论文标题
全球每日1公里的土地表面降水，基于云覆盖的降尺度
Global daily 1km land surface precipitation based on cloud cover-informed downscaling
论文作者
论文摘要
高分辨率气候数据对于环境研究中的许多应用至关重要。在这里，我们为每日降水开发了一种新的半机械缩减方法，该方法结合了高分辨率（30 ARC SEC）卫星衍生的云频率。降尺度算法结合了地形预测因子，例如风场，山谷博览会和边界层高度，并具有随后的偏置校正。我们将该方法应用于ERA5降水存档和MODIS月度云覆盖频率，以在2003年以1公里的分辨率开发每日栅格降水时间序列。将预测与现有网格产品和站数据进行比较表明，在预测降水时，缩放数据的时空性能有所改善。从地形上高度异质区域对云覆盖校正的区域审查进一步证实，与其他沉淀产品（例如数值天气模型）相比，Chelsa-Earthenv的性能很好。与ERA5相比，提出的Chelsa-Earthenv每日降水产品提高了时间准确性，空间精度的额外提高，并且可以更好地表示复杂地形的降水量
High-resolution climatic data are essential to many applications in environmental research. Here we develop a new semi-mechanistic downscaling approach for daily precipitation that incorporates high resolution (30 arc sec) satellite-derived cloud frequency. The downscaling algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. We apply the method to the ERA5 precipitation archive and MODIS monthly cloud cover frequency to develop a daily gridded precipitation time series in 1km resolution for the years 2003 onward. Comparison of the predictions with existing gridded products and station data indicates an improvement in the spatio-temporal performance of the downscaled data in predicting precipitation. Regional scrutiny of the cloud cover correction from a topographically highly heterogeneous area further confirms that CHELSA-EarthEnv performs well in comparison to other precipitation products such as numerical weather models. The presented CHELSA-EarthEnv daily precipitation product improves the temporal accuracy compared to ERA5 with an additional improved in spatial accuracy and much better representation of precipitation in complex terrain
