A consistent interpretation of the stochastic version of the Ensemble Kalman Filter

[thumbnail of Open Access]
Preview
Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.
| Preview
Available under license: Creative Commons Attribution
[thumbnail of SEnKF_clean.pdf]
Text - Accepted Version
· Restricted to Repository staff only
Restricted to Repository staff only

Please see our End User Agreement.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

van Leeuwen, P. J. (2020) A consistent interpretation of the stochastic version of the Ensemble Kalman Filter. Quarterly Journal of the Royal Meteorological Society, 146 (731). pp. 2815-2825. ISSN 1477-870X doi: 10.1002/qj.3819

Abstract/Summary

Ensemble Kalman Filters are used extensively in all geoscience areas. Often a stochastic variant is used, in which each ensemble member is updated via the Kalman Filter equation with an extra perturbation in the innovation. These perturbations are essential for the correct ensemble spread in a stochastic Ensemble Kalman Filter, and are applied either to the observations or to the modeled observations. This paper investigates if there is a preference for any of these two perturbation methods. Both versions lead to the same posterior mean and covariance when the prior and the likelihood are Gaussian in the state. However, ensemble verification methods, Bayes Theorem and the Best Linear Unbiased Estimate (BLUE) suggest that one should perturb the modeled observations. Furthermore, it is known that in non-Gaussian settings the perturbed modeled observation scheme is preferred, illustrated here for a skewed likelihood. Existing reasons for the perturbed observation scheme are shown to be incorrect, and no new arguments in favor of that scheme have been found. Finally, a new and consistent derivation and interpretation of the stochastic version of the EnKF equations is derived based on perturbing modeled observations. It is argued that these results have direct consequences for (iterative) Ensemble Kalman Filters and Smoothers, including 'perturbed observation' 3D- and 4DVars, both in terms of internal consistency and implementation.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/90781
Identification Number/DOI 10.1002/qj.3819
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher Royal Meteorological Society
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar