On the lidar-turbulence paradox  and possible countermeasures

Peña, Alfredo; Yankova, Ginka G.; Mallini, Vasiliki

doi:https://doi.org/10.5194/wes-10-83-2025

Articles | Volume 10, issue 1

https://doi.org/10.5194/wes-10-83-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

NAWEA/WindTech 2024

https://doi.org/10.5194/wes-10-83-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 10, issue 1

Research article

|

09 Jan 2025

Research article |

| 09 Jan 2025

On the lidar-turbulence paradox and possible countermeasures

Alfredo Peña, Ginka G. Yankova, and Vasiliki Mallini

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Final revised paper (published on 09 Jan 2025)
Preprint (discussion started on 09 Sep 2024)

Interactive discussion

Status: closed

RC1:
'Comment on wes-2024-108', Anonymous Referee #1, 29 Sep 2024

This article addresses an important problem in the atmospheric science community. I will accept it for publication after few revisions.

Citation: https://doi.org/10.5194/wes-2024-108-RC1
- AC1: 'Reply on RC1', Alfredo Peña, 07 Nov 2024
  
  please see attached file
  
  Citation: https://doi.org/10.5194/wes-2024-108-AC1
RC2:
'Comment on wes-2024-108', Anonymous Referee #2, 01 Oct 2024

Please see the attachment.

Citation: https://doi.org/10.5194/wes-2024-108-RC2
- AC2: 'Reply on RC2', Alfredo Peña, 07 Nov 2024
  
  please see attached file
  
  Citation: https://doi.org/10.5194/wes-2024-108-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Alfredo Peña on behalf of the Authors (07 Nov 2024) Author's response Author's tracked changes Manuscript

ED: Reconsider after major revisions (11 Nov 2024) by Etienne Cheynet

ED: Publish as is (13 Nov 2024) by Etienne Cheynet

ED: Publish as is (13 Nov 2024) by Julia Gottschall (Chief editor)

AR by Alfredo Peña on behalf of the Authors (15 Nov 2024)

Short summary

Lidars are vastly used in wind energy, but most users struggle when interpreting lidar turbulence measures. Here, we explain the difficulty in converting them into standard measurements. We show two ways of converting lidar to in situ turbulence measurements, both using neural networks: one of them is based on physics, while the other is purely data-driven. They show promising results when compared to high-quality turbulence measurements from a tall mast.