using regCoef for a regression-based model

Hi all,

I'm trying to use NCL to create a linear regression model such that
the leading EOFs of a scalar field X are used to "predict" another
scalar field Y. Assuming both X and Y have dimensions (lat,lon,time)
and we want to restrict the model to the first 5 EOFs, the NCL code
for obtaining the regression coefficients might look something like
this:

     n = 5
     eof_X = eofunc( X, n, False )
     ts_X = eofunc_ts( X, eof_X, False )
     rc = regCoef( ts_X, Y )

Here eof_X contains the leading five EOFs and has dimensions
(evn,lat,lon), ts_X contains the associated PCs and has dimensions
(evn,time), and rc contains the regression coefficents and has
dimensions (evn,lat,lon). Dimension evn is of size 5, one element for
each mode (EOF).

Now, here's the question: How can I use the regression coefficients rc
and PCs ts_X to reconstruct Y?

Ideally, what I'm looking for is something equivalent to the
'eof2data' function [say 'Y_reconst = regCoef2data( rc, ts_X )' ], but
a more "mathematically explicit" representation would be fine too.
Has anyone else done this successfully with NCL? Or am I missing
something fundamental here?

Thanks...

~Rob
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Received on Thu Aug 20 2009 - 18:59:15 MDT