Documentation of the Evaluation of CALPUFF and Other Long ...

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Figure C‐ ‐14. Global model perfoormance staatistics for thhe CAMx CTTEX5 sensitivvity tests usiing the PiG ssubgrid‐scale e puff moduule. The final panel in Fig gure C‐15 (boottom right) displays thee overall RANNK statistic. The RANK statistics orders the model m perfoormance of tthe CTEX5 CAAMx configuurations withhout PiG as follows: 1. CMAQ/PPM (1.92) ( (tied) 2. CMAQ/BOTT (1.92) (tied) ) 3. TKE/PPM (1.7 73) 4. TKE/BOTT (1. 71) 5. AACM2/BOTT (1.50) 6. AACM2/PPM ( 1.48) 7. OOB70/PPM (1 1.34) 8. OOB70/BOTT ( 1.33) As with CCTEX3, for th he CTEX5 expperiment thee CMAQ Kz aalgorithm is the best performing vertical mixing appproach in CAMx C based on both thee spatial and global statistical analysses. What diiffers in the CAAMx CTEX3 and a CTEX5 NoPiG sensitivity test perrformance iss the compossition of the RANK meetric. Spatial performannce for CTEX33 was signifiicantly betteer than for CTEX5 (10% ‐ 15% greater), thus FMS co ontributes leess to the RAANK statisticcal metric for CTEX5 commpared to CTTEX3. Similarly, , the PCC me etric is muchh more variaable across thhe Kz optionns with CTEXX5 experimennt, with esseentially no co ontribution of PCC to the RANK scorre for OB70 and ACM2 KKz options inn CTEX5. AAdditionally y, the KSP scoores comprisse a much greater portioon of the RAANK scores foor CTEX5. 18
Figure C‐ ‐15. Global model perfoormance staatistics for thhe CAMx sennsitivity testts without uusing the PiG ssubgrid‐scale e puff moduule for CAPTTEX Release 5 (NoPiG). C.3.7 SPATIAL PERFO ORMANCE FFOR CTEX5 PPIG EXPERIMMENTS As with tthe CTEX5 No oPiG experimments, interpretation off the spatial performancce for CTEX5 for the PiG CCAMx sensiti ivities posedd a slightly mmore difficultt challenge tto interpret ddue to similarities amongst the Kz/adveection solverr options forr the FMS meetric (Figure C‐16). The range of difference for f the FMS bbetween thee minimum aand maximuum for all of the eight combinations was less than 2%, with all in thhe range of 221.5% ‐ 23.33%, noting a slight degradattion across the board froom the correesponding NNoPiG experiments (0.2% % ‐ 3.1%). Examinattion of the extended e spaatial statisticcs reveals a ssimilar patteern in perforrmance compareed to the NoP PiG equivaleent tests. Grreater differences in perrformance are observed across thhe various Kz z/advection solver combbinations, especially for tthe POD andd TS metrics. For both of these metrics, the CMAQQ Kz combinations clearlly yield betteer spatial peerformance tthan the other Kz options (5% ‐ 10% bbetter for POOD and 2% ‐ 5% for TS thhan the secoond best Kz option (TTKE)). Consistennt with results from the NoPiG scenarios, the CMMAQ Kz option appears to perform best across thhe majority of o the spatial metrics. The largest diifference in spatial perfoormance is determinned by the user’s selection of the Kz option thann either the choice of addvection solvver or use of the subgrid sc cale PiG moddule in CAMxx. 19
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Documentation of the Evaluation of
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FOREWARD This report documents the
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2.4.3 ATMES‐II Model Evaluation A
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EXECUTIVE SUMMARY ABSTRACT The CALP
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OVERVIEW OF APPROACH Up to six LRT
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CALPUFF performance is evaluated by
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Table ES‐2. ATMES‐II spatial an
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• CALPUFF tended to overstate the
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• The best performing CALPUFF con
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2009a). The key findings from the C
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1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2
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2.4 2 1.6 1.2 0.8 0.4 0 EXP4A EXP4B
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Figure ESS‐4. RANK statistical pe
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Evaluatioon of Six LRT T Dispersion
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Table ES‐6. Summary of model rank
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eproduce the northwest to southeast
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ETEX LRT Dispersion Model Sensitivi
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CONCLUSIONS OF LRT DISPERSION MODEL
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The CAMx and CALGRID Eulerian photo
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July 1980. Both experiments examine
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1.3 ORGANIZATION OF REPORT Chapter
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puffs expand until they exceed the
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that performance evaluation be base
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The ETEX real‐time LRT modeling p
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The ETEX study has formulated the f
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In this study we expand the LRT mod
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AM ∩ AP FMS = × 100% (2‐2) A
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Factor of α (FAα): FAα represent
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3.0 1980 GREAT PLAINS FIELD STUDY 3
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compact discs, which were used to o
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ILEVZI = 1 Layer of winds to use in
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MCHEM = 0 No chemical transformatio
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Table 3‐6. CALPUFF/CALMET experim
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Table 3‐11. CALPUFF/MMIF sensitiv
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evaluation studies and evaluate whe
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Tables 3‐13 and Figures 3‐2 thr
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140% 120% 100% 80% 60% 40% 20% 0%
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30% 20% 10% 0% ‐10% ‐20% ‐30%
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120% 100% 80% 60% 40% 20% 0% ‐20%
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20% 10% 0% ‐10% ‐20% ‐30% ‐
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The fitted Gaussian plume statistic
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0% ‐10% ‐20% ‐30% ‐40% ‐5
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300% 250% 200% 150% 100% 50% 0% 300
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60% 40% 20% 0% ‐20% ‐40% ‐60%
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with APS, implementing the slug opt
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the amount of time that the tracer
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Figure 4‐1. CALPUFF/CALMET UTM mo
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compact discs, which were used to o
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Table 4‐4. CALPUFF parameters use
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Table 4‐8. CALPUFF/MMIF sensitivi
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the fitted Gaussian plume is not a
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Figure 4‐2. Comparison of predict
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Figure 5‐1. Location of Dayton an
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MM5 runs, the first without FDDA (i
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Table 5‐3. MM5 sensitivity tests
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Table 5‐6. Definition of the CALM
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performance at the monitor location
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35% 30% 25% 20% 15% 10% 5% 0% 35% 3
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40% 35% 30% 25% 20% 15% 10% 5% 0% F
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40% 35% 30% 25% 20% 15% 10% 5% 0% E
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5.4.1.4 Comparison of CALPUFF CTEX3
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0.48 0.36 0.24 0.12 0 ‐0.12 16% 1
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CTEX3 discussed in Section 5.4.1. A
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CALPUFF sensitivity simulations are
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14. Across all the spatial statisti
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sensitivity tests. The “B” seri
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‐0.1 ‐0.2 0 0.8 0.7 0.6 0.5 0.4
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6.0 1994 EUROPEAN TRACER EXPERIMENT
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Figure 6‐2a. Surface synoptic met
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Figure 6‐3a. Distribution of the
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36 kilometers and the vertical stru
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splitting flag near sunset (hour 17
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experienced during the original ETE
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2 1 0 ‐1 ‐2 3 2 1 0 23‐Oct 23
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70% 60% 50% 40% 30% 20% 10% 0% Figu
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Figure 6‐9. Factor of Exceedance
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eceiving a 0.0 score. Figure 6‐13
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Table 6‐1. Summary of model ranki
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plume spread and observed surface c
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Figure 6‐16c. Comparison of spati
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• NoPiG: The tracer emissions wer
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Using the NMSE statistical performa
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6.4.3.2 Effect of PiG on Model Perf
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80 70 60 50 40 30 20 10 0 1 2 3 4 5
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Table 6‐3. Summary of CALPUFF puf
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Page 173 and 174: Figure 6‐ ‐22 displays the t sp
Page 175 and 176: Figure 6‐ ‐23a. Global model pe
Page 177 and 178: Figure 6‐24. Figure of Merit (FMS
Page 179 and 180: 7.0 REFERENCES Anderson, B. 2008. T
Page 181 and 182: EPA, 1984: Interim Procedures for E
Page 183 and 184: Mlawer, E.J., S.J. Taubman, P.D. Br
Page 185 and 186: 148 Appendix A Evaluation of the MM
Page 187 and 188: Table A‐1. Wind speed and wind di
Page 189 and 190: Table A‐3. Definition of the CTEX
Page 191 and 192: Figure A‐ ‐1. Wind speed bias (
Page 193 and 194: Figure A‐ ‐3. Humidity bias and
Page 195 and 196: Table A‐5. Comparison of CTEX5 MM
Page 197 and 198: B.1 CALMET MODEL EVALUATION TO IDEN
Page 199 and 200: Figure B‐ ‐1 displays th he win
Page 201 and 202: Table B‐2a. Summary wind speed mo
Page 203 and 204: B.2 CONCLUSIONS OF CTEX3 CALMET SEN
Page 205 and 206: C.1 INTRODUCTION In this section, t
Page 207 and 208: C.2.2 HYYSPLIT GLOB BAL STATISTIICS
Page 209 and 210: The final panel in Figure C‐3 (bo
Page 211 and 212: Figure C‐ ‐5. Global model m pe
Page 213 and 214: C.3 CAMX SENSITIVITY TESTS Followin
Page 215 and 216: ACM2 Kzz combinatio ons rank as the
Page 217 and 218: Figure C‐ ‐10. Spatial model pe
Page 219 and 220: Figure C‐ ‐12. Global model per
Page 221: Figure C‐ ‐13. Spatial model pe
Page 225 and 226: Figure C‐ ‐17. Global model per
Page 227 and 228: Table C‐3. CAMx FMS and POD spati
Page 229 and 230: Figure C‐ ‐20. False Alarm Rate
Page 231 and 232: Figure C‐ ‐23. Factor of o Exce
Page 233 and 234: The PCC values for th he six LRT mo
Page 235 and 236: The RANK statistical performancce m
Page 237 and 238: Table C‐55. Summary y of model r
Page 239 and 240: Results foor the TS me etric are pr
Page 241 and 242: Figure C‐ ‐35. Factor of o 2 (F
Page 243 and 244: a negativve FB. The best b performm
Page 245 and 246: performing model the most often sco
Page 247: United States Environmental Protect
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