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#'Compute the Mean Bias
#'
#'The Mean Bias or Mean Error (Wilks, 2011) is defined as the mean difference
#'between the ensemble mean forecast and the observations. It is a deterministic
#'metric. Positive values indicate that the forecasts are on average too high
#'and negative values indicate that the forecasts are on average too low; however,
#'it gives no information about the typical magnitude of individual forecast errors.
#'
#'@param exp A named numerical array of the forecast with at least time
#' dimension.
#'@param 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'.
#'@param time_dim A character string indicating the name of the time dimension.
#' The default value is 'sdate'.
#'@param 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'
#' is already the ensemble mean. The default value is NULL.
#'@param ncores An integer indicating the number of cores to use for parallel
#' computation. The default value is NULL.
#'
#'@return
#'A numerical array of Bias with dimensions the dimensions of
#''exp' except 'time_dim' and 'memb_dim' dimensions.
#'
#'@references
#'Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7
#'
#'@examples
#'exp <- array(rnorm(1000), dim = c(dat = 1, lat = 3, lon = 5, member = 10, year = 50))
#'obs <- array(rnorm(1000), dim = c(dat = 1, lat = 3, lon = 5, year = 50))
#'bias <- Bias(exp = exp, obs = obs, time_dim = 'year', memb_dim = 'member', na.rm = FALSE, ncores = 1)
#'
#'@import multiApply
#'@export
Bias <- function(exp, obs, time_dim = 'sdate', memb_dim = NULL, na.rm = FALSE, ncores = NULL) {
## Checks
## Ensemble mean
if (!is.null(memb_dim)) {
exp <- MeanDims(exp, memb_dim, na.rm = na.rm)
}
## Mean bias
bias <- multiApply::Apply(data = list(exp, obs),
target_dims = time_dim,
fun = .Bias,
ncores = ncores)$output1
## Return the mean bias
bias <- MeanDims(bias, time_dim, na.rm = na.rm)
return(bias)
}
.Bias <- function(exp, obs) {
bias <- exp - obs
return(bias)
}