Comparing prognostic and diagnostic meteorological fields and their impacts on photochemical air quality modeling

Abstract

Photochemical air quality models are being used increasingly to make policy decisions. Meteorological data are one of the key inputs to these models. Currently, there are two primary techniques used to generate the gridded meteorological data for input to the air quality models: diagnostic analysis and prognostic modeling. Meteorological fields developed for the Southern California region using these two techniques are compared in two ways. First, the fields are compared directly to each other and with observed data. Second, the meteorological fields are used in photochemical airshed model simulations. The second test is used to investigate how the development of meteorological fields affects photochemical model performance, and how these fields impact control strategy evaluation. The results obtained from using prognostically-derived fields as input to a photochemical model are similar to those obtained by using diagnostically-derived fields, however, they show a lower peak ozone prediction. The reasons for these results are analyzed. The viability of using prognostic modeling to develop meteorological input to the air quality models is discussed.