Articles | Volume 6, issue 6
https://doi.org/10.5194/wes-6-1473-2021
© Author(s) 2021. 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-6-1473-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data
Alexander Basse
CORRESPONDING AUTHOR
Department of Integrated Energy Systems, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany
Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Königstor 59, 34119 Kassel, Germany
Doron Callies
Department of Integrated Energy Systems, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany
Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Königstor 59, 34119 Kassel, Germany
Anselm Grötzner
Ramboll Deutschland GmbH, Elisabeth-Consbruch-Straße 3, 34131 Kassel, Germany
Lukas Pauscher
Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Königstor 59, 34119 Kassel, Germany
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Wind Energ. Sci., 11, 1803–1820, https://doi.org/10.5194/wes-11-1803-2026, https://doi.org/10.5194/wes-11-1803-2026, 2026
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Using data collected over 13 months at an offshore wind farm, our study shows that a wind turbine’s position within the farm influences its energy output at a given nacelle-measured wind speed. Front-row turbines respond differently to similar wind speeds and turbulence than those further back. This finding suggests that current methods for characterizing inflow conditions may not fully capture actual wind behavior, underscoring the need for improved performance analysis techniques.
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In this paper we analyse the suitability of different reanalyses and meso-scale models for simulating wind energy in Germany. We found that all datasets overestimate energy production, with errors ranging from 5 % to 45 %. This suggests that the underlying models may not accurately reflect average wind conditions. CERRA and ERA5 performed the best, but they also require regional adjustment. Understanding the cause of these differences is crucial for improving weather and wind energy modelling.
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This study introduces an extended sea surface levelling method for the accurate offshore calibration of scanning lidars. This method can determine the alignment of the laser beam, including any vertical shift, and is independent of the scan pattern. Tests using real measurement data and a detailed uncertainty study confirm its reliability. The study offers a versatile calibration approach and improves confidence in offshore wind measurements with scanning lidars.
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Short summary
This study investigates systematic, seasonal biases in the long-term correction of short-term wind measurements (< 1 year). Two popular measure–correlate–predict (MCP) methods yield remarkably different results. Six reanalysis data sets serve as long-term data. Besides experimental results, theoretical findings are presented which link the mechanics of the methods and the properties of the reanalysis data sets to the observations. Finally, recommendations for wind park planners are derived.
This study investigates systematic, seasonal biases in the long-term correction of short-term...
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