Monthly surface solar radiation data over China (2000-2017) by merging satellite cloud and aerosol data with ground-based sunshine duration data

Surface incident solar radiation (Rs) is a key component of the surface radiation budget. It drives the global climate system and impacts the global energy balance and the hydrological and carbon cycles. Great progress has been made in the detection of variations in surface solar radiation (Rs) from meteorological observations, satellite retrieval and reanalysis. However, each type of estimation has its advantages and disadvantages. It has been shown that sunshine duration (SunDu)-derived Rs data can provide reliable long-term Rs variation over China; however, these data are spatially discontinuous. Therefore, we merged SunDu-derived Rs data with satellite-derived cloud fraction (MODAL2 M CLD) and CERES SYN aerosol optical depth (AOD) data to generate Rs data by the geographically weighted regression method. This dataset provides the monthly Rs from 2000 to 2017 over China with the spatial resolution of 0.1°.

Data and Resources

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Cite this as

Feng, Fei, Wang, Kaicun (2020). Dataset: Monthly surface solar radiation data over China (2000-2017) by merging satellite cloud and aerosol data with ground-based sunshine duration data. https://doi.org/10.1594/PANGAEA.921847

DOI retrieved: 2020

Additional Info

Field Value
Imported on November 30, 2024
Last update November 30, 2024
License CC-BY-4.0
Source https://doi.org/10.1594/PANGAEA.921847
Author Feng, Fei
Given Name Fei
Family Name Feng
More Authors
Wang, Kaicun
Source Creation 2020
Publication Year 2020
Subject Areas
Name: Lithosphere

Related Identifiers
Title: Merging ground-based sunshine duration with satellite cloud and aerosol data to produce high resolution long-term surface solar radiation over China
Identifier: https://doi.org/10.5194/essd-2020-231
Type: DOI
Relation: References
Source: Earth System Science Data Discussions
Authors: Feng Fei , Wang Kaicun .

Title: Monthly AOD CF (2000-03 to 2001-12)
Identifier: https://store.pangaea.de/Publications/Feng-Wang_2020/AOD_CF_3067.zip
Type: DOI
Relation: References
Authors: Feng Fei , Wang Kaicun .