Displacement data assimilation
Copyright policy )Displacement data assimilation
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 important. While the displacement transformation is not tied to any particular assimilation scheme, here we implement it within an ensemble Kalman Filter and demonstrate its effectiveness in tracking stochastically perturbed vortices.
26 Pages, 9 figures, 5 tables
- University of Miami United States
- Oregon State University United States
- Oregon State University United States
- Miami University United States
- University System of Ohio United States
93E11, 93B40, 76B47, 37N10, uncertainty quantification, Data smoothing in stochastic control theory, FOS: Physical sciences, Vortex flows for incompressible inviscid fluids, vortex dynamics, displacement assimilation, ensemble Kalman filter, Filtering in stochastic control theory, Physics - Atmospheric and Oceanic Physics, Physics - Data Analysis, Statistics and Probability, Atmospheric and Oceanic Physics (physics.ao-ph), PDEs with randomness, stochastic partial differential equations, data assimilation, Data Analysis, Statistics and Probability (physics.data-an)
93E11, 93B40, 76B47, 37N10, uncertainty quantification, Data smoothing in stochastic control theory, FOS: Physical sciences, Vortex flows for incompressible inviscid fluids, vortex dynamics, displacement assimilation, ensemble Kalman filter, Filtering in stochastic control theory, Physics - Atmospheric and Oceanic Physics, Physics - Data Analysis, Statistics and Probability, Atmospheric and Oceanic Physics (physics.ao-ph), PDEs with randomness, stochastic partial differential equations, data assimilation, Data Analysis, Statistics and Probability (physics.data-an)
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