Storm Surge Ensemble Prediction System for Lagoons and Transitional Environments

Abstract

Abstract We developed a storm surge ensemble prediction system (EPS) for lagoons and transitional environments. Lagoons are often threatened by storm surge events with consequent risks for human life and economic losses. The uncertainties connected with a classic deterministic forecast are many, thus, an ensemble forecast system is required to properly consider them and inform the end-user community accordingly. The technological resources now available allow us to investigate the possibility of operational ensemble forecasting systems that will become increasingly essential for coastal management. We show the advantages and limitations of an EPS applied to a lagoon, using a very high-resolution unstructured grid finite element model and 45 EPS members. For five recent storm surge events, the EPS generally improves the forecast skill on the third forecast day compared to just one deterministic forecast, while they are similar in the first two days. A weighting system is implemented to compute an improved ensemble mean. The uncertainties regarding sea level due to meteorological forcing, river runoff, initial boundaries, and lateral boundaries are evaluated for a special case in the northern Adriatic Sea, and the different forecasts are used to compose the EPS members. We conclude that the largest uncertainty is in the initial and lateral boundary fields at different time and space scales, including the tidal components. Significance Statement Storm surges are extreme sea level events that may threaten densely populated coastal areas. The purpose of this work is to improve the extreme sea level forecast for transitional areas with the understanding of what are the most important forcing generating uncertainties and find a technique to reach a reliable sea level forecast. This is achieved by implementing an ensemble prediction system running 45 members for each event considered. Results show that initial and lateral boundary conditions provide most of the uncertainty, including the tidal components. The weighting system applied to find the ensemble mean improves the forecast skill on the third forecast day while it is comparable with the deterministic forecast in the first two days.