Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields

Autores
Pérez Aracil, Jorge; Marina, Cosmin M.; Zorita, Eduardo; Barriopedro, David; Zaninelli, Pablo Gabriel; Giuliani, Matteo; Castelletti, Andrea; Gutiérrez, Pedro A.; Salcedo Sanz, Sancho
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents a novel hybrid approach for the probabilistic reconstruction of meteorological fields based on the combined use of the analogue method (AM) and deep autoencoders (AEs). The AE–AM algorithm trains a deep AE in the predictor fields, which the encoder filters towards a compressed space of reduced dimensionality. The AM is then applied in this latent space to find similar situations (analogues) in the historical record, from which the target field can be reconstructed. The AE–AM is compared to the classical AM, in which flow analogues are explicitly searched in the fully resolved field of the predictor, which may contain useless information for the reconstruction. We evaluate the performance of these two approaches in reconstructing the daily maximum temperature (target) from sea-level pressure fields (predictor) recorded during eight major European heat waves of the 1950–2010 period. We show that the proposed AE–AM approach outperforms the standard AM algorithm in reconstructing the magnitude and spatial pattern of the considered heat wave events. The improvement ranges from 7% to 22% in skill score, depending on the heat wave analyzed, demonstrating the potential added value of the hybrid method.
Fil: Pérez Aracil, Jorge. Universidad de Alcalá; España
Fil: Marina, Cosmin M.. Universidad de Alcalá; España
Fil: Zorita, Eduardo. Helmholtz-Zentrum Geesthacht; Alemania
Fil: Barriopedro, David. Consejo Superior de Investigaciones Científicas; España
Fil: Zaninelli, Pablo Gabriel. 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. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Giuliani, Matteo. Politecnico di Milano; Italia
Fil: Castelletti, Andrea. Politecnico di Milano; Italia
Fil: Gutiérrez, Pedro A.. Universidad de Córdoba; España
Fil: Salcedo Sanz, Sancho. Universidad de Alcalá; España
Materia
ANALOGUE METHOD
AUTOENCODERS
FIELD RECONSTRUCTION
HEAT WAVES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/260952
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/260952
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fieldsPérez Aracil, JorgeMarina, Cosmin M.Zorita, EduardoBarriopedro, DavidZaninelli, Pablo GabrielGiuliani, MatteoCastelletti, AndreaGutiérrez, Pedro A.Salcedo Sanz, SanchoANALOGUE METHODAUTOENCODERSFIELD RECONSTRUCTIONHEAT WAVEShttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1This paper presents a novel hybrid approach for the probabilistic reconstruction of meteorological fields based on the combined use of the analogue method (AM) and deep autoencoders (AEs). The AE–AM algorithm trains a deep AE in the predictor fields, which the encoder filters towards a compressed space of reduced dimensionality. The AM is then applied in this latent space to find similar situations (analogues) in the historical record, from which the target field can be reconstructed. The AE–AM is compared to the classical AM, in which flow analogues are explicitly searched in the fully resolved field of the predictor, which may contain useless information for the reconstruction. We evaluate the performance of these two approaches in reconstructing the daily maximum temperature (target) from sea-level pressure fields (predictor) recorded during eight major European heat waves of the 1950–2010 period. We show that the proposed AE–AM approach outperforms the standard AM algorithm in reconstructing the magnitude and spatial pattern of the considered heat wave events. The improvement ranges from 7% to 22% in skill score, depending on the heat wave analyzed, demonstrating the potential added value of the hybrid method.Fil: Pérez Aracil, Jorge. Universidad de Alcalá; EspañaFil: Marina, Cosmin M.. Universidad de Alcalá; EspañaFil: Zorita, Eduardo. Helmholtz-Zentrum Geesthacht; AlemaniaFil: Barriopedro, David. Consejo Superior de Investigaciones Científicas; EspañaFil: Zaninelli, Pablo Gabriel. 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. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Giuliani, Matteo. Politecnico di Milano; ItaliaFil: Castelletti, Andrea. Politecnico di Milano; ItaliaFil: Gutiérrez, Pedro A.. Universidad de Córdoba; EspañaFil: Salcedo Sanz, Sancho. Universidad de Alcalá; EspañaBlackwell Publishing2024-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/260952Pérez Aracil, Jorge; Marina, Cosmin M.; Zorita, Eduardo; Barriopedro, David; Zaninelli, Pablo Gabriel; et al.; Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields; Blackwell Publishing; Annals of the New York Academy of Sciences; 1541; 1; 10-2024; 230-2420077-8923CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://nyaspubs.onlinelibrary.wiley.com/doi/10.1111/nyas.15243info:eu-repo/semantics/altIdentifier/doi/10.1111/nyas.15243info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:03:52Zoai:ri.conicet.gov.ar:11336/260952instacron: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:34982025-10-22 11:03:52.653CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields
title Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields
spellingShingle Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields
Pérez Aracil, Jorge
ANALOGUE METHOD
AUTOENCODERS
FIELD RECONSTRUCTION
HEAT WAVES
title_short Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields
title_full Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields
title_fullStr Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields
title_full_unstemmed Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields
title_sort Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields
dc.creator.none.fl_str_mv Pérez Aracil, Jorge
Marina, Cosmin M.
Zorita, Eduardo
Barriopedro, David
Zaninelli, Pablo Gabriel
Giuliani, Matteo
Castelletti, Andrea
Gutiérrez, Pedro A.
Salcedo Sanz, Sancho
author Pérez Aracil, Jorge
author_facet Pérez Aracil, Jorge
Marina, Cosmin M.
Zorita, Eduardo
Barriopedro, David
Zaninelli, Pablo Gabriel
Giuliani, Matteo
Castelletti, Andrea
Gutiérrez, Pedro A.
Salcedo Sanz, Sancho
author_role author
author2 Marina, Cosmin M.
Zorita, Eduardo
Barriopedro, David
Zaninelli, Pablo Gabriel
Giuliani, Matteo
Castelletti, Andrea
Gutiérrez, Pedro A.
Salcedo Sanz, Sancho
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ANALOGUE METHOD
AUTOENCODERS
FIELD RECONSTRUCTION
HEAT WAVES
topic ANALOGUE METHOD
AUTOENCODERS
FIELD RECONSTRUCTION
HEAT WAVES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper presents a novel hybrid approach for the probabilistic reconstruction of meteorological fields based on the combined use of the analogue method (AM) and deep autoencoders (AEs). The AE–AM algorithm trains a deep AE in the predictor fields, which the encoder filters towards a compressed space of reduced dimensionality. The AM is then applied in this latent space to find similar situations (analogues) in the historical record, from which the target field can be reconstructed. The AE–AM is compared to the classical AM, in which flow analogues are explicitly searched in the fully resolved field of the predictor, which may contain useless information for the reconstruction. We evaluate the performance of these two approaches in reconstructing the daily maximum temperature (target) from sea-level pressure fields (predictor) recorded during eight major European heat waves of the 1950–2010 period. We show that the proposed AE–AM approach outperforms the standard AM algorithm in reconstructing the magnitude and spatial pattern of the considered heat wave events. The improvement ranges from 7% to 22% in skill score, depending on the heat wave analyzed, demonstrating the potential added value of the hybrid method.
Fil: Pérez Aracil, Jorge. Universidad de Alcalá; España
Fil: Marina, Cosmin M.. Universidad de Alcalá; España
Fil: Zorita, Eduardo. Helmholtz-Zentrum Geesthacht; Alemania
Fil: Barriopedro, David. Consejo Superior de Investigaciones Científicas; España
Fil: Zaninelli, Pablo Gabriel. 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. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Giuliani, Matteo. Politecnico di Milano; Italia
Fil: Castelletti, Andrea. Politecnico di Milano; Italia
Fil: Gutiérrez, Pedro A.. Universidad de Córdoba; España
Fil: Salcedo Sanz, Sancho. Universidad de Alcalá; España
description This paper presents a novel hybrid approach for the probabilistic reconstruction of meteorological fields based on the combined use of the analogue method (AM) and deep autoencoders (AEs). The AE–AM algorithm trains a deep AE in the predictor fields, which the encoder filters towards a compressed space of reduced dimensionality. The AM is then applied in this latent space to find similar situations (analogues) in the historical record, from which the target field can be reconstructed. The AE–AM is compared to the classical AM, in which flow analogues are explicitly searched in the fully resolved field of the predictor, which may contain useless information for the reconstruction. We evaluate the performance of these two approaches in reconstructing the daily maximum temperature (target) from sea-level pressure fields (predictor) recorded during eight major European heat waves of the 1950–2010 period. We show that the proposed AE–AM approach outperforms the standard AM algorithm in reconstructing the magnitude and spatial pattern of the considered heat wave events. The improvement ranges from 7% to 22% in skill score, depending on the heat wave analyzed, demonstrating the potential added value of the hybrid method.
publishDate 2024
dc.date.none.fl_str_mv 2024-10
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/11336/260952
Pérez Aracil, Jorge; Marina, Cosmin M.; Zorita, Eduardo; Barriopedro, David; Zaninelli, Pablo Gabriel; et al.; Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields; Blackwell Publishing; Annals of the New York Academy of Sciences; 1541; 1; 10-2024; 230-242
0077-8923
CONICET Digital
CONICET
url http://hdl.handle.net/11336/260952
identifier_str_mv Pérez Aracil, Jorge; Marina, Cosmin M.; Zorita, Eduardo; Barriopedro, David; Zaninelli, Pablo Gabriel; et al.; Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields; Blackwell Publishing; Annals of the New York Academy of Sciences; 1541; 1; 10-2024; 230-242
0077-8923
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://nyaspubs.onlinelibrary.wiley.com/doi/10.1111/nyas.15243
info:eu-repo/semantics/altIdentifier/doi/10.1111/nyas.15243
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Blackwell Publishing
publisher.none.fl_str_mv Blackwell Publishing
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
_version_ 1846781281957838848
score 12.982451

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