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

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15 BASED 1.79 80 18 ‐‐ No 16 EXP3A 1.79 36 12 10/100 Yes 17 EXP3B 1.79 36 12 100/200 Yes 18 EXP3C 1.79 36 12 500/1000 Yes 19 EXP3D 1.79 36 12 ‐‐ No 20 4KM_MMIF 1.78 4 ‐‐ ‐‐ No 21 EXP4C 1.72 12 12 10/100 Yes 22 36KM_MMIF 1.42 36 ‐‐ ‐‐ No 23 80KM_MMIF 1.42 80 ‐‐ ‐‐ No 24 12KM_MMIF 1.28 12 ‐‐ ‐‐ No Conclusions of the CAPTEX CALPUFF Tracer Sensitivity Tests There are some differences and similarities in CALPUFF’s ability to simulate the observed tracer concentrations in the CTEX3 and CTEX5 field experiments. The overall conclusions of the evaluation of the CALPUFF model using the CAPTEX tracer test field experiment data can be summarized as follows: • There is a noticeable variability in the CALPUFF model performance depending on the selected input options to CALMET. ‐ By varying CALMET inputs and options through their range of plausibility, CALPUFF can produce a wide range of concentrations estimates. • Regarding the effects of the RMAX1/RMAX2 parameters on CALPUFF/CALMET model performance, the “A” series (500/1000) performed best for CTEX3 but the “C” series (10/100) performed best for CTEX5 with both CTEX3 and CTEX5 agreeing that the “B” series (100/200) is the worst performing setting for RMAX1/RMAX2. ‐ This is in contrast to the CALMET wind evaluation that found the “B” series was the CALMET configuration that most closely matched observed surface winds. ‐ The CALMET wind evaluation was not an independent evaluation since some of the wind observations used in the model evaluation database were also used as input to CALMET. Evaluation of Six LRT Dispersion Models using the CTEX3 Database Six LRT dispersions models were applied for the CTEX3 experiment using common meteorological inputs based solely on MM5. Figure ES‐4 displays the RANK model performance statistic for the six LRT dispersion models. 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 2 ), 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. 18
Figure ESS‐4. RANK statistical peerformance mmetric for thhe six LRT mmodels and CCAPTEX Releease 3. Table ES‐ ‐5 summariz zes the rankiings between the six LRTT models forr the 11 perfformance statistics analyzed an nd comparess them to the rankings oobtained usinng the RANKK performance statistic. In testing th he efficacy of the RANK sstatistic for pproviding ann overall ranking of model performaance the ran nking of the ssix LRT models using thee average rank of the 111 performancce statistics (Table ES‐5) versus the ranking fromm the RANK statistical mmetric (Figuree ES‐4) are compareed as follows s: Raanking Avverage of 111 Statistics 1. CAAMx 2. SCCIPUFF 3. FLLEXPART 4. HYYSPLIT 5. CAALPUFF 6. CAALGRID 19 RANK CAMx SCIPUFF FLEXPARTT CALPUFF HYSPLIT CALGRID
Page 1 and 2: Documentation of the Evaluation of
Page 3 and 4: FOREWARD This report documents the
Page 5 and 6: 2.4.3 ATMES‐II Model Evaluation A
Page 7 and 8: EXECUTIVE SUMMARY ABSTRACT The CALP
Page 9 and 10: OVERVIEW OF APPROACH Up to six LRT
Page 11 and 12: CALPUFF performance is evaluated by
Page 13 and 14: Table ES‐2. ATMES‐II spatial an
Page 15 and 16: • CALPUFF tended to overstate the
Page 17 and 18: • The best performing CALPUFF con
Page 19 and 20: 2009a). The key findings from the C
Page 21 and 22: 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2
Page 23: 2.4 2 1.6 1.2 0.8 0.4 0 EXP4A EXP4B
Page 27 and 28: Evaluatioon of Six LRT T Dispersion
Page 29 and 30: Table ES‐6. Summary of model rank
Page 31 and 32: eproduce the northwest to southeast
Page 33 and 34: ETEX LRT Dispersion Model Sensitivi
Page 35 and 36: CONCLUSIONS OF LRT DISPERSION MODEL
Page 37 and 38: The CAMx and CALGRID Eulerian photo
Page 39 and 40: July 1980. Both experiments examine
Page 41 and 42: 1.3 ORGANIZATION OF REPORT Chapter
Page 43 and 44: puffs expand until they exceed the
Page 45 and 46: that performance evaluation be base
Page 47 and 48: The ETEX real‐time LRT modeling p
Page 49 and 50: The ETEX study has formulated the f
Page 51 and 52: In this study we expand the LRT mod
Page 53 and 54: AM ∩ AP FMS = × 100% (2‐2) A
Page 55 and 56: Factor of α (FAα): FAα represent
Page 57 and 58: 3.0 1980 GREAT PLAINS FIELD STUDY 3
Page 59 and 60: compact discs, which were used to o
Page 61 and 62: ILEVZI = 1 Layer of winds to use in
Page 63 and 64: MCHEM = 0 No chemical transformatio
Page 65 and 66: Table 3‐6. CALPUFF/CALMET experim
Page 67 and 68: Table 3‐11. CALPUFF/MMIF sensitiv
Page 69 and 70: evaluation studies and evaluate whe
Page 71 and 72: Tables 3‐13 and Figures 3‐2 thr
Page 73 and 74: 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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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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