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dc.contributor.authorSteptoe, H
dc.contributor.authorEconomou, T
dc.date.accessioned2021-04-30T12:10:34Z
dc.date.issued2021-04-29
dc.description.abstractWe use high-resolution (4.4 km) numerical simulations of tropical cyclones to produce exceedance probability estimates for extreme wind (gust) speeds over Bangladesh. For the first time, we estimate equivalent return periods up to and including a 1-in-200 year event, in a spatially coherent manner over all of Bangladesh, by using generalised additive models. We show that some northern provinces, up to 200 km inland, may experience conditions equal to or exceeding a very severe cyclonic storm event (maximum wind speeds in ≥64 kn) with a likelihood equal to coastal regions less than 50 km inland. For the most severe super cyclonic storm events (≥120 kn), event exceedance probabilities of 1-in-100 to 1-in-200 events remain limited to the coastlines of southern provinces only. We demonstrate how the Bayesian interpretation of the generalised additive model can facilitate a transparent decision-making framework for tropical cyclone warnings.en_GB
dc.description.sponsorshipInternational Climate Initiative (IKI)en_GB
dc.identifier.citationVol. 21, pp. 1313 - 1322en_GB
dc.identifier.doi10.5194/nhess-21-1313-2021
dc.identifier.urihttp://hdl.handle.net/10871/125519
dc.language.isoenen_GB
dc.publisherCopernicus Publications / European Geosciences Unionen_GB
dc.relation.urlhttps://doi.org/10.5281/zenodo.3953772
dc.relation.urlhttps://doi.org/10.5281/zenodo.3600201
dc.rights© Author(s) 2021. Open access. This work is distributed under the Creative Commons Attribution 4.0 License.en_GB
dc.titleExtreme wind return periods from tropical cyclones in Bangladesh: insights from a high-resolution convection-permitting numerical modelen_GB
dc.typeArticleen_GB
dc.date.available2021-04-30T12:10:34Z
dc.identifier.issn1561-8633
dc.descriptionThis is the final version. Available from Copernicus Publications via the DOI in this record.en_GB
dc.descriptionData availability: The data used in this study are available at https://doi.org/10.5281/zenodo.3600201 (Steptoe et al., 2020) and released under CC-BY 4.0en_GB
dc.descriptionCode availability: Python, R and data analysis code, including the fitted GAM model, is available at https://doi.org/10.5281/zenodo.3953772 (Steptoe, 2020)en_GB
dc.identifier.eissn1684-9981
dc.identifier.journalNatural Hazards and Earth System Sciencesen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2021-03-28
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2021-04-29
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-04-30T12:03:41Z
refterms.versionFCDVoR
refterms.dateFOA2021-04-30T12:11:17Z
refterms.panelBen_GB


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© Author(s) 2021. Open access. This work is distributed under the Creative Commons Attribution 4.0 License.
Except where otherwise noted, this item's licence is described as © Author(s) 2021. Open access. This work is distributed under the Creative Commons Attribution 4.0 License.