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

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Figure C‐ ‐40 lists the RANK model performance statistics for the six LLRT models. CAMx is thee highest rranked mode el using the RANK metricc with a valuue of 1.91 foollowed by HYSPLIT at 1. 8. It is importtant to note, , however, that both CAMx and HYSSPLIT exhibit high scores in all four areas of modell performanc ce (correlation, bias, spaatial and cummulative disttribution). Itt is an imporrtant attributee of model pe erformance to score higgh in all areas of model pperformancee. The next best performing model ac ccording to tthe RANK meetric is CALGGRID with a sscore of 1.577, followed bby SCIPUFF (1.53), FLEXPART (1.45), , and finally CALPUFF (1.28). Howevver, the CALGRID third bbest RANK meetric comes at the expense of low spatial (FMS) ) and zero coorrelation (PPCC or R2) performaance skill. Figure C‐ ‐40. RANK statistical s peerformance metric for thhe six LRT mmodels and CCAPTEX Releease 5. C.5.2.1 SSUMMARY OF O SIX LRT MMODEL RANKINGS FOR CTEX5 USING STATISTICCAL PERFORMMANCE MEA ASURES Table C‐66 summarize es the rankinngs of the sixx LRT modelss for the 11 performance statistics analyzed in CAPTEX Release R 5 annd comparess the averagiing ranking aacross the 11 statistics against the RANK me etric. Depennding on the statistical mmetric, five oof the six moodels were ranked as the best pe erforming mmodel for a pparticular staatistic. CALGGRID was thee only modeel not ranked fiirst for any performance p e statistics, aalthough it tiied with CAMMx for havinng the lowestt (best) FBB value (‐0.49 9), but it wass ranked behhind CAMx ( FB of +0.49) ) due to a deesire for regulatorry models to o not have an underestimmation bias. HYSPLIT waas ranked thhe best 40
performing model the most often scoring best in 4 of the 11 statistics (36% of the time). SCIPUFF, FLEXPART and CAMx all scored best with 2 of the 11 statistics (18%) with CALPUFF scoring best for just one statistical metric. In testing the efficacy of the RANK statistic, overall rank across all eleven statistics was used to come up with an average modeled ranking to compare with the RANK statistic rankings. The average rank across all 11 performance statistics and the RANK rankings are as follows: Ranking Average of 11 Statistics RANK 1. CAMx CAMx 2. HYSPLIT HYSPLIT 3. SCIPUFF CALGRID 4. FLEXPART SCIPUFF 5. CALPUFF FLEXPART 6. CALGRID CALPUFF The results from CAPTEX Release 5 present an interesting case study on the use of the RANK metric to characterize overall model performance. As noted in Table C‐6 and given above, the relative ranking of models using the average rankings across the 11 statistical metrics is considerably different than the RANK scores after the two highest ranked models. Both approaches rank CAMx as the best and HYSPLIT as the next best performing models for CTEX5, with rankings that are fairly close to each other. However, after that the two ranking techniques come to different conclusions regarding the ability of the models to simulate the observed tracer concentrations for the CTEX5 field experiment. The most noticeable feature of the RANK metric for ranking models in CTEX5 is the third highest ranking model using RANK, CALGRID (1.57). CALGRID ranks as the worst or second worst performing model in 9 of the 11 performance statistics (82% of the time) and have an average ranking of 5.0, which means on average it is the 5 th best performing model out of 6. In examining the contribution to the RANK metric for CALGRID, there is not a consistent contribution from all four broad categories to the composite score (Figure C‐40). Recall from equation 2‐12 in Section 2.4.3.2 that the RANK score is defined by the contribution of the four of the 11 statistics that represent measures of correlation/scatter (R2), bias (FB), spatial (FMS) and cumulative distribution: ( 1− FB / 2 ) + FMS / 100+ ( 1 KS / 100) 2 RANK = R + − The majority of CALGRID’s 1.57 RANK score comes from fractional bias and Kolmogorov‐ Smirnov parameter values. Recall from Figures C‐36 and C‐39 that the FOEX and FB metrics indicate that CALGRID consistently underestimates. The FB component to the composite score for CALGRID is one of the highest among the six models in this study, yet the underlying statistics indicate both marginal spatial skill and a degree of under‐prediction (likely due to the spatial skill of the model). The current form of the RANK score uses the absolute value of the fractional bias. This approach weights underestimation equally to overestimation. However, in a regulatory context, EPA is most concerned with models not being biased towards underestimation. When looking at all of the performance statistics, CALGRID is clearly one of the worst performing LRT 41
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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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0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.
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Figure 6‐ ‐22 displays the t sp
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Figure 6‐ ‐23a. Global model pe
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Figure 6‐24. Figure of Merit (FMS
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7.0 REFERENCES Anderson, B. 2008. T
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EPA, 1984: Interim Procedures for E
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Mlawer, E.J., S.J. Taubman, P.D. Br
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148 Appendix A Evaluation of the MM
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Table A‐1. Wind speed and wind di
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Table A‐3. Definition of the CTEX
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Figure A‐ ‐1. Wind speed bias (
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Page 195 and 196: Table A‐5. Comparison of CTEX5 MM
Page 197 and 198: B.1 CALMET MODEL EVALUATION TO IDEN
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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
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Page 217 and 218: Figure C‐ ‐10. Spatial model pe
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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: a negativve FB. The best b performm
Page 247: United States Environmental Protect
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