A two-stage framework for identifying and characterizing wind turbine noise data and its validation by listening tests

Könecke, Susanne; Jonscher, Clemens; Bohne, Tobias; Rolfes, Raimund

doi:10.5194/wes-11-1771-2026

Articles | Volume 11, issue 5

https://doi.org/10.5194/wes-11-1771-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-11-1771-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 5

Research article

|

19 May 2026

Research article |

| 19 May 2026

A two-stage framework for identifying and characterizing wind turbine noise data and its validation by listening tests

Susanne Könecke, Clemens Jonscher, Tobias Bohne, and Raimund Rolfes

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Final revised paper (published on 19 May 2026)
Preprint (discussion started on 16 Mar 2026)

Interactive discussion

Status: closed

RC1: 'Comment on wes-2026-44', Anonymous Referee #1, 02 Apr 2026

The paper presents a framework for the wind turbine noise identification. The work was validated with a listening test to identify the different noise sources. The results are interesting and encouraging.
The paper is well written and easy to follow. Maybe some paragraphs may be shortened as the paper is a bit long. The advantages and limitations of the methods are clearly addressed and discussed.
Maybe some additional details about atmospheric absorption could be of interest. The authors stated that this aspect does not significantly contribute. Is this true also for long listening time when the humidity may change considerably?
Could also the authors describe a bit the effect of ground in the detection method? Is the ground effect retained in the model?
Overall, the paper is a good quality work that can be accepted as is.

Citation: https://doi.org/10.5194/wes-2026-44-RC1
RC2: 'Comment on wes-2026-44', Anonymous Referee #2, 08 Apr 2026

The manuscript presents a work on identifying the wind turbine noise and distinguishing that noise from other environmental sounds. The paper is well written and easy to follow. All sections are clearly presented, possible issues addressed and discussed, and results clearly presented, analyzed and discussed. Authors included a listening test (where they explain shortfalls of the test), and present results that are very interesting.
I would maybe only add a sentence or two on if there might be some difference between different types of turbines in terms of noise production and how this method could be adopted to different types of turbines if there is a difference. Also, I know they tested the method for a specific set of atmospheric and geographic conditions (geography is set by a study area), but it would be nice to include a few thoughts on how this method could be adopted/changed for different geographies (different vegetation, orography,…) and different atmospheric conditions (maybe this could be mentioned as a possible future work?). I would be very interested in seeing those results as well.
Overall, I like the paper and presentation of the work, and would accepted it as is.

Citation: https://doi.org/10.5194/wes-2026-44-RC2
AC1: 'Comment on wes-2026-44', Susanne Könecke, 27 Apr 2026

We would like to thank both reviewers for their positive assessment of the manuscript and for their helpful comments. The remarks primarily address the applicability of the proposed method under varying boundary conditions, such as different atmospheric situations, topographical settings, and turbine types. This is a valid and important point. These aspects have been added to relevant sections and are additionally identified as areas for future work.
The supplement provides the answers to the reviewers’ comments. The lines used in the answers refer to the new version of the manuscript. The modified paragraphs are marked in the manuscript and are also given in colour in the corresponding responses in the supplement.

Citation: https://doi.org/10.5194/wes-2026-44-AC1

Peer review completion

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

AR by Susanne Könecke on behalf of the Authors (27 Apr 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (28 Apr 2026) by Alessandro Bianchini

ED: Publish as is (29 Apr 2026) by Nicolaos A. Cutululis (Chief editor)

AR by Susanne Könecke on behalf of the Authors (05 May 2026) Manuscript

Short summary

This paper presents a framework to identify wind turbine noise in long-term field measurements. By combining statistical criteria, turbine operating data, and physics-based signal analysis, periods dominated by wind turbine noise and its key components are detected. The framework is validated using a structured listening test and applied to a 1-month dataset. The framework, listening-test platform, and anonymized audio data are publicly available to support further research.