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Estimation of global tropical cyclone wind speed probabilities using the STORM dataset

Estimation of global tropical cyclone wind speed probabilities using the STORM dataset
Estimation of global tropical cyclone wind speed probabilities using the STORM dataset
Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments.
2052-4463
Bloemendaal, Nadia
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De Moel, Hans
c10ce4ce-4443-4f55-89c7-b59150fe611c
Muis, Sanne
d73531db-78f1-4f65-b1a0-f96ae1c46377
Haigh, Ivan D.
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Aerts, Jeroen C. J. H.
58ebafc6-3a46-4d27-8ae0-ca3c9e77f5a3
Bloemendaal, Nadia
8aaf62a9-9c7a-4650-ae4a-071a6f5b0ac1
De Moel, Hans
c10ce4ce-4443-4f55-89c7-b59150fe611c
Muis, Sanne
d73531db-78f1-4f65-b1a0-f96ae1c46377
Haigh, Ivan D.
945ff20a-589c-47b7-b06f-61804367eb2d
Aerts, Jeroen C. J. H.
58ebafc6-3a46-4d27-8ae0-ca3c9e77f5a3

Bloemendaal, Nadia, De Moel, Hans, Muis, Sanne, Haigh, Ivan D. and Aerts, Jeroen C. J. H. (2020) Estimation of global tropical cyclone wind speed probabilities using the STORM dataset. Scientific Data, 7 (1), [377]. (doi:10.1038/s41597-020-00720-x).

Record type: Article

Abstract

Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments.

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s41597-020-00720-x - Version of Record
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Accepted/In Press date: 14 October 2020
Published date: 10 November 2020

Identifiers

Local EPrints ID: 445000
URI: http://eprints.soton.ac.uk/id/eprint/445000
ISSN: 2052-4463
PURE UUID: 3ac77850-9aec-4f0c-8577-c5be2d237742

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Date deposited: 17 Nov 2020 17:39
Last modified: 25 Nov 2021 23:06

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Contributors

Author: Nadia Bloemendaal
Author: Hans De Moel
Author: Sanne Muis
Author: Ivan D. Haigh
Author: Jeroen C. J. H. Aerts

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