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

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Table 5‐8. CALPUFF model configuration used in the CTEX3 and CTEX5 sensitivity tests. Option Value Comment MGAUSS 1 Use Gaussian vertical distribution initially MCTADJ 0 No terrain adjustment MSLUG 0 Near‐field puffs not modeled as slugs MTRANS 1 Use transitional plume rise MTIP 1 Use stack tip downwash MBDW 1 Use ISC method to simulate building downwash MSHEAR 1 Model vertical wind shear above stack top MSPLIT 1 Use puff splitting MCHEM 0 No chemistry MWET 0 No wet deposition MDRY 0 No dry deposition MDISP 2 Dispersion from internally calculate sigma‐y and sigma‐z using turbulence MTURBW 3 Both sigma‐y and sigma‐z from PROFILE.DAT MDISP3 3 PG dispersion coefficients for rural areas MCTURB 2 Use AERMOD subroutine for turbulence variables MROUGH 0 Don’t adjust sigma‐y and sigma‐z for roughness MPARTL 1 Use partial plume penetration MTINV 0 Compute strength of temperature inversion MPDF 1 Use PDF for dispersion under convective conditions NSPLIT 3 Split puff into 3 puffs when performing vertical puff splitting IRESPLIT 24*1 Keep vertical puff splitting flag on all the time (default is just hour 17 = 1, rest 0) ZISPLIT 100 Vertical splitting is allowed if mixing height exceeds 100 m. ROLDMAX 0.25 Vertical splitting is allowed if ratio of maximum to current mixing height is > 0.25 NSPLITH 5 Number of puffs that result when horizontal splitting is performed SYSPLITH 1.0 Minimum width of puff (in grid cells) before horizontal splitting SHSPLITH 2.0 Minimum puff elongation factor for horizontal splitting CNSPLITH 1.E‐7 Minimum concentrations (g/m 3 ) in puff for horizontal splitting 5.4.1 CALPUFF CTEX3 Model Performance Evaluation Because of the large number of CALPUFF sensitivity tests performed for the CTEX3 tracer test field experiment, they are first compared by groups that used a common MM5/MM4 prognostic meteorological grid resolution output as input into CALMET or MMIF. We then compare the CALPUFF sensitivity tests using different MM4/MM5 grid resolutions but common CALMET/MMIF configurations to determine the sensitivity of MM4/MM5 grid resolution on CALPUFF tracer model performance. 5.4.1.1 CALPUFF CTEX3 Model Evaluation using 80 km MM4 Data Figure 5‐2 displays the spatial model performance statistics metrics for the CALPUFF CTEX3 sensitivity tests that used the 80 km MM4 data. There are variations in the rankings across the spatial statistical performance metrics for the CALPUFF sensitivity tests using the 80 km MM4 data. These sensitivity tests use the finest CALMET grid resolution tested in this series (12 km vs. 18 km) and minimizes the influence of the meteorological observations either through the lowest RMAX1/RMAX2 values (EXP1C) or not using meteorological observations at all by running CALMET in the NOOBS = 2 mode (EXP1D). 77
35% 30% 25% 20% 15% 10% 5% 0% 35% 30% 25% 20% 15% 10% 5% 0% Figure 5‐2. Spatial model performance statistics for the CTEX3 CALPUF sensitivity tests that used the 80 km MM4 data. The global model performance statistics for the CALPUFF sensitivity tests using 80 km MM4 data are compared in Figure 5‐3. 0% ‐2% ‐4% ‐6% ‐8% ‐10% ‐12% ‐14% ‐16% ‐18% 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Figure of Metric in Space (FMS) (Perfect = 100%) BASE A BASE B BASE C EXP 1A EXP 1B EXP 1C Probability of Detection (POD) (Perfect = 100%) BASE A BASE A BASE A BASE B BASE C EXP 1A EXP 1B EXP 1C Factor of Exceedance (FOEX) (Perfect = 0%) BASE B BASE B BASE C EXP 1A EXP 1B Fractional Bias (FB) (Perfect = 0) BASE C EXP 1A EXP 1B EXP 1C EXP 1C EXP 1D EXP 1D EXP 1D EXP 1D Figure 5‐3a. Global model performance statistics for the CTEX3 CALPUFF sensitivity tests using the 80 km MM4 data. 78 100% 20% 15% 10% 80% 60% 40% 20% 5% 0% 60 50 40 30 20 10 0 0% 60% 50% 40% 30% 20% 10% 0% BASE A BASE A BASE A False Alarm Rate (FAR) (Perfect = 0%) BASE B BASE B BASE B BASE C BASE C EXP 1A EXP 1A EXP 1B Threat Score (TS) (Perfect = 100%) BASE C EXP 1A EXP 1B EXP 1B EXP 1C EXP 1C Normalized Mean Square Error (NMSE) (Perfect = 0) EXP 1C Kolmogorov‐Smirnov Parameter (KSP) (Perfect = 0%) BASE A BASE B BASE C EXP 1A EXP 1B EXP 1C EXP 1D EXP 1D EXP 1D EXP 1D
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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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Page 65 and 66: Table 3‐6. CALPUFF/CALMET experim
Page 67 and 68: Table 3‐11. CALPUFF/MMIF sensitiv
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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%
Page 79 and 80: 20% 10% 0% ‐10% ‐20% ‐30% ‐
Page 81 and 82: The fitted Gaussian plume statistic
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Page 85 and 86: 300% 250% 200% 150% 100% 50% 0% 300
Page 87 and 88: 60% 40% 20% 0% ‐20% ‐40% ‐60%
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: performance at the monitor location
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
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Page 125 and 126: CTEX3 discussed in Section 5.4.1. A
Page 127 and 128: CALPUFF sensitivity simulations are
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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
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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 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
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Page 163 and 164: 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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