# This example computes the REOFs along forecast horizons and plots the one
# that explains the greatest amount of variability. The example data has low
# resolution so the result may not be explanatory, but it displays how to
# use this function.
# \dontshow{
startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
sampleData <- s2dv:::.LoadSampleData('tos', c('experiment'),
c('observation'), startDates,
leadtimemin = 1,
leadtimemax = 4,
output = 'lonlat',
latmin = 27, latmax = 48,
lonmin = -12, lonmax = 40)
# }
ano <- Ano_CrossValid(sampleData$mod, sampleData$obs)
ano <- MeanDims(ano$exp, c('dataset', 'member'))
res <- REOF(ano, lat = sampleData$lat, lon = sampleData$lon, ntrunc = 5)
if (FALSE) {
PlotEquiMap(eof$EOFs[1, , , 1], sampleData$lat, sampleData$lon)
}
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