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Advances in Meteorology
Volume 2016 (2016), Article ID 3764519, 8 pages
http://dx.doi.org/10.1155/2016/3764519
Research Article

Probabilistic Dressing of a Storm Surge Prediction in the Adriatic Sea

1Dipartimento di Ingegneria Civile, Edile ed Ambientale (DICEA), Università degli Studi di Padova, Via Marzolo 9, 35131 Padova, Italy
2Dipartimento Scienze e Tecnologie Biologiche e Ambientali (DISTEBA), Università del Salento, Via Tancredi 7, 73100 Lecce, Italy

Received 27 November 2015; Revised 12 April 2016; Accepted 8 May 2016

Academic Editor: Lars R. Hole

Copyright © 2016 R. Mel and P. Lionello. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Providing a reliable, accurate, and fully informative storm surge forecast is of paramount importance for managing the hazards threatening coastal environments. Specifically, a reliable probabilistic forecast is crucial for the management of the movable barriers that are planned to become operational in 2018 for the protection of Venice and its lagoon. However, a probabilistic forecast requires multiple simulations and a considerable computational time, which makes it expensive in real-time applications. This paper describes the ensemble dressing method, a cheap operational flood prediction system that includes information about the uncertainty of the ensemble members by computing it directly from the meteorological input and the local spread distribution, without requiring multiple forecasts. Here, a sophisticated error distribution form is developed, which includes the superposition of the uncertainty caused by inaccuracies of the ensemble prediction system, which depends on surge level and lead time, and the uncertainty of the meteorological forcing, which is described using a combination of cross-basin pressure gradients. The ensemble dressing is validated over a 3-month-long period in the year 2010, during which an exceptional sequence of storm surges occurred. Results demonstrate that this computationally cheap method can provide an acceptably realistic estimate of the uncertainty.