Articles | Volume 11, issue 4
https://doi.org/10.5194/wes-11-1205-2026
https://doi.org/10.5194/wes-11-1205-2026
Research article
 | 
14 Apr 2026
Research article |  | 14 Apr 2026

Bayesian uncertainty quantification of engineering models for wind farm–atmosphere interaction

Frederik Aerts, Koen Devesse, and Johan Meyers

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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-2025-196', Anonymous Referee #1, 26 Nov 2025
  • RC2: 'Comment on wes-2025-196', Anonymous Referee #2, 30 Nov 2025
  • AC1: 'Comment on wes-2025-196', Frederik Aerts, 19 Dec 2025

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Frederik Aerts on behalf of the Authors (19 Dec 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (02 Jan 2026) by Cristina Archer
RR by Anonymous Referee #2 (07 Jan 2026)
ED: Publish as is (21 Jan 2026) by Cristina Archer
ED: Publish as is (28 Jan 2026) by Sandrine Aubrun (Chief editor)
AR by Frederik Aerts on behalf of the Authors (03 Feb 2026)
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Short summary
This paper presents an improved Bayesian uncertainty quantification framework for calibrating and comparing wind farm flow models. It quantifies the parameter uncertainty in a joint posterior distribution as well as the model uncertainty. Applied to a large-eddy simulation dataset for wind farm blockage, it compares a standard wake model and an atmospheric perturbation model, showing the latter yields lower model uncertainty. The framework is available in the open-source Python package UMBRA (Uncertainty Modeling toolbox for Bayesian data Re-Analysis).
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