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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Nov 2020

03 Nov 2020

Review status
This preprint is currently under review for the journal ESSD.

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

Fei Feng1, and Kaicun Wang2, Fei Feng and Kaicun Wang
  • 1College of Forestry, Beijing Forestry University, Beijing 100083, China
  • 2State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
  • These authors contributed equally to this work.

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 (Feng and Wang, 2020).

Fei Feng and Kaicun Wang

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Fei Feng and Kaicun Wang

Fei Feng and Kaicun Wang


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