DynBiasCorrection {CSTools} | R Documentation |

## 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 used the functions 'CST_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

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

### Arguments

`exp` |
A multidimensional array with named dimensions with the experiment data. |

`obs` |
A multidimensional array with named dimensions with the observation 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

A multidimensional array with named dimensions with a bias correction performed conditioned by local dimension 'dim' or inverse of persistence 'theta'.

### Author(s)

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

### 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)
dynbias <- DynBiasCorrection(exp = expL, obs = obsL, method='QUANT',
proxy= "dim", quanti = 0.6)
```

*CSTools*version 5.2.0 Index]