**esacr**computes an auto correlation and

**escorc**computes an cross correlation at zero lag

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Data files for some examples# Correlations

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In addition to the functions demonstrated on this page, there are
two more that might be of interest:
**esacr** computes an auto correlation and
**escorc** computes an cross correlation at zero lag

corel_1.ncl: Calculates a cross
correlation.

**esccr**: Calculates the cross correlation between
two variables.

**dimsizes**: Calculates the size of the dimensions
of a variable.

corel_2.ncl: The intrinsic NCL
correlation function
**esccr** only calculates the positive side of a
correlation. This script demonstrates how a pos and neg lag can be created.
NCL coordinate variable syntax [ **{...}** ] is used to specify locations.
NCL index syntax [ **::-1** ] is used to reverse the array order.

corel_3.ncl: Demonstrates using
the same functions to do a 2D correlation in time.

The data must be reordered to put time as the rightmost dimension.

This example computes the cross-correlation at lags 0,1 and 2. If the
cross-correlation at 0-lag only were desired, then it would be more
efficient to use **escorc**.

Since the correlation values will always span -1 to 1, the two end
labelbar boxes representing values above 1 and below -1 were
removed by setting
res@*cnLabelBarEndStyle* to
"ExcludeOuterBoxes".

indices_soi_2.ncl:
Read gridded sea level pressure from the 20th Century Reanalysis; use proxy grid points
near Tahiti and Darwin to construct an SOI time series spanning 1950-2010;
perform lag-0 correlations between the SOI and SLP; SOI and temperature; and,
SOI and preciptation. The later uses the GPCP data which spans 1979-2010.
To more clearly delineate the main pattern structure correlations between,
-0.1 and +0.1 were set to _FillValue.

FYI: The linear correlation between the station based SOI (previous example) and the SOI derived from the 20th Century Reanalysis for the 1950-2010 period is 0.96.