globalchange  > 气候变化与战略
DOI: 10.1029/2019EA001058
Title:
Daily Global Solar Radiation in China Estimated From High-Density Meteorological Observations: A Random Forest Model Framework
Author: Zeng Z.; Wang Z.; Gui K.; Yan X.; Gao M.; Luo M.; Geng H.; Liao T.; Li X.; An J.; Liu H.; He C.; Ning G.; Yang Y.
Source Publication: Earth and Space Science
ISSN: 23335084
Publishing Year: 2020
Volume: 7, Issue:2
Language: 英语
Keyword: global solar radiation ; high-density meteorological observations ; random forest ; selection of variables
Scopus Keyword: accuracy assessment ; climate conditions ; error analysis ; estimation method ; geographical distribution ; observational method ; prediction ; renewable resource ; solar power ; solar radiation ; spatiotemporal analysis ; China
English Abstract: Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest-growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high-density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF-estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root-mean-square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root-mean-square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high-resolution DGSR network, which can not only be used to effectively evaluate the long-term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy. © 2020 The Authors.
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被引频次[WOS]:2   [查看WOS记录]     [查看WOS中相关记录]
Document Type: 期刊论文
Identifier: http://119.78.100.158/handle/2HF3EXSE/159535
Appears in Collections:气候变化与战略

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Affiliation: Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, China; Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing, China; Environment and Sustainability Institute, University of Exeter, Penryn, United Kingdom; Department of Geography, Hong Kong Baptist University, Hong Kong; School of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, China; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong; School of Resource and Environmental Sciences, Wuhan University, Wuhan, China; Plateau Atmospheric and Environment Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, China; CPI Power Engineering Co., LTD, Shanghai, China; National Meteorological Center, CMA, Beijing, China; School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China; State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China

Recommended Citation:
Zeng Z.,Wang Z.,Gui K.,et al. Daily Global Solar Radiation in China Estimated From High-Density Meteorological Observations: A Random Forest Model Framework[J]. Earth and Space Science,2020-01-01,7(2)
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