ACC: Computes Anomaly Correlation Coefficient

In s2dverification: Set of Common Tools for Forecast Verification

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).

Author(s)

History:
0.1 - 2013-08 (V. Guemas) - Original code
1.0 - 2013-09 (N. Manubens) - Formatting to CRAN
1.1 - 2013-09 (C. Prodhomme) - optimization
1.2 - 2014-08 (V. Guemas) - Bug-fixes: handling of NA & selection of domain + Simplification of code
1.3.0 - 2014-08 (V. Guemas) - Boostrapping over members
1.3.1 - 2014-09 (C. Prodhomme) - Add comments and minor style changes
1.3.2 - 2015-02 (N. Manubens) - Fixed ACC documentation and examples

References

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

Examples

# 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)
 
## End(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))
 
PlotACC(acc$ACC, startDates)
 

s2dverification documentation built on April 20, 2022, 9:06 a.m.