QuantileMapping {CSTools}

Quantile Mapping for seasonal or decadal forecast data

Description

This function is a wrapper of fitQmap and doQmap from package 'qmap' to be applied on multi-dimensional arrays. The quantile mapping adjustment between an experiment, typically a hindcast, and observation is applied to the experiment itself or to a provided forecast.

Usage

QuantileMapping(
  exp,
  obs,
  exp_cor = NULL,
  sdate_dim = "sdate",
  memb_dim = "member",
  window_dim = NULL,
  method = "QUANT",
  na.rm = FALSE,
  ncores = NULL,
  ...
)

Arguments

exp

A multidimensional array with named dimensions containing the hindcast.

obs

A multidimensional array with named dimensions containing the reference dataset.

exp_cor

A multidimensional array with named dimensions in which the quantile mapping correction should be applied. If it is not specified, the correction is applied on object 'exp'.

sdate_dim

A character string indicating the dimension name in which cross-validation would be applied when exp_cor is not provided. 'sdate' by default.

memb_dim

A character string indicating the dimension name where ensemble members are stored in the experimental arrays. It can be NULL if there is no ensemble member dimension. It is set as 'member' by default.

window_dim

A character string indicating the dimension name where samples have been stored. It can be NULL (default) in case all samples are used.

method

A character string indicating the method to be used: 'PTF', 'DIST', 'RQUANT', 'QUANT', 'SSPLIN'. By default, the empirical quantile mapping 'QUANT' is used.

na.rm

A logical value indicating if missing values should be removed (FALSE by default).

ncores

An integer indicating the number of cores for parallel computation using multiApply function. The default value is NULL (1).

...

Additional parameters to be used by the method choosen. See qmap package for details.

Value

An array containing the experimental data after applying the quantile mapping correction.

Author(s)

Nuria Perez-Zanon, [e-mail]

See Also

fitQmap and doQmap

Examples

# Use synthetic data
exp <- 1 : c(1 * 3 * 5 * 4 * 3 * 2)
dim(exp) <- c(dataset = 1, member = 3, sdate = 5, ftime = 4, 
             lat = 3, lon = 2)
obs <- 101 : c(100 + 1 * 1 * 5 * 4 * 3 * 2)
dim(obs) <- c(dataset = 1, member = 1, sdate = 5, ftime = 4,
             lat = 3, lon = 2)
res <- QuantileMapping(exp, obs)