Research Article Probabilistic Dressing of a Storm Surge Prediction in the Adriatic Sea R. Mel 1 and P. Lionello 2 1 Dipartimento di Ingegneria Civile, Edile ed Ambientale (DICEA), Universit` a degli Studi di Padova, Via Marzolo 9, 35131 Padova, Italy 2 Dipartimento Scienze e Tecnologie Biologiche e Ambientali (DISTEBA), Universit` a del Salento, Via Tancredi 7, 73100 Lecce, Italy Correspondence should be addressed to R. Mel; riccardo.mel@dicea.unipd.it Received 27 November 2015; Revised 12 April 2016; Accepted 8 May 2016 Academic Editor: Lars R. Hole Copyright © 2016 R. Mel and P. Lionello. is 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. 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. is 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. e 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. 1. Introduction Increasing population, tourism pressure, sea level (SL) rise, and increased storminess [1] pose a significant hazard for many coastal areas of the world. e generation of extreme coastal SLs and wind-waves produces overtopping of flood defenses and constitutes a significant threat to life and prop- erty, becoming a hazardous threat for coastal communities. Providing a fully informative forecast of storm surge and of associated flooding is of paramount importance for a wide range of problems related to coastal environments protection, receiving increasing attention from both the research and the operational communities. In fact, in the last decades, several countries have developed technologically advanced storm surges forecast systems [2, 3]. Venice Lagoon, Italy, which is the subject of the current study, is located at the shore of the Northern Adriatic Sea. Floods, which are locally called “acqua alta” (meaning “high water”), are a recurrent threat for this unique city, damaging monuments and buildings, frequently disrupting everyday life and affecting the local economy. e dynamics of floods in Venice and other islands of the lagoon have been described by several studies (e.g., [4–6]). e morphology of the Adriatic Sea, shallow in its northern part and deep in the south, and the basin’s shape, elongated, semienclosed, and surrounded by mountain chains, exposes the Venice Lagoon to intense storm surges, which are caused by cyclones moving along the North Atlantic storm track or secondary cyclones triggered by these systems in the northwestern MR [5, 7]. Because of catastrophic past episodes (1966, 1979, and 1986), increased frequency caused by mean SL rise in the past decades, and concern for future further intensification [8], a tide forecast centre was established by the Venice Municipality in 1980 (ICPSM, Istituzione Centro Previsioni e Segnalazioni Maree), which delivers the SL prediction using Hindawi Publishing Corporation Advances in Meteorology Volume 2016, Article ID 3764519, 8 pages http://dx.doi.org/10.1155/2016/3764519