Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology
In this study, we investigate the fully multivariate state and parameter estimation through idealised simulations of a dynamics-only model that uses the novel Maxwell elasto-brittle (MEB) sea-ice rheology and in which we estimate not only the sea-ice concentration, thickness and velocity, but also i...
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Online Access: | https://hdl.handle.net/11585/970436 https://doi.org/10.5194/tc-18-2381-2024 |
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ftunibolognairis:oai:cris.unibo.it:11585/970436 2024-06-23T07:56:38+00:00 Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology Chen, Yumeng Smith, Polly Carrassi, Alberto Pasmans, Ivo Bertino, Laurent Bocquet, Marc Finn, Tobias Sebastian Rampal, Pierre Dansereau, Véronique Chen, Yumeng Smith, Polly Carrassi, Alberto Pasmans, Ivo Bertino, Laurent Bocquet, Marc Finn, Tobias Sebastian Rampal, Pierre Dansereau, Véronique 2024 ELETTRONICO https://hdl.handle.net/11585/970436 https://doi.org/10.5194/tc-18-2381-2024 eng eng info:eu-repo/semantics/altIdentifier/wos/WOS:001221209600001 volume:18 issue:5 firstpage:2381 lastpage:2406 numberofpages:26 journal:THE CRYOSPHERE https://hdl.handle.net/11585/970436 doi:10.5194/tc-18-2381-2024 Data Assimilation Sea-Ice info:eu-repo/semantics/article 2024 ftunibolognairis https://doi.org/10.5194/tc-18-2381-2024 2024-06-03T14:11:26Z In this study, we investigate the fully multivariate state and parameter estimation through idealised simulations of a dynamics-only model that uses the novel Maxwell elasto-brittle (MEB) sea-ice rheology and in which we estimate not only the sea-ice concentration, thickness and velocity, but also its level of damage, internal stress and cohesion. Specifically, we estimate the air drag coefficient and the so-called damage parameter of the MEB model. Mimicking the realistic observation network with different combinations of observations, we demonstrate that various issues can potentially arise in a complex sea-ice model, especially in instances for which the external forcing dominates the model forecast error growth. Even though further investigation will be needed using an operational (a coupled dynamics-thermodynamics) sea-ice model, we show that, with the current observation network, it is possible to improve both the observed and the unobserved model state forecast and parameter accuracy. Article in Journal/Newspaper Sea ice IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) The Cryosphere 18 5 2381 2406 |
institution |
Open Polar |
collection |
IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) |
op_collection_id |
ftunibolognairis |
language |
English |
topic |
Data Assimilation Sea-Ice |
spellingShingle |
Data Assimilation Sea-Ice Chen, Yumeng Smith, Polly Carrassi, Alberto Pasmans, Ivo Bertino, Laurent Bocquet, Marc Finn, Tobias Sebastian Rampal, Pierre Dansereau, Véronique Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology |
topic_facet |
Data Assimilation Sea-Ice |
description |
In this study, we investigate the fully multivariate state and parameter estimation through idealised simulations of a dynamics-only model that uses the novel Maxwell elasto-brittle (MEB) sea-ice rheology and in which we estimate not only the sea-ice concentration, thickness and velocity, but also its level of damage, internal stress and cohesion. Specifically, we estimate the air drag coefficient and the so-called damage parameter of the MEB model. Mimicking the realistic observation network with different combinations of observations, we demonstrate that various issues can potentially arise in a complex sea-ice model, especially in instances for which the external forcing dominates the model forecast error growth. Even though further investigation will be needed using an operational (a coupled dynamics-thermodynamics) sea-ice model, we show that, with the current observation network, it is possible to improve both the observed and the unobserved model state forecast and parameter accuracy. |
author2 |
Chen, Yumeng Smith, Polly Carrassi, Alberto Pasmans, Ivo Bertino, Laurent Bocquet, Marc Finn, Tobias Sebastian Rampal, Pierre Dansereau, Véronique |
format |
Article in Journal/Newspaper |
author |
Chen, Yumeng Smith, Polly Carrassi, Alberto Pasmans, Ivo Bertino, Laurent Bocquet, Marc Finn, Tobias Sebastian Rampal, Pierre Dansereau, Véronique |
author_facet |
Chen, Yumeng Smith, Polly Carrassi, Alberto Pasmans, Ivo Bertino, Laurent Bocquet, Marc Finn, Tobias Sebastian Rampal, Pierre Dansereau, Véronique |
author_sort |
Chen, Yumeng |
title |
Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology |
title_short |
Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology |
title_full |
Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology |
title_fullStr |
Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology |
title_full_unstemmed |
Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology |
title_sort |
multivariate state and parameter estimation with data assimilation applied to sea-ice models using a maxwell elasto-brittle rheology |
publishDate |
2024 |
url |
https://hdl.handle.net/11585/970436 https://doi.org/10.5194/tc-18-2381-2024 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:001221209600001 volume:18 issue:5 firstpage:2381 lastpage:2406 numberofpages:26 journal:THE CRYOSPHERE https://hdl.handle.net/11585/970436 doi:10.5194/tc-18-2381-2024 |
op_doi |
https://doi.org/10.5194/tc-18-2381-2024 |
container_title |
The Cryosphere |
container_volume |
18 |
container_issue |
5 |
container_start_page |
2381 |
op_container_end_page |
2406 |
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1802649892006395904 |