Attachment 4_Horizontal Grid 

Definitions for MM5 and CMAQ.pdf
OTC Grid Definitions for MM5 and CMAQ 

Model Columns 

Dot 

(nx) 

Rows 

Dot 

(ny) 

X-Origin 

(km) 

Y-Origin 

(km) 

MM5 36-km 149 129 -2664 -2304 

CMAQ 36-km 145 102 -2628 -1728 

MM5 12-km 175 175 252 -900 

CMAQ 12-km 172 172 264 -888 

Appendix G Attachment 4
Attachment 5_Vertical Layer Definitions 

for MM5 and CMAQ.pdf
OTC Vertical Layer Definition for MM5 Simulations and Approach For 

Reducing CMAQ Layers By Collapsing Multiple MM5 Layers 

MM5 CMAQ

Layer Sigma Pres(mb) Height(m) Depth(m) Layer Sigma Pres(mb) Height(m) Depth(m)

29 0.000 50 18600 2145 23 0.000 50 18600 4290 

28 0.040 88.5 16450 2145 

27 0.080 127.1 14300 1460 21 0.080 127.1 14300 2920 

26 0.123 168.5 12800 1460 

25 0.168 211.8 11400 1200 20 0.168 211.8 11400 2390 

24 0.218 260.0 10200 1200 

23 0.268 308.1 8990 934 19 0.268 308.1 8990 1870 

22 0.318 356.3 8060 934 

21 0.368 404.5 7120 772 18 0.368 404.5 7120 1540 

20 0.418 452.6 6350 772 

19 0.468 500.8 5580 662 17 0.468 500.8 5580 1320 

18 0.518 549.0 4920 662 

17 0.568 597.1 4250 581 16 0.568 597.1 4250 1160 

16 0.618 645.3 3670 581 

15 0.668 693.4 3090 532 15 0.668 693.4 3090 532 

14 0.718 741.6 2560 455 14 0.781 741.6 2560 455 

13 0.763 785.0 2110 388 13 0.763 785.0 2110 388 

12 0.803 823.5 1720 337 12 0.803 823.5 1720 337 

11 0.839 858.2 1380 290 11 0.839 858.2 1380 290 

10 0.871 889.0 1090 247 10 0.871 889.0 1090 247 

9 0.899 916.0 844 207 9 0.899 916.0 844 207 

8 0.923 939.1 637 169 8 0.923 939.1 637 169 

7 0.943 958.3 468 133 7 0.943 958.3 468 133 

6 0.959 973.7 334 107 6 0.959 973.7 334 107 

5 0.972 986.3 227 82 5 0.972 986.3 227 82 

4 0.982 995.9 145 57 4 0.982 995.9 145 57 

3 0.989 1002.6 89 40 3 0.989 1002.6 89 40 

2 0.994 1007.5 48 27 2 0.994 1007.5 48 27 

1 0.9974 1010.7 21 21 1 0.9974 1010.7 21 21 

0 1.000 1013.24 0 0 0 1.000 1013.24 0 0 

Note: Layer-top pressures assume a surface pressure of 1013.24 hPa. 

Layer-top heights are determined by averaging MM5 (CMAQ)-calculated 

layer-top heights over time (August 2002) and space (the entire 172x172 

domain). 

Appendix G Attachment 5
Attachment 6_MM5 Meteorological 

Model Configuration.pdf
OTC MM5 Meteorological Model Configuration 

Science Options Configuration Details/Comments 

Model Code MM5 Version 3.6 

Horizontal Grid Mesh 36km/12km 

36-km grid 149x129 cells 

12-km grid 175x175 cells 

Vertical Grid Mesh 29 layers 

Grid Interaction No feedback Two-way nesting 

Initialization Eta first guess fields/LittleR 

Boundary Conditions Eta first guess fields/LittleR 

Microphysics Simple Ice 

Cumulus Scheme Kain-Fritsch 36km/12km grids 

Planetary Boundary Layer High-resolution Blackadar PBL 

Radiation Simple cooling 

Vegetation Data USGS 24 Category Scheme 

Land Surface Model Five-Layer Soil model 

Shallow Convection None 

Sea Surface Temperature Do not update SST 

Thermal Roughness Default 

Snow Cover Effects None 

4D Data Assimilation Analysis Nudging: 36km/12km 

Integration Time Step 75 seconds 

Simulation Periods 2002 

Platform Linux Cluster Done at UMD 

Appendix G Attachment 6
Attachment 

7a_Hao2005_METDataAnalysis-Hao.pdf
Meteorological Data Analy

Urban, Regional Modeling and Analysis S

Division of Air Resources

New York State Department of Environm

Conservation

November 16, 2005

Appendix G Attachment 7a Page Number:

1
36km Domain (145x102x22)12km Domain (172x172x22)

OTC Air Quality Modeling Domains

Appendix G Attachment 7a Page Number:

2
MM5 input and output files were received fro

for entire 2002 in 128 3.5-day chunks with hour overlapping period between adjacen

Evaluation was limited to 12km air quality m

domain for 12km MM5 output files over thmonth period of may to September 2002

Surface observation datasets include NCAR 

ds472.0 (around 800 stations) and CAST(around 50 stations)

Appendix G Attachment 7a Page Number:

3
METSTAT program from Environ was used 

examine surface wind speed and directiontemperature and humidity

Correlation coefficients were calculated for s

wind speed, temperature and humidity

Wind speed correlation with TDL are ranging from 0.7 tTemperature correlation with TDL are .96 and betterHumidity correlation with TDL are ranging from 0.8 to 0

Appendix G Attachment 7a Page Number:

4
Appendix G Attachment 7a Page Number:

5
Appendix G Attachment 7a Page Number:

6
Appendix G Attachment 7a Page Number:

7
Monthly total of MM5 predicted precipitation 

compared with 1/8-degree CPC rain gauganalysis

For months of May and September 2002, MM5 is doing 

capturing the rainfall patterns

For months of June, July and August, the model is not do

in terms of pattern and amount, probably is related to summertime convective activities

Appendix G Attachment 7a Page Number:

8
Obs May 2002 MM5

Appendix G Attachment 7a Page Number:

9
Obs June 2002 MM5

Appendix G Attachment 7a Page Number:

10
Obs July 2002 MM5

Appendix G Attachment 7a Page Number:

11
Obs August 2002 MM5

Appendix G Attachment 7a Page Number:

12
Obs September 2002 MM5

Appendix G Attachment 7a Page Number:

13
MM5 cloud cover was compared qualitative

UMD Surface Radiation Budget Groups’ p

The observed cloud interpolated from satellite base data 

0.5

o resolution

Total cloud fraction estimated by MCIP from MM5 low

and high cloud fraction

MM5 is doing a fair job to simulate cloud patterns for th

periods we examined

Appendix G Attachment 7a Page Number:

14
Observed and Simulated Cloud field on August 13, 

Appendix G Attachment 7a Page Number:

15
Observed and Simulated Cloud Field on August 14, 

Appendix G Attachment 7a Page Number:

16
Observed and Simulated Cloud Field on August 15, 

Appendix G Attachment 7a Page Number:

17
MM5 vertical wind speed profiles were comp

qualitatively with wind profilers observatiousing low level jets (LLJ) as an indicator

MM5 is doing a fair job capturing LLJ events

Appendix G Attachment 7a Page Number:

18
Appendix G Attachment 7a Page Number:

19
Appendix G Attachment 7a Page Number:

20
August 15, 2002

Appendix G Attachment 7a Page Number:

21
Attachment 

7b_HeEtAl2005_2002AnnualEvalof12k

mMM5SurfParams4O.pdf
Evaluation of 2002 Annual 12km M

Surface Parameters for OTC Mode

Shan He and Gary Kleiman

NESCAUM

And

Winston Hao

NYDEC

Review of Application and Assessment of CMAQ in OTC

Albany, NY

November 16, 2005

Appendix G Attachment 7b - Page Number:

1
Observation Network

Appendix G Attachment 7b - Page Number:

2
WIND

Appendix G Attachment 7b - Page Number:

3
WINTER Wind Speed 

Wind Speed - CASTNet

01234567

1

2

3

4

5

6

7

8

9

10

11

12

13

141516

17

18

1920

21

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343536

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4849

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5455

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336

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338

339

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341342

343

344

345

346

347348

349

350351

352

353

354

355356

357

358

359

360

361

362

363364

365

Julian Day

m/s

Obs Prd

Wind Speed - TDL

012345678

1

23

45

6

7

8

9

10

11

12

13

14

15

16

17

18

1920212223

24

25

2627

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53

54

55

56

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58

59

33

Julian Day

m/s

Bias Wind Speed

-2.0-1.5-1.0-0.5

0.00.51.01.52.0

1

2

34

5

6

7

8

9

10

11

12

13

14

15

16

17

1819

2021

22

23

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2526

27

28

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30

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3435

36

37

38394041

42

43

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45464748

49

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52535455

56

57

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335336337

338

339

340

341342

343

344

345

346

347

348

349350351

352

353

354355356

357

358

359

360

361

362

363364

365

Julian Day

m/s

CASTNet TDL

Wind Speed - CASTNet

01234567

1

2

3

4

5

6

7

8

9

1011121314

1516171819

2021

2223

2425

26

2728

2930

31

32

33

34

35

36

37

38

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40

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5253

54

55

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59

335

336337

338

339

340

341

342

343344

345346347

348

349350

35135

Julian Day

m/s

Obs 

Wind Speed - TDL

012345678

1

2

3

4

5

6

7

8

9

1011

12

13

1415

1617

18

1920

21

22232425

2627

28

2930

313233

3435363738

3940

41424344

45

4647

484950

51

52

53

54

555657

5859

335

336

337

338339340

341342343344

345346

347348349350351352

Julian Day

m/s

Obs 

Bias Wind Speed

-2.0-1.5-1.0-0.5

0.00.51.01.52.0

1

2

3

4

5

67

8

9

10

11

1213

1415

161718

19

20

2122

23

242526

27

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2930

31

3233

34353637

3839

40

41

42

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46

4748

4950

51

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53

54555657

58

59

335

336

337338339

340

341342343344345346347348

349350351352

Julian Day

m/s

CASTNet 

Appendix G Attachment 7b - Page Number:

4
WINTER Wind Speed (II)

RMSE Wind Speed - CASTNet

0.00.51.01.52.02.53.03.5

12

3456789

10111213141516

17

18

19

20

21

22

23

24

25

26

27

28

29

30

313233343536

3738394041

42434445

46474849

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336

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340341

342343344345346

347348

349350351

352353

354

355356

357

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360

361

362

363

364

365

Julian Day

m/s

RMSE RMSES RMSEU

RMSE Wind Speed - TDL

0.00.51.01.52.02.53.0

12

345

6

789

101112

131415

16

17

18

19

20

21

22

23

24

25

26

27

28

29

303132

33

34353637383940

4142434445464748

49

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55

56

Julian Day

m/s

RMSE 

IOA Wind Speed

0.00.10.20.30.40.50.60.70.80.91.0

1234567

89

101112131415

16

17

18

19

20

21

22

23

24

25

26

27

28

29

3031323334353637

383940

414243444546474849

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335

336

337

338339340341342343344

345346347

348349350351352353354355356

357

358

359

360

361

362

363

364

365

Julian Day

CASTNet TDL

IOA Wind Speed

0.00.10.20.30.40.50.60.70.80.91.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

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171819

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404142

43

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454647

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4950

5152

535455

5657

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339340341342

343

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34834935

Julian Day

CASTNet 

Appendix G Attachment 7b - Page Number:

5
WINTER Wind Direction

Wind Direction - CASTNet

0

306090

120150180210240270300330360

123456789

10

11

12

13

14

15

16

17

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252627282930

31323334353637383940

4142

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575859

335336337

338339340

341342343344345346347348349350351352

353

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356

357

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359

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361

362

363

364

365

Julian Day

Degree

Obs Prd

Wind Direction - TDL

0

306090

120150180210240270300330360

123456789

101112

1314

15

16

17

18

19

20

21

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2930

31323334353637

383940

4142434445

464748

49

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Julian Day

Degree

Obs Prd

Bias Wind Direction

-60-50-40-30-20-10

0

102030405060

123

45678

9

101112131415

16

17

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3031323334353637383940

414243444546

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339340341342

343344

345346347348349350351352353354355356357

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Julian Day

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CASTNet TDL

Bias Wind Direction

-60-50-40-30-20-10

0

102030405060

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4950515253

5455

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347348349350

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CASTNet 

Appendix G Attachment 7b - Page Number:

6
SPRING Wind Speed

Wind Speed - CASTNet 

0123456789

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

m/s

Obs Prd

Wind Speed - TDL

0123456789

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 

Julian Day

m/s

Bias Wind Speed

-2.0-1.5-1.0-0.5

0.00.51.01.52.0

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

m/s

CASTNet TDL

Wind Speed - CASTNet 

0123456789

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 13

Julian Day

m/s

Obs 

Wind Speed - TDL

0123456789

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 1

Julian Day

m/s

Obs 

Bias Wind Speed

-2.0-1.5-1.0-0.5

0.00.51.01.52.0

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 13

Julian Day

m/s

CASTNet 

Appendix G Attachment 7b - Page Number:

7
SPRING Wind Speed (II)

RMSE Wind Speed - CASTNet

0.00.51.01.52.02.53.03.54.0

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

m/s

RMSE RMSES RMSEU

RMSE Wind Speed - TDL

0

0.5

1

1.5

2

2.5

3

3.5

4

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 

Julian Day

m/s

RMSEWndSpd RMSES

IOA Wind Speed

0

0.10.20.30.40.50.60.70.80.9

1

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

CASTNet TDL

IOA Wind Speed

0

0.10.20.30.40.50.60.70.80.9

1

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 13

Julian Day

CASTNet 

Appendix G Attachment 7b - Page Number:

8
SPRING Wind Direction

Wind Direction - CASTNet

0

306090

120150180210240270300330360

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

Degree

Obs Prd

Wind Direction - TDL

0

306090

120150180210240270300330360

60 64 68 72 76 80 84 88 92 96 100 104 108 112 11

Julian Day

Degree

Obs Prd

Bias Wind Direction

-60-50-40-30-20-10

0

102030405060

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

Degree

CASTNet TDL

Bias Wind Direction

-60-50-40-30-20-10

0

102030405060

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 13

Julian Day

Degree

CASTNet 

Appendix G Attachment 7b - Page Number:

9
SUMMER Wind Speed

Wind Speed - CASTNet

01234567

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

m/s

Obs Prd

Wind Speed - TDL

01234567

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 2

Julian Day

m/s

Bias Wind Speed

-2.0-1.5-1.0-0.5

0.00.51.01.52.0

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

m/s

CASTNet TDL

Wind Speed - CASTNet

01234567

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 22

Julian Day

m/s

Obs 

Wind Speed - TDL

01234567

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 

Julian Day

m/s

Obs 

Bias Wind Speed

-2.0-1.5-1.0-0.5

0.00.51.01.52.0

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 22

Julian Day

m/s

CASTNet 

Appendix G Attachment 7b - Page Number:

10
SUMMER Wind Speed (II)

RMSE Wind Speed - CASTNet

0.00.51.01.52.02.53.03.54.0

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

m/s

RMSE RMSES RMSEU

RMSE Wind Speed - TDL

0.00.51.01.52.02.53.0

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 

Julian Day

m/s

RMSE RMSE

IOA Wind Speed

0.00.10.20.30.40.50.60.70.80.91.0

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

CASTNet TDL

IOA Wind Speed

0.00.10.20.30.40.50.60.70.80.91.0

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 22

Julian Day

CASTNet T

Appendix G Attachment 7b - Page Number:

11
SUMMER Wind Direction

Wind Direction - CASTNet

0

306090

120150180210240270300330360

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

Degree

ObsPrd

Wind Direction - TDL

0

306090

120150180210240270300330360

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 

Julian Day

Degree

Bias Wind Direction

-60-50-40-30-20-10

0

102030405060

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

Degree

CASTNet TDL

Bias Wind Direction

-60-50-40-30-20-10

0

102030405060

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 22

Julian Day

Degree

CASTNet 

Appendix G Attachment 7b - Page Number:

12
FALL Wind Speed

Wind Speed - CASTNet

01234567

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

m/s

Obs Prd

Wind Speed - TDL

012345678

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 3

Julian Day

m/s

Bias Wind Speed

-2.0-1.5-1.0-0.5

0.00.51.01.52.0

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

m/s

CASTNet TDL

Wind Speed - CASTNet

01234567

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 31

Julian Day

m/s

Obs 

Wind Speed - TDL

012345678

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 

Julian Day

m/s

Obs 

Bias Wind Speed

-2.0-1.5-1.0-0.5

0.00.51.01.52.0

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 

Julian Day

m/s

CASTNet 

Appendix G Attachment 7b - Page Number:

13
FALL Wind Speed (II)

RMSE Wind Speed - CASTNet

0.00.51.01.52.02.53.0

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

m/s

RMSE RMSES RMSEU

RMSE Wind Speed - TDL

0

0.5

1

1.5

2

2.5

3

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 

Julian Day

m/s

RMSE 

IOA Wind Speed

0.00.10.20.30.40.50.60.70.80.91.0

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

CASTNet TDL

IOA Wind Speed

0.00.10.20.30.40.50.60.70.80.91.0

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 

Julian Day

CASTNet 

Appendix G Attachment 7b - Page Number:

14
FALL Wind Direction

Wind Direction - CASTNet

0

306090

120150180210240270300330360

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

Degree

Obs Prd

Wind Direction - TDL

0

306090

120150180210240270300330360

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 

Julian Day

Degree

Bias Wind Direction

-60-50-40-30-20-10

0

102030405060

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

Degree

CASTNet TDL

Bias Wind Direction

-60-50-40-30-20-10

0

102030405060

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 31

Julian Day

Degree

CASTNet 

Appendix G Attachment 7b - Page Number:

15
WIND

• MM5 tends t

speed at CAunderestimasites

• Mean bias o

CASTNet w0.4m/s, whilwind speed about ~-0.5 

• MM5 wind s

IOA (~0.7 to CASTnet da

• MM5 wind d

variation to Cdirection thadirection

• However, m

direction to Cdirection is sbias of MM5 TDL wind di

Max Min Mean Max Min Mean Max Min Mean

CASTNET 1.16 -0.42 0.34 0.88 0.52 0.73 23.98 -22.55 2.54

TDL 0.11 -1.40 -0.53 0.90 0.55 0.74 11.42 -5.81 3.12

CASTNET 1.42 -0.61 0.31 0.92 0.47 0.75 18.55 -20.29 0.88

TDL 0.17 -1.51 -0.56 0.90 0.55 0.79 8.98 -2.80 3.31

CASTNET 1.70 -0.79 0.31 0.89 0.22 0.70 26.54 -20.04 1.93

TDL 0.21 -1.85 -0.59 0.91 0.53 0.76 38.45 -6.45 3.48

CASTNET 1.37 -0.76 0.38 0.89 0.42 0.73 32.74 -21.19 2.49

TDL 0.04 -1.33 -0.55 0.92 0.58 0.77 12.80 -4.59 3.61

CASTNET 1.60 -0.76 0.44 0.88 0.33 0.69 29.64 -20.58 2.33

TDL -0.04 -1.19 -0.52 0.91 0.54 0.74 11.25 -4.80 3.53

CASTNET 1.25 -0.63 0.28 0.87 0.41 0.69 30.65 -26.33 3.33

TDL -0.07 -1.11 -0.56 0.89 0.50 0.73 11.15 -3.65 3.89

CASTNET 1.26 -0.58 0.31 0.87 0.34 0.66 29.26 -33.34 1.44

TDL -0.14 -1.01 -0.58 0.86 0.38 0.68 12.30 -3.84 3.62

CASTNET 1.65 -0.87 0.24 0.95 0.16 0.65 31.83 -26.96 2.34

TDL -0.02 -1.22 -0.61 0.88 0.38 0.67 11.76 -4.67 2.74

CASTNET 1.47 -0.75 0.30 0.87 0.39 0.67 30.95 -32.73 3.01

TDL -0.09 -1.09 -0.54 0.88 0.48 0.72 11.54 -4.19 3.31

CASTNET 1.32 -0.72 0.32 0.89 0.50 0.72 35.10 -28.90 1.39

TDL -0.10 -1.05 -0.56 0.88 0.51 0.74 10.82 -4.09 2.81

CASTNET 1.39 -0.86 0.37 0.97 0.38 0.74 25.62 -17.68 2.35

TDL 0.02 -1.29 -0.57 0.88 0.49 0.75 9.30 -3.58 2.28

CASTNET 1.31 -0.53 0.39 0.92 0.43 0.74 30.70 -18.60 2.69

TDL -0.08 -1.17 -0.59 0.89 0.60 0.76 10.08 -5.47 3.41

OCT

NOV

DEC

JUN

JUL

AUG

SEP

FEB

MAR

APR

MAY

JAN

Bias IOA Bias

Wind Speed Wind Direction

2002 Network

Appendix G Attachment 7b - Page Number:

16
TEMPERATURE

Appendix G Attachment 7b - Page Number:

17
WINTER Temperature

Surface Temperature - CASTNet

245250255260265270275280285290

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342343344345346

347348349

350351352353354355356357358

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Obs Prd

Surface Temperature - TDL

250255260265270275280285290

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575859

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Julian Day

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Bias Surface Temperature

-5-4-3-2-1

012345

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678

9

101112131415

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CASTNet TDL

Surface Temperature - CASTNet

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Julian Day

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Obs P

Surface Temperature - TDL

250255260265270275280285290

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56

789

10111213

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15161718192021222324

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336337338

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348349

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351

3

Julian Day

K

Obs 

Bias Surface Temperature

-5-4-3-2-1

012345

1

2

34

5

67

8

9

10111213141516171819

202122232425

262728293031

3233

3435

3637

3839404142

43

44

454647

4849

505152535455

56

575859

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337338339

340

341342

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344345346

3473483493503513

Julian Day

K

CASTNet 

Appendix G Attachment 7b - Page Number:

18
WINTER Temperature (II)

RMSE Surface Temperature - CASTNet

0123456789

101112

12345678

9

1011121314

1516

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333435363738394041424344

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K

RMSE RMSES RMSEU

RMSE Surface Temperature - TDL

0.00.51.01.52.02.53.03.54.04.55.0

1

2345

6789

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12131415

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RM

IOA Surface Temperature

0.00.10.20.30.40.50.60.70.80.91.0

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CASTNet TDL

IOA Surface Temperature

0.00.10.20.30.40.50.60.70.80.91.0

1

2

34

56

7

8

9

1011

121314

15

1617181920

2122

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CASTNet 

Appendix G Attachment 7b - Page Number:

19
SPRING Temperature

Surface Temperature - CASTNet

260265270275280285290295300305

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

K

Obs Prd

Surface Temperature - TDL

260265270275280285290295300305

60 64 68 72 76 80 84 88 92 96 100 104 108 112 1

Julian Day

K

Bias Surface Temperature

-5-4-3-2-1

012345678

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

K

CASTNet TDL

Surface Temperature - CASTNet

260265270275280285290295300305

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 1

Julian Day

K

Obs 

Surface Temperature - TDL

260265270275280285290295300305

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 1

Julian Day

K

Obs 

Bias Surface Temperature

-5-4-3-2-1

012345678

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 13

Julian Day

K

CASTNet 

Appendix G Attachment 7b - Page Number:

20
SPRING Temperature (II)

RMSE Surface Temperature - CASTNet

0123456789

1011

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

K

RMSE RMSES RMSEU

RMSE Surface Temperature - TDL

0.00.51.01.52.02.53.03.54.0

60 64 68 72 76 80 84 88 92 96 100 104 108 112 11

Julian Day

K

RMSE 

IOA Surface Temperature

0.00.10.20.30.40.50.60.70.80.91.0

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

CASTNet TDL

IOA Surface Temperature

0.00.10.20.30.40.50.60.70.80.91.0

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 1

Julian Day

CASTNet 

Appendix G Attachment 7b - Page Number:

21
SUMMER Temperature

Surface Temperature - CASTNet

280285290295300305310

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

K

Obs Prd

Surface Temperature - TDL

280285290295300305310

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 

Julian Day

K

Bias Surface Temperature

-10

-9-8-7-6-5-4-3-2-1

0123456789

10

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

K

CASTNet TDL

Bias Surface Temperature

-10

-9-8-7-6-5-4-3-2-1

0123456789

10

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 22

Julian Day

K

CASTN

Appendix G Attachment 7b - Page Number:

22
SUMMER Temperature (II)

RMSE Surface Temperature - CASTNet

0123456789

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

K

RMSE RMSES RMSEU

RMSE Surface Temperature - TDL

0.00.51.01.52.02.53.0

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 

Julian Day

K

RM

IOA Surface Temperature

0.00.10.20.30.40.50.60.70.80.91.0

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

CASTNet TDL

IOA Surface Temperature

0.00.10.20.30.40.50.60.70.80.91.0

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 22

Julian Day

CASTNet 

Appendix G Attachment 7b - Page Number:

23
FALL Temperature

Surface Temperature - CASTNet

260265270275280285290295300305310

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

K

Obs Prd

Surface Temperature - TDL

260265270275280285290295300305310

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 

Julian Day

K

Bias Surface Temperature

-8-7-6-5-4-3-2-1

012345678

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

K

CASTNet TDL

Surface Temperature - CASTNet

260265270275280285290295300305310

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 31

Julian Day

K

Obs 

Surface Temperature - TDL

260265270275280285290295300305310

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 31

Julian Day

K

Obs 

Bias Surface Temperature

-8-7-6-5-4-3-2-1

012345678

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 31

Julian Day

K

CASTNet 

Appendix G Attachment 7b - Page Number:

24
FALL Temperature (II)

RMSE Surface Temperature - CASTNet

0123456789

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

K

RMSE RMSES RMSEU

RMSE Surface Temperature - TDL

0.00.51.01.52.02.53.03.54.0

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 

Julian Day

K

RMSE 

IOA Surface Temperature

0

0.10.20.30.40.50.60.70.80.9

1

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

CASTNet TDL

IOA Surface Temperature

0

0.10.20.30.40.50.60.70.80.9

1

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 31

Julian Day

CASTNet 

Appendix G Attachment 7b - Page Number:

25
TEMPERATURE

• MM5 tends to undere

temperature at TDL sat CASTNet sites for than 5month summe

• MM5 performs better 

for Summer than for 

• Unsystematic RMSE 

RMSE at TDL sites cat CASTNet sites, RMsimilar as RMSES

• MM5 shows good IO

(~0.9), better than at 

(~0.8)

Max Min Mean Max Min Mean

CASTNET 2.81 -3.59 -1.25 0.99 0.61 0.88

TDL 0.94 -2.73 -1.18 1.00 0.78 0.94

CASTNET 3.12 -3.77 -0.65 0.98 0.52 0.81

TDL 0.79 -2.64 -1.00 0.99 0.81 0.96

CASTNET 3.31 -4.39 -0.35 0.98 0.64 0.83

TDL 0.81 -2.65 -0.72 1.00 0.86 0.97

CASTNET 3.52 -2.97 -0.52 0.98 0.65 0.90

TDL 1.13 -2.06 -0.48 0.99 0.84 0.97

CASTNET 7.02 -2.83 0.67 0.99 0.51 0.87

TDL 1.27 -1.88 -0.18 0.99 0.86 0.96

CASTNET 7.13 -2.23 1.03 0.98 0.46 0.85

TDL 0.98 -1.21 -0.12 0.99 0.85 0.95

CASTNET 4.70 -2.40 0.34 0.97 0.60 0.85

TDL 0.74 -1.38 -0.34 0.98 0.75 0.92

CASTNET 9.03 -2.59 0.32 0.99 0.62 0.86

TDL 0.53 -1.28 -0.42 0.99 0.79 0.93

CASTNET 5.72 -2.37 0.76 0.97 0.52 0.86

TDL 0.34 -1.57 -0.54 0.99 0.76 0.94

CASTNET 5.17 -3.39 -0.56 0.99 0.55 0.89

TDL 0.29 -2.39 -0.79 0.99 0.86 0.97

CASTNET 2.83 -4.29 -1.25 0.99 0.58 0.88

TDL 0.25 -2.99 -1.35 0.99 0.82 0.95

CASTNET 1.98 -3.76 -1.17 0.98 0.52 0.88

TDL 0.29 -2.55 -1.20 0.99 0.80 0.94

SEPOCT

NOV

DECMAY

JUN

JUL

AUG

JAN

FEB

MAR

APR

Network Bias IOA

Temperature

2002

Appendix G Attachment 7b - Page Number:

26
HUMIDITY

Appendix G Attachment 7b - Page Number:

27
WINTER Humidity

Humidity - TDL

012345678

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g/kg

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Bias Humidity - TDL 

-1.0-0.8-0.6-0.4-0.2

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12

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RMSE Humidity - TDL

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1234567

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RMSE 

IOA Humidity TDL

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Appendix G Attachment 7b - Page Number:

28
SPRING Humidity

Humidity - TDL

02468

10121416

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

g/kg

Obs Prd

Bias Humidity - TDL

-2-1

01234

60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152

Julian Day

g/kg

RMSE Humidity - TDL

0.00.51.01.52.02.53.03.54.04.5

60 64 68 72 76 80 84 88 92 96 100 104 108 1

Julian Day

g/kg

RMSE 

IOA Humidity -TDL

0.00.10.20.30.40.50.60.70.80.91.0

60 64 68 72 76 80 84 88 92 96 100 104 108 11

Julian Day

Appendix G Attachment 7b - Page Number:

29
SUMMER Humidity

Humidity - TDL

02468

1012141618

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

g/kg

Obs Prd

Bias Humidity - TDL

-3.0-2.5-2.0-1.5-1.0-0.5

0.00.51.01.52.02.53.0

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244

Julian Day

g/kg

RMSE Humidity - TDL

0.00.51.01.52.02.53.03.5

152 156 160 164 168 172 176 180 184 188 192 196 200 20

Julian Day

g/kg

RMSE 

IOA Humidity - TDL

0.00.10.20.30.40.50.60.70.80.91.0

152 156 160 164 168 172 176 180 184 188 192 196 200 2

Julian Day

Appendix G Attachment 7b - Page Number:

30
FALL Humidity

Humidity - TDL

0123456789

101112131415

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

g/kg

Obs Prd

Bias Humidity - TDL

-2.0-1.5-1.0-0.5

0.00.51.01.52.0

243 247 251 255 259 263 267 271 275 279 283 287 291 295 299 303 307 311 315 319 323 327 331 335

Julian Day

g/kg

RMSE Humidity - TDL

0.00.51.01.52.02.53.0

243 247 251 255 259 263 267 271 275 279 283 287 291 2

Julian Day

g/kg

RMSE 

IOA Humidity - TDL

0.00.10.20.30.40.50.60.70.80.91.0

243 247 251 255 259 263 267 271 275 279 283 287 291 2

Julian Day

Appendix G Attachment 7b - Page Number:

31
HUMIDITY

• MM5 captures genera

humidity change

• MM5 tends to overest

humidity in Winter, Spbut underestimate hummonth Summer

• MM5 often shows larg

variations than observ

• Unsystematic RMSE d

RMSE

• MM5 shows good IOA 

year 

Max Min Mean Max Min Mean

JAN TDL -0.24 -1.54 0.45 0.99 0.60 0.89

FEB TDL 1.51 -0.07 0.48 0.99 0.54 0.87

MAR TDL 1.73 -0.10 0.52 0.99 0.47 0.91

APR TDL 3.48 -0.82 0.52 0.99 0.44 0.91

MAY TDL 0.92 -1.67 -0.02 0.99 0.48 0.91

JUN TDL 1.00 -2.27 -0.33 0.98 0.67 0.88

JUL TDL 0.87 -2.32 -0.55 0.97 0.57 0.86

AUG TDL 0.92 -2.17 -0.23 0.97 0.58 0.86

SEP TDL 1.64 -1.39 0.03 0.98 0.66 0.91OCT TDL 0.97 -0.67 0.15 0.99 0.80 0.96

NOV TDL 1.51 -0.17 0.34 0.99 0.56 0.92

DEC TDL 0.95 -0.17 0.34 0.99 0.54 0.91

Humidity

2002 Network Bias IOA

Appendix G Attachment 7b - Page Number:

32
Attachment 

7c_ZhangandZhang2005_MM5report2002data.pdf
1

Numerical Experimental Analysis Data for the Year of 2002

Da-Lin Zhang and Shunli Zhang

Department of Atmospheric and Oceanic Science, University of Maryland

College Park, MD 20742

Tel. (301) 405-2018; Email: dalin@atmos.umd.edu

1. Introduction

A total of 128 numerical experiments, in 3-day segments, for the year of 2002 (i.e.,

from 0000 UTC 14 December 2001 to 0000 UTC 1 January 2003) have been conducted 

on our newly purchased Cluster using the nested-grid (36/12 km) Version 3.6 of the

PSU/NCAR mesoscale model (i.e., MM5). The NCEP’s Eta analysis with 40-km

resolution was used to initialize the model integrations and specify the outmost lateral

boundary conditions. To minimize the influence of model errors but retain as many 

mesoscale circulations as possible, the dynamical nudging or four-dimensional data

assimilation (FDDA) technique was adopted to include observations of the surface winds

and upper-level meteorological information. More attention was paid to the accuracy of

surface winds due to their important roles in ozone transport. The model integrations

were re-initialized every 3.5 days, allowing a 12-h period for the model spin-up (i.e., the

first 12-h data could be truncated in the application of the datasets). Hourly model

outputs were archived for the period of 12.5 months. This four-dimensional high

resolution (in time and space) analysis dataset so assimilated was generated for air quality

modeling and for regional haze studies. These integrations yielded a total of 830 Gbytes

analysis data.

2. Model description

The Version 3.6 of MM5 with a Lambert conformal map projection, an MPP

Version developed for clusters, was used for this project. The (x, y) dimensions of the

coarse (36 km) and fine (12 km) mesh domains are 149 x 129 and 175 x 175,

respectively. The vertical discretion uses terrain-following σ-coordinates, but the pressure

at the σ-levels are determined from a reference state that is estimated using the

hydrostatic equation from a given sea-level pressure and temperature with a standard 

lapse rate. There are 30 uneven σ levels, giving 29 layers, with higher resolution in the

planetary boundary layer (PBL). The σ levels are placed at the following values:

1.000, 0.9974, 0.994, 0.989, 0.9820, 0.972, 0.959, 0.943, 0.923, 0.8990,

0.871, 0.839, 0.803, 0.763, 0.718, 0.668, 0.618, 0.568, 0.518, 0.468, 0.418,

0.368, 0.318, 0.268, 0.218, 0.168, 0.123, 0.080, 0.040, 0.00

The surface layer is defined at an altitude of about 10 m, the level at which surface winds

are typically observed. The model top is set at 50 hPa with a radiative upper boundary

condition. The time steps for the 36 km and 12 km resolution domains are 75 and 25

seconds, respectively.

Figs. 1 and 2 show the nested-grid (36/12 km) domain and the fine-mesh domain,

respectively, that were used for this project.

Domain 1 is centered at 400N latitude and 970W longitude with a grid size of 36 

km, and it covers the U.S. continents, Mexico, Canada, the Gulf of Mexico, and part of

the East Pacific and West Atlantic oceans.

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Fig. 1 The coarse-mesh (36 km) domain. Fig. 2 The fine-mesh (12-km) domain.

Domain 2 uses a grid size of 12 km, and it covers the northeastern, central and

southeastern US as well as Southeastern Canada.

The important model physics of the MM5 used for this project include:

(i) The latest version of the Kain-Fritsch (1993) convective scheme was used for

both 36- and 12-km resolution domains;

(ii) An explicit moisture scheme (without the mixed phase) containing prognostic

equations for cloud water (ice) and rainwater (snow) (Dudhia 1989; Zhang 1989);

(iii) A modified version of the Blackadar planetary boundary layer (PBL) scheme

(Zhang and Anthes 1982; Zhang and Zheng 2004);

(iv) A simple radiative cooling scheme (Grell et al. 1997);

(v) A multi-layer soil model to predict land surface temperatures using the surface

energy budget equation (Dudhia 1996).

Note that the Blackadar PBL scheme has been modified in order to reproduce the

diurnal cycles of surface winds and temperatures, after performing a comparative study

of the following five different PBL schemes: the Gayno-Seaman TKE scheme (Shafran et

al. 2000), Burk-Thompson (1989), Blackadar (Zhang and Anthes 1982), MRF (Hong and 

Pan 1996), and Miller-Yamada-Jajić (Miller and Yamada 1974; Jajić 1990, 1994). These

changes are given as follows (see Zhang and Zheng 2004 for more detail):

• K-coefficient is determined by the Richardson number according to Zhang and

Anthes (1982), where the critical Richardson number is set to be 0.25. In addition, the

mixing length is set to be the thickness of the model layer.

• Use of potential temperature rather than virtual potential temperature to calculate

the bulk Richardson number Rb.

3. Nudging Processes

The MM5 provides options for nudging observations for each domain during the

course of model integration (Stauffer and Seaman 1990; Stauffer et al. 1991). The Eta

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analyses of upper-air winds, temperature and water-vapor mixing ratio as well as their

associated surface fields, were nudged every 6 hours, and the higher-resolution surface

wind field was nudged every 3 hours. While only the surface winds were nudged, their

influences could be extended into the PBL (see Stauffer et al. 1991).

Based on our previous experience with many numerical experiments, the following

nudging coefficients have been used:

• Upper-air wind fields: 5.0E-4 for Domain 1, and 2.5E-4 for Domain 2;

• Upper-air temperature fields: 1.0E-5 for both Domains;

• Surface winds: 5.0E-4 for Domain 1, and 2.5E-4 for Domain 2; and

• Surface temperature and moisture: not nudged due to instability consideration.

4. Model initialization

The model is initialized with NCEP's Eta model analysis (ds609.2) as a first guess

that is then enhanced by observations at upper levels and the surface.

(i) NCEP’s ADP global upper-air observations (NCAR archive ds353.4) are used to

further enhance the upper-level Eta analysis.

(ii) The following two sets of surface observations have been introduced into the

model initial state to improve the Eta analysis of surface wind fields:

• The NCEP’s ADP global surface wind observations (NCAR archive ds464.0):

This dataset provides 6-hourly surface observations over land (i.e., at 0000, 0600, 1200,

1800 UTC) in one stream, and 3-hourly (i.e., at 0300, 0900, 1500, 2100 UTC) over both 

land and ocean surfaces in another stream.

• The TDL’s U.S. and Canadian surface observations (NCAR archive ds472.0): This

dataset provides hourly surface observations over the U.S. and Canadian regions.

The Eta model analysis has a domain covering the entire U.S. continents with a 40-

km horizontal resolution. It includes the following types of observations:

• Rawinsonde mass and wind;

• Piball winds;

• Dropwindsondes;

• Wind profiles;

• Surface land temperature and moisture;

• Oceanic surface data (ship and buoys);

• Aircraft winds;

• Satellite cloud-drift winds;

• Oceanic TOVS thickness retrievals;

• GOES and SSM/I precipitable water retrievals.

The Cressman objective analysis option was used to enhance the Eta analysis.

However, we analyzed the results and found that it still could not reproduce the right

diurnal cycle of surface winds and temperatures. Thus, we repeated the Cressman

procedures three more times to enhance the surface analyses. Results indicate that this

procedure significantly improved the results.

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Note that (i) because of the initial model spin-up, we recommend that the first 12-h

model integration of each run be discarded; and (ii) because the synoptic-scale upper-air

winds and temperatures were nudged, the flow fields above the PBL might contain less

smaller-scale features (e.g., in low-level jets, mountain-forced perturbations and etc.).

5. Data Archive

As mentioned above, we have conducted a total of 128 experiments, in 3-day 

segments, from 0000 UTC 14 December 2001 to 0000 UTC 1 January 2003. The

following table lists the experiments and their corresponding integration periods:

Exp. # Period Exp. # Period

1 00/15/12-00/18/12*01 2 00/18/12-12/21/12*01

3 00/21/12-00/24/12*01 4 00/24/12-00/27/12*01

5 00/27/12-00/30/12*01 6 00/30/12-00/02/01*02

7 00/02/01-00/05/01*02 8 00/05/01-00/08/01*02

9 00/08/01-00/11/01*02 10 00/11/01-00/14/01*02

11 00/14/01-00/17/01*02 12 00/17/01-00/20/01*02

13 00/20/01-00/23/01*02 14 00/23/01-00/26/01*02

15 00/26/01-00/29/01*02 16 00/29/01-00/01/02*02

17 00/01/02-00/04/02*02 18 00/04/02-00/07/02*02

Exp. # Period Exp. # Period

19 00/07/02-00/10/02*02 20 00/10/02-00/13/02*02

21 00/13/02-00/16/02*02 22 00/16/02-00/19/02*02

23 00/19/02-00/22/02*02 24 00/22/02-00/25/02*02

25 00/25/02-00/28/02*02 26 00/28/02-00/03/03*02

27 00/03/03-00/06/03*02 28 00/06/03-00/09/03*02

29 00/09/03-00/12/03*02 30 00/12/03-00/15/03*02

31 00/15/03-00/18/03*02 32 00/18/03-00/21/03*02

33 00/21/03-00/24/03*02 34 00/24/03-00/27/03*02

35 00/27/03-00/30/03*02 36 00/30/03-00/02/04*02

37 00/02/04-00/05/04*02 38 00/05/04-00/08/04*02

39 00/08/04-00/11/04*02 40 00/11/04-00/14/04*02

41 00/14/04-00/17/04*02 42 00/17/04-00/20/04*02

43 00/20/04-00/23/04*02 44 00/23/04-00/26/04*02

45 00/26/04-00/29/04*02 46 00/29/04-00/02/05*02

47 00/01/05-00/04/05*02 48 00/04/05-00/07/05*02

49 00/07/05-00/10/05*02 50 00/10/05-00/13/05*02

51 00/13/05-00/16/05*02 52 00/16/05-00/19/05*02

53 00/19/05-00/22/05*02 54 00/22/05-00/25/05*02

55 00/25/05-00/28/05*02 56 00/28/05-00/31/05*02

57 00/31/05-00/03/06*02 58 00/03/06-00/06/06*02

59 00/06/06-00/09/06*02 60 00/09/06-00/12/06*02

61 00/12/06-00/15/06*02 62 00/15/06-00/18/06*02

63 00/18/06-00/21/06*02 64 00/21/06-00/24/06*02

65 00/24/06-00/27/06*02 66 00/27/06-00/30/06*02

Appendix G Attachment 7c - Page Number:

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67 00/30/06-00/03/07*02 68 00/03/07-00/06/07*02

69 00/06/07-00/09/07*02 70 00/09/07-00/12/07*02

71 00/12/07-00/15/07*02 72 00/15/07-00/18/07*02

73 00/18/07-00/21/07*02 74 00/21/07-00/24/07*02

75 00/24/07-00/27/07*02 76 00/27/07-00/30/07*02

77 00/30/07-00/02/08*02 78 00/02/08-00/05/08*02

79 00/05/08-00/08/08*02 80 00/08/08-00/11/08*02

81 00/11/08-00/14/08*02 82 00/14/08-00/17/08*02

83 00/17/08-00/20/08*02 84 00/20/08-00/23/08*02

85 00/23/08-00/26/08*02 86 00/26/08-00/29/08*02

87 00/29/08-00/01/09*02 88 00/01/08-00/04/09*02

89 00/04/09-00/07/09*02 90 00/07/09-00/10/09*02

91 00/10/09-00/13/09*02 92 00/13/09-00/16/09*02

93 00/16/09-00/19/09*02 94 00/19/09-00/22/09*02

95 00/22/09-00/25/09*02 96 00/25/09-00/28/09*02

97 00/28/09-00/01/10*02 98 00/01/10-00/04/10*02

99 00/04/10-00/07/10*02 100 00/07/10-00/10/10*02

101 00/10/10-00/13/10*02 102 00/13/10-00/16/10*02

103 00/16/10-00/19/10*02 104 00/19/10-00/22/10*02

Exp. # Period Exp. # Period

105 00/22/10-00/25/10*02 106 00/25/10-00/28/10*02

107 00/28/10-00/31/10*02 108 00/31/10-00/03/11*02

109 00/03/11-00/06/11*02 110 00/06/11-00/09/11*02

111 00/09/11-00/12/11*02 112 00/12/11-00/15/11*02

113 00/15/11-00/18/11*02 114 00/18/11-00/21/11*02

115 00/21/11-00/24/11*02 116 00/24/11-00/27/11*02

117 00/27/11-00/30/11*02 118 00/30/11-00/03/12*02

119 00/03/12-00/06/12*02 120 00/06/12-00/09/12*02

121 00/09/12-00/12/12*02 122 00/12/12-00/15/12*02

123 00/15/12-00/18/12*02 124 00/18/12-00/21/12*02

125 00/21/12-00/24/12*02 126 00/24/12-00/27/12*02

127 00/27/12-00/30/12*02 128 00/30/12-00/02/01*03

The datasets listed above include the MM5 outputs from Domain 1 (36 km) and

Domain 2 (12 km), the analysis data used for FDDA, and initial and lateral boundary

conditions. If necessary, any of the experiments listed above could be re-run. The MM5 

outputs include the three-dimensional fields of temperature, horizontal winds, vertical

motion, pressure perturbations, moisture, cloud water/rain water/ice water/snow water

mixing ratio, and radiation tendency; and the two-dimensional fields of the map-scale

factor, longitude and latitude, Coriolis parameter, land use category, terrain height, PBL

depth, accumulated convective/non-convective precipitation, surface sensible/latent heat

flux. A FORTRAN program to read the datasets has also been included.

6. Acknowledgments

Appendix G Attachment 7c - Page Number:

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This project was funded by Maryland’s Department of Environment (MDE) and

Northeast States for Coordinated Air Use Management, Inc. (NESCAUM). New York

State Department of Environmental Conservation (NYDEC) has evaluated the MM5’s

performance with TDL and CASTNet measurements for the summer-season episodes of

6 – 16 August 2002 before the production of a complete 5-month simulation from 1 May 

to 30 September 2002. Similarly, NESCAUM evaluated the MM5 simulations of 23 – 29

January 2002 before the simulation of the winter-season episodes and the remaining

annual model simulations. We are very grateful to Gopal Sistla, Mike Ku, and Winston

Hao of NYSDEC, and Shan He and Gary Kleiman of NESCAUM for their careful

evaluations of the MM5 performance.

References

Burk, S. D., and W. T. Thompson, 1989: A vertically nested regional numerical weather

prediction model with second-order closure physics. Mon. Wea. Rev., 117, 2305–2324.

Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon

experiments using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–3107.

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1995: A description of the fifth-generation Penn

State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398 1 STR, 122 pp.

Hong, S.-H., and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range

forecast model. Mon. Wea. Rev., 124, 2322–2339.

Jajić, Z. I., 1990: The step-mountain coordinate: Physical package. Mon. Wea. Rev., 118, 1429-

1443.

, 1994: The step-mountain Eta coordinate model: Further development of the convection,

viscous sublayer and turbulent closure schemes. Mon. Wea. Rev., 122, 927-945.

Kain, J.S., and J.M. Fritsch, 1993: Convective parameterization for mesoscale models: The KainFritsch scheme. Cumulus Parameterization. Meteor. Monogr., 46, Amer. Meteor. Soc., 165-

170.

Mellor, G. L., and T. Yamada, 1974: A hierarchy of turbulence closure models for planetary

boundary layers. J. Atmos. Sci., 31, 1791–1806.

Shafran, P.C., N.L. Seaman, and G.A. Gayno, 2000: Evaluation of numerical predictions of 

boundary layer structure during the Lake Michigan ozone study. J. Appl. Meteor., 39, 412-

426.

Stauffer, D. R., N. L. Seaman and F. S. Binkowski, 1991: Use of four-dimensional data 

assimilation in a limited-area mesoscale model. Part II: Effects of data assimilation within the 

planetary boundary layer. Mon. Wea. Rev., 119, 734-754.

Stauffer, D. R. and N. L. Seaman, 1990: Use of four-dimensional data assimilation in a limitedarea mesoscale model. Part I: Experiments with synoptic-scale data. Mon. Wea. Rev., 118,

1250-1277.

Zhang, D.-L., 1989: The effect of parameterized ice microphysics on the simulation of vortex

circulation with a mesoscale hydrostatic model. Tellus, 41A, 132-147.

, and R. A. Anthes, 1982: A high-resolution model of the planetary boundary layer—

Sensitivity tests and comparisons with SESAME-79 data. J. Appl. Meteor., 21, 1594–1609.

, and W.-Z. Zheng, 2004: Diurnal cycles of surface winds and temperatures as simulated by

five boundary-layer parameterizations. J. Appl. Meteor., 43, 157-169.

Appendix G Attachment 7c - Page Number:

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Attachment 8a_SMOKE_ 

2002_Emissions Processing 

_Configuratio.pdf
 

Emission Processing for the Revised 2002 OTC Regional and 

Urban 12 km Base Case Simulations 

Bureau of Air Quality Analysis and Research 

Division of Air Resources 

New York State Department of Environmental Conservation 

September 19, 2006

Appendix G Attachment 8a - Page Number:

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1. Overview......................................................................................................................... 4 

2. Emission Inventories....................................................................................................... 4

2.1 MANE-VU................................................................................................................ 4 

2.1.1 Area Sources ...................................................................................................... 4 

2.1.2 Nonroad Sources................................................................................................ 4 

2.1.3 Mobile Sources .................................................................................................. 5 

2.1.4 Point Sources ..................................................................................................... 5 

2.2 CENRAP................................................................................................................... 5 

2.2.1 Area Sources ...................................................................................................... 5 

2.2.2 Nonroad Sources................................................................................................ 6 

2.2.3 Mobile Sources .................................................................................................. 6 

2.2.4 Point Sources ..................................................................................................... 6 

2.3 VISTAS..................................................................................................................... 6 

2.3.1 Area Sources ...................................................................................................... 7 

2.3.2 Nonroad Sources................................................................................................ 7 

2.3.3 Mobile Sources .................................................................................................. 7 

2.3.4 Point Sources ..................................................................................................... 7 

2.4 MRPO ....................................................................................................................... 7 

2.4.1 Area Sources ...................................................................................................... 8 

2.4.2 Nonroad Sources................................................................................................ 8 

2.4.3 Mobile Sources .................................................................................................. 8 

2.4.4 Point Sources ..................................................................................................... 8 

2.5 Canada....................................................................................................................... 9 

2.5.1 Area Sources ...................................................................................................... 9 

2.5.2 Nonroad Sources................................................................................................ 9 

2.5.3 Mobile Sources .................................................................................................. 9 

2.5.4 Point Sources ..................................................................................................... 9 

3. Mobile6 Processing....................................................................................................... 10 

3.1 MANE-VU.............................................................................................................. 10 

3.1.1 Mobile6 input files........................................................................................... 10 

3.1.2 SMOKE/Mobile6 auxiliary files...................................................................... 11 

3.1.3 Temperature averaging .................................................................................... 11 

3.2 CENRAP................................................................................................................. 11 

3.2.1 Mobile6 input files........................................................................................... 11 

3.2.2 SMOKE/Mobile6 auxiliary files...................................................................... 11 

3.2.3 Temperature averaging .................................................................................... 11 

3.3 VISTAS................................................................................................................... 12

3.3.1 Mobile6 input files........................................................................................... 12 

3.3.2 SMOKE/Mobile6 auxiliary files...................................................................... 12 

3.3.3 Temperature averaging .................................................................................... 12 

3.4 MRPO ..................................................................................................................... 12

3.4.1 Mobile6 input files........................................................................................... 12 

3.4.2 SMOKE/Mobile6 auxiliary files...................................................................... 12 

3.4.3 Temperature averaging .................................................................................... 13 

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4. Biogenic Emission Processing...................................................................................... 13 

5. Temporal Allocation ..................................................................................................... 13 

5.1 MANE-VU.............................................................................................................. 13 

5.1.1 Area and nonroad sources................................................................................ 13 

5.1.2 Mobile sources................................................................................................. 13 

5.1.3 Point Sources ................................................................................................... 13 

5.2 CENRAP................................................................................................................. 14 

5.3 VISTAS................................................................................................................... 14

5.4 MRPO ..................................................................................................................... 14

5.5 Canada..................................................................................................................... 15 

6. Speciation...................................................................................................................... 15 

7. Spatial Allocation.......................................................................................................... 15 

7.1 U.S. ......................................................................................................................... 15 

7.2 Canada..................................................................................................................... 15 

Appendix G Attachment 8a - Page Number:

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1. Overview 

All emissions processing for the revised 2002 OTC regional and urban 12 km 

base case simulations was performed with SMOKE2.1 compiled on a Red Hat 9.0 Linux 

operating system with the Portland group fortran compiler version 5.1. The emissions 

processing was performed on a month-by-month and RPO-by-RPO basis, i.e. SMOKE 

processing was performed for each month for each of the RPOs (MANE-VU, VISTAS, 

CENRAP, MRPO) individually as well as for Canada. For each month/RPO combination, 

a separate SMOKE ASSIGNS file was created, and the length of the episode in each of 

these ASSIGNS files was set to the entire month. Also, as discussed in Section 3, there 

was no difference between “episode-average” temperatures and “monthly-average” 

temperatures for the Mobile6 simulations that used the option of temperature averaging. 

This document is structured as follows: A listing of all emission inventories is 

given in Section 2, organized by RPO and source category. Section 3 discusses the 

Mobile6 processing approach employed for the different RPOs, while Section 4 describes 

the processing of biogenic emissions with BEIS3.12. Finally, Sections 5 through7 

describe the temporal allocation, speciation, and spatial allocation of the emissions 

inventories, respectively. 

2. Emission Inventories 

2.1 MANE-VU 

Version 3 of the MANE_VU inventory was utilized to generate CMAQ-ready 

emissions. This emissions inventory data were obtained from the MANEVU archive in 

April 2006. 

2.1.1 Area Sources

• Files: 

MANEVU_AREA_SMOKE_INPUT_ANNUAL_SUMMERDAY_040606.txt 

and MANEVU_AREA_SMOKE_INPUT_ANNUAL_WINTERDAY_040606.txt 

prepared by PECHAN, downloaded from ftp.marama.org (username mane-vu, 

password exchange) 

• Fugitive dust correction: This was applied as county-specific correction factors 

for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the 

correction factor file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA 

ftp site http://www.airmodelingftp.com (password protected).; this adjustment 

was performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing 

2.1.2 Nonroad Sources

• File: MANEVU_NRD2002_SMOKE_030306 prepared by PECHAN; 

downloaded from ftp.marama.org (username mane-vu, password exchange) 

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2.1.3 Mobile Sources

• VMT/Speed: MANEVU_2002_mbinv_02022006_addCT.txt prepared by 

PECHAN and NESCAUM; downloaded from 

http://bronze.nescaum.org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar 

2.1.4 Point Sources

• Files: 

MANEVU_Point_SMOKE_INPUT_ANNUAL_SUMMERDAY_041006.txt and 

MANEVU_Point_SMOKE_INPUT_ANNUAL_WINTERDAY_041006.txt 

prepared by PECHAN were downloaded from ftp.marama.org (username manevu, password exchange) 

• Fugitive dust correction: This was applied as county-specific correction factors 

for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the 

correction factor file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA 

ftp site http://www.airmodelingftp.com (password protected).; this adjustment 

was performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing 

• Corrected the omission of 2,100 tons/year VOC emissions from several point 

sources in NJ. NJDEP provided updated IDA files on June 30 that were used for 

modeling. 

2.2 CENRAP 

The inventory data were obtained from the CENRAP ftp site in March 2006 and 

reflect version BaseB of the CENRAP inventory. 

2.2.1 Area Sources

• Files: 

o CENRAP_AREA_SMOKE_INPUT_ANN_STATES_081705.txt 

o CENRAP_AREA_MISC_SMOKE_INPUT_ANN_STATE_071905.txt 

o CENRAP_AREA_BURNING_SMOKE_INPUT_ANN_TX_ 

NELI_071905.txt 

o CENRAP_AREA_MISC_SMOKE_INPUT_NH3_MONTH_{MMM} 

_072805.txt where {MMM} is JAN, FEB, ... DEC 

o CENRAP_AREA_SMOKE_INPUT_NH3_MONTH_{MMM} 

_071905.txt where {MMM} is JAN, FEB, ... DEC 

• Fugitive dust correction: This was applied as county-specific correction factors 

for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the 

correction factor file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA 

ftp site http://www.airmodelingftp.com (password protected).; this adjustment 

was performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing 

• Note about area and nonroad source SMOKE processing for the CENRAP region: 

All area source inventories (both annual and month-specific) were processed in 

Appendix G Attachment 8a - Page Number:

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one step through SMOKE. SMK_AVEDAY_YN was set to N, so seasonal 

profiles were used to apportion the annual inventories numbers by month. This 

setting was also used for the nonroad processing performed in a separate step. 

This was necessary since the month-specific files had zero in their ‘average-day’ 

column and the annual total column reflects the “monthly emissions as annual 

totals” as per header line. Therefore, seasonal profiles are used to apportion both 

the annual and month-specific files. As described below, we utilized the temporal 

profiles and cross-reference files generated by CENRAP. However, we did not 

verify that this approach indeed leads to the intended monthly allocation of 

ammonia and nonroad emissions. 

2.2.2 Nonroad Sources

• Files: 

o CENRAP_NONROAD_SMOKE_INPUT_ANN_071305.txt 

o CENRAP_NONROAD_SMOKE_INPUT_MONTH_{MMM}_071305.txt 

where {MMM} is JAN, FEB, ... DEC 

2.2.3 Mobile Sources

• VMT/Speed files: 

o mbinv02_vmt_cenrap_ce.ida 

o mbinv02_vmt_cenrap_no.ida 

o mbinv02_vmt_cenrap_so.ida 

o mbinv02_vmt_cenrap_we.ida 

2.2.4 Point Sources

• File: CENRAP_POINT_SMOKE_INPUT_ANNUAL_DAILY_072505.txt 

• Fugitive dust correction: This was applied as county-specific correction factors 

for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the 

correction factor file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA 

ftp site http://www.airmodelingftp.com (password protected).; this adjustment 

was performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing. 

2.3 VISTAS 

All VISTAS emission files were obtained from the Alpine Geophysics ftp site. They 

reflect version BaseG of the VISTAS inventory with the exception of fire emissions 

which reflect BaseF and BaseD. These files were downloaded between February and 

August, 2006. 

Appendix G Attachment 8a - Page Number:

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2.3.1 Area Sources

• Files: 

o arinv_vistas_2002g_2453922_w_pmfac.txt 

o ida_ar_fire_2002_vistaonly_basef.ida 

• Note: the header lines of these files indicate that the fugitive dust correction was 

already applied, so no further correction was performed. 

2.3.2 Nonroad Sources

• Files: 

o nrinv_vistas_2002g_2453908.txt 

o marinv_vistas_2002g_2453972.txt 

2.3.3 Mobile Sources

• VMT/Speed file: mbinv_vistas_02g_vmt_12jun06.txt 

2.3.4 Point Sources

• Files: 

o Annual: 

 egu_ptinv_vistas_2002typ_baseg_2453909.txt 

 negu_ptinv_vistas_2002typ_baseg_2453909.txt 

 ptinv_fires_{MM}_typ.vistas.ida where {MM} is 01, 02, 03, etc. 

depending on the month; these annual point fire files were 

generated as part of the VISTAS BaseD inventory and were 

obtained in January 2005 

o Hour-specific: 

 pthour_2002typ_baseg_{MMM}_28jun2006.ems where {MMM} 

is jan, feb, mar, etc. 

 pthour_fires_{MM}_typ.vistas.ida where {MM} is 01, 02, 03, etc. 

depending on the month; these hourly point fire files were 

generated as part of the VISTAS BaseD inventory and were 

obtained in January 2005 

• Note: No fugitive dust correction was performed for these files. 

2.4 MRPO 

MRPO emissions for SMOKE modeling were generated by Alpine Geophysics through a 

contract from MARAMA to convert the MRPO BaseK inventory from NIF to IDA 

format. The files were downloaded from the MARAMA ftp site ftp.marama.org

(username mane-vu, password exchange) between April and June 2006. 

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2.4.1 Area Sources

• Files: 

o Annual: 

 arinv_mar_mrpok_2002_27apr2006.txt 

 arinv_other_mrpok_2002_20jun2006.txt 

o Month-specific: 

 arinv_nh3_2002_mrpok_{mmm}_3may2006.txt where {mmm} is 

jan, feb, etc. 

 dustinv_2002_mrpok_{mmm}_23may2006.txt where {mmm} is 

jan, feb, etc. 

• Fugitive dust correction: This correction was performed only to the 

arinv_other_mrpok_2002_20jun2006.txt file using county-specific correction 

factors for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the correction factor 

file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA ftp site 

http://www.airmodelingftp.com (password protected).; this adjustment was 

performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing. 

• Note about area source SMOKE processing: SMOKE processing was performed 

separately for the annual and month-specific files. For the annual inventory 

processing, SMK_AVEDAY_YN was set to N, so seasonal profiles were used to 

apportion the annual inventories numbers by month. For the month-specific 

inventory processing, this variable was set to Y so that no seasonal profiles would 

be applied and the inventory numbers in the ‘average day’ column would be used. 

To save a SMOKE processing step, the annual “marine” inventory 

“arinv_mar_mrpok_2002_27apr2006.txt” was processed together with the annual 

“other area source” inventory “arinv_other_mrpok_2002_20jun2006.txt” even 

though it technically is part of the nonroad inventory. 

2.4.2 Nonroad Sources

• Files: nrinv_2002_mrpok_{mmm}_3may2006.txt where {mmm} is jan, feb, etc. 

2.4.3 Mobile Sources

• VMT/Speed file: mbinv_mrpo_02f_vmt_02may06.txt 

2.4.4 Point Sources

• Files: ptinv_egu_negu_2002_mrpok_1may2006.txt 

• Fugitive dust correction: This correction was performed only to the 

arinv_other_mrpok_2002_20jun2006.txt file using county-specific correction 

factors for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the correction factor 

file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA ftp site 

http://www.airmodelingftp.com (password protected).; this adjustment was 

Appendix G Attachment 8a - Page Number:

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9

performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing. 

2.5 Canada 

2.5.1 Area Sources

• File: AS2000_SMOKEready.txt obtained from 

ftp://ftp.epa.gov/EmisInventory/canada_2000inventory

• Fugitive dust correction: We applied “divide-by-four” correction for SCC’s listed 

at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; this adjustment was 

performed outside SMOKE with in-house Fortran programs. No county/provincespecific correction factors were available for Canada 

2.5.2 Nonroad Sources

• File: NONROAD2000_SMOKEready.txt obtained from 

ftp://ftp.epa.gov/EmisInventory/canada_2000inventory

2.5.3 Mobile Sources

• File: MOBILE2000_SMOKEready.txt obtained from 

ftp://ftp.epa.gov/EmisInventory/canada_2000inventory

• Fugitive dust correction: applied “divide-by-four” correction for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; this adjustment was 

performed outside of SMOKE with in-house Fortran programs. No 

county/province-specific correction factors were available for Canada. 

2.5.4 Point Sources

There has long been difficulty in obtaining an up-to-date Canadian criteria 

emissions inventory for point sources. This is due largely to confidentiality rights 

afforded to Canadian facilities. Thus far, the most recent inventory of Canadian point 

sources is rooted in the 1985 NAPAP data and is close to two decades old. Because there 

are a number of high emitting industrial facilities in southern Canada it is of particular 

importance to have a reasonably accurate inventory of these sources especially when 

modeling air quality over the Northeast and Midwest United States. Toward this end, an 

effort was made to obtain more recent Canadian point source data and incorporate it into 

an inventory database, which could then be used for the 2002 OTC air quality modeling. 

Perhaps the most accurate and publicly accessible source of Canadian pollutant 

data is now available from the National Pollutant Release Inventory (NPRI) database. 

This database contains 268 substances. Facilities that manufacture, process or otherwise 

use one of these substances and that meet reporting thresholds are required to report these 

emissions to Environment Canada on an annual basis. The NPRI data are available at 

Environment Canada’s website and can be found at the link 

Appendix G Attachment 8a - Page Number:

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10

http://www.ec.gc.ca/pdb/npri/npri_home_e.cfm. The page hosts an on-line search engine 

where one can locate emissions by pollutant or location. In addition, the entire database is 

available for download as an MS Access or Excel file. The NPRI database contains 

numerous pages with a rather comprehensive list of information. Detailed information is 

available about each facility, including location, activity and annual emissions. In 

addition, facilities having stacks with a height of 50 meters or more are required to report 

stack parameters. 

Unfortunately, one of the limitations of the NPRI database for modeling purposes 

is that the data are only available at the facility level. Emissions models require process 

level information, so in order to use this data, a few generalizations had to be made. Each 

facility has a Standard Industrial Classification (SIC) code associated with it; however, 

emissions models require Source Classification Codes (SCC’s). SCC’s are of critical 

importance as the emissions models use these codes for assignment of temporal and 

speciation profiles. SIC codes describe the general activity of a facility while SCC codes 

describe specific processes taking place at each facility. While no direct relationship 

exists between these two codes, a general albeit subjective association can be made. 

For the purposes of creating a model-ready inventory file it was necessary to obtain the 

whole NPRI database. After merging all the necessary components from the NPRI 

database required in the SMOKE inventory file, the SIC code from each facility was 

examined and assigned an SCC code. In most cases, only a SCC3 level code was 

assigned with confidence. While this is admittedly a less than desirable process, it does 

allow for the use of the most recent emissions from the NPRI database to be used in 

modeling. Furthermore, having some level of SCC associated with these emissions will 

ensure that they will be assigned a temporal and speciation profile by the model, other 

than the default. Once the model-ready inventory file was developed, it was processed 

through SMOKE. 

3. Mobile6 Processing 

3.1 MANE-VU 

3.1.1 Mobile6 input files

• Month-specific input files were prepared by PECHAN and NESCAUM and were 

downloaded from http://bronze.nescaum.org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar 

• Added the line “REBUILD EFFECTS :0.10” to each file before the 

SCENARIO record to override the Mobile6 default setting of 0.9 (90%) for the 

“chip reflash” effectiveness 

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3.1.2 SMOKE/Mobile6 auxiliary files

• SMOKE/Mobile6 auxiliary files were prepared by PECHAN and NESCAUM and 

were downloaded from http://bronze.nescaum.org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar 

3.1.3 Temperature averaging

• Following the setting in the MANEVU_2002_mvref.txt files, the following 

procedures were used by SMOKE for temporal and spatial temperature averaging 

in the calculation of emission factors: 

o Spatial averaging: temperatures were averaged over all counties that share 

a common reference county (i.e. Mobile6 input file) 

o Temporal averaging for May – September emissions processing: no 

temporal averaging was used, i.e. day-specific temperatures were used to 

calculate emission factors for each day. 

o Temporal averaging for non-summer-months emissions processing: 

Temporal averaging over the duration of the episode (i.e. the entire month, 

see introduction) was used, i.e. monthly average temperatures were used 

to calculate the emission factors. 

3.2 CENRAP 

3.2.1 Mobile6 input files

• Mobile6 input files for the CENRAP region for January and July were contained 

in the files central_M6_{MMM}.zip, north_M6_{MMM}.zip, 

south_M6_{MMM}.zip, west_M6_{MMM}.zip where {MMM} is either jan or 

jul. July input files were used for April – September processing, while January 

input files were used for the remaining months 

• All files were downloaded from the CENRAP ftp site in March 2006. 

3.2.2 SMOKE/Mobile6 auxiliary files

• SMOKE/Mobile6 auxiliary files were contained in the files central_M6_RD.zip, 

north_M6_RD.zip, south_M6_RD.zip, and west_M6_RD.zip. The SMOKE 

MCREF, MVREF, and MCODES files were contained in the file 

MOBILESMOKE_Inputs.zip. The MCREF and MVREF files were combined for 

the different regions (“central”, “east”, “west”, “north”) 

• All files were downloaded from the CENRAP ftp site in March 2006. 

3.2.3 Temperature averaging

• The following procedures were used by SMOKE for temporal and spatial 

temperature averaging in the calculation of emission factors according to the 

setting in the mvref files: 

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12

o Spatial averaging: no spatial averaging of temperatures, i.e. the 

temperatures for the reference county is used to calculate emission factors 

for all counties that share this reference county (i.e. Mobile6 input file) 

o Temporal averaging: Temporal averaging over the duration of the episode 

(i.e. the entire month, see introduction) was used, i.e. monthly average 

temperatures were used to calculate the emission factors. 

3.3 VISTAS 

3.3.1 Mobile6 input files

• Month-specific Mobile6 input files were obtained from the Alpine Geophysics ftp 

site in July 2006. They reflect version BaseG of the VISTAS inventory. 

3.3.2 SMOKE/Mobile6 auxiliary files

• SMOKE/Mobile6 auxiliary files utilized were obtained from the Alpine 

Geophysics ftp site in July 2006. They reflect version BaseG of the VISTAS 

inventory. 

3.3.3 Temperature averaging

• The following procedures were used by SMOKE for the temporal and spatial 

temperature averaging in the calculation of emission factors according to the 

setting in the mvref_baseg.36k.ag.txt file: 

o Spatial averaging: temperatures averaged over all counties that share a 

common reference county (i.e. Mobile6 input file) 

o Temporal averaging: Temporal averaging over the duration of the episode 

(i.e. the entire month, see introduction) was used, i.e. monthly average 

temperatures were used to calculate the emission factors. 

3.4 MRPO 

3.4.1 Mobile6 input files

• Month-specific Mobile6 input files for SMOKE modeling were generated by 

Alpine Geophysics through a contract from MARAMA. They are based on 

version BaseK of the MRPO inventory. The files were downloaded from the 

MARAMA ftp site ftp.marama.org (username mane-vu, password exchange) in 

May 2006. 

3.4.2 SMOKE/Mobile6 auxiliary files

• SMOKE/Mobile6 auxiliary files for SMOKE modeling were generated by Alpine 

Geophysics through a contract from MARAMA. They are based on version 

BaseK of the MRPO inventory. The files were downloaded from the MARAMA 

ftp site ftp.marama.org (username mane-vu, password exchange) in May 2006. 

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3.4.3 Temperature averaging

• The following procedures were used by SMOKE for the temporal and spatial 

temperature averaging in the calculation of emission factors according to the 

setting in the mvreg_mrpo_basek.txt file: 

o Spatial averaging: temperatures averaged over all counties that share a 

common reference county (i.e. Mobile6 input file) 

o Temporal averaging: Temporal averaging over the duration of the episode 

(i.e. the entire month, see introduction) was used, i.e. monthly average 

temperatures were used to calculate the emission factors. 

4. Biogenic Emission Processing 

Hourly gridded biogenic emissions for the 12 km and 36 km modeling domains 

were calculated by BEIS3.12 through SMOKE, using MCIP-processed MM5 fields for 

temperature (“TA”, layer-1 temperature), solar radiation (“RGRND”), surface pressure 

(“PRES”), and precipitation (“RN” and “RC”). A ‘seasonal switch’ file was generated by 

the SMOKE utility metscan to determine whether winter or summer emission factors 

should be used for any given grid cell on any given day. Winter emission factors are used 

from January 1st through the date of the last frost and again from the data of the first frost 

in fall through December 31st. Summer emission factors are used for the time period in 

between. This calculation is performed separately for each grid cell. 

5. Temporal Allocation 

5.1 MANE-VU 

5.1.1 Area and nonroad sources

• Generated as part of the MANE-VU version 1 inventory 

• amptpro.m3.us+can.manevu.030205.txt 

• amptref.m3.manevu.012405.txt 

• downloaded from ftp.marama.org (username mane-vu, password exchange) in 

January 2005 

5.1.2 Mobile sources

• MANEVU_2002_mtpro_02022006_addCT.txt 

• MANEVU_2002_mtref_02022006_addCT.txt 

• prepared by PECHAN and NESCAUM and downloaded from 

http://bronze.nescaum.org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar 

5.1.3 Point Sources

• Based on the same files as for the MANE-VU area and nonroad temporal files 

listed above, but added the CEM-based 2002 state-specific temporal profiles and 

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14

cross-references for EGU sources for the MANE-VU states that were generated 

by VISTAS for their BaseD modeling and obtained in February 2005. 

• No CEM-based hour-specific EGU emissions were utilized 

5.2 CENRAP 

The following temporal profiles and cross-reference files were used: 

• Area and nonroad sources: 

o amptpro.m3.us+can.cenrap.010605_incl_nrd.txt 

o amptref.m3.cenrap.010605_add_nh3_and_nrd.txt 

• Mobile sources: 

o mtpro.cenrap.v3.txt 

o mtref.cenrap.v3.txt 

• Point sources: 

o ptpro.{QQ}.cenrap_egus_cem.00-03avg.121205.txt where {QQ} is Q1 for 

January/February/March, Q2 for April/May/June, etc. 

o ptref.{QQ}.cenrap_egus_cem.00-03avg.121205.txt where {QQ} is Q1 for 

January/February/March, Q2 for April/May/June, etc. 

• All files were downloaded from the CENRAP ftp site in March 2006. 

5.3 VISTAS 

The following month-specific temporal profiles and cross-reference files were used: 

• Area and nonroad sources: 

o atpro_vistas_basef_15jul05.txt 

o atref_vistas_basef_15jul05.txt 

• Mobile sources: 

o mtpro_vistas_basef_04jul05.txt 

o mtref_us_can_vistas_basef_04jul05.txt 

• Point sources: 

o ptpro_typ_{MMM}_vistasg_28jun2006.txt where {MMM} is jan, feb, 

mar, etc. 

o ptref_typ_vistas_baseg_28jun2006.txt 

• These files were obtained from the Alpine Geophysics ftp site. They reflect 

version BaseG of the VISTAS inventory for the point source allocation files and 

version BaseF for the area, nonroad, and mobile source allocation files. These 

files were downloaded between February and July, 2006. 

5.4 MRPO 

The following month-specific temporal profiles and cross-reference files were used for 

all source categories: 

• amptpro_typ_us_can_{MMM}_vistas_27nov04.txt where {MMM} is jan, feb, 

mar, etc. 

• amptref_2002_us_can_vistas_17dec04.txt 

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15

• These files were obtained from VISTAS in January 2005 and reflect their BaseD 

modeling. No updated temporal profiles or cross-reference files were developed 

for use with the MRPO BaseK inventory. 

5.5 Canada 

For Canada, the SMOKE2.1 default temporal profiles and cross-reference files 

(amptpro.m3.us+can.txt and amptref.m3.us+can.txt) were utilized. 

6. Speciation 

The same speciation profiles (gspro.cmaq.cb4p25.txt) and cross-references 

(gsref.cmaq.cb4p25.txt) were utilized for all regions and all source categories. Different 

versions of these files were obtained (SMOKE2.1 default, EPA-CAIR modeling, 

VISTAS, CENRAP and MANE-VU) and compared. After comparing the creation dates 

and header lines of these files, it was determined that the EPA-CAIR and MANE-VU 

files had the most recent updates, and consequently the final speciation profile and crossreference files used for all regions and source categories was based on the EPA-CAIR 

files with the addition of MANE-VU specific updates. 

7. Spatial Allocation 

7.1 U.S. 

The spatial surrogates for the 12 km domain were extracted from the national grid 12 km 

U.S. gridding surrogates posted at EPA’s website at 

http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html

The gridding cross-references were also obtained from this website, but for the 

processing of MANE-VU area source emissions, MANE-VU specific cross-reference 

entries posted on the MARAMA ftp site were added. 

7.2 Canada 

The spatial surrogates for Canadian emissions for the 12 km domain were extracted from 

the national grid 12 km Canadian gridding surrogates posted at EPA’s website at 

http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html

The gridding cross-references were also obtained from this website. 

Appendix G Attachment 8a - Page Number:

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Attachment 

8b_SMOKE_2009_OTWOTB_ 

Emissions Processing.pdf
Emission Processing for OTC 2009 OTW/OTB

12km CMAQ Simulations

Office of Air Data Analysis

Air Division

Virginia Department of Environmental Quality

February, 2007

1

Appendix G Attachment 8b - Page Number:

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Overview

The OTC 2009 OTW/OTB emission modeling was conducted at the Virginia

Department of Environmental Quality (DEQ). The modeling followed and retained the

framework of the previous (original) OTC 2002/2009 emission modeling done by the New

York State Department of Environmental Conservation (NYSDEC). Several changes and

corrections had been made throughout the entire modeling period. Virginia DEQ was in

close contact with NYSDEC which provided many premerged netCDF files for inclusion

in the merging process to obtain final SMOKE outputs for CMAQ simulations.

Emissions for all source categories were processed by SMOKE2.1. The SMOKE

programs downloaded from Community Modeling and Analysis System (CMAS) website

have been compiled for LINUX system and ready for usage. If existing compiled codes

returned errors, such as in the case of large MCIP files, compiled versions provided by

NYSDEC and available at Ozone Research Center’s (ORC) ftp sites were used instead.

Data Sources

The majority of raw input data files were provided to DEQ by Greg Stella of

AlpineGeophysics through its ftp site at alpinegeophysics.com. Different versions of 2009

SMOKE emission modeling have been conducted over the years by AlpineGeophysics. The

version of input data files used for OTC 2009 OTW/OTB emission modeling was labeled

as BaseG of the AlpineGeophysics.

In some source categories, primarily in MANEVU and Canada regions, several changes

and corrections in emissions were made at various stage of SMOKE modeling, causing

the outputs using AlpineGeophysics files to be discarded. SMOKE modeling of those

categories (described below) was performed by NYSDEC which made netCDF outputs

available at Ozone Research Center’s ftp site at ozoneresearch.org. In such cases, DEQ

used the premerged netCDF files directly for final merging.

SMOKE Processing

The OTC 12km regional and urban scale modeling domain encompasses four RPOs:

VISTAS, MANEVU, CENRAP, and MRPO. Part of Canada also falls in the modeling

domain.

The OTC 2009 OTW/OTB emissions were processed roughly on a month-by-month

and RPO-by-RPO basis. SMOKE modeling was conducted for each month for each of the

four individual RPOs as well as for Canada (completed by NYSDEC), except for mobile

source category, which was done by two sub-RPOs: one for MANEVU and the other for

the combination of VISTAS, CENRAP, and MRPO. A separate SMOKE ASSIGNS file

was created for each RPO and/or source category. The episode length in the ASSIGNS

files varies from one month to the entire year.

2

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Five major emission source categories (listed below) were included in the OTC 2009

OTW/OTB SMOKE modeling. Sub-categories were lumped into the major categories

here for presentation purpose but were treated as separate categories in processing. For

example, low-level wildfire was treated as area source, whereas high-level wildfire was

modeled as point source. In addition, point source category was further divided into EGU

and non-EGU. Minor sources such as non-fossil fuels and marine vassel were processed as

well. Table 1 summarizes input files and other relevant information for each of the RPOs

and Canada.

(1). Area (including low-level wildfire and NH3);

(2). Nonroad (including marine vassel);

(3). Point (including EGU, Non-EGU, non-fossil fuels and wildfires);

(4). Mobile;

(5). Biogenic.

For VISTAS region (only), AlpineGeophysics has developed annual, daily, or hourly

emissions data for EGU and high-level wildfire source categories. SMOKE run script

parameters of DAY SPECIFIC YN and/or HOUR SPECIFIC YN were turned on (to Y)

and month-specific temporal profiles of BaseG were applied to make sure those more

detailed inventory files were used to override annual emissions.

Mobile source emissions were divided into two groups for processing. The original

input file (mbinv vistas 09g vmt 12jun06.txt) provided by AlpineGeophysics contains

VMT data for all four RPOs. The MANEVU portion was first removed from the original

file and the revised file (otherRPOs.mb.vmt.emis) which contains VMT data for the

remaining three RPOs (VISTAS, CENRAP, MRPO) was then used as the input inventory

for processing. The MANEVU portion removed from the original file was processed

separately on its own as another group.

MOBILE6 Processing

As described above, mobile source emissions for three RPOs — VISTAS, CENRAP,

and MRPO — were grouped and processed together. To estimate vehicle emission factors

in MOBILE6, temperature averaging of space and time were specified in input file of

mvref vistas 2009g 26aug06.txt as follows:

(1). Spatial averaging: temperatures were averaged over all counties that share a

common reference county;

(2). Temporal averaging: temperatures were averaged over the duration of the episode,

which in present case is one month.

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The averaging described above is consistent with the original OTC 2002/2009 emission

processing done by NYSDEC. DEQ also processed MANEVU portion of mobile source.

However, due to the inconsistency of temporal profile and cross-reference file used between

DEQ’s run and the original 2002/2009 run by NYSDEC, those outputs were discarded.

NYSDEC re-processed the MANEVU portion and provided netCDF files to DEQ for final

merging. The re-processed MANEVU run by NYSDEC reflects updated mobile source

information in New Jersey and Connecticut.

Speciations, Temporal and Spatial Allocations

For consistency, the OTC 2009 OTW/OTB input profiles for speciations, temporal,

and spatial allocations remained the same as the original OTC 2002/2009 emission

modeling done by NYSDEC, even though more up-to-date profiles (such as those marked

with BaseG or later) were available at the AlpineGeophysics. No attempt was made to

examine the effects of different versions of profiles on daily emissions.

Fugitive Dust Corrections

Fugitive dust emissions were corrected in SMOKE by two-step process. First,

SMKINVEN and CNTLMAT were executed with two separate input files: (1) the original

inventory file, and (2) a controlled matrix file of 2009 dust projection factors. A new

inventory file containing adjusted emissions was created in SMKINVEN/CNTLMAT run.

The new file was then used as the inventory input for regular SMOKE processing of

SMKINVEN, SPCMAT, GRDMAT, TEMPORAL, LAYPOINT (for point source), and

SMKMERGE. The source categories which went through this two-step process included

non-EGU for VISTAS, MANEVU, CENRAP, and MRPO, and area sources for MANEVU

and CENRAP.

Canadian and Biogenic Emissions

Canadian emissions of all four source categories (area, nonroad, point, mobile) and

domain-wide biogenic emissions were processed by NYSDEC. Details on how emission

modeling of these categories was conducted have been documented in “Emission Processing

for the Revised 2002 OTC Regional and Urban 12 km Base Case Simulations” by NYSDEC.

DEQ obtained premerged netCDF files for these source categories from ORC ftp site and

used them directly for final merging.

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Premerged netCDF Files

In December 2006, NYSDEC made further adjustments to ammonia and dust

emissions of MRPO region and ran through SMOKE with the adjusted emissions. Three

of MRPO’s source categories were affected: area, nonroad, and NH3. As a result,

outputs generated by DEQ for the three affected MRPO’s categories were discarded.

Canadian emissions of all four source categories (area, nonroad, point and mobile) were

also re-processed by NYSDEC with updated information. Seven newer versions, three for

MRPO and four for Canada, of premerged netCDF files reflecting the adjustments were

made available at ORC ftp site. The updated premerged netCDF files were used to replace

earlier versions in the final merging process..

SMOKE Merging

A total of twenty-seven netCDF files were merged together to produce daily total

emissions for use as inputs to CMAQ:

(1). Six for VISTAS (excluding mobile);

(2). Five for MANEVU (excluding mobile);

(3). Four for CENRAP (excluding mobile);

(4). Five for MRPO (excluding mobile);

(5). Two for mobile source emissions;

(6). Four for Canadian emissions;

(7). One for domain-wide biogenic emissions.

Table 1 lists the categories (indicated by sequential numbers) which were combined

in the merging process.

BOTW Emissions

The differences between 2009 BOTW and 2009 OTW/OTB emissions lie in the area

and non-EGU sources of MANEVU region where more controlled emissions are in effect

for BOTW than for OTW/OTB. NYSDEC generated premerged netCDF files for BOTW

run. To obtain 2009 OTC BOTW emissions, the two affected MANEVU source categories

for OTW/OTB run were substituted and replaced by the new BOTW premerged files in

the final merging process.

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Appendix G Attachment 8b - Page Number:

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Table 1. 2009 OTW/OTB Emissions Processing Summary

Files Files Source Notes

VISTAS

s 2009g 2453922 w pmfac.txt AlpineGeophysics

s 2009g 2453908.txt AlpineGeophysics

as 2009g 2453972.txt AlpineGeophysics marine vessel emissions

vistas 2009 baseg 2453957.txt AlpineGeophysics

vistas 2009 baseg 2453990.txt AlpineGeophysics annual emissions

9 baseg mon 2453990.ems AlpineGeophysics hourly emissions, mon=may,jun,...

fires vistas2002 baseg.ida AlpineGeophysics treated as area sources

e.vistasbaseg09.num.ida AlpineGeophysics treated as point sources; annual data

e.vistasbaseg09.num.ida AlpineGeophysics daily data; num=1,2,...

me.vistasbaseg09.num.ida AlpineGeophysics hourly data; num=1,2,...

.mb.vmt.emis revised from AlpineG contains VISTAS/CENRAP/MRPO

MANEVU

2009OTBAreaV3 1 woodburn.incl.IDA.txt AlpineGeophysics if BOTW, premerged netCDF for merging

EVUNRNIFV3 0 NonRoad IDA.NJfix.txt AlpineGeophysics

9noneguv3 0 point ida.txt AlpineGeophysics if BOTW, premerged netCDF for merging

009 manevu 10aug2006.txt AlpineGeophysics annual emissions

nfossil 2009 19sept2006.txt AlpineGeophysics non-fossil fuel emissions

e NYSDEC netCDF used for merging

age Number: 
Table 1. 2009 OTW/OTB Emissions Processing Summary (cont.)

Files Files Source Notes

CENRAP

ng smoke 2009 input ann tx neli 071905 2453959.txt AlpineGeophysics

2009 smoke input ann state 071905 2453959.txt AlpineGeophysics2009 smoke output nh3 annual 072805 rev 2453959.txt AlpineGeophysics

09 xfact.ida.txt AlpineGeophysics

e 2009 output nh3 annual 071905 rev 2453959.txt AlpineGeophysics

moke 2009 output annual 071305 rev.txt AlpineGeophysics

p2009 25aug2006.ida AlpineGeophysics

nrap 10aug2006.txt AlpineGeophysics annual emissions

mt.emis revised from AlpineG VISTAS/CENRAP/MRPO

MRPO

k 2009 10aug2006.txt AlpineGeophysics dust correction; premerged netCDF

ef4 2009 10nov05.ida AlpineGeophysics

ok ann 10aug2006.txt AlpineGeophysics dust correction; premerged netCDF

2453958 adj.txt AlpineGeophysics dust correction; premerged netCDF

2009 7aug2006.txt AlpineGeophysics

mrpok 10aug2006.txt AlpineGeophysics

rpok 10aug2006.txt AlpineGeophysics annual emissions

mt.emis revised from AlpineG VISTAS/CENRAP/MRPO

age Number: 
Table 1. 2009 OTW/OTB Emissions Processing Summary (cont.)

Files Source Notes

CANADA

le NYSDEC; downloaded from OTC ftp site premerged netCDF for merging

le NYSDEC; downloaded from OTC ftp site premerged netCDF for merging

le NYSDEC; downloaded from OTC ftp site premerged netCDF for merging

le NYSDEC; downloaded from OTC ftp site premerged netCDF for merging

BIOGENIC

le NYSDEC; downloaded from OTC ftp site domain-wide emissions; premerged netCDF for merging

age Number: 
Attachment 9_CMAQ Air Quality Model 

Configuration.pdf
OTC CMAQ Air Quality Model Configuration

Science Options Configuration Details/Comments 

Model CMAQ Version 4.5 

Horizontal Grid Mesh 36km/12km 

36-km grid 145x102 cells 

12-km grid 172x172 cells 

Vertical Grid Mesh 22 Layers 

Grid Interaction One-way nesting 

Boundary Conditions GEOS-CHEM 

Emissions 

Baseline Emissions 

Processing 

SMOKE (Version 2.1) 

model configuration 

MM5 meteorology input to SMOKE & 

CMAQ 

Sub-grid-scale Plumes No Plume –in-Grid 

(PinG) 

Chemistry 

Gas Phase Chemistry CBM-IV 

Aerosol Chemistry AE3/ISORROPIA 

Secondary Organic 

Aerosols 

Secondary Organic 

Aerosol Model 

(SORGAM) 

Aerosol Mass 

Conservation Patch Yes Schell et. al., (2001) 

Cloud Chemistry RADM-type aqueous 

chemistry Includes sub-grid cloud processes 

N2O5 Reaction 

Probability 0.01-0.001 

Meteorological 

Processor MCIP Version 3.0 

Horizontal Transport 

Eddy Diffusivity 

Scheme 

K-theory with Kh grid 

size dependence 

Multi-scale Smagorinsky (1963) 

approach 

Vertical Transport 

Eddy Diffusivity 

Scheme K-theory 

Diffusivity Lower Limit Kzmin = 1.0 

Planetary Boundary 

Layer No Patch 

Deposition Scheme M3dry Directly linked to Pleim-Xiu Land 

Surface Model parameters 

Numerics 

Gas Phase Chemistry Euler Backward Iterative Hertel et. Al. (1993) EBI solver ~2x 

Appendix G Attachment 9 - Page Number:

1
Science Options Configuration Details/Comments 

Solver (EBI) solver faster than MEBI 

Horizontal Advection 

Scheme 

Piecewise Parabolic 

Method (PPM) scheme 

Simulation Periods 2002 

Platform Linux Cluster 

Appendix G Attachment 9 - Page Number:

2