Description Usage Arguments Value Author(s) See Also

View source: R/setWholeTemperatureGeneratorParameters.R

Computes climatic and correlation information useful for creating an auto-regeressive random generation of maximum and minimun daily temparature. This function is called by `ComprehensiveTemperatureGenerator`

.

1 2 3 4 5 | ```
setComprehensiveTemperatureGeneratorParameters(station, Tx_all, Tn_all,
mean_climate_Tn = NULL, mean_climate_Tx = NULL, Tx_spline = NULL,
Tn_spline = NULL, year_max = 1990, year_min = 1961, leap = TRUE,
nmonth = 12, verbose = FALSE, cpf = NULL, normalize = TRUE,
sample = NULL, option = 2, yearly = FALSE)
``` |

`station` |
character vector of the IDs of the considered meteorological stations |

`Tx_all` |
data frame containing daily maximum temperature of all meteorological station. See |

`Tn_all` |
data frame containing daily minimum temperature of all meteorological station. See |

`mean_climate_Tn` |
a matrix containing monthly mean minimum daily temperature for the considered station or an object as returned by |

`mean_climate_Tx` |
a matrix containing monthly mean maximum daily temperature for the considered station or an object as returned by |

`Tx_spline` |
daily timeseries (from the first day of |

`Tn_spline` |
daily timeseries (from the first day of |

`year_max` |
start year of the recorded (calibration) period |

`year_min` |
end year of the recorded (calibration) period |

`leap` |
logical variables. It is |

`nmonth` |
number of months in one year. Default is 12. |

`verbose` |
logical variable |

`cpf` |
see |

`normalize` |
logical variable If |

`sample` |
see |

`option` |
integer value. If 1, the generator works with minimum and maximum temperature, if 2 (default) it works with the average value between maximum and minimum temperature and the respective daily thermal range. |

`yearly` |
logical value. If |

This function creates and returns the following gloabal variables:

`data_original`

matrix containing normalized and standardized data (i.e. `data_original`

)

`data_for_var`

matrix returned from `normalizeGaussian_severalstations`

by processing `data_original`

if `normalize`

is `TRUE`

), otherwise it is equal to `data_original`

.

`Tn_mes`

matrix containing measured minimum daily temperature in the analyzed time period ( *Tn_{mes}*)

`Tx_mes`

matrix containing measured maximum daily temperature in the analyzed time period ( *Tx_{mes}*)

`Tm_mes`

matrix calculated as to

*\frac{Tx_{mes}+Tn_{mes}}{2}*

`DeltaT_mes`

matrix corresponding to *Tx_{mes}-Tn_{mes}*

`monthly_mean_Tn`

matrix containing monthly means of minimum daily temperature for the considered station. It is calculated according to the input format `is.monthly.climate`

if `saveMonthlyClimate`

is `TRUE`

.

`monthly_mean_Tx`

matrix containing monthly means of maximum daily temperature for the considered station. It is calculated according to the input format `is.monthly.climate`

if `saveMonthlyClimate`

is `TRUE`

.

`Tx_spline`

matrix containing the averaged daily values of maximimum temperature obtained by a spline interpolation of the monthly climate `monthly_mean_Tx`

or `mean_climate_Tx`

using `splineInterpolateMonthlytoDailyforSeveralYears`

( *Tx_{s}*)

`Tn_spline`

matrix containing the averaged daily values of minimun temperature obtained by a spline interpolation of the monthly climate `monthly_mean_Tn`

or `mean_climate_Tn`

using `splineInterpolateMonthlytoDailyforSeveralYears`

( *Tn_{s}*)

`SplineAdvTm`

matrix calculated as *\frac{Tx_{s}+Tn_{s}}{2}*

`SplineAdvDeltaT`

, matrix corresponding to *Tx_{s}-Tn_{s}*

`stdTn`

vector containing the standard deviation of minimum temperature anomalies *Tn_{mes}-Tn_s* (*σ_{Tn}*)

`stdTx`

vector containing the standard deviation of maximum temperature anomalies *Tx_{mes}-Tx_s* (*σ_{Tx}*)

`stdTm`

vector containing the standard deviation of "mean" temperature anomalies *Tm_{mes}-Tm_s* (*σ_{Tm}*)

`Tn_mes_res`

standard core (standardization) of *Tn_mes* obtained
by solving column by column the expression

*\frac{Tn_{mes}-Tn_s}{σ_{Tn}}*

`Tx_mes_res`

standard core (standardization) of *Tx_mes* obtained
by solving column-by-column the expression

*\frac{Tx_{mes}-Tn_s}{sd_{Tm}}*

`Tm_mes_res`

standard core (standardization) of *Tm_mes* obtained
by solving column-by-column the expression

*\frac{Tm_{mes}-Tn_s}{sd_{Tm}}*

`DeltaT_mes_res`

equal to `DeltaT_mes`

`data_original`

matrix obtained as `cbind(Tx_mes_res,Tn_mes_res)`

if `option`

==1, or `cbind(Tm_mes_res,DeltaT_mes_res)`

if `option`

==2

See the R code for further details.

Emanuele Cordano, Emanuele Eccel

`splineInterpolateMonthlytoDailyforSeveralYears`

,`ComprehensiveTemperatureGenerator`

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