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Preprints
https://doi.org/10.5194/essd-2019-209
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-209
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Nov 2019

07 Nov 2019

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This preprint was under review for the journal ESSD but the revision was not accepted.

Surface global and diffuse solar radiation over China acquired from geostationary Multi-functional Transport Satellite data

Hou Jiang1,2, Ning Lu1,4, Jun Qin3, and Ling Yao1,4 Hou Jiang et al.
  • 1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
  • 2College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China
  • 3Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100085, China
  • 4Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China

Abstract. Surface solar radiation drives the water cycle and energy exchange on the earth's surface, being an indispensable parameter for many numerical models to estimate soil moisture, evapotranspiration and plant photosynthesis, and its diffuse component can promote carbon uptake in ecosystems as a result of improvements of plant productivity by enhancing canopy light use efficiency. To reproduce the spatial distribution and spatiotemporal variations of solar radiation over China, we generate the high-accuracy radiation datasets, including global solar radiation (GSR) and the diffuse radiation (DIF) with spatial resolution of 1/20 degree, based on the observations from the China Meteorology Administration (CMA) and Multi-functional Transport Satellite (MTSAT) satellite data, after tackling the integration of spatial pattern and the simulation of complex radiation transfer that the existing algorithms puzzle about by means of the combination of convolutional neural network (CNN) and multi-layer perceptron (MLP). All data cover a period from 2007 to 2018 in hourly, daily total and monthly total scales. The validation in 2008 shows that the root mean square error (RMSE) between our datasets and in-situ measurements approximates 73.79 W/m2 (0.27 MJ/m2) and 58.22 W/m2 (0.21 MJ/m2) for GSR and DIF, respectively. Besides, the spatially continuous hourly estimates properly reflect the regional differences and restore the diurnal cycles of solar radiation in fine scales. Such accurate knowledge is useful for the prediction of agricultural yield, carbon dynamics of terrestrial ecosystems, research on regional climate changes, and site selection of solar power plants etc. The datasets are freely available from Pangaea at https://doi.org/10.1594/PANGAEA.904136 (Jiang and Lu, 2019).

Hou Jiang et al.

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Hou Jiang et al.

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High-resolution surface global solar radiation and the diffuse component dataset over China J. Hou and L. Ning https://doi.org/10.1594/PANGAEA.904136

Hou Jiang et al.

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Short summary
This study produces a 12-year (2007–2018) hourly surface global and diffuse solar radiation dataset with 0.05° grids over China based on geostationary satellite data using deep learning technique. The produced data have much higher accuracy than results of traditional methods and current widely-used products due to integration of spatial pattern through convolutional neural network, enlightening studies involving spatial features, and reveal the long-term regional variations in fine scales.
This study produces a 12-year (2007–2018) hourly surface global and diffuse solar radiation...
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