Evaluating the performance of regional-scale photochemical modeling systems: Part II - Ozone predictions
In this paper, the concept of scale analysis is applied to evaluate ozone predictions from two regional-scale air quality models. To this end, seasonal time series of observations and predictions from the RAMS3b/UAM-V and MM5/MAQSIP (SMRAQ) modeling systems for ozone were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the scale analysis approach can help improve our understanding of the models' performance. The results indicate that UAM-V underestimates the total variance (energy) of the ozone time series when compared with observations, but shows a higher mean value than the observations. On the other hand, MAQSIP is able to better reproduce the average energy and mean concentration of the observations. However, both modeling systems do not capture the amount of variability present on the intra-day time scale primarily due to the grid resolution used in the models. For both modeling systems, the correlations between the predictions and observations are insignificant for the intra-day component, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and baseline components. This better model performance on longer time scales suggests that current regional-scale models are most skillful in characterizing average patterns over extended periods, rather than in predicting concentrations at specific locations, during 1-2 day episodic events. In addition, we discuss the implications of these results to using the model-predicted daily maximum ozone concentrations in the regulatory framework in light of the uncertainties introduced by the models' poor performance on the intra-day and diurnal time scales. Copyright © 2001 Elsevier Science Ltd.
Hogrefe, C; Rao, ST; Kasibhatla, P; Hao, W; Sistla, G; Mathur, R; McHenry, J
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