Correction of NWP Ocean Surface Wind Biases with Machine Learning

IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2024), Acting for Sustainability and Resilience, 7-12 July 2024, Athens, Greece.-- 4 pages, 3 figures, 1 table.-- © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses,...

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Detalles Bibliográficos
Autores: Makarova, Evgeniia, Portabella, Marcos, Stoffelen, Ad, Lin, Wenming
Tipo de recurso: otro
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/368594
Acceso en línea:http://hdl.handle.net/10261/368594
Access Level:acceso abierto
Palabra clave:Stress-equivalent winds
NWP biases
Scatterometer
Machine learning
Neural networks
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spelling Correction of NWP Ocean Surface Wind Biases with Machine LearningMakarova, EvgeniiaPortabella, MarcosStoffelen, AdLin, WenmingStress-equivalent windsNWP biasesScatterometerMachine learningNeural networksIEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2024), Acting for Sustainability and Resilience, 7-12 July 2024, Athens, Greece.-- 4 pages, 3 figures, 1 table.-- © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis work addresses the need for modelling and correcting the persistent Numerical Weather Prediction (NWP) local biases of the ocean surface wind forecasts. For such purpose, several NWP and ocean model output parameters are used as inputs to the machine learning and neural network models to generate the corrections of the NWP forecasts. The results show that such models are able to substantially reduce NWP local biases and therefore its overall error variance, opening the door for both its operational use as well the development of long-term data series of valuable ocean forcing datasetsWith the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewedInstitute of Electrical and Electronics EngineersAgencia Estatal de Investigación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/otherhttp://purl.org/coar/resource_type/c_3248Postprintinfo:eu-repo/semantics/acceptedVersioninfo:eu-repo/semantics/bookParthttp://hdl.handle.net/10261/368594reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1109/IGARSS53475.2024.10642503Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3685942026-05-22T06:33:51Z
dc.title.none.fl_str_mv Correction of NWP Ocean Surface Wind Biases with Machine Learning
title Correction of NWP Ocean Surface Wind Biases with Machine Learning
spellingShingle Correction of NWP Ocean Surface Wind Biases with Machine Learning
Makarova, Evgeniia
Stress-equivalent winds
NWP biases
Scatterometer
Machine learning
Neural networks
title_short Correction of NWP Ocean Surface Wind Biases with Machine Learning
title_full Correction of NWP Ocean Surface Wind Biases with Machine Learning
title_fullStr Correction of NWP Ocean Surface Wind Biases with Machine Learning
title_full_unstemmed Correction of NWP Ocean Surface Wind Biases with Machine Learning
title_sort Correction of NWP Ocean Surface Wind Biases with Machine Learning
dc.creator.none.fl_str_mv Makarova, Evgeniia
Portabella, Marcos
Stoffelen, Ad
Lin, Wenming
author Makarova, Evgeniia
author_facet Makarova, Evgeniia
Portabella, Marcos
Stoffelen, Ad
Lin, Wenming
author_role author
author2 Portabella, Marcos
Stoffelen, Ad
Lin, Wenming
author2_role author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Stress-equivalent winds
NWP biases
Scatterometer
Machine learning
Neural networks
topic Stress-equivalent winds
NWP biases
Scatterometer
Machine learning
Neural networks
description IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2024), Acting for Sustainability and Resilience, 7-12 July 2024, Athens, Greece.-- 4 pages, 3 figures, 1 table.-- © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/other
http://purl.org/coar/resource_type/c_3248
Postprint
info:eu-repo/semantics/acceptedVersion
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format other
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/368594
url http://hdl.handle.net/10261/368594
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1109/IGARSS53475.2024.10642503
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
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 15,811543

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