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A hybrid particle-ensemble kalman filter for lagrangian data assimilation (2014).
| Content Provider | CiteSeerX |
|---|---|
| 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 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 these experiments, the hybrid filter consistently outperformed the EnKF – both by better capturing the Bayesian posterior and by better tracking the truth. 1 |
| File Format | |
| Publisher Date | 2014-01-01 |
| Access Restriction | Open |
| Subject Keyword | Hybrid Particle-ensemble Kalman Filter Lagrangian Data Assimilation Velocity Field Hybrid Filter Ensemble Kalman Filter Passive Ocean Instrument Salinity Field Linear Shallow Water Equation Useful Source Nonlinear Drifter Position Pf Update High-dimensional Velocity Variable Enkf Update High-dimensional Variable Lagrangian Measurement Bayesian Posterior Accurate Method Widely-used Data Assimilation Algorithm Twin Experiment Particle Filter Ocean State |
| Content Type | Text |
