DINEOF (Data Interpolating Empirical Orthogonal Functions)

October 30, 2012

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

I finally got around to reproducing the DINEOF method (Beckers and Rixon, 2003) for optimizing EOF analysis on gappy data fields – it is especially useful for remote sensing data where cloud cover can result in large gaps in data. Their paper gives a nice overview of some of the various methods that have been used for such data sets. One of these approaches, which I have written about before,  involves deriving EOFs from a covariance matrix as calculated from available data. Unfortunately, as the author's point out, such covariance matrices are no longer positive-definite, which can lead to several problems. The DINEOF method seems to overcome several of these issues.

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