Articles | Volume 29, issue 2
Nonlin. Processes Geophys., 29, 241–253, 2022
https://doi.org/10.5194/npg-29-241-2022
Nonlin. Processes Geophys., 29, 241–253, 2022
https://doi.org/10.5194/npg-29-241-2022
Research article
22 Jun 2022
Research article | 22 Jun 2022

A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter

Andrey A. Popov et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Andrey Popov on behalf of the Authors (31 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (02 Feb 2022) by Olivier Talagrand
RR by Anonymous Referee #2 (21 Feb 2022)
RR by Alban Farchi (25 Feb 2022)
ED: Reconsider after major revisions (further review by editor and referees) (02 Mar 2022) by Olivier Talagrand
AR by Andrey Popov on behalf of the Authors (27 Apr 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (29 Apr 2022) by Olivier Talagrand
RR by Alban Farchi (10 May 2022)
ED: Publish subject to technical corrections (23 May 2022) by Olivier Talagrand
AR by Andrey Popov on behalf of the Authors (27 May 2022)  Author's response    Manuscript
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Short summary
Numerical weather prediction requires the melding of both computational model and data obtained from sensors such as satellites. We focus on one algorithm to accomplish this. We aim to aid its use by additionally supplying it with data obtained from separate models that describe the average behavior of the computational model at any given time. We show that our approach outperforms the standard approaches to this problem.