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EOF Analysis

EOF Analysis. EOF analysis. The empirical orthogonal function (EOF) analysis is a decomposition of a signal or data set in terms of orthogonal base functions which are determined from the data. It is also called principal component analysis.

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EOF Analysis

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  1. EOF Analysis

  2. EOF analysis • The empirical orthogonal function (EOF) analysis is a decomposition of a signal or data set in terms of orthogonal base functions which are determined from the data. It is also called principal component analysis. • It is different from the spectral analysis which uses triangle functions as bas functions. • IDL has math subroutines that are convenient for doing EOF analysis

  3. EOF analysis Y EOF1 Assume d is the data time series, its auto-covariance is C=ddT Calculate the singular value decomposition of C: C=UUT Then the columns of U are the empirical orthogonal functions (EOFs), and  gives the weight of each EOF. EOF2 x

  4. Mesh and shaded surface plots • Mesh surface plots surface, z, x, y, shades=bytscl(z,top=255) • Shaded surface plots shade_surf, z, x, y

  5. Image display • tv, d, x, y • tvscl, d, x, y

  6. In-class assignment VIII Data files are stored at: http://lightning.sbs.ohio-state.edu/geo820/data/ Read the netCDF file skt.mon.mean.nc for NCEP sea surface temperature (skt) data. Select 10 years of data for tropical Pacific (120E-280E, 20N-20S) and conduct the EOF analysis of the data. Plot for the first 4 modes the EOF, principal component, and the Fourier spectrum of the principal component. Try use surface plot for the EOF.

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