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Displacement data assimilation
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DOI：10.1016/j.jcp.2016.10.025.png)
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Abstract
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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. (C) 2016 Elsevier Inc. All rights reserved.
Keywords:
Displacement assimilation
Data assimilation
Uncertainty quantification
Ensemble Kalman Filter
Vortex dynamics
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