Computes the persistence climatology (i.e., the temporal autocorrelation at a given time lag)
Input grid (most likely annually aggregated)
Time lag at which to calculate persistence. Default is 1.
Coverage probability for confidence interval (in the range (0-1)). Default to
Logical. Should parallel execution be used?
Integer. Upper bound for user-defined number of cores.
Integer number of cores used in parallel computation. Self-selected number of
cores is used when
The function is a wrapper of
acf to compute the autocorrelation function. Significance at the given confidence
interval is calculated as in
In case of any missing values within the series, NA will be returned.
ci is specified (e.g.
ci=0.95), two global attributes are appended:
"signif:ci", indicating the confidence interval chosen
"is.signif", which is a logical matrix of dimension
lat x lon indicating which points exhibit a
A climatology grid (i.e.,
"time" dimension size = 1).
Parallel processing is enabled using the parallel package.
Parallelization is undertaken by a FORK-type parallel socket cluster formed by
ncores is not specified (default),
ncores will be one less than the autodetected number of cores.
The maximum number of cores used for parallel processing can be set with the
although this will be reset to the auto-detected number of cores minus 1 if this number is exceeded. Note that not all
code, but just some critical loops within the function are parallelized.
In practice, parallelization does not always result in smaller execution times, due to the parallel overhead. However, parallel computing may potentially provide a significant speedup for the particular case of large multimember datasets or large grids.
Parallel computing is currently not available for Windows machines.
plotClimatology, for conveniently plotting persistence climatology maps.
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