Combining hybrid and one-step-ahead smoothing for efficient short-range storm surge forecasting with an ensemble kalman filter

Abstract

This work combines two auxiliary techniques, namely the one-step-ahead (OSA) smoothing and the hybridformulation, to boost the forecasting skills of a storm surge ensemble Kalman filter (EnKF) forecastingsystem. Bayesian filtering with OSA-smoothing enhances the robustness of the ensemble background statistics by exploiting the data twice: first to constrain the sampling of the forecast ensemble with the futureobservation, and then to update the resulting ensemble. This is expected to improve the behavior of EnKFlike schemes during the strongly nonlinear surges periods, but requires integrating the ensemble with theforecast model twice, which could be computationally demanding. The hybrid flow-dependent/static formulation of the EnKF background error covariance is then considered to enable the implementation of thefilter with a small flow-dependent ensemble size, and thus less model runs. These two methods are combinedwithin an ensemble transform Kalman filter (ETKF). The resulting hybrid ETKF with OSA smoothing istested, based on twin experiments, using a realistic setting of the Advanced Circulation (ADCIRC) modelconfigured for storm surge forecasting in the Gulf of Mexico and assimilating pseudo-observations of seasurface levels from a network of buoys. The results of our numerical experiments suggest that the proposedfiltering system significantly enhances ADCIRC forecasting skills compared to the standard ETKF withoutincreasing the computational cost.