Abstract
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.
Highlights
Background & SummaryHigh resolution information on precipitation is essential in many scientific fields, ranging from ecology, agriculture, forestry, to global change impact studies[1,2,3]
Spatiotemporal precipitation data is usually derived from a range of different sources, including satellites, reanalysis, global circulation models, or precipitation gauges[4,5]
Each of these sources on their own have limitations in coverage, accuracy, or detail, impeding many downstream uses, especially those addressing large spatial and temporal extents[6,7]. Reanalysis data products such as ERA58,9, MERRA 210,11 or MSWEP12 overcome these constraints by combining data from a variety of sources
Summary
High resolution information on precipitation is essential in many scientific fields, ranging from ecology, agriculture, forestry, to global change impact studies[1,2,3]. Spatiotemporal precipitation data is usually derived from a range of different sources, including satellites, reanalysis, global circulation models, or precipitation gauges[4,5] Each of these sources on their own have limitations in coverage, accuracy, or detail, impeding many downstream uses, especially those addressing large spatial and temporal extents[6,7]. There are still interpolations and parametrizations involved which account for processes not resolved at the original model resolution This uneven distribution of gauges can be overcome by the use of satellite data[2,40,41,42,43], which offers spatially more complete information of precipitation patterns. The presented CHELSA-EarthEnv daily precipitation data at ~1 km horizontal resolution offers a more reliable characterization of precipitation in topographically heterogeneous regions and supports a range of applications that require high resolution precipitation data
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