CST_MultiMetric {CSTools}R Documentation

Multiple Metrics applied in Multiple Model Anomalies

Description

This function calculates correlation (Anomaly Correlation Coefficient; ACC), root mean square error (RMS) and the root mean square error skill score (RMSSS) of individual anomaly models and multi-models mean (if desired) with the observations.

Usage

CST_MultiMetric(
  exp,
  obs,
  metric = "correlation",
  multimodel = TRUE,
  time_dim = "ftime",
  memb_dim = "member",
  sdate_dim = "sdate"
)

Arguments

exp

an object of class s2dv_cube as returned by CST_Anomaly function, containing the anomaly of the seasonal forecast experiments data in the element named $data.

obs

an object of class s2dv_cube as returned by CST_Anomaly function, containing the anomaly of observed data in the element named $data.

metric

a character string giving the metric for computing the maximum skill. This must be one of the strings 'correlation', 'rms', 'rmsss' and 'rpss'. If 'rpss' is chossen the terciles probabilities are evaluated.

multimodel

a logical value indicating whether a Multi-Model Mean should be computed.

time_dim

name of the temporal dimension where a mean will be applied. It can be NULL, the default value is 'ftime'.

memb_dim

name of the member dimension. It can be NULL, the default value is 'member'.

sdate_dim

name of the start date dimension or a dimension name identifiying the different forecast. It can be NULL, the default value is 'sdate'.

Value

an object of class s2dv_cube containing the statistics of the selected metric in the element $data which is a list of arrays: for the metric requested and others for statistics about its signeificance. The arrays have two dataset dimensions equal to the 'dataset' dimension in the exp$data and obs$data inputs. If multimodel is TRUE, the first position in the first 'nexp' dimension correspons to the Multi-Model Mean.

Author(s)

Mishra Niti, niti.mishra@bsc.es

Perez-Zanon Nuria, nuria.perez@bsc.es

References

Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31.https://link.springer.com/10.1007/s00382-018-4404-z

See Also

Corr, RMS, RMSSS and CST_Load

Examples

library(zeallot)
mod <- 1 : (2 * 3 * 4 * 5 * 6 * 7)
dim(mod) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7)
obs <- 1 : (1 * 1 * 4 * 5 * 6 * 7)
dim(obs) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7)
lon <- seq(0, 30, 5)
lat <- seq(0, 25, 5)
exp <- list(data = mod, lat = lat, lon = lon)
obs <- list(data = obs, lat = lat, lon = lon)
attr(exp, 'class') <- 's2dv_cube'
attr(obs, 'class') <- 's2dv_cube'
c(ano_exp, ano_obs) %<-% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb = TRUE)
a <- CST_MultiMetric(exp = ano_exp, obs = ano_obs)
str(a)

exp <- lonlat_data$exp
obs <- lonlat_data$obs
a <- CST_MultiMetric(exp, obs, metric = 'rpss', multimodel = FALSE)
a <- CST_MultiMetric(exp, obs, metric = 'correlation')
a <- CST_MultiMetric(exp, obs, metric = 'rms')
a <- CST_MultiMetric(exp, obs, metric = 'rmsss')


[Package CSTools version 4.0.1 Index]