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The surface wind comparison is not an independent evaluation AOC 962W many of the surface wind observations in the evaluation database are also provided as input to CALMET. One exception was for puff splitting where more aggressive vertical puff splitting was allowed to occur throughout the day, rather than the default where vertical puff splitting is only allowed to occur once per day.

Below we present the results of the RANK performance statistic that is a composite statistic that represents four aspects of model performance: However, we have also found situations where the RANK statistic can provide misleading indications of the performance of dispersion models and recommend that all model performance attributes be examined to confirm that the RANK metric is providing a valid ranking of the dispersion model performance. The RANK metrics ranges from 0. Table ES-5 summarizes the AOC 962W between the six LRT models for the 11 performance statistics analyzed and compares them to the rankings obtained using the RANK performance statistic.

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Ranking 1. If not for this, the average rank across all 11 metrics would have been the same as Draxler's RANK score. However, the analyst should use discretion in relying too heavily upon RANK score without consideration to which performance metrics are important measures for the particular evaluation goals. For example, if performance goals are not concerned with a model's ability to perform well in space and time, then reliance upon spatial statistics, such as the FMS, in the composite RANK value may not be appropriate. Ranking Avg. In testing the efficacy of the RANK statistic, overall rankings across all eleven statics were obtained using an average modeled ranking. The average rank across all 11 performance statistics and the RANK model rankings are as follows: Both approaches show CAMx and HYSPLIT as the highest ranking models for CTEX5 with rankings that are fairly close to each other, however after that the two ranking techniques come to very different conclusions regarding the ability of the models to simulate the observed tracer concentrations for the CTEX5 field AOC 962W.

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The FB component to the RANK composite score for AOC 962W is one of the highest among the six models in this study, yet the underlying statistics indicate both marginal spatial skill and a large degree of under-prediction likely due to the spatial skill of the model. The current form of the RANK score uses the absolute value of the fractional bias. This approach weights AOC 962W equally to overestimation.

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However, in a regulatory context, EPA is most concerned with models not being biased towards under-prediction. Models can produce seemingly good low bias metrics AOC 962W compensating errors by averaging over- and under-predictions.


The use of an error statistic e. Adaptation of RANK score for regulatory use will require refinement of the individual components to insure that this situation does not develop and to insure AOC 962W the regulatory requirement of bias be accounted for when weighting the individual statistical measures to produce a composite score.


Summary of model rankings using the statistical performance metrics and comparison with the RANK metric. All five LRT dispersion models were AOC 962W using a common 36 km MM5 database for their meteorological inputs.

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