The surface incident solar radiation (Rs) is a key component of the surface radiation budget. It drives the global climate system, affects global energy balance, hydrology, and carbon cycling. Significant progress has been made in detecting changes in surface solar radiation (Rs) through meteorological observations, satellite retrieval, and reanalysis. However, each estimation method has its advantages and disadvantages. Previous studies have shown that solar radiation data derived from sunshine hours (SunDu) can provide reliable long-term solar radiation changes in China; However, these data are spatially discontinuous. Therefore, we merged the Rs data exported by SunDu with satellite exported cloud fraction (MODAL2 M CLD) and CERES SYN aerosol optical depth (AOD) data, and generated Rs data through geographically weighted regression method. This dataset provides monthly Rs data for China from 2000 to 2017, with a spatial resolution of 0.1 °.
The direct observation data of Rs from 2000 to 2017 were obtained from the China Meteorological Data Service Center (CMDC) of the China Meteorological Administration, http://data.cma.cn/data/index.html ）
Merge the Rs data exported by SunDu with satellite exported cloud fraction (MODAL2 M CLD) and CERES SYN aerosol optical depth (AOD) data, and generate Rs data through geographically weighted regression method.
The data quality is good.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.PANGAEA.DB4113.2023
10.1594/PANGAEA.921847
