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

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The RANK statistical performance metric was proposed by Draxler (2001) as a single model performance metric that equally ranks the combination of performance metrics for correlation (PCC or R), bias (FB), spatial analysis (FMS) and unpaired distribution comparisons (KS). The RANK metrics ranges from 0.0 to 4.0 with a perfect model receiving a score of 4.0. Figure 6‐15 lists the RANK model performance statistics for the five LRT models. CAMx is the highest ranked model using the RANK metric with a value of 1.9. Note that CAMx scores high in all four areas of model performance (correlation, bias, spatial and cumulative distribution). The next highest ranking models according to the RANK metric are SCIPUFF and HYSPLIT with a score of 1.8. 2.4 2.0 1.6 1.2 0.8 0.4 0.0 CALPUFF SCIPUFF Rank (RANK) (Perfect = 4) HYSPLIT Figure 6‐15. RANK statistical performance metric for the five LRT models and the ETEX tracer field experiment. 6.4.1.3 Summary of Model Ranking using Statistical Performance Measures Table 6‐1 summarizes the rankings between the five LRT models for the 11 performance statistics analyzed. Depending on the statistical metric, three different models were ranked first for a particular statistic with CAMx being ranked first most of the time (64%) and HYSPLIT ranked first second most (27%). In order to come up with an overall rank across all eleven statistics we average the modeled ranking order in order to come up with an average ranking that listed CAMx first, HYSPLIT second, SCIPUFF third, FLEXPART fourth and CALPUFF the fifth. This is the same ranking as produced by the RANK integrated statistics that combines the four statistics for correlation (PCC), bias (FB), spatial (FMS) and cumulative distribution (KS) giving credence that the RANK statistic is a potentially useful performance statistic for indicating over all model performance of a LRT dispersion model. 117 FLEXPART CAMx (1‐KS/100) FMS/100 (1‐FB/2) R^2
Table 6‐1. Summary of model ranking using the statistical performance metrics. Statistic 1 st 2 nd 3 rd 4 th 5 th FMS CAMx SCIPUFF HYSPLIT FLEXPART CALPUFF FAR HYSPLIT FLEXPART CAMx SCIPUFF CALPUFF POD CAMx SCIPUFF HYSPLIT FLEXPART CALPUFF TS CAMx HYSPLIT SCIPUFF FLEXPART CALPUFF FOEX CAMx SCIPUFF HYSPLIT FLEXPART CALPUFF FA2 CAMx SCIPUFF HYSPLIT FLEXPART CALPUFF FA5 CAMx SCIPUFF HYSPLIT FLEXPART CALPUFF NMSE HYSPLIT CAMx CALPUFF FLEXPART SCIPUFF PCC or R SCIPUFF HYSPLIT CAMx FLEXPART CALPUFF FB HYSPLIT CAMx CALPUFF FLEXPART SCIPUFF KS CAMx SCIPUFF HYSPLIT FLEXPART CALPUFF Avg. Ranking CAMx HYSPLIT SCIPUFF FLEXPART CALPUFF Avg. Score 1.55 2.27 2.73 3.82 4.64 RANK Ranking CAMx HYSPLIT SCIPUFF FLEXPART CALPUFF 6.4.2 Spatial Displays of Model Performance Figure 6‐16 displays the observed tracer distribution 24, 36, 48 and 60 hours after the beginning of the tracer release as well as the predicted tracer distribution by CALPUFF, SCIPUFF, FLEXPART and CAMx. Note that the observed tracer spatial distribution plots in Figure 6‐16 are color coded at the monitoring sites. Previously the spatial distribution of the observed tracer distribution was also presented using spatial interpolation from the monitoring sites in Figure 6‐ 3b. However, such an interpolation is in itself a model and may not be correct, so in Figure 6‐16 the observed tracer concentrations at the monitoring sites is presented for comparison with the five LRT models. 24 hours after the tracer release, the observed tracer was advected to the east‐northeast and was present across northern France and Germany (Figure 6‐16a, top left). CALPUFF advected the tracer with a more northeasterly direction than observed and underestimated the plume spread thereby missing the observed tracer concentrations in southern Germany (Figure 6‐16a, top right). SCIPUFF (Figure 6‐16a, middle left) also appeared to advect the tracer with more of a northeast direction than observed, but had more plume spread so was better able to capture the occurrence of observed tracer concentrations in southern Germany. FLEXPART (Figure 6‐ 16a, middle right) and HYSPLIT (Figure 6‐16a, bottom left) both correctly advect the tracer initially in the east‐northeast direction, but FLEXPART greatly underestimates the observed plume spread on the ground with HYSPLIT also underestimating the plume spread but not as much as FLEXPART. CAMx also appears to initially transport the tracer with more of a northeasterly than east‐northeast direction as seen with SCIPUFF. Like SCIPUFF, the CAMx tracer plume has a southerly bulge that begins to capture the occurrence of the observed tracer concentrations in southern Germany that the other three LRT dispersion models miss completely. All of the models fail to reproduce the leading edge of the observed tracer cloud in northeastern Germany, with SCIPUFF and CAMx best able to simulate the observed front of the tracer cloud. The LRT dispersion models underestimation of the location of the leading edge of the observed tracer cloud is likely related to the MM5 model wind speed underestimation bias (see Figure 6‐4a). SCIPUFF tends to have an overestimation bias of both concentrations and spatial extend of the observed tracer 24 hours after its release. The predicted and observed tracer distribution 36 hours after its release is shown in Figure 6‐ 16b. The observed tracer plume moved eastward and traverses Germany 36 hours after the 118
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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
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
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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
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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 and 150: 70% 60% 50% 40% 30% 20% 10% 0% Figu
Page 151 and 152: Figure 6‐9. Factor of Exceedance
Page 153: eceiving a 0.0 score. Figure 6‐13
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
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
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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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