# 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

- 0 =

return_val@trace)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**

**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*Note that the covariance of a grid point with itself yields standard cosine weighting:sqrt(cos(lat(x)))]*[y*sqrt(cos(lat(y)))] = x*y*sqrt(cos(lat(x)))*sqrt(cos(lat(y)))

[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.

## See Also

## Examples

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

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)

**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) ; denormalize the EOFs [units same asdata] 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

**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)

**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

**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

**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. ;conformis used to make wgt the same size as pt ptw = pt*conform(pt,wgt,0) evec=eofcov(ptw,neval)