摘要
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(R^(2)=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。
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(R^(2)=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.
作者
盛夏
石玉立
丁海勇
Sheng Xia;Shi Yuli;Ding Haiyong(School of Remote Sensing&Geomatics Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China)
出处
《遥感技术与应用》
CSCD
北大核心
2021年第3期571-580,共10页
Remote Sensing Technology and Application
基金
国家自然科学基金项目“异速增长和资源限制模型结合多源遥感数据估算森林地上生物量研究”(41471312)
国家自然科学基金项目“城市边缘区地表组分温度反演模型的构建”(41571350)。
关键词
GPM
降尺度
降水
青藏高原
随机森林
Global Precipitation Measurement(GPM)
Downscale
Precipitation
Tibetan Plateau
Random Forest
