A daily high-resolution surface net radiation dataset in China (2000-2019)

Surface net radiation (Rn) characterizes the energy available at the Earth's surface and is essential for studying atmospheric, water, and carbon cycles. While some global-scale Rn products are available, they often suffer from issues such as data gaps, low resolution, and large uncertainties. Therefore, we developed a high-resolution (0.05°×0.05°) daily Rn dataset (named CHiRAD) from 2000 to 2019 in China using routine meteorological variables from more than 2400 stations and remotely sensed albedo. To ensure the reliability of the dataset, we tested a series of net shortwave and longwave algorithms using ground-based measurements and then employed the optimal combination of algorithms to generate this dataset. The dataset was validated against Rn observations from 43 flux towers across China.  However, one may expect to use Rn data beyond this period in practice. To address this requirement, we also used AVHRR albedo to force the RI-PE algorithm and generated a long-series (1982–2020) Rn dataset (named Ext_CHiRAD).  The only difference between the Ext_CHiRAD and CHiRAD, except for the length of the data series, is the source of albedo data. This dataset may not be as accurate as CHiRAD, but it has the advantage of a longer time span (1982–2020). This advantage makes it an ideal source of Rn data for hydrologic and environmental models to simulate long-term changes in target variables.