On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model
International audience This paper examines the ability of optical re-flectance data assimilation to improve snow depth and snow water equivalent simulations from a chain of models with the SAFRAN meteorological model driving the detailed multi-layer snowpack model Crocus now including a two-stream r...
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Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2016
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Online Access: | https://hal-insu.archives-ouvertes.fr/insu-01388769 https://hal-insu.archives-ouvertes.fr/insu-01388769/document https://hal-insu.archives-ouvertes.fr/insu-01388769/file/CRYOSPHERE%20-%20On%20the%20assimilation%20of%20optical%20reflectances%20and%20snow%20depth%20observations%20into%20a%20detailed%20snowpack%20model.pdf https://doi.org/10.5194/tc-10-1021-2016-supplement |
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ftunivnantes:oai:HAL:insu-01388769v1 |
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record_format |
openpolar |
institution |
Open Polar |
collection |
Université de Nantes: HAL-UNIV-NANTES |
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ftunivnantes |
language |
English |
topic |
[SDE]Environmental Sciences |
spellingShingle |
[SDE]Environmental Sciences Charrois, Luc Cosme, Emmanuel Dumont, Marie Lafaysse, Matthieu Morin, Samuel Libois, Quentin Picard, Ghislain On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model |
topic_facet |
[SDE]Environmental Sciences |
description |
International audience This paper examines the ability of optical re-flectance data assimilation to improve snow depth and snow water equivalent simulations from a chain of models with the SAFRAN meteorological model driving the detailed multi-layer snowpack model Crocus now including a two-stream radiative transfer model for snow, TARTES. The direct use of reflectance data, allowed by TARTES, instead of higher level snow products, mitigates uncertainties due to commonly used retrieval algorithms. Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter , to represent simulation uncertainties. In snowpack mod-eling, uncertainties of simulations are primarily assigned to meteorological forcings. Here, a method of stochastic perturbation based on an autoregressive model is implemented to explicitly simulate the consequences of these uncertainties on the snowpack estimates. Through twin experiments, the assimilation of synthetic spectral reflectances matching the MODerate resolution Imaging Spectroradiometer (MODIS) spectral bands is examined over five seasons at the Col du Lautaret, located in the French Alps. Overall, the assimilation of MODIS-like data reduces by 45 % the root mean square errors (RMSE) on snow depth and snow water equivalent. At this study site, the lack of MODIS data on cloudy days does not affect the assimilation performance significantly. The combined assimilation of MODIS-like reflectances and a few snow depth measurements throughout the 2010/2011 season further reduces RMSEs by roughly 70 %. This work suggests that the assimilation of optical reflectances has the potential to become an essential component of spatialized snowpack simulation and forecast systems. The assimilation of real MODIS data will be investigated in future works. |
author2 |
Laboratoire de glaciologie et géophysique de l'environnement (LGGE) Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ) Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 )-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 ) Centre national de recherches météorologiques (CNRM) Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS) |
format |
Article in Journal/Newspaper |
author |
Charrois, Luc Cosme, Emmanuel Dumont, Marie Lafaysse, Matthieu Morin, Samuel Libois, Quentin Picard, Ghislain |
author_facet |
Charrois, Luc Cosme, Emmanuel Dumont, Marie Lafaysse, Matthieu Morin, Samuel Libois, Quentin Picard, Ghislain |
author_sort |
Charrois, Luc |
title |
On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model |
title_short |
On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model |
title_full |
On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model |
title_fullStr |
On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model |
title_full_unstemmed |
On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model |
title_sort |
on the assimilation of optical reflectances and snow depth observations into a detailed snowpack model |
publisher |
HAL CCSD |
publishDate |
2016 |
url |
https://hal-insu.archives-ouvertes.fr/insu-01388769 https://hal-insu.archives-ouvertes.fr/insu-01388769/document https://hal-insu.archives-ouvertes.fr/insu-01388769/file/CRYOSPHERE%20-%20On%20the%20assimilation%20of%20optical%20reflectances%20and%20snow%20depth%20observations%20into%20a%20detailed%20snowpack%20model.pdf https://doi.org/10.5194/tc-10-1021-2016-supplement |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
ISSN: 1994-0424 EISSN: 1994-0416 The Cryosphere https://hal-insu.archives-ouvertes.fr/insu-01388769 The Cryosphere, 2016, 10, pp.1021 - 1038. ⟨10.5194/tc-10-1021-2016-supplement⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-10-1021-2016-supplement insu-01388769 https://hal-insu.archives-ouvertes.fr/insu-01388769 https://hal-insu.archives-ouvertes.fr/insu-01388769/document https://hal-insu.archives-ouvertes.fr/insu-01388769/file/CRYOSPHERE%20-%20On%20the%20assimilation%20of%20optical%20reflectances%20and%20snow%20depth%20observations%20into%20a%20detailed%20snowpack%20model.pdf doi:10.5194/tc-10-1021-2016-supplement |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/tc-10-1021-2016-supplement |
_version_ |
1766216505881526272 |
spelling |
ftunivnantes:oai:HAL:insu-01388769v1 2023-05-15T18:32:24+02:00 On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model Charrois, Luc Cosme, Emmanuel Dumont, Marie Lafaysse, Matthieu Morin, Samuel Libois, Quentin Picard, Ghislain Laboratoire de glaciologie et géophysique de l'environnement (LGGE) Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ) Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 )-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 ) Centre national de recherches météorologiques (CNRM) Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS) 2016 https://hal-insu.archives-ouvertes.fr/insu-01388769 https://hal-insu.archives-ouvertes.fr/insu-01388769/document https://hal-insu.archives-ouvertes.fr/insu-01388769/file/CRYOSPHERE%20-%20On%20the%20assimilation%20of%20optical%20reflectances%20and%20snow%20depth%20observations%20into%20a%20detailed%20snowpack%20model.pdf https://doi.org/10.5194/tc-10-1021-2016-supplement en eng HAL CCSD Copernicus info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-10-1021-2016-supplement insu-01388769 https://hal-insu.archives-ouvertes.fr/insu-01388769 https://hal-insu.archives-ouvertes.fr/insu-01388769/document https://hal-insu.archives-ouvertes.fr/insu-01388769/file/CRYOSPHERE%20-%20On%20the%20assimilation%20of%20optical%20reflectances%20and%20snow%20depth%20observations%20into%20a%20detailed%20snowpack%20model.pdf doi:10.5194/tc-10-1021-2016-supplement info:eu-repo/semantics/OpenAccess ISSN: 1994-0424 EISSN: 1994-0416 The Cryosphere https://hal-insu.archives-ouvertes.fr/insu-01388769 The Cryosphere, 2016, 10, pp.1021 - 1038. ⟨10.5194/tc-10-1021-2016-supplement⟩ [SDE]Environmental Sciences info:eu-repo/semantics/article Journal articles 2016 ftunivnantes https://doi.org/10.5194/tc-10-1021-2016-supplement 2023-03-01T06:07:19Z International audience This paper examines the ability of optical re-flectance data assimilation to improve snow depth and snow water equivalent simulations from a chain of models with the SAFRAN meteorological model driving the detailed multi-layer snowpack model Crocus now including a two-stream radiative transfer model for snow, TARTES. The direct use of reflectance data, allowed by TARTES, instead of higher level snow products, mitigates uncertainties due to commonly used retrieval algorithms. Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter , to represent simulation uncertainties. In snowpack mod-eling, uncertainties of simulations are primarily assigned to meteorological forcings. Here, a method of stochastic perturbation based on an autoregressive model is implemented to explicitly simulate the consequences of these uncertainties on the snowpack estimates. Through twin experiments, the assimilation of synthetic spectral reflectances matching the MODerate resolution Imaging Spectroradiometer (MODIS) spectral bands is examined over five seasons at the Col du Lautaret, located in the French Alps. Overall, the assimilation of MODIS-like data reduces by 45 % the root mean square errors (RMSE) on snow depth and snow water equivalent. At this study site, the lack of MODIS data on cloudy days does not affect the assimilation performance significantly. The combined assimilation of MODIS-like reflectances and a few snow depth measurements throughout the 2010/2011 season further reduces RMSEs by roughly 70 %. This work suggests that the assimilation of optical reflectances has the potential to become an essential component of spatialized snowpack simulation and forecast systems. The assimilation of real MODIS data will be investigated in future works. Article in Journal/Newspaper The Cryosphere Université de Nantes: HAL-UNIV-NANTES |