@article{Rosenthal_Displacement_2017,
	abstract = {Abstract 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. },
	author = {W. Steven Rosenthal and Shankar Venkataramani and Arthur J. Mariano and Juan M. Restrepo},
	date-added = {2017-01-29 16:07:29 +0000},
	date-modified = {2017-01-29 20:11:00 +0000},
	doi = {http://dx.doi.org/10.1016/j.jcp.2016.10.025},
	issn = {0021-9991},
	journal = {Journal of Computational Physics},
	keywords = {Displacement assimilation; Data assimilation; Uncertainty quantification; Ensemble Kalman Filter; Vortex dynamics; pubs},
	pages = {594 - 614},
	title = {Displacement data assimilation},
	url_arxiv = {https://arxiv.org/abs/1602.02209},
	url_journal = {http://www.sciencedirect.com/science/article/pii/S002199911630523X},
	volume = {330},
	year = {2017},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.jcp.2016.10.025},
	Bdsk-Url-2 = {http://www.sciencedirect.com/science/article/pii/S002199911630523X}}

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