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

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tracer evaluation bears more weight. Other aspects of the EPA‐FLM recommended settings generally produced better CALPUFF tracer model performance including use of prognostic meteorological data as input to CALPUFF. The CALPUFF evaluation also found better CALPUFF performance when 12 km grid resolution is used in MM5 or CALMET as opposed to 80 or 36 km. CALPUFF Model Performance using CALMET versus MMIF: The CALPUFF tracer model performance using meteorological inputs based on the MMIF tool versus CALMET was mixed. The variations of the CALPUFF model predictions using MMIF were much less than when CALMET was used and the CALPUFF/MMIF model performance was usually within the range of the performance exhibited by CALPUFF/CALMET. Specific examples from the tracer tests are as follows: • For the GP80 100 km receptor arc, the CALPUFF/MMIF exhibited better fitted plume observed tracer model performance statistics than all of the CALPUFF/CALMET configurations except when CALMET was run using MM5 and surface meteorological observations but no upper‐air meteorological observations. • CALPUFF/CALMET using no MM5 data and just meteorological observations exhibited the best plume centerline location on the GP80 100 km receptor arc with CALPUFF/CALMET using just MM5 data and no observations and CALMET/MMIF exhibiting the worst plume centerline location. • For the GP80 600 km receptor arc, the CALPUFF/MMIF fitted plume model performance statistics are in the middle of the performance statistics for the CALPUFF/CALMET configurations. • The slug option was needed for CALPUFF/CALMET to produce good 600 km receptor arc tracer residence time statistics but had little effect on CALPUFF/MMIF. However, use of puff splitting greatly improved the CALPUFF/MMIF tracer residence time statistics. • Of all the CALPUFF sensitivity tests examined, CALPUFF/MMIF using the slug option and puff splitting produced the best CALPUFF fitted plume tracer model performance statistics for the GP80 600 km receptor arc. • In an opposite fashion to the GP80 100 km receptor arc, for the SRL75 100 km receptor arc the best plume centerline offset was achieved when CALPUFF was run with just MM5 data and no meteorological observations (either with CALMET or MMIF) with performance degraded when meteorological observations are used with CALMET. • The CALPUFF model performance using the MMIF tool and 36 and 12 km MM5 data performed better than all of the CALPUFF/CALMET sensitivity tests for the CAPTEX CTEX3 experiment. However, the CALPUFF/MMIF using 36 and 12 km MM5 data performed worse than all of the CALPUFF/CALMET sensitivity tests for the CAPTEX CTEX5 experiment. Comparison of Model Performance of LRT Dispersion Models: Six LRT dispersion modeled were evaluated using the CAPTEX Release 3 and 5 tracer database and five LRT dispersion models were evaluated using the ETEX tracer test field experiment. In each case the same MM5 meteorological data were used as input into all of the dispersion models, although different MM5 configuration options were selected for each tracer experiement. 30
The CAMx and CALGRID Eulerian photochemical grid models, FLEXPART Lagrangian particle model, HYSPLIT Lagrangian particle, puff and particle/puff hybrid model and CALPUFF and SCIPUFF Gaussian puff models were evaluated. For all three tracer experiments (CTEX3, CTEX5 and ETEX), the CAMx model consistently ranked highest when looking across all of the model performance statistics or when using the RANK composite performance statistic. For the CTEX3 field experiment, the RANK composite performance statistic gave consistent rankings of model performance with the suite of statistical metrics with CAMx being the higheset RANK score (1.91) followed by SCICHEM (1.71). The rankings of the models using all of the statistics versus the RANK composite statistic were inconsistent for the CTEX5 experiment. Both approaches showed CAMx and HYSPLIT were the highest ranking LRT dispersion model for the CTEX5 field experiment. However, the RANK statistic ranked CALGRID as the 3 rd best performing model, whereas when looking at all the performance statistics it was the worst performing model because it exhibited a large spread underestimation bias, had no correlation with the observations and little skill in reproducing the spatial distribution of the observed tracer. The CTEX5 LRT model evaluation points out the need to examine all performance statistics and not rely solely on the RANK composite statistic. It also points out the need to define a RANK‐type composite statistic that focuses on the regulatory application of LRT dispersion models where an underestimation bias is undesirable. Of the three top performing LRT dispersion models, CAMx had the highest RANK composite statistic and scored the highest for most (64%) of the other ATMES‐II statistical model performance metrics, with HYSPLIT scoring the highest for 27% of the metrics. Additional findings of the ETEX tracer test evaluation are as follows: • The model performance rankings were preserved closer to the source (e.g., within 300 km) as well as further downwind. • CALPUFF puff splitting sensitivity tests had little effect on CALPUFF model performance. • CAMx vertical mixing and horizontal advection solver sensitivity tests found that use of the MM5CAMx CMAQ‐like vertical mixing diffusion coefficients and the PPM advection solver produced the best tracer test model performance. Similar results were seen in the CTEX3 and CTEX5 sensitivity modeling. • HYSPLIT sensitivity tests using solely particle, solely puff and hybrid particle/puff and puff/particle combinations found that the hybrid configurations performed best and the puff configuration performed worst, with the CTEX3 and CTEX5 sensitivity test producing similar results. 31
Page 1 and 2: Documentation of the Evaluation of
Page 3 and 4: FOREWARD This report documents the
Page 5 and 6: 2.4.3 ATMES‐II Model Evaluation A
Page 7 and 8: EXECUTIVE SUMMARY ABSTRACT The CALP
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: CONCLUSIONS OF LRT DISPERSION MODEL
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
Page 57 and 58: 3.0 1980 GREAT PLAINS FIELD STUDY 3
Page 59 and 60: compact discs, which were used to o
Page 61 and 62: ILEVZI = 1 Layer of winds to use in
Page 63 and 64: MCHEM = 0 No chemical transformatio
Page 65 and 66: Table 3‐6. CALPUFF/CALMET experim
Page 67 and 68: Table 3‐11. CALPUFF/MMIF sensitiv
Page 69 and 70: evaluation studies and evaluate whe
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
Page 83 and 84: 0% ‐10% ‐20% ‐30% ‐40% ‐5
Page 85 and 86: 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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