Can principal component analysis provide atmospheric circulation or teleconnecion patterns?
This investigation examines principal component (PC) methodology and the interpretation of the displays, such as eigenvalue magnitude, loadings and scores, which the methodology provides. The key question posed is, to what extent can S- and T-mode decompositions of a dispersion matrix yield the kind...
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2008
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_08998418_v28_n6_p703_Compagnucci http://hdl.handle.net/20.500.12110/paper_08998418_v28_n6_p703_Compagnucci |
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paper:paper_08998418_v28_n6_p703_Compagnucci2023-06-08T15:49:39Z Can principal component analysis provide atmospheric circulation or teleconnecion patterns? Atmospheric circulation Circulation change Circulation patterns Principal component analysis Regionalization S-Mode T-Mode Teleconnections Atmospheric movements Correlation methods Covariance matrix Eigenvalues and eigenfunctions Flow patterns Principal component analysis Amalgamations Atmospheric circulation Synoptic flow patterns Meteorology atmospheric circulation correlation covariance analysis eigenvalue flow pattern methodology principal component analysis teleconnection This investigation examines principal component (PC) methodology and the interpretation of the displays, such as eigenvalue magnitude, loadings and scores, which the methodology provides. The key question posed is, to what extent can S- and T-mode decompositions of a dispersion matrix yield the kinds of interpretations placed on them typically? In particular, a series of experiments are designed based on various amalgamations of three distinct synoptic flow patterns. Since these flow patterns are known, a priori, this allows testing via subtle alterations of the methodology to determine whether there is equivalence between the S- and T-mode decompositions, the degree to which the flow patterns or teleconnections can be recovered by each mode, and the interpretation of each mode. The findings are examined in two contexts: how well they classify the flow patterns, and how well they provide meaningful teleconnections. Both correlation and covariance dispersion matrices are used to determine differences that arise from the standardization. Additionally, unrotated and rotated results are included. By examining a variety of commonly applied methodologies, the results hold for a wider range of studies. Key findings are that eigenvalue degeneracy can influence one mode (but not the other) or both modes for any set of flow patterns resulting in pattern intermixing at times. Similarly, such degeneracy is found in one or both dispersion matrices. Congruence coefficients are used to provide a measure of validity by matching the PC loadings to the parent correlations and covariances. This matching is vital as the loadings exhibit dipoles that have been interpreted historically as physically meaningful, but the present work indicates they may arise purely through the methodology. Overall, we observe that S-mode results can be interpreted as teleconnection patterns and T-mode as flow patterns for well-designed analyses that are meticulously scrutinized for methodological problems. Copyright © 2007 Royal Meteorological Society. 2008 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_08998418_v28_n6_p703_Compagnucci http://hdl.handle.net/20.500.12110/paper_08998418_v28_n6_p703_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 Circulation change Circulation patterns Principal component analysis Regionalization S-Mode T-Mode Teleconnections Atmospheric movements Correlation methods Covariance matrix Eigenvalues and eigenfunctions Flow patterns Principal component analysis Amalgamations Atmospheric circulation Synoptic flow patterns Meteorology atmospheric circulation correlation covariance analysis eigenvalue flow pattern methodology principal component analysis teleconnection |
| spellingShingle |
Atmospheric circulation Circulation change Circulation patterns Principal component analysis Regionalization S-Mode T-Mode Teleconnections Atmospheric movements Correlation methods Covariance matrix Eigenvalues and eigenfunctions Flow patterns Principal component analysis Amalgamations Atmospheric circulation Synoptic flow patterns Meteorology atmospheric circulation correlation covariance analysis eigenvalue flow pattern methodology principal component analysis teleconnection Can principal component analysis provide atmospheric circulation or teleconnecion patterns? |
| topic_facet |
Atmospheric circulation Circulation change Circulation patterns Principal component analysis Regionalization S-Mode T-Mode Teleconnections Atmospheric movements Correlation methods Covariance matrix Eigenvalues and eigenfunctions Flow patterns Principal component analysis Amalgamations Atmospheric circulation Synoptic flow patterns Meteorology atmospheric circulation correlation covariance analysis eigenvalue flow pattern methodology principal component analysis teleconnection |
| description |
This investigation examines principal component (PC) methodology and the interpretation of the displays, such as eigenvalue magnitude, loadings and scores, which the methodology provides. The key question posed is, to what extent can S- and T-mode decompositions of a dispersion matrix yield the kinds of interpretations placed on them typically? In particular, a series of experiments are designed based on various amalgamations of three distinct synoptic flow patterns. Since these flow patterns are known, a priori, this allows testing via subtle alterations of the methodology to determine whether there is equivalence between the S- and T-mode decompositions, the degree to which the flow patterns or teleconnections can be recovered by each mode, and the interpretation of each mode. The findings are examined in two contexts: how well they classify the flow patterns, and how well they provide meaningful teleconnections. Both correlation and covariance dispersion matrices are used to determine differences that arise from the standardization. Additionally, unrotated and rotated results are included. By examining a variety of commonly applied methodologies, the results hold for a wider range of studies. Key findings are that eigenvalue degeneracy can influence one mode (but not the other) or both modes for any set of flow patterns resulting in pattern intermixing at times. Similarly, such degeneracy is found in one or both dispersion matrices. Congruence coefficients are used to provide a measure of validity by matching the PC loadings to the parent correlations and covariances. This matching is vital as the loadings exhibit dipoles that have been interpreted historically as physically meaningful, but the present work indicates they may arise purely through the methodology. Overall, we observe that S-mode results can be interpreted as teleconnection patterns and T-mode as flow patterns for well-designed analyses that are meticulously scrutinized for methodological problems. Copyright © 2007 Royal Meteorological Society. |
| title |
Can principal component analysis provide atmospheric circulation or teleconnecion patterns? |
| title_short |
Can principal component analysis provide atmospheric circulation or teleconnecion patterns? |
| title_full |
Can principal component analysis provide atmospheric circulation or teleconnecion patterns? |
| title_fullStr |
Can principal component analysis provide atmospheric circulation or teleconnecion patterns? |
| title_full_unstemmed |
Can principal component analysis provide atmospheric circulation or teleconnecion patterns? |
| title_sort |
can principal component analysis provide atmospheric circulation or teleconnecion patterns? |
| publishDate |
2008 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_08998418_v28_n6_p703_Compagnucci http://hdl.handle.net/20.500.12110/paper_08998418_v28_n6_p703_Compagnucci |
| _version_ |
1768545792926154752 |