Air quality model evaluation and uncertainty.

Abstract

The many advances made in air quality model evaluation procedures during the past ten years are discussed and some components of model uncertainty presented. Simplified statistical procedures for operational model evaluation are suggested. The fundamental model performance measures are the mean bias, the mean square error, and the correlation. The bootstrap resampling technique is used to estimate confidence limits on the performance measures, in order to determine if a model agrees satisfactorily with data or if one model is significantly different from another model. Applications to two tracer experiments are described. It is emphasized that review and evaluation of the scientific components of models are often of greater importance than the strictly statistical evaluation. A necessary condition for acceptance of a model should be that it is scientifically correct. It is shown that even in research-grade tracer experiments, data input errors can cause errors in hourly-average model predictions of point concentrations almost as large as the predictions themselves. The turbulent or stochastic component of model uncertainty has a similar magnitude. These components of the uncertainty decrease as averaging time increases.