Displacement data assimilation

JOURNAL OF COMPUTATIONAL PHYSICS

卷:330

Article

Rosenthal, W. Steven¹  Venkataramani, Shankar^2,3  Mariano, Arthur J.⁴  Restrepo, Juan M.⁵ 

[1] Pacific Northwest Lab, Richland, WA 99354 USA

[2] Univ Arizona, Dept Math, Tucson, AZ 85721 USA

[3] Univ Arizona, Program Appl Math, Tucson, AZ 85721 USA

[4] Univ Miami, Rosenstiel Sch Marine & Atmospher Sci, 4600 Rickenbacker Causeway, Miami, FL 33149 USA

[5] Oregon State Univ, Dept Math, Corvallis, OR 97331 USA

关键词: Displacement assimilation;    Data assimilation;    Uncertainty quantification;    Ensemble Kalman Filter;    Vortex dynamics;   

DOI  :  10.1016/j.jcp.2016.10.025

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier Inc. All rights reserved.

【 授权许可】

Free