@article{10902/32395,
year = {2024},
month = {4},
url = {https://hdl.handle.net/10902/32395},
abstract = {Waves produced by tropical cyclones (TCs) can be estimated using non-stationary wave models forced with timevarying wind fields. However, dynamical simulations are time and computationally demanding at regional-scale domains since high temporal and spatial resolutions are required to correctly simulate TC-induced wave propagation processes. Applications such as early warning systems, coastal risk assessments and future climate projections benefit from fast and accurate estimates of wave fields induced by close-to-real storm tracks geometry. The proposed SHyTCWaves methodology constitutes a novel tool capable of estimating the spatiotemporal variability of directional wave spectra produced by TCs in deep waters, using a hybrid approach and statistical techniques to reduce CPU time effort. This work demonstrates that TC-induced waves can be reconstructed using a stop-motion approach based on the addition of successive 6 h periods of time-varying storm conditions. The developed hybrid model reduces a TC track to a number of segments that are parameterized in terms of 10 representative TC features, and generates a library of cases dynamically pre-computed which allow to ensemble consecutive 6 h analog segments representing the original TC track. The metamodel has been compared and corrected with available satellite data, and its applicability is exemplified for TC Ofa in the South Pacific},
organization = {The authors would like to acknowledge the funding from the projects CE4Wind (CPP2022-010118 MCIN/AEI/10.13039/501100011033 and European Union - Next GenerationEU/PRTR), Perfect-Storm (2023/ TCN/003 - Goverment of Cantabria/FEDER, UE) and MyFlood (PLEC2022-009362 - MCIN/AEI/10.13039/501100011033 and Euro pean Union - Next GenerationEU/PRTR). Ron Hoeke’s contributions were supported by the CSIRO’s Research Office and in-kind contribu tions from CSIRO Environment. Laura Cagigal acknowledges the fund ing from the Juan de la Cierva – Formacion ´ FJC2021-046933-I/MCIN/ AEI/10.13039/501100011033 and the European Union “NextGener ationEU”/PRTR.},
publisher = {Elsevier Ltd},
publisher = {Ocean Modelling, 2024, 188, 102341},
title = {SHyTCWaves: A stop-motion hybrid model to predict tropical cyclone induced waves},
author = {Ortega Van Vloten, Sara and Cagigal Gil, Laura and Pérez Díaz, Beatriz and Hoeke, Ron and Méndez Incera, Fernando Javier},
}