A Hybrid Particle–Ensemble Kalman Filter for Lagrangian Data Assimilation
Abstract
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...
Paper Details
Title
A Hybrid Particle–Ensemble Kalman Filter for Lagrangian Data Assimilation
Published Date
Jan 1, 2015
Journal
Volume
143
Issue
1
Pages
195 - 211
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