Articles | Volume 22, issue 2
https://doi.org/10.5194/nhess-22-431-2022
https://doi.org/10.5194/nhess-22-431-2022
Research article
 | 
14 Feb 2022
Research article |  | 14 Feb 2022

Statistical estimation of spatial wave extremes for tropical cyclones from small data samples: validation of the STM-E approach using long-term synthetic cyclone data for the Caribbean Sea

Ryota Wada, Jeremy Rohmer, Yann Krien, and Philip Jonathan

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Cited articles

Barbier, E. B.: Policy: Hurricane Katrina's lessons for the world, Nat. News, 524, 285, https://doi.org/10.1038/524285a, 2015. a
Bloemendaal, N., Muis, S., Haarsma, R. J., Verlaan, M., Apecechea, M. I., de Moel, H., Ward, P. J., and Aerts, J. C.: Global modeling of tropical cyclone storm surges using high-resolution forecasts, Clim. Dynam., 52, 5031–5044, 2019. a
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Dasgupta, R., Basu, M., Kumar, P., Johnson, B. A., Mitra, B. K., Avtar, R., and Shaw, R.: A rapid indicator-based assessment of foreign resident preparedness in Japan during Typhoon Hagibis, Int. J. Disast. Risk Reduct., 51, 101849, https://doi.org/10.1016/j.ijdrr.2020.101849, 2020. a
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Short summary
Characterizing extreme wave environments caused by tropical cyclones in the Caribbean Sea near Guadeloupe is difficult because cyclones rarely pass near the location of interest. STM-E (space-time maxima and exposure) model utilizes wave data during cyclones on a spatial neighbourhood. Long-duration wave data generated from a database of synthetic tropical cyclones are used to evaluate the performance of STM-E. Results indicate STM-E provides estimates with small bias and realistic uncertainty.
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