ABSTRACT
A new method is developed to generate the meteorological input fields required for use with photochemical airshed models that seek to predict the effect of pollutant emissions on the long-term frequency distribution of peak O3 concentrations. Instead of using meteorological fields derived from interpolation of direct weather observations, this method uses synthetically generated meteorological data. These synthetic meteorological fields are created by first constructing a semi-Markov process that generates a time series of large-scale synoptic weather conditions that statistically resemble the occurrence and persistence of synoptic weather patterns during specific months of the year. Then for each day within each synoptic weather category, local weather variables indicative of the meteorological potential for ozone formation are drawn from the approximated joint distribution of the summation of three pressure gradients across the airshed and the 850 mb temperature measured in the early morning. The synthetic initial conditions are combined with boundary values that are extracted from historical days that match the chosen synoptic class, temperature, and pressure gradient values as closely as possible for use in a prognostic mesoscale meteorological model. The prognostic mesoscale meteorological model generates the meteorological input fields necessary for the photochemical airshed model. The airshed model driven by synthetically generated meteorological data is executed for a 31 day period that statistically resembles weather during the month of August in Southern California using pollutant emissions data from the year 1987. The procedure produced a frequency of occurrence of peak 8 h average ozone concentrations that compared well both to that produced by the deterministic model as well as to the O3 concentrations observed over the August months of the years 1984-1990.
