s2dv (version 2.1.0)

ROCSS: Compute the Relative Operating Characteristic Skill Score

Description

The Relative Operating Characteristic Skill Score (ROCSS; Kharin and Zwiers, 2003) is based on the ROC curve, which gives information about the hit rates against the false-alarm rates for a particular category or event. The ROC curve can be summarized with the area under the ROC curve, known as the ROC score, to provide a skill value for each category. The ROCSS ranges between minus infinite and 1. A positive ROCSS value indicates that the forecast has higher skill than the reference forecast, meaning the contrary otherwise.
The function accepts either the data or the probabilities of each data as inputs. If there is more than one dataset, RPSS will be computed for each pair of exp and obs data.

Usage

ROCSS(
  exp,
  obs,
  ref = NULL,
  time_dim = "sdate",
  memb_dim = "member",
  cat_dim = NULL,
  dat_dim = NULL,
  prob_thresholds = c(1/3, 2/3),
  indices_for_clim = NULL,
  cross.val = FALSE,
  ncores = NULL
)

Value

A numerical array of ROCSS with dimensions c(nexp, nobs, cat, the rest dimensions of 'exp' except 'time_dim' and 'memb_dim' dimensions). nexp is the number of experiment (i.e., dat_dim in exp), and nobs is the number of observation (i.e., dat_dim in obs). If dat_dim is NULL, nexp and nobs are omitted. dimension 'cat' refers to the probabilistic category, i.e., 1 + length(prob_thresholds).

Arguments

exp

A named numerical array of either the forecast with at least time and member dimensions, or the probabilities with at least time and category dimensions. The probabilities can be generated by s2dv::GetProbs.

obs

A named numerical array of either the observations with at least time dimension, or the probabilities with at least time and category dimensions. The probabilities can be generated by s2dv::GetProbs. The dimensions must be the same as 'exp' except 'memb_dim' and 'dat_dim'.

ref

A named numerical array of the reference forecast with at least time and member dimensions, or the probabilities with at least time and category dimensions. The probability can be generated by s2dv::GetProbs. The dimensions must be the same as 'exp' except 'memb_dim' and 'dat_dim'. If there is only one reference dataset, it should not have dataset dimension. If there is corresponding reference for each experiment, the dataset dimension must have the same length as in 'exp'. If 'ref' is NULL, the random forecast is used as reference forecast. The default value is NULL.

time_dim

A character string indicating the name of the time dimension. The default value is 'sdate'.

memb_dim

A character string indicating the name of the member dimension to compute the probabilities of the forecast and the reference forecast. The default value is 'member'. If the data are probabilities, set memb_dim as NULL.

cat_dim

A character string indicating the name of the category dimension that is needed when exp, obs, and ref are probabilities. The default value is NULL, which means that the data are not probabilities.

dat_dim

A character string indicating the name of dataset dimension. The length of this dimension can be different between 'exp' and 'obs'. The default value is NULL.

prob_thresholds

A numeric vector of the relative thresholds (from 0 to 1) between the categories. The default value is c(1/3, 2/3), which corresponds to tercile equiprobable categories.

indices_for_clim

A vector of the indices to be taken along 'time_dim' for computing the thresholds between the probabilistic categories. If NULL, the whole period is used. The default value is NULL.

cross.val

A logical indicating whether to compute the thresholds between probabilistic categories in cross-validation. The default value is FALSE.

ncores

An integer indicating the number of cores to use for parallel computation. The default value is NULL.

References

Kharin, V. V. and Zwiers, F. W. (2003): https://doi.org/10.1175/1520-0442(2003)016

Examples

# Use data as input
exp <- array(rnorm(1000), dim = c(lon = 3, lat = 2, sdate = 60, member = 10))
ref <- array(rnorm(1000), dim = c(lon = 3, lat = 2, sdate = 60, member = 10))
obs <- array(rnorm(1000), dim = c(lon = 3, lat = 2, sdate = 60))
ROCSS(exp = exp, obs = obs) ## random forecast as reference forecast
ROCSS(exp = exp, obs = obs, ref = ref) ## ref as reference forecast
# Use probs as input
exp_probs <- GetProbs(exp, memb_dim = 'member')
obs_probs <- GetProbs(obs, memb_dim = NULL)
ref_probs <- GetProbs(ref, memb_dim = 'member')
ROCSS(exp = exp_probs, obs = obs_probs, ref = ref_probs, memb_dim = NULL, cat_dim = 'bin')