It is well known that meteorological conditions are important input data for air quality models. Since Taiwan is an island with steep mountains, the air flows in Taiwan are significantly affected by local topography as well as surface parameters such as sea surface temperature and roughness. In general, it is a difficult task to collect accurate input data for a meteorological model. Hence, it is worth to investigate the effects of input data and computation algorithm of a meteorological model on the simulation results of a photochemical air quality model.
The mesoscale meteorological model, RAMS, and photochemical air quality model, CAMx, was used in this study. Several runs were carried out by varying the parameters that control four-dimensional data assimilation (FDDA), topographic initialization methods, and sea surface temperature data. The effects of these modifications on the results of simulated meteorological fields, including the distributions of wind, temperature, and humidity, were studied. Then, the simulated meteorological fields were used as input of CAMx. Model. The performances of the CAMx model were evaluated by comparing the results with observations.
The RAMS model employs Newtonian relaxation method for FDDA. While the analysis nudging is used, the nudging term will be controlled by a time scale parameter. Too strong nudging should be avoided, otherwise some circulations, such as land-sea breezes, mountain-valley winds, and topography effects will be diminished. For topographic initialization, the results obtained by the reflected envelop topography scheme are superior to that obtained by the conventional averaging approach. Two sea surface temperature data set were used, i.e., climatologically data and optimal interpolated real time data. The results of air quality simulation obtained by using two dataset are similar.