Articles | Volume 2, issue 1
https://doi.org/10.5194/wes-2-77-2017
https://doi.org/10.5194/wes-2-77-2017
Research article
 | 
10 Feb 2017
Research article |  | 10 Feb 2017

An error reduction algorithm to improve lidar turbulence estimates for wind energy

Jennifer F. Newman and Andrew Clifton

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (21 Aug 2016) by Jakob Mann
AR by Jennifer Newman on behalf of the Authors (28 Oct 2016)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (07 Nov 2016) by Jakob Mann
RR by Anonymous Referee #1 (21 Nov 2016)
RR by Rozenn Wagner (28 Nov 2016)
ED: Reconsider after major revisions (04 Dec 2016) by Jakob Mann
AR by Jennifer Newman on behalf of the Authors (22 Dec 2016)  Author's response   Manuscript 
ED: Publish as is (16 Jan 2017) by Jakob Mann
ED: Publish as is (16 Jan 2017) by Jakob Mann (Chief editor)
AR by Jennifer Newman on behalf of the Authors (18 Jan 2017)  Manuscript 
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Short summary
Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure mean wind speeds accurately but measure different values of turbulence than an instrument on a tower. In this paper, a model is described that improves lidar turbulence estimates. The model can be applied to commercially available lidars in real time or post-processing. Results indicate that the model performs well under most atmospheric conditions but retains some errors under daytime conditions.
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