Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 3
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.
https://doi.org/10.5194/gmd-14-1595-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 3
Development and technical paper
 | 
19 Mar 2021
Development and technical paper |  | 19 Mar 2021

CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework

Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont

Data sets

CrocO: manuscript Dataset 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

CrocO: model source code and external librairies Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont https://doi.org/10.5281/zenodo.3774861

bertrandcz/CrocO_toolbox: CrocO_toolbox : GMD mauscript version Bertrand Cluzet https://doi.org/10.5281/zenodo.3784980

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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.
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