Preprints
https://doi.org/10.5194/wes-2025-269
https://doi.org/10.5194/wes-2025-269
19 Jan 2026
 | 19 Jan 2026
Status: this preprint is currently under review for the journal WES.

50-year Wind Speed Maps for Tropical Cyclone-affected Regions using Best Track data

Keeta Chapman-Smith, Xiaoli Guo Larsén, and Mark Laier Brodersen

Abstract. Accurate estimation of extreme wind speeds from tropical cyclones is a significant challenge within tropical cyclone prone regions. This study presents a method to estimate the 50-year return wind speed at heights relevant to wind turbines. The International Best Track Archive for Climate Stewardship data is combined with the Holland parametric model and the Gumbel distribution to assess extreme winds within three tropical cyclone-affected regions within the Northern Hemisphere. These regions are Taiwan, Japan, and the east coast of the United States of America. To assess the uncertainty within the results from differing input parameters, Monte Carlo simulations are used. The method performs well in Taiwan and Japan which can be attributed to the large sample size of data points located within a limited spatial area. The east coast of the United States performs less well, which conversely, is due to the smaller sample size and wider spatial region of which they cover. This study shows that combining International Best Track Archive for Climate Stewardship data with parametric and statistical models provides a practical approach to estimate extreme wind speeds while highlighting the need for an understanding of regional characteristics to ensure reliability of the results.

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Keeta Chapman-Smith, Xiaoli Guo Larsén, and Mark Laier Brodersen

Status: open (until 16 Feb 2026)

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Keeta Chapman-Smith, Xiaoli Guo Larsén, and Mark Laier Brodersen
Keeta Chapman-Smith, Xiaoli Guo Larsén, and Mark Laier Brodersen
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Short summary
This study presents a method to estimate wind speeds which could occur in a 50-year period. The 50-year wind speed is calculated for three regions: Taiwan, Japan, and the east coast of the United States of America. The method performs well in Taiwan and Japan which can be attributed to the large dataset size located within a limited spatial area. The east coast of the United States performs less well due to the smaller dataset size and wider spatial region of which they cover.
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