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
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
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
Liu, Boming
Ma, Xin
Guo, Jianping
Wen, Renqiang
Li, Hui
Jin, Shikuan
Ma, Yingying
Guo, Xiaoran
Gong, Wei

DOI
10.5194/acp-24-4047-2024
URN
urn:nbn:de:101:1-2024041104523429084656
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
14.08.2025, 10:55 AM CEST

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Associated

  • Liu, Boming
  • Ma, Xin
  • Guo, Jianping
  • Wen, Renqiang
  • Li, Hui
  • Jin, Shikuan
  • Ma, Yingying
  • Guo, Xiaoran
  • Gong, Wei

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