CST_RegimesAssign {CSTools}

Function for matching a field of anomalies with a set of maps used as a reference (e.g. clusters obtained from the WeatherRegime function)

Description

This function performs the matching between a field of anomalies and a set of maps which will be used as a reference. The anomalies will be assigned to the reference map for which the minimum Eucledian distance (method =’distance’) or highest spatial correlation (method = 'ACC') is obtained.

Usage

CST_RegimesAssign(
  data,
  ref_maps,
  method = "distance",
  composite = FALSE,
  memb = FALSE,
  ncores = NULL
)

Arguments

data

An 's2dv_cube' object.

ref_maps

An 's2dv_cube' object as the output of CST_WeatherRegimes.

method

Whether the matching will be performed in terms of minimum distance (default = 'distance') or the maximum spatial correlation (method = 'ACC') between the maps.

composite

A logical parameter indicating if the composite maps are computed or not (default = FALSE).

memb

A logical value indicating whether to compute composites for separate members (default FALSE) or as unique ensemble (TRUE). This option is only available for when parameter 'composite' is set to TRUE and the data object has a dimension named 'member'.

ncores

The number of multicore threads to use for parallel computation.

Value

A list with two elements $data (a 's2dv_cube' object containing the composites cluster=1,..,K for case (*1) or only k=1 for any specific cluster, i.e., case (*2)) (only when composite = 'TRUE') and $statistics that includes $pvalue (array with the same structure as $data containing the pvalue of the composites obtained through a t-test that accounts for the serial dependence of the data with the same structure as Composite.)(only when composite = 'TRUE'), $cluster (array with the same dimensions as data (except latitude and longitude which are removed) indicating the ref_maps to which each point is allocated.), $frequency (A vector of integers (from k=1,...k n reference maps) indicating the percentage of assignations corresponding to each map.).

Author(s)

Verónica Torralba - BSC, [e-mail]

References

Torralba, V. (2019) Seasonal climate prediction for the wind energy sector: methods and tools for the development of a climate service. Thesis. Available online: https://eprints.ucm.es/56841/

Examples

data <- array(abs(rnorm(1280, 282.7, 6.4)), dim = c(dataset = 2, member = 2, 
                                                   sdate = 3, ftime = 3, 
                                                   lat = 4, lon = 4))
coords <- list(lon = seq(0, 3), lat = seq(47, 44))
exp <- list(data = data, coords = coords)
class(exp) <- 's2dv_cube'
regimes <- CST_WeatherRegimes(data = exp, EOFs = FALSE, 
                             ncenters = 4)
res1 <- CST_RegimesAssign(data = exp, ref_maps = regimes, 
                         composite = FALSE)