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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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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.
Preprints
https://doi.org/10.5194/wes-2020-134
https://doi.org/10.5194/wes-2020-134

  08 Feb 2021

08 Feb 2021

Review status: this preprint is currently under review for the journal WES.

Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

Alexander Basse1,2, Doron Callies1,2, Anselm Grötzner3, and Lukas Pauscher2 Alexander Basse et al.
  • 1Department of Integrated Energy Systems, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany
  • 2Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Königstor 59, 34119 Kassel, Germany
  • 3Ramboll Deutschland GmbH, Elisabeth-Consbruch-Straße 3, 34131 Kassel, Germany

Abstract. Measure-Correlate-Predict (MCP) approaches are often used to correct wind measurements to the long-term wind conditions on site. This paper investigates systematic errors in MCP-based long-term corrections which occur if the measurement on site covers only a few months (seasonal biases). In this context, two common linear MCP methods are tested and compared, namely Variance Ratio and Linear Regression with Residuals. Wind measurement data from 18 sites with different terrain complexity in Germany are used (measurement heights between 100 and 140 m). Six different reanalysis data sets serve as the reference (long-term) wind data in the MCP calculations. Besides experimental results, theoretical considerations are presented which provide the mathematical background for understanding the observations. General relationships are derived which trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. This allows the transfer of the results of this study to different measurement durations, other reference data sets and other regions of the world. In this context, it is shown both theoretically and experimentally that the results do not only depend on the selected reference data set but also significantly change with the choice of the MCP method.

Alexander Basse et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2020-134', Anonymous Referee #1, 16 Mar 2021 reply

Alexander Basse et al.

Alexander Basse et al.

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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.
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