Swiss Federal Research Institute for Forest, Snow, and Landscape Research (WSL), Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.
Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT, 06520-8106, USA.
Sci Data. 2021 Nov 26;8(1):307. doi: 10.1038/s41597-021-01084-6.
High-resolution climatic data are essential to many questions and applications in environmental research and ecology. Here we develop and implement a new semi-mechanistic downscaling approach for daily precipitation estimate that incorporates high resolution (30 arcsec, ≈1 km) 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 1 km resolution for the years 2003 onward. Comparison of the predictions with existing gridded products and station data from the Global Historical Climate Network indicates an improvement in the spatio-temporal performance of the downscaled data in predicting precipitation. Regional scrutiny of the cloud cover correction from the continental United States further indicates that CHELSA-EarthEnv performs well in comparison to other precipitation products. The CHELSA-EarthEnv daily precipitation product improves the temporal accuracy compared with a large improvement in the spatial accuracy especially in complex terrain.
高分辨率气候数据对于环境研究和生态学中的许多问题和应用至关重要。在这里，我们开发并实施了一种新的半机理降尺度方法，用于估计每日降水，该方法结合了高分辨率（30 弧秒，约 1 公里）卫星衍生的云频率。降尺度算法结合了地形预测因子，如风场、山谷方位和边界层高度，并进行了后续的偏差修正。我们将该方法应用于 ERA5 降水档案和 MODIS 每月云覆盖频率，以开发 2003 年以来的每日网格化降水时间序列，分辨率为 1 公里。与现有网格化产品和全球历史气候网络的站数据的预测比较表明，降尺度数据在预测降水方面的时空性能得到了改善。对来自美国大陆的云覆盖修正的区域检查进一步表明，与其他降水产品相比，CHELSA-EarthEnv 的表现非常出色。与大幅度提高空间精度相比，CHELSA-EarthEnv 的每日降水产品提高了时间精度，尤其是在复杂地形中。
