Selecting and weighting dynamical models using data-driven approaches
- Autores
- Le Bras, Pierre; Sévellec, Florian; Tandeo, Pierre; Ruiz, Juan Jose; Ailliot, Pierre
- Año de publicación
- 2024
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- In geosciences, multi-model ensembles are helpful to explore the robustness of a range of results. Toobtain a synthetic and improved representation of the studied dynamic system, the models are usually weighted.The simplest method, namely the model democracy, gives equal weights to all models, while more advancedapproaches base weights on agreement with available observations. Here, we focus on determining weights forvarious versions of an idealized model of the Atlantic Meridional Overturning Circulation. This is done by assessing their performance against synthetic observations (generated from one of the model versions) within a dataassimilation framework using the ensemble Kalman filter (EnKF). In contrast to traditional data assimilation, weimplement data-driven forecasts using the analog method based on catalogs of short-term trajectories. This approach allows us to efficiently emulate the model’s dynamics while keeping computational costs low. For eachmodel version, we compute a local performance metric, known as the contextual model evidence, to compareobservations and model forecasts. This metric, based on the innovation likelihood, is sensitive to differences inmodel dynamics and considers forecast and observation uncertainties. Finally, the weights are calculated usingboth model performance and model co-dependency and then evaluated on averages of long-term simulations.Results show good performance in identifying numerical simulations that best replicate observed short-termvariations. Additionally, it outperforms benchmark approaches such as strategies based on model democracy orclimatology when reconstructing missing distributions. These findings encourage the application of the proposedmethodology to more complex datasets in the future, like climate simulations.
Fil: Le Bras, Pierre. Imt Atlantique Bretagne Pays de la Loire.; Francia
Fil: Sévellec, Florian. Imt Atlantique Bretagne Pays de la Loire.; Francia
Fil: Tandeo, Pierre. Imt Atlantique Bretagne Pays de la Loire.; Francia
Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina. Universite de Bretagne Occidentale; Francia
Fil: Ailliot, Pierre. Imt Atlantique Bretagne Pays de la Loire.; Francia - Materia
-
Model selection
Data assimilation
Machine learning - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/261257
Ver los metadatos del registro completo
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Selecting and weighting dynamical models using data-driven approachesLe Bras, PierreSévellec, FlorianTandeo, PierreRuiz, Juan JoseAilliot, PierreModel selectionData assimilationMachine learninghttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1In geosciences, multi-model ensembles are helpful to explore the robustness of a range of results. Toobtain a synthetic and improved representation of the studied dynamic system, the models are usually weighted.The simplest method, namely the model democracy, gives equal weights to all models, while more advancedapproaches base weights on agreement with available observations. Here, we focus on determining weights forvarious versions of an idealized model of the Atlantic Meridional Overturning Circulation. This is done by assessing their performance against synthetic observations (generated from one of the model versions) within a dataassimilation framework using the ensemble Kalman filter (EnKF). In contrast to traditional data assimilation, weimplement data-driven forecasts using the analog method based on catalogs of short-term trajectories. This approach allows us to efficiently emulate the model’s dynamics while keeping computational costs low. For eachmodel version, we compute a local performance metric, known as the contextual model evidence, to compareobservations and model forecasts. This metric, based on the innovation likelihood, is sensitive to differences inmodel dynamics and considers forecast and observation uncertainties. Finally, the weights are calculated usingboth model performance and model co-dependency and then evaluated on averages of long-term simulations.Results show good performance in identifying numerical simulations that best replicate observed short-termvariations. Additionally, it outperforms benchmark approaches such as strategies based on model democracy orclimatology when reconstructing missing distributions. These findings encourage the application of the proposedmethodology to more complex datasets in the future, like climate simulations.Fil: Le Bras, Pierre. Imt Atlantique Bretagne Pays de la Loire.; FranciaFil: Sévellec, Florian. Imt Atlantique Bretagne Pays de la Loire.; FranciaFil: Tandeo, Pierre. Imt Atlantique Bretagne Pays de la Loire.; FranciaFil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina. Universite de Bretagne Occidentale; FranciaFil: Ailliot, Pierre. Imt Atlantique Bretagne Pays de la Loire.; FranciaEuropean Geosciences Union2024-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/261257Le Bras, Pierre; Sévellec, Florian; Tandeo, Pierre; Ruiz, Juan Jose; Ailliot, Pierre; Selecting and weighting dynamical models using data-driven approaches; European Geosciences Union; Nonlinear Processes in Geophysics; 31; 3; 7-2024; 303-3171607-7946CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://npg.copernicus.org/articles/31/303/2024/info:eu-repo/semantics/altIdentifier/doi/10.5194/npg-31-303-2024info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:44:28Zoai:ri.conicet.gov.ar:11336/261257instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:44:28.845CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Selecting and weighting dynamical models using data-driven approaches |
title |
Selecting and weighting dynamical models using data-driven approaches |
spellingShingle |
Selecting and weighting dynamical models using data-driven approaches Le Bras, Pierre Model selection Data assimilation Machine learning |
title_short |
Selecting and weighting dynamical models using data-driven approaches |
title_full |
Selecting and weighting dynamical models using data-driven approaches |
title_fullStr |
Selecting and weighting dynamical models using data-driven approaches |
title_full_unstemmed |
Selecting and weighting dynamical models using data-driven approaches |
title_sort |
Selecting and weighting dynamical models using data-driven approaches |
dc.creator.none.fl_str_mv |
Le Bras, Pierre Sévellec, Florian Tandeo, Pierre Ruiz, Juan Jose Ailliot, Pierre |
author |
Le Bras, Pierre |
author_facet |
Le Bras, Pierre Sévellec, Florian Tandeo, Pierre Ruiz, Juan Jose Ailliot, Pierre |
author_role |
author |
author2 |
Sévellec, Florian Tandeo, Pierre Ruiz, Juan Jose Ailliot, Pierre |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
Model selection Data assimilation Machine learning |
topic |
Model selection Data assimilation Machine learning |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
In geosciences, multi-model ensembles are helpful to explore the robustness of a range of results. Toobtain a synthetic and improved representation of the studied dynamic system, the models are usually weighted.The simplest method, namely the model democracy, gives equal weights to all models, while more advancedapproaches base weights on agreement with available observations. Here, we focus on determining weights forvarious versions of an idealized model of the Atlantic Meridional Overturning Circulation. This is done by assessing their performance against synthetic observations (generated from one of the model versions) within a dataassimilation framework using the ensemble Kalman filter (EnKF). In contrast to traditional data assimilation, weimplement data-driven forecasts using the analog method based on catalogs of short-term trajectories. This approach allows us to efficiently emulate the model’s dynamics while keeping computational costs low. For eachmodel version, we compute a local performance metric, known as the contextual model evidence, to compareobservations and model forecasts. This metric, based on the innovation likelihood, is sensitive to differences inmodel dynamics and considers forecast and observation uncertainties. Finally, the weights are calculated usingboth model performance and model co-dependency and then evaluated on averages of long-term simulations.Results show good performance in identifying numerical simulations that best replicate observed short-termvariations. Additionally, it outperforms benchmark approaches such as strategies based on model democracy orclimatology when reconstructing missing distributions. These findings encourage the application of the proposedmethodology to more complex datasets in the future, like climate simulations. Fil: Le Bras, Pierre. Imt Atlantique Bretagne Pays de la Loire.; Francia Fil: Sévellec, Florian. Imt Atlantique Bretagne Pays de la Loire.; Francia Fil: Tandeo, Pierre. Imt Atlantique Bretagne Pays de la Loire.; Francia Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina. Universite de Bretagne Occidentale; Francia Fil: Ailliot, Pierre. Imt Atlantique Bretagne Pays de la Loire.; Francia |
description |
In geosciences, multi-model ensembles are helpful to explore the robustness of a range of results. Toobtain a synthetic and improved representation of the studied dynamic system, the models are usually weighted.The simplest method, namely the model democracy, gives equal weights to all models, while more advancedapproaches base weights on agreement with available observations. Here, we focus on determining weights forvarious versions of an idealized model of the Atlantic Meridional Overturning Circulation. This is done by assessing their performance against synthetic observations (generated from one of the model versions) within a dataassimilation framework using the ensemble Kalman filter (EnKF). In contrast to traditional data assimilation, weimplement data-driven forecasts using the analog method based on catalogs of short-term trajectories. This approach allows us to efficiently emulate the model’s dynamics while keeping computational costs low. For eachmodel version, we compute a local performance metric, known as the contextual model evidence, to compareobservations and model forecasts. This metric, based on the innovation likelihood, is sensitive to differences inmodel dynamics and considers forecast and observation uncertainties. Finally, the weights are calculated usingboth model performance and model co-dependency and then evaluated on averages of long-term simulations.Results show good performance in identifying numerical simulations that best replicate observed short-termvariations. Additionally, it outperforms benchmark approaches such as strategies based on model democracy orclimatology when reconstructing missing distributions. These findings encourage the application of the proposedmethodology to more complex datasets in the future, like climate simulations. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-07 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/261257 Le Bras, Pierre; Sévellec, Florian; Tandeo, Pierre; Ruiz, Juan Jose; Ailliot, Pierre; Selecting and weighting dynamical models using data-driven approaches; European Geosciences Union; Nonlinear Processes in Geophysics; 31; 3; 7-2024; 303-317 1607-7946 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/261257 |
identifier_str_mv |
Le Bras, Pierre; Sévellec, Florian; Tandeo, Pierre; Ruiz, Juan Jose; Ailliot, Pierre; Selecting and weighting dynamical models using data-driven approaches; European Geosciences Union; Nonlinear Processes in Geophysics; 31; 3; 7-2024; 303-317 1607-7946 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://npg.copernicus.org/articles/31/303/2024/ info:eu-repo/semantics/altIdentifier/doi/10.5194/npg-31-303-2024 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
European Geosciences Union |
publisher.none.fl_str_mv |
European Geosciences Union |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844613399759028224 |
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13.070432 |