Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators

Gaukhar Shaimerdenova; H{\aa}kon Hoel; Ra\'ul Tempone

Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2002.00480 v2 pith:BF7EXZBS submitted 2020-02-02 math.NA cs.NA

H{aa}kon Hoel , Gaukhar Shaimerdenova , Raúl Tempone This is my paper · ORCID

classification math.NAcs.NA

keywords methodenkfensemblefilteringkalmanmlenkfindependentmultilevel

verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments

read the original abstract

We introduce a new multilevel ensemble Kalman filter method (MLEnKF) which consists of a hierarchy of independent samples of ensemble Kalman filters (EnKF). This new MLEnKF method is fundamentally different from the preexisting method introduced by Hoel, Law and Tempone in 2016, and it is suitable for extensions towards multi-index Monte Carlo based filtering methods. Robust theoretical analysis and supporting numerical examples show that under appropriate regularity assumptions, the MLEnKF method has better complexity than plain vanilla EnKF in the large-ensemble and fine-resolution limits, for weak approximations of quantities of interest. The method is developed for discrete-time filtering problems with finite-dimensional state space and linear observations polluted by additive Gaussian noise.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations
math.NA 2019-08 conditional novelty 7.0 of 10

A localized ensemble Kalman-Bucy filter for nonlinear short-range models has component-wise mean-squared error of order sqrt(ε) independent of state dimension, with pathwise error growing only logarithmically in time.

Pith tools