A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment
International audience The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertai...
Published in: | The Cryosphere |
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Online Access: | https://hal.archives-ouvertes.fr/hal-01923173 https://hal.archives-ouvertes.fr/hal-01923173/document https://hal.archives-ouvertes.fr/hal-01923173/file/an2018-pub00057932.pdf https://doi.org/10.5194/tc-12-2287-2018 |
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ftccsdartic:oai:HAL:hal-01923173v1 2023-05-15T18:32:12+02:00 A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment Un schéma de filtre particulaire pour l'assimilation de données multi-variée dans un modèle de neige à l'échelle ponctuelle dans les Alpes Piazzi, G. Thirel, Guillaume Campo, L. Gabellani, S. Hydrosystèmes continentaux anthropisés : ressources, risques, restauration (UR HYCAR) Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA) 2018 https://hal.archives-ouvertes.fr/hal-01923173 https://hal.archives-ouvertes.fr/hal-01923173/document https://hal.archives-ouvertes.fr/hal-01923173/file/an2018-pub00057932.pdf https://doi.org/10.5194/tc-12-2287-2018 en eng HAL CCSD Copernicus info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-12-2287-2018 hal-01923173 https://hal.archives-ouvertes.fr/hal-01923173 https://hal.archives-ouvertes.fr/hal-01923173/document https://hal.archives-ouvertes.fr/hal-01923173/file/an2018-pub00057932.pdf doi:10.5194/tc-12-2287-2018 IRSTEA: PUB00057932 info:eu-repo/semantics/OpenAccess ISSN: 1994-0424 EISSN: 1994-0416 The Cryosphere https://hal.archives-ouvertes.fr/hal-01923173 The Cryosphere, Copernicus 2018, 12, pp.2287-2306. ⟨10.5194/tc-12-2287-2018⟩ snow hydrological model MODELE HYDROLOGIQUE NEIGE [SDE]Environmental Sciences info:eu-repo/semantics/article Journal articles 2018 ftccsdartic https://doi.org/10.5194/tc-12-2287-2018 2021-11-07T02:34:09Z International audience The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims to investigate the performance of a multivariate sequential importance resampling - particle filter scheme, designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameter perturbation on the filter updating of the snowpack state; the system sensitivity to (3) the frequency of the assimilated observations, and (4) the ensemble size.The perturbation of the meteorological forcing data generally turns out to be insufficient for preventing the sample impoverishment of the particle sample, which is highly limited when jointly perturbating key model parameters. However, the parameter perturbation sharpens the system sensitivity to the frequency of the assimilated observations, which can be successfully relaxed by introducing indirectly estimated information on snow-mass-related variables. The ensemble size is found not to greatly impact the filter performance in this point-scale application. Article in Journal/Newspaper The Cryosphere Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) The Cryosphere 12 7 2287 2306 |
institution |
Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
snow hydrological model MODELE HYDROLOGIQUE NEIGE [SDE]Environmental Sciences |
spellingShingle |
snow hydrological model MODELE HYDROLOGIQUE NEIGE [SDE]Environmental Sciences Piazzi, G. Thirel, Guillaume Campo, L. Gabellani, S. A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment |
topic_facet |
snow hydrological model MODELE HYDROLOGIQUE NEIGE [SDE]Environmental Sciences |
description |
International audience The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims to investigate the performance of a multivariate sequential importance resampling - particle filter scheme, designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameter perturbation on the filter updating of the snowpack state; the system sensitivity to (3) the frequency of the assimilated observations, and (4) the ensemble size.The perturbation of the meteorological forcing data generally turns out to be insufficient for preventing the sample impoverishment of the particle sample, which is highly limited when jointly perturbating key model parameters. However, the parameter perturbation sharpens the system sensitivity to the frequency of the assimilated observations, which can be successfully relaxed by introducing indirectly estimated information on snow-mass-related variables. The ensemble size is found not to greatly impact the filter performance in this point-scale application. |
author2 |
Hydrosystèmes continentaux anthropisés : ressources, risques, restauration (UR HYCAR) Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA) |
format |
Article in Journal/Newspaper |
author |
Piazzi, G. Thirel, Guillaume Campo, L. Gabellani, S. |
author_facet |
Piazzi, G. Thirel, Guillaume Campo, L. Gabellani, S. |
author_sort |
Piazzi, G. |
title |
A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment |
title_short |
A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment |
title_full |
A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment |
title_fullStr |
A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment |
title_full_unstemmed |
A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment |
title_sort |
particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an alpine environment |
publisher |
HAL CCSD |
publishDate |
2018 |
url |
https://hal.archives-ouvertes.fr/hal-01923173 https://hal.archives-ouvertes.fr/hal-01923173/document https://hal.archives-ouvertes.fr/hal-01923173/file/an2018-pub00057932.pdf https://doi.org/10.5194/tc-12-2287-2018 |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
ISSN: 1994-0424 EISSN: 1994-0416 The Cryosphere https://hal.archives-ouvertes.fr/hal-01923173 The Cryosphere, Copernicus 2018, 12, pp.2287-2306. ⟨10.5194/tc-12-2287-2018⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-12-2287-2018 hal-01923173 https://hal.archives-ouvertes.fr/hal-01923173 https://hal.archives-ouvertes.fr/hal-01923173/document https://hal.archives-ouvertes.fr/hal-01923173/file/an2018-pub00057932.pdf doi:10.5194/tc-12-2287-2018 IRSTEA: PUB00057932 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/tc-12-2287-2018 |
container_title |
The Cryosphere |
container_volume |
12 |
container_issue |
7 |
container_start_page |
2287 |
op_container_end_page |
2306 |
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1766216283422982144 |