Articles | Volume 11, issue 2
https://doi.org/10.5194/wes-11-661-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-661-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Feb 2026
Research article |
| 24 Feb 2026
| 24 Feb 2026
Evaluating the impact of inter-annual variability on long-term wind speed predictions
Johanna Borowski
CORRESPONDING AUTHOR
Fraunhofer IWES, Fraunhofer Institute for Wind Energy Systems IWES, Küpkersweg 70, 26129 Oldenburg, Germany
Carl von Ossietzky Universität Oldenburg, School of Mathematics and Science, Institute of Physics, Ammerländer Heerstraße 114–118, 26129 Oldenburg, Germany
Sandra Schwegmann
Fraunhofer IWES, Fraunhofer Institute for Wind Energy Systems IWES, Küpkersweg 70, 26129 Oldenburg, Germany
Kerstin Avila
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Carl von Ossietzky Universität Oldenburg, School of Mathematics and Science, Institute of Physics, Ammerländer Heerstraße 114–118, 26129 Oldenburg, Germany
Martin Dörenkämper
Fraunhofer IWES, Fraunhofer Institute for Wind Energy Systems IWES, Küpkersweg 70, 26129 Oldenburg, Germany
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This study introduces a methodology that integrates climate model ensembles into the measure-correlate-predict framework for site-specific wind resource assessments. Using ERA5 reanalysis data and tall tower measurements from European sites, the approach bias-corrects climate model data to local conditions and reproduces historical wind climates. Results indicate decreases in summer wind speeds (typically 0.1–0.4 m s−¹) and slight winter increases but with greater ensemble spread in this season.
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We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
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This research explores the ground-generation airborne wind energy system (AWES) design space and investigates scaling effects by varying design parameters such as aircraft wing size, aerodynamic efficiency and mass. Therefore, representative simulated onshore and offshore wind data are implemented into an AWES trajectory optimization model. We estimate optimal annual energy production and capacity factors as well as a minimal operational lift-to-weight ratio.
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Scanning lidar measurements combined with meteorological sensors and mesoscale simulations reveal the strong directional and stability dependence of the wake strength in the direct vicinity of wind farm clusters.
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Short summary
Assessing wind resources and mitigating the associated uncertainties are crucial to wind farm profitability. The study quantifies the uncertainty due to inter-annual variability, averaging 6.5 % and ranging from 1 % to 14 %, using long-term, quality-controlled wind measurements from tall met masts in terrain of varying complexity. Further, the results indicate that machine learning models are beneficial in mitigating the impact of inter-annual variability in heterogeneous and complex terrain.
Assessing wind resources and mitigating the associated uncertainties are crucial to wind farm...
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