Displacement data assimilation

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

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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
StatePublished - Feb 1 2017


  • 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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