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A new method to compute the principal components from self‐organizing maps: an application to monsoon intraseasonal oscillations
Author(s) -
Sahai A. K.,
Chattopadhyay R.,
Joseph Susmitha,
Abhilash S.,
Borah Nabanita,
Goswami B. N.
Publication year - 2014
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.3885
Subject(s) - empirical orthogonal functions , principal component analysis , orthogonal functions , similarity (geometry) , mathematics , phase space , computer science , pattern recognition (psychology) , artificial intelligence , physics , mathematical analysis , statistics , image (mathematics) , thermodynamics
The study develops a self‐organizing map ( SOM )‐based local principal component analysis ( PCA ) to obtain the linearly decorrelated principal components (PCs) of monsoon intraseasonal oscillations ( MISOs ). Although the SOM ‐derived feature maps are not orthogonal like empirical orthogonal function ( EOFs ), we show that in the case of MISOs simple mathematical substitution can make the SOM ‐derived PCs linearly decorrelated to each other. Thus, the SOM ‐based local PCA is seen also to be statistically equivalent to extended EOF analysis when applied to MISO analysis. The life cycle and phase evolution of MISO through SOM ‐based PCA is robust and conforms to the results based on extended EOFs besides having potential to give new information. The remarkable similarity of results with the symmetric SOM configurations shows the effectiveness of these methods for use as empirical reduction models for climate patterns. The asymmetric SOM lattice configurations derive similar phase evolution as compared to a symmetric lattice, but asymmetric temporal evolutions in the PC ‐defined phase space. The results once again endorse the mathematical basis of PCA through SOM for geophysical applications.