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

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0% ‐5% ‐10% ‐15% ‐20% 0.82 0.8 0.78 0.76 0.74 0.72 0.7 0.68 0.66 0.64 BASE A Factor of Exceedance (FOEX) (Perfect = 0%) BASE B BASE C BASE D 80K MMIF Figure 5‐11a. Global model performance statistics for the CTEX5 CALPUFF sensitivity tests using the 80 km MM5data (lower values indicate better performance). 0.8 0.6 0.4 0.2 0 BASE A Fractional Bias (FB) (Perfect = 0) BASE B BASE C 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% BASE A BASE D Pearson's Correlation Coefficient (PCC) (Perfect = 1) BASE A BASE B BASE C BASE D 80K MMIF Figure 5‐11b. Global model performance statistics for the CTEX5 CALPUFF sensitivity tests using the 80 km MM5 data (higher values indicate better performance). 5.4.2.2 CALPUFF CTEX5 Model Evaluation using 36 km MM5 Data Figure 5‐12 displays the spatial statistical metrics for the CTEX5 CALPUFF sensitivity tests using the 36 km MM5 data. Note that the CALMET simulation for EXP3D encountered an error in HTOLD so no CALPUFF sensitivity modeling results are available. The “A” and “B” series of 89 20 15 10 5 0 60% 50% 40% 30% 20% 10% 0% Factor of 2 and 5 (Perfect = 100%) 80K MMIF BASE B BASE C BASE D 2.4 2.0 1.6 1.2 0.8 0.4 0.0 BASEA Normalized Mean Square Error (NMSE) (Perfect = 0) BASE A BASE A BASE B BASE B BASE C BASE C BASE D Kolmogorov‐Smirnov Parameter (KSP) (Perfect = 0%) 80K MMIF BASEB FA2 FA5 Rank (RANK) (Perfect = 4) BASEC BASED 80KM_MMIF BASE D (1‐KS/100) FMS/100 (1‐FB/2) R^2 80K MMIF 80K MMIF
CALPUFF sensitivity simulations are performing best for the spatial performance statistics with the 36KM_MMIF performing worst. 80% 70% 60% 50% 40% 30% 20% 10% 0% 30% 25% 20% 15% 10% 5% 0% EXP 3A Figure of Metric in Space (FMS) (Perfect = 100%) EXP 3A EXP 3B EXP 3C EXP 3D EXP 5A EXP 5B EXP 5C EXP 5D Probability of Detection (POD) (Perfect = 100%) EXP 3B EXP 3C EXP 3D EXP 5A EXP 5B EXP 5C EXP 5D 36K MMIF 36K MMIF Figure 5‐12. Spatial model performance statistics for the CTEX5 CALPUF sensitivity tests that used the 36 km MM5 data. The global statistics for the CALPUFF sensitivity tests using the 36 km MM5 data are shown in Figure 5‐13. EXP5D and 36KM_MMIF have the FOEX that is closest to zero. The EXP5B and EXP5D sensitivity simulations have the lowest bias and error followed by EXP3C with 36KM‐ MMIF having the worst bias and error metrics. The lowest (best) KSP statistics is given by EXP5D followed by 36KM_MMIF and EXP3C. EXP3B and EXP5A have the best FA2 and FA5 values, with 36KM_MMIF having the worst ones. EXP3A, EXP3C and EXP5A all have correlation coefficients above 0.7 with 36km_MMIF having the lowest correlation coefficient that is below 0.3. Using the overall composite RANK statistics, EXP3C and EXP5D (2.1) are ranked first followed by EXP3A and EXP5A (2.0) with 36KM_MMIF (1.4) having the lowest RANK statistic. 90 100% 80% 60% 40% 20% 0% 50% 40% 30% 20% 10% 0% EXP 3A EXP 3A False Alarm Rate (FAR) (Perfect = 0%) EXP 3B EXP 3B EXP 3C EXP 3D EXP 5A EXP 5B Threat Score (TS) (Perfect = 100%) EXP 3C EXP 3D EXP 5A EXP 5B EXP 5C EXP 5C EXP 5D EXP 5D 36K MMIF 36K MMIF
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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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Page 77 and 78: 120% 100% 80% 60% 40% 20% 0% ‐20%
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Page 81 and 82: The fitted Gaussian plume statistic
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Page 89 and 90: with APS, implementing the slug opt
Page 91 and 92: the amount of time that the tracer
Page 93 and 94: Figure 4‐1. CALPUFF/CALMET UTM mo
Page 95 and 96: compact discs, which were used to o
Page 97 and 98: Table 4‐4. CALPUFF parameters use
Page 99 and 100: Table 4‐8. CALPUFF/MMIF sensitivi
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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: CTEX3 discussed in Section 5.4.1. A
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Page 131 and 132: sensitivity tests. The “B” seri
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Page 135 and 136: 6.0 1994 EUROPEAN TRACER EXPERIMENT
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Page 139 and 140: Figure 6‐3a. Distribution of the
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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 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
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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
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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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Figure A‐ ‐3. Humidity bias and
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Table A‐5. Comparison of CTEX5 MM
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B.1 CALMET MODEL EVALUATION TO IDEN
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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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