Mapping of 10-km daily diffuse solar radiation across China from reanalysis data and a Machine-Learning method

Abstract

Diffuse solar radiation (DSR) plays a critical role in renewable energy utilization and efficient agricultural production. However, there is a scarcity of high-precision, long-term, and spatially continuous datasets for DSR in the world, and particularly in China. To address this gap, a 41-year (1982–2022) daily diffuse solar radiation dataset (CHDSR) is constructed with a spatial resolution of 10 km, based on a new ensemble model that combines the clear-sky irradiance estimated by the REST2 model and a machine-learning technique using precise cloud information derived from reanalysis data. Validation against ground-based measurements indicates strong performance of the new hybrid model, with a correlation coefficient, root mean square error and mean bias error (MBE) of 0.94, 13.9 W m−2 and −0.49 W m−2, respectively. The CHDSR dataset shows good spatial and temporal continuity over the time horizon from 1982 to 2022, with a multi-year mean value of 74.51 W m−2. This dataset is now freely available on figshare to the potential benefit of any analytical work in solar energy, agriculture, climate change, etc (https://doi.org/10.6084/m9.figshare.21763223.v3).