Bathymetry estimation using ensemble adjustment Kalman filter in the numerical simulation of M2 tide

Data assimilation can estimate the uncertain parameters in the numerical model while adjusting the state variables with observations to improve the simulation results by enhancing the numerical model. Based on the Ensemble Adjustment Kalman Filter (EAKF) and the external mode of the Princeton Ocean model with the generalized coordinate system (POMgcs), a bathymetry estimate is performed in the M₂ tide simulation of the Bohai Sea and part of the Yellow Sea. In the practical data assimilation experiment of the NAO.99Jb and tide gauge data, by comparing with the 34 tide gauges, the model simulated amplitude and phase lag errors of M₂ tide are reduced by 40.18% and 49.19%, respectively, by the use of the posterior estimate of the bathymetry.