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Generation of a global synthetic tropical cyclone hazard dataset using STORM

Generation of a global synthetic tropical cyclone hazard dataset using STORM
Generation of a global synthetic tropical cyclone hazard dataset using STORM
Over the past few decades, the world has seen substantial tropical cyclone (TC) damages, with the 2017 Hurricanes Harvey, Irma and Maria entering the top-5 costliest Atlantic hurricanes ever. Calculating TC risk at a global scale, however, has proven difficult given the limited temporal and spatial information on TCs across much of the global coastline. Here, we present a novel database on TC characteristics on a global scale using a newly developed synthetic resampling algorithm we call STORM (Synthetic Tropical cyclOne geneRation Model). STORM can be applied to any meteorological dataset to statistically resample and model TC tracks and intensities. We apply STORM to extracted TCs from 38 years of historical data from IBTrACS to statistically extend this dataset to 10,000 years of TC activity. We show that STORM preserves the TC statistics as found in the original dataset. The STORM dataset can be used for TC hazard assessments and risk modeling in TC-prone regions.
2052-4463
Bloemendaal, Nadia
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Haigh, Ivan
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de Moel, Hans
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Muis, Sanne
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Haarsma, Reindert J.
52de705c-7df6-48c6-a891-99c8a01f4b09
Aerts, Jeroen C. J. H.
5dcc3360-4ec6-4e04-8071-71993ec461a2
Bloemendaal, Nadia
8aaf62a9-9c7a-4650-ae4a-071a6f5b0ac1
Haigh, Ivan
945ff20a-589c-47b7-b06f-61804367eb2d
de Moel, Hans
c10ce4ce-4443-4f55-89c7-b59150fe611c
Muis, Sanne
d73531db-78f1-4f65-b1a0-f96ae1c46377
Haarsma, Reindert J.
52de705c-7df6-48c6-a891-99c8a01f4b09
Aerts, Jeroen C. J. H.
5dcc3360-4ec6-4e04-8071-71993ec461a2

Bloemendaal, Nadia, Haigh, Ivan, de Moel, Hans, Muis, Sanne, Haarsma, Reindert J. and Aerts, Jeroen C. J. H. (2020) Generation of a global synthetic tropical cyclone hazard dataset using STORM. Scientific Data, 7 (1), [40]. (doi:10.1038/s41597-020-0381-2).

Record type: Article

Abstract

Over the past few decades, the world has seen substantial tropical cyclone (TC) damages, with the 2017 Hurricanes Harvey, Irma and Maria entering the top-5 costliest Atlantic hurricanes ever. Calculating TC risk at a global scale, however, has proven difficult given the limited temporal and spatial information on TCs across much of the global coastline. Here, we present a novel database on TC characteristics on a global scale using a newly developed synthetic resampling algorithm we call STORM (Synthetic Tropical cyclOne geneRation Model). STORM can be applied to any meteorological dataset to statistically resample and model TC tracks and intensities. We apply STORM to extracted TCs from 38 years of historical data from IBTrACS to statistically extend this dataset to 10,000 years of TC activity. We show that STORM preserves the TC statistics as found in the original dataset. The STORM dataset can be used for TC hazard assessments and risk modeling in TC-prone regions.

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e-pub ahead of print date: 6 February 2020
Published date: 6 February 2020
Additional Information: Funding Information: We thank SURFsara (www.surf.nl) for the support in using the Lisa Computer Cluster. NB and JCJHA are funded by a VICI grant from the Netherlands Organization for Scientific Research (NWO) (Grant Number 453-13-006). IDH was funded by NERC Grant CompFlood (Grant Number NE/S003150/1). SM received funding from the research programme MOSAIC with project number ASDI.2018.036, which is financed by the Dutch Research Council (NWO). Publisher Copyright: © 2020, The Author(s).

Identifiers

Local EPrints ID: 438952
URI: http://eprints.soton.ac.uk/id/eprint/438952
ISSN: 2052-4463
PURE UUID: 1ec34afb-b48e-4c8d-97e1-576e98e1e552
ORCID for Ivan Haigh: ORCID iD orcid.org/0000-0002-9722-3061

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Date deposited: 30 Mar 2020 16:30
Last modified: 21 Sep 2022 01:40

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Contributors

Author: Nadia Bloemendaal
Author: Ivan Haigh ORCID iD
Author: Hans de Moel
Author: Sanne Muis
Author: Reindert J. Haarsma
Author: Jeroen C. J. H. Aerts

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