Preprints
https://doi.org/10.5194/wes-2024-27
https://doi.org/10.5194/wes-2024-27
13 Mar 2024
 | 13 Mar 2024
Status: this preprint is currently under review for the journal WES.

Underestimation of strong wind speeds offshore in ERA5: evidence, discussion, and correction

Rémi Gandoin and Jorge Garza

Abstract. Offshore Wind power plants have become an important element of the European electrical grid. Studies of metocean site conditions (wind, sea state, currents, water levels) form a key input to the design of these large infrastructure projects. Such studies heavily rely on reanalysis datasets which provide decades-long model time series over large areas. In this article, we address a known deficiency of one these reanalysis datasets, ERA5, namely that it underestimates strong wind speeds offshore. For doing so, comparisons are made against CFSR/CFSv2 reanalyses as well as high quality wind energy specific in-situ measurements from floating LiDAR systems. The ERA5 surface drag formulation and its sea state dependency are analysed in detail, the conditions of the bias identified, and a correction method is suggested. The article concludes with proposing practical and simple ways to incorporate publicly available, high-quality wind energy measurement datasets in air-sea interaction studies alongside legacy measurements such as met buoys.

Rémi Gandoin and Jorge Garza

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2024-27', Anonymous Referee #1, 09 Apr 2024
  • RC2: 'Comment on wes-2024-27', Anonymous Referee #2, 09 Apr 2024
Rémi Gandoin and Jorge Garza
Rémi Gandoin and Jorge Garza

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Short summary
ERA5 has become the workhorse of most wind resource assessment applications, as it compares better with in-situ measurements than the other reanalyses. However, for design purposes, ERA5 suffers from a drawback: it underestimates strong wind speeds offshore (approx. from 10 m/s). This is not widely discussed in the scientific literature. We address this bias and proposes a simple, robust correction. This article support the growing need for use-case specific validations of reanalysis datasets.
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