Preprints
https://doi.org/10.5194/gmd-2020-130
https://doi.org/10.5194/gmd-2020-130

Submitted as: development and technical paper 10 Jul 2020

Submitted as: development and technical paper | 10 Jul 2020

Review status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

CrocO_v1.0 : a Particle Filter to assimilate snowpack observations in a spatialised framework

Bertrand Cluzet1, Matthieu Lafaysse1, Emmanuel Cosme2, Clément Albergel3, Louis-François Meunier3, and Marie Dumont1 Bertrand Cluzet et al.
  • 1Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, Centre d’Études de la Neige, Grenoble, France
  • 2Institut des Géosciences de l’Environnement, IGE, UGA-CNRS, Grenoble, France
  • 3CNRM, Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, France

Abstract. Monitoring the evolution of the snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In-situ and remotely sensed observations provide precious information on the snowpack but usually offer a limited spatio-temporal coverage of bulk or surface variables only. In particular, visible-near infrared (VIS-NIR) reflectance observations can inform on the snowpack surface properties but are limited by terrain shading and clouds. Snowpack modelling enables to estimate any physical variable, virtually anywhere, but is affected by large errors and uncertainties. Data assimilation offers a way to combine both sources of information, and to propagate information from observed areas to non observed areas. Here, we present CrocO, (Crocus-Observations) an ensemble data assimilation system able to ingest any snowpack observation (applied as a first step to the height of snow (HS) and VIS-NIR reflectances) in a spatialised geometry. CrocO uses an ensemble of snowpack simulations to represent modelling uncertainties, and a Particle Filter (PF) to reduce them. The PF is known to collapse when assimilating a too large number of observations. Two variants of the PF were specifically implemented to ensure that observations information is propagated in space while tackling this issue. The global algorithm ingests all available observations with an iterative inflation of observation errors, while the klocal algorithm is a localised approach performing a selection of the observations to assimilate based on background correlation patterns. Experiments are carried out in a twin experiment setup, with synthetic observations of HS and VIS-NIR reflectances available in only a 1/6th of the simulation domain. Results show that compared against runs without assimilation, analyses exhibit an average improvement of snow water equivalent Continuous Rank Probability Score (CRPS) of 60 % when assimilating HS with a 40-member ensemble, and an average 20 % CRPS improvement when assimilating reflectance with a 160-member ensemble. Significant improvements are also obtained outside the observation domain. These promising results open a way for the assimilation of real observations of reflectance, or of any snowpack observations in a spatialised context.

Bertrand Cluzet et al.

 
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Bertrand Cluzet et al.

Data sets

Input and output data, manuscript figure scripts Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont https://doi.org/10.5281/zenodo.3775007

Model code and software

Ensemble Data assimilation system code: SURFEX model (Crocus Snow model and Particle Filter), snowtools (pre-post processing software) and vortex (HPC software) Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont https://doi.org/10.5281/zenodo.3774861

CrocO_toolbox (pre-post procesing software) Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont https://doi.org/10.5281/zenodo.3784980

Bertrand Cluzet et al.

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Short summary
In mountainous areas, snowpack models suffer from large errors and observations are scarce, a challenge for data assimilation. Here, we develop two variants of the Particle Filter in order to propagate the information content of observations over a complex topography. By adjusting observation errors and exploiting background correlation patterns, these variants demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy in a whole domain.