From: Rob Nicholas <rnicholas_at_nyahnyahspammersnyahnyah>

Date: Thu, 20 Aug 2009 17:59:15 -0700

Date: Thu, 20 Aug 2009 17:59:15 -0700

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

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