AbsBiasSS: Compute the Absolute Mean Bias Skill Score

In s2dv: A Set of Common Tools for Seasonal to Decadal Verification

Compute the Absolute Mean Bias Skill Score

Description

The Absolute Mean Bias Skill Score is based on the Absolute Mean Error (Wilks, 2011) between the ensemble mean forecast and the observations. It measures the accuracy of the forecast in comparison with a reference forecast to assess whether the forecast presents an improvement or a worsening with respect to that reference. The Mean Bias Skill Score ranges between minus infinite and 1. Positive values indicate that the forecast has higher skill than the reference forecast, while negative values indicate that it has a lower skill. Examples of reference forecasts are the climatological forecast (average of the observations), a previous model version, or another model. It is computed as AbsBiasSS = 1 - AbsBias_exp / AbsBias_ref. The statistical significance is obtained based on a Random Walk test at the confidence level specified (DelSole and Tippett, 2016). If there is more than one dataset, the result will be computed for each pair of exp and obs data.

Usage

AbsBiasSS(
  exp,
  obs,
  ref = NULL,
  time_dim = "sdate",
  memb_dim = NULL,
  dat_dim = NULL,
  na.rm = FALSE,
  sig_method.type = "two.sided.approx",
  alpha = 0.05,
  ncores = NULL
)

Arguments

exp	A named numerical array of the forecast with at least time dimension.
obs	A named numerical array of the observation with at least time dimension. The dimensions must be the same as 'exp' except 'memb_dim' and 'dat_dim'.
ref	A named numerical array of the reference forecast data with at least time dimension. 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 experiement, the dataset dimension must have the same length as in 'exp'. If 'ref' is NULL, the climatological 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 ensemble mean; it should be set to NULL if the parameter 'exp' and 'ref' are already the ensemble mean. The default value is NULL.
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.
na.rm	A logical value indicating if NAs should be removed (TRUE) or kept (FALSE) for computation. The default value is FALSE.
sig_method.type	A character string indicating the test type of the significance method. Check RandomWalkTest() parameter test.type for details. The default is 'two.sided.approx', which is the default of RandomWalkTest().
alpha	A numeric of the significance level to be used in the statistical significance test. The default value is 0.05.
ncores	An integer indicating the number of cores to use for parallel computation. The default value is NULL.

Value

$biasSS	A numerical array of BiasSS with dimensions nexp, nobs and the rest dimensions of 'exp' except 'time_dim' and 'memb_dim'.
$sign	A logical array of the statistical significance of the BiasSS with the same dimensions as $biasSS. 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.

References

Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7 DelSole and Tippett, 2016; https://doi.org/10.1175/MWR-D-15-0218.1

Examples

exp <- array(rnorm(1000), dim = c(dat = 1, lat = 3, lon = 5, member = 10, sdate = 50))
ref <- array(rnorm(1000), dim = c(dat = 1, lat = 3, lon = 5, member = 10, sdate = 50))
obs <- array(rnorm(1000), dim = c(dat = 1, lat = 3, lon = 5, sdate = 50))
biasSS1 <- AbsBiasSS(exp = exp, obs = obs, ref = ref, memb_dim = 'member')
biasSS2 <- AbsBiasSS(exp = exp, obs = obs, ref = NULL, memb_dim = 'member')

s2dv documentation built on Oct. 13, 2024, 9:07 a.m.