Articles | Volume 6, issue 5
Wind Energ. Sci., 6, 1117–1142, 2021
https://doi.org/10.5194/wes-6-1117-2021
Wind Energ. Sci., 6, 1117–1142, 2021
https://doi.org/10.5194/wes-6-1117-2021

Research article 09 Sep 2021

Research article | 09 Sep 2021

Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics

Davide Conti et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2020-135', Anonymous Referee #1, 11 May 2021
  • RC2: 'Comment on wes-2020-135', Anonymous Referee #2, 18 May 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Davide Conti on behalf of the Authors (30 Jun 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (06 Jul 2021) by Sandrine Aubrun
ED: Publish as is (19 Jul 2021) by Gerard J.W. van Bussel(Chief Editor)
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Short summary
We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected from the Scaled Wind Farm Technology (SWiFT) facility in Texas. The analysis includes the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric-stability conditions.