Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems
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 meteor...
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2001
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_08998418_v21_n2_p197_Compagnucci http://hdl.handle.net/20.500.12110/paper_08998418_v21_n2_p197_Compagnucci |
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paper:paper_08998418_v21_n2_p197_Compagnucci2023-06-08T15:49:36Z Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems Compagnucci, Rosa Hilda Araneo, Diego C. Canziani, Pablo Osvaldo 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 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. 2001 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_08998418_v21_n2_p197_Compagnucci http://hdl.handle.net/20.500.12110/paper_08998418_v21_n2_p197_Compagnucci |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| 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 |
| spellingShingle |
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 Compagnucci, Rosa Hilda Araneo, Diego C. Canziani, Pablo Osvaldo Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems |
| topic_facet |
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 |
| 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. |
| author |
Compagnucci, Rosa Hilda Araneo, Diego C. Canziani, Pablo Osvaldo |
| author_facet |
Compagnucci, Rosa Hilda Araneo, Diego C. Canziani, Pablo Osvaldo |
| author_sort |
Compagnucci, Rosa Hilda |
| title |
Principal sequence pattern analysis: A new approach to classifying the evolution of atmospheric systems |
| 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 |
| publishDate |
2001 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_08998418_v21_n2_p197_Compagnucci http://hdl.handle.net/20.500.12110/paper_08998418_v21_n2_p197_Compagnucci |
| work_keys_str_mv |
AT compagnuccirosahilda principalsequencepatternanalysisanewapproachtoclassifyingtheevolutionofatmosphericsystems AT araneodiegoc principalsequencepatternanalysisanewapproachtoclassifyingtheevolutionofatmosphericsystems AT canzianipabloosvaldo principalsequencepatternanalysisanewapproachtoclassifyingtheevolutionofatmosphericsystems |
| _version_ |
1768546216036007936 |