Displacement data assimilation

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

Research output: Contribution to journalArticle

5 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 languageEnglish (US)
Pages (from-to)594-614
Number of pages21
JournalJournal of Computational Physics
Volume330
DOIs
StatePublished - Feb 1 2017

Keywords

  • Data assimilation
  • Displacement assimilation
  • Ensemble Kalman Filter
  • Uncertainty quantification
  • Vortex dynamics

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)
  • Computer Science Applications

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  • Cite this

    Rosenthal, W. S., Venkataramani, S., Mariano, A. J., & Restrepo, J. M. (2017). Displacement data assimilation. Journal of Computational Physics, 330, 594-614. https://doi.org/10.1016/j.jcp.2016.10.025