Wind wave footprint of tropical cyclones from satellite data
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Identificadores
URI: https://hdl.handle.net/10902/27320DOI: 10.1002/joc.7764
ISSN: 0899-8418
ISSN: 1097-0088
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Cagigal Gil, Laura


Fecha
2023-01Derechos
© 2022 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society
Publicado en
International Journal of Climatology, 43, 1, 372-381
Editorial
John Wiley and Sons Ltd
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Palabras clave
Clustering techniques
Satellite data
Self-organizing maps
Tropical cyclones
Wave footprint
Wind waves
Resumen/Abstract
ABSTRACT: 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.
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