ComprehensiveTemperatureGenerator.R/setWholeTemperatureGeneratorParameters.R
setComprehensiveTemperatureGeneratorParameters.RdComputes climatic and correlation information useful for creating an auto-regeressive random generation of maximum and minimun daily temparature. This function is called by ComprehensiveTemperatureGenerator.
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
)character vector of the IDs of the considered meteorological stations
data frame containing daily maximum temperature of all meteorological station. See TEMPERATURE_MAX for formatting.
data frame containing daily minimum temperature of all meteorological station. See TEMPERATURE_MIN for formatting.
a matrix containing monthly mean minimum daily temperature for the considered station or an object as returned by getMonthlyMean. If NULL, it is calculated. See input of is.monthly.climate
a matrix containing monthly mean maximum daily temperature for the considered station or an object as returned by getMonthlyMean. If NULL, it is calculated. See input of is.monthly.climate
daily timeseries (from the first day of year_min to the last day of year_max) of averaged maximum temperature which can be obtained by a spline interpolation of monthly mean values. Default is NULL and returned as output. See for spline interpolation utilized: splineInterpolateMonthlytoDailyforSeveralYears.
daily timeseries (from the first day of year_min to the last day of year_max) of averaged minimum temperature which can be obtained by a spline interpolation of monthly mean values. Default is NULL and returned as output. See for spline interpolation utilized: splineInterpolateMonthlytoDailyforSeveralYears.
start year of the recorded (calibration) period
end year of the recorded (calibration) period
logical variables. It is TRUE (Default) if leap years are considered
number of months in one year. Default is 12.
logical variable
logical variable If TRUE normalizeGaussian_severalstations is used, otherwise it is not. If option is 2, it is always TRUE.
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.
logical value. If TRUE the monthly mean values are calculated for each year from year_min to year_max separately. Default is FALSE.
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 \(Tn_{mes}-Tn_s\) (\(\sigma_{Tn}\))
stdTx vector containing the standard deviation of maximum temperature anomalies \(Tx_{mes}-Tx_s\) (\(\sigma_{Tx}\))
stdTm vector containing the standard deviation of "mean" temperature anomalies \(Tm_{mes}-Tm_s\) (\(\sigma_{Tm}\))
Tn_mes_res standard core (standardization) of \(Tn_mes\) obtained
by solving column by column the expression $$\frac{Tn_{mes}-Tn_s}{\sigma_{Tn}}$$
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.