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Displacement data assimilation. (English) Zbl 1378.93127

Summary: We show that modifying a Bayesian data assimilation scheme by incorporating kinematically-consistent displacement corrections produces a scheme that is demonstrably better at estimating partially observed state vectors in a setting where feature information is important. While the displacement transformation is generic, here we implement it within an ensemble Kalman filter framework and demonstrate its effectiveness in tracking stochastically perturbed vortices.

MSC:

93E11 Filtering in stochastic control theory
93E14 Data smoothing in stochastic control theory
76B47 Vortex flows for incompressible inviscid fluids
35R60 PDEs with randomness, stochastic partial differential equations

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References:

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