Spatial downscaling of GPM precipitation products: A case study of Guizhou Province
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摘要: 为提高GPM卫星降水产品的空间分辨率,扩展其应用范围,以贵州省为研究区域,通过建立空间降尺度模型对其进行降尺度研究。首先,以经度、纬度、高程、坡度、坡向等地形因子为解释变量,以原始GPM卫星降水数据为目标变量,分别建立多元线性回归、地理加权回归、极限学习机、支持向量机、随机森林回归等降尺度模型; 然后对多年平均时间尺度进行应用与评价; 最后选择效果最佳的模型分别对典型年的年、月降水量进行空间降尺度研究。结果表明,除随机森林回归模型外的其余4种空间降尺度模型均表现良好,其中以多元线性回归模型表现最为稳定、效果最优; 多元线性回归模型的降尺度结果在观测精度和空间相关性上均有较大程度的提升。该研究可为贵州省提供高分辨率的网格化降水产品,对区域水文气象研究等工作提供支持。Abstract: To improve the spatial resolution and expand the application scopes of GPM precipitation products, the downscaling study of GPM precipitation products was conducted based on the precipitation data of Guizhou Province by establishing multiple spatial downscaling models. Firstly, with the topographic factors including longitude, latitude, elevation, slope, and aspect as explanatory variables and the original GPM precipitation data as target variables, multiple downscaling models were established based on the methods of multivariate linear regression, geographically weighted regression, extreme learning machine, support vector machine, and random forest regression. Then multiyear average precipitation data were applied and assessed, and the optimal model was selected to conduct the spatial downscaling study of the annual and monthly precipitation amount in typical years in Guizhou Province. According to the results, the downscaling models except for the random forest regression model all performed well. Most especially, the multivariate linear regression model performed the most stably and effectively and yielded the highly improved downscaling results in terms of observation accuracy and spatial correlation. This study will provide a set of high-resolution gridded precipitation products for Guizhou Province and provide support for regional hydrometeorological research.
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