Wave Data Assimilation to Modify Wind Forcing Using an Ensemble Kalman Filter

Abstract

In order to improve the predictability of winter storm waves in the East Sea, this article explores the use of the ensemble Kalman filter technique for data assimilation in wave modeling. The nested wave model has been established using SWAN along the east coast of Korea to simulate wave transformation and wave dissipation in coastal areas to obtain a better modeling performance with regard to wind waves and swells in the East Sea. The regional atmospheric model is used to provide high-resolution forcing winds. These are adjusted by directly assimilating measurements of offshore wave heights into the wave model state. The model setup, data assimilation parameters, and validation of prediction are described with optimal conditions during the stormy periods in 2015. The ensemble Kalman filter data assimilation has shown itself to be very efficient, leading to large reductions of up to 40% in the root-mean- square error of the signification wave height compared to the results with and without data assimilation at locations other than those of the observations used. It shows that the wave modeling with ensemble Kalman filter data assimilation is very feasible to predict coastal waves, in particular storm events in the East Sea. kw]Keywords -winter storm waves, wave modeling, data assimilation, wind forcing, East Sea