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

Viewed

Total article views: 3,333 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,460 783 90 3,333 142 180
  • HTML: 2,460
  • PDF: 783
  • XML: 90
  • Total: 3,333
  • BibTeX: 142
  • EndNote: 180
Views and downloads (calculated since 10 Jul 2020)
Cumulative views and downloads (calculated since 10 Jul 2020)

Viewed (geographical distribution)

Total article views: 3,333 (including HTML, PDF, and XML) Thereof 3,033 with geography defined and 300 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 22 Nov 2025
Download
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.
Read more
Share