Description Usage Arguments Value Note Author(s) See Also Examples

View source: R/ComprehensiveTemperatureGenerator.R

The Comprehensive Temperature Generator

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
ComprehensiveTemperatureGenerator(station = c("T0001", "T0010", "T0099"),
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 = TRUE, p = 1, type = "none",
lag.max = NULL, ic = "AIC", activateVARselect = FALSE,
year_max_sim = year_max, year_min_sim = year_min,
mean_climate_Tn_sim = NULL, mean_climate_Tx_sim = NULL,
Tn_spline_sim = NULL, Tx_spline_sim = NULL, onlygeneration = FALSE,
varmodel = NULL, normalize = TRUE, type_quantile = 3, sample = NULL,
extremes = TRUE, option = 2, yearly = FALSE, yearly_sim = yearly,
n_GPCA_iteration = 0, n_GPCA_iteration_residuals = n_GPCA_iteration,
exogen = NULL, exogen_sim = exogen, is_exogen_gaussian = FALSE,
exogen_all = NULL, exogen_all_col = station, nscenario = 1,
seed = NULL, noise = NULL)
``` |

`station` |
see respective input parameter on |

`Tx_all, Tn_all, mean_climate_Tn, mean_climate_Tx, Tx_spline, Tn_spline` |
see respective input parameter on |

`year_max, year_min, leap, nmonth, verbose` |
see respective input parameter on |

`p, type, lag.max, ic, activateVARselect` |
see respective input parameter on |

`year_max_sim` |
last year of the simulation period. Default is equal to |

`year_min_sim` |
first year of the simulation period. Default is equal to |

`mean_climate_Tn_sim` |
monthly averaged daily minimum temperatures for the simulated scenario and used by the random generator . Default is |

`mean_climate_Tx_sim` |
monthly averaged daily maximum temperatures for the simulated scenario and used by the random generator . Default is |

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

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

`onlygeneration` |
logical variable. If |

`varmodel` |
the comprehensinve VAR model as a |

`normalize, sample, extremes` |
see |

`type_quantile` |
see |

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

`yearly` |
logical value. If |

`yearly_sim` |
logical value. If |

`n_GPCA_iteration` |
number of iterations of Gaussianization process for data. Default is 0 (no Gaussianization) |

`n_GPCA_iteration_residuals` |
number of iterations of Gaussianization process for VAR residuals. Default is 0 (no Gaussianization) |

`exogen` |
data frame or matrix containing the (normalized or not) exogenous variables (predictors) for the recorded (calibration) period. Default is |

`exogen_sim` |
data frame or matrix containing the (normalized or not) exogenous variables (predictors) for the simulation period. Default is |

`is_exogen_gaussian` |
logical value, If |

`exogen_all` |
data frame containing exogenous variable formatted like |

`exogen_all_col` |
vector of considered columns of |

`nscenario` |
number of generated scenarios for daily maximum and minimum temperature |

`seed` |
seed for stochastic random generation see |

`noise` |
stochastic noise to add for variabile generation. Default is |

A list of the following variables:

`input`

list of variables returned by `setComprehensiveTemperatureGeneratorParameters`

`var`

varest object containing the used VAR model (if useVAR is true), `NULL`

(otherwise)

`output`

list variables returned by `generateTemperatureTimeseries`

(i.e. generated timeseries)

It pre-processes series and generates multi-site temperature fields by using `setComprehensiveTemperatureGeneratorParameters`

,`getVARmodel`

and `generateTemperatureTimeseries`

. Detailed examples can be viewed of this function in this presentation.

Emanuele Cordano, Emanuele Eccel

`setComprehensiveTemperatureGeneratorParameters`

, `generateTemperatureTimeseries`

,`generateTemperatureTimeseries`

,`splineInterpolateMonthlytoDailyforSeveralYears`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
data(trentino)
set.seed(1222) # set the seed for random generations!
year_min <- 1961
year_max <- 1990
year_min_sim <- 1982
year_max_sim <- 1983
n_GPCA_iter <- 5
n_GPCA_iteration_residuals <- 5
p <- 1
vstation <- c("B2440","B6130","B8570","B9100","LAVIO","POLSA","SMICH","T0001",
"T0010","T0014","T0018","T0032","T0064","T0083","T0090","T0092",
"T0094","T0099","T0102","T0110","T0129","T0139","T0147","T0149",
"T0152","T0157","T0168","T0179","T0189","T0193","T0204","T0210",
"T0211","T0327","T0367","T0373")
## Not Run: the call to ComprehensiveTemperatureGenerator may elapse
## too long time (more than 5 eseconds) and is not executed by CRAN check.
## Please uncomment the following line to run the example on your own PC.
# generation00 <-ComprehensiveTemperatureGenerator(station=vstation[16],
# Tx_all=TEMPERATURE_MAX,Tn_all=TEMPERATURE_MIN,year_min=year_min,year_max=year_max,
# p=p,n_GPCA_iteration=n_GPCA_iter,n_GPCA_iteration_residuals=n_GPCA_iteration_residuals,
# sample="monthly",year_min_sim=year_min_sim,year_max_sim=year_max_sim)
``` |

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