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
- Institución
- Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
- OAI Identificador
- paperaa:paper_08998418_v21_n2_p197_Compagnucci
Ver los metadatos del registro completo
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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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12.712165 |