**eofunc_n_Wrap**,

**eofunc_ts_n_Wrap**and

**eofunc_north**. If no meta data is desired:

**eofunc_n**and

**eofunc_ts_n**.

See the eof_*n*_640.ncl scripts below for examples of using the new *xxxx*_n functions.

NCL Home>
Application examples>
Data Analysis ||
Data files for some examples# NCL Graphics: EOFs

**EOF:** **Standard Empirical Orthogonal Analysis**
**EEOF: Extended Empirical Orthogonal Analysis**
**References:**
**EEOF: Extended Empirical Orthogonal Analysis Examples**
**S-mode** EEOF using
longitude and latitude spatial extensions.

Example pages containing:
tips |
resources |
functions/procedures

For historical reasons, NCL contains a suite of EOF functions.
The recommended functions are:
**eofunc_n_Wrap**,
**eofunc_ts_n_Wrap** and
**eofunc_north**.
If no meta data is desired:
**eofunc_n** and
**eofunc_ts_n**.

See the eof_*n*_640.ncl scripts below for examples of using the new *xxxx*_n functions.

Standard EOF (*aka* eigenvector,
**principal component**)
analysis yields patterns and time series which are both orthogonal.
The derived patterns are a function of the domain and the time period being used.
The EOF represntation is optimal in the sense that maximum variance
may be accounted for by choosing in order the eigenvectors associated with the
largest eigenvalues of the covariance matrix (Kutzback, 1967).
However, **the EOF procedure is strictly mathematical and
is not based upon physics**. The results may produce patterns that are similar
to physical modes within the the system. However, physical meaning is dependent
on your interpretation of the mathematical result.

To be clear, EOF analysis is not a statistical procedure. However, because the results are orthoganal (ie, independent), they have been used as predictor variables in some statistical applications.

**Note on signs of EOF analysis** (conributed by Andrew Dawson, U. East Anglia)

EOFs are eigenvectors of the covariance matrix formed from the input data. Since an eigenvector can be multiplied by any scalar and still remain an eigenvector, the sign is arbitrary. In a mathematical sense the sign of an eigenvector is rather unimportant. This is why the EOF analysis may yield different signed EOFs for slightly different inputs. Sign only becomes an issue when you wish to interpret the physical meaning (if any) of an eigenvector.

You should approach the interpretation of EOFs by looking at both the EOF pattern and the associated time series together. For example, consider an EOF of sea surface temperature. If your EOF has a positive centre and the associated time series is increasing, then you will interpret this centre as a warming signal. If your EOF had come out the other sign (ie. a negative centre), then the associated time series would also be the opposite sign and you would still interpret the centre as a warming signal.

In essence, the sign flip does not change the physical interpretation of the result. Hence, it is up to you to choose which sign to associate with your EOF patterns for visualisation (remembering that any sign change to an EOF must be applied to the associated time series also). Usually you would simply adjust the sign so that all your EOF patterns with the same physical interpretation also look the same

**EEOF** provides the ability to discern 'common patterns of variability'
shared among multiple datasets in both space and time.
The "extension" may be in space (**S-mode**)
or in time (**T-mode**). A visual depiction of these modes is
**Figure 1**
of the Neeti and Eastman (2014) reference.
A disadvantage of EEOF "is the possibility that
little coherrent persistence exists in the data field so that the
functions may obfuscate the spatial coherence identified by traditional technique" (Weare, 1982).

See **EEOF** examples below.

A Cautionary Note on the Interpretation of EOFsDietmar, D. and Latif, M. J. Climate, 2002: 15: 216-225.Empirical eigenvectors of sea-level pressure, surface temperature and precipitation complexes over North AmericaKutzbach, J.E. Journal of Applied Meteorology (1967): 6, pp. 791-802Novel approaches in Extended Principal Component Analysis to compare spatio-temporal patterns among multiple image time seriesNeeti, N. and J.R. Eastman Remote Sensing of Environment: 148, 25 May 2014, Pages 84-96Sampling Errors in the Estimation of Empirical Orthogonal FunctionsNorth, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moeng Mon. Wea. Rev., 1982: 110, 699-706.Application of Extended Empirical Orthogonal Function Analysis to Interrelationships and Sequential Evolution of Monsoon FieldsSingh, S,V., R.H. Kriplani Mon. Wea. Rev., 1986: 114, 1603-1610.Empirical Orthogonal Function Analysis of Atlantic Ocean Surface TemperaturesWeare, B.C., R.E. Newell Quart. J. Roy. Meteor. Soc., 1977: 103, 467-478.Examples of Extended Empirical Orthogonal Function AnalysesWeare, B.C., J.S. Nasstrom Mon. Wea. Review, 1982: 481-485

eof_0.ncl /
eof_0_640.ncl:
These scripts verify the output from **eofunc** and **eofunc_ts**
by reading an ascii file containing box data from the book:
**The results match the book exactly.**

John C Davis Satistics and Data Analysis in Geology Wiley, 2nd Edition, 1986 Source Data: page 524 , EOF results: page 537

The eof_0.ncl and eof_0_640.ncl scripts produce identical results;
eof_0_640.ncl uses functions **eofunc_n**
and **eofunc_ts_n** (added in NCL V6.4.0) to avoid
having to reorder the data.

Further illustrated:

- reconstructing the original array from the calculated EOFs and Principal Components (time series)
- computing the sum-of-squares of each EOF to verify that they are normalized
- computing cross correlations of each component to verify there are no cross-component correlations

The source data is available **here**.

The output from running the above script is **here**.

eof_0a.ncl:
Same as **Eof_0** but here the data are weighted (cosine of latitude). The script illustrates how to 'unweight' the data.

The output from running the above script is **here**.

eof_1.ncl /
eof_1_640.ncl:
Read sea level pressure;
extract the data spanning 1979-2003; calculate the first three EOFs
over the North Atlantic region for the winter (DJF) season.
The first EOF pattern is commonly identified as the
North Atlantic Oscillation (NAO) mode.

**lonFlip** is used to rearrange the
data to span -180 to 180. Then
coordinate
subscripting is used to extract the region of interest.

Finally, the resulting principal component time series is normalized by the weights used to get the time series of the mean areal amplitudes.

The eof_0.ncl and eof_0_640.ncl scripts produce identical results;
eof_0_640.ncl uses functions **eofunc_n**
and **eofunc_ts_n** (added in NCL V6.4.0) to avoid
having to reorder the data.

The dataset use can be downloaded from:

http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.surface.html

eof_2.ncl
eof_2_640.ncl:
This is an example posted by
David Pierce (Scripps). NCL uses code posted at the the
Scripps site. The Scripps example only shows the results
from the first EOF. This example shows three patterns and time series.
This example was created to illustrate that NCL's **eofunc** /
**eofunc_n**
and **eofunc_ts** / **eofunc_ts_n** results match Pierce's.
Note that no areal weighting was performed.

eof_3.ncl / eof_3_640.ncl: From a directory with many POP files, (1) read 30 years spanning years 270-279; (2) compute climatology; (3) compute anomalies; (4) mask out all regions but the Atlantic basin; (5) weight grid by area; (5) compute EOFs; and, (7) plot.

eof_4.ncl / eof_4_640.ncl: Use the approach outlined at the Climate Prediction Center to compute the Antarctic Oscillation (AAO) for the period 1979-2000: Read geopotential height data (source: Reanalysis-2); select 700hPa and data poleward of 20S; compute monthly climatologies and anomalies; weight observations; compute EOFs; normalize the principal components; and project the anomalies via regression.

eof_5.ncl /
eof_5_640.ncl:
Compute EOFs for the northern hemisphere height field at 500hPa.
Use **cd_calendar** to create an array containing
time in the form of YYYYMM. Then use **ind**
to determine the index values corresponding data from 1979-2011.
Read the desired data using NCL's coordinate subscripting.
Compute standard EOFs via **eofunc_Wrap** and the
corresponding time series via **eofunc_ts_Wrap**.
Because we want to do a varimax rotation
6 EOFs were computed. This (neof=6) is arbitrary but it should be large enough to
allow **eofunc_varimax_Wrap**
to reorthogonalize the subset. Finally,
**eofunc_varimax_reorder**
is used to place the output into descending order. The attribute **pcvar_varimax**
represents the variance explained after the varimax rotation has been applied.

eof_6.ncl /
eof_6_640.ncl:
Compute EOFs for the northern hemisphere January sea level pressures from 1989-2009.
The **eofunc_north** is used to assess if eigenvalues
are distinct.

if the *prinfo* argument is set to True. The following will be printed.

dlam low pcvar highwhere 'dlam' is the 'shift' (North's term).sig(0) 8.28161 18.5539 26.8355 35.1171 True (1) 5.68727 12.7416 18.4289 24.1161 True (2) 3.76075 8.42548 12.1862 15.947 True (3) 2.67866 6.00119 8.67985 11.3585 False

Shikha Singh (Scientist, Indian Institute of Tropical Meteorology, Pune, India) has suggested multiple methods for combining different variables with common dimensionality: (time,lat,lon).

For simplicity, two variables (var1, var2) with dimensionality (ntim,nlat,mlon) are used to illustrate the T-mode approach. Generally for climate, it is assumed that the variables contain anomalies from the mean annual cycle. Because the anomaly variables may have different variances, normalized values should be input to the function. Specifically:

var1 = ...anomalies... var2 = ...anomalies... dimv =Dr. Singh also suggested the following fordimsizes(var1) ntim = dimv(0) nlat = dimv(1) mlon = dimv(2) std1 =dim_stddev_n(var1,0) ; local (grid point, station) standard deviations std2 =dim_stddev_n(var2,0) or std1 =stddev(var1) ; overall (field) standard deviation std2 =stddev(var2) nvar = 2 ; T-mode ; 0 , 1 , 2 , 3 ; dimension numbers new_data =new((/nvar, ntim, nlat, mlon/),typeof(var1),getFillValue(var1)) new_data(0,:,:,:) = var1/std1 new_data(1,:,:,:) = var2/std2 NEW_DATA =reshape(new_data,(/2*ntim,nlat,mlon/))printVarSummary(NEW_DATA) neof = 3 ; # of desired EOFs ndim = 1 ; dimension number for 'time' eeof =(NEW_DATA, neof, False, ndim)eofunc_nprintVarSummary(eeof)

; Joining the data along the longitude axis new_data1 =new((/2*mlon, nlat, time/),typeof(var1),getFillValue(var1)) new_data1(0:mlon-1,:,:) = var1 new_data1(mlon:2*mlon-1,:,:) = var2 ; Joining the data along latitude axis new_data2 =new((/mlon, 2*nlat, time/), typeof(var1),typeof(var1),getFillValue(var1)) new_data2(:,0:nlat-1,:,:) = var1 new_data2(:,nlat:2*nlat-1,:,:)= var2

mjoclivar_14.ncl:

The data from OLR, Uwind, Vwind are averaged along latitude to make 2D data. The 3 variables are then combined into a bigger matrix: (time,space).

Multivariate (Combined) EOF; cross-correlations between EOF1 and EOF2; time-series of (PC1^2 + PC2^2) and 91-day running mean.

**This script was updated 2 March 2015**.
Marcus N. Morgan (Florida Institute of Technology) submitted the code segment that calculates the
percent variance for each component in the multivariate EOF.

**This script was updated 29 July 2016**.
Eun-Pa Lim: Bureau of Meteorology, Australis

For comparison, the equivalent 3 plots from the
** Korean MJO-Diagnostics** are
(a) **eof_all**;
(b) **lag_all**
(c) **time series**