부산대학교 도서관

In this study, measured and model-predicted ozone time series are spectrally decomposed into their different temporal components to explore the forcing mechanisms that distinguish days of high ozone concentrations from average or non-episodic conditions. The modeled values are the results from a 3-month simulation of a regional photochemical modeling system. It is shown that the ozone process can be represented as the sum of four components and that it is the longer-term components (with periods greater than 1 day) that distinguish episodic ozone days from non-episodic days. The magnitude of intra-day fluctuations, however, remains almost invariant in time. Furthermore, it is shown that model predictions capture these features and that model performance is better on longer than on shorter time scales. Therefore, the authors argue that, in a regulatory setting, model simulations need to be performed for longer time periods than just for the duration of an ozone episode.