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

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

doi:https://doi.org/10.5194/tc-16-1281-2022

Articles | Volume 16, issue 4

https://doi.org/10.5194/tc-16-1281-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-16-1281-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Data sets

Data_TC_Cluzet (v0.1) Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont https://doi.org/10.5281/zenodo.5115557

Model code and software

CrocO_v1.1: model source code and external libraries Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont https://doi.org/10.5281/zenodo.5111449

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.