Articles | Volume 16, issue 4
Research article
11 Apr 2022
Research article |  | 11 Apr 2022

Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-225', Anonymous Referee #1, 21 Sep 2021
    • AC1: 'Reply on RC1', Bertrand Cluzet, 04 Jan 2022
  • RC2: 'Comment on tc-2021-225', Anonymous Referee #2, 27 Sep 2021
    • AC2: 'Reply on RC2', Bertrand Cluzet, 04 Jan 2022
  • RC3: 'Comment on tc-2021-225', Anonymous Referee #3, 30 Sep 2021
    • AC3: 'Reply on RC3', Bertrand Cluzet, 04 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (07 Jan 2022) by Masashi Niwano
AR by Bertrand Cluzet on behalf of the Authors (11 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (15 Feb 2022) by Masashi Niwano
RR by Anonymous Referee #2 (25 Feb 2022)
ED: Publish as is (01 Mar 2022) by Masashi Niwano
Short summary
The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.