Computes empirical orthogonal functions (aka: Principal Component Analysis, Eigen Analysis) given an index that specifies which dimension contains the number of observations, and retains metadata.
Available in version 6.4.0 and later.
load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl" function eofunc_n_Wrap ( data : numeric, neval : integer, optEOF : logical, dim  : integer ) return_val : numeric
A multi-dimensional array in which the dim index specifies the dimension that contains the number of observations. Generally, this is the time dimension. Commonly, the data array contains anomalies from some base climatology, however, this is not required.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.optEOF
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:
optEOF = True optEOF@jopt = 1optEOF@jopt = 1: uses correlation matrix to compute EOFs. The default is to use a covariance matrix (optEOF@jopt = 0).
optEOF = True optEOF@pcrit = 85optEOF@pcrit = %: a float value that indicates the percentage of non-missing points that must exist at any single point in order to be calculated. The default is 50%. Points that contain all missing values will automatically be set to missing.
The dimension index of data that represents the dimension containing the number of observations. Generally, this is the time dimension.
A multi-dimensional array of the same size as data with the dimension
indicated by dim 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:
- eval: a one-dimensional array of size neval that contains the eigenvalues.
- pcvar: a one-dimensional array of size neval equal to the percent variance associated with each eigenvalue.
- pcrit: The same value and type of optEOF@pcrit if the user changed the default.
- matrix: A string indicating the type of matrix used, "correlation" or "covariance".
- method: A string indicating the status of the matrix, "transpose" or "no transpose"
- sig: (v6.3.0 onward) A one-dimensional logical array of size neval indicating if each eigenvalue is significantly separated from adjacent eigenvalues. See eofunc_north.
This function is identical to eofunc_Wrap, except it has an extra dim argument that allows you to specify which dimension index is the "time" dimension. This keeps you from having to unnecessarily reorder the data to force "time" to be the rightmost dimension.
This function computes Empirical Orthogonal Functions (EOFs) via an anomaly covariance matrix or, optionally, via a correlation matrix and retains metadata. This is also known as Principal Component Analysis or Eigen Analysis. The eigenvectors are calculated using LAPACK's "dspevx" routine. The returned values are normalized such that the sum of squares for each EOF pattern equals one. To denormalize the returned EOFs multiply by the square root of the associated eigenvalue (aka,the singular value).
Most commonly, the input data consists of anomalies. Missing values are ignored when computing the covariance or correlation matrix.
Comments on weighting observations
Generally, when performing an 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:
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.
If desired, EOF spatial patterns may be tested for orthogonality by using the dot product:
d01 = sum(eof(0,:,:)*eof(1,:,:)) d12 = sum(eof(1,:,:)*eof(2,:,:)) d02 = sum(eof(0,:,:)*eof(2,:,:)) print("d01="+d01+" d12="+d12+" d02="+d02) ; may be +/- 1e-8
Quadrelli, Roberta, Christopher S. Bretherton, John M. Wallace, 2005: On Sampling Errors in Empirical Orthogonal Functions. J. Climate, 18, 3704-3710 North, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moeng, Sampling errors in the estimation of empirical orthogonal functions, Mon. Wea. Rev., 110, 699-706, 1982. Dawson, A.: EOF Analysis
Acknowledgement: The code used is a modified version of David Pierce's Fortran code.
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". Commonly, 'x' contains anomalies.
neval = 3 ; calculate 3 EOFs out of 7 ev = eofunc_n_Wrap(x,neval,False,1) ; ev(neval,nvar) option = True option@jopt = 1 ; use correlation matrix ev_cor = eofunc_n_Wrap(x,neval,option,1) ; ev_cor(neval,nvar)Example 2
Let x be three-dimensional with dimensions of time, lat, lon:
neval = nvar ; calculate all EOFs ev = eofunc_n_Wrap(y,neval,False,0) ; 0=index of time dimension ; ev(neval,nlat,nlon)Example 3
Let z be four-dimensional with dimensions time, lev, lat, lon:
neval = 3 ; calculate 3 EOFs out of klev*nlat*mlon ev = eofunc_n_Wrap(z,neval,False,0) ; ev will be dimensioned neval, level, lat, lonExample 4
Calculate the EOFs at every other lat/lon grid point.
neval = 5 ; calculate 5 EOFs out of nlat*mlon ev = eofunc_n_Wrap(z(:,::2,::2),neval,False,0) ; 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 = eofunc_n_Wrap(z(kl,:,:,:),neval,False,3) ; ev will be dimensioned neval, lat, lonExample 6
Let z be four-dimensional with dimensions time, lev, lat, lon.
kl = 3 ; specify level neval = 8 ; calculate 8 EOFs out of nlat*mlon ev = eofunc_n_Wrap(z,neval,False,0) ; ev will be dimensioned neval, lat, lonExample 7
Area-weight the data prior to calculation. Let p be three-dimensional with dimensions time x lat x lon. 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)) pw = p ; create an array with metadata ; weight each point prior to calculation. ; conform is used to make wgt the same size as pt pw = p*conform(p, wgt, 1) evec= eofunc_n_Wrap(pw,neval,80.,0)