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A Hybrid Particle–Ensemble Kalman Filter for Lagrangian Data Assimilation
2015
Monthly Weather Review
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
doi:10.1175/mwr-d-14-00051.1
fatcat:nh5fdijzgfem3bp53rrargb4we