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arXiv · 2012.10108

Global daily 1km land surface precipitation based on cloud cover-informed downscaling

Dirk Nikolaus Karger·Adam M. Wilson·Colin Mahony·Niklaus E. Zimmermann·Walter Jetz
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Abstract

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

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Dirk Nikolaus Karger, Adam M. Wilson, Colin Mahony, Niklaus E. Zimmermann, Walter Jetz. 2020-12-18. Global daily 1km land surface precipitation based on cloud cover-informed downscaling. https://arxiv.org/abs/2012.10108

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