Articles | Volume 15, issue 24
Research article
16 Dec 2022
Research article |  | 16 Dec 2022

High-fidelity retrieval from instantaneous line-of-sight returns of nacelle-mounted lidar including supervised machine learning

Kenneth A. Brown and Thomas G. Herges


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-35', Anonymous Referee #1, 09 Jun 2022
  • RC2: 'Comment on amt-2022-35', Anonymous Referee #2, 02 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Kenneth Brown on behalf of the Authors (03 Oct 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (09 Oct 2022) by Gerd Baumgarten
RR by Anonymous Referee #1 (28 Oct 2022)
ED: Publish as is (07 Nov 2022) by Gerd Baumgarten
Short summary
The character of the airflow around and within wind farms has a significant impact on the energy output and longevity of the wind turbines in the farm. For both research and control purposes, accurate measurements of the wind speed are required, and these are often accomplished with remote sensing devices. This article pertains to a field experiment of a lidar mounted to a wind turbine and demonstrates three data post-processing techniques with efficacy at extracting useful airflow information.