Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems
- Autores
- Compagnucci, Rosa H.; Araneo, Diego; Canziani, Pablo Osvaldo
- 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.
Laboratorio de Investigación de Sistemas Ecológicos y Ambientales - Materia
-
Ciencias Naturales
Atmospheric circulation
Extended empirical orthogonal function
Principal components analysis
Principal sequence pattern analysis
Synoptic climatology
T-mode approach - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/84959
Ver los metadatos del registro completo
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Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systemsCompagnucci, Rosa H.Araneo, DiegoCanziani, Pablo OsvaldoCiencias NaturalesAtmospheric circulationExtended empirical orthogonal functionPrincipal components analysisPrincipal sequence pattern analysisSynoptic climatologyT-mode approachA 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.Laboratorio de Investigación de Sistemas Ecológicos y Ambientales2001info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf197-217http://sedici.unlp.edu.ar/handle/10915/84959enginfo:eu-repo/semantics/altIdentifier/issn/0899-8418info:eu-repo/semantics/altIdentifier/doi/10.1002/joc.601info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-10T12:18:58Zoai:sedici.unlp.edu.ar:10915/84959Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-10 12:18:59.08SEDICI (UNLP) - Universidad Nacional de La Platafalse |
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, Rosa H. Ciencias Naturales Atmospheric circulation Extended empirical orthogonal function Principal components analysis Principal sequence pattern analysis Synoptic climatology T-mode approach |
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, Rosa H. Araneo, Diego Canziani, Pablo Osvaldo |
author |
Compagnucci, Rosa H. |
author_facet |
Compagnucci, Rosa H. Araneo, Diego Canziani, Pablo Osvaldo |
author_role |
author |
author2 |
Araneo, Diego Canziani, Pablo Osvaldo |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Naturales Atmospheric circulation Extended empirical orthogonal function Principal components analysis Principal sequence pattern analysis Synoptic climatology T-mode approach |
topic |
Ciencias Naturales Atmospheric circulation Extended empirical orthogonal function Principal components analysis Principal sequence pattern analysis Synoptic climatology T-mode approach |
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. Laboratorio de Investigación de Sistemas Ecológicos y Ambientales |
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. |
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 Articulo 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://sedici.unlp.edu.ar/handle/10915/84959 |
url |
http://sedici.unlp.edu.ar/handle/10915/84959 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/issn/0899-8418 info:eu-repo/semantics/altIdentifier/doi/10.1002/joc.601 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf 197-217 |
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