Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems

Autores
Compagnucci, R.H.; Araneo, D.; Canziani, P.O.
Año de publicación
2001
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A new eigentechnique approach, Principal Sequence Pattern Analysis (PSPA), is introduced for the analysis of spatial pattern sequence, as an extension of the traditional Principal Component Analysis set in the T-Mode. In this setting, the variables are sequences of k spatial fields of a given meteorological variable. PSPA is described and applied to a sample of 256 consecutive daily 1000 hPa geopotential height fields. The results of the application of the technique to 5-day sequences demonstrate the advantages of this procedure in identifying field pattern sequences, thereby allowing the determination of the evolution and development of the systems, together with cyclogenesis and anticyclogenesis processes. In order to complete the study, the more traditional Extended Empirical Orthogonal Function (EEOF) analysis, which is the S-mode equivalent of the PSPA, was applied to the same dataset. For EEOF, it was not possible to identify any real sequences that could correspond to the sequences of patterns yielded by the EEOF. Furthermore, the explained variance distribution in the EEOF was significantly different from that obtained with PSPA. Conversely, the PSPA approach allowed for the identification of the sequences corresponding to those sequences observed in the data. Using diagrams of the squares of the component loadings values, as a function of time, the study of the times of occurrence of dominant field characteristics was also possible. In other words, successful determination of periods where the basic flow was dominant and times when strongly perturbed transient events with a significant meridional component occurred, was facilitated by PSPA. © 2001 Royal Meteorological Society.
Fil:Compagnucci, R.H. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Araneo, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Canziani, P.O. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fuente
Int. J. Climatol. 2001;21(2):197-217
Materia
Atmospheric circulation
Extended empirical orthogonal function
Principal components analysis
Principal sequence pattern analysis
Synoptic climatology
T-mode approach
atmospheric dynamics
empirical analysis
numerical method
principal component analysis
synoptic meteorology
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
paperaa:paper_08998418_v21_n2_p197_Compagnucci

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oai_identifier_str paperaa:paper_08998418_v21_n2_p197_Compagnucci
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repository_id_str 1896
network_name_str Biblioteca Digital (UBA-FCEN)
spelling Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systemsCompagnucci, R.H.Araneo, D.Canziani, P.O.Atmospheric circulationExtended empirical orthogonal functionPrincipal components analysisPrincipal sequence pattern analysisSynoptic climatologyT-mode approachatmospheric dynamicsempirical analysisnumerical methodprincipal component analysissynoptic meteorologyA new eigentechnique approach, Principal Sequence Pattern Analysis (PSPA), is introduced for the analysis of spatial pattern sequence, as an extension of the traditional Principal Component Analysis set in the T-Mode. In this setting, the variables are sequences of k spatial fields of a given meteorological variable. PSPA is described and applied to a sample of 256 consecutive daily 1000 hPa geopotential height fields. The results of the application of the technique to 5-day sequences demonstrate the advantages of this procedure in identifying field pattern sequences, thereby allowing the determination of the evolution and development of the systems, together with cyclogenesis and anticyclogenesis processes. In order to complete the study, the more traditional Extended Empirical Orthogonal Function (EEOF) analysis, which is the S-mode equivalent of the PSPA, was applied to the same dataset. For EEOF, it was not possible to identify any real sequences that could correspond to the sequences of patterns yielded by the EEOF. Furthermore, the explained variance distribution in the EEOF was significantly different from that obtained with PSPA. Conversely, the PSPA approach allowed for the identification of the sequences corresponding to those sequences observed in the data. Using diagrams of the squares of the component loadings values, as a function of time, the study of the times of occurrence of dominant field characteristics was also possible. In other words, successful determination of periods where the basic flow was dominant and times when strongly perturbed transient events with a significant meridional component occurred, was facilitated by PSPA. © 2001 Royal Meteorological Society.Fil:Compagnucci, R.H. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Araneo, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Canziani, P.O. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.2001info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12110/paper_08998418_v21_n2_p197_CompagnucciInt. J. Climatol. 2001;21(2):197-217reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar2025-10-16T09:30:14Zpaperaa:paper_08998418_v21_n2_p197_CompagnucciInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-10-16 09:30:15.329Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems
title Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems
spellingShingle Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems
Compagnucci, R.H.
Atmospheric circulation
Extended empirical orthogonal function
Principal components analysis
Principal sequence pattern analysis
Synoptic climatology
T-mode approach
atmospheric dynamics
empirical analysis
numerical method
principal component analysis
synoptic meteorology
title_short Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems
title_full Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems
title_fullStr Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems
title_full_unstemmed Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems
title_sort Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems
dc.creator.none.fl_str_mv Compagnucci, R.H.
Araneo, D.
Canziani, P.O.
author Compagnucci, R.H.
author_facet Compagnucci, R.H.
Araneo, D.
Canziani, P.O.
author_role author
author2 Araneo, D.
Canziani, P.O.
author2_role author
author
dc.subject.none.fl_str_mv Atmospheric circulation
Extended empirical orthogonal function
Principal components analysis
Principal sequence pattern analysis
Synoptic climatology
T-mode approach
atmospheric dynamics
empirical analysis
numerical method
principal component analysis
synoptic meteorology
topic Atmospheric circulation
Extended empirical orthogonal function
Principal components analysis
Principal sequence pattern analysis
Synoptic climatology
T-mode approach
atmospheric dynamics
empirical analysis
numerical method
principal component analysis
synoptic meteorology
dc.description.none.fl_txt_mv A new eigentechnique approach, Principal Sequence Pattern Analysis (PSPA), is introduced for the analysis of spatial pattern sequence, as an extension of the traditional Principal Component Analysis set in the T-Mode. In this setting, the variables are sequences of k spatial fields of a given meteorological variable. PSPA is described and applied to a sample of 256 consecutive daily 1000 hPa geopotential height fields. The results of the application of the technique to 5-day sequences demonstrate the advantages of this procedure in identifying field pattern sequences, thereby allowing the determination of the evolution and development of the systems, together with cyclogenesis and anticyclogenesis processes. In order to complete the study, the more traditional Extended Empirical Orthogonal Function (EEOF) analysis, which is the S-mode equivalent of the PSPA, was applied to the same dataset. For EEOF, it was not possible to identify any real sequences that could correspond to the sequences of patterns yielded by the EEOF. Furthermore, the explained variance distribution in the EEOF was significantly different from that obtained with PSPA. Conversely, the PSPA approach allowed for the identification of the sequences corresponding to those sequences observed in the data. Using diagrams of the squares of the component loadings values, as a function of time, the study of the times of occurrence of dominant field characteristics was also possible. In other words, successful determination of periods where the basic flow was dominant and times when strongly perturbed transient events with a significant meridional component occurred, was facilitated by PSPA. © 2001 Royal Meteorological Society.
Fil:Compagnucci, R.H. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Araneo, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Canziani, P.O. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
description A new eigentechnique approach, Principal Sequence Pattern Analysis (PSPA), is introduced for the analysis of spatial pattern sequence, as an extension of the traditional Principal Component Analysis set in the T-Mode. In this setting, the variables are sequences of k spatial fields of a given meteorological variable. PSPA is described and applied to a sample of 256 consecutive daily 1000 hPa geopotential height fields. The results of the application of the technique to 5-day sequences demonstrate the advantages of this procedure in identifying field pattern sequences, thereby allowing the determination of the evolution and development of the systems, together with cyclogenesis and anticyclogenesis processes. In order to complete the study, the more traditional Extended Empirical Orthogonal Function (EEOF) analysis, which is the S-mode equivalent of the PSPA, was applied to the same dataset. For EEOF, it was not possible to identify any real sequences that could correspond to the sequences of patterns yielded by the EEOF. Furthermore, the explained variance distribution in the EEOF was significantly different from that obtained with PSPA. Conversely, the PSPA approach allowed for the identification of the sequences corresponding to those sequences observed in the data. Using diagrams of the squares of the component loadings values, as a function of time, the study of the times of occurrence of dominant field characteristics was also possible. In other words, successful determination of periods where the basic flow was dominant and times when strongly perturbed transient events with a significant meridional component occurred, was facilitated by PSPA. © 2001 Royal Meteorological Society.
publishDate 2001
dc.date.none.fl_str_mv 2001
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/20.500.12110/paper_08998418_v21_n2_p197_Compagnucci
url http://hdl.handle.net/20.500.12110/paper_08998418_v21_n2_p197_Compagnucci
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Int. J. Climatol. 2001;21(2):197-217
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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