View source: R/CST_MultiMetric.R
CST_MultiMetric | R Documentation |
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.
CST_MultiMetric(
exp,
obs,
metric = "correlation",
multimodel = TRUE,
time_dim = "ftime",
memb_dim = "member",
sdate_dim = "sdate"
)
exp |
An object of class |
obs |
An object of class |
metric |
A character string giving the metric for computing the maximum skill. This must be one of the strings 'correlation', 'rms', 'rmsss' and 'rpss'. If 'rpss' is chossen the terciles probabilities are evaluated. |
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'. |
An object of class s2dv_cube
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
first position in the first 'nexp' dimension correspons to the Multi-Model Mean.
Mishra Niti, niti.mishra@bsc.es
Perez-Zanon Nuria, nuria.perez@bsc.es
Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00382-018-4404-z")}
Corr
, RMS
,
RMSSS
and CST_Load
mod <- rnorm(2*2*4*5*2*2)
dim(mod) <- c(dataset = 2, member = 2, sdate = 4, ftime = 5, lat = 2, lon = 2)
obs <- rnorm(1*1*4*5*2*2)
dim(obs) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 2, lon = 2)
lon <- seq(0, 30, 5)
lat <- seq(0, 25, 5)
coords <- list(lat = lat, lon = lon)
exp <- list(data = mod, coords = coords)
obs <- list(data = obs, coords = coords)
attr(exp, 'class') <- 's2dv_cube'
attr(obs, 'class') <- 's2dv_cube'
a <- CST_MultiMetric(exp = exp, obs = obs)
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