s2dverification: RatioSDRMS – R documentation

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RatioSDRMS

Computes the ratio between the ensemble spread and RMSE

Description

Arrays var_exp & var_obs should have dimensions between
c(nmod/nexp, nmemb/nparam, nsdates, nltime)
and
c(nmod/nexp, nmemb/nparam, nsdates, nltime, nlevel, nlat, nlon)
The ratio between the standard deviation of the members around the ensemble mean in var_exp and the RMSE between var_exp and var_obs is output for each experiment and each observational dataset.
The p-value is provided by a one-sided Fischer test.

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

Usage

RatioSDRMS(var_exp, var_obs, pval = TRUE)

.RatioSDRMS(exp, obs, pval = TRUE)

Arguments

`var_exp`	Model data: c(nmod/nexp, nmemb/nparam, nsdates, nltime) up to c(nmod/nexp, nmemb/nparam, nsdates, nltime, nlevel, nlat, nlon)
`var_obs`	Observational data: c(nobs, nmemb, nsdates, nltime) up to c(nobs, nmemb, nsdates, nltime, nlevel, nlat, nlon)
`pval`	Whether to compute the p-value of Ho : SD/RMSE = 1 or not.
`exp`	N by M matrix of N forecasts from M ensemble members.
`obs`	Vector of the corresponding observations of length N.

Value

RatioSDRMS: Array with dimensions c(nexp/nmod, nobs, 1 or 2, nltime) up to c(nexp/nmod, nobs, 1 or 2, nltime, nlevel, nlat, nlon).
The 3rd dimension corresponds to the ratio (SD/RMSE) and the p.value (only present if pval = TRUE) of the one-sided Fisher test with Ho: SD/RMSE = 1.

.RatioSDRMS:

$ratio The ratio of the ensemble spread and RMSE,
$p_val Corresponds to the p values of the ratio (only present if pval = TRUE).

Author(s)

History:
0.1 - 2011-12 (V. Guemas, virginie.guemas@ic3.cat) - Original code
1.0 - 2013-09 (N. Manubens, nicolau-manubens@ic3.cat) - Formatting to CRAN
1.1 - 2017-02 (A. Hunter, alasdair.hunter@bsc.es) - Adapted to veriApply()

Examples

# Load sample data as in Load() example:
example(Load)
rsdrms <- RatioSDRMS(sampleData$mod, sampleData$obs)
# Reorder the data in order to plot it with PlotVsLTime
rsdrms_plot <- array(dim = c(dim(rsdrms)[1:2], 4, dim(rsdrms)[4]))
rsdrms_plot[, , 2, ] <- rsdrms[, , 1, ]
rsdrms_plot[, , 4, ] <- rsdrms[, , 2, ]
 
PlotVsLTime(rsdrms_plot, toptitle = "Ratio ensemble spread / RMSE", ytitle = "", 
           monini = 11, limits = c(-1, 1.3), listexp = c('CMIP5 IC3'), 
           listobs = c('ERSST'), biglab = FALSE, siglev = TRUE, 
           fileout = 'tos_rsdrms.eps')
 

# The following example uses veriApply combined with .RatioSDRMS instead of RatioSDRMS
 ## Not run: 
require(easyVerification)  
RatioSDRMS2 <- s2dverification:::.RatioSDRMS
rsdrms2 <- veriApply("RatioSDRMS2",
                    sampleData$mod,
                    # see ?veriApply for how to use the 'parallel' option
                    Mean1Dim(sampleData$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