Merging ground-based sunshine duration observations with satellite cloud and aerosol retrievals to produce high-resolution long-term surface solar radiation over China

[摘要] Although great progress has been made in estimating surface solar radiation ( R s ) from meteorological observations, satellite retrieval, and reanalysis, getting best-estimated long-term variations in R s are sorely needed for climate studies. It has been shown that R s 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 R s with satellite-derived cloud fraction and aerosol optical depth (AOD) to generate high-spatial-resolution (0.1 ∘ ) R s 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 R s from 97 meteorological observation stations, which are co-located with those that direct R s measurement sites, the GWR incorporated with satellite cloud fraction and AOD data produces monthly R s with R 2 =0.97 and standard deviation  =11.14   W m −2 , while GWR driven by only cloud fraction produces similar results with R 2 =0.97 and standard deviation  =11.41   W m −2 . This similarity is because SunDu-derived R s has included the impact of aerosols. This finding can help to build long-term R s 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 R s 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).

[学科分类] 眼科学