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

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Based on this analysis the puff‐particle and particle‐puff hybrid configurations of the HYSPLIT system are clearly the best performing, indicating a distinct operational advantage over pure puff or particle configurations. 6.5 CONCLUSIONS OF THE MODEL PERFORMANCE EVALUATION OF THE LRT DISPERSION MODELS USING THE ETEX TRACER EXPERIMENT FIELD STUDY DATA The evaluation of the five LRT dispersion models using a common MM5 dataset and the ETEX database has provided interesting results about the current capability of LRT models to reproduced observed tracer concentrations. Four of the five LRT models were able to reproduce the observed tracer bifurcation at the farther downwind distances. The CALPUFF model was unable reproduce the observed bifurcation of the tracer cloud and kept the estimated tracer cloud in a circular Gaussian distribution that was advected too far north. CALPUFF puff splitting sensitivity tests were performed to determine whether it would help simulate the bifurcation of the tracer cloud but puff splitting had little effect on the CALPUFF predictions. CAMx sensitivity tests were conducted to examine vertical mixing and horizontal advection solvers and the best performing CAMx model configuration was the one that is most frequently used in applications, which includes using the CMAQ‐like vertical diffusion coefficients in MM5CAMx and the PPM advection solver. The vertical diffusion algorithm had a much bigger effect on CAMx model performance than the choice of horizontal advection solver. The HYSPLIT sensitivity tests with different particle‐puff variations resulted in a wide range of model performance with RANK scores that varied from 1.01 to 2.09. 141
7.0 REFERENCES Anderson, B. 2008. The USEPA MM5CALPUFF Software Project. 12th Annual George Mason University Conference on Atmospheric Transport and Dispersion Modeling, Fairfax, VA, July 8‐10, 2008. Anthes, R.A. and T.T. Warner. 1978. Development of Hydrodynamic Models Suitable for Air Pollution and other Mesometeorological Studies. Mon. Wea. Rev., 106, 1045‐1078. 1078. (http://journals.ametsoc.org/doi/abs/10.1175/1520‐ 0493(1978)106%3C1045%3ADOHMSF%3E2.0.CO%3B2). Bott, A. 1989. A Positive Definite Advection Scheme Obtained by Nonlinear Renormalization of the Advective Fluxes. Mon. Wea. Rev., 117, 1006‐1015. Boybeyi, Z., N. Ahmad, D. Bacon, T. Dunn, M. Hall, P. Lee, R. Sarma, and T. Wait. 2001. Evaluation of the Operational Multiscale Environment Model with Grid Adaptivity against the European Tracer Experiment. J. App. Meteor., 40, 1541‐1558. Brashers, B. and C. Emery. 2011. The Mesoscale Model Interface Program (MMIF) Version 2.0 – Draft User’s Manual. Prepared by ENVIRON International Corporation Novato California and Lynnwood Washington. EPA Contract No. EP‐D‐07‐102, Work Assignments 2‐06 and 4‐06. September 30. Brashers, B. and C. Emery. 2012. The Mesoscale Model Interface Program (MMIF) Version 2.1, 2012‐01‐31 – Draft User’s Manual. Prepared by ENVIRON International Corporation Novato California and Lynnwood Washington. EPA Contract No. EP‐D‐07‐102, Work Assignments 2‐06, 4‐06 and 5‐08. January 31. Byun, D.W., and J.K.S. Ching. 1999. “Science Algorithms of the EPA Models‐3 Community Multiscale Air Quality (CMAQ) Modeling System”, EPA/600/R‐99/030. Carhart, R.A., A. Policastro, M. Watag and L. Coke. 1989. Evaluation of Eight Short‐Term Long‐ Range Transport Models using Field Data. Atmos. Env., 23, 85‐105. Chang, J.C., K. Chayantrakom, and S.R. Hanna. 2003. Evaluation of CALPUFF, HPAC, and VLSTRACK with Two Mesoscale Field Datasets. J. App. Meteor., 42, 453‐466. Chang, J. C., S.R. Hanna, Z. Boybeyi, and P. Franzese. 2005. Use of Salt Lake City URBAN 2000 Field Data to Evaluate the Urban Hazard Prediction Assessment Capability (HPAC) Dispersion Model. J. App. Meteor., 44, 485 – 501. Chen, F. and J. Dudhia. 2001. Coupling an advanced land‐surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569 – 585. Colella, P., and P.R. Woodward. 1984. The Piecewise Parabolic Method (PPM) for Gasdynamical Simulations. J. Comp. Phys., 54, 174-201. D’Amours, R. 1998. Modeling the ETEX Plume Dispersion with the Canadian Emergency Response Model. Atmos. Environ., 32, 4335 – 4341. 34 Deng, A. N.L. Seaman, G.K. Hunter, and D.R. Stauffer, 2004: Evaluation of Interregional Transport Using the MM5‐SCIPUFF System. J. App. Meteor., 43, 1864‐1885. 142
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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
Page 127 and 128: CALPUFF sensitivity simulations are
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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 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
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: Figure 6‐24. Figure of Merit (FMS
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
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
Page 221 and 222: Figure C‐ ‐13. Spatial model pe
Page 227 and 228: 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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