the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Surface global and diffuse solar radiation over China acquired from geostationary Multi-functional Transport Satellite data
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).
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RC1: 'Some comments', Anonymous Referee #1, 06 Dec 2019
- AC1: 'Response to Referee #1', Ning lu, 15 Dec 2019
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RC2: 'comment for essd-2019-209', Anonymous Referee #2, 15 Dec 2019
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AC2: 'Response to Referee #2', Ning lu, 18 Dec 2019
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RC3: 'comment for essd-2019-209', Anonymous Referee #2, 04 Jan 2020
- SC1: 'Response to Referee #2 (Round 2)', Hou Jiang, 09 Jan 2020
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RC3: 'comment for essd-2019-209', Anonymous Referee #2, 04 Jan 2020
-
AC2: 'Response to Referee #2', Ning lu, 18 Dec 2019
-
RC1: 'Some comments', Anonymous Referee #1, 06 Dec 2019
- AC1: 'Response to Referee #1', Ning lu, 15 Dec 2019
-
RC2: 'comment for essd-2019-209', Anonymous Referee #2, 15 Dec 2019
-
AC2: 'Response to Referee #2', Ning lu, 18 Dec 2019
-
RC3: 'comment for essd-2019-209', Anonymous Referee #2, 04 Jan 2020
- SC1: 'Response to Referee #2 (Round 2)', Hou Jiang, 09 Jan 2020
-
RC3: 'comment for essd-2019-209', Anonymous Referee #2, 04 Jan 2020
-
AC2: 'Response to Referee #2', Ning lu, 18 Dec 2019
Data sets
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
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Cited
3 citations as recorded by crossref.
- Long-term trend of ozone pollution in China during 2014–2020: distinct seasonal and spatial characteristics and ozone sensitivity W. Wang et al. 10.5194/acp-22-8935-2022
- A global long-term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network J. Xu et al. 10.5194/essd-14-2315-2022
- Analysis of global and net radiation fluxes in relation to surface albedo at DACCIWA site in Ile-Ife, southwest Nigeria A. Ajao et al. 10.1007/s00704-023-04396-2