A Hybrid Particle-Ensemble Kalman Filter for Lagrangian Data Assimilation

Lagrangian measurements from passive ocean instruments provide a useful source of data for estimating and forecasting the ocean’s state (velocity field, salinity field, etc). However, trajectories from these instruments are often highly nonlinear, leading to difficulties with widely-used data assimilation algorithms such as the ensemble Kalman filter (EnKF). Additionally, the velocity field is often modeled as a high-dimensional variable, which precludes the use of more accurate methods such as the particle filter (PF). Here, we develop a hybrid particle-ensemble Kalman filter which applies the EnKF update to the potentially high-dimensional velocity variables, and the PF update to the relatively low-dimensional, highly nonlinear drifter position variable. We test this algorithm with twin experiments on the linear shallow water equations. In experiments with infrequent observations, the hybrid filter consistently outperformed the EnKF – both by better capturing the Bayesian posterior and by better tracking the truth.