Effect of Determining Initial Conditions by Four-Dimensional Variational Data Assimilation on Storm Surge Forecasting

A tangent linear model and an adjoint model of the three-dimensional, time-dependent, nonlinear Princeton Ocean Model (POM) were developed to construct a 4-dimensional variational data assimilation (4D-Var) system for coastal oceanic data assimilation. To verify and evaluate the performance of this 4D-Var system, data assimilation experiments were conducted for a storm surge case using model generated “pseudo-observations”. The pseudo-observations were generated by a nested-grid high-resolution numerical model which is coupled to a wetting and drying model that is not included in the original POM.

The 4D-Var system was tested thoroughly for code accuracy. The assimilation cycles led to effective convergence between the forecast and the “observations”. Assimilating water level alone or together with surface currents both led to significant improvements in storm surge forecasts within and several hours beyond the data assimilation window. It is worth noting that, assimilating water level alone produced comparable improvements in storm surge forecasts as those by assimilating both water level and currents, suggesting that optimizations of water level and currents were linked through the 4D-Var assimilation cycles. However, it is also worth noting that, the benefit resulting from the reduction of initial error in water level and/or surface currents through data assimilation decreased rapidly in time outside the assimilation window. This suggests that determining initial conditions of water level and/or surface currents via data assimilation is only effective within and a few hours beyond the assimilation window for storm surge forecasting. Thus, alternative data assimilation approaches are needed to improve the accuracy and lead time in operational storm surge forecasting.