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

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RANK statistic (1.22) exhibited by EXP4B (12 km CALMET and 100/200) and EXP6B (4 km CALMET and 100/200). 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 EXP4A EXP4B EXP4C Rank (RANK) (Perfect = 4) EXP4D EXP6A Figure ES‐1. RANK performance statistics for CTEX3 CALPUFF sensitivity tests that used 12 km MM5 as input to CALMET or MMIF. Figure ES‐2 compares the RANK model performance statistics for “B” (RMAX1/RMAX2 = 100/200) and “D” (no observations) series of CALPUFF/CALMET sensitivity tests using different CALMET/MM5 grid resolutions of 18/80 (BASEB), 12/80 (EXP1), 12/36 (EXP3), 12/12 (EXP4) 4/36 (EXP5) and 4/12 (EXP6) along with the CALPUFF/MMIF runs using 36 and 12 km MM5 data. The CALPUFF/CALMET sensitivity tests using no observations (“D” series) generally have a higher rank metric than when meteorological observations are used with CALPUFF (“B” series). The CALMET/MMIF sensitivity test using 36 and 12 km MM5 data are the configurations with the highest RANK metric.The CALPUFF/MMIF show a strong relationship between observed and predicted winds than the CALPUFF/CALMET sensitivity tests, which had no to slightly negative correlations with the tracer observations. EXP6B 14 EXP6C EXP6D 12KM_MMIF (1‐KS/100) FMS/100 (1‐FB/2) R^2
1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 BASEB EXP1B EXP1D EXP3B Rank (RANK) (Perfect = 4) EXP3D EXP4B EXP4D Figure ES‐2. RANK performance statistics for CTEX3 CALPUFF sensitivity tests that used RMAX1/RMAX2 = 100/200 (“B” series) or no observations in CALMET (“D” series) and various CALMET/MM5 grid resolutions plus CALMET/MMIF using 36 and 12 km MM5 data. Table ES‐3 ranks all of the CALPUFF CTEX3 sensitivity tests using the RANK statistics. It is interesting to note that the EXP3A and EXP4A CALPUFF/CALMET sensitivity test that uses the, respectively, 36 km and 12 km MM5 data with 12 km CALMET grid resolution and RMAX1/RMAX2 values of 500/1000 have a rank metric that is third highest, but the same model configuration with alternative RMAX1/RMAX2 values of 10/100 (EXP3C and EXP4C) degrades the model performance of the CALPUFF configuration according to the RANK statistic, with a RANK value of 1.12. This is largely due to decreases in the FMS and KS metrics. Note that the finding that CALPUFF/CALMET model performance using CALMET wind fields based on setting RMAX1/RMAX2 = 100/200 (i.e., the “B” series) produces worse CALPUFF model performance for simulating the observed atmospheric tracer concentrations is in contrast to the CALMET surface wind field comparison that found the “B” series most closely matched observations at surface meteorological stations. Since the CALPUFF tracer evaluation is an independent evaluation of the CALMET/CALPUFF modeling system, whereas the CALMET surface wind evaluation is not, the CALPUFF tracer evaluation may be a better indication of the best performing CALMET configuration. The CALMET “B” series approach for blending the wind observations in the wind fields may just be the best approach for getting the CALMET winds to match the observations at the monitoring sites, but possibly at the expense of degrading the wind fields away from the monitoring sites resulting in worse overall depiction of transport conditions. Table ES‐3. Final Rankings of CALPUFF CTEX3 Sensitivity Tests using the RANK model performance statistics. EXP5B 15 EXP5D EXP6B EXP6D 12KM_MMIF 36KM_MMIF (1‐KS/100) FMS/100 (1‐FB/2) R^2
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: 2009a). The key findings from the C
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 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
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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
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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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