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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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NPG | Articles | Volume 25, issue 4
Nonlin. Processes Geophys., 25, 765–807, 2018
https://doi.org/10.5194/npg-25-765-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Numerical modeling, predictability and data assimilation in...

Nonlin. Processes Geophys., 25, 765–807, 2018
https://doi.org/10.5194/npg-25-765-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 12 Nov 2018

Review article | 12 Nov 2018

Review article: Comparison of local particle filters and new implementations

Alban Farchi and Marc Bocquet

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Alban Farchi on behalf of the Authors (24 Jul 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (25 Jul 2018) by Olivier Talagrand
RR by Anonymous Referee #1 (30 Jul 2018)
RR by Anonymous Referee #3 (16 Sep 2018)
ED: Publish subject to minor revisions (review by editor) (19 Sep 2018) by Olivier Talagrand
AR by Alban Farchi on behalf of the Authors (27 Sep 2018)  Author's response    Manuscript
ED: Publish as is (08 Oct 2018) by Olivier Talagrand
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Short summary
Data assimilation looks for an optimal way to learn from observations of a dynamical system to improve the quality of its predictions. The goal is to filter out the noise (both observation and model noise) to retrieve the true signal. Among all possible methods, particle filters are promising; the method is fast and elegant, and it allows for a Bayesian analysis. In this review paper, we discuss implementation techniques for (local) particle filters in high-dimensional systems.
Data assimilation looks for an optimal way to learn from observations of a dynamical system to...
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