Multi-index ensemble Kalman filtering
Abstract
In this work we combine ideas from multi-index Monte Carlo and ensemble Kalman filtering (EnKF) to produce a highly efficient filtering method called multi-index EnKF (MIEnKF). MIEnKF is based on independent samples of four-coupled EnKF estimators on a multi-index hierarchy of resolution levels, and it may be viewed as an extension of the multilevel EnKF (MLEnKF) method developed by the same authors in 2020. Multi-index here refers to a two-index method, consisting of a hierarchy of EnKF estimators that are coupled in two degrees of freedom: time discretization and ensemble size. Under certain assumptions, when strong coupling between solutions on neighboring numerical resolutions is attainable, the MIEnKF method is proven to be more tractable than EnKF and MLEnKF. Said efficiency gains are also verified numerically in a series of test problems.
Keywords
Cite
@article{arxiv.2104.07263,
title = {Multi-index ensemble Kalman filtering},
author = {Håkon Hoel and Gaukhar Shaimerdenova and Raúl Tempone},
journal= {arXiv preprint arXiv:2104.07263},
year = {2022}
}
Comments
29 pages, 11 figures, 1 algorithm. Accepted by the Journal of Computational Physics. The computer code can be downloaded from https://github.com/GaukharSH/mienkf