Abstract
A probabilistic framework for incorporating uncertainty in air quality models is described. The quantitative dependence of the uncertainty in calculated air quality concentrations on the uncertainty in the input meteorological data is illustrated using a simple Second-order Closure integrated Model Plume in combination with the EPRI Plume Model Validation and Development Data Set. Evaluation of the model results demonstrate that even though individual hourly samples cannot be deterministically predicted downwind of a powerplant stack, statistical representations of the observed cumulative distribution of the sample values are quite predictable. We discuss the data needed to improve the definition of the range of meteorological uncertainty within an ensemble of flows defined by given meteorological data, and thus provide for improvements in predictability models of the type illustrated. We argue that attempts to collect the data needed to define more precisely the variance within the ensemble of compatible flows will prove more productive than attempts to eliminate meteorological uncertainties in given datasets.
