Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1595-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1595-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Mar 2021
Development and technical paper |
| 19 Mar 2021
| 19 Mar 2021
CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
Matthieu Lafaysse
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
Emmanuel Cosme
Institut des Géosciences de l'Environnement, IGE, UGA-CNRS, Grenoble, France
Clément Albergel
CNRM, University of Toulouse, Météo-France, CNRS, 31057 Toulouse, France
now at: European Space Agency Climate Office, ECSAT, Harwell Campus, Oxfordshire, Didcot OX11 0FD, UK
Louis-François Meunier
CNRM, University of Toulouse, Météo-France, CNRS, 31057 Toulouse, France
Marie Dumont
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
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Cited
15 citations as recorded by crossref.
- Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products J. Revuelto et al. 10.3390/rs13224513
- The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021) M. Vernay et al. 10.5194/essd-14-1707-2022
- Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area E. Alonso-González et al. 10.5194/hess-25-4455-2021
- Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles J. Odry et al. 10.5194/tc-16-3489-2022
- Improving the Spatial Distribution of Snow Cover Simulations by Assimilation of Satellite Stereoscopic Imagery C. Deschamps‐Berger et al. 10.1029/2021WR030271
- Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network B. Cluzet et al. 10.5194/tc-16-1281-2022
- Black carbon and dust alter the response of mountain snow cover under climate change M. Réveillet et al. 10.1038/s41467-022-32501-y
- Improving the estimation of snow depth in the Noah-MP model by combining particle filter and Bayesian model averaging Y. You et al. 10.1016/j.jhydrol.2022.128877
- Reanalysis Surface Mass Balance of the Greenland Ice Sheet Along K‐Transect (2000–2014) M. Navari et al. 10.1029/2021GL094602
- Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
- Sample Regenerating Particle Filter Combined With Unequal Weight Ensemble Kalman Filter for Nonlinear Systems X. Li et al. 10.1109/ACCESS.2021.3100486
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al. 10.5194/hess-27-4637-2023
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. 10.5194/gmd-15-9127-2022
- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al. 10.3389/feart.2024.1381323
- The Challenges of Simulating SWE Beneath Forest Canopies are Reduced by Data Assimilation of Snow Depth E. Smyth et al. 10.1029/2021WR030563
15 citations as recorded by crossref.
- Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products J. Revuelto et al. 10.3390/rs13224513
- The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021) M. Vernay et al. 10.5194/essd-14-1707-2022
- Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area E. Alonso-González et al. 10.5194/hess-25-4455-2021
- Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles J. Odry et al. 10.5194/tc-16-3489-2022
- Improving the Spatial Distribution of Snow Cover Simulations by Assimilation of Satellite Stereoscopic Imagery C. Deschamps‐Berger et al. 10.1029/2021WR030271
- Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network B. Cluzet et al. 10.5194/tc-16-1281-2022
- Black carbon and dust alter the response of mountain snow cover under climate change M. Réveillet et al. 10.1038/s41467-022-32501-y
- Improving the estimation of snow depth in the Noah-MP model by combining particle filter and Bayesian model averaging Y. You et al. 10.1016/j.jhydrol.2022.128877
- Reanalysis Surface Mass Balance of the Greenland Ice Sheet Along K‐Transect (2000–2014) M. Navari et al. 10.1029/2021GL094602
- Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
- Sample Regenerating Particle Filter Combined With Unequal Weight Ensemble Kalman Filter for Nonlinear Systems X. Li et al. 10.1109/ACCESS.2021.3100486
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al. 10.5194/hess-27-4637-2023
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. 10.5194/gmd-15-9127-2022
- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al. 10.3389/feart.2024.1381323
- The Challenges of Simulating SWE Beneath Forest Canopies are Reduced by Data Assimilation of Snow Depth E. Smyth et al. 10.1029/2021WR030563
Latest update: 29 Jun 2024
Short summary
In the mountains, the combination of large model error and observation sparseness is a challenge for data assimilation. Here, we develop two variants of the particle filter (PF) in order to propagate the information content of observations into unobserved areas. By adjusting observation errors or exploiting background correlation patterns, we demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy with the PF in an idealised setting.
In the mountains, the combination of large model error and observation sparseness is a challenge...
