A decision-tree-based measure–correlate–predict approach for peak wind gust estimation  from a global reanalysis dataset

Kartal, Serkan; Basu, Sukanta; Watson, Simon J.

doi:https://doi.org/10.5194/wes-8-1533-2023

Articles | Volume 8, issue 10

https://doi.org/10.5194/wes-8-1533-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-8-1533-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 8, issue 10

Research article

|

16 Oct 2023

Research article |

| 16 Oct 2023

A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset

Serkan Kartal, Sukanta Basu, and Simon J. Watson

Model code and software

Peak Wind Gust Estimation S. Kartal https://github.com/serkankartal/PeakWindGustEstimation

Short summary

Peak wind gust is a crucial meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.