Articles | Volume 24, issue 7
Research article
04 Apr 2024
Research article |  | 04 Apr 2024

Extending the wind profile beyond the surface layer by combining physical and machine learning approaches

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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2727', Anonymous Referee #1, 03 Jan 2024
    • AC1: 'Reply on RC1', Boming Liu, 05 Feb 2024
  • RC2: 'Comment on egusphere-2023-2727', Anonymous Referee #2, 13 Jan 2024
    • AC2: 'Reply on RC2', Boming Liu, 05 Feb 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Boming Liu on behalf of the Authors (05 Feb 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (05 Feb 2024) by Yuan Wang
RR by Anonymous Referee #1 (13 Feb 2024)
ED: Publish as is (13 Feb 2024) by Yuan Wang
AR by Boming Liu on behalf of the Authors (17 Feb 2024)
Short summary
Accurate wind profile estimation, especially for the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. We propose a novel method that combines the power-law method with the random forest algorithm to extend wind profiles beyond the surface layer. Compared with the traditional algorithm, this method has better stability and spatial applicability and can be used to obtain the wind profiles on different land cover types.
Final-revised paper