Among the most detrimental natural phenomena, storm surges heavily endanger the environment, the economy and the everyday life of sea-side countries and coastal zones. Considering that 120.000.000 people live in the Mediterranean area, with additional 200.000.000 presences in Summer for tourism purposes, the correct prediction of storm surges is crucial to avoid fatalities and economic losses. Earth Observation (EO) can play an important role in operational storm surge forecasting, yet it is not widely diffused in the storm surge community. In 2011 the European Space Agency (ESA), through its Data User Element (DUE) programme, financed two projects aimed at encouraging the uptake of EO data in this sector: eSurge and eSurge-Venice (eSV). The former was intended to address the issues of a wider users' community, while the latter was focused on a restricted geographical area: the northern Adriatic Sea and the Gulf of Venice. Among the objectives of the two projects there were a number of storm surge hindcast experiments using satellite data, to demonstrate the improvements on the surge forecast brought by EO. We report here the results of the hindcast experiments of the eSV project. They were aimed to test the sensitivity of a storm surge model to a forcing wind field modified with scatterometer data in order to reduce the bias between simulated and observed winds. Hindcast experiments were also performed to test the response of the storm surge model to the assimilation, with a dual 4D-Var system, of satellite altimetry observations as model errors of the initial state of the sea surface level. Remarkable improvements on the storm surge forecast have been obtained for what concerns the modified model wind forcing. Encouraging results have been obtained also in the assimilation experiments.

Improvements of storm surge modelling in the gulf of venice with satellite data: The ESA due esurge-venice project

De Biasio F.
Primo
;
Bajo M.
Secondo
;
Vignudelli S.;Della Valle A.;Umgiesser G.;Zecchetto S.
2016

Abstract

Among the most detrimental natural phenomena, storm surges heavily endanger the environment, the economy and the everyday life of sea-side countries and coastal zones. Considering that 120.000.000 people live in the Mediterranean area, with additional 200.000.000 presences in Summer for tourism purposes, the correct prediction of storm surges is crucial to avoid fatalities and economic losses. Earth Observation (EO) can play an important role in operational storm surge forecasting, yet it is not widely diffused in the storm surge community. In 2011 the European Space Agency (ESA), through its Data User Element (DUE) programme, financed two projects aimed at encouraging the uptake of EO data in this sector: eSurge and eSurge-Venice (eSV). The former was intended to address the issues of a wider users' community, while the latter was focused on a restricted geographical area: the northern Adriatic Sea and the Gulf of Venice. Among the objectives of the two projects there were a number of storm surge hindcast experiments using satellite data, to demonstrate the improvements on the surge forecast brought by EO. We report here the results of the hindcast experiments of the eSV project. They were aimed to test the sensitivity of a storm surge model to a forcing wind field modified with scatterometer data in order to reduce the bias between simulated and observed winds. Hindcast experiments were also performed to test the response of the storm surge model to the assimilation, with a dual 4D-Var system, of satellite altimetry observations as model errors of the initial state of the sea surface level. Remarkable improvements on the storm surge forecast have been obtained for what concerns the modified model wind forcing. Encouraging results have been obtained also in the assimilation experiments.
2016
Istituto di Scienze dell'Atmosfera e del Clima - ISAC
Istituto di Scienze Marine - ISMAR
Istituto di Biofisica - IBF
Venice
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/519076
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