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

15 BASED 1.79 80 18 ‐‐ No
16 EXP3A 1.79 36 12 10/100 Yes
17 EXP3B 1.79 36 12 100/200 Yes
18 EXP3C 1.79 36 12 500/1000 Yes
19 EXP3D 1.79 36 12 ‐‐ No
20 4KM_MMIF 1.78 4 ‐‐ ‐‐ No
21 EXP4C 1.72 12 12 10/100 Yes
22 36KM_MMIF 1.42 36 ‐‐ ‐‐ No
23 80KM_MMIF 1.42 80 ‐‐ ‐‐ No
24 12KM_MMIF 1.28 12 ‐‐ ‐‐ No
Conclusions of the CAPTEX CALPUFF Tracer Sensitivity Tests
There are some differences and similarities in CALPUFF’s ability to simulate the observed tracer
concentrations in the CTEX3 and CTEX5 field experiments. The overall conclusions of the
evaluation of the CALPUFF model using the CAPTEX tracer test field experiment data can be
summarized as follows:
• There is a noticeable variability in the CALPUFF model performance depending on the
selected input options to CALMET.
‐ By varying CALMET inputs and options through their range of plausibility, CALPUFF
can produce a wide range of concentrations estimates.
• Regarding the effects of the RMAX1/RMAX2 parameters on CALPUFF/CALMET model
performance, the “A” series (500/1000) performed best for CTEX3 but the “C” series
(10/100) performed best for CTEX5 with both CTEX3 and CTEX5 agreeing that the “B”
series (100/200) is the worst performing setting for RMAX1/RMAX2.
‐ This is in contrast to the CALMET wind evaluation that found the “B” series was the
CALMET configuration that most closely matched observed surface winds.
‐ The CALMET wind evaluation was not an independent evaluation since some of the
wind observations used in the model evaluation database were also used as input to
CALMET.
Evaluation of Six LRT Dispersion Models using the CTEX3 Database
Six LRT dispersions models were applied for the CTEX3 experiment using common
meteorological inputs based solely on MM5. Figure ES‐4 displays the RANK model performance
statistic for the six LRT dispersion models. The RANK statistical performance metric was
proposed by Draxler (2001) as a single model performance metric that equally ranks the
combination of performance metrics for correlation (PCC or R 2 ), bias (FB), spatial analysis (FMS)
and unpaired distribution comparisons (KS). The RANK metrics ranges from 0.0 to 4.0 with a
perfect model receiving a score of 4.0.
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