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eofunc_ts_Wrap

Calculates the time series of the amplitudes associated with each eigenvalue in an EOF and retains metadata.

Prototype

load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"

	function eofunc_ts (
		data    : numeric,  
		evec    : numeric,  
		optETS  : logical   
	)

	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 eofunc.

optETS

A logical variable to which various optional arguments may be assigned as attributes. These optional arguments alter the default behavior of the function. Must be set to True prior to setting the attributes which are assigned using the @ operator:

• jopt - integer (default is 0)

  optETS      = True
  optETS@jopt = 1
  

  optETS@jopt = 1: Use the standardized data matrix to compute the time series. The default is to use data and evec

Return value

A two-dimensional array dimensioned by the number of eigen values selected in eofunc 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

Warning: A user has reported an instance where the EOFs returned by eofunc were not consistent with those from eofcov. eofunc is potentially much faster than the eofcov function. To be confident in your results, please try both functions and compare the results. Please send any information on differences to shea@ucar.edu.

Calculates the time series of the amplitudes associated with each eigenvalue in an EOF and retains metadata. These amplitudes are also called principal components, expansion coefficients, scores, etc. They are derived via the dot product of the data and the EOF spatial patterns. The mean is subtracted from the value of each component time series.

See Also

eofunc, eofunc_varimax

Examples

Example 1

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

  neval  = 3                         ; calculate 3 EOFs out of 7 
  ev     = eofunc(x,neval,False)   ; ev(neval,nvar)
  
  option      = True
  option@jopt = 1                    ; use correlation matrix
  ev_cor = eofunc(x,neval,option)  ; ev_cor(neval,nvar)

  ev_ts = eofunc_ts_Wrap(x,ev_cor,False)

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     = eofunc(y(lat|:,lon|:,time|:),neval,False)   
  ; ev(neval,nlat,nlon)
  ev_ts = eofunc_ts_Wrap(y,ev,False)

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     = eofunc(z,neval,False)      
; ev will be dimensioned neval, level, lat, lon
  ev_ts = eofunc_ts_Wrap(z,ev,False)

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     = eofunc(zTemp,neval,False)   ; ev(neval,nlat/2,mlon/2)
  ev_ts = eofunc_ts_Wrap(zTemp,ev,False)

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     = eofunc(z(kl,:,:,:),neval,False)  
; ev will be dimensioned neval, lat, lon 

  optETS      = True
  optETS@jopt = 1
  ev_rot = eofunc_ts_Wrap(z,ev,optETS)

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     = eofunc(zTemp,neval,False)      
; ev will be dimensioned neval, lat, lon
  ev_ts = eofunc_ts_Wrap(zTemp,ev,False)

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     = eofunc(ptw,neval,80.)   
  evec_ts = eofunc_ts_Wrap(ptw,evec,False)