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• W3199954893 abstract "Diffuse solar radiation is an important, but understudied, component of the Earth's surface radiation budget, with most global climate models not archiving this variable and a dearth of ground-based observations. Here, we describe the development of a global 40-year (1980-2019) monthly database of total shortwave radiation, including its diffuse and direct beam components, called BaRAD (Bias-adjusted RADiation dataset). The dataset is based on a random forest algorithm trained using Global Energy Balance Archive (GEBA) observations and applied to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset at the native MERRA-2 resolution (0.5° by 0.625°). The dataset preserves seasonal, latitudinal, and long-term trends in the MERRA-2 data, but with reduced biases than MERRA-2. The mean bias error is close to 0 (root mean square error = 10.1 W m-2) for diffuse radiation and -0.2 W m-2 (root mean square error = 19.2 W m-2) for the total incoming shortwave radiation at the surface. Studies on atmosphere-biosphere interactions, especially those on the diffuse radiation fertilization effect, can benefit from this dataset." @default.
• W3199954893 created "2021-09-27" @default.
• W3199954893 creator A5019874233 @default.
• W3199954893 creator A5039626546 @default.
• W3199954893 date "2021-09-15" @default.
• W3199954893 modified "2023-10-04" @default.
• W3199954893 title "Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset" @default.
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• W3199954893 doi "https://doi.org/10.1038/s41597-021-01016-4" @default.
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