CST_DynBiasCorrection {CSTools}

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

Description

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'

Usage

CST_DynBiasCorrection(
  exp,
  obs,
  method = "QUANT",
  wetday = FALSE,
  proxy = "dim",
  quanti,
  ncores = NULL
)

Arguments

exp

An s2v_cube object with the experiment data.

obs

An s2dv_cube object with the reference data.

method

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

wetday

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').

proxy

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

quanti

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

ncores

The number of cores to use in parallel computation.

Value

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

Author(s)

Carmen Alvarez-Castro, [e-mail]

Maria M. Chaves-Montero, [e-mail]

Veronica Torralba, [e-mail]

Davide Faranda, [e-mail]

References

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: 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.

Examples

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 <- as.POSIXct(paste(rep("01", 100), rep("01", 100), 1920:2019, sep = "-"), 
                       format = "%d-%m-%y")
time_expL <- as.POSIXct(paste(rep("01", 100), rep("01", 100), 1929:2019, sep = "-"), 
                       format = "%d-%m-%y")
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, coords = list(lon = lon, lat = lat),
                 Dates = time_expL)
obsL <- s2dv_cube(data = obsL, coords = list(lon = lon, lat = lat),
                 Dates = 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)