Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Teleconexão Ortogonal Empírica× | Análise de Covariância Máxima× | |
|---|---|---|
| Área | Meteorologia | Meteorologia |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1956 | 1992 |
| Autor original≠ | Lorenz, Wallace | Bretherton, Wallace |
| Tipo≠ | Data analysis and pattern identification | Covariance decomposition method |
| Fonte seminal≠ | Wallace, J. M., & Gutzler, D. S. (1981). Teleconnections in the geopotential height field during the Northern Hemisphere winter. Monthly Weather Review, 109(4), 784-812. DOI ↗ | Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., & Blade, I. (1992). The effective number of spatial degrees of freedom of a time-varying field. Journal of the Atmospheric Sciences, 49(11), 1063-1083. link ↗ |
| Outros nomes | EOF analysis, Empirical orthogonal function, Teleconnection patterns, PCA meteorology | MCA, Singular value decomposition, SVD analysis, Covariance analysis |
| Relacionados | 2 | 2 |
| Resumo≠ | Empirical orthogonal function (EOF) analysis is a statistical technique that identifies dominant spatial patterns and temporal variability in atmospheric or oceanic data. When applied to geographically distant locations, EOF analysis reveals teleconnection patterns—coherent patterns of variability that link weather systems across ocean basins and continents. | Maximum covariance analysis (MCA) is a statistical technique that identifies coupled patterns of variability between two spatially distributed fields (e.g., sea surface temperature and precipitation). Unlike EOF analysis which focuses on variance in a single field, MCA identifies spatial patterns that are maximally correlated between two different fields. |
| ScholarGateConjunto de dados ↗ |
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