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eofcor_ts

Calculates the time series of the amplitudes associated with each eigenvalue in an EOF which was calculated using a correlation matrix.

Prototype

	function eofcor_ts (
		data  : numeric,  
		evec  : numeric   
	)

	return_val  : numeric

Arguments

data

A multi-dimensioned array in which the rightmost dimension is the number of observations. Generally, this is the time dimension.

evec

A multi-dimensional array containing the EOFs calculated using eofcor.

Return value

A two-dimensional array dimensioned by the number of eigen values selected in eofcor by the size of the time dimension of data.

Description

This function is deprecated and has been replaced by the faster eofunc_ts.

Calculates the time series of the amplitudes associated with each eigenvalue in an EOF which was calculated using a correlation matrix. They are derived via the dot product of the data and the EOF spatial patterns.

See Also

This function is deprecated and has been replaced by the faster eofunc_ts.

Examples

Example 1

Let x be two dimensional with dimensions variables (size = nvar) and time:

  neval  = 3                         ; calculate 3 EOFs out of 7 
  ev     = eofcor(x,neval)   ; ev(neval,nvar)
  ev_ts = eofcor_ts(x,ev_cor)
Example 2

Let x be three-dimensional with dimensions of time, lat, lon. Reorder x so that time is the rightmost dimension:

  y!0    = "time"                  ; name dimensions if not already done 
  y!1    = "lat"                   ; must be named to reorder
  y!2    = "lon"                   

  neval  = nvar                                  ; calculate all EOFs 
  ev     = eofcor(y(lat|:,lon|:,time|:),neval)   
  ; ev(neval,nlat,nlon)
  ev_ts = eofcor_ts(y,ev)
Example 3

Let z be four-dimensional with dimensions lev, lat, lon, and time:

  neval  = 3                       ; calculate 3 EOFs out of klev*nlat*mlon 
  ev     = eofcor(z,neval)      
; ev will be dimensioned neval, level, lat, lon
  ev_ts = eofcor_ts(z,ev)
Example 4

Calculate the EOFs at every other point rather. Use of a temporary array is NOT necessary but it avoids having to reorder the array twice in this example:

  neval  = 5                          ; calculate 5 EOFs out of nlat*mlon 
  zTemp  = z(lat|::2,lon|::2,time|:)  ; reorder and use temporary array
  ev     = eofcor(zTemp,neval)   ; ev(neval,nlat/2,mlon/2)
  ev_ts = eofcor_ts(zTemp,ev)
Example 5

Let z be four-dimensional with dimensions level, lat, lon, time. Calculate the EOFs at one specified level:

  kl     = 3                               ; specify level
  neval  = 8                               ; calculate 8 EOFs out of nlat*mlon 
  ev     = eofcor(z(kl,:,:,:),neval)  
; ev will be dimensioned neval, lat, lon 

  optETS      = True
  optETS@jopt = 1
  ev_rot = eofcor_ts(z,ev)
Example 6

Let z be four-dimensional with dimensions time, lev, lat, lon. Reorder x so that time is the rightmost dimension and calculate on one specified level:

  kl     = 3                             ; specify level
  neval  = 8                             ; calculate 8 EOFs out of nlat*mlon 
  zTemp  = z(lev|kl,lat|:,lon|:,time|:)   
  ev     = eofcor(zTemp,neval)      
; ev will be dimensioned neval, lat, lon
  ev_ts = eofcor_ts(zTemp,ev)
Example 7

Area weight the data prior to calculation. Let p be four-dimensional with dimensions lat, lon, and time. The array lat contains the latitudes.

; calculate the weights using the square root of the cosine of the latitude and
; also convert degrees to radians
  wgt = sqrt(cos(lat*0.01745329)) 
  
; reorder data so time is fastest varying                                      
  pt  = p(lat|:,lon|:,time|:)         ; (lat,lon,time)
  ptw = pt                            ; create an array with metadata

; weight each point prior to calculation. 
; conform is used to make wgt the same size as pt
  ptw = pt*conform(pt,wgt,0)        
                                      
  evec     = eofcor(ptw,neval)   
  evec_ts = eofcor_ts(ptw,evec)