Data assimilation of ocean surface waves using Sentinel-1 SAR during typhoon Malakas

Abstract In this study, a data assimilation system is constructed in a third generation ocean surface wave model, MASNUM-WAM, to improve wave simulations. The data assimilation system uses Ensemble Adjustment Kalman Filter (EAKF) method, which is based on dynamic sampling. Difference between 24 h-interval wave parameter fields during the period 7-day before and after assimilation time, is used to construct dynamic ensemble, which is an approximation to background error. Eight experiments are carried out during typhoon Malakas to investigate the impact of different assimilating wave parameters to the simulation errors of significant wave height (SWH). Wave spectrum observations from satellite Sentinel-1 SAR are used for data assimilation. SWH, peak wave period, mean wave direction and wave spectrum are adjusted simultaneously when an observation is available. Results show that the data assimilation system improves the simulation of SWH during typhoon Malakas.