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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Bertrand Cluzet on behalf of the Authors (16 Nov 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (20 Nov 2020) by Richard Mills
RR by Anonymous Referee #1 (21 Nov 2020)
ED: Publish as is (04 Dec 2020) by Richard Mills
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.