青藏高原GPM降水数据空间降尺度研究

高分辨率的降水数据对于复杂地形区的精确水文预报和气候模拟至关重要。利用青藏高原的植被、地形和地理位置特征，建立了与降水的回归模型，将全球降水测量（GPM）IMERG的年降水量从0.1° 降尺度至1 km，通过分解年降水获得月降水量数据，并用气象站点的实测数据进行校准。得出以下结论：①GPM IMERG月降水量略大于地面观测值，与2015~2017年的站点数据相关性较高（R²=0.79）；②通过建立降尺度模型，提高了研究区GPM IMERG的空间分辨率；③利用站点数据校准后的月降水量，可以反映降水的细节特征，尤其是在雨季和湿润地区。该模型可用于获得地形复杂地区的高空间分辨率降水资料，对水文学和气象学研究具有重要意义。

Spatial Downscaling of GPM Precipitation over the Tibetan Plateau

Precipitation dataset with high resolution are essential for accurate hydrology predictions and meteorology simulations over complex terrains. A regression model was built to downscale the Global Precipitation Measurement （GPM） IMERG precipitation data from 0.1° to 1 km on an annual scale， using vegetation， topography and geographical location features over the Tibetan Plateau. Then monthly precipitation data were obtained by disaggregating the annual downscaled estimates， which were calibrated with observations of local rain gauge stations. The major conclusions are summarized as follows： （1） Monthly GPM IMERG precipitation demonstrated good agreement with the rain gauge data during the period 2015 to 2017 （R²=0.79）， though GPM was slightly larger than ground observations； （2） Annual downscaled precipitation improved the spatial resolution of the GPM IMERG in the study area； （3） Monthly donscaled precipitation calibrated with rain gauge data reflected detailed characteristics with better predictive performance especially in summer or in wet regions.We concluded that the model can be used to obtain precipitation data with high spatial resolution from heavy rain to light one over the areas with complex tography， which is meaning for applications in hydrology and metorology studies.