Articles | Volume 16, issue 9
https://doi.org/10.5194/tc-16-3489-2022
© 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-16-3489-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Sep 2022
Research article |
| 01 Sep 2022
| 01 Sep 2022
Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Quebec, Canada
Marie-Amélie Boucher
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Quebec, Canada
Simon Lachance-Cloutier
Quebec Ministère de l’Environnement et de la Lutte contre les Changements Climatiques, Quebec City, Quebec, Canada
Richard Turcotte
Quebec Ministère de l’Environnement et de la Lutte contre les Changements Climatiques, Quebec City, Quebec, Canada
Pierre-Yves St-Louis
Quebec Ministère de l’Environnement et de la Lutte contre les Changements Climatiques, Quebec City, Quebec, Canada
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Short summary
The research deals with the assimilation of in-situ local snow observations in a large-scale spatialized snow modeling framework over the province of Quebec (eastern Canada). The methodology is based on proposing multiple spatialized snow scenarios using the snow model and weighting them according to the available observations. The paper especially focuses on the spatial coherence of the snow scenario proposed in the framework.
The research deals with the assimilation of in-situ local snow observations in a large-scale...
