s2dverification (version 2.10.3)

ACC: Computes Anomaly Correlation Coefficient

Description

Calculates the Anomaly Correlation Coefficient for the ensemble mean of each model and the corresponding references for each startdate and each leadtime. The domain of interest can be specified by providing the list of longitudes/latitudes (lon/lat) of the grid together with the corners of the domain: lonlatbox = c(lonmin, lonmax, latmin, latmax).

Usage

ACC(
  var_exp,
  var_obs,
  lon = NULL,
  lat = NULL,
  lonlatbox = NULL,
  conf = TRUE,
  conftype = "parametric",
  siglev = 0.95
)

Arguments

var_exp

Array of experimental anomalies with dimensions: c(nexp, nsdates, nltimes, nlat, nlon) or c(nexp, nsdates, nmembers, nltimes, nlat, nlon).

var_obs

Array of observational anomalies, same dimensions as var_exp except along the first dimension and the second if it corresponds to the member dimension.

lon

Array of longitudes of the var_exp/var_obs grids, optional.

lat

Array of latitudes of the var_exp/var_obs grids, optional.

lonlatbox

Domain to select: c(lonmin, lonmax, latmin, latmax), optional.

conf

TRUE/FALSE: confidence intervals and significance level provided or not.

conftype

"Parametric" provides a confidence interval for the ACC computed by a Fisher transformation and a significance level for the ACC from a one-sided student-T distribution. "Bootstrap" provides a confidence interval for the ACC and MACC computed from bootstrapping on the members with 100 drawings with replacement. To guarantee the statistical robustness of the result, make sure that your experiments/oservations/startdates/ leadtimes always have the same number of members.

siglev

The confidence level for the computed confidence intervals.

Value

ACC

If conf = TRUE, array with dimensions: c(nexp, nobs, nsdates, nleadtimes, 4) The fifth dimension of length 4 corresponds to the lower limit of the siglev% confidence interval, the ACC, the upper limit of the siglev% confidence interval and the siglev% significance level. If conf = FALSE, the array of the Anomaly Correlation Coefficient has dimensions c(nexp, nobs, nsdates, nleadtimes).

MACC

The array of the Mean Anomaly Correlation Coefficient with dimensions c(nexp, nobs, nleadtimes).

References

Joliffe and Stephenson (2012). Forecast Verification: A Practitioner's Guide in Atmospheric Science. Wiley-Blackwell.

Examples

# NOT RUN {
# See ?Load for explanations on the first part of this example.
 
# }
# NOT RUN {
data_path <- system.file('sample_data', package = 's2dverification')
expA <- list(name = 'experiment', path = file.path(data_path, 
            'model/$EXP_NAME$/$STORE_FREQ$_mean/$VAR_NAME$_3hourly',
            '$VAR_NAME$_$START_DATE$.nc'))
obsX <- list(name = 'observation', path = file.path(data_path,
            '$OBS_NAME$/$STORE_FREQ$_mean/$VAR_NAME$',
            '$VAR_NAME$_$YEAR$$MONTH$.nc'))

# Now we are ready to use Load().
startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
sampleData <- Load('tos', list(expA), list(obsX), startDates, 
                  leadtimemin = 1, leadtimemax = 4, output = 'lonlat',
                  latmin = 27, latmax = 48, lonmin = -12, lonmax = 40)
 
# }
# NOT RUN {
 
# }
# NOT RUN {
sampleData$mod <- Season(sampleData$mod, 4, 11, 12, 2)
sampleData$obs <- Season(sampleData$obs, 4, 11, 12, 2)
clim <- Clim(sampleData$mod, sampleData$obs)
ano_exp <- Ano(sampleData$mod, clim$clim_exp)
ano_obs <- Ano(sampleData$obs, clim$clim_obs)
acc <- ACC(Mean1Dim(ano_exp, 2), Mean1Dim(ano_obs, 2))
 
# }
# NOT RUN {
PlotACC(acc$ACC, startDates)
 
# }