Displacement data assimilation

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• 作者：W. Steven Rosenthal^a ; Shankar Venkataramani^b ; Arthur J. Mariano^c ; Juan M. Restrepo^d ; ^{restrepo@math.oregonstate.edu}
• 关键词：Displacement assimilation ; Data assimilation ; Uncertainty quantification ; Ensemble Kalman Filter ; Vortex dynamics
• 刊名：Journal of Computational Physics
• 出版年：2017
• 出版时间：1 February 2017
• 年：2017
• 卷：330
• 期：Complete
• 页码：594-614
• 全文大小：2353 K
• 卷排序：330

文摘

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.