Articles | Volume 29, issue 1
Nonlin. Processes Geophys., 29, 77–92, 2022
https://doi.org/10.5194/npg-29-77-2022
Nonlin. Processes Geophys., 29, 77–92, 2022
https://doi.org/10.5194/npg-29-77-2022

Research article 18 Feb 2022

Research article | 18 Feb 2022

Ensemble Riemannian data assimilation: towards large-scale dynamical systems

Sagar K. Tamang 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
  • RC1: 'Comment on npg-2021-28', Anonymous Referee #1, 11 Oct 2021
    • AC1: 'Reply on RC1', Sagar Tamang, 03 Dec 2021
  • RC2: 'Comment on npg-2021-28', Anonymous Referee #2, 13 Nov 2021
    • AC2: 'Reply on RC2', Sagar Tamang, 03 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Sagar Tamang on behalf of the Authors (03 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (04 Jan 2022) by Natale Alberto Carrassi
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Short summary
The outputs from Earth system models are optimally combined with satellite observations to produce accurate forecasts through a process called data assimilation. Many existing data assimilation methodologies have some assumptions regarding the shape of the probability distributions of model output and observations, which results in forecast inaccuracies. In this paper, we test the effectiveness of a newly proposed methodology that relaxes such assumptions about high-dimensional models.