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 (US)
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

Keywords

Data assimilation
Displacement assimilation
Ensemble Kalman Filter
Uncertainty quantification
Vortex dynamics

ASJC Scopus subject areas

Numerical Analysis
Modeling and Simulation
Physics and Astronomy (miscellaneous)
General Physics and Astronomy
Computer Science Applications
Computational Mathematics
Applied Mathematics

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10.1016/j.jcp.2016.10.025

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title = "Displacement data assimilation",

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.",

keywords = "Data assimilation, Displacement assimilation, Ensemble Kalman Filter, Uncertainty quantification, Vortex dynamics",

author = "Rosenthal, {W. Steven} and Shankar Venkataramani and Mariano, {Arthur J.} and Restrepo, {Juan M.}",

note = "Publisher Copyright: {\textcopyright} 2016 Elsevier Inc.",

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AU - Rosenthal, W. Steven

AU - Venkataramani, Shankar

AU - Mariano, Arthur J.

AU - Restrepo, Juan M.

PY - 2017/2/1

Y1 - 2017/2/1

N2 - 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.

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.

KW - Data assimilation

KW - Displacement assimilation

KW - Ensemble Kalman Filter

KW - Uncertainty quantification

KW - Vortex dynamics

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VL - 330

SP - 594

EP - 614

JO - Journal of Computational Physics

JF - Journal of Computational Physics

ER -

Displacement data assimilation

Abstract

Keywords

ASJC Scopus subject areas

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