A Conditional Probability Density Function for Forecasting Ozone Air Quality Data

Probabilistic forecasts are often employed to estimate the potential for high pollutant concentrations. To develop a probabilistic forecast of ozone concentrations, we suggest that use be made of the inherent properties of seasonality and autocorrelation in 0, time series. A non-stationary, autocorrelated stochastic process is used to simulate a conditional probability density function (p.d.f.) which quantifies the effects of seasonality and autocorrelation. To illustrate the utility of such a model, the simulated conditional p.d.f. is shown to be clearly superior to an ordinary p.d.f. developed from summer ozone data. Key word index: O,, autocorrelation, seasonality, simulation, non-stationarity, forecasting.