Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion

Version 1.2.0.0 (1,78 ko) par Zhou Chunlüe
Empirical Orthogonal Function (EOF) analysis is often used in Meteorology and Climatology
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Mise à jour 25 avr. 2016

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In statistics and signal processing, the method of empirical orthogonal function (EOF) analysis is a decomposition of a signal or data set in terms of orthogonal basis functions which are determined from the data. It is the same as performing a principal components analysis on the data, except that the EOF method finds both time series and spatial patterns. The term is also interchangeable with the geographically weighted PCAs in geophysics.
if there are too many spatial grids, the spatiotemporal convertion is often performed to quicken the process, other than EOF_analysis.
As required by users, a new version of Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion is provided here.

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Zhou Chunlüe (2024). Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion (https://www.mathworks.com/matlabcentral/fileexchange/54675-empirical-orthogonal-function-eof-with-spatiotemporal-convertion), MATLAB Central File Exchange. Récupéré le .

Compatibilité avec les versions de MATLAB
Créé avec R2011b
Compatible avec toutes les versions
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Inspiré par : Empirical Orthogonal Function (EOF) analysis

A inspiré : EOF

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Version Publié le Notes de version
1.2.0.0

update the figure
add some example figures
As required by users, a new version of Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion is provided here.
1.1.0.0

As required by users, a new version of Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion is provided here.
1.0.0.0