05 Aug 2021

05 Aug 2021

Review status: this preprint is currently under review for the journal TC.

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

Bertrand Cluzet1, Matthieu Lafaysse1, César Deschamps-Berger1,2, Matthieu Vernay1, and Marie Dumont1 Bertrand Cluzet et al.
  • 1Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France
  • 2Centre d’Etudes Spatiales de la Biosphère, CESBIO, Univ. Toulouse, CNES/CNRS/INRA/IRD/UPS, 31401 Toulouse, France

Abstract. The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterisations, and unresolved terrain features. In-situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large scale modelling errors by means of data assimilation. In this work, we assimilate HS observations from an in-situ network of 295 stations covering the French Alps, Pyrenees and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialised snow cover modelling framework, we investigate whether such observations can be used to correct neighbouring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modelling, based on a Particle Filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localisation strategies to assimilate snow observations. These approaches are evaluated in a Leave-One-Out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that intermediate localisation radius of 35–50 km yield a slightly lower root mean square error (RMSE), and a better Spread-Skill than the strategy assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modelling errors are the largest, e.g. the Haute-Ariège, Andorra and the Extreme Southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses, and therefore snow simulations. In-situ HS observations thus shows an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas.

Bertrand Cluzet et al.

Status: final response (author comments only)

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

Bertrand Cluzet et al.

Data sets

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

Model code and software

CrocO_v1.1: model source code and external libraries Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, Marie Dumont

Bertrand Cluzet et al.


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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 spatialised version of the Particle Filter, in which information from in-situ snow depth observations is successfully used to constrain nearby simulations.