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EVALUATION OF THE MM5 AND CALMET METEOROLOGICAL MODELS USING THE CAPTEX CTEX5 FIELD EXPERIMENT DATA Statistical evaluation of the prognostic (MM5) and diagnostic (CALMET) meteorological model applications for the CTEX5 CAPTEX release was conducted using surface meteorological measurements. For the MM5 datasets, performance for meteorological parameters of wind (speed and direction), temperature, and humidity (mixing ratio) was examined. For the CALMET experiments, CALMET estimated winds (speed and direction) were examined because the two‐dimensional temperature and relative humidity fields output are simple interpolated fields of the observations. Therefore, the evaluation for CALMET was restricted to winds where the majority of change can be induced by both diagnostic terrain adjustments and varying the OA strategy. Note that except for the NOOBS = 2 CALMET sensitivity tests (i.e., the “D” series of CALMET sensitivity tests), surface meteorological observations are blended with the wind fields in the CALMET STEP2 objective analysis (OA) procedure. Thus, the evaluation of the CALMET wind fields is not a true independent evaluation as the surface meteorological observations used in the evaluation are also used as input into CALMET. So we expect the CALMET wind fields to compare better with observations than MM5, but that does not mean that CALMET is producing better meteorological fields. As clearly shown by EPA (2009a,b), the CALMET diagnostic (STEP1) and blending of observations using the STEP2 OA procedure can introduce discontinuities and artifacts in the wind fields generated by the MM5/WRF prognostic meteorological model that is used as input to CALMET, even though the CALMET winds may match the observed surface winds at the locations of the monitoring sites does not necessarily mean that CALMET is performing better than MM5/WRF. The METSTAT software (Emery et al., 2001) was used to match MM5 output with observation data. The MMIFStat software (McNally, 2010) tool was used to match CALMET output with observation data. Emery and co‐workers (2001) have developed a set of “benchmarks” for comparing prognostic meteorological model performance statistics metrics. These benchmarks were developed after examining the performance of the MM5 and RAMS prognostic meteorological models for over 30 applications. The purpose of the benchmarks is not to assign a passing or failing grade, rather it is to put the prognostic meteorological model performance in context. The surface meteorological model performance benchmarks from Emery et al., (2001) are displayed in Table A‐1. Note that the wind speed RMSE benchmark was also used for wind speed MNGE given the similarity of the RMSE and MNGE performance statistics. These benchmarks are not applicable for diagnostic model evaluations. 1
Table A‐1. Wind speed and wind direction benchmarks used to help judge the performance of prognostic meteorological models (Source: Emery et al., 2001). Wind Speed Root Mean Squared Error (RMSE) Mean Normalized Bias (NMB) Index of Agreement (IOA) Wind Direction Mean Normalized Gross Error (MNGE) Mean Normalized Bias (MNB) Temperature Mean Normalized Gross Error (MNGE) Mean Normalized Bias (NMB) Index of Agreement (IOA) Humidity Mean Normalized Gross Error (MNGE) Mean Normalized Bias (NMB) Index of Agreement (IOA) 2 ≤ 2.0 m/s ≤ ±0.5 m/s ≥ 0.6 ≤ 30° ≤ ±10° ≤ 2.0 K ≤ ±0.5 m/s ≥ 0.8 ≤ 2.0 g/kg ≤ ±1.0 g/kg ≥ 0.6 Table A‐2 lists the CTEX5 MM5 sensitivity tests that are evaluated in this section. For the first set of MM5 experiments (EXP1) MM5 was configured as it would be run during the late 1980s and early 1990s using only 16 vertical layers, a single 80 km grid resolution and older (Blackadar) planetary boundary layer (PBL) and land soil module (LSM). There were several four dimensional data assimilation (FDDA) experiments using this first MM5 configuration from none (EXP1A) to analysis nudging above the PBL and at the surface (EXP1C). The second set of MM5 experiments (EXP2A‐C) used a more recent MRF PBL scheme and 33 vertical layers with three levels of grid nesting (108/26/12 km) and was meant to represent the way MM5 was run in the late 1990s/early 2000s. Three different levels of FDDA were used with this MM5 configuration: none (EXP2A), analysis nudging above the PBL (EXP2B) and analysis nudging above the PBL as well as at the surface (EXP2C). Note that additional sensitivity experiments were planned using this second MM5 configuration (e.g., EXP2D and EXP2E), but the MM5 model performance using the MRF PBL scheme was so poor that this MM5 configuration was abandoned. The third set of MM5 experiments (EXP2F‐J) used a MM5 configuration similar to the second set of MM5 experiments only with more vertical layers (43) and going back to the Blackadar PBL scheme due to the poor performance of MRF. Additional FDDA sensitivity tests were performed that increased the FDDA nudging strength by a factor of 2 and then added in observation nudging. The final MM5 configuration (EXP3) was exactly the same as the third configuration MM5 experiment EXP2H, only using the Pleim‐Xiu PBL/LSM scheme. The CALMET sensitivity tests are listed in Table A‐3. The MM5 output from either MM5 EXP1C (80 km) or MM5 EXP2H (36 and 12 km) were used as initial guess winds in the CALMET experiments. The CALMET sensitivity tests varied by the CALMET grid resolution, the source and grid resolution of the MM5 output data used and how the surface and upper‐air meteorological data were blended into the STEP1 wind fields in the STEP2 OA procedure. There were seven basic CALMET configurations:
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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
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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
Page 135 and 136: 6.0 1994 EUROPEAN TRACER EXPERIMENT
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Page 139 and 140: Figure 6‐3a. Distribution of the
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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
Page 161 and 162: • NoPiG: The tracer emissions wer
Page 163 and 164: Using the NMSE statistical performa
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Page 169 and 170: Table 6‐3. Summary of CALPUFF puf
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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: 148 Appendix A Evaluation of the MM
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
Page 211 and 212: Figure C‐ ‐5. Global model m pe
Page 213 and 214: C.3 CAMX SENSITIVITY TESTS Followin
Page 215 and 216: ACM2 Kzz combinatio ons rank as the
Page 217 and 218: Figure C‐ ‐10. Spatial model pe
Page 219 and 220: Figure C‐ ‐12. Global model per
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Page 227 and 228: Table C‐3. CAMx FMS and POD spati
Page 229 and 230: Figure C‐ ‐20. False Alarm Rate
Page 231 and 232: Figure C‐ ‐23. Factor of o Exce
Page 233 and 234: The PCC values for th he six LRT mo
Page 235 and 236: 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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