Using a Particle Filter to Estimate the Spatial Distribution of the Snowpack Water Equivalent

Philippe Cantet Département de génie civil et génie du bâtiment, Université de Sherbrooke, Sherbrooke, Quebec, Canada

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M. A. Boucher Département de génie civil et génie du bâtiment, Université de Sherbrooke, Sherbrooke, Quebec, Canada

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S. Lachance-Coutier Direction de l’expertise hydrique, Ministère du Développement durable, de l’Environnement et de la Lutte contre les changements climatiques, Quebec, Quebec, Canada

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R. Turcotte Direction de l’expertise hydrique, Ministère du Développement durable, de l’Environnement et de la Lutte contre les changements climatiques, Quebec, Quebec, Canada

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V. Fortin Centre Météorologique Canadien, Recherche en prévision numérique environnementale, Environnement et Changement climatique Canada, Dorval, Quebec, Canada.

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Abstract

A snow model forced by temperature and precipitation is used to simulate the spatial distribution of snow water equivalent (SWE) over a 600 000 km2 portion of the province of Quebec, Canada. We propose to improve model simulations by assimilating SWE data from sporadic manual snow surveys with a particle filter. A temporally and spatially correlated perturbation of the meteorological forcing is used to generate the set of particles. The magnitude of the perturbations is fixed objectively. First, the particle filter and direct insertion were both applied on 88 sites for which measured SWE consisted of more or less five values per year over a period of 17 years. The temporal correlation of perturbations enables us to improve the accuracy and the ensemble dispersion of the particle filter, while the spatial correlation leads to a spatial coherence in the particle weights. The spatial estimates of SWE obtained with the particle filter are compared with those obtained through optimal interpolation of the snow survey data, which is the current operational practice in Quebec. Cross-validation results as well as validation against an independent dataset show that the proposed particle filter enables us to improve the spatial distribution of the snow water equivalent compared with optimal interpolation.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Philippe Cantet, cantet.philip@gmail.com

Abstract

A snow model forced by temperature and precipitation is used to simulate the spatial distribution of snow water equivalent (SWE) over a 600 000 km2 portion of the province of Quebec, Canada. We propose to improve model simulations by assimilating SWE data from sporadic manual snow surveys with a particle filter. A temporally and spatially correlated perturbation of the meteorological forcing is used to generate the set of particles. The magnitude of the perturbations is fixed objectively. First, the particle filter and direct insertion were both applied on 88 sites for which measured SWE consisted of more or less five values per year over a period of 17 years. The temporal correlation of perturbations enables us to improve the accuracy and the ensemble dispersion of the particle filter, while the spatial correlation leads to a spatial coherence in the particle weights. The spatial estimates of SWE obtained with the particle filter are compared with those obtained through optimal interpolation of the snow survey data, which is the current operational practice in Quebec. Cross-validation results as well as validation against an independent dataset show that the proposed particle filter enables us to improve the spatial distribution of the snow water equivalent compared with optimal interpolation.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Philippe Cantet, cantet.philip@gmail.com
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