dc.contributor.author | Ricondo Cueva, Alba | |
dc.contributor.author | Cagigal Gil, Laura | |
dc.contributor.author | Rueda Zamora, Ana Cristina | |
dc.contributor.author | Hoeke, Ron | |
dc.contributor.author | Storlazzi, Curt D. | |
dc.contributor.author | Méndez Incera, Fernando Javier | |
dc.contributor.other | Universidad de Cantabria | es_ES |
dc.date.accessioned | 2023-09-27T17:33:43Z | |
dc.date.available | 2023-09-27T17:33:43Z | |
dc.date.issued | 2023-08 | |
dc.identifier.issn | 1463-5003 | |
dc.identifier.issn | 1463-5011 | |
dc.identifier.other | PID2019-107053RB-I00 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10902/30018 | |
dc.description.abstract | Long-term and accurate wave hindcast databases are often required in different coastal engineering projects.
The assessment of the nearshore wave climate is often accomplished by using downscaling techniques to
translate offshore waves to coastal areas. However, dynamical downscaling approaches may incur huge
computational cost. Additionally, the common use of bulk parameterizations are often not accurate for
multidimensional waves. To overcome these limitations, we present a hybrid downscaling approach that
combines mathematical algorithms (statistical downscaling) and numerical modeling (dynamical downscaling)
over the individual spectral partitions. Every wave partition is downscaled and aggregated afterward by using
principles of wave linear theory. By assuming linearity in the propagation of the wave celerity, the application
of the method is limited from offshore to intermediate water depths. In addition, the method proposed uses
a technique to simplify the spectral boundary conditions in complex domains. The methodology has been
applied and validated in the island states of Samoa, American Samoa, Majuro, and Kwajalein, showing good
skill at reproducing the spectral hourly time series of significant wave height, peak period, and peak direction.
Moreover, an accurate representation of the observed energy spectrum was achieved. This study provides
insight into the numerical approximation of the combined sea-swell states while improving the quality of fast
spectral forecasting and early warning systems. | es_ES |
dc.description.sponsorship | This work would not have been possible without funding from the Spanish Ministry of Science and Innovation, project Beach4cast PID2019-107053RB-I00. The authors would like to acknowledge CSIRO, for making the spectral hindcast data publicly available, PacIOOS (www.pacioos.org), part of the U.S. Integrated Ocean Observing System (IOOS®), for providing the Kalo and Aunu’u wave buoy measurements, the U.S. Geological Survey (www.sciencebase.gov) for providing the Kwajalein and Roi-Namur field observations, and Oceanor-SOPAC (today Fugro-SPC) for the Apolima buoy data. LC acknowledges the funding from the Juan de la Cierva – Formación FJC2021-046933-I/ MCIN/ AEI/ 10.13039/501100011033 and the European Union ‘‘NextGenerationEU’’/ PRTR. ARu acknowledges the funding from the Juan de la Cierva-Incorporación IJC2020-043907-I/ MCIN/AEI/ 10.13039/5011 00011033 and the European Union ‘‘NextGenerationEU’’/PRTR. ARi
is funded by a Concepción Arenal studentship from the Universidad de Cantabria. We thank Kai Parker for conducting a USGS internal review of this manuscript. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. I understand that any modifications made to a published article require careful consideration and adherence to the journal’s policies. Therefore, I kindly request your guidance on the appropriate procedure to follow for making this minor modification. I am more than willing to provide any necessary documentation or clarification to support this request. | es_ES |
dc.format.extent | 11 p. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier Ltd | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source | Ocean Modelling, 2023, 184, 102210 | es_ES |
dc.subject.other | Hybrid downscaling | es_ES |
dc.subject.other | Data mining | es_ES |
dc.subject.other | Multimodal wave climate | es_ES |
dc.subject.other | Spectral partitioning | es_ES |
dc.subject.other | Directional wave spectra | es_ES |
dc.title | HyWaves: Hybrid downscaling of multimodal wave spectra to nearshore areas | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherVersion | https://doi.org/10.1016/j.ocemod.2023.102210 | es_ES |
dc.rights.accessRights | openAccess | es_ES |
dc.identifier.DOI | 10.1016/j.ocemod.2023.102210 | |
dc.type.version | publishedVersion | es_ES |