A Lagrangian uncertainty quantification approach to validate ocean model datasets

This work presents a methodology to measure how well the material transport produced by different ocean models aligns with observational data, using their trajectories as a basis for comparison. To this end, recent results that relate an uncertainty metric to invariant dynamic structures are used. T...

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Detalles Bibliográficos
Autores: García-Sánchez, G., Agaoglou, M., Smith, E.M.C., Mancho, A.M.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/420763
Acceso en línea:http://hdl.handle.net/10261/420763
https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004208281&doi=10.1016%2Fj.physd.2025.134690&partnerID=40&md5=8944ae9e24d515cef2fb1f52aee819ce
Access Level:acceso abierto
Palabra clave:Invariant dynamical structures
Material transport
Ocean models
Uncertainty quantification
Ocean engineering
Dynamic structure
Dynamical structure
Invariant dynamical structure
Invariant dynamics
Lagrangian
Observational data
Ocean model
Uncertainty
Uncertainty quantifications
Lagrange multipliers
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spelling A Lagrangian uncertainty quantification approach to validate ocean model datasetsGarcía-Sánchez, G.Agaoglou, M.Smith, E.M.C.Mancho, A.M.Invariant dynamical structuresMaterial transportOcean modelsUncertainty quantificationOcean engineeringDynamic structureDynamical structureInvariant dynamical structureInvariant dynamicsLagrangianObservational dataOcean modelUncertaintyUncertainty quantificationsLagrange multipliersThis work presents a methodology to measure how well the material transport produced by different ocean models aligns with observational data, using their trajectories as a basis for comparison. To this end, recent results that relate an uncertainty metric to invariant dynamic structures are used. These connections shed light on how to implement statistical averaging strategies to systematically assess the quality of the ocean data set and its performance in terms of Lagrangian transport. The method is applied using both reanalysis and analysis data in the North Atlantic, where observed drifter trajectory data serve as benchmarks for validation. To assess the reliability of the proposed methodology, it is tested alongside a comparable, purpose-built example conducted under controlled conditions within the same region. We present evidence that the proposed methodology provides valuable information on model performance on different spatial and temporal scales. © 2025 The AuthorsGS and AMM acknowledge the support of a CSIC PIE project Ref. 202250E001. AMM, GGS and MA acknowledge the support from grant PID2021-123348OB-I00 funded by MCIN/AEI/10.13039/501100011033/ and by FEDER A way to make Europe. MA acknowledges the support from the grant CEX2019-000904-S and IJC2019-040168-I funded by: MCIN/AEI/10.13039/501100011033. AMM acknowledges the support from grant EIN2020-112235 funded by MCIN/ AEI /10.13039/501100011033/ and by the European Union NextGenerationEU/PRTR . Part of this work was done during a stay of EMCS at ICMAT within the program Erasmus +.Peer reviewedElsevierMinisterio de Ciencia e Innovación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/420763https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004208281&doi=10.1016%2Fj.physd.2025.134690&partnerID=40&md5=8944ae9e24d515cef2fb1f52aee819cereponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésPhysica D: Nonlinear Phenomenahttps://doi.org/10.1016/j.physd.2025.134690Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4207632026-05-22T06:33:51Z
dc.title.none.fl_str_mv A Lagrangian uncertainty quantification approach to validate ocean model datasets
title A Lagrangian uncertainty quantification approach to validate ocean model datasets
spellingShingle A Lagrangian uncertainty quantification approach to validate ocean model datasets
García-Sánchez, G.
Invariant dynamical structures
Material transport
Ocean models
Uncertainty quantification
Ocean engineering
Dynamic structure
Dynamical structure
Invariant dynamical structure
Invariant dynamics
Lagrangian
Observational data
Ocean model
Uncertainty
Uncertainty quantifications
Lagrange multipliers
title_short A Lagrangian uncertainty quantification approach to validate ocean model datasets
title_full A Lagrangian uncertainty quantification approach to validate ocean model datasets
title_fullStr A Lagrangian uncertainty quantification approach to validate ocean model datasets
title_full_unstemmed A Lagrangian uncertainty quantification approach to validate ocean model datasets
title_sort A Lagrangian uncertainty quantification approach to validate ocean model datasets
dc.creator.none.fl_str_mv García-Sánchez, G.
Agaoglou, M.
Smith, E.M.C.
Mancho, A.M.
author García-Sánchez, G.
author_facet García-Sánchez, G.
Agaoglou, M.
Smith, E.M.C.
Mancho, A.M.
author_role author
author2 Agaoglou, M.
Smith, E.M.C.
Mancho, A.M.
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Invariant dynamical structures
Material transport
Ocean models
Uncertainty quantification
Ocean engineering
Dynamic structure
Dynamical structure
Invariant dynamical structure
Invariant dynamics
Lagrangian
Observational data
Ocean model
Uncertainty
Uncertainty quantifications
Lagrange multipliers
topic Invariant dynamical structures
Material transport
Ocean models
Uncertainty quantification
Ocean engineering
Dynamic structure
Dynamical structure
Invariant dynamical structure
Invariant dynamics
Lagrangian
Observational data
Ocean model
Uncertainty
Uncertainty quantifications
Lagrange multipliers
description This work presents a methodology to measure how well the material transport produced by different ocean models aligns with observational data, using their trajectories as a basis for comparison. To this end, recent results that relate an uncertainty metric to invariant dynamic structures are used. These connections shed light on how to implement statistical averaging strategies to systematically assess the quality of the ocean data set and its performance in terms of Lagrangian transport. The method is applied using both reanalysis and analysis data in the North Atlantic, where observed drifter trajectory data serve as benchmarks for validation. To assess the reliability of the proposed methodology, it is tested alongside a comparable, purpose-built example conducted under controlled conditions within the same region. We present evidence that the proposed methodology provides valuable information on model performance on different spatial and temporal scales. © 2025 The Authors
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/420763
https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004208281&doi=10.1016%2Fj.physd.2025.134690&partnerID=40&md5=8944ae9e24d515cef2fb1f52aee819ce
url http://hdl.handle.net/10261/420763
https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004208281&doi=10.1016%2Fj.physd.2025.134690&partnerID=40&md5=8944ae9e24d515cef2fb1f52aee819ce
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Physica D: Nonlinear Phenomena
https://doi.org/10.1016/j.physd.2025.134690
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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