s2dverification (version 2.4.0)

RMSSS: Computes Root Mean Square Skill Score

Description

Arrays var_exp & var_obs should have the same dimensions except along posloop 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). RMSSS computes the Root Mean Square Skill Score of each jexp in 1:nexp against each jobs in 1:nobs which gives nexp x nobs RMSSS for each other grid point of the matrix (each latitude/longitude/level/leadtime). The RMSSS are computed along the posRMS dimension which should correspond to the startdate dimension. The p-value is provided by a one-sided Fisher test.

Usage

RMSSS(var_exp, var_obs, posloop = 1, posRMS = 2)

Value

Array with dimensions : c(length(posloop) in var_exp, length(posloop) in var_obs, 2, all other dimensions of var_exp & var_obs except posRMS). The dimension 2 corresponds to the RMSSS and the p.value of the one-sided Fisher test with Ho: RMSSS = 0.

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)
rmsss <- RMSSS(Mean1Dim(ano_exp, 2), Mean1Dim(ano_obs, 2))
rmsss2 <- array(dim = c(dim(rmsss)[1:2], 4, dim(rmsss)[4]))
rmsss2[, , 2, ] <- rmsss[, , 1, ]
rmsss2[, , 4, ] <- rmsss[, , 2, ]
PlotVsLTime(rmsss, toptitle = "Root Mean Square Skill Score", ytitle = "", 
            monini = 11, limits = c(-1, 1.3), listexp = c('CMIP5 IC3'), 
            listobs = c('ERSST'), biglab = FALSE, hlines = c(-1, 0, 1), 
            fileout = 'tos_rmsss.eps')