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

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observations) divided by the number of monitor‐time events that either a prediction or observed tracer occurred at a monitor (i.e., either a predicted or observed hits), with a perfect score of 100% (which means there were no occurrences when there was a predicted hit but an observed miss and vice versa). 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Threat Score (TS) (Perfect = 100%) CALPUFF SCIPUFF HYSPLIT FLEXPART CAMx Figure 6‐8. Threat Score (TS) statistical performance metric for the five LRT models and the ETEX tracer field experiment. 6.4.1.2 Global Analysis of Model Performance Eight global statistical analysis metrics are used to evaluate the five LRT model performance using the ETEX data base that are described in Section 2.4 and consist of the FOEX, FA2, FA5, NMSE, PCC, FB, KS and RANK statistical metrics. The Factor of Exceedance (FOEX) gives a measure of the scatter of the modeled predicted and observed and a level of underestimation versus overestimation of the model. FOEX is bounded by ‐50% to +50%. The within a Factor of α (FAα), where we used within a Factor of 2 (FA2) and 5 (FA5), also gives an indication of the amount of scatter in the predicted and observed tracer pairs, but no information on whether the model is over‐ or under‐predicting. A perfect model would have an FAα score of 100%. A good performing model would have a FOEX score near zero and high FAα values. A model with a large negative FOEX and low FAα values would indicate an under‐prediction tendency. Whereas a model with a large positive FOEX and low FAα would suggest a model that over‐predicts. Figure 6‐9 displays the FOEX performance metrics for the five LRT models and the ETEX modeling period. 113
Figure 6‐9. Factor of Exceedance (FOEX) statistical performance metric for the five LRT models and the ETEX tracer field experiment. The rankings of the five LRT models are the same whether using the FA2 or FA5 performance metric. 25% 20% 15% 10% 20% 10% 0% ‐10% ‐20% ‐30% ‐40% 5% 0% Factor of Exceedance (FOEX) (Perfect = 0%) CALPUFF SCIPUFF HYSPLIT FLEXPART CAMx CALPUFF Factor of 2 and 5 (Perfect = 100%) SCIPUFF HYSPLIT Figure 6‐10. Factor of 2 (FA2, top) and Factor of 5 (FA5, bottom) statistical performance metric for the five LRT models and the ETEX tracer field experiment. The scores for the Normalized Mean Squared Error (NMSE) statistical metrics for the five LRT models are given in Figure 6‐11. The NMSE provides an indication of the deviations between the predicted and observed tracer concentrations paired by time and location with a perfect model receiving a 0.0 score. 114 FLEXPART CAMx FA2 FA5
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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
Page 99 and 100: Table 4‐8. CALPUFF/MMIF sensitivi
Page 101 and 102: the fitted Gaussian plume is not a
Page 103 and 104: Figure 4‐2. Comparison of predict
Page 105 and 106: Figure 5‐1. Location of Dayton an
Page 107 and 108: MM5 runs, the first without FDDA (i
Page 109 and 110: 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
Page 133 and 134: ‐0.1 ‐0.2 0 0.8 0.7 0.6 0.5 0.4
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
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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: 70% 60% 50% 40% 30% 20% 10% 0% Figu
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 and 162: • NoPiG: The tracer emissions wer
Page 163 and 164: Using the NMSE statistical performa
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
Page 171 and 172: 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.
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
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Table B‐2a. Summary wind speed mo
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B.2 CONCLUSIONS OF CTEX3 CALMET SEN
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C.1 INTRODUCTION In this section, t
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C.2.2 HYYSPLIT GLOB BAL STATISTIICS
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The final panel in Figure C‐3 (bo
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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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