DADA: data assimilation for the detection and attribution of weather and climate-related events

Autores
Hannart, Alexis; Carrasi, A.; Bocquet, M.; Ghil, M.; Naveau, P.; Pulido, M.; Ruiz, Juan Jose; Tandeo, P.
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We describe a new approach that allows for systematic causal attribution of weather and climate-related events, in near-real time. The method is designed so as to facilitate its implementation at meteorological centers by relying on data and methods that are routinely available when numerically forecasting the weather. We thus show that causal attribution can be obtained as a by-product of data assimilation procedures run on a daily basis to update numerical weather prediction (NWP) models with new atmospheric observations; hence, the proposed methodology can take advantage of the powerful computational and observational capacity of weather forecasting centers. We explain the theoretical rationale of this approach and sketch the most prominent features of a “data assimilation–based detection and attribution” (DADA) procedure. The proposal is illustrated in the context of the classical three-variable Lorenz model with additional forcing. The paper concludes by raising several theoretical and practical questions that need to be addressed to make the proposal operational within NWP centers.
Fil: Hannart, Alexis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina
Fil: Carrasi, A.. Mohn-Sverdrup Center; Noruega
Fil: Bocquet, M.. Université Paris-Est; Francia
Fil: Ghil, M.. Ecole Normale Supérieure; Francia. University of California at Los Angeles; Estados Unidos
Fil: Naveau, P.. Centre National de la Recherche Scientifique; Francia
Fil: Pulido, M.. Universidad Nacional del Nordeste; Argentina
Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina
Fil: Tandeo, P.. Télécom Bretagne; Francia
Materia
Data assimilation
Detection and attribution
Climate change
Casualty theory
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/17809
Acceder
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network_name_str CONICET Digital (CONICET)
spelling DADA: data assimilation for the detection and attribution of weather and climate-related eventsHannart, AlexisCarrasi, A.Bocquet, M.Ghil, M.Naveau, P.Pulido, M.Ruiz, Juan JoseTandeo, P.Data assimilationDetection and attributionClimate changeCasualty theoryhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1We describe a new approach that allows for systematic causal attribution of weather and climate-related events, in near-real time. The method is designed so as to facilitate its implementation at meteorological centers by relying on data and methods that are routinely available when numerically forecasting the weather. We thus show that causal attribution can be obtained as a by-product of data assimilation procedures run on a daily basis to update numerical weather prediction (NWP) models with new atmospheric observations; hence, the proposed methodology can take advantage of the powerful computational and observational capacity of weather forecasting centers. We explain the theoretical rationale of this approach and sketch the most prominent features of a “data assimilation–based detection and attribution” (DADA) procedure. The proposal is illustrated in the context of the classical three-variable Lorenz model with additional forcing. The paper concludes by raising several theoretical and practical questions that need to be addressed to make the proposal operational within NWP centers.Fil: Hannart, Alexis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; ArgentinaFil: Carrasi, A.. Mohn-Sverdrup Center; NoruegaFil: Bocquet, M.. Université Paris-Est; FranciaFil: Ghil, M.. Ecole Normale Supérieure; Francia. University of California at Los Angeles; Estados UnidosFil: Naveau, P.. Centre National de la Recherche Scientifique; FranciaFil: Pulido, M.. Universidad Nacional del Nordeste; ArgentinaFil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; ArgentinaFil: Tandeo, P.. Télécom Bretagne; FranciaSpringer2016-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/17809Hannart, Alexis; Carrasi, A.; Bocquet, M.; Ghil, M.; Naveau, P.; et al.; DADA: data assimilation for the detection and attribution of weather and climate-related events; Springer; Climatic Change; 136; 2; 5-2016; 155-1740165-00091573-1480enginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10584-016-1595-3info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s10584-016-1595-3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:47:47Zoai:ri.conicet.gov.ar:11336/17809instacron: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 10:47:47.299CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv DADA: data assimilation for the detection and attribution of weather and climate-related events
title DADA: data assimilation for the detection and attribution of weather and climate-related events
spellingShingle DADA: data assimilation for the detection and attribution of weather and climate-related events
Hannart, Alexis
Data assimilation
Detection and attribution
Climate change
Casualty theory
title_short DADA: data assimilation for the detection and attribution of weather and climate-related events
title_full DADA: data assimilation for the detection and attribution of weather and climate-related events
title_fullStr DADA: data assimilation for the detection and attribution of weather and climate-related events
title_full_unstemmed DADA: data assimilation for the detection and attribution of weather and climate-related events
title_sort DADA: data assimilation for the detection and attribution of weather and climate-related events
dc.creator.none.fl_str_mv Hannart, Alexis
Carrasi, A.
Bocquet, M.
Ghil, M.
Naveau, P.
Pulido, M.
Ruiz, Juan Jose
Tandeo, P.
author Hannart, Alexis
author_facet Hannart, Alexis
Carrasi, A.
Bocquet, M.
Ghil, M.
Naveau, P.
Pulido, M.
Ruiz, Juan Jose
Tandeo, P.
author_role author
author2 Carrasi, A.
Bocquet, M.
Ghil, M.
Naveau, P.
Pulido, M.
Ruiz, Juan Jose
Tandeo, P.
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Data assimilation
Detection and attribution
Climate change
Casualty theory
topic Data assimilation
Detection and attribution
Climate change
Casualty theory
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We describe a new approach that allows for systematic causal attribution of weather and climate-related events, in near-real time. The method is designed so as to facilitate its implementation at meteorological centers by relying on data and methods that are routinely available when numerically forecasting the weather. We thus show that causal attribution can be obtained as a by-product of data assimilation procedures run on a daily basis to update numerical weather prediction (NWP) models with new atmospheric observations; hence, the proposed methodology can take advantage of the powerful computational and observational capacity of weather forecasting centers. We explain the theoretical rationale of this approach and sketch the most prominent features of a “data assimilation–based detection and attribution” (DADA) procedure. The proposal is illustrated in the context of the classical three-variable Lorenz model with additional forcing. The paper concludes by raising several theoretical and practical questions that need to be addressed to make the proposal operational within NWP centers.
Fil: Hannart, Alexis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina
Fil: Carrasi, A.. Mohn-Sverdrup Center; Noruega
Fil: Bocquet, M.. Université Paris-Est; Francia
Fil: Ghil, M.. Ecole Normale Supérieure; Francia. University of California at Los Angeles; Estados Unidos
Fil: Naveau, P.. Centre National de la Recherche Scientifique; Francia
Fil: Pulido, M.. Universidad Nacional del Nordeste; Argentina
Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina
Fil: Tandeo, P.. Télécom Bretagne; Francia
description We describe a new approach that allows for systematic causal attribution of weather and climate-related events, in near-real time. The method is designed so as to facilitate its implementation at meteorological centers by relying on data and methods that are routinely available when numerically forecasting the weather. We thus show that causal attribution can be obtained as a by-product of data assimilation procedures run on a daily basis to update numerical weather prediction (NWP) models with new atmospheric observations; hence, the proposed methodology can take advantage of the powerful computational and observational capacity of weather forecasting centers. We explain the theoretical rationale of this approach and sketch the most prominent features of a “data assimilation–based detection and attribution” (DADA) procedure. The proposal is illustrated in the context of the classical three-variable Lorenz model with additional forcing. The paper concludes by raising several theoretical and practical questions that need to be addressed to make the proposal operational within NWP centers.
publishDate 2016
dc.date.none.fl_str_mv 2016-05
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/11336/17809
Hannart, Alexis; Carrasi, A.; Bocquet, M.; Ghil, M.; Naveau, P.; et al.; DADA: data assimilation for the detection and attribution of weather and climate-related events; Springer; Climatic Change; 136; 2; 5-2016; 155-174
0165-0009
1573-1480
url http://hdl.handle.net/11336/17809
identifier_str_mv Hannart, Alexis; Carrasi, A.; Bocquet, M.; Ghil, M.; Naveau, P.; et al.; DADA: data assimilation for the detection and attribution of weather and climate-related events; Springer; Climatic Change; 136; 2; 5-2016; 155-174
0165-0009
1573-1480
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s10584-016-1595-3
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s10584-016-1595-3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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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score 13.070432

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