Evaluating the performance of regional-scale photochemical modeling systems: Part I - meteorological predictions

In this study, the concept of scale analysis is applied to evaluate two sta te-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal tim e series of observations and predictions for temperature, water vapor, and wind speed were spectrally decomposed into fluctuations operating on the in tra-day, diurnal, synoptic and longer-term time scales. Traditional model e valuation statistics are also presented to illustrate how the method of spe ctral decomposition can help provide additional insight into the models' pe rformance. The results indicate that both meteorological models under-repre sent the variance of fluctuations on the intra-day time scale. Correlations between model predictions and observations for temperature and wind speed are insignificant on the intra-day time scale, high for the diurnal compone nt because of the inherent diurnal cycle but low for (lie amplitude of the diurnal component, and highest for the synoptic and longer-term components. This better model performance on longer time scales suggests that current regional-scale models are most skillful for characterizing average patterns over extended periods. The implications of these results to using meteorol ogical models to drive photochemical models are discussed. (C) 2001 Elsevie r Science Ltd. All rights reserved.