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dc.contributor.authorCagigal Gil, Laura 
dc.contributor.authorMéndez Incera, Fernando Javier 
dc.contributor.authorOrtega Van Vloten, Sara
dc.contributor.authorRueda Zamora, Ana Cristina 
dc.contributor.authorCoco, Giovanni
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2023-01-19T14:31:14Z
dc.date.available2023-01-19T14:31:14Z
dc.date.issued2023-01
dc.identifier.issn0899-8418
dc.identifier.issn1097-0088
dc.identifier.otherPID2019-107053RB-I00es_ES
dc.identifier.urihttps://hdl.handle.net/10902/27320
dc.description.abstractABSTRACT: Tropical cyclones are associated with extreme winds, waves, and storm surge, being among most destructive natural phenomena. Developing capability for a rapid impact estimate is crucial for coastal applications and risk preparedness. When predicting waves characteristics associated to tropical cyclones, the traditional approach involves a two-step procedure (a) a Holland-type wind vortex model and (b) numerical simulations using a wave generation model, using buoy and satellite measurements for validation. In this work, we take advantage of the increasing amount of remote sensing observational data and propose a new empirical model to estimate the wind wave footprint of tropical cyclones. For this purpose, we construct a dataset with over a million satellite observations of waves triggered by tropical cyclones assuming a circular shape of the TC influence area and defining composites of significant wave height as a function of representative parameters of the track characteristics like the minimum pressure, its forward velocity, and its latitude. The validation against buoy data confirms the usefulness of the model for a first and rapid estimation of the wave footprint, although an underestimation of the most extreme events is observed due to the relatively small number of observations recorded. Due to its efficiency, the model can be applied for rapid estimations of wave footprints in operational systems, reconstruction of historical or synthetic events and risk assessments.es_ES
dc.description.sponsorshipThis work would not have been possible without funding from the Strategic Environmental Research and Development Program's grant DOD/SERDP RC-2644 and from the Spanish Ministry of Science and Innovation, project Beach4cast PID2019-107053RB-I00. Ana Rueda funded by a Juan de la Cierva Incorporación Scholarship (IJC2020-04390). Laura Cagigal is funded by a scholarship from the University of Auckland. Open access publishing facilitated by The University of Auckland, as part of the Wiley - The University of Auckland agreement via the Council of Australian University Librarians.es_ES
dc.format.extent10 p.es_ES
dc.language.isoenges_ES
dc.publisherJohn Wiley and Sons Ltdes_ES
dc.rights© 2022 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Societyes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceInternational Journal of Climatology, 43, 1, 372-381es_ES
dc.subject.otherClustering techniqueses_ES
dc.subject.otherSatellite dataes_ES
dc.subject.otherSelf-organizing mapses_ES
dc.subject.otherTropical cycloneses_ES
dc.subject.otherWave footprintes_ES
dc.subject.otherWind waveses_ES
dc.titleWind wave footprint of tropical cyclones from satellite dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1002/joc.7764es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1002/joc.7764
dc.type.versionpublishedVersiones_ES


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© 2022 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological SocietyExcepto si se señala otra cosa, la licencia del ítem se describe como © 2022 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society