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

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Figure 6‐16d. Comparison of spatial distribution of the ETEX tracer concentrations 60 hours after release for the observed (top left), CALPUFF (top right), SCIPUFF (middle left), FLEXPART (middle right), HYSPLIT (bottom left) and CAMx (bottom right). 6.4.3 CAMx Sensitivity Tests Sixteen CAMx sensitivity tests were conducted to investigate the effects of vertical diffusion, horizontal advection solvers and use of the sub‐grid scale Plume‐in‐Grid (PiG) module on the model performance for the ETEX tracer experiment. Plume‐in‐Grid (PiG) Module: The PiG module treats the near‐source plume dispersion (and chemistry if applicable) of a point source plume using a subgrid‐scale Lagrangian puff module. The mass from the PiG puff module is transferred to the grid model when the plume size is commensurate with the grid cell size used in the CAMx simulation. Two types of PiG sensitivity tests were conducted in this study to investigate the effects of the PiG module on model performance: 123
• NoPiG: The tracer emissions were released directly into the CAMx 36 km grid cell containing the tracer release location calculating plume rise using the local meteorological conditions to inject the emissions into the appropriate vertical layer. • PiG: Calculate plume rise using local meteorological conditions and simulate the early evolution of plume dispersion using the PiG module. Vertical Diffusion Coefficients (Kz): The Kz coefficients define the rate of vertical mixing in a column of grid cells in CAMx. MM5 meteorological model does not directly output Kz, thus the MM5CAMx pre‐processor has several different algorithms for diagnosing the Kz coefficients. Four different Kz algorithms were evaluated in the CAMx sensitivity tests in this study: • OB70: O’Brien 1970 algorithm for calculation Kz values by diagnosing them from the MM5 output. • TKE: The Eta planetary boundary layer (PBL) scheme used in the ETEX MM5 meteorological modeling has a Turbulent Kinetic Energy (TKE) formulation. When using a TKE PBL scheme, MM5CAMx can calculate the Kz coefficients directly from the TKE values, rather than diagnosing them from the other meteorological variables in the MM5 output. • ACM2: The Asymmetric Convective Mixing (ACM2) algorithm has two components: a standard Kz scheme that calculates diffusion between two adjacent grid cells in a column; and a non‐local diffusion scheme that can calculate diffusion between grid cells in a column that are not adjacent. In CAMx, the ACM2 scheme will deduce when convective activity is present in a column of grid cells and add the non‐local diffusion to the standard local diffusion based on the Kz coefficients. • CMAQ: Use the algorithm for calculating Kz from the CMAQ modeling system (Byun and Ching, 1999). Horizontal Advection Solver: Horizontal advection (transport) is solved in CAMx using finite difference algorithms that were explicitly developed for simulating transport and limit numerical diffusion that can artificially reduce concentration peaks. Two horizontal transport algorithms are implemented in CAMx and their effect on model performance for the ETEX experiment was evaluated: Bott: The Bott (1989) scheme is a positive definite transport scheme that limits numerical diffusion. PPM: The Piecewise Parabolic Method (PPM; Colella and Woodward, 1984) is a higher order positive definite transport scheme that is also designed to limit numerical diffusion. The configuration of CAMx presented in the previous sections comparing model performance against the other four LRT models was a standard configuration used in many regional model applications: • Don’t use PiG subgrid‐scale puff module (NoPiG) • Use of CMAQ‐like Kz vertical diffusion coefficients (CMAQ) • Use of PPM horizontal advection solver (PPM) 6.4.3.1 NoPiG CAMx Sensitivity Tests Figure 6‐17 displays the CAMx spatial model performance statistics for the sensitivity tests that were run without using the PiG subgrid‐scale puff module. For the FMS statistic, the CMAQ Kz and PPM horizontal transport sensitivity test (CMAQ/PPM) is performing the best with a FMS value of 51.8% followed by CMAQ/Bott (50.9%) and ACM2/PPM (50.8%). Vertical diffusion has 124
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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
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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
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
Page 143 and 144: splitting flag near sunset (hour 17
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 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: Figure 6‐16c. Comparison of spati
Page 163 and 164: Using the NMSE statistical performa
Page 165 and 166: 6.4.3.2 Effect of PiG on Model Perf
Page 167 and 168: 80 70 60 50 40 30 20 10 0 1 2 3 4 5
Page 169 and 170: Table 6‐3. Summary of CALPUFF puf
Page 171 and 172: 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.
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
Page 205 and 206: C.1 INTRODUCTION In this section, t
Page 207 and 208: C.2.2 HYYSPLIT GLOB BAL STATISTIICS
Page 209 and 210: 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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