Multilevel ensemble Kalman filtering for spatially extended models
Numerical Analysis
2016-08-31 v1
Abstract
This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF), thereby yielding a multilevel ensemble Kalman filter (MLEnKF) which has provably superior asymptotic cost to a given accuracy level. The development of MLEnKF for finite-dimensional state-spaces in the work [20] is here extended to models with infinite-dimensional state- spaces in the form of spatial fields. A concrete example is given to illustrate the results.
Keywords
Cite
@article{arxiv.1608.08558,
title = {Multilevel ensemble Kalman filtering for spatially extended models},
author = {Alexey Chernov and Haakon Hoel and Kody Law and Fabio Nobile and Raul Tempone},
journal= {arXiv preprint arXiv:1608.08558},
year = {2016}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1502.06069