s2dverification (version 2.4.0)

Corr: Computes Correlation Skill Measure (Temporal Correlation Along Start Dates)

Description

Matrix var_exp & var_obs should have the same dimensions except along posloop dimension where the length can be different, with the number of experiments/models for var_exp (nexp) and the number of obserational datasets for var_obs (nobs). Corr computes the correlation skill of each jexp in 1:nexp against each jobs in 1:nobs which gives nexp x nobs correlation skill measures for each other grid point of the matrix (each latitude/longitude/level/leadtime). The correlations are computed along the poscor dimension which should correspond to the startdate dimension. If compROW is given, the correlations are computed only if rows along the compROW dimension are complete between limits[1] and limits[2], that mean with no NA between limits[1] and limits[2]. This option can be activated if the user wishes to account only for the forecasts for which observations are available at all leadtimes. Default: limits[1] = 1 and limits[2] = length(compROW dimension). The confidence interval is computed by a Fisher transformation. The significance level relies on a one-sided student-T distribution. We can modifiy the treshold of the test modifying siglev (default value=0.95).

Usage

Corr(var_exp, var_obs, posloop = 1, poscor = 2, compROW = NULL, limits = NULL, 
     siglev = 0.95, method = 'pearson')

Value

Matrix with dimensions : c(length(posloop) in var_exp, length(posloop) in var_obs, 4, all other dimensions of var_exp & var_obs except poscor). The third dimension of length 4 corresponds to the lower limit of the 95% confidence interval, the correlation, the upper limit of the 95% confidence interval and the 95% significance level given by a one-sided T-test.

Examples

# Load sample data as in Load() example:
example(Load)
clim <- Clim(sampleData$mod, sampleData$obs)
ano_exp <- Ano(sampleData$mod, clim$clim_exp)
ano_obs <- Ano(sampleData$obs, clim$clim_obs)
runmean_months <- 12
dim_to_smooth <- 4  # Smooth along lead-times
smooth_ano_exp <- Smoothing(ano_exp, runmean_months, dim_to_smooth)
smooth_ano_obs <- Smoothing(ano_obs, runmean_months, dim_to_smooth)
dim_to_mean <- 2  # Mean along members
required_complete_row <- 3  # Discard start dates which contain any NA lead-times
leadtimes_per_startdate <- 60
corr <- Corr(Mean1Dim(smooth_ano_exp, dim_to_mean), 
             Mean1Dim(smooth_ano_obs, dim_to_mean), 
             compROW = required_complete_row, 
             limits = c(ceiling((runmean_months + 1) / 2), 
                        leadtimes_per_startdate - floor(runmean_months / 2)))
PlotVsLTime(corr, toptitle = "correlations", ytitle = "correlation", 
            monini = 11, limits = c(-1, 2), listexp = c('CMIP5 IC3'), 
            listobs = c('ERSST'), biglab = FALSE, hlines = c(-1, 0, 1), 
            fileout = 'tos_cor.eps')