Commentary: On the Efficiency of Covariance Localisation of the Ensemble Kalman Filter Using Augmented Ensembles

Bishop, Craig H.; Whitaker, Jeffrey S.; Lei, Lili

doi:10.3389/fams.2020.00002

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

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Commentary: On the Efficiency of Covariance Localisation of the Ensemble Kalman Filter Using Augmented Ensembles

2020
By Bishop, Craig H. ; Whitaker, Jeffrey S. ; Lei, Lili
Source: Frontiers in Applied Mathematics and Statistics, 6

[PDF-221.80 KB]

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Journal Title:

Frontiers in Applied Mathematics and Statistics
Personal Author:

Bishop, Craig H. ; Whitaker, Jeffrey S. ; Lei, Lili

Bishop, Craig H. ; Whitaker, Jeffrey S. ; Lei, Lili Less -
NOAA Program & Office:

ESRL (Earth System Research Laboratory) ; OAR (Oceanic and Atmospheric Research)

ESRL (Earth System Research Laboratory) ; OAR (Oceanic and Atmospheric Research) Less -
Description:

In discussing Equation (39) (Equation (25) of Bocquet [1]), Farchi and Bouquet [2] state that “This perturbation update has been rediscovered by Bishop et al. [3] and included in their gain ETKF (GETKF) algorithm. However, the update formula used in the GETKF is prone to numerical cancellation errors as opposed to Equation (39)”.
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Applied Mathematics Statistics And Probability
Source:

Frontiers in Applied Mathematics and Statistics, 6
DOI:

https://doi.org/10.3389/fams.2020.00002
ISSN:

2297-4687
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PDF
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Frontiers Media SA
Document Type:

Journal Article
Funding:

Grant no. NA14OAR4830123
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https://creativecommons.org/licenses/by/4.0/
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CC BY
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urn:sha-512:3527cca08293b242cda6e72f2894575b7b081d66195cd053a281423f929cf1e1f21e65f868147758a1c27a8934f077b2333e6ba63690e595b77088027d4b587f
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Commentary: On the Efficiency of Covariance Localisation of the Ensemble Kalman Filter Using Augmented Ensembles

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