Extending the wind profile beyond the surface layer by combining physical and machine learning approaches
Abstract α R 2 - 1 R 2 - 1, respectively. This indicates that the estimates from the PLM-RF method are much closer to observations than those from the PLM and RF methods. Moreover, the RMSE of the wind profiles estimated by the PLM-RF model is relatively large for highlands, while it is small for plains. This result indicates that the performance of the PLM-RF model is affected by the terrain factor. Finally, the PLM-RF model is applied to three atmospheric radiation measurement sites for independent validation, and the wind profiles estimated by the PLM-RF model are found to be consistent with Doppler wind lidar observations. This confirms that the PLM-RF model has good applicability. These findings have great implications for the weather, climate, and renewable energy sector.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Extending the wind profile beyond the surface layer by combining physical and machine learning approaches ; volume:24 ; number:7 ; year:2024 ; pages:4047-4063 ; extent:17
Atmospheric chemistry and physics ; 24, Heft 7 (2024), 4047-4063 (gesamt 17)
- Creator
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Liu, Boming
Ma, Xin
Guo, Jianping
Wen, Renqiang
Li, Hui
Jin, Shikuan
Ma, Yingying
Guo, Xiaoran
Gong, Wei
- DOI
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10.5194/acp-24-4047-2024
- URN
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urn:nbn:de:101:1-2024041104523429084656
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:55 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Liu, Boming
- Ma, Xin
- Guo, Jianping
- Wen, Renqiang
- Li, Hui
- Jin, Shikuan
- Ma, Yingying
- Guo, Xiaoran
- Gong, Wei