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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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Discussion papers
https://doi.org/10.5194/wes-2019-42
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-42
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 26 Jul 2019

Submitted as: research article | 26 Jul 2019

Review status
A revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

How to improve your metocean datasets

Erik Quaeghebeur and Michiel B. Zaaijer Erik Quaeghebeur and Michiel B. Zaaijer
  • Wind Energy Section, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands

Abstract. We present an analysis of three metocean datasets of 10-minute statistics and our resulting recommendations to both producers and users of such datasets. Many of our recommendations are more generally of interest to all numerical measurement data producers. The datasets analyzed originate from offshore meteorological masts installed to support offshore wind farm planning and design: the Dutch OWEZ and MMIJ, and the German FINO 1. Our analysis shows that such datasets contain issues that users should look out for and whose prevalence can be reduced by producers. We also present expressions to derive uncertainty and bias values for the statistics from information typically available about sample uncertainty. We also observe that the format in which the data is disseminated is sub-optimal from the users' perspective and discuss how producers can create more immediately useful dataset files. Effectively, we advocate using an established binary format (HDF5 or netCDF4) instead of the typical text-based one (comma-separated values), as this allows for the inclusion of relevant metadata and the creation of significantly smaller directly accessible dataset files. Next to informing producers of the advantages of these formats, we also provide concrete pointers to their effective use. Our conclusion is that datasets such as the ones we analyzed can be improved substantially in usefulness and convenience with limited effort.

Erik Quaeghebeur and Michiel B. Zaaijer

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Erik Quaeghebeur and Michiel B. Zaaijer

Model code and software

equaeghe/met-data-scripts: Version 0.2.0 E. Quaeghebeur https://doi.org/10.5281/zenodo.3352011

Erik Quaeghebeur and Michiel B. Zaaijer

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Latest update: 16 Feb 2020
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Short summary
Meteorological and oceanic datasets are fundamental to the modeling of offshore wind farms. Data quality issues in one such dataset led us to conduct a study to establish whether such issues are more generally present in these datasets. The answer is yes and users should be aware of this. We therefore also investigated how such issues can be avoided. The result is a set of techniques and recommendations for dataset producers, leading to substantial quality improvements with limited extra effort.
Meteorological and oceanic datasets are fundamental to the modeling of offshore wind farms. Data...
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