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A Guide to Empirical Orthogonal Functions for Climate Data AnalysisGeneralizations: Rotated, Complex, Extended and Combined EOF

A Guide to Empirical Orthogonal Functions for Climate Data Analysis: Generalizations: Rotated,... [We have seen in the last section that the difficulty in identifying real physical patterns from EOF stems from their orthogonal nature. Orthogonality translates into the fact that typical patterns appear in secondary (higher order) EOF. Very often the first EOF has little structure, the second has a positive and a negative center, the third more centers and so on, in a way so as to maintain orthogonality.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Guide to Empirical Orthogonal Functions for Climate Data AnalysisGeneralizations: Rotated, Complex, Extended and Combined EOF

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References (1)

Publisher
Springer Netherlands
Copyright
© Springer Science+Business Media B.V. 2010
ISBN
978-90-481-3701-5
Pages
69 –96
DOI
10.1007/978-90-481-3702-2_5
Publisher site
See Chapter on Publisher Site

Abstract

[We have seen in the last section that the difficulty in identifying real physical patterns from EOF stems from their orthogonal nature. Orthogonality translates into the fact that typical patterns appear in secondary (higher order) EOF. Very often the first EOF has little structure, the second has a positive and a negative center, the third more centers and so on, in a way so as to maintain orthogonality.]

Published: Nov 27, 2009

Keywords: Full Column Rank; Time Coefficient; Target Matrix; Quarter Wavelength; Quarter Period

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