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Ensemble forecasting of storm surges

By Jonathan Flowerdew, Kevin Horsburgh and Ken Mylne


The overtopping of flood defenses by coastal storm surges constitutes a significant threat to life and property. Like all forecasts, storm surge predictions have an associated uncertainty, but this is not directly predicted by current operational systems. The dominant source of this uncertainty is thought to be uncertainty in the driving atmospheric forecast of conditions at the sea surface, which can vary substantially depending on the meteorological situation. Ensemble prediction is a technique used to assess uncertainty in forecasts of complex nonlinear systems such as weather, where small errors can quickly grow to produce significantly different outcomes. It works by running not one but several forecasts, using slightly different initial conditions, boundary conditions, and/or model physics. These are chosen to sample the range of uncertainty in model inputs and formulation so that the corresponding forecasts will sample the range of possible results that are consistent with those uncertainties. The United Kingdom Met Office has recently developed the Met Office Global and Regional Ensemble Prediction System (MOGREPS), which provides 24 different predictions of meteorological evolution over a North Atlantic and European domain with a 24 km grid length. The aim of the present project is to run a barotropic storm surge prediction for each MOGREPS ensemble member, and thereby estimate the risk of damaging events given the forecast uncertainties which are sampled by the ensemble. The system forecasts 54 hours ahead and runs twice per day. In most situations, the ensemble develops rather little spread, suggesting a fairly predictable situation and a high degree of confidence in the forecast. On some occasions, however, the spread is much larger, suggesting a greater degree of uncertainty. Initial verification results are encouraging, although statistical evaluation suggests the ensemble spread is generally too smal

Topics: Marine Sciences
Publisher: Taylor and Francis
Year: 2009
DOI identifier: 10.1080/01490410902869151
OAI identifier:
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