CST_DynBiasCorrection {CSTools}R Documentation

Performing a Bias Correction conditioned by the dynamical properties of the data.


This function perform a bias correction conditioned by the dynamical properties of the dataset. This function internally uses the functions 'Predictability' to divide in terciles the two dynamical proxies computed with 'CST_ProxiesAttractor'. A bias correction between the model and the observations is performed using the division into terciles of the local dimension 'dim' and inverse of the persistence 'theta'. For instance, model values with lower 'dim' will be corrected with observed values with lower 'dim', and the same for theta. The function gives two options of bias correction: one for 'dim' and/or one for 'theta'


  method = "QUANT",
  wetday = FALSE,
  proxy = "dim",
  ncores = NULL



an s2v_cube object with the experiment data


an s2dv_cube object with the reference data


a character string indicating the method to apply bias correction among these ones: "PTF","RQUANT","QUANT","SSPLIN"


logical indicating whether to perform wet day correction or not OR a numeric threshold below which all values are set to zero (by default is set to 'FALSE').


a character string indicating the proxy for local dimension 'dim' or inverse of persistence 'theta' to apply the dynamical conditioned bias correction method.


a number lower than 1 indicating the quantile to perform the computation of local dimension and theta


The number of cores to use in parallel computation


dynbias an s2dvcube object with a bias correction performed conditioned by local dimension 'dim' or inverse of persistence 'theta'


Carmen Alvarez-Castro, carmen.alvarez-castro@cmcc.it

Maria M. Chaves-Montero, mdm.chaves-montero@cmcc.it

Veronica Torralba, veronica.torralba@cmcc.it

Davide Faranda, davide.faranda@lsce.ipsl.fr


Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large scale atmospheric predictability.Nature Communications, 10(1), 1316. DOI = https://doi.org/10.1038/s41467-019-09305-8 "

Faranda, D., Gabriele Messori and Pascal Yiou. (2017). Dynamical proxies of North Atlantic predictability and extremes. Scientific Reports, 7-41278, 2017.


# example 1: simple data s2dvcube style
expL <- rnorm(1:2000)
dim (expL) <- c(time =100,lat = 4, lon = 5)
obsL <- c(rnorm(1:1980),expL[1,,]*1.2)
dim (obsL) <- c(time = 100,lat = 4, lon = 5)
time_obsL <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
time_expL <- paste(rep("01", 100), rep("01", 100), 1929 : 2019, sep = "-")
lon <- seq(-1,5,1.5)
lat <- seq(30,35,1.5)
# qm=0.98 # too high for this short dataset, it is possible that doesn't
# get the requirement, in that case it would be necessary select a lower qm
# for instance qm=0.60
expL <- s2dv_cube(data = expL, lat = lat, lon = lon,
                Dates = list(start = time_expL, end = time_expL))
obsL <- s2dv_cube(data = obsL, lat = lat, lon = lon,
                Dates = list(start = time_obsL, end = time_obsL))
# to use DynBiasCorrection
dynbias1 <- DynBiasCorrection(exp = expL$data, obs = obsL$data, proxy= "dim",
                                 quanti = 0.6)
# to use CST_DynBiasCorrection
dynbias2 <- CST_DynBiasCorrection(exp = expL, obs = obsL, proxy= "dim",
                                quanti = 0.6)

[Package CSTools version 4.0.1 Index]