Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment
International audience The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface tem...
Published in: | The Cryosphere |
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Main Authors: | , , , |
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Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2023
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Online Access: | https://hal.science/hal-04570294 https://hal.science/hal-04570294/document https://hal.science/hal-04570294/file/Alonso-Gonza%CC%81lez%20et%20al.%20-%202023%20-%20Exploring%20the%20potential%20of%20thermal%20infrared%20remote.pdf https://doi.org/10.5194/tc-17-3329-2023 |
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English |
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[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [SDE.IE]Environmental Sciences/Environmental Engineering [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology |
spellingShingle |
[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [SDE.IE]Environmental Sciences/Environmental Engineering [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology Alonso-González, Esteban Gascoin, Simon Arioli, Sara Picard, Ghislain Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment |
topic_facet |
[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [SDE.IE]Environmental Sciences/Environmental Engineering [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology |
description |
International audience The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface temperature (LST) can be observed from space, but its potential to improve SWE simulations remains underexplored. This is likely due to the insufficient temporal or spatial resolution offered by the current thermal infrared (TIR) missions. However, three planned missions will provide global-scale TIR data at much higher spatiotemporal resolution in the coming years.To investigate the value of TIR data to improve SWE estimation, we developed a synthetic data assimilation (DA) experiment at five snow-dominated sites covering a latitudinal gradient in the Northern Hemisphere. We generated synthetic true LST and SWE series by forcing an energy balance snowpack model with the ERA5-Land reanalysis. We used this synthetic true LST to recover the synthetic true SWE from a degraded version of ERA5-Land. We defined different observation scenarios to emulate the revisiting times of Landsat 8 (16 d) and the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) (3 d) while accounting for cloud cover. We replicated the experiments 100 times at each experimental site to assess the robustness of the assimilation process with respect to cloud cover under both revisiting scenarios. We performed the assimilation using two different approaches: a sequential scheme (particle filter) and a smoother (particle batch smoother).The results show that LST DA using the smoother reduced the normalized root mean square error (nRMSE) of the SWE simulations from 61 % (open loop) to 17 % and 13 % for 16 d revisit and 3 d revisit respectively in the absence of clouds. We found similar but higher nRMSE values by removing observations due to cloud cover but with a substantial increase in the standard deviation of the ... |
author2 |
Centre d'études spatiales de la biosphère (CESBIO) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université de Toulouse (UT)-Université de Toulouse (UT)-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 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) |
format |
Article in Journal/Newspaper |
author |
Alonso-González, Esteban Gascoin, Simon Arioli, Sara Picard, Ghislain |
author_facet |
Alonso-González, Esteban Gascoin, Simon Arioli, Sara Picard, Ghislain |
author_sort |
Alonso-González, Esteban |
title |
Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment |
title_short |
Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment |
title_full |
Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment |
title_fullStr |
Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment |
title_full_unstemmed |
Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment |
title_sort |
exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04570294 https://hal.science/hal-04570294/document https://hal.science/hal-04570294/file/Alonso-Gonza%CC%81lez%20et%20al.%20-%202023%20-%20Exploring%20the%20potential%20of%20thermal%20infrared%20remote.pdf https://doi.org/10.5194/tc-17-3329-2023 |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
ISSN: 1994-0424 EISSN: 1994-0416 The Cryosphere https://hal.science/hal-04570294 The Cryosphere, 2023, 17 (8), pp.3329 - 3342. ⟨10.5194/tc-17-3329-2023⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-17-3329-2023 hal-04570294 https://hal.science/hal-04570294 https://hal.science/hal-04570294/document https://hal.science/hal-04570294/file/Alonso-Gonza%CC%81lez%20et%20al.%20-%202023%20-%20Exploring%20the%20potential%20of%20thermal%20infrared%20remote.pdf doi:10.5194/tc-17-3329-2023 WOS: 001050001700001 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/tc-17-3329-2023 |
container_title |
The Cryosphere |
container_volume |
17 |
container_issue |
8 |
container_start_page |
3329 |
op_container_end_page |
3342 |
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spelling |
ftmeteofrance:oai:HAL:hal-04570294v1 2024-09-15T18:38:59+00:00 Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment Alonso-González, Esteban Gascoin, Simon Arioli, Sara Picard, Ghislain Centre d'études spatiales de la biosphère (CESBIO) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université de Toulouse (UT)-Université de Toulouse (UT)-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 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) 2023 https://hal.science/hal-04570294 https://hal.science/hal-04570294/document https://hal.science/hal-04570294/file/Alonso-Gonza%CC%81lez%20et%20al.%20-%202023%20-%20Exploring%20the%20potential%20of%20thermal%20infrared%20remote.pdf https://doi.org/10.5194/tc-17-3329-2023 en eng HAL CCSD Copernicus info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-17-3329-2023 hal-04570294 https://hal.science/hal-04570294 https://hal.science/hal-04570294/document https://hal.science/hal-04570294/file/Alonso-Gonza%CC%81lez%20et%20al.%20-%202023%20-%20Exploring%20the%20potential%20of%20thermal%20infrared%20remote.pdf doi:10.5194/tc-17-3329-2023 WOS: 001050001700001 info:eu-repo/semantics/OpenAccess ISSN: 1994-0424 EISSN: 1994-0416 The Cryosphere https://hal.science/hal-04570294 The Cryosphere, 2023, 17 (8), pp.3329 - 3342. ⟨10.5194/tc-17-3329-2023⟩ [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [SDE.IE]Environmental Sciences/Environmental Engineering [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology info:eu-repo/semantics/article Journal articles 2023 ftmeteofrance https://doi.org/10.5194/tc-17-3329-2023 2024-06-25T00:01:47Z International audience The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface temperature (LST) can be observed from space, but its potential to improve SWE simulations remains underexplored. This is likely due to the insufficient temporal or spatial resolution offered by the current thermal infrared (TIR) missions. However, three planned missions will provide global-scale TIR data at much higher spatiotemporal resolution in the coming years.To investigate the value of TIR data to improve SWE estimation, we developed a synthetic data assimilation (DA) experiment at five snow-dominated sites covering a latitudinal gradient in the Northern Hemisphere. We generated synthetic true LST and SWE series by forcing an energy balance snowpack model with the ERA5-Land reanalysis. We used this synthetic true LST to recover the synthetic true SWE from a degraded version of ERA5-Land. We defined different observation scenarios to emulate the revisiting times of Landsat 8 (16 d) and the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) (3 d) while accounting for cloud cover. We replicated the experiments 100 times at each experimental site to assess the robustness of the assimilation process with respect to cloud cover under both revisiting scenarios. We performed the assimilation using two different approaches: a sequential scheme (particle filter) and a smoother (particle batch smoother).The results show that LST DA using the smoother reduced the normalized root mean square error (nRMSE) of the SWE simulations from 61 % (open loop) to 17 % and 13 % for 16 d revisit and 3 d revisit respectively in the absence of clouds. We found similar but higher nRMSE values by removing observations due to cloud cover but with a substantial increase in the standard deviation of the ... Article in Journal/Newspaper The Cryosphere Météo-France: HAL The Cryosphere 17 8 3329 3342 |