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

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a quarter of the models had FMS>55%, with a maximum FMS value of 71%. In ETEX, at 36 hours one tenth of the models had a zero FMS (i.e., no overlap of the predicted and observed tracer cloud) and a quarter had an FMS>45%, with a maximum FMS of 67%. At 60 hours in ATMES‐II half of the models (against only a quarter of the models of ETEX) had a FMS>30% and the maximum FMS was 58%, while the maximum FMS for ETEX models was 52%. Temporal Analysis: The temporal analysis was carried out at two arcs of receptors at distances of approximately 600 and 1,200‐1,400 km from the release point. In general, the LRT models were better at predicting the time of arrival, duration and peak concentration of the tracer cloud for the central stations of the two arcs, and less satisfactory for the external stations. The Figure of Merit in Time (FMT, see Section 2.4 for definition) the best performances were observed for the central stations of the two arcs. For all the stations selected for the time analysis, FMT of models in ATMES‐II improved when compared to the first ETEX release exercise. Global Statistics: The global statistical indexes also indicate a general improvement of models' performance in ATMES‐II compared to the ETEX real time modeling exercise. For instance, only eight models out of 49 (16%) had a bias higher than 0.4 ngm ‐3 (400 pg/m 3 ) in absolute value; the number of models above the same threshold in ETEX real time was 24 out of the 28 (86%) participants. Almost all models showed a satisfactory agreement with the measured values. However, few models were distinguished by a particularly good (or bad) performance in all respects. More than half of the models showed a relatively small error (NMSE), indicating a limited spread of the predictions around the corresponding measurements. Again, while in the ETEX real‐time exercise only four models had an NMSE less than 100, 42 models were below this threshold in ATMES‐II. Improvements compared to ETEX could also be seen in the number of predicted and observed pairs within a factor of 2 (FA2) and 5 (FA5) of each other; whereas in ATMES‐II half of the models had FA5>45%, in ETEX no model reached that value. There was no negative Pearson correlation coefficient, with the best models showing values slightly less than 0.7. Conclusions: The three main original objectives of ETEX as follows: • to test the capability of institutes involved in emergency response to produce predictions of the cloud evolution in real‐time; • to evaluate the validity of their predictions; and • to assemble a database that allows the evaluation of long‐range atmospheric dispersion models. 11
The ETEX study has formulated the following conclusions: • The objectives stated in the project design were met. • ETEX demonstrated the feasibility of conducting a continental scale tracer experiment across Europe using the perfluorocarbon tracer technique. • There is a large number of institutes that can (and will in the event of a real accident) predict the long‐range atmospheric dispersion of a pollutant cloud. • The rapidity of LRT dispersion modeling groups in predicting the tracer cloud evolution and transmitting the results to a central point was excellent. • Regarding the quality of the predictions, differences between observations and calculations of 3 to 6 hours in arrival time and a factor of 3 in maximum airborne concentrations at ground level should be viewed as the best achievable with current LRT models. • The simulation of cloud dispersion at short and mesoscale distances seems to have considerable influence on the long‐range cloud development. • The transition of the dispersion scales from local to long‐range modeling should be investigated in more detail. • ETEX assembled a unique experimental database of tracer concentrations and meteorological data accessible via the Internet. • ETEX created widespread interest and resulted in considerable dispersion model development as well as the reinforcement of communication and collaboration between national institutes and international organizations. • The ETEX network of national institutes and international organizations should be maintained and improved to continue model development and demonstrate the technical capability necessary to support emergency management in real cases. • Further investigations are needed to determine the quality of predictions under complex meteorological conditions, and to quantify the uncertainty of models for emergency management. 2.3.5 Data Archive of Tracer Experiments and Meteorology (DATEM) The Data Archive of Tracer Experiments and Meteorology (DATEM 17 ) is not a single particular study but an archive of tracer experiment and meteorological data and suggested procedures for evaluating LRT dispersion models using atmospheric tracer data (Draxler, Heffter and Rolph, 2002). The DATEM archive currently incorporates data from five long‐range dispersion experiments, which represent a collection of more than 19,000 air concentration samples, re‐ analysis fields from the National Center for Atmospheric Research (NCAR) / National Centers for Environmental Prediction (NCEP) re‐analysis project, and statistical analysis programs based upon the ATMES‐II evaluation of ETEX. All the emissions and sampling data are in space delimited text files, easily used by FORTRAN programs or imported into any spreadsheet. Meteorological data fields have been reformatted for use by HYSPLIT and are available for download. The statistical programs are all written in FORTRAN and include PC executables with the source code so that they can be compiled on other platforms. The five long range transport tracer field experiments whose atmospheric and meteorological data reside on the DATEM website are as follows: 17 http://www.arl.noaa.gov/DATEM.php 12
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: The ETEX real‐time LRT modeling p
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
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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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Page 225 and 226:
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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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