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PANGAEA.
Data Publisher for Earth & Environmental Science

Jiang, Hou; Lu, Ning (2019): High-resolution surface global solar radiation and the diffuse component dataset over China (2017). PANGAEA, https://doi.org/10.1594/PANGAEA.908092, In: Jiang, H; Lu, N (2019): High-resolution surface global solar radiation and the diffuse component dataset over China. PANGAEA, https://doi.org/10.1594/PANGAEA.904136

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Abstract:
Surface solar radiation drives the water cycle and energy exchange on the earth's surface, and its diffuse component can promote carbon uptake in ecosystems by increasing the plant productivity. The accurate knowledge of their spatial distribution is of great importance to many studies and applications, such as the estimation of agricultural yield, carbon dynamics of terrestrial systems, site selection of solar power plants, as well as trends of regional climate changes. Therefore, we produce the hourly surface radiation datasets based on the hourly Multi-functional Transport Satellite (MTSAT) satellite imagery and the ground observations from the China Meteorology Administration (CMA) through deep learning techniques. The deep network is trained using training samples in 2008, and then utilized to generate the hourly radiation for other years. This dataset provides the gridded surface global and diffuse solar radiation in 2017 within 71.025°E - 141.025°E and 14.975°N - 59.975°N with an increment of 0.05°. Both the direct predicted hourly values and the integrated daily and monthly total values are available. The dataset should be useful for the analysis of the regional differences and temporal cycles of solar radiation in fine scales, and the impact of diffuse radiation on plant growth etc.
Keyword(s):
China; deep learning; diffuse radiation; geostationary satellite; solar radiation
Related to:
Jiang, Hou; Lu, Ning; Qin, Jun; Tang, Wenjun; Yao, Ling (2019): A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data. Renewable & Sustainable Energy Reviews, 114, 109327, https://doi.org/10.1016/j.rser.2019.109327
Coverage:
Latitude: 36.000000 * Longitude: 109.000000
Event(s):
China * Latitude: 36.000000 * Longitude: 109.000000
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
1File contentContentJiang, Hou
2File nameFile nameJiang, Hou
3File formatFile formatJiang, Hou
4File sizeFile sizekByteJiang, Hou
5Uniform resource locator/link to fileURL fileJiang, Hou
Size:
95 data points

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