Canonical Correlation Analysis for finding patterns in coupled fields

March 25, 2012

(This article was first published on me nugget, and kindly contributed to R-bloggers)

First CCA pattern of Sea Level Pressure (SLP) and Sea Surface Temperature (SST) monthly anomalies for the region between -180 °W to -70 °W and +30 °N to -30 °S.

The following post demonstrates the use of Canonical Correlation Analysis (CCA) for diagnosing coupled patterns in climate fields. The method produces similar results to that of  Maximum Covariance Analysis (MCA), but patterns reflect maximum correlation rather than maximum covariance. Furthermore, the output of the model is a combination of linear models that can be used for field prediction.

This particular method was illustrated by Barnett and Preisendorfer (1997) – it constructs a CCA model based on a truncated subset of EOF coefficients (i.e. "principle components") instead of using the original field (as with MCA). This truncation has several benefits for the fitting of the model – First, one reduces the amount of noise in the problem by eliminating the higher EOF modes, which represent poorly organized, small-scale features of the fields. Second, by using orthogonal functions, the algebra of the problem is simplified (see von Storch and Zweiers 1999 for details). Bretherton etal. (1992) reviewed several techniques for diagnosing coupled patterns and found the Barnett and Preisendorfer method (hereafter "BPCCA") and MCA to be the most robust.

Read more »

To leave a comment for the author, please follow the link and comment on their blog: me nugget. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)