Articles | Volume 25, issue 8
https://doi.org/10.5194/nhess-25-2863-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
26 Aug 2025
Research article |
| 26 Aug 2025
| 26 Aug 2025
Reask UTC: a machine learning modeling framework to generate climate-connected tropical cyclone event sets globally
Cited articles
Aon: Weather, Climate and Catastrophe Insight, Aon, https://www.aon.com/getmedia/1b516e4d-c5fa-4086-9393-5e6afb0eeded/20220125-2021-weather-climate-catastrophe-insight.pdf.aspx (last access: August 2024), 2021.
Aon: Q1 Global Catastrophe Recap (PDF) (Report), Aon Benfield, https://www.aon.com/reinsurance/getmedia/af1248d6-9332-4878-8c92-572c1bf3c19d/20221204-q1-2022-catastrophe-recap.pdf (last access: 22 August 2025), 2022.
Arthur, W. C.: A statistical–parametric model of tropical cyclones for hazard assessment, Nat. Hazards Earth Syst. Sci., 21, 893–916, https://doi.org/10.5194/nhess-21-893-2021, 2021.
Bloemendaal, N., Haigh, H., de Moel, I. D., Haarsma, R., and Aerts, J.: Generation of a global synthetic tropical cyclone hazard dataset using STORM, Scientific Data, 7, 40, https://doi.org/10.1038/s41597-020-0381-2, 2020.
Breiman, L.: Statistical modelling: The two cultures, Stat. Sci., 16, 199–231, https://doi.org/10.1214/ss/1009213726, 2001.
