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

More documents

Recommendations

Info

Table C‐2. CTEX3 CAMx RANK model performance statistic and model rankings for different model Kz/advection solver configurations with and without using the PiG subgrid‐scale puff model. Without PiG Module With PiG Module PiG‐NoPiG Model Model Model Configuration RANK Ranking RANK Ranking ΔRANK Percent OB70/BOTT 1.56 8 1.25 8 ‐0.41 ‐26.4% OB70/PPM 1.67 7 1.26 7 ‐0.41 ‐24.5% TKE/BOTT 1.89 3 1.74 4 ‐0.15 ‐8.0% TKE/PPM 1.97 1 1.83 2 ‐0.14 ‐7.1% ACM2/BOTT 1.83 6 a 1.69 5 ‐0.14 ‐7.6% ACM2/PPM 1.87 4 1.68 6 ‐0.19 ‐10.1% CMAQ/BOTT 1.83 5 a 1.81 3 ‐0.02 ‐1.0% CMAQ/PPM a tied 1.91 2 1.86 1 ‐0.05 ‐2.6% C.3.5 SPATIAL PERFORMANCE FOR CTEX5 NOPIG EXPERIMENTS Spatial performance for CTEX5 and the NoPiG CAMx sensitivities posed a slightly more difficult challenge to interpret due to similarities amongst the Kz/advection solver options for the FMS metric (Figure C‐13). The range of difference for the FMS between the minimum and maximum for all of the eight combinations was less than 2.1%, with all in the range of 22% to 24%. This would indicate that each of the model configurations performs similarly across all concentration ranges. However, in the extended spatial statistics of FAR, POD, and TS, greater differentiation in model spatial performance metrics are seen. For example, for POD and TS, the CMAQ Kz combinations perform best of all of the vertical diffusion options, and have POD/TS statistics that are nearly twice as good as the OB70 diffusion combinations with POD/TS values of ~60%/~22% for CMAQ versus ~33%/~10% for OB70 diffusion algorithm options. Since these statistics are valid for concentration ranges above the 100 pg m ‐3 concentration level, similarity in model performance for the FMS metric is likely due to better performance of OB70 and ACM2 at levels below the threshold concentration used for the FAR, POD and TS statistics. Above the concentration threshold spatial performance for OB70 and ACM2 lags behind that of the TKE and CMAQ, indicating that the TKE and CMAQ Kz options perform better across all concentration ranges compared to similar performance at the lower concentration levels below the threshold. Overall, it appears that the CMAQ Kz option yields the best performance of the diffusion options when examining the performance across all of the spatial metrics. 16
Figure C‐ ‐13. Spatial model perfoormance staatistics for the CAMx CTTEX5 sensitivvity tests without using the PiG subgrid‐sccale puff moodule (NoPiGG). C.3.6 Gloobal Statistical Performmance for CTEX5 NoPiG EExperiments Figures CC‐14 and C‐15 displays thhe global staatistics for thhe CAMx No for the sttatistical me etrics with thhe best perfoorming model has the, r highest sscore. For th he FOEX mettric, all of the Kz/advectiion solver op other (1% % ‐ 5%), with h the best peerformance coming fromm OB70 and slightly across the AC CM2 and CMMAQ options. The NMSSE, FB, and PCC P metrics pprovide cleaar differentiaation in perfo options, with the TKE E and CMAQQ options yieelding significcantly bette OB70 or ACM2. For NMSE, the CCMAQ combbinations havve the best s followed by the TKE combinationns with 12.6 – 13.6 pg m for OB700 and ACM2. A similar reelationship is found with CMAQ annd TKE perfo orming signifficantly bettter than eith ‐3 s PiG CTEX5 sensitivity tessts espectively, lowest and ptions are wwithin 4% of eeach TKE optionss and degradding ormance acrross the Kz er performannce than eithher scores with 99.3 – 9.4 pg m . NMSE vaalues are nearly double h the FB andd PCC metrics, with the er ACM2 or OB70. ‐3 , that 17
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 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
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
Page 87 and 88:
60% 40% 20% 0% ‐20% ‐40% ‐60%
Page 89 and 90:
with APS, implementing the slug opt
Page 91 and 92:
the amount of time that the tracer
Page 93 and 94:
Figure 4‐1. CALPUFF/CALMET UTM mo
Page 95 and 96:
compact discs, which were used to o
Page 97 and 98:
Table 4‐4. CALPUFF parameters use
Page 99 and 100:
Table 4‐8. CALPUFF/MMIF sensitivi
Page 101 and 102:
the fitted Gaussian plume is not a
Page 103 and 104:
Figure 4‐2. Comparison of predict
Page 105 and 106:
Figure 5‐1. Location of Dayton an
Page 107 and 108:
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
Page 133 and 134:
‐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
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 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
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: Figure C‐ ‐12. Global model per
Page 223 and 224: Figure C‐ ‐15. Global model per
Page 225 and 226: Figure C‐ ‐17. Global model per
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
Page 237 and 238: Table C‐55. Summary y of model r
Page 239 and 240: Results foor the TS me etric are pr
Page 241 and 242: Figure C‐ ‐35. Factor of o 2 (F
Page 243 and 244: a negativve FB. The best b performm
Page 245 and 246: performing model the most often sco
Page 247: United States Environmental Protect
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?