本研究主要是使用臺灣地區高解析度(簡稱CTCI資料)的土地利用資料，用以取代WRF模式中原始的USGS (The U.S. Geological Survey)資料。此高解析度土地利用資料是由中鼎工程顧問公司數位化內政部所出版的1/25,000地形圖所得。更新的土地利用資料主要為修正臺灣地區都市、水田、混和林與旱田的分佈；和臺灣地區空照圖相比，新的資料較USGS資料更精確反應出臺灣地區土地利用的現況。本研究預期可提供WR模式在一個合理的土地 利用背景八料下進行更正確的預報與模擬。 本研究同時也針對兩組土地利用資料進行個案模擬，模擬結果顯示模式對更新後的土地利用資料均能產生合理的反應，並具體表現在地表能量通量的計算結果。此外，土地利用資料修正後也反應在模式對降水與溫度場的預報，特別是地面風場的差異隨著預報時間由沿海往內陸發展，並在午後達到最大，顯示模式中土地利用資料的改變會對局部環流產生顯著的影響。 本研究進一步針下CTCI土地利用類型資料進行連續一個月的預報實驗，結果顯示平均預報誤差改善0.12℃，改善幅度達11.5%. 選取CTCI組中為urban土地利用類型的測站進行統計，則新的土地利用資料的預報誤差平均減少0.22℃，改善幅度達24.5%，顯見在模式中改善urban土地利用類型可以獲得更大的預報改善。

Landduse type is one of the most important parameters in the model to describe the exchange of the heat, moisture, and momentum in between land and atmosphere. In the WRF model, the vegetation fraction, albedo, roughness, and emissivity are parameterized according to the landuse type. Therefore, to well describe the landuse type in the model plays an important role to improve the interaction of land surface and planetry boundary layer. This paper is to create a new landuse dataset and replace the original USGS (The U.S. Geological Survey) data used in the WRF model. The new dataset improves the description of urban, irrigated cropland and pasture, mixed forecast, and dry land cropland and pasture over Taiwan Island. A case study shows that the model has reasonable response to the improved landuse data set on the ground temperature, latent heat flux, and sensible heat flux. The different landuse data also contribute to the difference of the forecast precipitation, surface temperature, and wind field. The difference of local circulation propagates from the coastline to the inland, and reaches to its maximum in the afternoon. It is shown that the improved landuse dataset could result in remarkable change of the local circulation. A 1-month forecast experiment was conducted to further evaluate the impact of the CTCI landuse dataset on model forecast. The results shows that the new landuse dataset has improvement of 0.12℃ on the surface temperature. In particular the stations denoted ass urban catalog in CTCI dataset has the improvement up to 0.22℃. It is shown that the up-to-date landuse description improve the forecast apparently.