Displacement data assimilation

W. Steven Rosenthal; Shankar Venkataramani; Arthur J. Mariano; Juan M. Restrepo

doi:10.1016/j.jcp.2016.10.025

Displacement data assimilation

W. Steven Rosenthal, Shankar Venkataramani, Arthur J. Mariano, Juan M. Restrepo

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

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.

Original language	English
Pages (from-to)	594-614
Number of pages	21
Journal	Journal of Computational Physics
Volume	330
DOIs	https://doi.org/10.1016/j.jcp.2016.10.025
State	Published - Feb 1 2017
Externally published	Yes

Funding

This research was made possible by a grant from The Gulf of Mexico Research Initiative, and by NSF DMS grant 0304890, and NSF OCE grant 1434198. Some of the research on this project was undertaken when JMR was a participant in Stockholm University's Rossby Faculty Fellowship program.

Funders	Funder number
NSF DMS	0304890
National Science Foundation
Directorate for Geosciences	1434198
Gulf of Mexico Research Initiative
Stockholms Universitet

Keywords

Data assimilation
Displacement assimilation
Ensemble Kalman Filter
Uncertainty quantification
Vortex dynamics

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10.1016/j.jcp.2016.10.025

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AU - Mariano, Arthur J.

AU - Restrepo, Juan M.

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AB - 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.

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KW - Ensemble Kalman Filter

KW - Uncertainty quantification

KW - Vortex dynamics

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Displacement data assimilation

Abstract

Funding

Keywords

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