中文摘要：

应用TRMM降水数据,进行国内典型区域降尺度相关研究,可弥补应用气象站点数据研究带来的局限。以陕西秦巴山区为研究区,基于TRMM降水数据和NDVI数据,应用GWR模型和比例指数,获得GWR年、月降尺度数据并进行检验,最后分析地形对降尺度结果的影响。结果表明：获得的1 km分辨率的GWR降尺度降水数据,具有较强的细节表现能力;降尺度数据与实测降水数据年尺度上相关系数为0.88,月为0.93,相关性较好;与TRMM原始数据对比,降尺度结果降水值略小,整体低估降水;区内秦岭山地GWR降尺度结果精度变化幅度最小,相似地形条件下,海拔越高,GWR降尺度结果表现越好;采用GWR模型进行秦巴山区TRMM降水数据的降尺度研究,具有较强的适用性。

英文摘要：

The precipitation in mountain area has important research value. However,the uneven distribution and the lack of the rain gauge stations in mountain area result in the difficulties in the study of precipitation which is related to the ecological,hydrological and meteorological processes. High-resolution satellite rainfall data provides a great opportunity to monitor precipitation frequently over large and remote areas. The Tropical Rainfall Measuring Mission（TRMM）is one of these products and can provide reliable precipitation data for regional hydrology study at the monthly and yearly scales. Unfortunately,in some small region,especially the mountain region with abundant rainfall and complex terrain,the TRMM rainfall product cannot be used directly because of its coarse resolution（0.25°）. Therefore,it is very necessary to improve its resolution. In this paper,taking Shaanxi Qinling-Daba Mountains which were further divided into four different terrain zones as the study area,the relationship between TRMM and NDVI explored by geographically weighted regression（GWR）was used to construct the precipitation downscaling model,it could produce 1 km downscaled precipitation data in yearly time scale;then,selecting year 2010 as a typical case,the monthly downscaled precipitation was obtained from the annual downscaled precipitation data according to the proportion of the monthly precipitation over annual precipitation in TRMM;finally,the effects of topography on the reliability of the downscaled results were evaluated.In the study,the accuracies of the TRMM precipitation and GWR downscaled precipitation were evaluated by using a network of 23 rain gauges over Shaanxi Qinling-Daba Mountains,and the R,RMSE and BIAS were calculated to evaluate the data. The results show as follows：（1）the downscaled precipitation data improves the spatial resolution（from 0.25° to 1 km）,which can provide more details about the precipitation;（2）the R of annual downscaled precipitation data is 0.88,and at monthly scale