Daily Global Solar Radiation in China Estimated From High-Density Meteorological Observations: A Random Forest Model Framework
dc.contributor.author | Zeng, Z | |
dc.contributor.author | Wang, Z | |
dc.contributor.author | Gui, K | |
dc.contributor.author | Yan, X | |
dc.contributor.author | Gao, M | |
dc.contributor.author | Luo, M | |
dc.contributor.author | Geng, H | |
dc.contributor.author | Liao, T | |
dc.contributor.author | Li, X | |
dc.contributor.author | An, J | |
dc.contributor.author | Liu, H | |
dc.contributor.author | He, C | |
dc.contributor.author | Ning, G | |
dc.contributor.author | Yang, Y | |
dc.date.accessioned | 2020-03-23T10:18:34Z | |
dc.date.issued | 2020-01-26 | |
dc.description.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. | en_GB |
dc.description.sponsorship | National Natural Science Foundation of China | en_GB |
dc.description.sponsorship | State Key Laboratory of Loess and Quaternary Geology | en_GB |
dc.identifier.citation | Vol. 7 (2), article e2019EA001058 | en_GB |
dc.identifier.doi | 10.1029/2019EA001058 | |
dc.identifier.grantnumber | 41776195 | en_GB |
dc.identifier.grantnumber | 41531069 | en_GB |
dc.identifier.grantnumber | 41871029 | en_GB |
dc.identifier.grantnumber | SKLLQG1842 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/120372 | |
dc.language.iso | en | en_GB |
dc.publisher | American Geophysical Union (AGU) | en_GB |
dc.rights | © 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. | en_GB |
dc.subject | global solar radiation | en_GB |
dc.subject | high‐density meteorological observations | en_GB |
dc.subject | random forest | en_GB |
dc.subject | selection of variables | en_GB |
dc.title | Daily Global Solar Radiation in China Estimated From High-Density Meteorological Observations: A Random Forest Model Framework | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-03-23T10:18:34Z | |
dc.description | This is the final version. Available on open access from the American Geophysical Union via the DOI in this record | en_GB |
dc.identifier.eissn | 2333-5084 | |
dc.identifier.journal | Earth and Space Science | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_GB |
dcterms.dateAccepted | 2020-01-16 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-01-26 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-03-23T10:15:16Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-03-23T10:18:37Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © 2020 The Authors.
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.