Abstract
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).
Funder
National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
National Basic Research Program of China
Fundamental Research Funds for the Central Universities
State Key Laboratory of Earth Surface Processes and Resource Ecology
Subject
General Earth and Planetary Sciences
Cited by
14 articles.
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