Displacement Data Assimilation

TL;DR

This paper introduces a displacement correction to Bayesian data assimilation, enhancing the estimation of partially observed states, demonstrated within an ensemble Kalman Filter for tracking vortices.

Contribution

It presents a novel displacement correction method integrated into existing assimilation schemes, improving state estimation accuracy for features like vortices.

Findings

Displacement correction improves vortex tracking accuracy.

The method enhances state estimation with partial observations.

Effective within ensemble Kalman Filter framework.

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 important. While the displacement transformation is not tied to any particular assimilation scheme, here we implement it within an ensemble Kalman Filter and demonstrate its effectiveness in tracking stochastically perturbed vortices.