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
Univ.
Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’études de la Neige,
1441 rue de la Piscine, 38400 Saint-Martin d'Hères, France
WSL – Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Univ.
Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’études de la Neige,
1441 rue de la Piscine, 38400 Saint-Martin d'Hères, France
Univ.
Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’études de la Neige,
1441 rue de la Piscine, 38400 Saint-Martin d'Hères, France
Centre d’Etudes Spatiales de la Biosphère, CESBIO, Univ. Toulouse, CNES/CNRS/INRA/IRD/UPS, 31401 Toulouse, France
Matthieu Vernay
Univ.
Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’études de la Neige,
1441 rue de la Piscine, 38400 Saint-Martin d'Hères, France
Univ.
Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’études de la Neige,
1441 rue de la Piscine, 38400 Saint-Martin d'Hères, France
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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.
The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover...
