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

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Revised manuscript accepted for WES
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Cited articles

Ágústsson, H. and Ólafsson, H.: Forecasting wind gusts in complex terrain, Meteorol. Atmos. Phys., 103, 173–185, 2009. a
AMS: Gust. Glossary of Meteorology, (last access: 14 October 2023), 2023. a, b
Asadi, M. and Pourhossein, K.: Wind farm site selection considering turbulence intensity, Energy, 236, 121480,, 2021. a
Ashcroft, J.: The relationship between the gust ratio, terrain roughness, gust duration and the hourly mean wind speed, J. Wind Eng. Indust. Aerodynam., 53, 331–355, 1994. a
Azorin-Molina, C., Guijarro, J.-A., McVicar, T. R., Vicente-Serrano, S. M., Chen, D., Jerez, S., and Espírito-Santo, F.: Trends of daily peak wind gusts in Spain and Portugal, 1961–2014, J. Geophys. Res.-Atmos., 121, 1059–1078, 2016. a
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.
Final-revised paper