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

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⎛ b ⎞ TS = ⎜ ⎟× 100% (2‐5) ⎝ a + b + d ⎠ 2.4.3.2 Temporal Analysis In Section 2.4.1 temporal statistics related to the timing of when the predicted and observed tracer arrives at a monitor or arc of monitors, its residence time over a monitor (or arc) and when the tracer leaves the monitor (or arc) were discussed. Another temporal analysis statistics is the Figure of Merit in Time (FMT), which is analogous to the FMS only it is calculated at a fixed location ( x ) rather than a fixed time as the FMS. The FMT evaluates the overlap between the measures (M) and predicted (P) concentration at location x and time tj. The FMT is normalized to the maximum predicted or measured value at each time interval and is expressed as a percentage value in the same manner as the FMS (Mosca et al., 1998). The FMT is sensitive to both differences between measured and predicted and any temporal shifts that may occur. 2.4.3.3 Global Analysis Following Draxler et al. (2002), four broad categories were used for global analysis of model evaluation. These broad categories are: (1) scatter; (2) bias; (3) spatial distribution of predictions relative to measurements; and (4) differences in the distribution of unpaired measured and predicted values. One or more statistical measures are used from each of the four categories in the global analysis. These include the percent over‐prediction, number of calculations within a factor of 2 and 5 of the measurements, normalized mean square error, correlation coefficient, bias, fractional bias, figure of merit in space, and the Kolmogorov‐ Smirnov parameter representing the differences in cumulative distributions (Draxler et al., 2002). Factor of Exceedance: In the scatter category, better model performance is observed when the Factor of Exceedance (FOEX) measure is close to zero and FA2 (described next) has a high percentage. A high positive FOEX and high percentage of FA5 would indicate a model’s tendency towards over‐prediction when compared to observed values. ⎡ N FOEX = ⎢ ⎣ N ( P > ) i Nii ⎤ − 0 . 5⎥ × 100% ⎦ Where, N in the numerator is the number of pairs when the prediction (P) exceeds the measurement (M) and the N in the denominator is the total number of pairs in the evaluation. In FOEX, all 0‐0 pairs are excluded from the analysis. FOEX can range from ‐50% to +50% with a perfect model receiving a 0% value. 17 (2‐6) (2‐7)
Factor of α (FAα): FAα represents the percentage of predicted values that are within a factor of α, where we have used α = 2 or 5. As with FOEX, in FAα all 0‐0 pairs are excluded. Normalized Mean Squared Error (NMSE): Normalized mean squared error is the average of the square of the differences divided by the product of the means. NMSE gives information about the deviations, but does not yield estimations of model over‐prediction or under‐prediction. ( ) 2 1 NMSE = ∑ Pi − M i (2‐9) N PM Pearson’s Correlation Coefficient (PCC): Also referred to as the linear correlation coefficient, its value ranges between ‐1.0 and +1.0. A value of +1.0 indicates “perfect positive correlation” or having all pairings of (Mi, Pi) lay on straight line on a scatter diagram with a positive slope. Conversely, a value of ‐1.0 indicates “perfect negative correlation” or having all pairings of (Mi, Pi) lie on a straight line with a negative slope. A value of near 0.0 indicates the clear absence of relationship between the model predictions and observed values. R = ⎡ ⎢ ⎣ ∑ ( y − y0 = [ x x0] α) ⎡N − ⎤ FAα = ⎢ ⎥ × 100 (2‐8) ⎣ N ⎦ ∑ i ( M − M ) • ( P − P) i 2 ⎤⎡ 2 ⎤ ( M i − M ) ⎥⎢ ∑( Pi − P) ⎥⎦ ⎦⎣ Fractional Bias (FB): Calculated as the mean difference in prediction minus observation pairings divided by the average of the predicted and observed values. ( P M ) FB = 2 B + (2‐11) Kolmogorov‐Smirnov Parameter (KS): The KS parameter is defined as the maximum difference between two cumulative distributions. The KS parameter provides a quantitative estimate where C is the cumulative distribution of the measured and predicted concentrations over the range of k. The KS is a measure of how well the model reproduces the measured concentration distribution regardless of when or where it occurred. The maximum difference between any two distributions cannot be more than 100%. ( M ) C( P ) KS = MaxC − (2‐12) k RANK: Given the large number of metrics, a single measure describing the overall performance of a model could be useful. Stohl et al. (1998) evaluated many of the above measures and 18 i k (2‐10)
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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: AM ∩ AP FMS = × 100% (2‐2) A
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
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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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