Extremewave analysis by integrating model and wave buoy data

Estimating the extreme values of significant wave height (HS), generally described by the HS return period TR function HS(TR) and by its confidence intervals, is a necessity in many branches of coastal science and engineering. The availability of indirect wave data generated by global and regional w...

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Published in:Water
Main Authors: Dentale F., Furcolo P., Carratelli E. P., Reale F., Contestabile P., Tomasicchio G. R.
Other Authors: Dentale, F., Furcolo, P., Carratelli, E. P., Reale, F., Contestabile, P., Tomasicchio, G. R.
Format: Article in Journal/Newspaper
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/11591/412387
https://doi.org/10.3390/w10040373
http://www.mdpi.com/2073-4441/10/4/373/pdf
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spelling ftuncampaniairis:oai:iris.unicampania.it:11591/412387 2024-04-14T08:15:52+00:00 Extremewave analysis by integrating model and wave buoy data Dentale F. Furcolo P. Carratelli E. P. Reale F. Contestabile P. Tomasicchio G. R. Dentale, F. Furcolo, P. Carratelli, E. P. Reale, F. Contestabile, P. Tomasicchio, G. R. 2018 http://hdl.handle.net/11591/412387 https://doi.org/10.3390/w10040373 http://www.mdpi.com/2073-4441/10/4/373/pdf eng eng info:eu-repo/semantics/altIdentifier/wos/WOS:000434954900027 volume:10 issue:4 firstpage:373 journal:WATER http://hdl.handle.net/11591/412387 doi:10.3390/w10040373 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85044362596 http://www.mdpi.com/2073-4441/10/4/373/pdf Gulf of Mexico Mediterranean Sea North Atlantic Spanish coast Small scale storm variation Wave extreme event Wave modeling info:eu-repo/semantics/article 2018 ftuncampaniairis https://doi.org/10.3390/w10040373 2024-03-21T16:14:41Z Estimating the extreme values of significant wave height (HS), generally described by the HS return period TR function HS(TR) and by its confidence intervals, is a necessity in many branches of coastal science and engineering. The availability of indirect wave data generated by global and regional wind and wave model chains have brought radical changes to the estimation procedures of such probability distribution-weather and wave modeling systems are routinely run all over the world, and HS time series for each grid point are produced and published after assimilation (analysis) of the ground truth. However, while the sources of such indirect data are numerous, and generally of good quality, many aspects of their procedures are hidden to the users, who cannot evaluate the reliability and the limits of the HS(TR) deriving from such data. In order to provide a simple engineering tool to evaluate the probability of extreme sea-states as well as the quality of such estimates, we propose here a procedure based on integrating HS time series generated by model chains with those recorded by wave buoys in the same area. Article in Journal/Newspaper North Atlantic Università degli Studi della Campania "Luigi Vanvitelli": CINECA IRIS V: Water 10 4 373
institution Open Polar
collection Università degli Studi della Campania "Luigi Vanvitelli": CINECA IRIS V:
op_collection_id ftuncampaniairis
language English
topic Gulf of Mexico
Mediterranean Sea
North Atlantic Spanish coast
Small scale storm variation
Wave extreme event
Wave modeling
spellingShingle Gulf of Mexico
Mediterranean Sea
North Atlantic Spanish coast
Small scale storm variation
Wave extreme event
Wave modeling
Dentale F.
Furcolo P.
Carratelli E. P.
Reale F.
Contestabile P.
Tomasicchio G. R.
Extremewave analysis by integrating model and wave buoy data
topic_facet Gulf of Mexico
Mediterranean Sea
North Atlantic Spanish coast
Small scale storm variation
Wave extreme event
Wave modeling
description Estimating the extreme values of significant wave height (HS), generally described by the HS return period TR function HS(TR) and by its confidence intervals, is a necessity in many branches of coastal science and engineering. The availability of indirect wave data generated by global and regional wind and wave model chains have brought radical changes to the estimation procedures of such probability distribution-weather and wave modeling systems are routinely run all over the world, and HS time series for each grid point are produced and published after assimilation (analysis) of the ground truth. However, while the sources of such indirect data are numerous, and generally of good quality, many aspects of their procedures are hidden to the users, who cannot evaluate the reliability and the limits of the HS(TR) deriving from such data. In order to provide a simple engineering tool to evaluate the probability of extreme sea-states as well as the quality of such estimates, we propose here a procedure based on integrating HS time series generated by model chains with those recorded by wave buoys in the same area.
author2 Dentale, F.
Furcolo, P.
Carratelli, E. P.
Reale, F.
Contestabile, P.
Tomasicchio, G. R.
format Article in Journal/Newspaper
author Dentale F.
Furcolo P.
Carratelli E. P.
Reale F.
Contestabile P.
Tomasicchio G. R.
author_facet Dentale F.
Furcolo P.
Carratelli E. P.
Reale F.
Contestabile P.
Tomasicchio G. R.
author_sort Dentale F.
title Extremewave analysis by integrating model and wave buoy data
title_short Extremewave analysis by integrating model and wave buoy data
title_full Extremewave analysis by integrating model and wave buoy data
title_fullStr Extremewave analysis by integrating model and wave buoy data
title_full_unstemmed Extremewave analysis by integrating model and wave buoy data
title_sort extremewave analysis by integrating model and wave buoy data
publishDate 2018
url http://hdl.handle.net/11591/412387
https://doi.org/10.3390/w10040373
http://www.mdpi.com/2073-4441/10/4/373/pdf
genre North Atlantic
genre_facet North Atlantic
op_relation info:eu-repo/semantics/altIdentifier/wos/WOS:000434954900027
volume:10
issue:4
firstpage:373
journal:WATER
http://hdl.handle.net/11591/412387
doi:10.3390/w10040373
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85044362596
http://www.mdpi.com/2073-4441/10/4/373/pdf
op_doi https://doi.org/10.3390/w10040373
container_title Water
container_volume 10
container_issue 4
container_start_page 373
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