MultiMetric {CSTools}

Multiple Metrics applied in Multiple Model Anomalies

Description

This function calculates correlation (Anomaly Correlation Coefficient; ACC), root mean square error (RMS) and the root mean square error skill score (RMSSS) of individual anomaly models and multi-models mean (if desired) with the observations on arrays with named dimensions.

Usage

MultiMetric(
  exp,
  obs,
  metric = "correlation",
  multimodel = TRUE,
  time_dim = "ftime",
  memb_dim = "member",
  sdate_dim = "sdate"
)

Arguments

exp

A multidimensional array with named dimensions.

obs

A multidimensional array with named dimensions.

metric

A character string giving the metric for computing the maximum skill. This must be one of the strings 'correlation', 'rms' or 'rmsss.

multimodel

A logical value indicating whether a Multi-Model Mean should be computed.

time_dim

Name of the temporal dimension where a mean will be applied. It can be NULL, the default value is 'ftime'.

memb_dim

Name of the member dimension. It can be NULL, the default value is 'member'.

sdate_dim

Name of the start date dimension or a dimension name identifiying the different forecast. It can be NULL, the default value is 'sdate'.

Value

A list of arrays containing the statistics of the selected metric in the element $data which is a list of arrays: for the metric requested and others for statistics about its signeificance. The arrays have two dataset dimensions equal to the 'dataset' dimension in the exp$data and obs$data inputs. If multimodel is TRUE, the greatest position in the first dimension correspons to the Multi-Model Mean.

Author(s)

Mishra Niti, [e-mail]

Perez-Zanon Nuria, [e-mail]

References

Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31. doi: 10.1007/s00382-018-4404-z

See Also

Corr, RMS, RMSSS and CST_Load

Examples

exp <- array(rnorm(2*2*4*5*2*2), 
            dim = c(dataset = 2, member = 2, sdate = 4, ftime = 5, lat = 2, 
                    lon = 2))
obs <- array(rnorm(1*1*4*5*2*2),
            dim = c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 2, 
                    lon = 2))
res <- MultiMetric(exp = exp, obs = obs)