Review article: Comparison of local particle filters and new implementations

Farchi, Alban; Bocquet, Marc

doi:https://doi.org/10.5194/npg-25-765-2018

Articles | Volume 25, issue 4

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...

https://doi.org/10.5194/npg-25-765-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 4

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

Download

Final revised paper (published on 12 Nov 2018)
Preprint (discussion started on 05 Mar 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'A comprehensive review of current localisation literature', Anonymous Referee #1, 22 Mar 2018
- AC1: 'Response to reviewer 1', Alban Farchi, 24 Jul 2018
RC2: 'Reviewer response', S.G. Penny, 18 Jun 2018
- AC2: 'Response to reviewer 2', Alban Farchi, 24 Jul 2018

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

AR by Alban Farchi on behalf of the Authors (08 Oct 2018)

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.