ComprehensiveTemperatureGenerator: The Comprehensive Temperature Generator
In RMAWGEN: Multi-Site Auto-Regressive Weather GENerator

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

View source: R/ComprehensiveTemperatureGenerator.R

The Comprehensive Temperature Generator

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 `setComprehensiveTemperatureGeneratorParameters`
`Tx_all, Tn_all, mean_climate_Tn, mean_climate_Tx, Tx_spline, Tn_spline`	see respective input parameter on `setComprehensiveTemperatureGeneratorParameters`
`year_max, year_min, leap, nmonth, verbose`	see respective input parameter on `setComprehensiveTemperatureGeneratorParameters`
`p, type, lag.max, ic, activateVARselect`	see respective input parameter on `getVARmodel`
`year_max_sim`	last year of the simulation period. Default is equal to `year_max`
`year_min_sim`	first year of the simulation period. Default is equal to `year_min`
`mean_climate_Tn_sim`	monthly averaged daily minimum temperatures for the simulated scenario and used by the random generator . Default is `mean_climate_Tn`
`mean_climate_Tx_sim`	monthly averaged daily maximum temperatures for the simulated scenario and used by the random generator . Default is `mean_climate_Tx`
`Tn_spline_sim`	daily timeseries (from the first day of `year_min_sim` to the last day of `year_max_sim`) of averaged minimum temperature which can be obtained by a spline interpolation of monthly mean values (for the generation period). Default is `Tn_spline`. See for spline interpolation utilized `splineInterpolateMonthlytoDailyforSeveralYears`.
`Tx_spline_sim`	daily timeseries (from the first day of `year_min_sim` to the last day of `year_max_sim`) of averaged maximum temperature which can be obtained by a spline interpolation of monthly mean values (for the generation period). Default is `Tx_spline`. See for spline interpolation utilized `splineInterpolateMonthlytoDailyforSeveralYears`.
`onlygeneration`	logical variable. If `TRUE` the VAR model `varmodel` is given as input and only random generation is done, otherwise (default) is calculated from measured data
`varmodel`	the comprehensinve VAR model as a `varest2` or `GPCAvarest2` S4 object or a `NULL` object. If `NULL` (default), the comprehensinve VAR is estimated from measured data within the function, otherwise it is given as input and only random generation is done.
`normalize, sample, extremes`	see `normalizeGaussian_severalstations` or `setComprehensiveTemperatureGeneratorParameters`
`type_quantile`	see `type` on `quantile`
`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 `TRUE` the monthly mean values are calculated for each year from `year_min` to `year_max` separately. Default is `FALSE`.
`yearly_sim`	logical value. If `TRUE` the monthly mean values are calculated for each year from `year_min_sim` to `year_max_sim` separately. Default is `yearly`.
`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 `NULL`.
`exogen_sim`	data frame or matrix containing the (normalized or not) exogenous variables (predictors) for the simulation period. Default is `NULL`. If it is `NULL`, `exogen_sim` is set equal to `exogen` within the function.
`is_exogen_gaussian`	logical value, If `TRUE`, `exogen_sim` and `exogen` are given as already normalized variables, otherwhise they are not normalized. Default is `FALSE`
`exogen_all`	data frame containing exogenous variable formatted like `Tx_all` and Tn_all. Default is `NULL`. It is alternative to `exogen` and if it not `NULL`,`is_exogen_gaussian` is automatically set to `FALSE`
`exogen_all_col`	vector of considered columns of `exogen_all`. Default is `station`.
`nscenario`	number of generated scenarios for daily maximum and minimum temperature
`seed`	seed for stochastic random generation see `set.seed`
`noise`	stochastic noise to add for variabile generation. Default is `NULL`. See `newVARmultieventRealization`. Not used in case that `nscenario>1`.

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.

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)