| Summary: | 碩士 === 淡江大學 === 水資源及環境工程學系碩士班 === 96 === The land use dataset and surface parameters for different land categories are important inputs for mesoscale meteorological models. The Global Land Cover Characterization (GLCC) dataset generated form AVHRR data were widely used by mesoscale meteorological models such as RAMS. However, the GLCC data have several drawbacks. They are of out of date, low resolution, and inaccurate, especially for urban classification. Another newer dataset is MOD12Q1. This dataset uses new data, has higher resolution and more accurate classification for urban area. There are significant differences between these two dataset for Taiwan area. The urban areas in MODIS dataset are much larger than that of GLCC dataset. When compare with the distributions of population and building density in Taiwan, it is found that MODIS dataset is more reasonable. We also noted that the surface parameters, especially surface roughness, are important for urban meteorological simulation. Since the buildings are higher in Taiwan area, the default setting of surface roughness in RAMS is too low when it was used in Taiwan. In this study, we investigate the effects of different land use dataset and urban roughness on the results of meteorological simulations.
When the MODIS dataset was used, the surface temperatures in urban areas increased about 2℃ and the water contents in urban areas decreased about 2g/kg-air. Significant urban heat island effects were noted. If the surface roughness were increased, the surface wind speeds may decrease 3m/s in urban areas and have better agreement when compared with observations.
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