SkillScore: Calculate a skill score and assess uncertainty.

In sieste/SpecsVerification: Forecast Verification Routines for Ensemble Forecasts of Weather and Climate

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

data(eurotempforecast)
SkillScore(EnsCrps(ens, obs), EnsCrps(ens[, 1:2], obs))

sieste/SpecsVerification documentation built on May 29, 2019, 9:59 p.m.