conditions. Typically, this type of operational evaluation is comprised principally of statistical

assessments of model versus observed pairs. Operational evaluations are generally accompanied

by graphical and other qualitative descriptions of the model's ability to replicate historical air

quality patterns. The robustness of an operational evaluation is directly proportional to the

amount and quality of the ambient data available for comparison.

The second type of model performance assessment, a diagnostic evaluation, can be made

in several ways. One way to evaluate the response of the model is to examine predicted and

observed ratios of “indicator species”. Indicator species techniques have been developed for both

ozone and secondary PM species (in particular nitrate) (Sillman, 1995 and 1998; Ansari and

Pandis, 1998; Blanchard et al., 2000). If ratios of observed indicator species are very high or very

low, they provide a sense of whether further ozone or secondary PM2.5 production at the

monitored location is likely to be limited by availability of NOx or VOC (or NH3). Agreement

between paired observed and predicted ratios suggests a model may correctly predict the

sensitivity of ozone or secondary PM2.5 at the monitored locations to emission control strategies.

Thus, the use of indicator species has the potential to evaluate models in a way which is most

closely related to how they will be used in attainment demonstrations. A second way for

assessing a model's performance in predicting the sensitivity of ozone or PM2.5 species to changes

in emissions is to perform a retrospective analysis. This involves comparing model predicted

historical trends with observed trends. Retrospective analyses provide potentially useful means

for diagnosing why a strategy did or did not work as expected. They also provide an important

opportunity to evaluate model performance in a way which is closely related to how models are

used to support an attainment demonstration. More types of diagnostic analyses are provided in

Section 18.5. We recommend that diagnostic analyses be performed during the initial phase of

the model application and during any mid-course review.

 

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