Generation of a global synthetic tropical cyclone hazard dataset using STORM.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
06 02 2020
06 02 2020
Historique:
received:
27
03
2019
accepted:
23
01
2020
entrez:
8
2
2020
pubmed:
8
2
2020
medline:
8
2
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32029746
doi: 10.1038/s41597-020-0381-2
pii: 10.1038/s41597-020-0381-2
pmc: PMC7005259
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
40Subventions
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)
ID : 453-13-006
Pays : International
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)
ID : ASDI.2018.036
Pays : International
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