RMSSS: Computes Root Mean Square Skill Score

In s2dverification: Set of Common Tools for Forecast Verification

Computes Root Mean Square Skill Score

Description

Computes the root mean square error skill score between an array of forecasts, var_exp and an array of observations, var_obs, which should have the same dimensions except along posloop where the lengths 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 optionally provided by a one-sided Fisher test.

.RMSSS provides the same functionality but taking a matrix of ensemble members as input (exp).

Usage

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

.RMSSS(exp, obs, pval = TRUE)

Arguments

var_exp	Array of experimental data.
var_obs	Array of observational data, same dimensions as var_exp except along posloop dimension, where the length can be nobs instead of nexp.
posloop	Dimension nobs and nexp.
posRMS	Dimension along which the RMSE are to be computed (the dimension of the start dates).
pval	Whether to compute or not the p-value of the test Ho : RMSSS = 0. TRUE by default.
exp	N by M matrix of N forecasts from M ensemble members.
obs	Vector of the corresponding observations of length N.

Value

RMSSS: Array with dimensions:
c(length(posloop) in var_exp, length(posloop) in var_obs, 1 or 2, all other dimensions of var_exp & var_obs except posRMS).
The 3rd dimension corresponds to the RMSSS and, if pval = TRUE, the p-value of the one-sided Fisher test with Ho: RMSSS = 0.

.RMSSS:

• $rmsss The RMSSS.

• $p_val Corresponds to the p values (only present if pval = TRUE) for the RMSSS.

Author(s)

History:
0.1 - 2012-04 (V. Guemas) - Original code
1.0 - 2013-09 (N. Manubens) - Formatting to R CRAN
1.1 - 2017-02 (A. Hunter) - Adapted to veriApply()

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))
rmsss_plot <- array(dim = c(dim(rmsss)[1:2], 4, dim(rmsss)[4]))
rmsss_plot[, , 2, ] <- rmsss[, , 1, ]
rmsss_plot[, , 4, ] <- rmsss[, , 2, ]
 
PlotVsLTime(rmsss_plot, 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')
 
# The following example uses veriApply combined with .RMSSS instead of RMSSS
 ## Not run: 
require(easyVerification)
RMSSS2 <- s2dverification:::.RMSSS
rmsss2 <- veriApply("RMSSS2", ano_exp,
                   # see ?veriApply for how to use the 'parallel' option
                   Mean1Dim(ano_obs, 2),
                   tdim = 3, ensdim = 2)
 
## End(Not run)

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