The present study analyses the propagation of the uncertainties through an integrated numerical modeling chain that is composed by the meteorological model COSMO, the wave/oceanographic models (SWAN and ROMS) and the coastal model XBeach. The ensemble approach was used to investigate how the uncertainty propagates from off-shore meteorological forecasts to on-shore vulnerability to coastal flood. The research focused on a storm event occurred in the winter 2015along the coasts of the Emilia-Romagna region located in the North-East of Italy. The outputs of the models were compared with the measured data collected during the storm. The research confirms that the inaccuracy of the meteorological forecasts propagates up to the coastal model, affecting the forecasts of the morphological variations. The ensemble approach seems to be the most promising methodology to quantify the overall uncertainty.

PROPAGATION OF UNCERTAINTY ACROSS THE MODELING CHAIN FOR EARLY WARNING OF COASTAL STORMS IN THE EMILIA-ROMAGNA REGION

UNGUENDOLI, SILVIA;ZANUTTIGH, BARBARA;PACCAGNELLA, TIZIANA
In corso di stampa

Abstract

The present study analyses the propagation of the uncertainties through an integrated numerical modeling chain that is composed by the meteorological model COSMO, the wave/oceanographic models (SWAN and ROMS) and the coastal model XBeach. The ensemble approach was used to investigate how the uncertainty propagates from off-shore meteorological forecasts to on-shore vulnerability to coastal flood. The research focused on a storm event occurred in the winter 2015along the coasts of the Emilia-Romagna region located in the North-East of Italy. The outputs of the models were compared with the measured data collected during the storm. The research confirms that the inaccuracy of the meteorological forecasts propagates up to the coastal model, affecting the forecasts of the morphological variations. The ensemble approach seems to be the most promising methodology to quantify the overall uncertainty.
In corso di stampa
Coastal Dynamics 2017
74
84
Unguendoli, S.; Zanuttigh, B.; Paccagnella, T.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/600824
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