Articles | Volume 27, issue 24
Research article
22 Dec 2023
Research article |  | 22 Dec 2023

Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation

Esteban Alonso-González, Kristoffer Aalstad, Norbert Pirk, Marco Mazzolini, Désirée Treichler, Paul Leclercq, Sebastian Westermann, Juan Ignacio López-Moreno, and Simon Gascoin


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-954', Anonymous Referee #1, 31 Aug 2023
    • AC1: 'Reply on RC1', Esteban Alonso-González, 20 Oct 2023
  • RC2: 'Comment on egusphere-2023-954', Anonymous Referee #2, 30 Sep 2023
    • AC2: 'Reply on RC2', Esteban Alonso-González, 20 Oct 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (further review by editor) (30 Oct 2023) by Jan Seibert
AR by Esteban Alonso-González on behalf of the Authors (30 Oct 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (12 Nov 2023) by Jan Seibert
AR by Esteban Alonso-González on behalf of the Authors (15 Nov 2023)
Short summary
Here we explore how to improve hyper-resolution (5 m) distributed snowpack simulations using sparse observations, which do not provide information from all the areas of the simulation domain. We propose a new way of propagating information throughout the simulations adapted to the hyper-resolution, which could also be used to improve simulations of other nature. The method has been implemented in an open-source data assimilation tool that is readily accessible to everyone.