Ensemble Kalman filtering with a divided state-space strategy for coupled data assimilation problems

TL;DR

This paper introduces a divided state-space approach for ensemble Kalman filtering in coupled systems, allowing separate or combined sub-system assimilation to improve efficiency and accuracy in data assimilation tasks.

Contribution

It proposes novel variants of the ensemble Kalman filter that operate on divided state-spaces, addressing challenges in coupled data assimilation problems.

Findings

Variants outperform conventional EnKF in multi-scale Lorentz 96 model

Divided state-space methods offer better efficiency and accuracy trade-offs

Extensions further improve performance in coupled data assimilation scenarios

Abstract

This study considers the data assimilation problem in coupled systems, which consists of two components (sub-systems) interacting with each other through certain coupling terms. A straightforward way to tackle the assimilation problem in such systems is to concatenate the states of the sub-systems into one augmented state vector, so that a standard ensemble Kalman filter (EnKF) can be directly applied. In this work we present a divided state-space estimation strategy, in which data assimilation is carried out with respect to each individual sub-system, involving quantities from the sub-system itself and correlated quantities from other coupled sub-systems. On top of the divided state-space estimation strategy, we also consider the possibility to run the sub-systems separately. Combining these two ideas, a few variants of the EnKF are derived. The introduction of these variants is mainly inspired by the current status and challenges in coupled data assimilation problems, and thus might be of interest from a practical point of view. Numerical experiments with a multi-scale Lorentz 96 model are conducted to evaluate the performance of these variants against that of the conventional EnKF. In addition, specific for coupled data assimilation problems, two prototypes of extensions of the presented methods are also developed in order to achieve a trade-off between efficiency and accuracy.