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eofcov_ts

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

Prototype

	function eofcov_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 eofcov.

Return value

A two-dimensional array dimensioned by the number of eigen values selected in eofcov by the size of the time dimension of data. Will contain the following attribute:

ts_mean: an array of the same size and type as evec containing the means removed from data as part of the calculation.

This attribute can be accessed using the @ operator:

print(return_val@ts_mean)

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This function is deprecated and has been replaced by the faster eofunc_ts.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 covariance matrix. They are derived via the dot product of the data and the EOF spatial patterns.

Examples

Example 1

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

  neval  = 3                         ; calculate 3 EOFs out of 7 
  ev     = eofcov(x,neval)   ; ev(neval,nvar)
  ev_ts = eofcov_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     = eofcov(y(lat|:,lon|:,time|:),neval)   
  ; ev(neval,nlat,nlon)
  ev_ts = eofcov_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     = eofcov(z,neval)      
; ev will be dimensioned neval, level, lat, lon
  ev_ts = eofcov_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     = eofcov(zTemp,neval)   ; ev(neval,nlat/2,mlon/2)
  ev_ts = eofcov_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     = eofcov(z(kl,:,:,:),neval)  
; ev will be dimensioned neval, lat, lon 

  optETS      = True
  optETS@jopt = 1
  ev_rot = eofcov_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     = eofcov(zTemp,neval)      
; ev will be dimensioned neval, lat, lon
  ev_ts = eofcov_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.