SpecsVerification (version 0.5-3)

SkillScore: Calculate a skill score and assess uncertainty.

Description

A skill score is defined as (mean score - mean reference score) / (perfect score - mean reference score). The skill score is zero if the mean score of the forecast equals the mean score of the reference forecast, and equals one if the mean score of the forecast equals the best possible score. Uncertainty is assessed by estimating the standard deviation of the skill score by propagation of uncertainty.

Usage

SkillScore(
  scores,
  scores.ref,
  N.eff = NA,
  score.perf = 0,
  handle.na = c("na.fail", "use.pairwise.complete")
)

Arguments

scores

vector of verification scores

scores.ref

vector of verification scores of the reference forecast, must be of the same length as `scores`

N.eff

user-defined effective sample size to be used to estimate the sampling uncertainty; if NA, the length of `scores` is used; default: NA

score.perf

a numeric constant, indicating the value that the score would assign to the perfect forecast

handle.na

how should missing values in scores vectors be handled; possible values are 'na.fail' and 'use.pairwise.complete'; default: 'na.fail'

Value

vector with skill score and its estimated standard deviation

See Also

ScoreDiff

Examples

# NOT RUN {
data(eurotempforecast)
SkillScore(EnsCrps(ens, obs), EnsCrps(ens[, 1:2], obs))
# }