NCL Home > Documentation > Functions > Empirical orthogonal functions

eofcov

Calculates empirical orthogonal functions via a covariance matrix (original version).

Prototype

	function eofcov (
		data   : numeric,  
		neval  : integer   
	)

	return_val  :  numeric

Arguments

data

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

neval

A Scalar integer that specifies the number of eigenvalues and eigenvectors to be returned. This is usually less than or equal to the minimum number of observations or number of variables, and is typically 3 to 5.

Return value

A multi-dimensional array containing normalized EOFs. The returned array of the same size as data with the rightmost dimension removed and an additional leftmost dimension of the same size as neval added. Double if data is double, float otherwise.

Will contain the following attributes:

trace: A scalar value equal to the trace of the correlation/covariance matrix
eval: A one-dimensional array the same size as neval containing the eigenvalues in descending order.
pcvar: A one-dimensional array the same size as neval containing the percent variance associated with each eigenvalue
eof_function: A scalar integer:
- 0 = eofcov was used to compute the EOFs
- 1 = eofcor was used to compute the EOFs
- 2 = eofcov_pcmsg was used to compute the EOFs
- 3 = eofcor_pcmsg was used to compute the EOFs

These attributes can be accessed using the @ operator:

print(return_val@trace)
print(return_val@eval)

Description

eofcov is the original NCL function for calculating EOFs. It can be slow if the input matrix is large. There is a faster function for calculating EOFs, eofunc. The answers may not match exactly because eofunc examines the input data array and may use a different covariance matrix than eofcov. If you do not want this feature, use eofcov.

eofcov calculates empirical orthogonal functions [EOFs] via a covariance matrix. [It does not use the singular value decomposition approach.] This function computes the covariance matrix by removing the appropriate means and calculating the covariance matrix using anomalies. The eigenvalues and eigenvectors are calculated using LAPACK's "dspevx" routine.

The returned EOFs are normalized to one. To denormalize the returned EOFs multiply by the square root of the associated eigenvalue.

Missing values are ignored but each grid point must have at least some non-missing data. If grid points with all missing values exist, use eofcov_pcmsg

Use the eofcov_Wrap function if metadata retention is desired. The interface is identical.

Note: weighting observations

Generally, when performing and EOF analysis on observations over the globe or a portion of the globe, the values are weighted prior to calculating. This is usually required to account for the convergence of the meridions (area weighting) which lessens the impact of high-latitude grid points that represent a small area of the globe. Most frequently, the square root of the cosine of the latitude is used to compute the area weight. The square root is used to create a covariance matrix that reflects the area of each matrix element. If weighted in this manner, the resulting covariance values will include quantities calculated via:

[x*sqrt(cos(lat(x)))]*[y*sqrt(cos(lat(y)))] = x*y*sqrt(cos(lat(x)))*sqrt(cos(lat(y)))

Note that the covariance of a grid point with itself yields standard cosine weighting:

[x*sqrt(cos(lat(x)))]*[x*sqrt(cos(lat(x)))] = x^2 * cos(lat(x)).

Note: EOF analysis and physical interpretation

Conventional EOF analysis yields patterns and time series which are both orthogonal. The derived patterns are a function of the domain. The calculated patterns may resemble physical modes of the system. However, the procedure is strictly mathematical (not statistical) and is not based upon physics.

Note: sub-sampling the data to improve speed and reduce memory requirements

Generally, the datasets input to eofcov contain highly correlated data. These correlated data contribute no significant information to the resulting eigenvalues and patterns. Hence, the datasets may be sub-sampled ["thinning"] without compromising the resulting information content. Sub-sampling is quite easy via NCL's array syntax. Example 4 illustrates how to sub-sample or thin the data.

Examples

In the following, the attribute pcvar can be output via:

  print(ev@pcvar)             ; 1D vector of length "neval"

This attribute could also be used in graphics. For example, it is it could be used in a title.

  title = "%=" + ev@pcvar(1)

sprintf can be used to format the title more precisely:

  title = "%=" + sprintf("%5.2f", ev@pcvar(1) )

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)

  ; Use eofcov_Wrap if metadata retention is desired
  ; ev     = eofcov_Wrap(x,neval)   ; ev(neval,nvar)

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)

  ; Use eofcov_Wrap if metadata retention is desired
  ; ev     = eofcov_Wrap(y(lat|:,lon|:,time|:),neval) ; ev(neval,nlat,nlon)  


                                   ; denormalize the EOFs [units same as data]
  do ne=0,neval-1
     ev(ne,:,:) = ev(ne,:,:)*sqrt( ev@eval(ne) )
  end do

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

  ; Use eofcov_Wrap if metadata retention is desired
  ; ev     = eofcov_Wrap(z,neval)

Example 4

Calculate the EOFs by subsampling ("thinning") the input data array. This simple operation of using every other grid point reduces the memory requirements by a factor of four and substantially reduces the computational time without compromising the information content of the results. User can use their knowledge of the system to make even larger sub-samplings. The thinning need not be the same in the north-south and east west directions.

In this example, 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)

  ; Use eofcov_Wrap if metadata retention is desired
  ; ev     = eofcov_Wrap(zTemp,neval)   ; ev(neval,nlat/2,mlon/2)

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 

  ; Use eofcov_Wrap if metadata retention is desired
  ; ev     = eofcov_Wrap(z(kl,:,:,:),neval)

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

  ; Use eofcov_Wrap if metadata retention is desired
  ; ev     = eofcov_Wrap(zTemp,neval)

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.