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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Apr 2022
Research article |
| 11 Apr 2022
| 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
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
Matthieu Lafaysse
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
César Deschamps-Berger
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
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
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Cited
15 citations as recorded by crossref.
- Evaluation of snow depth retrievals from ICESat-2 using airborne laser-scanning data C. Deschamps-Berger et al. https://doi.org/10.5194/tc-17-2779-2023
- A high-resolution snow dataset for Switzerland (2016–2025) combining physics-based simulations and in situ observations M. Oberrauch et al. https://doi.org/10.5194/tc-20-3387-2026
- Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting S. Horton & P. Haegeli https://doi.org/10.5194/tc-16-3393-2022
- Radar-based high-resolution ensemble precipitation analyses over the French Alps M. Vernay et al. https://doi.org/10.5194/amt-18-1731-2025
- Operational snow-hydrological modeling for Switzerland R. Mott et al. https://doi.org/10.3389/feart.2023.1228158
- Assimilation of synthetic observations of radar backscatters at Ku-band improves SWE estimates N. Leroux et al. https://doi.org/10.5194/tc-20-2773-2026
- Evaluating the utility of Sentinel-1 in a Data Assimilation System for estimating snow depth in a mountainous basin B. Mirza et al. https://doi.org/10.5194/tc-19-6691-2025
- Ensemble-based snow depth data assimilation for a multi-layer snow scheme over the European Arctic Å. Bakketun et al. https://doi.org/10.5194/tc-20-737-2026
- Quantifying Storm-Time Neutral Density Uncertainties using a Physics-Based Particle Filter Framework N. Dietrich & T. Matsuo https://doi.org/10.1007/s40295-026-00595-x
- SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme M. Baron et al. https://doi.org/10.5194/gmd-17-1297-2024
- Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter M. Mazzolini et al. https://doi.org/10.5194/tc-19-3831-2025
- Exploring how Sentinel-1 wet-snow maps can inform fully distributed physically based snowpack models B. Cluzet et al. https://doi.org/10.5194/tc-18-5753-2024
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. https://doi.org/10.5194/gmd-15-9127-2022
- Snow depth time series Generation: Effective simulation at multiple time scales H. Abdelmoaty et al. https://doi.org/10.1016/j.hydroa.2024.100177
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al. https://doi.org/10.5194/hess-27-4637-2023
15 citations as recorded by crossref.
- Evaluation of snow depth retrievals from ICESat-2 using airborne laser-scanning data C. Deschamps-Berger et al. https://doi.org/10.5194/tc-17-2779-2023
- A high-resolution snow dataset for Switzerland (2016–2025) combining physics-based simulations and in situ observations M. Oberrauch et al. https://doi.org/10.5194/tc-20-3387-2026
- Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting S. Horton & P. Haegeli https://doi.org/10.5194/tc-16-3393-2022
- Radar-based high-resolution ensemble precipitation analyses over the French Alps M. Vernay et al. https://doi.org/10.5194/amt-18-1731-2025
- Operational snow-hydrological modeling for Switzerland R. Mott et al. https://doi.org/10.3389/feart.2023.1228158
- Assimilation of synthetic observations of radar backscatters at Ku-band improves SWE estimates N. Leroux et al. https://doi.org/10.5194/tc-20-2773-2026
- Evaluating the utility of Sentinel-1 in a Data Assimilation System for estimating snow depth in a mountainous basin B. Mirza et al. https://doi.org/10.5194/tc-19-6691-2025
- Ensemble-based snow depth data assimilation for a multi-layer snow scheme over the European Arctic Å. Bakketun et al. https://doi.org/10.5194/tc-20-737-2026
- Quantifying Storm-Time Neutral Density Uncertainties using a Physics-Based Particle Filter Framework N. Dietrich & T. Matsuo https://doi.org/10.1007/s40295-026-00595-x
- SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme M. Baron et al. https://doi.org/10.5194/gmd-17-1297-2024
- Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter M. Mazzolini et al. https://doi.org/10.5194/tc-19-3831-2025
- Exploring how Sentinel-1 wet-snow maps can inform fully distributed physically based snowpack models B. Cluzet et al. https://doi.org/10.5194/tc-18-5753-2024
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. https://doi.org/10.5194/gmd-15-9127-2022
- Snow depth time series Generation: Effective simulation at multiple time scales H. Abdelmoaty et al. https://doi.org/10.1016/j.hydroa.2024.100177
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al. https://doi.org/10.5194/hess-27-4637-2023
Saved (final revised paper)
Latest update: 06 Jul 2026
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.
The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover...
