Preprints
https://doi.org/10.5194/tc-2021-322
https://doi.org/10.5194/tc-2021-322
 
27 Oct 2021
27 Oct 2021
Status: this preprint is currently under review for the journal TC.

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

Jean Odry1, Marie-Amélie Boucher1, Simon Lachance-Cloutier2, Richard Turcotte2, and Pierre-Yves St-Louis2 Jean Odry et al.
  • 1Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Canada
  • 2Quebec Ministère de l’Environnement et de la Lutte contre les Changements Climatiques, Quebec, Canada

Abstract. The use of particle filters for data assimilation is increasingly popular because of its minimal assumptions. Nevertheless, implementing a particle filter over domains of large spatial dimensions remains challenging, as the number of required particles rises exponentially as domain size increases. A common solution to overcome this issue is to localize the particle filter and consider a collection of local applications rather than a single regional one. Although this solution can solve the dimensionality limit, it can also create some spatial discontinuity inside the particles. This issue can become even more problematic when additional data is assimilated. The purpose of this study is to test the possibility of remedying the spatial discontinuities of the particles by locally reordering the particles.

We implement a spatialized particle filter to estimate the snow water equivalent (SWE) over a large territory in eastern Canada by assimilating local manual snow survey observations. We apply two reordering strategies based on 1) a simple ascending order sorting and 2) the Schaake Shuffle and evaluate their ability to maintain the spatial structure of the particles. To increase the amount of assimilated data, we investigate the inclusion of a second data set, in which SWE is indirectly estimated from snow depth. The two reordering solutions maintain the spatial structure of the individual particles throughout the winter season, which significantly reduces the random noise in the distribution of the particles and decreases the uncertainty associated with the estimation. The Schaake Shuffle proves to be a better tool for maintaining a realistic spatial structure for all particles, although we also found that sorting provides a simpler and satisfactory solution. The assimilation of the secondary data set improved SWE estimates in ungauged sites when compared with the open-loop model, but we noted no significant improvement when both snow courses and the SR50 data were assimilated.

Jean Odry et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-322', Bertrand Cluzet, 02 Dec 2021
  • RC2: 'Comment on tc-2021-322', Anonymous Referee #2, 05 Jan 2022

Jean Odry et al.

Data sets

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

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

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

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

Model code and software

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

Jean Odry et al.

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Short summary
The research deals with the assimilation of in-situ local snow observation 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 then according to the available observations. Especially, the paper focuses on the spatial coherence of the snow scenario proposed in the framework.