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

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the biggest effect with the ranking of the algorithms from best to worst using the FMS statistic being CMAQ, ACM2, TKE and OB70. Whereas, for the horizontal advection solver the PPM algorithm performs slightly better than Bott using the FMS statistic. For the FAR statistic, CMAQ/Bott has the best score (39.0%) followed by CMAQ/PPM (41.0%). Overall CMAQ is the best performing vertical diffusion formulation and Bott performs better than PPM for horizontal advection using the FAR statistic. For the POD and TS spatial statistics, the CMAQ and TKE vertical diffusion algorithms perform substantially better than the OB70 and ACM2 approaches. There are much smaller differences in the model performance using the two advection solvers for the POD and TS statistics. In summary, based on the spatial statistics, the CMAQ Kz algorithm appears to be the best performing approach for vertical mixing followed by TKE. And with the exception of the FAR statistic, PPM produces slightly better spatial model performance statistics than the Bott horizontal advection solver. The differences in vertical diffusion algorithms has a greater effect on CAMx model performance than the differences in horizontal advection solvers. 55% 52% 49% 46% 43% 40% 60% 55% 50% 45% 40% 35% 30% OB70/BOTT OB70/BOTT Figure of Metric in Space (FMS) (Perfect = 100%) OB70/PPM TKE/BOTT TKE/PPM ACM2/BOTT ACM2/PPM Probability of Detection (POD) (Perfect = 100%) OB70/PPM TKE/BOTT TKE/PPM ACM2/BOTT ACM2/PPM CMAQ/BOTT CMAQ/BOTT CMAQ/PPM CMAQ/PPM Figure 6‐17. Spatial model performance statistics for the CAMx sensitivity tests without using the PiG subgrid‐scale puff module (NoPiG). Figure 6‐18 displays the global statistics for the CAMx NoPiG sensitivity tests with Figures 6‐18a and 6‐18b containing the statistical metrics where the best performing model has the, respectively, lowest and highest score. For the FOEX metric, the vertical diffusion algorithm has the biggest effect with the ACM2 scoring the best with an essentially zero FOEX score followed by OB70 with values ‐2.4% (OB70/Bott) and ‐3.4% (OB70/PPM). The TKE (8.5%) and PPM (8.7% and 9.1%) have the highest (worst) FOEX scores. The FOEX metrics using the two alternative horizontal advection algorithms are essentially the same. 125 50% 47% 44% 41% 38% 35% 45% 40% 35% 30% 25% OB70/BOTT OB70/BOTT OB70/PPM OB70/PPM False Alarm Rate (FAR) (Perfect = 0%) TKE/BOTT TKE/PPM ACM2/BOTT Threat Score (TS) (Perfect = 100%) TKE/BOTT TKE/PPM ACM2/BOTT ACM2/PPM ACM2/PPM CMAQ/BOTT CMAQ/BOTT CMAQ/PPM CMAQ/PPM
Using the NMSE statistical performance metric, the CMAQ vertical diffusion scheme performs best with OB70 and TKE producing very similar results next, with the ACM2 exhibiting the worst NMSE performance results (Figure 6‐18a, top right). The PPM horizontal advection scheme is performing slightly better than the Bott algorithm based on the NMSE metric. The CMAQ vertical diffusion scheme is also the best performing method according to the FB metrics followed by the TKE then ACM2 and then OB70 in last. According to the FB metrics, PPM performs slightly better than Bott. For the KS parameter, the OB70 is the best vertical mixing method with CMAQ barely beating out ACM2 in second and TKE slightly worse. The PPM horizontal advection solver is performing slightly better than Bott for the KS parameter. For the within a factor of 2 and 5 metrics (FA2 and FA5, Figure 6‐18b, top), the CMAQ and TKE vertical mixing approaches are clearly performing better than the OB70 and ACM2 methods and the PPM horizontal advection solver is clearly performing better than Bott. For the FA2, the TKE/PPM is the best performing configuration (11.4%) followed by CMAQ/PPM (10.9%), Whereas for the FA5 the reverse is true with CMAQ/PPM being the best performing configuration (23.4%) followed by TKE/PPM (22.2%). There is essentially no difference in the PCC statistic using the two horizontal advection solvers (Figure 6‐18b, bottom right). According to the PCC metric, CMAQ is the best performing vertical diffusion approach (0.52) followed by TKE (0.37 and 0.38), OB70 (0.35) and ACM2 (0.26 and 0.27). The final panel in Figure 6‐18b (bottom right) displays the overall RANK statistic. The RANK statistics orders the model performance of the CAMx configurations without PiG as follows: 1. CMAQ/PPM (1.94) 2. CMAQ/Bott (1.90) 3. TKE/PPM (1.70) 4. OB70/PPM (1.66) 5. TKE/Bott (1.65) 6. ACM2/PPM (1.60) (tied) 7. OB70/Bott (1.60) (tied) 8. ACM2/Bott (1.54) Based on this analysis the CMAQ Kz coefficients is the best performing vertical diffusion approach followed by TKE and the PPM horizontal advection algorithm is performing slightly better than Bott. The vertical diffusion algorithm has a greater effect on CAMx model performance compared to the choice of horizontal advection solvers. 126
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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
Page 111 and 112: Table 5‐6. Definition of the CALM
Page 113 and 114: performance at the monitor location
Page 115 and 116: 35% 30% 25% 20% 15% 10% 5% 0% 35% 3
Page 117 and 118: 40% 35% 30% 25% 20% 15% 10% 5% 0% F
Page 119 and 120: 40% 35% 30% 25% 20% 15% 10% 5% 0% E
Page 121 and 122: 5.4.1.4 Comparison of CALPUFF CTEX3
Page 123 and 124: 0.48 0.36 0.24 0.12 0 ‐0.12 16% 1
Page 125 and 126: CTEX3 discussed in Section 5.4.1. A
Page 127 and 128: CALPUFF sensitivity simulations are
Page 129 and 130: 14. Across all the spatial statisti
Page 131 and 132: sensitivity tests. The “B” seri
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Page 135 and 136: 6.0 1994 EUROPEAN TRACER EXPERIMENT
Page 137 and 138: Figure 6‐2a. Surface synoptic met
Page 139 and 140: Figure 6‐3a. Distribution of the
Page 141 and 142: 36 kilometers and the vertical stru
Page 143 and 144: splitting flag near sunset (hour 17
Page 145 and 146: experienced during the original ETE
Page 147 and 148: 2 1 0 ‐1 ‐2 3 2 1 0 23‐Oct 23
Page 149 and 150: 70% 60% 50% 40% 30% 20% 10% 0% Figu
Page 151 and 152: Figure 6‐9. Factor of Exceedance
Page 153 and 154: eceiving a 0.0 score. Figure 6‐13
Page 155 and 156: Table 6‐1. Summary of model ranki
Page 157 and 158: plume spread and observed surface c
Page 159 and 160: Figure 6‐16c. Comparison of spati
Page 161: • NoPiG: The tracer emissions wer
Page 165 and 166: 6.4.3.2 Effect of PiG on Model Perf
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Page 169 and 170: 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
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C.3 CAMX SENSITIVITY TESTS Followin
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ACM2 Kzz combinatio ons rank as the
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Figure C‐ ‐10. Spatial model pe
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Figure C‐ ‐12. Global model per
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Figure C‐ ‐13. Spatial model pe
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Table C‐3. CAMx FMS and POD spati
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Figure C‐ ‐20. False Alarm Rate
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Figure C‐ ‐23. Factor of o Exce
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The PCC values for th he six LRT mo
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The RANK statistical performancce m
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Table C‐55. Summary y of model r
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Results foor the TS me etric are pr
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Figure C‐ ‐35. Factor of o 2 (F
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a negativve FB. The best b performm
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performing model the most often sco
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United States Environmental Protect
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