s2dverification: RMSSS – R documentation

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RMSSS

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, vguemas@bsc.es) - Original code
1.0 - 2013-09 (N. Manubens, nicolau.manubens@bsc.es) - Formatting to R CRAN
1.1 - 2017-02 (A. Hunter, alasdair.hunter@bsc.es) - 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

Set of Common Tools for Forecast Verification

v2.10.0

Apache License 2.0

Authors

BSC-CNS [aut, cph], Virginie Guemas [aut], Nicolau Manubens [aut], An-Chi Ho [ctb, cre], Nuria Perez-Zanon [ctb], Javier Garcia-Serrano [aut], Neven Fuckar [aut], Louis-Philippe Caron [aut], Omar Bellprat [aut], Luis Rodrigues [aut], Veronica Torralba [aut], Alasdair Hunter [aut], Chloe Prodhomme [aut], Martin Menegoz [aut], Domingo Manubens [ctb], Constantin Ardilouze [ctb], Lauriane Batte [ctb], Fabian Lienert [ctb], Julia Giner [ctb], Jean-Philippe Baudouin [ctb], Nube Gonzalez [ctb], Ludovic Auger [ctb], Nicola Cortesi [ctb], Eleftheria Exarchou [ctb], Ruben Cruz [ctb], Isabel Andreu-Burillo [ctb], Ramiro Saurral [ctb]

Initial release