Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 295–309, 2021
https://doi.org/10.5194/npg-28-295-2021
Nonlin. Processes Geophys., 28, 295–309, 2021
https://doi.org/10.5194/npg-28-295-2021

Research article 06 Jul 2021

Research article | 06 Jul 2021

Ensemble Riemannian data assimilation over the Wasserstein space

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-2021-11', Anonymous Referee #1, 12 Apr 2021
    • AC1: 'Reply on RC1', Sagar Tamang, 14 May 2021
  • RC2: 'Comment on npg-2021-11', Anonymous Referee #2, 02 May 2021
    • AC2: 'Reply on RC2', Sagar Tamang, 14 May 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Sagar Tamang on behalf of the Authors (14 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Reconsider after major revisions (further review by editor and referees) (19 May 2021) by Alberto Carrassi
ED: Referee Nomination & Report Request started (28 May 2021) by Alberto Carrassi
RR by Anonymous Referee #1 (07 Jun 2021)
ED: Publish as is (07 Jun 2021) by Alberto Carrassi
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Short summary
Data assimilation aims to improve hydrologic and weather forecasts by combining available information from Earth system models and observations. The classical approaches to data assimilation usually proceed with some preconceived assumptions about the shape of their probability distributions. As a result, when such assumptions are invalid, the forecast accuracy suffers. In the proposed methodology, we relax such assumptions and demonstrate improved performance.