Radar-derived precipitation climatology for wind turbine blade leading edge erosion

Letson, Frederick; Barthelmie, Rebecca J.; Pryor, Sara C.

doi:https://doi.org/10.5194/wes-5-331-2020

Articles | Volume 5, issue 1

https://doi.org/10.5194/wes-5-331-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Wind Energy Science Conference 2019

https://doi.org/10.5194/wes-5-331-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 5, issue 1

Research article

|

06 Mar 2020

Research article |

| 06 Mar 2020

Radar-derived precipitation climatology for wind turbine blade leading edge erosion

Frederick Letson, Rebecca J. Barthelmie, and Sara C. Pryor

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Final revised paper (published on 06 Mar 2020)
Preprint (discussion started on 13 Aug 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Erosion of Wind Turbine Blade Leading Edge by Precipitation', Anonymous Referee #1, 02 Oct 2019
RC2: 'Review', Anonymous Referee #2, 17 Oct 2019
EC1: 'Please answer reviewers comments', Julio J. Melero, 17 Oct 2019
AC1: 'Authors' Response to Reviewers', Frederick Letson, 08 Nov 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Frederick Letson on behalf of the Authors (08 Nov 2019) Author's response

ED: Referee Nomination & Report Request started (20 Nov 2019) by Julio J. Melero

RR by Anonymous Referee #2 (24 Nov 2019)

ED: Publish as is (29 Nov 2019) by Julio J. Melero

ED: Publish as is (02 Dec 2019) by Joachim Peinke(Chief editor)

Short summary

Wind turbine blade leading edge erosion (LEE) is potentially a significant source of energy loss and expense for wind farm operators. This study presents a novel approach to characterizing LEE potential from precipitation across the contiguous USA based on publicly available National Weather Service dual-polarization RADAR data. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments.