Version 1.2 (14.6 MB) by Chad Greene
Empirical Orthogonal Functions tailored for spatiotemporal analysis, with a tutorial.
Updated 9 Jul 2019

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This standalone version of the EOF function is no longer being maintained. It still works fine, but you'll find the most up-to-date version in the Climate Data Toolbox for MATLAB here: https://www.mathworks.com/matlabcentral/fileexchange/70338. If the eof function has been useful for you, please cite our Climate Data Toolbox for MATLAB paper!

This function simplifies the process of applying Empirical Orthogonal Functions (spatiotemporal principal component analysis) to 3D datasets such as climate data. EOF analysis is not terribly difficult to implement, but much time is often spent trying to figure out how to reshape a big 3D dataset, get the EOFs, and then un-reshape. This function does all the reshaping for you, and performs EOF analysis in a computationally efficient manner. The analysis method is a streamlined and optimized version of Guillame MAZE's caleof function, method 2.

For a full description and an in-depth tutorial describing how to perform EOF analysis on climate data, click on the Example tab above.

Cite As

Greene, C. A., Thirumalai, K., Kearney, K. A., Delgado, J. M., Schwanghart, W., Wolfenbarger, N. S., et al. (2019). The Climate Data Toolbox for MATLAB. Geochemistry, Geophysics, Geosystems, 20. https://doi.org/10.1029/2019GC008392

MATLAB Release Compatibility
Created with R2012b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes

updated citation.


Fixed the issues that arose from rounding the explained variance values, fixed the issue of results going complex for large numbers of modes, updated and expanded the Tutorial.

Typo fix in the documentation.
Added a simple example in the tutorial.