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  4. 黄土与第四纪地质国家重点实验室（2010~）
Daily Global Solar Radiation in China Estimated From High-Density Meteorological Observations: A Random Forest Model Framework

文献类型：期刊论文

作者Zeng, Zhaoliang10; Wang, Zemin10; Gui, Ke11; Yan, Xiaoyu12; Gao, Meng13; Luo, Ming2,3,4; Geng, Hong9; Liao, Tingting1; Li, Xiao8; An, Jiachun11
刊名EARTH AND SPACE SCIENCE
出版日期2020-02-01
卷号7期号:2页码:15
DOI10.1029/2019EA001058
通讯作者Wang, Zemin(zmwang@whu.edu.cn) ; Yang, Yuanjian(yyj1985@nuist.edu.cn)
英文摘要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/m(2), and mean bias of -0.04 MJ/m(2). 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.
WOS关键词PHOTOSYNTHETICALLY ACTIVE RADIATION ; AIR-POLLUTION ; IRRADIANCE FORECASTS ; PREDICTION ; SATELLITE ; SUNSHINE ; TEMPERATURE ; PRODUCTS
资助项目National Natural Science Foundation of China[41776195] ; National Natural Science Foundation of China[41531069] ; National Natural Science Foundation of China[41871029] ; open funding of State Key Laboratory of Loess and Quaternary Geology[SKLLQG1842]
WOS研究方向Astronomy & Astrophysics ; Geology
语种英语
出版者AMER GEOPHYSICAL UNION
WOS记录号WOS:000529236000017
资助机构National Natural Science Foundation of China ; open funding of State Key Laboratory of Loess and Quaternary Geology
源URL[http://ir.ieecas.cn/handle/361006/14751]  
专题地球环境研究所_黄土与第四纪地质国家重点实验室（2010~）
通讯作者Wang, Zemin; Yang, Yuanjian
作者单位1.Chengdu Univ Informat Technol, Coll Atmospher Sci, Plateau Atmospher & Environm Lab Sichuan Prov, Chengdu, Peoples R China
2.Chinese Univ Hong Kong, Inst Environm Energy & Sustainabil, Hong Kong, Peoples R China
3.Sun Yat Sen Univ, Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou, Peoples R China
4.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou, Peoples R China
5.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian, Peoples R China
6.Nanjing Univ Informat Sci & Technol, Sch Atmospher Phys, Nanjing, Peoples R China
7.CMA, Natl Meteorol Ctr, Beijing, Peoples R China
8.CPI Power Engn Co LTD, Shanghai, Peoples R China
9.Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China
10.Wuhan Univ, Chinese Antarctic Ctr Surveying & Mapping, Wuhan, Peoples R China
推荐引用方式
GB/T 7714		 Zeng, Zhaoliang,Wang, Zemin,Gui, Ke,et al. Daily Global Solar Radiation in China Estimated From High-Density Meteorological Observations: A Random Forest Model Framework[J]. EARTH AND SPACE SCIENCE,2020,7(2):15.
APA Zeng, Zhaoliang.,Wang, Zemin.,Gui, Ke.,Yan, Xiaoyu.,Gao, Meng.,...&Yang, Yuanjian.(2020).Daily Global Solar Radiation in China Estimated From High-Density Meteorological Observations: A Random Forest Model Framework.EARTH AND SPACE SCIENCE,7(2),15.
MLA Zeng, Zhaoliang,et al."Daily Global Solar Radiation in China Estimated From High-Density Meteorological Observations: A Random Forest Model Framework".EARTH AND SPACE SCIENCE 7.2(2020):15.

入库方式： OAI收割

来源：地球环境研究所

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