Merging ground-based sunshine duration
with satellite cloud and aerosol data to
produce high resolution long-term surface
solar radiation over China
Abstract. Although great progress has been made in estimating surface solar radiation (Rs) from meteorological observations, satellite retrieval and reanalysis, getting best estimated of long-term variations in Rs are sorely needed for climate studies. It has been shown that sunshine duration (SunDu)-derived Rs data can provide reliable long-term Rs variation. Here, we merge SunDu-derived Rs data with satellite-derived cloud fraction and aerosol optical depth (AOD) data to generate high spatial resolution (0.1°) Rs over China from 2000 to 2017. The geographically weighted regression (GWR) and ordinary least squares regression (OLS) merging methods are compared, and GWR is found to perform better. Whether or not AOD is taken as input data makes little difference for the GWR merging results. Based on the SunDu-derived Rs from 97 meteorological observation stations, the GWR incorporated with satellite cloud fraction and AOD data produces monthly Rs with R2 = 0.97 and standard deviation = 11.14 W/m2, while GWR driven by only cloud fraction produces similar results with R2 = 0.97 and standard deviation = 11.41 w/m2. This similarity is because SunDu-derived Rs has included the impact of aerosols. This finding can help to build long-term Rs variations based on cloud data, such as Advanced Very High Resolution Radiometer (AVHRR) cloud retrievals, especially before 2000, when satellite AOD retrievals are not unavailable. The merged Rs product at a spatial resolution of 0.1° in this study can be downloaded at https://doi.pangaea.de/10.1594/PANGAEA.921847 (Feng and Wang, 2020).