Preprints
https://doi.org/10.5194/wes-2025-147
https://doi.org/10.5194/wes-2025-147
08 Sep 2025
 | 08 Sep 2025
Status: this preprint is currently under review for the journal WES.

Under-resolved gradients: slow wake recovery and fast turbulence decay with mesoscale Wind Farm Parameterizations

William C. Radünz, Jens H. Kasper, Richard J. A. M. Stevens, and Julie K. Lundquist

Abstract. Numerical Weather Prediction (NWP) and climate models equipped with Wind Farm Parameterizations (WFPs) can simulate cluster wake effects affecting downstream wind farms in both onshore and offshore environments. This study evaluates the strengths and limitations of the NWP-WFP approach using the Weather Research and Forecasting (WRF) model with the Fitch WFP, benchmarked against large-eddy simulations (LES) of an idealized offshore wind farm under neutral atmospheric stability. Wake recovery is underestimated in NWP-WFP simulations because of two interconnected issues. First, the spatial gradients in the wind velocity field within the near-farm wake are necessarily under-resolved at mesoscale grid resolutions compared to the LES. Second, the faster decay of farm-added turbulence kinetic energy (TKE) in the mesoscale simulations is likely not due to excessive dissipation, but rather due to the underestimation of spatial gradients needed to sustain elevated TKE levels via shear production. A key insight is that the slow recovery in the near-farm wake, although confined to a short downstream distance, has lasting consequences for the far wake region. This fact underscores the need to address under-resolved spatial gradients to improve wake recovery and reduce far wake biases in NWP-WFP simulations.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Share
William C. Radünz, Jens H. Kasper, Richard J. A. M. Stevens, and Julie K. Lundquist

Status: open (until 06 Oct 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
William C. Radünz, Jens H. Kasper, Richard J. A. M. Stevens, and Julie K. Lundquist
William C. Radünz, Jens H. Kasper, Richard J. A. M. Stevens, and Julie K. Lundquist

Viewed

Total article views: 37 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
35 1 1 37 0 0
  • HTML: 35
  • PDF: 1
  • XML: 1
  • Total: 37
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 08 Sep 2025)
Cumulative views and downloads (calculated since 08 Sep 2025)

Viewed (geographical distribution)

Total article views: 37 (including HTML, PDF, and XML) Thereof 37 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 09 Sep 2025
Download
Short summary
Wind farms extract energy from the wind, creating slower, more turbulent flows that can affect other farms downstream. Using high-fidelity simulations for comparison, we find that models using coarser resolution to represent wind farms may underestimate how quickly the wind recovers. This appears to result from missing sharp wind changes and losing turbulence too quickly. Improving these aspects can help better predict wind energy production over long distances.
Share
Altmetrics