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Abstract. Although great progress has been made in estimating surface solar radiation (Rs) from meteorological observations, satellite retrieval, and reanalysis, getting best-estimated long-term variations in Rs are sorely needed for climate studies. It has been shown that Rs data derived from sunshine duration (SunDu) can provide reliable long-term variability, but such data are available at sparsely distributed weather stations. Here, we merge SunDu-derived Rs with satellite-derived cloud fraction and aerosol optical depth (AOD) 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. Based on the SunDu-derived Rs from 97 meteorological observation stations, which are co-located with those that direct Rs measurement sites, the GWR incorporated with satellite cloud fraction and AOD data produces monthly Rs with R2=0.97 and standard deviation =11.14 W m−2, while GWR driven by only cloud fraction produces similar results with R2=0.97 and standard deviation =11.41 W m−2. 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.org/10.1594/PANGAEA.921847 (Feng and Wang, 2020).
Feng et al. (Mon,) studied this question.