BiasCorrection {CSTools}

Bias Correction based on the mean and standard deviation adjustment

Description

This function applies the simple bias adjustment technique described in Torralba et al. (2017). The adjusted forecasts have an equivalent standard deviation and mean to that of the reference dataset.

Usage

BiasCorrection(
  exp,
  obs,
  exp_cor = NULL,
  na.rm = FALSE,
  memb_dim = "member",
  sdate_dim = "sdate",
  dat_dim = NULL,
  ncores = NULL
)

Arguments

exp

A multidimensional array with named dimensions containing the seasonal forecast experiment data with at least time and member dimensions.

obs

A multidimensional array with named dimensions containing the observed data with at least time dimension.

exp_cor

A multidimensional array with named dimensions containing the seasonal forecast experiment to be corrected with at least time and member dimension. If it is NULL, the 'exp' forecast will be corrected. If there is only one corrected dataset, it should not have dataset dimension. If there is a corresponding corrected dataset for each 'exp' forecast, the dataset dimension must have the same length as in 'exp'. The default value is NULL.

na.rm

A logical value indicating whether missing values should be stripped before the computation proceeds, by default it is set to FALSE.

memb_dim

A character string indicating the name of the member dimension. By default, it is set to 'member'.

sdate_dim

A character string indicating the name of the start date dimension. By default, it is set to 'sdate'.

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.

ncores

An integer that indicates the number of cores for parallel computations using multiApply function. The default value is NULL.

Value

An array containing the bias corrected forecasts with the dimensions nexp, nobs and same dimensions as in the 'exp' object. 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. If 'exp_cor' is provided the returned array will be with the same dimensions as 'exp_cor'.

Author(s)

Verónica Torralba, [e-mail]

References

Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56, 1231-1247, doi: 10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS)

Examples

mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7)
dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, time = 5, lat = 6, lon = 7)
obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7)
dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, time = 5, lat = 6, lon = 7)
a <- BiasCorrection(exp = mod1, obs = obs1)