Preprints
https://doi.org/10.5194/wes-2023-116
https://doi.org/10.5194/wes-2023-116
27 Nov 2023
 | 27 Nov 2023
Status: a revised version of this preprint is currently under review for the journal WES.

Mesoscale weather systems and associated potential wind power variations in a mid-latitude sea strait (Kattegat)

Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig

Abstract. Mesoscale weather systems cause spatiotemporal variability in offshore wind power and insight in their fluctuations can support grid operations. In this study, a 10-year model integration with the kilometre-scale atmospheric model COSMO-CLM served a wind and potential power fluctuation analysis in the Kattegat, a mid-latitude sea strait of 130 km width with an irregular coastline. The model agrees well with scatterometer data away from coasts and small islands, with a spatiotemporal root mean square difference of 1.35 m/s. A comparison of 10 minute wind speed at about 100 metre with lidar data for a 2 year period reveals a very good performance with a slight model overestimation of 0.08 m/s and a high value for the Perkins Skill Score (0.97). From periodograms made using the Welch method it was found that the wind speed variability on a sub-hourly timescale is higher in winter compared to summer. In contrast, the wind power varies more in summer when winds often drop below the rated power threshold. During winter, variability is largest in the northeastern part of the Kattegat due to a spatial spin up of convective systems over the sea during the predominant southwesterly winds. Summer convective systems are found to develop over land, driving spatial variability in offshore winds during this season. On average over the 10 summers the mesoscale wind speeds are up to 20 % larger than the synoptic background at 17 h UTC with a clear diurnal cycle. The winter averaged mesoscale wind component is up to 10 % larger with negligible daily variation. Products with a lower resolution like ERA5 substantially underestimate this ratio between the mesoscale and synoptic wind speed. Moreover, taking into account mesoscale spatial variability is important for correctly representing temporal variability of power production. The root mean square difference between two power output time series, one ignoring and one accounting for mesoscale spatial variability, is 14 % of the total power generation.

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 preprint. The responsibility to include appropriate place names lies with the authors.
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig

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-2023-116', Anonymous Referee #1, 18 Dec 2023
    • AC1: 'Reply on RC1', Jérôme Neirynck, 19 Feb 2024
    • AC3: 'Reply on RC1', Jérôme Neirynck, 06 Mar 2024
  • RC2: 'Comment on wes-2023-116', Anonymous Referee #2, 28 Dec 2023
    • AC2: 'Reply on RC2', Jérôme Neirynck, 19 Feb 2024
    • AC4: 'Reply on RC2', Jérôme Neirynck, 06 Mar 2024
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig

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Short summary
In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We've observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we've noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.
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