Class I areas across 

the western U.S., TSSA almost always estimates that the “Other” category is one of the largest 

contributors. This likely occurs because the BC contribution is included in the “Other” category 

and because the mass conservation and adjustment terms are a relatively larger share of the total 

SO4 mass under cleaner conditions. Although the two source attribution methods differ on the 

rankings of the remainder of the sources, they both agree that point sources from more distant 

states to the northwest of GRCA (such as OR, ID, and WA) contribute to SO4 at GRCA on this 

cleanest day of 2002. This is consistent with the Clean Air Corridor concept that the cleanest 

days at GRCA occur when winds are out of the northwest. 

Up at the Fort Peck, MT (FOPE) site, July 4 was the 6th worst visibility day at FOPE during 

2002. TSSA and PSAT both estimate that different source groups contribute to the SO4

concentrations than is the case at GRCA (Figure 9-10); this is expected, given their different 

geographic locations. Both source attribution methods agree that point sources from North 

Dakota (Pts_ND) contribute the most to SO4 at FOPE on this day, although the PSAT Pts_ND 

contribution is almost twice that of TSSA. The two methods also agree that the next three source 

groups contributing to SO4 at FOPE on July 4 are much smaller and are due to nonroad 

emissions from ND and MT and point sources east of the WRAP states. Again the TSSA 

“Other” category is the second largest contributor to SO4 at FOPE on this day. Since PSAT 

suggests that most of the significant contributions at FOPE are due to identified sources that are 

tagged in the TSSA run (i.e., not fires or BCs), this suggests that the “Other” category at FOPE 

on this day is due primarily to mass conservation errors and the mass adjustment terms. 
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The SO4 contributions at Rocky Mountains National Park (RMNP) on July 1, 2002, estimated by 

TSSA and PSAT are shown in Figure 9-11. This was the worst visibility day at RMNP during 

2002. If fires are included in the TSSA “Other” category, then TSSA and PSAT exhibit good 

agreement in the source contributions to SO4 at RMNP on this day, with fires from Utah and 

Colorado having significant contributions along with point sources in CO, NV, and UT and 

nonroad sources in CO and CA. 

The TSSA and PSAT SO4 contributions at the Salmon, Idaho (SALM) site in eastern ID are 

shown in Figure 9-12. With the exception of the large “Other” contribution in TSSA and the BC 

contribution in PSAT, the two source attribution methods agree on the top contribution sources 

groups (i.e., point sources in OR and WA and nonroad sources in OR, WA, ID, and MT). 

Examining the BC and fire contributions to SO4 at SALM estimated by PSAT does not explain 

the TSSA highest “Other” contribution, suggesting that the mass conservation errors and mass 

adjustment terms in PSAT have a large influence on the SO4 predictions at SALM on July 1. 

9.5.4 Conclusions on PM Source Attribution 

The comparisons of the CMAQ TSSA and CAMx PSAT PM source attribution modeling results 

for SO4 for February and July 2002 were confounded due to the presence of the TSSA “Other” 

category, which includes untagged source terms, such as BCs and fires, and the accumulation of 

mass conservation errors and the CMAQ mass adjustment term designed to insure mass 

consistency. The PSAT SO4 source attribution accounts for all sources in the region, so there are 

no untagged sources and the CAMx model is formulated without a mass adjustment step. It is 

unfortunate that the WRAP CMAQ TSSA modeling used Version 4.4beta of CMAQ that was 

later found to have mass conservation errors in the vertical transport algorithm; this has been 

fixed in the final Version 4.4 of CMAQ. Even with the confounding influence of the TSSA 

“Other” category, the SO4 contributions from TSSA and PSAT were generally consistent in 

identifying the same top contributors to SO4 across the western U.S. 

Until the relative magnitudes of the contributions of untagged sources, mass conservation errors, 

and mass adjustment terms in the CMAQ TSSA “Other” category can be resolved, the TSSA PM 

source attribution should be viewed qualitatively. As the CAMx PSAT PM source attribution 

does not suffer these limitations, it can be viewed in more quantitative terms. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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24-hr average contributions to SO4 at GRCA on 2002 182

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

All other

Fir_CO

Pts_UT

Mob_NV

Mob_CA

ANR_NM

Mob_Mex

Mob_AZ

Fir_UT

Pts_Est

Pts_NM

Pts_CA

ANR_CA

ANR_AZ

ANR_Mex

ANR_NV

BC

Fir_AZ

Pts_AZ

Pts_Mex

Pts_NV

ug/m3

Figure 9-7. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Grand Canyon National Park on July 1, 2002 (day 182). 
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24-hr average contributions to SO4 at GRCA on 2002 188

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

All other

Pts_WA

Fir_NM

ANR_OR

Fir_AZ

Fir_UT

Mob_Mex

Pts_NM

Mob_CA

Fir_CA

ANR_NV

Pts_UT

Mob_AZ

BC

ANR_AZ

ANR_Mex

Pts_CA

ANR_CA

Pts_AZ

Pts_Mex

Pts_NV

ug/m3

Figure 9-8. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Grand Canyon National Park on July 7, 2002 (day 188). 
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24-hr average contributions to SO4 at GRCA on 2002 032

0.00

0.02

0.04

0.06

0.08

0.10

0.12

All other

Mob_OR

Mob_WA

Pts_CO

Mob_UT

ANR_ID

Pts_Mex

ANR_AZ

Mob_AZ

ANR_WA

ANR_OR

Pts_WA

ANR_Mex

Pts_OR

Pts_WY

Pts_ID

Pts_NM

ANR_UT

Pts_AZ

BC

Pts_UT

ug/m3

Figure 9-9. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Grand Canyon National Park on February 1, 2002 (day 32). 
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24-hr average contributions to SO4 at FOPE on 2002 185

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

All other

Pts_UTPts_NV

Pts_WA

Mob_MT

Pts_ID

BC

Fir_MT

ANR_WY

Fir_UT

ANR_Mex

Fir_WY

Pts_CO

ANR_Est

ANR_SD

Pts_MT

Pts_WY

Pts_Est

ANR_MT

ANR_ND

Pts_ND

ug/m3

Figure 9-10. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Fort Peck, Montana on July 4, 2002 (day 185). 
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24-hr average contributions to SO4 at RMHQ on 2002 182

0.00

0.05

0.10

0.15

0.20

0.25

0.30

All other

Mob_CAANR_AZ

Pts_AZ

Pts_NM

ANR_UT

Fir_CA

ANR_Mex

ANR_NV

Mob_CO

Pts_WY

BC

Pts_Mex

Pts_CA

ANR_CO

ANR_CA

Pts_UT

Fir_CO

Pts_NV

Pts_CO

Fir_UT

ug/m3

Figure 9-11. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution at 

Rocky Mountain National Park, CO, on July 1, 2002 (day 182). 
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24-hr average contributions to SO4 at SALM on 2002 182

0.00

0.02

0.04

0.06

0.08

0.10

0.12

All other

Fir_WA

Pts_CA

ANR_Mex

ANR_CA

Fir_OR

Pts_Mex

Pts_ID

Pts_MT

Mob_WA

Fir_ID

Mob_OR

Mob_MT

Mob_ID

ANR_MT

ANR_WA

ANR_ID

Pts_WA

BC

ANR_OR

Pts_OR

ug/m3

Figure 9-12. Comparison of TSSA (top) and PSAT (bottom) sulfate source attribution 

at Salmon, ID, on July 1, 2002 (day 182). 
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9.6 Status of Task 7 Deliverables 

Table 9-2 gives the status of each Task 7 deliverable listed in the 2004 WRAP RMC work plan, 

plus any additional deliverables agreed upon after the work plan was prepared. 

Table 9-2. Status of the Task 7 deliverables. 

Deliverable Status 

Acquisition of the CMAQ TSSA results, latest 2002 MM5 data, and Pre02d 

emissions 

Completed 

January 2005 

Initial testing and evaluation of CAMx and comparison of the CAMx PSAT and 

CMAQ TSSA PM source attribution techniques 

To be completed 

March 2005 

PowerPoint presentation at WRAP Modeling Forum meeting (available at 

http://pah.cert.ucr.edu/aqm/308/meetings/March_2005/03-08_09-

05.SF_CA/Alternative_Model_Mar8-9_2005_MF_Meeting.ppt) 

To be completed 

March 2005 
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10. Task 9: Testing and Further Improvements to the 

Windblown Dust Emissions Modeling Methodology 

10.1 Introduction 

The WRAP area-source emissions inventory currently does not include fugitive windblown dust 

because of strong concern that the methodology previously used to calculate these particulate 

emissions yielded inaccurate and misleading results. WRAP has funded a task under which the 

RMC is addressing this lack of windblown dust emissions. The task objective is to develop a 

model and inventory of PM dust emissions from wind erosion. We documented our fugitive 

windblown dust methodology and results from Phase I of this task in a final report (ENVIRON, 

2004a) and technical memoranda (Mansell, 2003a,b). Many of the assumptions employed in the 

Phase I methodology were related to a lack of detail in the underlying data used to characterize 

vacant land use types and soil conditions. In addition, the Phase I methodology relied on arbitrarily assigned threshold friction velocities and dust reservoir characteristics. The results of the 

initial model runs and subsequent sensitivity simulations demonstrated a need to revise and/or 

update various assumptions associated with the development of the emissions inventory. 

We are currently in Phase II of the task, during which we have developed a general Phase II 

estimation methodology for PM dust emissions from wind erosion. Various approaches for 

refining and enhancing the existing version of the windblown dust model were presented and 

discussed in Mansell et al. (2004b). These refinements focus primarily on improving the 

determination of surface friction velocities and threshold friction velocities, as well as the 

calculation of dust emission fluxes. We also considered the characterization of the disturbance 

level of vacant land parcels. Land use datasets to more accurately characterize vacant lands were 

identified for use in the revised dust emission estimation methodology. In addition, we identified 

a number of studies for review, and incorporated algorithms and methodologies from these 

studies, as appropriate, into the revised windblown dust model. 

Section 10.2 briefly summarizes the Phase I methodology, assumptions, and shortcomings, and 

discusses the literature review conducted for Phase II. The methodologies, assumptions, and data 

sources for the Phase II revised windblown dust model are documented in Section 10.3. Results 

for calendar year 2002 are presented and discussed (Section 10.4), as are preliminary results of 

an initial model performance evaluation (Section 10.5). 

10.2 Summary of Phase I Methodology and Phase II Literature 

Review 

10.2.1 Summary of Phase I methodology 

The development and implementation of the Phase I windblown dust model, including various 

assumptions incorporated in the estimation methodology, have been documented previously 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 263 

(ENVIRON, 2004a, 2003a,b; Mansell, 2003a,b). The estimation method that was ultimately 

implemented in Phase I is referred to as the MacDougall method (see ENVIRON [2004] for 

more details). In summary, it relies on the characterization of vacant land use types and soil 

conditions, and numerous assumptions regarding dust reservoir characteristics. Wind erosion is 

initiated in the model based on an arbitrary wind speed assignment, independent of surface conditions. Emission factors, or dust fluxes, were derived from limited wind tunnel study results as a 

function of wind speed and soil texture. Adjustments were applied to the resulting emission rates 

based on the vegetation density of vacant land parcels. Surface disturbance levels were based on 

land use types. In addition, adjustments were applied for agricultural lands based on nonclimatic 

factors. Land use characterization was based on BELD3; soil texture was derived from the 

STATSGO database. 

The relative lack of detail in the datasets used for characterizing the physical conditions of land 

parcels and soils required a number of assumptions to be employed in the methodology. These 

assumptions are presented and discussed in detail in Mansell (2003b) and Mansell et al. (2004a). 

The primary assumptions affecting the model results can be summarized as follows: 

• Threshold Wind Velocity: The threshold wind velocity is assumed to be 20 mph, independent of land use and soil texture. 

• Vacant Land Stability: The methodology relies on specification of the stability of vacant 

land parcels. The stability characteristics are based solely on the land use type. 

• Dust Reservoirs: The amount of erodible soil available for suspension into the atmosphere for a given vacant land parcel is referred to as the reservoir. Reservoir properties 

are based on the stability characteristics of land parcels, and determine the duration of 

dust events. Limited reservoirs emit dust for a shorter time than unlimited reservoirs. 

Assumptions are made concerning the amount of time a reservoir will emit windblown 

dust. Also assumed are the reservoir recharge intervals. 

• Rain, Snow, and Freeze Events: Assumptions are included that determine the time intervals after which land parcels will emit dust following precipitation, snow, and freeze 

events. These assumptions greatly affect the number of wind events treated in the 

methodology as well as the total dust emissions generated. 

• Vegetation Density: The percentage of vegetative, or canopy, cover is determined by the 

general land use category of vacant land parcels. These percentages are constant for a 

given land use type. Estimated emission factors, or emission rates, are attenuated based 

on the assumed canopy cover percentage. 

The above assumptions have a number of implications with respect to the estimation of fugitive 

dust from wind erosion. However, in many cases the data necessary to address these issues for a 

regional-scale domain are lacking. These issues and their implications are discussed in Mansell 

et al. (2004a). Phase II of the windblown dust task, documented in this section, seeks to address 

these assumptions and limitations and improve the overall estimation methodology and dust 

model implementation. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 264 

10.2.2 Review of recent literature 

A number of windblown dust studies have been identified in the literature and are summarized 

below with respect to the algorithms and physical parameters considered. These studies are compared with each other and summarized in regard to improving the model application for the 

Phase II dust study. A more detailed comparison of the salient features of each of the windblown 

dust emission estimation methodologies is presented in Mansell et al. (2004a). 

10.2.2.1 Recent dust emission models 

Draxler et al. (2001) constructed a regional model for estimating PM10 from windblown dust 

using the concept of threshold friction velocity, which is dependent on the aerodynamic roughness length of the surface, z0. PM10 vertical mass flux was calculated using the Marticorena et al. 

(1997) algorithm. The flux is a function of wind velocity, threshold wind velocity, and a coefficient that relates the surface soil texture to PM10 emissions. Emissions start when the friction 

velocity is greater than the threshold friction velocity at that height. Friction velocity was 

calculated as a function of z0. 

Threshold friction velocity is calculated as the ratio of the threshold velocity for a smooth surface (u*ts) to the effective friction velocity (feff). Marticorena and Bergamette (1995) define feff as 

the ratio of friction velocity for a smooth surface to actual friction velocity. To determine this 

ratio, the aerodynamic roughness length for a smooth surface (z0s)—which is defined as the mean 

soil particle diameter (Dp) divided by 30 (Greeley and Iversen, 1985)—is divided by the actual 

aerodynamic roughness length. Soil samples were collected from the modeled area to determine 

Dp. The value for z0s was calculated using the measured Dp. Using a mean value of 22 cm/s for 

u*ts, the actual threshold friction velocity was calculated for different values of aerodynamic 

roughness length. 

Using images of the area, a map of surface conditions and geomorphology, and the u*t data from 

the Mojave Desert (Gillette et al., 1980, 1982), the authors estimated the threshold friction velocity and surface roughness length for each surface classification. The coefficient that relates the 

surface soil texture to PM10 emissions was estimated using data for several soils from semi-arid 

areas (Gillette et al., 1997) showing the ratio of vertical flux of PM10 to total horizontal mass flux 

as a function of friction velocity. 

In their work, Draxler et al. (2001) considered a special case of windblown dust. Land with 

vegetative cover has a relatively high threshold velocity and was not considered as an emission 

source. Two types of soil surface conditions were considered: loose undisturbed soil and disturbed soil. All the area was considered dry and therefore the effect of rain and snow was not 

considered. 

Zender et al. (2003) developed a Dust Entrainment and Deposition (DEAD) model for studying 

dust-related processes at both local and global scales. They considered three major factors that 

affect the dust flux: wind friction velocity, vegetation cover, and surface soil moisture content. 

The approach developed by Marticorena and Bergamette (1995) was used to develop the model. 

For computing the threshold friction velocity they used a semi-empirical equation developed by 

Iversen and White (1982). In this equation, the friction velocity is a function of soil density and 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 265 

particle size, and air density and kinematic viscosity. A constant surface roughness length of 

0.01 cm was assumed for the entire domain. 

The change in threshold friction velocity was calculated using the equation developed by 

Marticorena and Bergamette (1995), similar to what Draxler et al. (2001) used in their model. 

Zender et al. used one global value of 0.0033 cm for the roughness length for a smooth surface. 

The effect of moisture content of the surface soil was considered in the Zender et al. model. A 

threshold moisture content was calculated as a function of the mass fraction of clay, as adopted 

from Fécan et al. (1999). Land covered by vegetation was not considered as a dust-emitting 

source. 

The vertical mass flux was calculated as a function of the horizontal mass flux, a global tuning 

factor, a source erodibility factor, the fraction of bare soil, and the fraction of clay mass. The horizontal mass flux was calculated as a function of friction velocity and threshold friction velocity. 

Shao (2001) developed an emission flux model as a function of the horizontal mass flux, 

threshold friction velocity, and an empirical function of the diameters of saltating and emitted 

particles. In his work, Shao emphasized the microscale forces working on saltating particles and 

the impact of these particles on dust emissions. In this model, a particle size distribution of the 

soil is required. 

10.2.2.2 ENVIRON/RMC model 

The “ENVIRON/RMC model” refers to the estimation methodology proposed by the RMC’s 

Phase I fugitive windblown dust task team and documented in ENVIRON (2003a). Based on a 

review of wind tunnel studies, we noted that the two important components for characterizing 

dust emission process from an erodible surface are (1) the threshold friction velocity, which 

defines the inception of the emission process as a function of the wind speed and as influenced 

by the surface characteristics; and (2) the strength of the emissions that follow the commencement of particle movement. The two critical factors affecting emission strength are the wind 

speed (wind friction velocity), which drives the saltation system, and the soil characteristics. 

10.2.2.2.1 Threshold friction velocities 

The methodology relies on the determination of threshold surface friction velocities, u*t, as a 

function of aerodynamic surface roughness length, z0. In addition to aerodynamic roughness, the 

degree of disturbance of the surface also plays a key role in estimating threshold friction velocities. Based on the work of Marticorena et al. (1997), relationships between u*t and z0 were 

identified and compared with wind tunnel data from Gillette et al. (1980, 1982), Gillette (1988), 

and Nickling and Gillies (1989). This comparison is presented in Figure 10-1. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 266 

u*t = 0.31e7.44x(Zo)

R2 = 0.60

u*t = 0.30e7.22x(Zo)

0

0.5

1

1.5

2

2.5

3

0.00001 0.0001 0.001 0.01 0.1 1

zo (cm)

u*t (m s-1)

wind tunnel data Marticorena et al. 1997

Expon. (wind tunnel data) Expon. (Marticorena et al. 1997)

Figure 10-1. Comparison between (1) the Marticorena et al. (1997) modeled relationship of 

threshold friction velocity and aerodynamic roughness length and (2) wind tunnel data 

from Gillette et al. (1980, 1982), Gillette (1988), and Nickling and Gillies (1989).] 

Several general relationships can be described for threshold friction velocity data. Two major 

factors have the greatest influence on the threshold of wind-erodible soils: the degree of disturbance and the aerodynamic roughness. For loose or disturbed soils, the most important factor 

controlling the threshold friction velocity is aerodynamic roughness. The effect of surface disturbance on threshold friction velocity has been evaluated and documented by others (Gillette et 

al., 1980,1 982; Gillette, 1988; Nickling and Gillies, 1989). For a given surface type, the effect of 

disturbance is to lower the threshold to between ~90% and ~20% of the undisturbed value. 

Applying the relationship shown in Figure 10-1 to assign a threshold friction velocity to a surface requires information on a surface’s aerodynamic roughness length. This type of information 

is not generally available in land use databases, because they were not specifically developed to 

quantify aerodynamic properties of surfaces. Based on the designation of land use type, the 

aerodynamic roughness can be assigned using previously reported values for similar surfaces. 

10.2.2.2.2 Emission factors 

Field and wind tunnel experiments suggest that the dust emissions are proportional to wind 

friction speed and approximate theoretical model predictions, but the considerable scatter in the 

available data makes it impossible to clearly define this dependence (Nickling and Gillies, 1993). 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 267 

Different surfaces appear to have different constants of proportionality for the flux versus wind 

friction velocity relationship, implying that the flux is predictable, but that surface and soil properties affect the magnitude of the flux. A detailed discussion of wind tunnel studies, including 

various limitations and measured data, is provided in ENVIRON (2003a,b). The findings of the 

various wind tunnel studies are briefly summarized here. 

Alfaro et al. (2003) re-analyzed the Nickling and Gillies (1989) data and found that the tendency 

of a surface to emit dust does not depend primarily on its textural qualities, but instead on the 

size distribution of the loose soil aggregates available for saltation, and the aerodynamic roughness length that conditions the emission threshold. The re-analysis was based in part on the work 

of Chatenet et al. (1996) in which they found that desert soils could be broadly divided into four 

populations based upon their soil aggregate populations. The differences between the four groups 

are based upon the estimated geometric mean diameter of the soil particles. The four size classes 

are 125 mm, 210 mm, 520 mm, and 690 mm, which they label FSS (silt), FS (sandy silt), MS 

(silty sand), and CS (sand). The emission flux relationships are displayed in Figure 10-2. 

FFS

F = 2.45x10-6 (u*)

3.97

FS

F = 9.33x10-7 (u*)

2.44

MS

F = 1.243x10-7(u*)

2.64

CS

F = 1.24x10-7 (u*)

3.44

0.000000001

0.00000001

0.0000001

0.000001

0.00001

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Friction Velocity (m s-1)

Emission Flux (F, g cm-2 s-1)

FSS

FS

MS

CS

Power (FSS)

Power (FS)

Power (MS)

Power (CS)

Figure 10-2. The emission flux as a function of friction velocity predicted by the Alfaro 

and Gomes (2001) model constrained by the four geometric-mean-diameter 

soil classes of Alfaro et al. (2003). 

Using the Alfaro et al. (2003) approach, emissions of dust for soils can be confined to four 

different emission factors, depending on the geometric mean grain size, as determined by the 
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 268 

methods of Chatenet et al. (1996). The model predictions were tested against the wind tunnel 

dataset of Nickling and Gillies (1989) and found to fit the measured data satisfactorily. Of key 

importance is that Chatenet et al. (1996) established relationships between the 12 soil types that 

are defined in the classical soil texture triangle and their four dry soil types (FSS, FS, MS, and 

CS). The soil texture categorization and the relationships among texture assignments and soil 

groupings are discussed in Section 10.3.7.2. 

10.3 Phase II Windblown Dust Emission Estimation Methodology 

We found that all of the dust model applications we reviewed (Section 10.2.2), including the 

ENVIRON/RMC methodology, utilize a similar approach for determining threshold friction 

velocities and dust emission flux rates. With each particular application, certain simplifying 

assumptions are incorporated. These assumptions are generally required because there is not 

enough detailed information to fully characterize physical conditions and parameters of erodible 

surfaces. 

The Phase II dust model implementation follows the same general approach as the 

ENVIRON/RMC methodology originally proposed by the RMC team during the Phase I work 

effort and the other models discussed above, but with improvements. The approach for determining and applying each of the required elements of the model is presented below. 

10.3.1 Friction velocities 

Surface friction velocities are determined from the aerodynamic surface roughness lengths and 

the 10-m wind speeds from the MM5 model simulations. Friction velocity, u*, is related to the 

slope of the velocity versus the natural logarithm of height through this relationship: 

* o

z

z

z ln 1

u

u

κ = 

where 

uz = wind velocity at height z (m s-1) 

u* = friction velocity (m s-1) 

κ = von Karman’s constant (0.4) 

z0 = aerodynamic roughness length (m) 

10.3.2 Threshold friction velocities 

The threshold friction velocities, u*t, are determined from the relationships developed by 

Marticorena as a function of the aerodynamic surface roughness length, z0. This relationship was 

presented in Figure 10-1. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 269 

10.3.3 Surface roughness lengths 

Surface friction velocities, including the threshold friction velocity, are a function of the aerodynamic surface roughness lengths. The surface roughness lengths are in turn dependent on 

surface characteristics, particularly land use/land cover. While these values can vary considerably for a given type of land, published data are available that provide a range of surface roughness lengths for various land use types and vegetation covers. Table 10-1 summarizes the assignments of surface roughness for each land cover type considered for wind erosion. Other land 

cover types were not considered erodible due to the large assumed surface roughness lengths. 

Table 10-1. Summary of surface characteristics for application of the Phase II dust model. 

Parameter Dust Code 3 Dust Code 4 Dust Code 6 Dust Code 7 

Land use category Agricultural Grassland Shrubland Barren 

Surface roughness length (cm) 0.031 0.1 0.05 0.002 

Threshold friction velocity (mi/h) 8.33 13.81 9.62 6.81 

Threshold wind velocity at 38 m 

height (mi/h) 29.5 44.3 32.8 28.5 

10.3.4 Emission fluxes 

Emission fluxes, or emission rates, are determined as a function of surface friction velocity and 

soil texture. The relationships developed by Alfaro and Gomes (2001) for each of the soil texture 

groups of Alfaro et al. (2003) is applied for estimating dust emission fluxes. These relationships 

were presented in Figure 10-2 above. 

10.3.5 Reservoir characteristics 

Dust emissions from vacant lands are limited by the amount of erodible soil available for suspension into the atmosphere. In addition to the amount of soil present, the condition of the soil 

(including texture and stability) and climatological factors influence the total windblown dust 

emission potential of a given parcel of vacant land. The amount of soil available for a given land 

parcel is referred to as the reservoir and can be classified as limited or unlimited. The classification chosen has implications with respect to the duration of time over which the dust emissions 

are generated. In general, the reservoirs should be classified in terms of the type of soils, the 

depth of the soil layer, soil moisture content, and meteorological parameters. Finally, the time 

required for a reservoir to recharge following a wind event is influenced by a number of factors, 

including precipitation and snow events and freezing conditions of the soils. 

Given that the soils database for use in this task does not provide information concerning the 

moisture content or the depth of the soil layer, we made certain assumptions regarding the determination and classification of soil reservoirs. These assumptions are based primarily on the land 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 270 

use type and stability of the vacant land parcel. Reservoirs are classified as limited for stable land 

parcels and unlimited for unstable land parcels. 

The duration and amount of precipitation and snow and freeze events also affect the dust emissions from wind erosion. In the Phase I application, these parameters were somewhat arbitrarily 

assigned. Barnard (2003) has compiled a set of conditions for treating these events based on 

season, soil characteristics, and the amounts of rainfall and snow cover. These conditions were 

based on limited information found in the literature and additional assumptions. The results of 

the analysis of Barnard are summarized in Tables 10-2 and 10-3. 

Table 10-2. Number of days after precipitation event to re-initiate 

wind erosion for rainfall amounts (constant) ≥2 in. 

Soil type Spring/Fall Summer Winter 

Sand 3 2.1 4.2 

Sandy loam 3 2.1 4.2 

Fine sand loam 3 2.1 4.2 

Loam 4 2.9 3.8 

Silt loam 4 2.9 3.8 

Sandy clay loam 4 2.9 3.8 

Clay loam 5 3.6 7.2 

Silty clay loam 6 4.3 8.6 

Clay 7 5.0 10.0 

Table 10-3. Number of days after precipitation event to re-initiate 

wind erosion for rainfall amounts (constant) <2 in. 

Soil type Spring/Fall Summer Winter 

Sand 1 0.7 1.4 

Sandy loam 1 0.7 1.4 

Fine sand loam 1 0.7 1.4 

Loam 2 1.4 2.8 

Silt loam 2 1.4 2.8 

Sandy clay loam 2 1.4 2.8 

Clay loam 3 2.0 4.0 

Silty clay loam 4 2.8 5.6 

Clay 5 3.6 7.2 
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 271 

10.3.6 Soil disturbance 

The level of disturbance of an erodible surface is an important parameter in estimating windblown dust emissions. Disturbed surfaces tend to generate more dust than undisturbed ones. In 

the application of the Phase I model, different emission rates were applied for disturbed versus 

undisturbed surfaces. The disturbance level of surfaces was assumed to be determined by the 

land use type and to be invariant in time and across the modeling domain. Thus, assumptions 

were required to assign surface disturbance based on land cover type. As noted previously, the 

disturbance level of a surface more appropriately has the effect of altering the threshold surface 

friction velocity; disturbed surfaces have lower thresholds while undisturbed surfaces exhibit 

higher threshold friction velocities. 

The disturbance level of various surfaces across a regional-scale simulation is difficult to determine, given the lack of detail in both the LULC and soils data available for use in the model. 

Except for agricultural lands, which are treated separately in the model (see Section 10.3.8), 

vacant land parcels are typically undisturbed unless some activity is present that causes a disturbance—for example, off-road vehicle activity in desert lands, or animal grazing on rangelands. 

For the Phase II model application, we chose to consider all nonagricultural land use types as 

undisturbed surfaces, since there is no a priori information to indicate otherwise for the regionalscale modeling domain being considered. The effects of assumed disturbance levels were evaluated with sensitivity simulations (discussed in Section 10.4). For the sensitivity simulations, 

threshold surface friction velocities for the assumed disturbed land use types were based on 

limited experimental data. 

10.3.7 Data sources 

The various datasets required for implementing the Phase II approach to windblown dust estimation are summarized below. 

10.3.7.1 Land use/land cover 

The land use/land cover data used for Phase II were based on the National Land Cover Database 

(NLCD). These are the same datasets used in developing the ammonia emissions inventory, and 

are documented in Section 2. A summary of land use types for the U.S. portion of the RPO 

Unified domain is given in Table 10-4. 

Table 10-4. Percentage of each land use type for the U.S. portion of the modeling domain. 

Land Use Type Total Area 

(Acres) 

% of Total When 

Water Included 

% of Total When 

Water Excluded 

Water 98,484,739 5.0% — 

Urban 35,629,865 1.8% 1.9% 

Barren 37,204,176 1.9% 2.0% 

Forest 556,424,387 28.1% 29.6% 
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Land Use Type Total Area 

(Acres) 

% of Total When 

Water Included 

% of Total When 

Water Excluded 

Shrubland 355,796,082 18.0% 18.9% 

Grassland 302,601,621 15.3% 16.1% 

Agricultural 515,624,831 26.0% 27.4% 

Wetlands 78,127,135 3.9% 4.2% 

Total when water included 1,979,892,836 100.0% — 

Total when water excluded 1,881,408,097 — 100.0% 

10.3.7.2 Soil characteristics 

Application of the emission factor relationships described above requires the characterization of 

soil texture in terms of the four soil groups considered by the model. The type (characteristics) of 

soil is one of the parameters of primary importance when applying the emission estimation relationships derived from wind tunnel study results. We used the STATSGO database to determine 

the types of soils present in the modeling domain. These data were developed from the same 

databases as the soils data for the ammonia emissions inventory (see Section 2). 

The classification of soil textures and soil group codes is based on the standard soil triangle that 

classifies soil texture in terms of percent sand, silt, and clay. Combining the soil groups defined 

by the work of Alfaro et al. (2003) and Chatenet et al. (1996) and the standard soil triangle provides the mapping of the 12 soil textures to the four soil groups considered in their study (see 

Section 10.2.2.2.2). This soil texture/soil group mapping (Table 10-5) allows the application of 

emission factor data. 

Table 10-5. STATSGO soil texture and soil group codes. 

STATSGO Soil 

Texture 

Soil 

Group 

STATSGO Soil 

Texture 

Soil 

Group 

No data N/A Sandy clay loam MS 

Sand CS Silty clay loam FFS 

Loamy sand CS Clay loam MS 

Sandy loam MS Sandy clay MS 

Silt loam FS Silty clay FFS 

Silt FFS Clay FS 

Loam MS 
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10.3.7.3 Meteorology 

As with the Phase I model implementation, gridded hourly meteorological data are required for 

the dust estimation methodology. The meteorological data for Phase II are based on MM5 model 

simulation results. Data fields required include wind speeds, precipitation rates, soil temperatures, and ice/snow cover. The 2002 MM5 model results on the RPO Unified domain (see Section 4), at both 12- and 36-km resolutions, are processed as in Phase I and used in the application 

of the Phase II windblown dust model. 

10.3.8 Agricultural land adjustments 

Unlike other types of vacant land, windblown dust emissions from agricultural land are subject 

to a number of nonclimatic influences, including irrigation and seasonal crop growth. We therefore developed several nonclimatic correction or adjustment factors to apply in calculating the 

agricultural wind erosion emissions. These factors address: 

• long-term effects of irrigation (i.e., soil “cloddiness”); 

• crop canopy cover; 

• post-harvest vegetative cover (i.e., residue); 

• bare soil (e.g., barren areas within an agriculture field that do not develop crop canopy for 

various reasons); and 

• field borders (i.e., bare areas surrounding and adjacent to agricultural fields). 

The methodology used to develop individual nonclimatic correction factors for the Phase I study 

was described in ENVIRON (2004a). Most of these methods were based upon previous similar 

work performed by CARB in their development of California-specific adjustment factors for 

USDA’s Wind Erosion Equation (WEQ) (CARB, 1997). We developed these correction factors 

for specific soil textures, crop types, and geographic locations and then applied them to the wind 

erosion estimates developed from the wind tunnel studies. Correction factors were developed 

only for the 17 field crops specifically identified in the BELD3.1 dataset (i.e., alfalfa, barley, 

corn, cotton, grass, hay, oats, pasture, peanuts, potatoes, rice, rye, sorghum, soybeans, tobacco, 

wheat, and miscellaneous crops). Due to the insufficient characterization of the wind erosion 

emission processes for orchards and vineyards, correction factors for this type of agricultural 

land were not developed. 

For the Phase II dust model implementation, we applied these same nonclimatic adjustments. 

However, because the BELD3 database was not used, these factors were instead related to the 

agricultural land use types available in the NLCD LULC data. The existing county-level crop 

percentages from the BELD3 database were linked to the aggregated agricultural land parcels 

from the NLCD data. Note that for all states outside of the WRAP and CENRAP regions, no 

agricultural data were collected. Therefore, for those states no agricultural adjustments were 

applied. 
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10.4 Model Results for 2002 

This section discusses the results obtained from running the Phase II revised windblown dust 

emission model using the estimation methodologies and data described above. However, note 

that after this set of model runs was performed, a minor error was discovered in the meteorological data processor. The effect of the error was to shift the wind fields a fixed number of grid cells 

to the west. The model is therefore being rerun to correct this error and to utilize the latest MM5 

simulation results. The revised windblown dust emission estimates, as well as the emission 

estimates based on the 12-km resolution meteorological data, will be documented in the draft 

final report for this task, which is schedule for completion in March 2005. Nevertheless, the 

results from the four sensitivity scenarios described below are still relevant because in each case 

the wind field shift is the same. The results of these scenarios can still be compared with each 

other to provide some insight into the sensitivity of the model and the estimation methodology to 

various assumptions. 

Four scenarios were conducted to evaluate the sensitivity of the model to the various assumptions incorporated in the methodology. These scenarios were designed to consider the effects of 

soil disturbance and reservoir characteristics. The scenarios were as follows: 

a. No limitation on dust event duration; all soils loose and undisturbed 

b. Dust event duration limited to 10 hours per day; all soils loose and undisturbed 

c. No limitation on dust event duration for disturbed or undisturbed soils; 10% of grassland, 

shrubland, and barren land area assumed disturbed; threshold friction velocity for disturbed grass and shrubland = 0.5 · (undisturbed value); threshold friction velocity for 

barren land = 0.27 · (undisturbed value) 

d. Dust event duration limited to 10 hours per day for undisturbed soils; no limitation on 

dust event duration for disturbed soils; 10% of grassland, shrubland, and barren land area 

assumed disturbed; threshold friction velocity for disturbed grass and shrubland = 

0.5 · (undisturbed value); threshold friction velocity for barren land = 0.27 ·(undisturbed 

value) 

Sections 10.4.1 through 10.4.4 present plots of coarse PM (PMc) and PM10 for each scenario. 

Section 10.4.5 discusses the results for the scenarios, and recommends the scenario to use for 

further evaluation and air quality modeling. Displays of the spatial distribution of dust emissions 

are presented in terms of PMc, while state-level and domainwide results (bar charts) are 

presented in terms of PM10. The spatial displays are generated from the gridded model-ready 

emission files, which represent PMc as required by the air quality models. PM10 emission 

estimates are available as text files output directly from the dust model and are summarized by 

land type and/or state for presentation. 

10.4.1 Scenario a 

Scenario a considered all soils to be loose and undisturbed. In addition, no limit on the duration 

of dust events was imposed. The spatial distribution of PMc emissions is displayed in Figure 
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10-3. Figure 10-4 shows the distribution of predicted PM10 dust emissions by land use type for 

each of the WRAP states.

Figure 10-3. Spatial distribution of total 2002 annual PMc dust emissions for Scenario a. 
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Dust from all Categories for Scenario a

PM10 Yearly Total

0

30,000

60,000

90,000

120,000

150,000

180,000

210,000

240,000

270,000

300,000

330,000

360,000

390,000

420,000

 AZ

 CA

 CO

 ID

 MT

 NV

 NM

 ND OR

 SD UT

 WA

 WY

State

tons/yr

Dust Code 3 - Ag

Dust Code 4 - Grasslands

Dust Code 6 - Shrublands

Dust Code 7 - Barren

Figure 10-4. Distribution of total annual 2002 PM10 dust emissions by land use type 

and state for Scenario a. 
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10.4.2 Scenario b 

As in Scenario a, Scenario b considered all soils to be loose and undisturbed. In contrast to 

Scenario a, however, the duration of dust events was limited to 10 hours per day. Figure 10-5 

shows the spatial distribution of PMc emissions. The distribution of predicted PM10 dust 

emissions by land use type for each of the WRAP states is displayed in Figure 10-6. 

Figure 10-5. Spatial distribution of total 2002 annual PMc dust emissions for Scenario b. 
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Dust from all Categories for Scenario b

PM10 Yearly Total

0

30,000

60,000

90,000

120,000

150,000

180,000

210,000

240,000

 AZ

 CA

 CO

 ID

 MT

 NV

 NM

 ND OR

 SD UT

 WA

 WY

State

tons/yr

Dust Code 3 - Ag

Dust Code 4 - Grasslands

Dust Code 6 - Shrublands

Dust Code 7 - Barren

Figure 10-6. Distribution of total annual 2002 PM10 dust emissions by land use type 

and state for Scenario b. 
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10.4.3 Scenario c 

Scenario c assumed that 10% of the grassland, shrubland, and barren land area was disturbed. 

For the disturbed areas, threshold friction velocities were reduced, resulting in the initiation of 

wind erosion at lower wind speeds. For disturbed grassland and shrubland the threshold friction 

velocity was assumed equal to 0.5 times the undisturbed value; for disturbed barren land the 

threshold friction velocity was assumed equal to 0.27 times the undisturbed value. In addition, no 

limit on the duration of dust events was imposed for either disturbed or undisturbed soils. The 

spatial distribution of PMc emissions is displayed in Figure 10-7, while Figure 10-8 shows the 

distribution of predicted PM10 dust emissions by land use type for each WRAP state. 

Figure 10-7. Spatial distribution of total 2002 annual PMc dust emissions for Scenario c. 
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Dust from all Categories for Scenario c

PM10 Yearly Total

0

30,000

60,000

90,000

120,000

150,000

180,000

210,000

240,000

270,000

300,000

330,000

360,000

390,000

420,000

 AZ

 CA

 CO

 ID

 MT

 NV

 NM

 ND OR

 SD UT

 WA

 WY

State

tons/yr

Dust Code 3 - Ag

Dust Code 4 - Grasslands

Dust Code 6 - Shrublands

Dust Code 7 - Barren

Figure 10-8. Distribution of total annual 2002 PM10 dust emissions by land use type 

and state for Scenario c. 
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10.4.4 Scenario d 

Scenario d made the same assumption as Scenario c concerning the disturbance of grassland, 

shrubland, and barren land. However, a 10-hour-per-day limit on dust event duration was imposed for all undisturbed soils, while no limit on the dust event duration was imposed for disturbed soils. This scenario differs from the previous scenario in that Scenario c has no limit on 

the event duration imposed for either disturbed or undisturbed soils. Figure 10-9 shows the 

spatial distribution of PMc emissions. The distribution of predicted PM10 dust emissions by land 

use type and WRAP state is displayed in Figure 10-10. 

Figure 10-9. Spatial distribution of total 2002 annual PMc dust emissions for Scenario d. 
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Dust from all Categories for Scenario d

PM10 Yearly Total

0

30,000

60,000

90,000

120,000

150,000

180,000

210,000

240,000

 AZ

 CA

 CO

 ID

 MT

 NV

 NM

 ND OR

 SD UT

 WA

 WY

State

tons/yr

Dust Code 3 - Ag

Dust Code 4 - Grasslands

Dust Code 6 - Shrublands

Dust Code 7 - Barren

 

Figure 10-10. Distribution of total annual 2002 PM10 dust emissions by land use type 

and state for Scenario d. 
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10.4.5 Summary of results and recommendations 

The results of the four sensitivity simulations are compared below. Figure 10-11 is a state-bystate comparison of the total annual 2002 PM10 dust emissions for the four scenarios. Also 

displayed in this figure are the total annual PM10 dust emissions for 1996 estimated as part of 

Phase I of this task. Figure 10-12 shows the distribution by month of total PM10 for the entire 

domain for the four scenarios. The emissions for Scenarios a and c (no limitation on dust event 

duration) are consistently higher than those from their counterparts, Scenarios b and d (dust 

event duration limited to 10 hours a day). This result is expected, since the lack of limitation on 

dust events causes higher emissions to be generated in regions where winds exceed the land-usedependent threshold surface velocities for longer time periods. Also as expected, the cases with 

assumed disturbance of grassland, shrubland, and barren land (Scenarios c and d) show consistently higher emissions than those with no assumed soil disturbance (Scenarios a and b). 

Dust Yearly Total PM10 by State

WRAP States

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

 AZ

 CA

 CO

 ID

 MT

 NV

 NM

 ND

 OR

 SD

 UT

 WA WY

State

tons/yr

scen a

scen b

scen c

scen d

1996

Figure 10-11. Distribution of total annual 2002 PM10 dust emissions by scenario and state. 
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Monthly Dust Emissions for the Entire Domain 

0

400,000

800,000

1,200,000

1,600,000

2,000,000

2,400,000

2,800,000

1 2 3 4 5 6 7 8 9 10 11 12

Month

tons/mon

scen a

scen b

scen c

scen d

Figure 10-12. Monthly distribution of total 2002 PM10 dust emissions for each 

scenario across the entire WRAP domain. 

The seasonal variation of predicted dust emissions can also be discerned from Figure 10-12. The 

dust emissions tend to peak in the spring months due to generally higher winds throughout most 

of the domain. In addition, for agricultural areas this time period corresponds to the spring planting when the crop canopy is relative small or absent. A second, smaller peak occurs in the fall for 

similar reasons. 

Figure 10-13 displays the percentage of dust by land use type across the WRAP domain for each 

scenario. In all cases, the dust emissions are dominated by those from agricultural lands. Shrubland and grassland make up the majority of the remaining dust emissions, while barren land contributes only a minor portion. Although barren lands have the lowest surface threshold velocity 

based on the assumed surface roughness length, there are only limited areas across the domain 

characterized as barren lands based on the NLCD used for the project. The impact of the various 

assumed disturbance levels of the soils and reservoir characteristics are reflected in the differences between the four pie charts and are seen to be consistent with expectations. For example, a 

comparison of Scenario a and Scenario c shows the expected increase in estimated dust emissions for the assumed disturbed lands. A comparison of Scenario b and Scenario d shows similar 

results. 
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Dust emissions, Scenario a

18.4% 60.2%

20.4%

1.0%

Dust Code 3 - Ag

Dust Code 4 - Grasslands

Dust Code 6 - Shrublands

Dust Code 7 - Barren

Dust emissions, Scenario b

15.7% 61.7%

21.5%

1.1% Dust Code 3 - Ag

Dust Code 4 - Grasslands

Dust Code 6 - Shrublands

Dust Code 7 - Barren

Dust emissions, Scenario c

48.6%

26.0%

23.3%

2.1% Dust Code 3 - Ag

Dust Code 4 - Grasslands

Dust Code 6 - Shrublands

Dust Code 7 - Barren

Dust emissions, Scenario d

42.9%

28.6%

25.7%

2.8%

Dust Code 3 - Ag

Dust Code 4 - Grasslands

Dust Code 6 - Shrublands

Dust Code 7 - Barren

Figure 10-13. Distribution of total 2002 PM10 dust emissions by land use type for each 

scenario for all WRAP states combined. 

Based on review and comparison of the results of the sensitivity simulations, we recommend that 

the results of Scenario b be used for further evaluation and air quality modeling. Scenario b 

assumes that all soils are loose and undisturbed, and imposes a 10-hour-per-day limit on the duration of dust events. Given the limitations of the data used for this task, this scenario appears to 

be the most appropriate, for the following reasons: 

• Dust Event Duration: It is reasonable to expect that a limit on the dust event duration is 

related to the type and amount of soil available for erosion due to winds. Therefore, the 

assumed limit of 10 hours per day, although only an assumption, is more appropriate than 

imposing no limit at all on the dust reservoirs. 

• Soil Disturbance: Although clearly not all soils are undisturbed, there is no information 

available to assign disturbance levels to various areas throughout the region. Scenarios c 

and d assumed a 10% level of disturbance, but that was only for conducting sensitivity 

simulations. Clearly the percent disturbance will vary by region, soil type, and season. In 

addition, the threshold surface velocities for the disturbed soil cases are based on only 

very limited test results and are therefore not likely to apply regionwide. 
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Given the lack of supporting data for the dust reservoir and soil disturbance characteristics, we 

feel that the results of Scenario b would be more universally applicable on a regional scale for 

the WRAP’s air quality modeling purposes. 

The spatial distributions of modeled coarse PM dust from Scenario b are presented in Figures 

10-14 (annual total)*

 and 10-15 (seasonal totals).† The dust emissions are highest in agricultural 

areas in the central portion of the U.S. and in parts of the West that are dominated by shrub- and 

grassland. Echoing the results shown in Figure 10-12, the seasonal plots in Figure 10-15 indicate 

that the highest predicted dust emission occur during the spring, when winds are typically 

stronger throughout the region and agricultural crops have recently been planted. Lower overall 

dust emissions are predicted in the summer months, corresponding to the higher level of crop 

canopy cover and, in the Southwest, to increased periods of precipitation. 

Figure 10-14. Spatial distribution of annual 2002 PMc dust emissions for Scenario b. 

 

*

 This figure looks different from Figure 10-5 because Figure 10-5 uses tons/yr while Figure 10-14 uses log(tons/yr). 

†

 Note that these figures show only the western portion of the domain. Due to the lack of detailed agricultural 

information for states outside the WRAP and CENRAP regions, we could not apply the nonclimatic agricultural 

adjustments described above outside those two regions. Because the assumed surface roughness for agricultural 

lands is representative of a bare field, these areas have a greater potential for wind erosion. Therefore, since no 

agricultural adjustment could be applied to the eastern portion of the domain, results for that region are overestimated and are therefore neither realistic nor applicable. The displays were prepared for the western region only in 

order to highlight spatial details not apparent when the entire domain is displayed. 
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Figure 10-15. Spatial distribution of 2002 PMc dust emissions by season for Scenario b. 

10.5 Model Performance Evaluation 

As part of Phase II of this task, the RMC is to conduct a model performance evaluation of the 

revised windblown dust emission model. We have initiated this evaluation, but at this time only 

preliminary information has been collected and analyzed, so there are no results to discuss yet. 

The model performance evaluation has been delayed due to the need to rerun the windblown dust 

emissions model with the latest MM5 meteorological data for the WRAP 12-km modeling 

domain. These results will be fully documented in this task’s Phase II final report, to be completed in early March 2005. In this section we summarize the model performance evaluation 

approach and procedures. 
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The 2002 windblown dust emissions model will be evaluated several ways for the reasonableness and accuracy of its output. One of the challenges in this evaluation is that measurements 

generally do not distinguish between windblown dust and other forms of dust (e.g., from paved 

and unpaved roads). Moreover, measurements of coarse mass, which is typically assumed to be 

dust, can contain other nondust compounds (e.g., sulfate and nitrate compounds). 

The IMPROVE monitoring network measures a fine soil species and a CM species that are generally assumed to be due to dust emissions. The fine soil species consists of mineral elements, so 

in the absence of interference by fires it is probably a fairly good estimate of the fine dust contribution. The CM measurement, on the other hand, is the difference between the PM10 and PM2.5

mass measurements, so it can contain compounds other than dust. 

WRAP is performing a study titled “Assessment of the Major Causes of Dust-Resultant Haze in 

the WRAP” (Etyemezian et al., 2004) that will characterize dust events at IMPROVE monitors 

for 2001-2003. This study is using visibility extinction due to fine soil (FS) and CM to define 

extinction due to dust: 

Bdust = [FS] + 0.6 [CM] 

Dust events at the WRAP IMPROVE monitors are being characterized as follows: 

• Transcontinental events 

• Regional windblown dust events 

• Wildfire-related events 

• Local windblown dust 

• Other events 

• Unknown source 

This analysis will be very valuable as we evaluate the windblown and other dust emissions. 

However, results from this study are not yet available. 

We initially intended to conduct the model performance evaluation in three parts: (1) an initial 

comparison of modeled dust emissions with measured dust at IMPROVE monitors; (2) an 

evaluation of the CMAQ model results with and without the windblown dust emissions; and 

(3) a refined evaluation using the results of the Causes of Dust project. However, due to the 

delays in the Causes of Dust project and the desire to complete the windblown dust emissions 

inventory in a reasonable time frame, the enhanced model performance evaluation was dropped 

from the scope of work for this task. The remaining two parts of the model performance 

evaluation are described in Sections 10.5.1 and 10.5.2. 

10.5.1 Evaluation, Part 1: Comparisons of windblown dust emissions with 

the occurrence of enhanced “dust” at IMPROVE monitors 

For each 24-hour IMPROVE sample during 2002, we will match the IMPROVE dust extinction 

(Bdust) with modeled windblown dust emissions for the concurrent day in the nine (3 x 3) and 25 
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(5 x 5) grid cells centered on the IMPROVE monitor. Statistics will be generated to identify the 

occurrence of four different situations: 

1. near-zero Bdust and zero modeled windblown dust (nonoccurrence of both measured and 

modeled dust); 

2. near-zero Bdust and enhanced (i.e., exceeding background levels) modeled windblown 

dust (no measured dust, but modeled windblown dust in vicinity of monitor); 

3. enhanced Bdust and no windblown dust (occurrence of measured dust, but no modeled 

windblown dust); and 

4. enhanced Bdust and modeled windblown dust emissions (occurrence of both enhanced 

measured and enhanced modeled dust). 

These classifications allow a qualitative evaluation of the windblown dust emissions. It is expected that type 1 would occur most frequently, especially at more northerly sites and during the 

winter months, and there should be a lot of agreement between the modeled values and the 

measurements. Type 2 does not necessarily indicate a problem with the windblown dust model, 

as the dust cloud may be real but may not have impacted the monitor. Likewise, the elevated 

Bdust in type 3 may be due to sources other than windblown dust, so type 3 also does not necessarily indicate a problem. Type 4 may provide the most information, especially if a positive 

correlation can be established between windblown dust emissions and Bdust. If the above analysis 

provides useful information, it would be repeated for the fine and coarse components of the 

measured (FS and CM) dust and modeled windblown dust emissions. 

10.5.2 Evaluation, Part 2: Enhancements to CMAQ to separately track dust 

CMAQ currently tracks all fine PM that is not SO4, NO3, EC, or OC as a single species (A25). 

Similarly, all coarse PM is typically tracked as one species (ACORS).*

 We will modify CMAQ 

by adding two additional fine and coarse PM species, one set to track fine and coarse windblown 

dust emissions and the other set to track fine and coarse “other dust” emissions. This will allow 

the separate accounting of CMAQ’s fine and coarse PM species into windblown dust, other dust, 

and other primary fine and coarse PM emissions species. 

We will run the modified CMAQ and compare the model estimates against the IMPROVE 

measures for fine soil and CM. The evaluation will be performed for dust (windblown and other) 

species alone and with and without windblown dust to determine whether adding the windblown 

dust emissions improves model performance. The performance of CMAQ using all other fine 

and coarse emissions would also be evaluated, as has been done in the past. 

10.6 Summary 

This section summarizes the major findings so far of Phase II of the windblown dust model task. 

It also lists recommendations with regard to improvements in data quality, and suggests 

enhancements and refinements of the estimation methodology. As noted at the beginning of 

 

*

Note that there are other coarse species for sea salt and soil, but they are not typically used. 
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Section 10.4, we are currently revising the windblown dust emissions estimates due to a minor 

error that occurred in processing the wind fields used in the estimation methodology. However, 

the general qualitative discussion of the results presented here is not expected to change 

significantly. The revised emission estimates will be presented in the draft final task report. 

Several of the recommendations provided below also will be addressed in that report. 

10.6.1 Results 

Based on a review of recent studies concerning dust emission from wind erosion, we developed a 

general Phase II estimation methodology for PM dust emissions from wind erosion. Emission 

rates were developed by soil type and stability class. Land use/land cover data were based on the 

NLCD database and used to determine the surface roughness lengths necessary for estimating 

threshold surface friction velocities. Wind speed, precipitation rates, and soil temperatures were 

based on MM5 model simulation results at a spatial resolution of 36 km. Although the overall 

estimation methodology was updated and improved upon from the Phase I methodology, a number of assumptions were still required to implement the methodology, primarily related to limitations in the input data used in the model (see Section 10.3). 

We implemented the Phase II estimation methodology for calendar year 2002 on the RPO Unified domain. A number of sensitivity simulations were performed to investigate the effects of the 

various assumptions regarding dust reservoir characteristics and the disturbance levels of the 

soils across the domain. The results were presented and discussed in Section 10.4. Qualitatively, 

the results appear to be consistent with what would be expected based on the various assumptions made. 

A more detailed analysis of the model results and a comparison with ambient data are currently 

underway and will be documented in the task final report. 

10.6.2 Recommendations 

As the results of the task are still under review and a more detailed model performance evaluation is underway, it is too early to present a full set of recommendations. However, based on a 

preliminary assessment of the results from the Phase II fugitive windblown emission estimation 

methodology, we can make the following initial set of recommendations: 

• An attempt should be made to identify more detailed data on land use types and soil 

characteristics for the regional domain. 

• The lack of information needed to apply agricultural adjustments to the eastern U.S. 

should be addressed. 

• The dust model should be rerun using the latest 36-km annual MM5 modeling results 

from Task 2. 

• The model should be applied using the 12-km-resolution MM5 meteorology data when 

they become available from Task 2. 
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• To validate the methodology, the model should be applied to a smaller local domain for 

which detailed data are available. 

• The transport fractions developed by the EPA should be applied. This should be implemented from within the dust model to make use of the higher resolution of the land use 

data, as opposed to application at the county level. 

10.7 Status of Task 9 Deliverables 

Table 10-6 below gives the status of each Task 9 deliverable listed in the 2004 WRAP RMC 

work plan, plus any additional deliverables agreed upon after the work plan was prepared. 

Table 10-6. Status of the Task 9 deliverables. 

Deliverable Status 

Draft final task report Expected completion in early March 2005. 

Final task report To be completed after receiving comments on draft final task report. 

Updated model results To be included in the draft final task report. 
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11. Task 10: Continued Improvement to Model Evaluation 

Software 

11.1 Work Performed from March 2004 through February 2005 

During this project year, the RMC continued to use and improve the UCR model performance 

evaluation (MPE) software that was developed earlier in the project and that has been supported 

primarily by WRAP. We note that funding from VISTAS was also used to improve the MPE 

software during 2003; in return, VISTAS realized substantial benefits from applying the MPE 

software. This example represents one of many benefits of this project to multiple RPOs. 

Only minor changes in the MPE software were planned for 2004. The original work plan included tasks to process Photochemical Assessment Monitoring Station (PAMS) and PM Supersite data, and to develop software for creating contour plots of error and bias. In consultation 

with the WRAP Air Quality Modeling Forum, however, it was agreed that these were low 

priority tasks, so instead those resources were reallocated for the acquisition of additional disk 

storage that was needed for the project. Because there is relatively minimal benefit in evaluating 

the small amount of PAMS (primarily located in urban areas) and Supersite data for the WRAP 

region, we do not recommend continuing those tasks in the 2005 work plan. 

We continued to revise and improve the MPE software during 2004. For a snapshot of the MPE 

software as of January 2005, please refer to Appendix F, which is MPE draft documentation 

titled “User’s Guide, Air Quality Model Evaluation Software, Version 2.0.1.” Because model 

performance guidance does not yet exist for visibility modeling, we continue to explore new 

evaluation methods, and sometimes discover improved methods for presenting model results that 

necessitate changes in the MPE software. In particular, during 2004 we experimented with 

various types of plots for summarizing and effectively presenting the results of the model-toambient-data comparisons, including soccer plots and bugle plots. Beginning in January 2005 

we also adapted a Microsoft Access Database program originally developed by Air Resource 

Specialists (Cassie Archuleta, Air Resource Specialists, Inc., personal communication) to 

produce stacked-bar time-series plots. 

While there is not yet official EPA guidance on which model performance metrics or which type 

of evaluation products should be used, we have found that the stacked-bar time-series plots and 

the bugle plots are most effective for conveying both qualitative and quantitative information 

about model performance. Although we continue to compute a wide variety of model performance metrics, we typically use mean fractional bias (MFB) and mean fractional error (MFE) 

when presenting evaluation results because these metrics provide the most balanced and symmetrical approach for characterizing model underpredictions and overpredictions. 

Figures 11-1 through 11-4 present examples of the newer model evaluation products. We present 

these to illustrate the types of model evaluation results that can be produced using the MPE. 
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An advantage of our traditional MPE products (i.e., time-series plots and scatterplots) was that 

they were produced automatically by the MPE software with minimal staff time. The new evaluation products (soccer plots, bugle plots, stacked-bar time-series plots) have been very resource 

intensive to produce because we have prepared them “manually” by importing error and bias 

metrics into Excel spreadsheets or Access and then preparing the plots. Therefore, we spent 

additional effort in 2004 to begin automating development of the stacked-bar time-series plots. 

We plan to release a public version of the MPE software after completing revisions to the MPE 

user’s guide given in Appendix F. This documentation will also be posted to the RMC web site. 

During this project year, we also invested substantial effort in attempting to identify updated site 

information for IMPROVE sites that can be used in the MPE software. There are at least three 

different versions of site classifications that are referenced in policy guidance from EPA, so we 

made an effort to determine the most correct list of IMPROVE sites for use in the MPE software. 

We have implemented additional sets of site lists into the routine model evaluation. These 

include some sites that are in Class I areas but for which there are no monitoring data. These 

sites can be used for evaluating the results of different model sensitivity experiments and 

emissions reductions or for producing source attribution products. However, these sites cannot be 

used in model performance evaluations for which ambient monitoring data are required. 

11.2 Status of Task 10 Deliverables 

Table 11-1 gives the status of each Task 10 deliverable listed in the 2004 WRAP RMC work 

plan, plus any additional deliverables agreed upon after the work plan was prepared. 

Table 11-1. Status of the Task 10 deliverables. 

Deliverable Status 

Visualization tools for 

source attribution 

Completed in March 2004, with periodic updates afterward. Source code 

available to WRAP members on request. 

GUI for using model 

evaluation tools and 

visualization tools 

We developed GUI tools for using the model performance evaluation software, 

as described in the MPE documentation. However, we believe that the script 

version of the MPE software is easier to use and maintain, and we 

recommended continuing with only a script version of the software. Therefore, 

per agreement with the WRAP Air Quality Modeling Forum, we agreed to drop 

this deliverable to focus resources in other areas. 

Revised version of 

MPE package 

Periodic updates were made throughout the project year. 

List of sites used in evaluation in October 2004 was revised to accommodate 

vague EPA guidance on identification of site definitions. 

Source code is available to WRAP members on request. We plan to release a 

public, open-source version for wider distribution when the MPE 

documentation is completed in March 2005. 

Documentation of 

model software 

To be completed in March 2005. 

 
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12. Task 11: Sensitivity Studies Designed to Evaluate 

Uncertainties in Fire Emissions 

With the Fire Emissions Joint Forum and Air Sciences Inc., the RMC developed several emission scenario combinations to study the effect of fires on air quality in general, and on visibility 

in particular. Some of these scenarios include 2018 emissions and fires; others include 2002 

emissions and fires. In general, we compared CMAQ results created using emissions with no 

fires in the WRAP domain against CMAQ results created after adding either one type of fire or 

all types of fires to the emissions. Development of fire emissions data was completed by Air 

Sciences Inc. under contract from WRAP. Processing of fire emissions is described in Section 3 

of this report. Figure 12-1a, discussed in more detail in Section 3.1, shows the locations and 

magnitudes of total fire emissions for July 2002. 

Sections 12.1 through 12.6 describe the results of the fire sensitivity modeling scenarios. We 

have completed emissions processing and CMAQ simulations and analysis for all of the fire 

sensitivity model simulations that were included in the revised 2004 work plan. The primary 

analysis products include the following: 

• Monthly-average spatial visibility plots using both deciview (dV, or dcv) and the 

extinction coefficient (Bext) and concentrations of selected PM species and ozone. These 

include plots showing spatial distributions of concentration for a model base case 

simulation, and difference plots that show the spatial change in concentration for a 

sensitivity scenario compared to the base case simulation. The base case spatial plots are 

included in the analysis and evaluation of the Pre02b, Pre02c and Pre02d base case 

simulations at the RMC web page http://pah.cert.ucr.edu/aqm/308/cmaq.shtml. 

Therefore, our analysis here focuses on the difference plots for the fire sensitivity 

simulations. Key results are discussed below; for complete results, see 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#fire. 

• Stacked-bar time-series plots showing the summed contribution to Bext at each selected 

sites. Example plots are shown in Figure 12-2, for the anthropogenic and natural fire 

emissions at the Grand Canyon IMPROVE site. At sites for which monitoring data are 

available, these time-series plots can also be compared to a stacked-bar time-series plot of 

the ambient monitoring data. Alternatively, model-versus-model results can be compared 

at sites for which ambient data are not available. Stacked bar plots for the Pre02d base 

case are available at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml, so here we focus on the 

stacked-bar difference plots comparing the fire sensitivity runs to a base case run. Note 

that in some instances we may compare the differences between two different fire 

sensitivity cases rather than a sensitivity case and base case. These plots have been used 

primarily as a qualitative approach for assessing source attribution to fire emissions. 

Table 12-1 lists the new model simulations completed, including a short name used to identify 

each modeled fire sensitivity case. Each of the sensitivity scenarios is also summarized below. 

In addition to these new model simulations, we also completed further analysis for some fire 
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model simulations carried out using the 1996 and 2018 scenarios that were developed during the 

Section 309 modeling effort. 

Table 12-1. Summary of new air quality model simulations used to evaluate effects 

of fire emissions. 

Model Case Motivation Case ID 

Base case (no fire) Base case to compare with modeled fire sensitivity 

simulations 

Pre02b 

Base + ag vertical 

sens (Scenario 4) 

Evaluate model sensitivity to uncertainty in plume rise of 

emissions from agricultural burning. 

Pre02b_agmod 

Base + wildfires Evaluate effect of all wildfires on model-simulated visibility Pre02b_wf02 

Base + “natural” fires 

(Scenario 5a) 

Evaluate effect of natural fires on model-simulated visibility 

(includes all wildfires and some prescribed burning). 

Pre02e 

Base + ag burning Evaluate effect of agricultural burning emissions on modelsimulated visibility Pre02b_ag18 

Base + Rx burning Evaluate effect of prescribed burning on model-simulated 

visibility 

Pre02b_rx02 

Base + all fire emis 

(Scenario 1a)

Reference case with all fires to compare with Pre02b (base 

case) and with other fire sensitivity cases. 

Pre02c 

Base + anthro + 

natural fires 

Uses new fire emissions classes. The total of the anthro plus 

natural fire emissions is identical to the total Pre02c fire 

emissions in scenario 1a. 

Pre02f 

12.1 Scenario 1(a) 

This scenario was designed to investigate the combined effects of all fire types on air quality. It 

compares the 2002 CMAQ results that were based on the preliminary Pre02b base case 

emissions scenario against the 2002 CMAQ results that were based on emissions scenario 

Pre02c, which includes Pre02b emissions plus the combined emissions of three fire types: 

(1) 2002 wildfires (wf02) 

(2) 2018 typical agricultural fires (agbase.073102, also referred to below as ag18) 

(3) 2002 prescribed fires (rx02) 

The fire emissions are described in 5.2.10. A detailed description of the other (i.e., nonfire) 

emissions sources used in these model simulations is also available at 

http://www.cert.ucr.edu/rmc/2002/emissions/WRAP_2004_Emissions_Workplan_v1-2.xls. 
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Figure 12-1 shows results of this analysis averaged for the month of July. The July average is 

shown here as illustrative of the relation between emissions and visibility impact because it is 

one of the months with large total fire emissions, and total fire emissions of carbon monoxide 

(CO), shown in Figure 12-1a, are used here as an indicator of fire size and location. Plots of 

emissions of other gas and particulate species, which are included on the RMC web site, have the 

same relative intensity and distribution as the CO emissions. Figure 12-1b shows the effect of 

combined wild fire, ag burning, and prescribed burning fire emissions on visibility (in deciviews) 

calculated as the July average. We note that the Pre02f case (Figure 12-1b), which combines 

natural and anthropogenic fire emissions, is identical to the Pre02d case, which combines wild 

fire, ag burning, and prescribed burning emissions. Figure 12-1 shows that fire emissions have 

large effects on visibility throughout the western states. The peak impact of 20.6 dV is in western 

Colorado; also, large regions show visibility changes greater than 4 dV. 

Figures 12-3 and 12-4 show the monthly average effects on visibility as deciviews for all 12 

months. The effects of the wildfire emissions in June through August are by far the largest 

contribution to fire emissions, with the highest modeled deciview values occurring August. 

12.2 Scenario 1(b) 

This scenario was created to investigate the effect of each individual fire type on air quality. It is 

made up of three subscenarios that compare the CMAQ scenario with no fires (Pre02b) with a 

CMAQ sensitivity run that includes the Pre02b emissions plus one type of fire: 

1. Scenario Pre02b_wf02: Pre02b vs. Pre02b + wf02 (wild fires for 2002, as identified by 

SCC in the SMOKE point file). SMOKE was run to merge Pre02b and wf02 emissions to 

create the emissions input for CMAQ. 

2. Scenario Pre02b_ag18: Pre02b vs. Pre02b + agbase.073102. SMOKE was run to merge 

Pre02b and ag18 emissions to create emissions input to CMAQ. 

3. Scenario Pre02b_rx02: Pre02b vs. Pre02b + rx02 (prescribed fires for 2002, as identified 

by SCC in the SMOKE PT file). SMOKE was run to merge Pre02b and rx02 emissions to 

create emissions input to CMAQ. 

Figure 12-5 shows the results of each of these three scenarios as spatial plots in PAVE for the 

month of July, for which the monthly average was calculated from the model simulation results 

over all hours in the month. Note that the left panels show CO emissions for each fire category, 

but that the color scale varies across the three plots, and that wildfire emissions are an order of 

magnitude larger than emissions from prescribed and agricultural burning. The visibility effects 

of wildfire emissions in Figure 12-5b are very similar to the effects of the combined fire 

emissions in Figure 12-1b. 

12.3 Scenario 2 

This scenario compares CMAQ results based on using the 2018 “All control” Base Smoke 

Management scenario (Allcntl_bsm) with those using the 2018 “All control” Optimal Smoke 
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Management scenario (Allcntl_osm), where base and optimal smoke management have been 

defined by the Fire Emissions Joint Forum. The Allcntl_bsm emissions include 2018 emissions 

with controls applied to some point sources and to all three base 2018 typical fire emissions. The 

Allcntl_osm emissions include 2018 emissions with controls applied to some point sources and 

fire emissions reductions derived from the optimal smoke management strategy for 2018 typical 

agricultural and prescribed fire emissions, and to the wildfires. No new SMOKE or CMAQ runs 

were required for this scenario because we used model simulation results that we developed in 

the §309 modeling effort. 

Figures 12-6(a-d) show the seasonal differences in CO emissions as a result of changing from the 

base smoke management (BSM) scenario to the optimal smoke management (OSM) scenario; 

blue colors indicate reductions in emissions. As expected, the OSM scenario produced only 

reduced emissions with no increases relative to BSM. The highest reduction was during the 

spring and the lowest reductions were during the winter. Figures 12-6(a-d) and 12-7(a-d) show 

the seasonal change in visibility (in deciviews) for OSM compared to BSM. The pattern of 

temporal (seasonal) and spatial changes in visibility is similar to changes in emissions. The 

changes in Bext were as low as -15.69 1/Mm (plots for Bext are not shown here but their spatial 

pattern is identical to that of the deciview plots); in deciviews they were as low as -1.0 deciview. 

Figures 12-8 and 12-9 show the monthly average changes in visibility, as monthly average 

deciviews, for the OSM case compared to the BSM case. The monthly average results are 

consistent with the seasonal results; the greatest absolute improvement in visibility occurs in 

September, with a reduction extinction coefficient of 29.7 1/Mm improvement in visibility (plots 

of extinction coefficient are not shown here but are included in the September 2004 FEJF 

meeting presentation (see the PowerPoint file at http://pah.cert.ucr.edu/aqm/308/meetings.shtml). 

In terms of deciviews, the largest improvements were in the spring and fall months, both in 

magnitude and in spatial extent of the improvement, with the greatest improvement being a 2.3-

dV improvement in June. There were small improvements in visibility in the summer and winter 

months.

12.4 Scenario 3 

This scenario will compare CMAQ results using emissions simulation Pre02c with those using a 

modified Pre02c in which the prescribed fires and wildfires will be replaced with new inputs in 

which some of the smaller fires will be zeroed out. This activity has been deferred until the FEJF 

develops these data. 

12.5 Scenario 4 

This scenario, named Pre02b_agmod, was designed to investigate the effect of a vertical redistribution of fire emissions by removing 50% of the agricultural burning emissions from the model’s 

surface layer and redistributing an equal mass of emissions to higher layers in the model. The 

total agricultural fire emissions in each grid cell for the Pre02b_agmod scenario were the same as 

those in Scenario Pre02b_ag18 (see Section 12.2). New PTHOUR files were created in which the 

emission fractions in the first layer (L1FRAC) were reduced by the ratio 38/80. The rest of the 
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first-layer emissions (42/80) were placed in the higher layers. SMOKE and CMAQ runs were 

required to complete this scenario. Results are available on the RMC web site (see 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#fire) and in PowerPoint files (see the files from the 

FEJF meetings at http://pah.cert.ucr.edu/aqm/308/meetings.shtml). The results are not shown 

here because the change in vertical distribution had essentially no effect on visibility. 

There are two probable reasons why this sensitivity did not show an effect on visibility. First, the 

agricultural burning emissions were small compared to wildfire emissions, so changes in the 

agricultural burning vertical profile should logically have only a small relative effect on visibility 

(Figure 12-5f). Second, the agricultural burning emissions are generally small fires for which the 

plume rise does not exceed the height of the planetary boundary layer (PBL). Because the PBL is 

well mixed during the daytime when most agricultural burning emissions occur, these emissions 

tend to be well mixed in the PBL regardless of which layer they are injected within the model. 

However, we would expect a different result for large wildfires for which the plume rise height 

may extend into the free troposphere, and which also have much larger emissions than 

agricultural burning, and therefore a larger effect on visibility in general. Additional sensitivity 

simulations should still be performed to evaluate the effects of plume rise height for large fires. 

12.6 Scenario 5 

To evaluate the effects of natural versus anthropogenic fire emissions, we created two new 

emission scenarios using new fire emissions datasets provided by Air Sciences. 

1. Scenario Pre02e: Includes Pre02b base emissions and the new natural fire emissions files 

only (i.e., no new anthropogenic fire emissions), using fire emissions received from Air 

Sciences on September 10, 2004. 

2. Scenario Pre02f: Includes Pre02b base emissions, the new natural fire emissions, the new 

anthropogenic fire emissions, and the old agricultural burning emissions. 

The following comparisons using these datasets were performed: 

• Compare Pre02e to Pre02b to assess source attribution for natural fire emissions. 

• Compare Pre02f to Pre02e to assess source attribution for anthropogenic fire emissions. 

• Compare Pre02f to Pre02b to assess source attribution for total fire emissions. 

• Compare Pre02f to Pre02c to evaluate the effects of the new fire emissions inventory 

compared to the older fire emissions data used in earlier 2002 modeling. 

Complete results are given on the RMC web site (see 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#fire); here, we present summary results. Figure 

12-10a shows the July total natural fire emissions of CO. Comparing this with Figure 12-1a, it is 

apparent that the natural fire emissions are almost identical to the wildfire emissions. Natural fire 

emissions were defined by the FEJF to include all wildfire emissions and a portion of the 

prescribed burning emissions, while anthropogenic emissions were defined to include all ag 

burning and the remaining portion of the prescribed burning. Because the wildfire emissions are 
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much larger than the other two categories, the effects of natural fire emissions on visibility, as 

shown in Figure 12-10b, were nearly identical to the effects of the wildfire emissions. Moreover, 

the effects of the anthropogenic emissions on visibility (Figure 12-10c) were small, with peak 

increases of 1.2 dV in Oregon and increases of less than 0.5 dV in all other regions. Finally, 

comparing Figure 12-10b to Figure 12-10d shows that the effects of the natural fire emissions 

were nearly identical to the combined natural and anthropogenic fire emissions in Figure 12-10d. 

In retrospect, it is not surprising that the anthropogenic emissions had a relatively small effect on 

visibility, given the small magnitude of anthropogenic agricultural burning compare to total fire 

emissions. However, anthropogenic fire emissions might show larger effects in model 

simulations with finer grid resolutions. For example, results shown here were from model 

simulations on a 36-km grid. It is possible that on a 4-km grid the emissions intensity could be as 

much as 81 times greater than the 36-km grid because each 36-km grid cell contains 9x9 of the 

4-km grid cells. Thus, it is possible that anthropogenic fire emissions might have more 

significant effects on visibility in model simulations using higher grid resolutions. This would be 

a special concern for emissions located in or near a Class I area. Thus, the significance of 

anthropogenic emissions should not be discounted based on these results, and additional studies 

should be performed at finer grid resolutions. 

12.7 Status of Task 11 Deliverables 

Table 12-1 gives the status of each Task 11 deliverable listed in the 2004 WRAP RMC work 

plan, plus any additional deliverables agreed upon after the work plan was prepared.
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Table 12-2. Status of the Task 11 deliverables. 

Deliverable Status 

Report on analysis of existing 

results 

Results were presented at the Fire Emissions Joint Forum meeting in Idaho on Sept 

9, 2004. PowerPoint files are available on the RMC web site: 

Preliminary Fire Emissions Sensitivity Results in CMAQ 

Report results of 

additional model 

runs. 

Deliverables include the PowerPoint presentations given at the Fire Emissions 

Joint Forum Meetings, available at http://pah.cert.ucr.edu/aqm/308/meetings.shtml. 

FEJF Meeting in Las Vegas, NV, December 8, 2004: 

• Additional Fire Emissions Sensitivity Results in CMAQ 

• Example Stacked Bar Time-Series Plot 

FEJF Meeting in Idaho, Sept 9, 2004: 

• Preliminary Fire Emissions Sensitivity Results in CMAQ 

Additional results are available on the RMC web site at 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#fire. Results of analyzing the air 

quality model simulations are available on the RMC web site for the following 

comparisons: 

Monthly average spatial difference plots to examine source attribution fire 

emissions:

• Pre02f (natural+anthropogenic fire) vs. Pre02e (natural fire only) 

• Pre02f (natural+anthropogenic fire) vs. Pre02b (no fire) 

• Pre02f (natural+anthropogenic fire) vs. Pre02c (old fire) 

• Pre02e (natural fire) vs. Pre02b (no fire) 

Daily difference stacked-bar plots for extinction contribution: 

• Pre02f (natural+anthropogenic fire) vs. Pre02e (natural fire only) 

• Pre02f (natural+anthropogenic fire) vs. Pre02b (no fire) 

• Pre02e (natural fire) vs. Pre02b (no fire) 

Monthly average spatial difference plots for the three fire categories individually compared to no fire emissions: 

• Pre02b_wf02 (Wild Fires) vs. Pre02b (no fire) 

• Pre02b_rx02 (Prescribed burning) vs. Pre02b (no fire) 

• Pre02b_ag18 (Ag Burning) vs. Pre02b (no fire) 

Sensitivity of model to changes in vertical distribution of ag burning emissions, 

with half of surface layer emissions redistributed to upper layers in the model: 

• Pre02b_agmod (Redistributed vertical Ag Burning) vs. Pre02b_ag18 

(original Ag Burning) 
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Figure 12-1. (a) The July total fire emissions of carbon monoxide, used as an indicator of 

fire size and location; (b) the effect of combined wild fire, agricultural burning, and 

prescribed burning fire emissions on visibility, calculated as the July average. 

(a) 

(b) 

Log(tons/mo) 
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Figure 12-2. Two example plots of results showing effects of fire emissions on extinction 

coefficient at the Grand Canyon IMPROVE site. Top panel is for anthropogenic emissions; 

bottom panel is for natural emissions. 
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Figure 12-3. Monthly average for January through June (as labeled in each plot) showing 

effect of total fire emissions calculated as the difference of the Pre02f case with both 

natural and anthropogenic fire emissions minus the Pre02b case with no fire emissions. 
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Figure 12-4. Monthly averages for July through December (as labeled in each plot) 

showing effect of total fire emissions calculated as the difference of the Pre02f case with 

both natural and anthropogenic fire emissions minus the Pre02 case with no fire emissions. 
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Figure 12-5. Left panels show change in carbon monoxide emissions. Right panels shows 

the change in visibility from all aerosol species, as monthly average deciviews, for each fire 

sensitivity case: (b) July wild fires; (d); November prescribed burning; (f) November 

agricultural burning.. 

(a) 

(c) 

(e) 

(b) 

(d) 

(f) 
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Figure 12-6. Seasonal total change in carbon monoxide emissions for the Optimal Smoke 

Management emissions compared to the Base Smoke Management emissions. 
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Figure 12-7. Seasonal average change in deciviews showing effect of Optimal Smoke 

Management emissions compared to Base Smoke Management emissions. 
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Figure 12-8. Monthly average for January through June (as labeled in each plot) showing 

effect of Optimal Smoke Management compared to Base Smoke Management emissions. 
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Figure 12-9. Monthly average for July through December (as labeled in each plot) showing 

effect of Optimal Smoke Management compared to Base Smoke Management emissions. 
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Figure 12-10. (a) The July total natural fire emissions of carbon monoxide. Panels (b) 

through (d) show the effect of fire sensitivity simulations on visibility, calculated as deciviews and averaged for the month of July, as the difference of each fire sensitivity case 

minus base case (i.e., Pre02b) for (b) the natural fire sensitivity case; (c) the anthropogenic 

fire emissions, and (d) the combined natural and anthropogenic fire emissions. 

(a) 

(c) (d) 

(b) 
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13. Task 12: Preliminary Meteorological, Emissions, and Air 

Quality Modeling Activities for Alaska 

13.1 Introduction 

As explained in Section 1.1.1, EPA’s Regional Haze Rule calls for state and Federal agencies to 

work together to improve visibility in 156 Federally mandated Class I areas that include national 

parks and wilderness areas. The objective of the RHR is to achieve natural visibility conditions 

in these Federally protected lands by the year 2064. The rule requires that states develop and 

implement air quality SIPs and TIPs to reduce the pollution that causes visibility impairment. 

WRAP is charged with implementing regional planning processes to improve visibility in western U.S. Class I areas. 

The State of Alaska is developing a plan to protect visibility and comply with the intent of the 

RHR. Alaska poses some unique challenges for regional haze modeling. Geographically, it is far 

enough removed from the rest of the WRAP states that using a unified modeling domain would 

be quite large and inefficient. Also, Alaska has much lower emission densities than the other 

WRAP states (except during fire events), and there are longer transport distances between the 

various source regions and Class I areas. In addition, the influence of long-range (international) 

transport of pollutants from Eurasia may dominate regional haze events on occasion (this 

phenomenon is known as Arctic haze). 

There are four Class I areas in Alaska (Figure 13-1): 

• Denali National Park and Preserve 

• Tuxedni Wilderness Area 

• Simeonof Wilderness Area 

• Bering Sea Wilderness Area 

Both the Simeonof and Bering Sea Wilderness Areas are quite far away (>500 mi) from 

Anchorage and Fairbanks, the two largest cities in Alaska. However, the Tuxedni Wilderness 

Area and Denali National Park are close enough that they may be affected by emissions from, 

respectively, Anchorage and Fairbanks, as well as other emission sources in the region. 
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Figure 13-1. Locations of the four Class I areas in Alaska and of the two biggest 

Alaskan cities, Anchorage and Fairbanks. 

As part of the WRAP Alaska effort, the RMC is developing techniques for credible modeling of 

regional haze in Alaska, and providing an initial evaluation of the potential contributions to regional haze in the Alaska Class I areas that are more likely to be affected by in-state emissions: 

Denali National Park, Tuxedni Wilderness Area, and (less so) Simeonof Wilderness Area. (Due 

to its remoteness, the Bering Sea Wilderness Area will not be addressed in this initial modeling 

analysis). There are three main components of regional haze modeling: meteorological modeling, 

emissions modeling, and air quality modeling. 

The State of Alaska is developing a statewide emissions inventory for all sources. It is therefore 

premature to perform emissions modeling, as not all of the data are currently available in a 

suitable form. Consequently, it is also premature to perform photochemical grid modeling with a 

model such as CMAQ, since source-oriented models require complete emissions inventories. 

Thus, the approach in our WRAP Alaska modeling work is to develop meteorological modeling 

techniques to simulate the unique and complex meteorological conditions of Alaska, and to 

perform some preliminary air quality modeling using a simplified Lagrangian (trajectory) model 

to provide an initial assessment of the potential emissions contributions from Anchorage and 

Fairbanks, as well as major stationary sources, to visibility at Denali National Park and Tuxedni 

Wilderness Area. The Lagrangian model chosen for preliminary WRAP Alaska visibility 

modeling is the California Puff Model (CALPUFF) air quality modeling system. 

We are using MM5 to develop hourly meteorological fields for the Alaska region. In the initial 

modeling phase, MM5 has been applied to simulate two different and diverse episodes (i.e., a 
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summer and a winter period) to develop appropriate methods for running MM5 over Alaska, 

including Cook Inlet, the Alaska Range, and the region north of Fairbanks. The meteorological 

conditions of Alaska, described in Section 13.2.2, required some special consideration. 

We have developed a modeling protocol for the meteorological modeling component using 

MM5, as well as a brief outline of the air quality modeling component, and are proceeding with 

the meteorological component of the 2002 annual run. Section 13.2 describes the meteorological 

modeling approach. It overviews MM5 (Section 13.2.1) and the meteorology of Alaska (Section 

13.2.2), and discusses the MM5 configuration we chose (based on some preliminary sensitivity 

studies) and the procedure used to simulate the year 2002 meteorology (Sections 13.2.3 and 

13.2.4). Section 13.2.5 presents the plan for evaluating MM5’s performance in replicating the 

evolution of observed winds, temperature, humidity, and boundary layer morphology to the 

extent that resources and data availability allow; this will be the primary approach to assessing 

the reliability of the meteorological fields to make sure they adequately characterize the state of 

the atmosphere for input to CALPUFF. Sections 13.3 and 13.4 briefly discuss the emissions 

modeling and air quality modeling approaches. 

13.2 Meteorological Modeling Approach 

The CALPUFF modeling system includes the CALPUFF dispersion model and the diagnostic 

meteorological preprocessor CALMET (California Meteorological Model). CALPUFF requires 

inputs of 3-D gridded wind, temperature, humidity, cloud/precipitation, and boundary layer parameters. These fields are generated by CALMET, which can reprocess output fields from MM5 

and supply them in a format suitable for CALPUFF. MM5 is a state-of-the-science atmosphere 

model that has proven very useful for air quality applications and has been used extensively in 

past local, state, regional, and national modeling efforts. It has undergone extensive peer review, 

with all of its components continually undergoing development and scrutiny by the modeling 

community. The MM5 modeling system software is freely provided and supported by the 

Mesoscale Prediction Group in the Mesoscale and Microscale Meteorology Division of NCAR. 

For these reasons, MM5 is the most widely used public-domain prognostic model. In-depth 

descriptions of MM5 can be found in Dudhia (1993) and Grell et al. (1994) and at 

http://www.mmm.ucar.edu/mm5. 

13.2.1 Overview of MM5 

MM5 is a limited-area, terrain-following (sigma-coordinate), prognostic meteorological model. It 

solves the full suite of nonhydrostatic prognostic primitive equations for the 3-D wind, temperature, water, and pressure fields. It can be run with multiple one-way or two-way nested grids to 

resolve a range of atmospheric processes and circulations on spatial scales ranging from one to 

several thousands of kilometers. The model is highly modular, facilitating the interchange of 

physics and data assimilation options. Several options exist for boundary layer schemes; resolved 

and sub-grid-scale cloud and precipitation treatments; soil heat budget models; and radiative 

transfer. The model equations are solved horizontally on an Arakawa-B grid structure defined on 

a number of available map projections. The vertical coordinate is a terrain-following normalized 

pressure coordinate, referred to as “sigma-p.” Typically, 30 to 50 vertical levels are used to 

resolve the troposphere and lower stratosphere to ~15 km. 
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13.2.2 The meteorology of Alaska 

The meteorology of Alaska poses some unique and special challenges for MM5 modeling. In 

winter, there is little liquid water or insolation, and temperatures and specific humidities are 

extremely low north of the Alaska Range. In the northern part of the domain, there is extensive 

sea ice in the winter in the Bering Sea and Arctic Ocean; interactions between sea ice and the air 

above it have historically been difficult for models to simulate well, and the energy exchange 

between the sea ice and the overlying air is not well understood (Curry et al., 2001). In the dark 

of winter, the ice undergoes strong radiative cooling, which sets up a strong temperature inversion near the surface. This also occurs over the snow-covered inland areas from the Alaska 

Range north. This creates an extremely stable boundary layer, which can then decouple from the 

flow aloft. It is therefore possible to have air masses with different origins and properties superimposed in the vertical. This can be significant in terms of air quality modeling if the overlying 

air mass has different chemical properties than the air below it. In addition, it is difficult for an 

atmosphere model to handle this type of extremely stable boundary layer well, as turbulent flows 

in stable layers are not well understood (Mahrt, 1998). These stable layers can be broken up by 

intermittent turbulence, sometimes arising from gravity-wave generation over the Alaska Range. 

These events are notoriously difficult for an atmosphere model to simulate (Jeff Tilley, University of North Dakota, personal communication, 2004). 

MM5-modeled temperature fields are very sensitive to the cloud field. Small errors in cloud 

cover can produce large errors in the temperatures. Because clouds can be a sub-grid-scale phenomenon, errors in characterization of the cloud field are inevitable, and may lead to significant 

temperature errors, particularly in winter. For example, in a region with strong radiative cooling 

over sea ice, if the model places a cloud where there is none in the real world, then the outgoing 

longwave radiation trapped by the modeled cloud and re-emitted downward will cause a spurious 

warming below the cloud. This will cause the model to overestimate the surface temperature and 

underestimate or entirely fail to produce the inversion that should be there. This may have serious implications for subsequent air quality modeling. 

Further complicating the characterization of the cloud field is the fact that some Arctic clouds 

have unusual properties. There are clouds composed of ice crystals that can extend down to the 

ground, (known as “diamond dust”). Multiple thin cloud decks may appear in the statically stable 

atmosphere, and convective plumes may appear in gaps in the sea ice (Curry et al., 2001). All of 

these phenomena may be difficult or impossible to capture in a model running at a maximum 

resolution of 15 km. 

Another issue is that Alaska is so cold in winter that the some of the physical assumptions underpinning the parameterizations of moist processes may no longer be valid. MM5 has a POLAR 

option (Cassano et al., 2001) for use at high latitude that attempts to compensate for this 

problem. 

Given the low population density and often harsh conditions, it is not surprising that the observing network in Alaska is sparse compared to that of the U.S. mainland. This complicates model 

validation, particularly in a 15-km-resolution domain, where representative observations may be 

very widely spaced if one or more observing sites are down (Jeff Tilley, personal communication, 2004). The rugged Alaskan terrain can also cause problems in model validation. Many 
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stations are located in regions where meso-gamma and microscale effects forced by topography 

can form a significant part of the signal in temperature and winds (Jeff Tilley, personal communication, 2004). 

In the spring and fall, the model must account for the breakup and formation of sea ice. Sea ice 

has a large effect on the surface energy budget, and a mischaracterization of the extent of sea ice 

will degrade MM5’s simulation of the thermal structure of the overlying atmospheric column. 

13.2.3 MM5 configuration 

The WRAP MM5 modeling system configuration for the Alaska application is based on the work 

of the Mesoscale Modeling and Applications Group at the University of Alaska Fairbanks 

(UAF). The UAF group has extensive experience with operational numerical weather prediction 

in high latitudes using MM5 modeling (http://knik.iarc.uaf.edu/AtmGroup). We are building on 

their experience by incorporating some facets of their MM5 configuration for use in our Alaska 

application. 

13.2.3.1 Modeling domain 

We have configured MM5 to run on two grids: a large-scale grid with 45-km grid point spacing, 

and a smaller, nested grid with 15-km grid point spacing focused on the Class I areas (Figure 

13-2). The model is run on both grids simultaneously, but in a “one-way” nesting mode. In this 

approach, information from the 45-km grid is transferred to the 15-km domain through boundary 

conditions during the simulation, but there is no feedback of the 15-km fields up to the 45-km 

domain. Another alternative is “two-way” nesting, wherein MM5 is run for both grids simultaneously and information propagates both down- and up-scale between the two. Our experience 

suggests that the choice of one-way versus two-way nesting does not significantly affect model 

performance for regional applications with coarse grid spacing. 

Our 45-km Alaska Grid is defined on a polar stereographic projection, with central latitude 59o N 

and central longitude 151o W. The grid has 109 (east-west) by 90 (north-south) dot points, and 

108 (east-west) by 89 (north-south) cross points. The 15-km subregional grid is defined on the 

same polar stereographic projection, but covers the populated areas and Class I areas (see in 

Figure 13-2) with 15-km grid point spacing. 
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Figure 13-2. Spatial coverage of the Alaska Grid with 45-km grid point 

spacing (D01) and the nested 15-km grid (D02). 

13.2.3.2 Physics options and FDDA 

This section details the rationale behind our choices of physics options and FDDA for the Alaska 

MM5 modeling effort. As mentioned above, the 2002 annual MM5 application has been configured in many aspects according to the optimum arrangement identified by the UAF group. We 

have also used some information from additional sensitivity tests specific to this application; 

these tests are discussed below. 

When modeling a full year over the Alaska domain, the annual cycle of sea ice must be taken 

into account. The sea ice, which forms part of the atmosphere’s lower boundary condition, grows 

in spatial extent during fall and winter, and retreats during the spring and early summer. MM5 

has an option whereby the sea ice fraction in a grid cell is diagnosed using the sea surface temperature, and this flag must be used during the winter months to ensure adequate treatment of the 

atmosphere’s lower boundary condition. Use of the sea ice option limits the available physics 

options in MM5, however, as it requires the use of the five-layer land surface model. Thus, for 

winter modeling, we use the sea ice option, the five-layer LSM, and the Eta PBL scheme. During 

summer, however, we turn off the sea ice option, which allows us to choose a more detailed land 

surface model. The NOAH LSM has been shown to be effective in simulating the Alaskan summer when used with the Eta PBL scheme (Jeff Tilley, personal communication, 2004), so we use 

this configuration as our starting point. Note that although the LSM/PBL/sea ice configuration 

changes in going from summer to winter, the rest of the physics options are held constant 

throughout the year. 
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To treat stratiform clouds, we use the Reisner 2 cloud microphysics scheme (Reisner et al., 

1998). Although this scheme is more computationally intensive than the “simple ice” option, 

EPA recommends that a mixed-phase ice scheme be employed in MM5 to drive aqueous chemistry and wet scavenging in CMAQ. This is because the simple ice approach treats all condensed 

water forms as a single liquid variable, which when passed to CMAQ overstates the quantity of 

liquid cloud and precipitation water available for chemistry and removal. Furthermore, the next 

version of CMAQ will include separate contributions from precipitating ice (graupel). Although 

the initial Alaska visibility modeling will be done with CALPUFF, subsequent air quality 

modeling of Alaska may be done with CMAQ, so we selected the Reisner 2 cloud microphysics 

scheme to allow the use of CMAQ at a later date. For cumulus convection, we selected the Grell 

scheme, which has been used successfully by the UAF group. Cumulus convection is not 

expected to play a large role in the Alaska simulation during winter, but significant convection 

can occur in the summer, particularly in the Denali area. Convection in this region is driven 

largely by topographic/land surface forcings (Jeff Tilley, personal communication, 2004). Sensitivity tests regarding the cumulus parameterization may be carried out at a later date, depending 

on model performance. 

Next, we needed to select a radiation scheme and an FDDA configuration. We performed several 

sensitivity tests to determine the appropriate configuration. These tests are summarized in Table 

13-1. Below is listing of the various schemes tested in this study and an explanation of the 

abbreviations used in the table.

• Land Surface Models: NOAH LSM; Pleim-Xiu LSM (PX); five-layer LSM

• Planetary Boundary Layer Schemes: Eta PBL; Pleim-Xiu PBL (PX)

• Cumulus Scheme: Grell 

• Radiation Schemes: Rapid Radiative Transfer Model (RRTM); Community Climate 

Model 2 (CCM2); CLOUD 

• IPOLAR: Y = sea-surface temperatures (SSTs) vary with time; N = SSTs do not vary 

with time

• IEXSI: 0 = no sea ice fraction information; 1 = sea ice fraction is diagnosed from SST 

• Cloud Microphysics Scheme: Reisner 2

• Four-Dimensional Data Assimilation: W = wind; T = temperature; RH= relative humidity

Table 13-1. Summary of MM5 configuration for sensitivity tests. 

Run ID* LSM PBL Cumulus 

Radiation IPOLAR IEXSI Microphysics 

 Analysis FDDA 

 3-D Surface 

Obs 

FDDA

Run_S1 NOAH Eta Grell RRTM Y 0 Reisner 2 W/T/RH W/T/RH None 

Run_S2 NOAH Eta Grell CCM2 Y 0 Reisner 2 W/T/RH W/T/RH None 

Run_S4 PX PX Grell RRTM Y 0 Reisner 2 W/T/RH W/T/RH None 
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Run ID* LSM PBL Cumulus 

Radiation IPOLAR IEXSI Microphysics 

 Analysis FDDA 

 3-D Surface 

Obs 

FDDA

Run_W1 5-layer Eta Grell RRTM Y 1 Reisner 2 W/T/RH W/T/RH None 

Run_W1b 5-layer Eta Grell RRTM Y 1 Reisner 2 W/T/RH W/T/RH W 

Run_W2 5-layer Eta Grell CCM2 Y 1 Reisner 2 W/T/RH W/T/RH None 

Run_W3 5-layer Eta Grell CLOUD Y 1 Reisner 2 W/T/RH W/T/RH None 

*In the run IDs, “S” stands for “summer” (July) and “W” stands for “winter” (January)

The results of the tests are discussed below. As in Section 4, we organize our analysis discussion 

around soccer plots, which display average performance statistics for a particular modeled time 

period. Soccer plots are shown for wind speed bias versus wind speed RMSE, temperature bias 

versus temperature error, and humidity bias versus humidity error. In each plot, a solid blue line 

indicates the benchmark. The benchmark was generated by considering the performance statistics from approximately 50 MM5 and RAMS simulations performed in support of air quality 

modeling studies in the continental U.S. (Tesche et al., 2002). A soccer plot, therefore, places the 

current MM5 model run in the context of prior meteorological databases used for air quality 

modeling. A data point that falls inside the box represents a model run that meets the performance benchmark. Perfect model performance is indicated by a data point at (0,0). The closer a 

data point is to the origin, the better the model’s performance. We emphasize that the benchmarks are not used as acceptance/rejection criteria of the MM5 model simulation. Rather, they 

put the MM5 model performance into perspective and allow the identification of potential 

problems in the MM5 fields. Furthermore, the benchmarks are mainly for ozone episodes in the 

continental U.S., which typically occur in meteorological conditions very different from those 

found in Alaska. 

Most prior modeling efforts in Alaska have used the RRTM or CCM2 radiation schemes. They 

showed that RRTM performed better in some cases while CCM2 did better in others, and there 

was no guidance to determine a priori which would be the best choice for this application (Jeff 

Tilley, personal communication, 2004). We tested three MM5 radiation schemes: CCM2, 

RRTM, and CLOUD. The CLOUD radiative package was widely used before the advent of the 

RRTM scheme, and has a simpler treatment of the longwave absorption than RRTM, which uses 

the correlated-k method of calculating absorption coefficients. The shortwave treatments in 

CLOUD and RRTM are similar. We tested the CLOUD scheme in order to have a range of 

complexity of parameterization when evaluating the sensitivity of the Alaska simulation to the 

choice of radiation scheme. We ran MM5 for a five-day period in July and a five-day period in 

January to span the range of climatological conditions the model was likely to encounter in the 

annual 2002 run. We examined the results on both the 15-km and 45-km domains. 

The surface wind performance for the three runs testing the different radiation schemes is shown 

in Figure 13-3. Runs W1_45km, W2_45km, and W3_45km are otherwise identical 45-km January runs that differ only in the radiation scheme used (the W1b January run, designed to test the 

effect of observational nudging, is discussed later). The winter runs are shown by blue symbols 

without borders. The corresponding 15-km model runs are shown by blue symbols with black 

borders. The summer runs (S1 andS2) are displayed using the same convention for 45- and 15-
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km runs, but are shown in red*

 (the S4 July run, designed to test the effect of LSM scheme, is 

discussed later). 

Alaska: 45 km and 15 km Wind Performance 

0

0.5

1

1.5

2

2.5

3

3.5

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Wind Speed Bias (m/s)

WindSpeed RMSE (m/s)

Benchmark W1 45 km W2 45 km W3 45 km S1 45 km S2 45 km W1 15 km

W2 15 km W3 15 km W1b S1 15 km S2 15 km S4 15 km

Run 1: RRTM

Run 2: CCM2

Run 3: CLOUD

Figure 13-3. Wind soccer plot for winter and summer Alaska sensitivity tests. 

A comparison of winter Runs W1_45km through W3_45km shows that there is not much difference among the three radiation schemes in terms of model performance; the three points are 

tightly clustered and lie outside the benchmark, with a slight low wind speed bias. At 15 km, the 

three winter runs (Runs W1_15km, W2_15km, W3_15km) were again quite similar in terms of 

performance, and there was a stronger low wind speed bias on the higher-resolution 15-km grid.

Figure 13-4 displays the temperature performance of the winter runs W1 through W3 and the 

summer runs S1 and S2. Most of the runs fell within or near the benchmark for bias, but outside 

the benchmark for error. In the 45-km series of winter runs, the RRTM (Run W1_45km) and 

CLOUD (Run W3_45km) runs were comparable, but the CCM2 (Run W2_45km) run had a cold 

bias that was nearly twice as large as those of RRTM and CLOUD. There was little difference in 

the error among the three runs. In winter at 15 km, the bias changed sign and increased somewhat for RRTM and CLOUD, and the error decreased slightly. For CCM2, however, bias and 

error both increased. 

 

*

Note that the summer runs used different LSM schemes, a different IEXSI setting, and, in the case of run S4, a 

different PBL scheme than the winter runs (see Table 13-1), so the summer and winter runs are not directly 

comparable with each other for examining the effects of the radiation schemes. 
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Alaska: 45 and 15 km Temperature Performance 

0

0.5

1

1.5

2

2.5

3

3.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Temperature Bias (K)

Temeprature Error (K)

Benchmark W1 45 km W2 45 km W3 45 km S1 45 km

S2 45 km W1 15 km W2 15 km W3 15 km W1b 15 km

S1 15 km S2 15 km S4 15 km

Run 1: RRTM

Run 2: CCM2

Run 3: CLOUD

Figure 13-4. Temperature soccer plot for winter and summer Alaska sensitivity tests. 

In the summer 45-km runs, there was not much difference in temperature between the RRTM 

(Run S1_45km) and CCM2 (Run S2_45km). At 15 km, both bias and error in the CCM2 run 

(Run S2_15km) were reduced to the point where they lay within the benchmark. In the RRTM 

run (Run S1_15km), on the other hand, the bias decreased but error increased. 

The humidity soccer plot (Figure 13-5) shows all runs in summer and winter tightly clustered 

within the benchmark. Humidity performance is quite good relative to the benchmark, but it is 

worth noting again that the benchmark is based on MM5 and RAMS simulations of regions in 

the continental U.S. In these regions, the specific humidities are generally higher than in Alaska, 

and the fact that the Alaska runs fall within the benchmark may be a reflection of the lower 

Alaskan humidity rather than an indication of good model performance. Since the humidity 

performance was consistent across the sensitivity tests, humidity was not a useful metric for 

selecting a radiation scheme. 
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Alaska: 45 km and 15 km Humidity Performance 

0

1

2

3

4

5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

Humidity Bias (g/kg)

Humidity Error (g/kg)

Benchmark W1 45 km W2 45 km W3 45 km S1 45 km

S2 45 km W1 15 km W2 15 km W3 15 km W1b 15 km

S1 15 km S2 15 km S4 15 km

Run 1: RRTM

Run 2: CCM2

Run 3: CLOUD

Figure 13-5. Humidity soccer plot for winter and summer Alaska sensitivity tests. 

Based on the surface wind and temperature performance, the CCM2 scheme did not sufficiently 

improve MM5 performance to justify its added computational burden (~20%), so we selected the 

RRTM parameterization to be the radiation scheme for summer and winter. 

In the next sensitivity test we looked at the choice of LSM for the summer months. Recall that in 

winter, we must use the five-layer LSM in order to turn on the sea ice parameterization. In summer, however, we are free to select the best-performing LSM scheme for this application. The 

Pleim-Xiu scheme has been shown to perform well over the continental U.S. (Olerud and Sims, 

2003; Kemball-Cook et al., 2004) and also allows use of the Pleim deposition scheme in subsequent CMAQ modeling; we therefore made a run (S4) using the Pleim-Xiu scheme and the 

RRTM radiation scheme for the July time period. The 15-km results for wind (Figure 13-3) show 

a reduction in the low wind speed bias relative to the other 15-km summer runs, and little change 

in the RMSE. For temperature (Figure 13-4), the bias increased and the error decreased relative 

to the other 15-km run that used the RRTM radiation scheme (Run S1_15km). The humidity 

soccer plot (Figure 13-5) shows a change of sign in the bias, and a small increase in error. Overall, the Pleim-Xiu scheme did not cause significant improvement, so we elected to use the 

NOAH/Eta LSM/PBL combination for summer. 

In summary, Tables 13-2 and 13-3 give the MM5 physics options we used for the winter and 

summer months, respectively. 
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Table 13-2. Physics options selected for the 2002 

WRAP winter Alaska MM5 simulation. 

Physics Option Parameterization 

Cloud microphysics Reisner 2 

Cumulus parameterization Grell 

Planetary boundary layer Eta 

Land surface model Five-layer Model 

Radiation RRTM 

Shallow convection None 

Varying SST with time? (IPOLAR) Yes 

Sea ice (IEXSI) Yes 

Snow cover Simple Snow Model 

Table 13-3. Physics options selected for the 2002 

WRAP summer Alaska MM5 simulation. 

Physics Option Parameterization 

Cloud microphysics Reisner 2 

Cumulus parameterization Grell 

Planetary boundary layer Eta 

Land surface model NOAH

Radiation RRTM 

Shallow convection None 

Varying SST with time? (IPOLAR) Yes 

Sea ice (IEXSI) No 

Snow cover No 

Regarding FDDA capabilities to use in the Alaska simulations, we configured MM5 to use 

FDDA to nudge the model toward observed wind, temperature, and moisture fields on both the 

45-km and 15-km grids throughout the 2002 annual simulation. Specifically, analysis (or grid) 

nudging was performed at three-hourly intervals for both the 2-D surface fields and the 3-D 

fields aloft, excluding the boundary layer depth. Excluding the boundary layer in the FDDA 

process removes the potential for damping out resolved mesoscale forcings in the model that are 

important to boundary layer development and thus the vertical fluxes of momentum, heat, and 

moisture into the free atmosphere and to the surface. Two-dimensional surface nudging was 

based on NCAR/National Centers for Environmental Prediction (NCEP) Reanalysis Project 

(NNRP) surface analyses, but were enhanced via the “little_R” program using hourly NWS 
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surface observations from NCAR dataset ds472.*

 The analysis nudging coefficients we chose to 

use are shown in Table 13-4. 

Table 13-4. FDDA analysis nudging coefficients (s-1). 

Data Type 45 km 15 km 

Wind 2.5×10-4 1.0×10-4

Temperature 2.5×10-4 1.0×10-4

Water vapor 1.0×10-5 1.0×10-5

In addition to analysis nudging, FDDA may be used to nudge the model solution toward observational data at individual measurement sites (i.e., “station” or “observational” nudging). Usually 

this option is best suited for smaller, high-resolution grids in which data from a very dense 

network of measurement sites are available (such as an intensive field study). The next sensitivity test was designed to test the effect of observational (obs) nudging using the ds472 surface 

meteorological observation network (Figure 13-6). Obs nudging of the winds was performed for 

the winter W1 configuration (Run W1b in Table 13-1), and the effect on the surface wind speed 

performance is shown in Figure 13-3. With obs nudging turned on, the low wind speed bias 

increased and the RMSE decreased relative to the comparable un-nudged run W1. For temperature (Figure 13-4) and humidity (Figure 13-5), the difference in performance between runs W1 

and W1b is nearly indistinguishable. Although this test of nudging was not conclusive, we 

elected to proceed with observational nudging of winds. 

 

*

 Note that the NNRP analysis fields are used instead of the usual EDAS fields because Alaska falls out of the 

EDAS domain. 
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Figure 13-6. Stations contributing data used for observational nudging. 

13.2.4 Procedure to simulate the year 2002 

Once we had selected model configurations for summer and winter that would ensure reasonable 

model performance in both seasons, the annual simulation for the year 2002 was begun. The 

January-June 2002 time period of the simulation was done at UCR and July-December 2002 was 

done at ENVIRON. The 2002 annual simulation was made in sequential five-day run segments, 

each with an initial spin-up period of 12 h that overlaps the last 12 h of the preceding run. This is 

done so that the air quality model can be started at either 00Z or 12Z without including the MM5 

re-initialization period. MM5 was re-initialized at the beginning of each five-day period to 

reduce error propagation through the simulation. The 2002 annual simulation included the final 

two weeks of December 2001 to allow sufficient spin-up time for photochemical/visibility 

applications with start dates at the beginning of January 2002. The model was run with 90-s and 

30-s time steps on the 45-km and 15-km grids, respectively. The MM5 annual run for 2002 is 

now complete, and analysis of the results is underway. 

13.2.5 Evaluation procedures for the 2002 annual run 

13.2.5.1 Overview 

The goal of the evaluation is to determine whether the meteorological fields are sufficiently 

accurate to properly characterize the transport, chemistry, and removal processes in CALPUFF. 
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If errors in the meteorological fields are too large, the ability of the air quality model to replicate 

regional pollutant levels over the entire base year will be compromised. To provide a reasonable 

meteorological characterization to the photochemical/visibility model, MM5 must represent the 

following with some fidelity: 

• Large-scale weather patterns (i.e., synoptic patterns depicted in the 850-300–mb height 

fields), as these are key forcings for mesoscale circulations 

• Mesoscale and regional wind, temperature, PBL height, humidity, and cloud/precipitation 

patterns 

• Mesoscale circulations such as sea breezes and mountain/drainage circulations 

• Diurnal cycles in PBL depth, temperature, and humidity 

For visibility applications, the moisture and condensate fields are particularly important, as they 

significantly impact PM chemical formation, removal, and light scattering efficiency. In addition, cloud and precipitation fields are a good measure of the integrated performance of the 

model because these are model-derived quantities and not nudged to observations. Because of 

the coarse resolutions of 45 and 15 km, the model cannot be expected to faithfully simulate the 

pattern or variability of the convective precipitation, but should reproduce the synoptic precipitation and cloud patterns. 

In this study, the basis for the performance assessment is a comparison of the predicted 

meteorological fields with available surface and aloft data that are collected, analyzed, and disseminated by NWS. This is being carried out both graphically and statistically to evaluate model 

performance for winds, temperature, humidity, and the placement, intensity, and evolution of key 

weather phenomena. For example, Figure 13-7 shows a comparison of modeled and observed 

upper-air soundings for a weather station in the Alaska domain. A specific set of statistics has 

been identified for use in establishing benchmarks for acceptable model performance (Emery et 

al., 2001); these benchmarks, similar to current EPA guidance criteria for air quality model performance, are intended to allow for a consistent comparison of various meteorological simulations for important variables at the surface and in the boundary layer. ENVIRON has developed 

a statistical analysis software package, METSTAT, to calculate and graphically present the 

statistics described above. 

13.2.5.2 Surface statistical analyses 

The statistical evaluation of the Alaska MM5 surface fields using NCAR dataset ds472, which 

contains hourly observations of the commonly measured variables from airports in the U.S. and 

Canada. The network of ds472 stations used in the Alaska MM5 evaluation is shown in Figure 

13-8. Variables in the ds472 data set include temperature, dew point, wind speed/direction and 

gusts, cloud cover fraction and cloud base for multiple cloud layers, visual range, precipitation 

rates and snow cover, and a descriptive weather code. The key data of interest were extracted for 

the modeled domain, and processed into the appropriate formats for METSTAT that is used to 

generate time-series plots of observed and modeled surface wind, temperature, and humidity and 

relevant performance statistics. Example time series of observed and estimated temperature 

performance for a preliminary Alaska MM5 simulation for January 2002 are shown in Figure 

13-9. 
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Figure 13-7. Observed (black) and modeled (red) temperature and dew point soundings 

for a sample weather station (BRW) in the Alaska domain for July 3, 2002. 
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-175 -170 -165 -160 -155 -150 -145 -140 -135 -130 -125

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Figure 13-8. NCAR ds472 surface observing network stations in the 45-km Alaska domain. 
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Figure 13-9. Example of METSTAT graphics from a WRAP Alaska 

January 2002 sensitivity test. 
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As described above, care must be taken in selecting an area for averaging when calculating 

model performance statistics. One problem with average evaluation statistics is that the more 

data pairings a given metric includes, the better the statistic generally looks, so calculating a 

single set of statistics for a very large area would not yield significant insight into performance. 

Therefore, a balance must be struck between taking an area large enough to create a 

representative sample but not choosing such a large area as to smear out the signal of interest. 

For Alaska, the observing network is sufficiently sparse that we must average over the entire 

domain or else we may not have enough data points to form a meaningful average. However, 

special attention needs to be given to the meteorological model performance near the Class I 

areas of interest.

The METSTAT statistical package is used to calculate hourly and daily statistical measures, and 

compare them against the Emery et al. (2001) benchmarks (see Section 13.2.5.1) for acceptable 

model performance. Potential reasons for any poor statistical performance for particular 

variables, times, and/or subregions are being investigated and documented in an evaluation 

report. 

13.2.6 CALMET modeling 

To generate the 3-D gridded wind, temperature, humidity, cloud/precipitation, and boundary 

layer parameters required by CALPUFF for air quality modeling, the CALMET meteorological 

preprocessor was used. As we noted earlier, CALMET can reprocess output fields from MM5 

and supply them in a format suitable for CALPUFF. The 2002 MM5 output discussed in Section 

4 was processed using the CALMM program to create files of the format and with the variables 

needed by CALMET. CALMET was run initially using the MM5 data as input in the NOOBS 

mode; this mode uses the MM5 data only, without any surface or upper-air observations. The 

feasibility of running with meteorological observations in addition to the MM5 data is being 

investigated along with the evaluation of the use of 15-km versus 5-km grid resolution for 

CALMET/CALPUFF modeling. 

13.3 Emissions Modeling Approach 

In June 2004, the Alaska Department of Environmental Conservation (ADEC) submitted 2002 

annual emissions for major point sources and railroad emissions to EPA for inclusion in EPA’s 

initial 2002 NEI. These data are undergoing corrections and will be resubmitted in the future. 

Previously, ADEC submitted 1999 criteria pollutant inventories for Anchorage, Fairbanks, and 

Juneau for all source categories (point, area, and mobile) that are included in the EPA 1999 NEI 

Version 2 (NEI99 v2). Day-specific 2002 emissions for major point sources, such as EGUs, are 

available from EPA’s Acid Rain Database (ARDB). ADEC intends to have additional 2002 

emissions data ready by the March-April 2005 time frame. Also, WRAP has contracted to obtain 

aviation and rural area emissions for Alaska that should be ready in spring 2005. 

The Alaska air quality sensitivity modeling was initiated in late 2004 and will be completed 

during 2005. The following approach is being used to develop an Alaskan emissions inventory 

for sensitivity modeling: 
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• Acquire (1) the latest 2002 point-source and railroad emissions inventories and (2) the 

1999 Anchorage, Fairbanks, and Juneau criteria pollutant emissions inventories for the 

three main Alaska urban areas. 

• Download the Alaska 2002 major-point-source emissions from the ARDB. 

• Combine the 2002 ARDB, 2002 point-source and railroad, and 1999 criteria pollutant 

emissions databases, eliminating duplicate sources by keeping the source characteristics 

according to the following priorities: 2002 ARDB, 2002 point/railroad, and 1999 criteria. 

• Process emissions into CALPUFF format in separate files by major source categories, 

such as major point sources, minor point sources, railroads, on-road mobile sources, and 

other sources. 

13.4 Air Quality Modeling Approach 

As discussed at the beginning of Section 13.2, preliminary air quality modeling using the 2002 

MM5 meteorological fields for Alaska, processed first through CALMET (see Section 13.2.6), 

will be conducted using CALPUFF, a Lagrangian (trajectory) puff model. This model is typically used to simulate the concentration and deposition impacts due to a source or group of 

sources. Species that CALPUFF treats include primary PM (e.g., PM2.5, PM10, EC, and OC), 

SO2, NOx, and secondary sulfate and nitrate PM. We will use the model to simulate the effect of 

primary emissions of PM, NOx, and SO2 on atmospheric PM (e.g., primary PM and SO4 and 

NO3) in Alaska, including the three Class I areas in the Alaska CALMET/CALPUFF modeling 

domain. Separate CALPUFF simulations will be made by major source category, and the results 

will be summed by CALPOST to estimate visibility impairment at the Class I areas. 

At this time we are not proposing to use a more comprehensive photochemical grid model, such 

as CMAQ, to simulate air quality in Alaska because such models require emissions data from all 

sources, which as noted in Section 13.3 are not yet available for Alaska. The CALPUFF air 

quality modeling of Alaska is ongoing and will be completed in 2005. 

13.5 Status of Task 12 Deliverables 

Table 13-5 gives the status of each Task 12 deliverable listed in the 2004 WRAP RMC work 

plan, plus any additional deliverables agreed upon after the work plan was prepared. 

Table 13-5. Status of the Task 12 deliverables. 

Deliverable Status 

Conduct 2002 MM5 sensitivity runs for January and July 2002 Completed December 2004 

Conduct 2002 annual MM5 simulations Completed February 2005 

Initiate CALMET/CALPUFF runs To be completed in 2005 
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14. Task 13: Training Courses for the WRAP States and 

Tribes 

14.1 Work Performed from March 2004 through February 2005 

Training needs were uncertain at the beginning of 2004, so a contingency fund was included in 

the 2004 funding to allow training to be conducted as needed. Basic training in CMAQ and 

SMOKE is routinely being provided through the Community Modeling and Analysis System 

(CMAS) Center, which is hosted at UNC-CEP. Since this is the most efficient method to provide 

basic SMOKE/CMAQ training, it was determined that the RMC would provide “advanced 

training.” Based on discussions with WRAP members, we developed a one-day training class for 

managers (i.e., non-modeling staff) that provides an introduction to the technical aspects of the 

modeling program, including definition of terminology, an introduction to the modeling system, 

and explanations of the products from the modeling activity and how to access them. A major 

goal of this class is to enhance the ability of managers to present and explain modeling results to 

their constituents. 

The content of the training was selected after extensive discussions with WRAP members. An 

initial one-day training class was conducted in conjunction with the Attribution of Haze meeting 

held in September 2004. Additional training classes will be offered as needed. 

During 2004, another training opportunity emerged: training to assist staff at state and tribal air 

pollution agencies in setting up and operating the visibility modeling system, including the 

aspects of hardware, operating systems, and installation and operation of models and datasets 

and the associated computers. We are engaged in several activities related to this need. For 

example, we are producing a frequently asked questions (FAQ) sheet that explains how to install 

an operating system and the modeling system on a Linux PC, and includes recommendations for 

computer hardware appropriate for the modeling system users’ needs. This FAQ sheet will be 

posted on the project web page. 

After providing some assistance to one state in setting up a specialized operating system for 

managing parallel computers, we determined that the best use of RMC resources would be to 

limit the assistance we provide to supporting a single, standard installation of Linux and the 

modeling system. 

The original funding budgeted for training and technology transfer in the 2004 RMC work plan 

was $85,000, with activities to be defined depending on training needs during 2004. Per 

agreement with the WRAP Modeling Forum, some of these funds were rebudgeted for other 

activities. On July 30, 2004, we rebudgeted $20,000 to perform additional MM5 testing and 

sensitivity simulations to improve the meteorology data used in the air quality modeling. The 

additional MM5 testing was a high priority because the MM5 performance in the WRAP region 

was inferior to that in other regions of the United States. The additional MM5 sensitivity testing 

resulted in new parameters for the MM5 configuration that substantially improved MM5’s 
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performance in the WRAP states. In a September 2004 conference call, we rebudgeted an 

additional $20,000 to purchase new computer equipment, including additional RAID5 disk 

storage and parts to build eight additional dual-CPU compute nodes. UCR charged $25,000 for 

training activities in 2004. At the end of December 2004, UCR carried over a balance of 

$100,420, which included funding of $20,000 budgeted for training and technology transfer. 

14.2 Status of Task 13 Deliverables 

Table 14-1 gives the status of each Task 13 deliverable listed in the 2004 WRAP RMC work 

plan, plus any additional deliverables agreed upon after the work plan was prepared. 

Table 14-1. Status of the Task 13 deliverables. 

Deliverable Status 

Develop material for a one-day 

introductory training class 

Completed. Training agenda and PowerPoint files are available at 

http://pah.cert.ucr.edu/aqm/308/reports/training/Model_Training.htm

Offer first class in Salt Lake 

City in September 2004 

Completed. 

Provide support in computer 

systems configuration to 

Arizona DEQ 

Initial support task complete. Additional support available as needed. 

Develop a hardware and 

software FAQ sheet to be 

accessed on the RMC web site 

In progress; to be updated periodically. 
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15. Summary of Work for March 1, 2004, through February 

28, 2005 

15.1 Task 0.5: 2002 Ammonia Emissions Inventory for WRAP Region 

The RMC has developed an ammonia emissions inventory that includes livestock operations, 

fertilizer application, native soils, domestic sources, and wild animals, to be used for gridded 

modeling in the WRAP region. The inventory was created using the improvements and estimation methodologies described in Section 2 and documented in detail in Chitjian and Mansell 

(2003a,b). These inventory enhancements were associated with the effects of environmental 

parameters on the emission factors, and with the temporal and spatial allocation of NH3 emissions for several source categories. The improvements were based on the results of a literature 

survey of recent research in NH3 emissions inventory development. We developed a GIS-based 

NH3 emissions modeling system and applied it for calendar year 2002 to generate a gridded, 

hourly emissions inventory for the WRAP modeling domain at a spatial resolution of 36 km. The 

data sources and environmental factors used in the inventory improvements were presented 

previously and in more detail in the draft task report (Mansell, 2004a). 

On a regional scale, the 2002 NH3 emissions inventory is dominated by fertilizer application and 

livestock operation emissions. This result is entirely consistent with the current understanding of 

NH3 emission within the air quality and emissions modeling community. Ammonia emission 

from native soils, while a major component of the overall inventory, remains highly uncertain. 

This uncertainty arises mainly from the current differences of opinion in the research community 

with respect to whether soils act as a source or a sink of NH3. Ammonia emission from domestic 

sources, while only a small contributor to the total regional NH3 inventory, can be a major source 

of emissions on smaller, urban scales. The wild animal NH3 emissions are only a small portion of 

the inventory. 

Section 2.3.6 compares the WRAP NH3 inventory with similar results obtained from the CMU 

Ammonia Model; this is discussed in greater detail in Mansell (2004a). Based on state-level 

comparison, the results of this task are seen to be consistent with existing NH3 emissions 

inventories. Evaluation of the differences and similarities, and a further investigation of the two 

inventories for detailed source categories at the county level, may provide useful insight into 

potential improvements and enhancements to the inventory and to the emissions modeling 

system. 

Based on the results of this task, we make a number of recommendations (see Section 2.4.2) 

regarding improvements in data quality and refinements to the NH3 inventory and emissions 

modeling system. 
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15.2 Task 1: Project Administration 

The major activities in this task included: 

• Communication with WRAP to define the scope of work, approach and specific tasks, 

and deliverables, and to communicate project results 

• Coordination among the three contractors (one prime contractor, two subcontractors) 

• Computer systems administration, including computer hardware, software, project web 

site, and mailing lists 

• Contract administration 

Because of the evolving and sometimes uncertain nature of the regulatory framework and 

guidance for visibility SIPs and TIPs, extensive discussion is required to carefully define the 

scope of work and deliverables. Moreover, because the RMC includes three different contractors 

who receive data from several WRAP contractors and WRAP forums, the project requires 

frequent communication. Project communication and coordination is handled through monthly 

conference calls with the WRAP modeling forum cochairs on the second Monday of each month, 

and through a conference call with the WRAP Modeling Forum on the following Monday of 

each month. Additional conference calls are scheduled as needed, typically at least one call per 

week, for detailed follow-up on specific tasks. Progress and refinement of task definitions are 

also frequently discussed by e-mail and phone communications. 

Computer systems for the RMC are maintained at UCR. ENVIRON and UNC staff have 

accounts on these computer systems so that all project staff can perform simulations on the RMC 

computers. Because there are “down times” between model simulations, computer resources 

funded by WRAP and other agencies can be shared. This provides an important efficiency in the 

use of computer resources and allows WRAP to benefit by having access to a larger number of 

computers when WRAP simulations are being performed. 

The RMC web site and e-mail listserv are the primary method of communicating project results. 

These are updated regularly. 

Finally, contract administration is also a complex and time-consuming activity that involves 

contract administration staff at WGA, UCR, UNC, and ENVIRON. Difficulty in communicating 

with or between administrative staff members at these institutions can sometimes be a significant 

source of frustration and delay in managing the project. 

15.3 Task 2: Test, Improve, Quality Control, Obtain External Peer 

Review, and Finalize 36-km and 12-km MM5 Simulations for 

Eventual Use in CMAQ 

The RMC has carried out MM5 simulations for the entirety of 2002 to support CMAQ visibility 

modeling for the §308 SIPs/TIPs. During the fall of 2003, we made an initial MM5 run for 2002 

on the RPO Unified Continental 36-km Modeling Grid domain. This simulation used an MM5 

model configuration similar to what has been used by the other RPOs (i.e., VISTAS, CENRAP, 
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MRPO). For this run, the modeling system performed better in the central and eastern United 

States than in the West, and in general performed better in winter than in summer (Morris et al., 

2004a; Kemball-Cook et al., 2004). In the western U.S., the amplitude of the diurnal temperature 

cycle was persistently underestimated during the summer, especially in the Southwest. In the 

desert Southwest, the humidity was greatly overestimated during the summer as well, and there 

was a pronounced cold bias. 

WRAP then requested that we perform further MM5 sensitivity tests to identify a better-performing model configuration for the western states. We ran sensitivity tests in which four physics 

options were changed: (1) the cumulus parameterization, (2) the LSM, (3) the PBL model, and 

(4) the FDDA. As a result of these tests, we obtained an improved MM5 configuration that 

provided better results. 

The 36-km MM5 runs we made with this improved configuration showed a dramatic reduction in 

the summertime cold, wet bias in the desert Southwest. The surface temperature and humidity 

performance are now within benchmarks for all WRAP subdomains, except the desert Southwest 

for temperature. The new configuration gives a more accurate representation of the diurnal temperature cycle in the desert Southwest as well as a more realistic precipitation pattern over the 

western United States. Model performance over the eastern U.S. also improved. 

After settling on a configuration for the 36-km annual run, we turned our attention to MM5's 

performance on the nested WRAP 12-km grid. Further sensitivity tests at this resolution were 

undertaken, allowing for the possibility that the 36-km physics options might differ from those 

selected for the 12-km run. Improved MM5 performance using the new configuration was also 

seen on the 12-km grid. Therefore, a new annual 36/12-km 2002 MM5 simulation was 

conducted and the results subjected to a model performance evaluation. The revised WRAP 

2002 MM5 36-km results were compared against those for the initial WRAP 2002 36-km 

simulation, as well as the CENRAP and VISTAS 2002 36-km MM5 simulations. The results 

from the new WRAP 36-km and 12-km results were also compared. Details of these activities 

can be found at http://pah.cert.ucr.edu/aqm/308/mm5_reports04.shtml. 

15.4 Task 3: 2002 Base Year Emissions Modeling, Processing, and 

Analysis 

The four preliminary 2002 emissions simulations conducted by the RMC during this project year 

have served to prepare us for the final 2002 simulation and emissions sensitivities that will begin 

in 2005. The WRAP emissions modeling QA protocol—which includes the generation of 

comprehensive documentation, implementation of the Bugzilla project management system, and 

using CVS for version control—has proved to be a success. Using a team of emissions modelers, 

each with well-defined roles, worked well for managing the complex emissions modeling project 

at the RMC. 

A summary of the major emission modeling tasks completed in 2004 by the WRAP RMC 

includes the following: 

• 2002 WRAP emissions inventory database development: This task included collection, QA, and enhancement of 2002 emissions inventories for the U.S., Canada, and 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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Mexico. We collected inventory data from WRAP inventory contractors, other RPOs, 

and EPA to build the WRAP inventory database for preliminary 2002 emissions 

modeling. The database is hosted at the RMC and will provided the basis for the final 

2002 modeling that will be completed in 2005. 

• Simulation Pre02a_36: We used this first preliminary 2002 simulation to test the 

emissions modeling QA protocol, collect a first set of 2002 inventories, and diagnose 

any problems with the emissions modeling system. 

• Simulation Pre02b_36: The annual extension of simulation Pre02a_36, this simulation contained some data updates and produced the first results used in CMAQ 

modeling by the RMC. 

• Simulation Pre02c_36: This annual simulation was created by the addition of actual 

2002 wildfires and prescribed fires and typical-year agricultural fires to Pre02b_36. 

• Simulation Pre02c_36s01: A one-month sensitivity to test the effects of the diesel 

retrofit emissions control program in the WRAP region. 

• Simulation Pre02d_36: Several data updates, including new 2002 fires, actual 2002 

VISTAS inventories, windblown dust, and anthropogenic ammonia emissions, 

created the last annual simulation of the year. 

• Simulation Pre02d_12: An annual fine-grid nest of simulation Pre02d_36 on the 

WRAP 12-km modeling grid. 

• Simulation Pre02e_36: An annual fire sensitivity to test the effects of natural fires. 

• Simulation Pre02f_36: An annual fire sensitivity to test the effects of the updated 

2002 fire inventories. 

With the emissions modeling and QA infrastructure now fully developed, we are well prepared 

to begin the final 2002 emissions modeling, as well as the host of derivative sensitivities for 

evaluating the impacts of natural emissions on regional haze (Task 6 in the 2004 work plan, to be 

continued in 2005). 

15.5 Task 4: Air Quality Model Evaluation for 2002 Annual Simulation 

The RMC performed a CMAQ 2002 annual model simulation on the RPO Unified Continental 

36-km Modeling Grid domain and the WRAP western U.S. 12-km domain using the final WRAP 

2002 MM5 meteorological fields and the Base Case D (pre02d) emissions inputs. We then 

conducted an operational model performance evaluation that compared the CMAQ model 

estimates against observations from the IMPROVE, CASTNet, STN, and NADP networks in the 

WRAP region. 

• Model performance for sulfate was reasonably good, albeit with a winter overestimation 

bias of 20% to 80% and a summer underestimation bias that reached as low as –20%. 

• Nitrate performance was generally poor, with a winter overestimation (~ +100%) and 

summer underestimation (~ -100%) bias and large (100%-140%) errors. With the 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

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exception of California, summer NO3 levels are low, so the summer underestimation bias 

is not as big a concern as the overestimation bias in winter, when NO3 makes up a larger 

fraction of the visibility extinction. 

• Organic carbon performance exhibited less seasonal variation and is overstated in the 

winter. The CMAQ 36-km and 12-km OC performances are quite different from each 

other: summer OC bias was generally within ±10% using the 36-km grid, but OC is 

underestimated by -30% when the 12-km grid is used. 

• Performance for elemental carbon was generally fairly good, with low (<±20%) monthly 

bias across the year, although with a fair amount of scatter (i.e., errors of 60%-80%). 

• “Other fine PM” (soil) was overstated in the winter, with fairly low bias in the summer. 

Coarse mass, on the other hand, was understated throughout the year, with winter bias 

of -40% to -80% and summer bias exceeding -100%. The reasons for the poor soil and 

CM performance are related to (1) model-versus-measurement incommensurability, 

whereby modeled soil and CM may include different components from those included in 

the measured values; (2) uncertainties and missing emissions (e.g., windblown dust); and 

(3) local (sub-grid-scale) impacts that are not captured by the model. 

In summary, although there are noticeable areas of improvement needed in the 2002 CMAQ 

pre02d model performance, the performance is substantially better than the CMAQ 1996 simulation used in the §309 analysis. Performance for SO4 and EC is generally fairly good, whereas 

performance of NO3 and CM is poor. 

15.6 Task 5: Preparation and Reporting of Geographic Source 

Apportionment Results 

During 2003, UCR developed the Tagged Species Source Apportionment (TSSA) algorithm in 

CMAQ version 4.2.2 to assess source attribution. In 2004 we ported this code to a beta release 

of CMAQ version 4.4. However, it was discovered that there were problems with mass 

conservation in the model that made it difficult to fully attribute PM mass at receptor sites to 

emissions sources. We expended significant effort in the source attribution modeling task, which 

included debugging and refining the algorithm, completing the annual source attribution model 

simulation, and presenting and providing results to WRAP and its other contractors. Some 

uncertainty remains in the source attribution because of problems with mass conservation in 

CMAQ. This creates uncertainty in some of the source attribution results, especially with respect 

to the contribution of boundary conditions. However, we have more confidence in the ranking of 

the relative source attribution for the tagged sources other than boundary conditions. As part of 

Task 7, we also performed source attribution simulations for February and July using the CAMx 

Particulate Source Apportionment Technology (PSAT). In general the PSAT results were 

consistent with the CMAQ TSSA results, although there were significant differences in the 

relative ranking of sources at some receptor sites. Any additional source attribution modeling 

should be performed using the CAMx PSAT model while we complete further testing of the 

TSSA algorithm in a new release of CMAQ that corrects some mass conservation errors. 
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15.7 Task 6: Further Analysis of Model Performance in Regard to the 

Contribution of Natural Emissions to Visibility Impairment 

The RMC used 2004 to focus on identifying the sources of haze impacting the WRAP region. By 

reviewing cursory model simulations we performed in 2003, EPA regional haze guidance 

documentation, and research completed by other RPOs, we established a direction for defining 

the distinction between natural and anthropogenic emissions sources contributing to regional 

visibility impairment. We circulated a memorandum in 2004 defining the various natural 

emissions sources that would be the focus of continuing research. Based on this memo, we 

completed a literature review to identify the availability of emission factors for modeling natural 

emissions sources. We used this information to develop emissions estimates for previously 

unaccounted-for natural sources of haze, and to implement new inventories created by the 

WRAP Emissions Forums that define natural versus anthropogenic emissions. We now have the 

information needed to begin modeling to assess the impacts of natural emissions sources on 

visibility impairment in the WRAP region. 

15.8 Task 7: Evaluation and Comparison of Alternative Models 

The CMAQ modeling system is the primary modeling tool being used in the WRAP 2002 

modeling. However, there are many advantages to operating alternative models, such as being 

able to diagnose model performance, corroborate the primary model’s results, quantify model 

uncertainty, and provide a backup modeling tool. A review of available alternative models 

identified the Comprehensive Air-quality Model with extensions (CAMx) as the optimal 

alternative model because it’s similar state-of-science formulations as CMAQ, its use by several 

other RPOs (e.g., MRO and CENRAP) and availability of a PM Source Apportionment 

Technology (PSAT) capability. The CAMx modeling system was applied for February and July 

2002 on the RPO Unified Continental 36-km Modeling Grid domain, and the model performance 

compared with CMAQ. In general CMAQ and CAMx exhibited similar model performance 

attributes with neither model exhibiting superior model performance across all species and 

subregions. The CAMx PSAT sulfate source apportionment was also compared with the PM 

source apportionment modeling results from the CMAQ TSSA runs. The CAMx PSAT and 

CMAQ TSSA sulfate source apportionment produced similar rankings of source region 

contributions to sulfate at western Class I areas, with the exception that the TSSA “Other” 

category that includes non-tagged sources, mass adjustments and mass conservation errors is not 

present in the PSAT results that accounts for the contributions of all sources. 

15.9 Task 9: Testing and Further Improvements to the Windblown 

Dust Emissions Modeling Methodology 

In 2004 we implemented the Phase II estimation methodology for PM dust emissions from wind 

erosion for calendar year 2002 on the RPO Unified Continental 36-km Modeling Grid domain. A 

number of sensitivity simulations were performed to investigate the effects of the various 

assumptions regarding dust reservoir characteristics and disturbance levels of the soils across the 

domain. 
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Based on a review of recent studies concerning dust emission from wind erosion, we developed a 

general estimation methodology. Emission rates were developed by soil type and wind speed. 

Land use/land cover data came from the NLCD database were used to determine the surface 

roughness lengths necessary for estimating threshold surface friction velocities. Wind speed, 

precipitation rates, and soil temperatures were based on MM5 model simulation results at a spatial resolution of 36 km. Although we improved the overall estimation methodology compared 

with our Phase I methodology, a number of assumptions were still required to implement the 

methodology (see Section 10.3). 

The results, presented and discussed in Section 10.4, qualitatively appear to be consistent with 

the various assumptions made. Variations in estimated dust emissions by land use type and 

season are described in that section. A more detailed analysis of the model results and a comparison with ambient data are currently underway and will be documented in the final task report. 

Although the results of the project are still being reviewed, we can already make a number of 

recommendations with regard to improvements in data quality and in the estimation methodology. These include incorporating more detailed land use type and soil characteristics data, 

implementing the model using higher-resolution meteorological data, applying transport fractions to the estimated dust emissions, and validating the methodology using a smaller-scale local 

domain. 

15.10 Task 10: Continued Improvement to Model Evaluation Software 

We continued to revise and improve the model performance evaluation (MPE) software during 

2004. Because model performance guidance does not yet exist for visibility modeling, we 

continue to explore new evaluation methods and sometimes discover improved methods for 

presenting model results that necessitate changes in the MPE software. In particular, during 2004 

we experimented with various types of plots for summarizing and effectively presenting the 

results of the model-to-ambient-data comparisons, including soccer plots and bugle plots. 

Beginning in January 2005 we also adapted a Microsoft Access Database program originally 

developed by Air Resources Inc. to produce stacked-bar time-series plots. 

Although we continue to compute a wide variety of model performance metrics, we typically use 

mean fractional bias (MFB) and mean fractional error (MFE) when presenting evaluation results 

because these metrics provide the most balanced and symmetrical approach for characterizing 

model underpredictions and overpredictions. 

We currently recommend presenting model evaluation results using a combination of bugle 

plots, stacked-bar time-series plots, and soccer plots. We are continuing to modify the MPE 

software to make it easier to generate these plots. We still need guidance from EPA on official 

criteria for defining acceptable performance for visibility modeling. 

The MPE software is available for use to all WRAP contractors and WRAP members. We plan 

to release a public version of the MPE after completing revisions to the MPE documentation. A 

draft of the MPE user’s guide is included as Appendix F. 
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 340 

15.11 Task 11: Sensitivity Studies Designed to Evaluate Uncertainties 

in Fire Emissions 

With the Fire Emissions Joint Forum and Air Sciences Inc., the RMC developed several emission scenario combinations to study the effect of fires on air quality in general, and on visibility 

in particular. We completed CMAQ simulations for each of these scenarios and compared the 

results to the CMAQ results with no fire emissions. Fire emissions were classified as wildfires, 

prescribed burning, or agricultural burning. They were further classified as either natural or 

anthropogenic, with wildfires defined exclusively as natural, prescribed burning divided between 

natural and anthropogenic, and agricultural burning defined as exclusively anthropogenic. The 

fire scenario evaluations completed include the following: 

• Comparison of the 2018 Base Smoke Management (BSM) and Optimal Smoke 

Management (OSM) strategies. 

• Comparison of natural versus anthropogenic emissions. 

• Evaluation of individual contributions of wildfires, prescribed burning, and agricultural 

burning. 

• Evaluation of the sensitivity of the CMAQ results to changes in the vertical distribution 

of agricultural burning emissions. 

Wildfires and natural fire emissions were the major component of the fire emissions inventory 

and by far the largest contributors among the three categories to ambient PM. Anthropogenic 

emissions had a relatively small effect on visibility because of the small magnitude of agricultural burning compare to total fire emissions. However, anthropogenic fire emissions might show 

larger effects in model simulations that use finer grid resolution. This would be a special 

concern for emissions located in or near Class I area. Thus, the significance of anthropogenic 

emissions should not be discounted based on these results, and additional studies should be 

performed at finer grid resolutions. 

The CMAQ results were not sensitive to changes in the vertical distribution of agricultural 

burning emissions because these were small compared to the wildfire emissions. However, we 

would expect a different result for large wildfires for which the plume rise height may extend 

into the free troposphere, and which also have much larger emissions than agricultural burning, 

and therefore a larger effect on visibility in general. Additional sensitivity simulations should 

still be performed to evaluate the effects of plume rise height for large fires. 

15.12 Task 12: Preliminary Meteorological, Emissions, and Air Quality 

Modeling Activities for Alaska 

The State of Alaska is developing a plan to protect visibility and comply with the intent of the 

RHR. As part of the WRAP Alaska effort, we are developing techniques for credible modeling 

of regional haze in Alaska, and providing a preliminary evaluation of the potential contributions 

to regional haze in the Alaska Class I areas that are more likely to be affected by in-state emis-
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 341 

sions. There are three main components of regional haze modeling: meteorological modeling, 

emissions modeling, and air quality modeling. 

The State of Alaska is developing a statewide emissions inventory. It is thus premature to 

perform emissions and photochemical grid modeling, as not all of the data are currently available 

in a suitable form. Therefore, our current modeling approach for Alaska is to begin by 

developing meteorological modeling techniques to simulate the unique and complex 

meteorological conditions of the region. The MM5 model was applied for year 2002 on a 45/15-

km grid and evaluated against available surface, upper-air, and synoptic data. The 2002 MM5 

data are being used to perform preliminary air quality modeling using a simplified Lagrangian 

(trajectory) model in 2005 to provide an initial assessment of the potential effects of emissions 

from Anchorage and Fairbanks, as well as major stationary sources, on visibility at Denali 

National Park and Tuxedni Wilderness Area. The Lagrangian model we will use for preliminary 

WRAP Alaska visibility modeling is CALPUFF. CALPUFF is being applied for 2002 and the 

results compared against available observations for reasonableness. 

15.13 Task 13: Training Courses for the WRAP States and Tribes 

Basic CMAQ and SMOKE training classes are available through the CMAS Center. Therefore, 

the RMC focuses its efforts on advanced training and technology transfer. A one-day training 

class on the use of visibility modeling results was developed and offered in conjunction with the 

Attribution of Haze meeting held in September 2004. 

The primary training need concerns the transfer of the models and datasets to the states and 

tribes, and instruction or assistance in setting up the modeling system. This includes aspects of 

hardware, operating systems, and installation and operation of models and datasets and the 

associated computers. We are engaged in several activities related to this need. For example, we 

are producing a frequently asked questions (FAQ) sheet that explains how to install an operating 

system and the modeling system on a Linux PC, and includes recommendations for computer 

hardware appropriate for the modeling system users’ needs. This FAQ sheet will be posted on the 

project web site. 

After providing some assistance to one state in setting up a specialized operating system for 

managing parallel computers, we determined that the best use of RMC resources would be to 

limit the assistance we provide to supporting a single, standard installation of Linux and the 

modeling system. 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 342 

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Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 349 

Appendices A through E: 

Five Appendices to Section 5, “Task 3: 2002 Base Year 

Emissions Modeling, Processing, and Analysis” 

Five appendices are referred to in Section 5, which covers Task 3. They are contained in a 

separate file located at 

http://pah.cert.ucr.edu/aqm/308/reports/final/2004_RMC_final_report_Appendices_A-E.pdf

The titles of the appendices are as follows: 

• Appendix A: Complete Listing of Simulation Pre02d_36 Input/Output Files and 

SMOKE Configuration Settings 

• Appendix B: Area-Source Agricultural SCCs Replaced by Process-Based NH3

Emissions Model 

• Appendix C: Preliminary 2002 Emissions Density Maps 

• Appendix D: Preliminary 2002 Bar Charts 

• Appendix E: SMOKE QA Checklist for Simulation Pre02d_36 
Final Report for the WRAP Regional Modeling Center for the Project Period March 1, 2004 – February 28, 2005 

 350 

Appendix F: 

Appendix to Section 11, “Task 10: Continued 

Improvement to Model Evaluation Software” 

One appendix is referred to in Section 11, which covers Task 10. This appendix is a draft of the 

model performance evaluation (MPE) documentation prepared by RMC staff at CE-CERT, 

University of California, Riverside and titled “User’s Guide: Air Quality Model Evaluation 

Software, Version 2.0.1.” The appendix is contained in a separate file located at 

http://pah.cert.ucr.edu/aqm/308/reports/final/2004_RMC_final_report_Appendix_F.pdf