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

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HYSPLIT). Both approaches show CAMx and HYSPLIT as the highest ranking models for CTEX5 with rankings that are fairly close to each other, however after that the two ranking techniques come to very different conclusions regarding the ability of the models to simulate the observed tracer concentrations for the CTEX5 field experiment. The most noticeable feature of the RANK metric for ranking models in CTEX5 is the third highest ranking model using RANK, CALGRID (1.57). CALGRID ranks as the worst or second worst performing model in 9 of the 11 performance statistics, so is one of the worst performing model 82% of the time and has an average ranking of 5 th best model out of the 6 LRT dispersion models. In examining the contribution to the RANK metric for CALGRID, there is not a consistent contribution from all four broad categories to the composite scores (Figure ES‐5). As noted in Table ES‐2, the RANK score is defined by the contribution of the four of the 11 statistics that represent measures of correlation/scatter (R 2 ), bias (FB), spatial (FMS) and cumulative distribution (KS): ( 1− FB / 2 ) + FMS / 100+ ( 1 KS / 100) 2 RANK = R + − The majority of CALGRID’s 1.57 RANK score comes from the fractional bias (FB) and Kolmogorov‐Smirnov (KS) performance statistics with little or no contributions from the correlation (R 2 ) or spatial (FMS) statistics. As shown in Table ES‐6, CALGRID performs very poorly for the FOEX and FA2/FA5 statistics due to a large underestimation bias. The FB component to the RANK composite score for CALGRID is one of the highest among the six models in this study, yet the underlying statistics indicate both marginal spatial skill and a large degree of under‐prediction (likely due to the spatial skill of the model). The current form of the RANK score uses the absolute value of the fractional bias. This approach weights underestimation equally to overestimation. However, in a regulatory context, EPA is most concerned with models not being biased towards under‐prediction. Models can produce seemingly good (low) bias metrics through compensating errors by averaging over‐ and under‐predictions. The use of an error statistic (e.g., NMSE) instead of a bias statistic (i.e., FB) in the RANK composite metrics would alleviate this problem. Adaptation of RANK score for regulatory use will require refinement of the individual components to insure that this situation does not develop and to insure that the regulatory requirement of bias be accounted for when weighting the individual statistical measures to produce a composite score. 22
Table ES‐6. Summary of model rankings using the statistical performance metrics and comparison with the RANK metric. Statistic 1 st 2 nd 3 rd 4 th 5 th 6 th FMS SCIPUFF CAMx HYSPLIT CALPUFF FLEXPART CALGRID FAR FLEXPART HYSPLIT CAMx SCIPUFF CALGRID CALPUFF POD SCIPUFF CAMx HYSPLIT FLEXPART CALPUFF CALGRID TS FLEXPART HYSPLIT CAMx SCIPUFF CALPUFF CALGRID FOEX CALPUFF CAMx HYSPLIT CALGRID SCIPUFF FLEXPART FA2 HYSPLIT CAMx CALPUFF SCIPUFF FLEXPART CALGRID FA5 HYSPLIT CAMx SCIPUFF CALPUFF FLEXPART CALGRID NMSE CAMx SCIPUFF FLEXPART HYSPLIT CALPUFF CALGRID PCC or R HYSPLIT CAMx SCIPUFF FLEXPART CALGRID CALPUFF FB CAMx CALGRID FLEXPART SCIPUFF HYSPLIT CALPUFF KS HYSPLIT CALPUFF CALGRID CAMx FLEXPART SCIPUFF Avg. Ranking CAMx HYSPLIT SCIPUFF FLEXPART CALPUFF CALGRID Avg. Score 2.20 2.4 3.4 3.8 4.3 5.0 RANK Ranking CAMx HYSPLIT CALGRID SCIPUFF FLEXPART CALPUFF RANK 1.91 1.80 1.57 1.53 1.45 1.28 European Tracer Experiment (ETEX) The European Tracer Experiment (ETEX) was conducted in 1994 with two tracer releases from northwest France that was measured at 168 samplers located in 17 European countries. Five LRT dispersion models were evaluated for the first (October 23, 1994) ETEX tracer release period (CALPUFF, SCICHEM, HYSPLIT, FLEXPART and CAMx). All five LRT dispersion models were exercised using a common 36 km MM5 database for their meteorological inputs. For CALPUFF, the MMIF tool was used to process the MM5 data. Default model options were mostly selected for the LRT dispersion models. An exception to this is that for CALPUFF puff splitting was allowed to occur throughout the day, instead of once per day which is the default setting. The MM5 simulation was evaluated using surface meteorological variables. The MM5 performance did not always meet the model performance benchmarks and exhibited a wind speed and temperature underestimation bias. However, since all five LRT dispersion models used the same MM5 fields, this did not detract from the LRT model performance intercomparison. The ATMES‐II model evaluation approach was used in the evaluation that calculated 12 model performance statistics of spatial, scatter, bias, correlation and cumulative distribution. ETEX LRT Dispersion Model Performance Evaluation Figure ES‐6 displays the ranking of the five LRT dispersion models using the RANK model performance statistic with Table ES‐7 summarizing the rankings for the other 11 ATMES‐II performance statistics. Depending on the statistical metric, three different models were ranked as the best performing model 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 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 23
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 and 24: 2.4 2 1.6 1.2 0.8 0.4 0 EXP4A EXP4B
Page 25 and 26: Figure ESS‐4. RANK statistical pe
Page 27: Evaluatioon of Six LRT T Dispersion
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%
Page 75 and 76: 30% 20% 10% 0% ‐10% ‐20% ‐30%
Page 77 and 78: 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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