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

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6.1 DESCRIPTION OF THE 1994 EUROPEAN TRACER EXPERIMENT 98 6.1.1 ETEX Field Study 98 6.1.2 Synoptic Conditions 99 6.2 MODEL CONFIGURATION AND APPLICATION 102 6.2.1 Experimental Design 102 6.2.2 Meteorological Inputs 103 6.2.3 LRT Model Configuration and Inputs 104 6.3 QUALITY ASSURANCE 107 6.3.1 Quality Assurance of the Meteorological Inputs 107 6.3.2 Quality Assurance of the LRT Model Inputs 110 6.4 MODEL PERFORMANCE EVALUATION 110 6.4.1 Statistical Model Performance Evaluation 110 6.4.2 Spatial Displays of Model Performance 118 6.4.3 CAMx Sensitivity Tests 123 6.4.4 CALPUFF Sensitivity Tests 129 6.4.5 HYSPLIT Sensitivity Tests 134 6.5 CONCLUSIONS OF THE MODEL PERFORMANCE EVALUATION OF THE LRT DISPERSION MODELS USING THE ETEX TRACER EXPERIMENT FIELD STUDY DATA 141 7.0 REFERENCES 142 Appendices Appendix A: Evaluation of the MM5 and CALMET Meteorological Models Using the CAPTEX CTEX5 Field Experiment Data Appendix B: Evaluation of Various Configurations of the CALMET Meteorological Model Using the CAPTEX CTEX3 Field Experiment Data Appendix C: Intercomparison of LRT Models against the CAPTEX Release 3 and Release 5 Field Experiment Data
EXECUTIVE SUMMARY ABSTRACT The CALPUFF Long Range Transport (LRT) air quality dispersion modeling system is evaluated against several atmospheric tracer field experiments. Meteorological inputs for CALPUFF were generated using MM5 prognostic meteorological model processed using the CALMET diagnostic wind model with and without meteorological observations. CALPUFF meteorological inputs were also generated using the Mesoscale Model Interface (MMIF) tool that performs a direct “pass through” of the MM5 meteorological variables to CALPUFF without any adjustments or re‐diagnosing of meteorological variables, as is done by CALMET. The effects of alternative options in CALMET on the CALMET meteorological model performance and the performance of the CALPUFF LRT dispersion model for simulating observed atmospheric tracer concentrations was analyzed. The performance of CALPUFF was also compared against past CALPUFF evaluation studies using an earlier version of CALPUFF and some of the same tracer test field experiments as used in this study. In addition, up to five other LRT dispersion models were also evaluated against some of the tracer field experiments. CALPUFF and the other LRT models represent three distinct types of LRT dispersion models: Gaussian puff, particle and Eulerian photochemical grid models. Numerous sensitivity tests were conducted using CALPUFF and the other LRT models to elucidate the effects of alternative meteorological inputs on dispersion model performance for the tracer field studies, as well as to intercompare the performance of the different dispersion models. INTRODUCTION Near‐Source and Far‐Field Dispersion Models Dispersion models, such as the Industrial Source Complex Short Term (ISCST) or American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) typically assume steady‐state, horizontally homogeneous wind fields instantaneously over the entire modeling domain and are usually limited to distances of less than 50 kilometers from a source. However, dispersion model applications of distances of hundreds of kilometers from a source require other models or modeling systems. At these distances, the transport times are sufficiently long that the mean wind fields can no longer be considered steady‐state or homogeneous. As part of the Prevention of Significant Deterioration (PSD) program, new sources or proposed modifications to existing sources may be required to assess the air quality and Air Quality Related Value (AQRV) impacts at Class I and sensitive Class II areas that may be far away from the source (e.g., > 50 km). AQRVs include visibility and acid (sulfur and nitrogen) deposition. At these far downwind distances, the steady‐state Gaussian plume assumptions of models like ISCST and AERMOD are likely not valid and Long Range Transport (LRT) dispersion models are required. The Interagency Workgroup on Air Quality Modeling (IWAQM) consists of the U.S. EPA and Federal Land Managers (FLMs; i.e., NPS, USFS and FWS) and was formed to provide a focus for the development of technically sound recommendations regarding assessment of air pollutant source impacts on Federal Class I areas. One objective of the IWAQM is the recommendation of LRT dispersion models for assessing air quality and AQRVs at Class I areas. One such LRT dispersion model is the CALPUFF Gaussian puff modeling system, which includes the CALMET diagnostic wind model and the CALPOST post‐processor. In 1998, EPA published a report that evaluated CALPUFF against two short‐term tracer test field experiments (EPA, 1998a). Later in 1998 IWAQM released their Phase II recommendations (EPA, 1998b) that included 1
Page 1 and 2: Documentation of the Evaluation of
Page 3 and 4: FOREWARD This report documents the
Page 5: 2.4.3 ATMES‐II Model Evaluation A
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 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
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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
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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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