The Cryosphere
The Cryosphere
TC
Articles | Volume 16, issue 9
Articles | Volume 16, issue 9
The Cryosphere, 16, 3489–3506, 2022
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 Cryosphere, 16, 3489–3506, 2022
https://doi.org/10.5194/tc-16-3489-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 9
Research article
01 Sep 2022
Research article | 01 Sep 2022

Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles

Jean Odry et al.

Data sets

Meteorological inputs Marie-Amélie Boucher https://doi.org/10.7910/DVN/BXXRHL

HYDROTEL Snow Model Parameters Marie-Amélie Boucher https://doi.org/10.7910/DVN/RJSZIP

Historical Snow Simulation (Open Loop) Marie-Amélie Boucher https://doi.org/10.7910/DVN/CJYMCV

Snow Observation Data Marie-Amélie Boucher https://doi.org/10.7910/DVN/NPB1JY

Model code and software

TheDroplets/Snow_spatial_particle_filter: First release of the codes for the spatial particle filte Jean Odry https://doi.org/10.5281/zenodo.5531771

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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.
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