Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment
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 o...
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2023
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00068375 2023-09-05T13:23:43+02:00 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 2023-08 electronic https://doi.org/10.5194/tc-17-3329-2023 https://noa.gwlb.de/receive/cop_mods_00068375 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066804/tc-17-3329-2023.pdf https://tc.copernicus.org/articles/17/3329/2023/tc-17-3329-2023.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-17-3329-2023 https://noa.gwlb.de/receive/cop_mods_00068375 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066804/tc-17-3329-2023.pdf https://tc.copernicus.org/articles/17/3329/2023/tc-17-3329-2023.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2023 ftnonlinearchiv https://doi.org/10.5194/tc-17-3329-2023 2023-08-20T23:20:20Z 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 nRMSE of the ... Article in Journal/Newspaper The Cryosphere Niedersächsisches Online-Archiv NOA The Cryosphere 17 8 3329 3342 |
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article Verlagsveröffentlichung 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 |
article Verlagsveröffentlichung |
description |
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 nRMSE of the ... |
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 |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-3329-2023 https://noa.gwlb.de/receive/cop_mods_00068375 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066804/tc-17-3329-2023.pdf https://tc.copernicus.org/articles/17/3329/2023/tc-17-3329-2023.pdf |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-17-3329-2023 https://noa.gwlb.de/receive/cop_mods_00068375 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066804/tc-17-3329-2023.pdf https://tc.copernicus.org/articles/17/3329/2023/tc-17-3329-2023.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt 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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1776204302678753280 |