Effect of determining initial conditions by four-dimensional variational data assimilation on storm surge forecasting

Abstract

A tangent linear model and an adjoint model of the three-dimensional, time-dependent, nonlinear Princeton Ocean Model (POM) are developed to construct a four-dimensional variational data assimilation (4D-Var) algorithm for coastal ocean prediction. To verify and evaluate the performance of this 4D-Var method, a suite of numerical experiments are conducted for a storm surge case using model-generated “pseudo-observations”. The pseudo-observations are generated by a nested-grid high-resolution numerical model which is coupled to an inundation/drying scheme that is not included in the original POM. The 4D-Var algorithm based on POM is tested thoroughly for both code accuracy and the potential application in storm surge forecasting. The assimilation cycles lead to effective convergence between the forecasts and the “observations”. Assimilating water level alone or together with surface currents both lead 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 produces improvements in storm surge forecasts that are comparable to those by assimilating both water level and surface currents, suggesting that optimizations of water level and surface currents are 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 decreases 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.