Maintainer:	Emanuele Cordano <emanuele.cordano@gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Title:	Multi-Site Auto-Regressive Weather GENerator
Type:	Package
Description:	S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time series of temperature and precipitation. These tools make use of Vector AutoRegressive models (VARs). The weather generator model is then saved as an object and is calibrated by daily instrumental "Gaussianized" time series through the 'vars' package tools. Once obtained this model, it can it can be used for weather generations and be adapted to work with several climatic monthly time series.
Version:	1.3.9.3
Repository:	CRAN
Date:	2025-04-08
Depends:	R (≥ 3.5.0), chron, date, vars, methods
Imports:	Matrix
Suggests:	lubridate
URL:	https://ecor.github.io/RMAWGEN/,https://github.com/ecor/RMAWGEN, https://docs.google.com/file/d/0B66otCUk3Bv6V3RPbm1mUG4zVHc/edit
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-04-12 12:49:19 UTC; ecor
Author:	Emanuele Cordano [aut, cre, ctb], Emanuele Eccel [aut], Eike Luedeling [ctb]
Date/Publication:	2025-04-12 13:30:05 UTC

R - Multi-site Autoregressive WEather Generator

Description

Multi-site autoregressive Models for Daily Weather Generation. The modeling in climate change applications for agricultural or hydrological purposes often requires daily time-series of precipitation and temperature. This is the case of downscaled series from monthly or seasonal predictions of Global Climate Models (GCMs). The R package RMAWGEN (R Multi-Sites Auto regressive Weather GENerator) is built to generate daily temperature and precipitation time series in several sites by using the theory of vectorial autoregressive models (VAR). The VAR model is used because it is able to maintain the temporal and spatial correlations among the several series. In particular, observed time series of daily maximum and minimum temperature and precipitation are used to calibrate the parameters of a VAR model (saved as ”GPCAvarest2” or ”varest2” classes, which inherit the "varest" S3 class defined in the package vars [Pfaff, 2008]). Therefore the VAR model, coupled with monthly mean weather variables downscaled by GCM predictions, allows to generate several stochastic daily scenarios. The structure of the package consists in functions that transform precipitation and temperature time series into Gaussian-distributed random variables through deseasonalization and Principal Component Analysis. Then a VAR model is calibrated on transformed time series. The time series generated by VAR are then inversely re transformed into precipitation and/or temperature series. An application dateset is included in the RMAWGEN package as an example; it is presented by using a dataset with daily weather time series recorded in 59 different sites of Trentino (Italy) and its neighborhoods for the period 1958-2007. The software is distributed as a Free Software with General Public License (GPL) and is available on CRAN and Github. A presentation of the package is available on https://docs.google.com/file/d/0B66otCUk3Bv6V3RPbm1mUG4zVHc/edit. Example script files about package usage are available on https://github.com/ecor/RMAWGENCodeCorner.

Details

Note

First release of RMAWGEN was created in the frame of ACE-SAP and ENVIROCHANGE projects funded by Provincia Autonoma di Trento, Italy.

RMAWGEN is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

RMAWGEN is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Author(s)

Emanuele Cordano emanuele.cordano@gmail.org, Emanuele Eccel emanuele.eccel@fmach.it

References

Cordano E. and Eccel E. (2016), Tools for stochastic weather series generation in R environment, Italian Journal of Agrometeorology doi:10.19199/2016.3.2038-5625.031

Pfaff B. (2008). VAR, SVAR and SVEC Models: Implementation Within R Package vars. Journal of Statistical Software 27(4). https://www.jstatsoft.org/v27/i04/(doi:10.18637/jss.v027.i04)

The comprehensive Precipitation Generator

Description

The comprehensive Precipitation Generator

Usage

ComprehensivePrecipitationGenerator(
  station = c("T0001", "T0010", "T0099"),
  prec_all,
  mean_climate_prec = NULL,
  year_max = 1990,
  year_min = 1961,
  leap = TRUE,
  nmonth = 12,
  cpf = NULL,
  verbose = TRUE,
  p = 1,
  type = "none",
  lag.max = NULL,
  ic = "AIC",
  activateVARselect = FALSE,
  exogen = NULL,
  exogen_sim = NULL,
  is_exogen_gaussian = FALSE,
  year_max_sim = year_max,
  year_min_sim = year_min,
  mean_climate_prec_sim = NULL,
  onlygeneration = FALSE,
  varmodel = NULL,
  type_quantile = 3,
  qnull = NULL,
  valmin = 0.5,
  step = 0,
  n_GPCA_iteration = 0,
  n_GPCA_iteration_residuals = n_GPCA_iteration,
  sample = NULL,
  extremes = TRUE,
  exogen_all = NULL,
  exogen_all_col = station,
  no_spline = FALSE,
  nscenario = 1,
  seed = NULL,
  noise = NULL,
  nearPD = FALSE
)

Arguments

Value

A list of the following variables:

prec_mes matrix containing measured daily precipitation (the data is copied by the measured data given as input for the period and the station considered for varmodel estimation)

prec_spline matrix containing climatic "spline-interpolated" daily preciptation from mean_climate_prec

data_prec matrix containing normalized measured precipitation variable

prec_gen matrix containing generated daily precipitation [mm]

prec_spline_sim matrix containing climatic "spline-interpolated" daily preciptation from mean_climate_prec_sim

data_prec_gen matrix containing normalized generated precipitation variable

mean_climate_prec matrix containing monthly means of daily precipitation (historical scenario)

mean_climate_prec_sim matrix containing monthly means of daily precipitation (predicted/simulated scenario)

var a varest object containing the used VAR model

Note

It pre-processes and generates a multi-site precipitation fields. It uses getVARmodel. Detailed examples can be viewed of this function in this presentation. Unfortunately, using this approach, the spatial correlations are underestimated. This is due to the persinstence of zeros in the precipitation records. This problem is known in literature and can be solved in the future versions of RMAWGEN. See the R code for further details

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

splineInterpolateMonthlytoDailyforSeveralYears

Examples

data(trentino)
set.seed(1222) # set the seed for random generations!
year_max <- 1990
year_min <- 1961
year_max_sim <- 1982
year_min_sim <- 1981

n_GPCA_iter <- 2
p <- 1
nscenario=1
station <- c("T0090","T0083")

## Not Run: the call to ComprehensivePrecipitationGenerator may elapse too
## long time (more than 5 eseconds) and is not executed  by default CRAN check.
## Please uncomment the following line to run the example on your own PC.
generation00 <- ComprehensivePrecipitationGenerator(station=station,
prec_all=PRECIPITATION,year_min=year_min,year_max=year_max,
year_min_sim=year_min_sim,year_max_sim=year_max_sim,p=p,
n_GPCA_iteration=n_GPCA_iter,n_GPCA_iteration_residuals=0,
sample="monthly",nscenario=nscenario,no_spline=TRUE)

The Comprehensive Temperature Generator

Description

The Comprehensive Temperature Generator

Usage

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,
  nearPD = FALSE
)

Arguments

Value

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)

Note

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.

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

setComprehensiveTemperatureGeneratorParameters, generateTemperatureTimeseries ,generateTemperatureTimeseries,splineInterpolateMonthlytoDailyforSeveralYears.

Examples

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)

Extracts the elevation of a meteorological station expressed in meters above a reference (sea level)

Description

Extracts the elevation of a meteorological station expressed in meters above a reference (sea level)

Usage

ElevationOf(name, station_names, elevation)

Arguments

Value

the elevation given the vectors of station IDs and the respective elevations

Examples

data(trentino)
ElevationOf("T0099",station_names=STATION_NAMES,elevation=ELEVATION)

This function makes a Gaussianization procedure based on PCA iteration ( see GPCA_iteration)

Description

This function makes a Gaussianization procedure based on PCA iteration ( see GPCA_iteration)

Usage

GPCA(x_prev, n = 30, extremes = TRUE, nearPD = FALSE)

Arguments

Value

A GPCA-class S3 object returned by GPCA_iteration at each iteration and the final results of the G-PCA procedure (matrix final_results)

Note

This function re-iterates the equation (1) of "PCA Gaussianization for One-Class Remote Sensing Image" by V. Laparra et al., https://www.uv.es/lapeva/papers/SPIE09_one_class.pdf,https://www.uv.es/vista/vistavalencia/papers/SPIE_09_Gaussianization_presentation.pdf

Author(s)

Emanuele Cordano

See Also

GPCA,GPCA_iteration,inv_GPCA_iteration,inv_GPCA,GPCA-class for 'GPCA' S3 class

Examples

library(RMAWGEN)
set.seed(1222)
nIterations <- 30
N <- 20
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

GPCA <- GPCA(df,n=nIterations,extremes=TRUE)

x <- rnorm(N)
y <- x+rnorm(N)
dfn <- data.frame(x=x,y=y)

GPCAn <- GPCA(dfn,n=nIterations,extremes=TRUE)

GPCA-class

Description

GPCA S3 class returned by GPCA

Details

list of GPCA_iteration

subsequent GPCA iterations

final_results

data.frame or matrix of the "gaussianized" data

Note

Formal definition with setOldClass for the S3 class GPCA

Author(s)

Emanuele Cordano

Examples

showClass("GPCA")

 

This function makes an iteration of PCA-Gaussianization process

Description

This function makes an iteration of PCA-Gaussianization process

Usage

GPCA_iteration(x_prev, extremes = TRUE, nearPD = FALSE)

Arguments

Value

A GPCA_iteration S3 object which contains the following objects:

x_prev Previous set of random variable, x_prev input variable

x_gauss_prev Marginal Gaussianization of x_prev obtained through normalizeGaussian_severalstations

B_prev rotation matrix (i. e. eigenvector matrix of the covariance matrix of x_gauss_prev

x_next results obtained by multiplying B_prev by x_gauss_prev (see equation 1 of the reference)

Note

This function is based on equation (1) of "PCA Gaussianization for One-Class Remote Sensing Image" by V. Laparra et al., https://www.uv.es/lapeva/papers/SPIE09_one_class.pdf and https://ieeexplore.ieee.org/document/5413808/

Author(s)

Emanuele Cordano

See Also

GPCA,GPCA_iteration,inv_GPCA_iteration,inv_GPCA

Examples

library(RMAWGEN)
set.seed(1222)
N <- 20
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

GPCA <- GPCA_iteration(df,extremes=TRUE)

x <- rnorm(N)
y <- x+rnorm(N)
dfn <- data.frame(x=x,y=y)

GPCAn <- GPCA_iteration(dfn,extremes=TRUE)

GPCAiteration-class

Description

GPCAiteration S3 class returned by GPCA_iteration

Details

x_prev

Previous set of random variable, x_prev input variable of GPCA_iteration

x_gauss_prev

Marginal Gaussianization of x_prev obtained through normalizeGaussian_severalstations

B_prev

rotation matrix (i. e. eigenvector matrix of the covariance matrix of x_gauss_prev)

x_next

results obtained by multiplying B_prev by x_gauss_prev (see equation 1 of the reference in GPCA_iteration)

Note

Formal definition with setOldClass for the S3 class GPCAiteration

Author(s)

Emanuele Cordano

Examples

showClass("GPCAiteration")

 

GPCAvarest2-class

Description

This class inherits varest2 and contains all information about GPCA (GPCA transformation.

Details

GPCA_data:

A "GPCA" S3 object containing the parameters of the Multi-variate Gaussianization of the time series, it is the result of GPCA function applied to the input data of getVARmodel

GPCA_residuals:

A "GPCA" S3 object containing the parameters of the Multi-variate Gaussianization of the residuals of the VAR model contained in the VAR slot; it is NULL if no Gaussiatization of residuals is applied. Object of class "list"

VAR:

S3 Object of class "varest"

#' @note A GPCAvarest2 object can be created by new("GPCAvarest2", ...) or returned by the function getVARmodel

Author(s)

Emanuele Cordano

Examples

showClass("GPCAvarest2")

Generates a new realization of a VAR model

Description

Generates a new realization of a VAR model

Usage

NewVAReventRealization(var, xprev, noise, exogen = NULL, B = NULL)

Arguments

Value

a vector of values

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

forecastEV,forecastResidual

Gets the last day in a precipitation time series, expressed in decimal julian days since 1970-1-1 00:00 UTC

Description

@author Emanuele Cordano, Emanuele Eccel

Usage

PrecipitationEndDay(name, station_names, end_day)

Arguments

Value

the precipitation measurement end day given the vectors of station IDs and the precipitation measurement end days

Examples

data(trentino)
PrecipitationEndDay("T0099",station_names=STATION_NAMES,end_day=PRECIPITATION_MEASUREMENT_END_DAY) 

Gets the first day in a precipitation time series, expressed in decimal julian days since 1970-1-1 00:00 UTC

Description

@author Emanuele Cordano

Usage

PrecipitationStartDay(name, station_names, start_day)

Arguments

Value

the precipitation measurement start day given the vectors of station IDs and the respective precipitation measurement start days

Examples

data(trentino)
PrecipitationStartDay("T0099",
     station_names=STATION_NAMES,
     start_day=PRECIPITATION_MEASUREMENT_START_DAY) 

Gets the last day in a temperature time series, expressed as decimal julian days since 1970-1-1 00:00 UTC

Description

Gets the last day in a temperature time series, expressed as decimal julian days since 1970-1-1 00:00 UTC

Usage

TemperatureEndDay(name, station_names, end_day)

Arguments

Value

the temperature measurement end day given the vectors of station IDs and the temperature measurement end days

Author(s)

Emanuele Cordano, Emanuele Eccel

Examples

data(trentino)
TemperatureEndDay("T0099",station_names=STATION_NAMES,end_day=TEMPERATURE_MEASUREMENT_END_DAY) 

Gets the first day in a temperature time series, expressed as decimal julian days since 1970-1-1 00:00 UTC

Description

@author Emanuele Cordano, Emanuele Eccel

Usage

TemperatureStartDay(name, station_names, start_day)

Arguments

Value

the temperature measurement start day given the vectors of station IDs and the respective temperature measurement start days

@examples data(trentino) TemperatureStartDay("T0099",station_names=STATION_NAMES,start_day=TEMPERATURE_MEASUREMENT_START_DAY)

Modified version of VAR function allowing to describe white-noise as VAR-(0) model (i. e. varest objects)

Description

Modified version of VAR function allowing to describe white-noise as VAR-(0) model (i. e. varest objects)

Usage

VAR_mod(
  y,
  p = 1,
  type = c("const", "trend", "both", "none"),
  season = NULL,
  exogen = NULL,
  lag.max = NULL,
  ic = c("AIC", "HQ", "SC", "FPE")
)

Arguments

Value

a Vector Auto-Regeressive model (VAR) as varest object

Gets the toponym where a meteorological station is located

Description

Gets the toponym where a meteorological station is located

Usage

WhereIs(name, station_names, location)

Arguments

Value

the location toponym given the vectors of station IDs and the respective location toponyms

Author(s)

Emanuele Cordano, Emanuele Eccel

Examples

data(trentino)
WhereIs("T0099",station_names=STATION_NAMES,location=LOCATION) 

Plots the auto- and cross- covariance functions between measured and simulated data for several stations

Description

Plots the auto- and cross- covariance functions between measured and simulated data for several stations

Usage

acvWGEN(measured, simulated, titles = c("Sim.", "Mes."), station = NULL)

Arguments

Value

0 in case of success

Note

It uses acf function

Inserts three columns (year,month,day) passing dates to a matrix or to a dataframe

Description

Inserts three columns (year,month,day) passing dates to a matrix or to a dataframe

Usage

adddate(data, origin = "1961-1-1")

Arguments

Value

a data frame with dates and data values

See Also

findDate

Adds suffixes for daily maximum and minimum temperature to the names of a column data frame

Description

Adds suffixes for daily maximum and minimum temperature to the names of a column data frame

Usage

addsuffixes(
  names = c("T0001", "T0099", "T0001", "T0099"),
  suffix = c("_Tx", "_Tn"),
  sep = ""
)

Arguments

Details

This function is used for data frames with duplicated field names

Value

the vector of names with suffixes added

See Also

getVARmodel

Examples

names <- addsuffixes()

arch.test function for varest2 object

Description

arch.test function for varest2 object

Usage

arch_test(object, interval = NULL, overlap = 20, list.output = FALSE, ...)

Arguments

Details

This function is a wrapper of arch.test. It can compute the test also for some subsets (intervals) of the time-series or for all the time-series divided in overlapping intervals. The intervals considered for the ARCH test are defined with the argument interval. If interval is an integer number instead of a vector, it indicates the length of the intervals in which the time-series is split. If interval is set to NULL, the test is done on the comprehensive residual time-series without splitting.

Value

One object or a list of objects with class attribute varcheck as reported in arch.test

See Also

arch.test

Collinear Dataset

Description

It is an artificial example dataset contaning 16 variables with collinearity among some of them.

Usage

data(collinear_dataset)

Format

Data frame

Details

The user can easily use the package with his/her own data after replacing the values of such variables.

Source

This dataset is intended for research purposes only, being distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY.

Calculates the continuity ratio of a set of precipitation measured or generated data in several sites as defined by Wilks, 1998 (see reference link)

Description

Calculates the continuity ratio of a set of precipitation measured or generated data in several sites as defined by Wilks, 1998 (see reference link)

Usage

continuity_ratio(data, lag = 0, valmin = 0.5)

Arguments

Value

A list containing the following matrices:

continuity_ratio : lag-day lagged continuity ratio ,

occurrence : joint probability of lag-day lagged precipitation occurrence

nooccurrence : joint probability of lag-day lagged no precipitation occurrence.

nooccurrence_occurrence : joint probability of lag-day lagged no precipitation and precipitation occurrence respectively.

occurrence_nooccurrence : joint probability of lag-day lagged precipitation and no precipitation occurrence respectively.

probability_continuity_ratio: lag-day lagged ratio about precipitation probability contitioned to no precipitation/preciitation occurrence in the other site

Note

If lag==0 the function returns the continuity ratio and joint probability as described by Wilks, 1998. Otherwise the precipitation values for each couple of rain gauges are taken with lag-day lag.

References

Mhanna, M. and Bauwens, W. (2012), A stochastic space-time model for the generation of daily rainfall in the Gaza Strip. Int. J. Climatol., 32: 1098-1112. doi:10.1002/joc.2305

D.S. Wilks (1998),Multisite generalization of a daily stochastic precipitation generation model,Journal of Hydrology, doi:10.1016/S0022-1694(98)00186-3

Examples

data(trentino)

year_min <- 1961
year_max <- 1990
origin <- paste(year_min,1,1,sep="-")

period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max
station <- names(PRECIPITATION)[!(names(PRECIPITATION) %in% c("day","month","year"))]
prec_mes <- PRECIPITATION[period,station]

## removing nonworking stations (e.g. time series with NA)
accepted <- array(TRUE,length(names(prec_mes)))
names(accepted) <- names(prec_mes)
for (it in names(prec_mes)) {
		 accepted[it]  <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it]))
}

prec_mes <- prec_mes[,accepted]
## the dateset is reduced!!!
prec_mes <- prec_mes[,1:2]

continuity_ratio <-continuity_ratio(data=prec_mes,lag=0,valmin=0.5)
continuity_ratio1 <-continuity_ratio(data=prec_mes,lag=-1,valmin=0.5)

counts NAs in each row of data

Description

counts NAs in each row of data

Usage

countNAs(data)

Arguments

Value

the vector with numbers of NA values for each data column

Calculates the covariance matrix of the normally standardized variables obtained from the columns of x

Description

Calculates the covariance matrix of the normally standardized variables obtained from the columns of x

Usage

covariance(
  x,
  data = x,
  cpf = NULL,
  mean = 0,
  sd = 1,
  step = NULL,
  prec = 10^-4,
  use = "pairwise.complete.obs",
  type = 3,
  extremes = TRUE,
  sample = NULL,
  origin_x = NULL,
  origin_data = origin_x
)

Arguments

Value

a matrix with the normalized variable or its inverse

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

normalizeGaussian_severalstations,normalizeGaussian

@note It applies normalizeGaussian_severalstations to x and data and then calculates the covariances among the column. See the R code for further details

Extracts generated time series of Daily Minimum Temperature from a random multi-realization obtained by generateTemperatureTimeseries function

Description

Extracts generated time series of Daily Minimum Temperature from a random multi-realization obtained by generateTemperatureTimeseries function

Usage

extractTnFromAnomalies(res_multigen, std, SplineAdv)

Arguments

Value

a matrix with generated minimum temperature

Author(s)

Emanuele Cordano, Emanuele Eccel

Extracts generated time series of Daily Maximum Temperature from a random multi-realization obtained by generateTemperatureTimeseries function

Description

Extracts generated time series of Daily Maximum Temperature from a random multi-realization obtained by generateTemperatureTimeseries function

Usage

extractTxFromAnomalies(res_multigen, std, SplineAdv)

Arguments

Value

a matrix with generated maximum temperature

Author(s)

Emanuele Cordano, Emanuele Eccel

Extracts the rows of a matrix corresponding to the requested days (expressed as dates YYYY-MM-DD) given the date (origin) of the first row

Description

Extracts the rows of a matrix corresponding to the requested days (expressed as dates YYYY-MM-DD) given the date (origin) of the first row

Usage

extractdays(
  data = array(1:ndim_max, dim = c(ndim_max, 1)),
  ndim_max = 1e+05,
  when = "1990-1-1",
  origin = "1961-1-1",
  nday = 1
)

Arguments

Value

a matrix containing the requested rows

Note

It uses julian

Examples

extractdays()

Extracts the rows of a matrix corresponding to requested months of a year given the date (origin) of the first row

Description

Extracts the rows of a matrix corresponding to requested months of a year given the date (origin) of the first row

Usage

extractmonths(
  data = array(1:ndim_max, dim = c(ndim_max, 1)),
  ndim_max = 1e+05,
  when = c("Dec", "Jan", "Feb"),
  year = NULL,
  origin = "1961-1-1"
)

Arguments

Value

a matrix containing the requested rows

Note

It uses months and julian

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

extractdays

Examples

extractmonths()

data(trentino)
dates <- sprintf("%02d-%02d-%02d",TEMPERATURE_MAX$year,TEMPERATURE_MAX$month,TEMPERATURE_MAX$day)
origin <- dates[1]
out <- extractmonths(data=TEMPERATURE_MAX,origin=origin)

Extracts the elements of a data frame corresponding to a period between year_min and year_max for the stations listed in station

Description

Extracts the elements of a data frame corresponding to a period between year_min and year_max for the stations listed in station

Usage

extractyears(
  data,
  year_min = 1961,
  year_max = 1990,
  station = c("T0001", "T0014", "T0129")
)

Arguments

Value

a matrix containing the requested daily data where each day corresponds to a row and each station corresponds to a column

Note

The input data frame data must have the following fields: year,month,day,variables_ID1,variables_ID2,... where the fields ,variables_ID1,variables_ID2,... contain the daily variables referred to the respective stations and the field names are replaced with the respective station ID.

Finds the date corresponding a row index of a matrix given the date (origin) of the first row

Description

Finds the date corresponding a row index of a matrix given the date (origin) of the first row

Usage

findDate(
  k,
  origin = "1961-1-1",
  data.frame = TRUE,
  decimal = FALSE,
  character = FALSE
)

Arguments

Value

the date(s) corresponding to k under different formats

Note

It uses functions of time package. It works like an inverse functions of extractdays. If k is a vector, the function returns several dates for each element of k

See Also

date.mdy,extractdays

Examples

findDate <- findDate(100,origin="1961-1-1",data.frame=FALSE,character=TRUE)

Forecasts the expected value of a VAR realization given the prievious one

Description

Forecasts the expected value of a VAR realization given the prievious one

Usage

forecastEV(var, xprev = NULL, exogen = NULL)

Arguments

Value

a vector of values

See Also

forecastResidual

@export

Forecasts the residual value of a VAR realization given the white noise covariance matrix

Description

Forecasts the residual value of a VAR realization given the white noise covariance matrix

Usage

forecastResidual(var, xprev = NULL, B = NULL)

Arguments

Value

a vector of values

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

forecastEV,NewVAReventRealization

Returns time series of Daily Maximum and Minimum with a random multi-realization obtained by using newVARmultieventRealization. This function is called by ComprehensiveTemperatureGenerator.

Description

Returns time series of Daily Maximum and Minimum with a random multi-realization obtained by using newVARmultieventRealization. This function is called by ComprehensiveTemperatureGenerator.

Usage

generateTemperatureTimeseries(
  std_tn,
  std_tx,
  SplineTx,
  SplineTn,
  SplineTm,
  SplineDeltaT,
  std_tm,
  var = NULL,
  exogen = NULL,
  normalize = TRUE,
  type = 3,
  extremes = TRUE,
  sample = NULL,
  option = 1,
  original_data,
  origin_x = NULL,
  origin_data = NULL,
  noise = NULL
)

Arguments

Value

This function returns a list of the following variables:

res_multigen matrix containing standardized values of daily maximum and minimum temperature anomalies

Tx_spline matrix containing climatic "spline-interpolated" daily maximum temperature

Tn_spine matrix containing climatic "spline-interpolated" daily minimum temperature

Tx_gen matrix containing generated daily maximum daily temperature (Tx_{gen})

Tn_gen matrix containing generated daily minimum daily temperature (Tn_{gen})

Tm_gen matrix containing generated "mean" daily temperature defined as \frac{Tx_{gen}+Tn_{gen}}{2}

DeltaT_gen matrix containing generated daily thermal range defined as Tx_{gen}-Tn_{gen}

See the R code for further details

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

newVARmultieventRealization,normalizeGaussian_severalstations

Calculates the daily means of a range of days around each date of a data frame corresponding to a period between year_min and year_max for stations listed in station

Description

Calculates the daily means of a range of days around each date of a data frame corresponding to a period between year_min and year_max for stations listed in station

Usage

getDailyMean(
  data,
  year_min = 1961,
  year_max = 1990,
  station = c("T0001", "T0010"),
  origin = "1961-1-1",
  lag = 5
)

Arguments

Value

a matrix containing the requested daily mean data where each day corresponds to a row and each station corresponds to a column

Note

The input data frame data must have the following fields: year,month,day,variables_ID1,variables_ID2,... where the fields ,variables_ID1,variables_ID2,... contain the daily variables referred to the respective stations and the field names are replaced with the respective station ID.

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

extractyears

Calculates the monthly means of a data frame corresponding to a period between year_min and year_max for stations listed in station

Description

@author Emanuele Cordano, Emanuele Eccel

Usage

getMonthlyMean(
  data,
  year_min = 1961,
  year_max = 1990,
  station = names(data),
  no_date = FALSE,
  origin = "1961-1-1",
  yearly = FALSE
)

Arguments

Value

a matrix containing the requested monthly means where each month corresponds to a row and each station corresponds to a column or a list of such matrices in case the monthly mean values are calculated separately for each year (if yearly is TRUE)

Note

The input data frame data must have the following fields: year,month,day,variables_ID1,variables_ID2,... where the fields ,variables_ID1,variables_ID2,... contain the daily variables referred to the respective stations and the field names are replaced with the respective station ID. In case yearly is TRUE the returned output is a list of matrices whose names are the corresponding year.

See Also

extractyears

Either creates a VAR model or chooses a VAR model by using VAR or VARselect commands of vars package

Description

Either creates a VAR model or chooses a VAR model by using VAR or VARselect commands of vars package

Usage

getVARmodel(
  data,
  suffix = c("_Tx", "_Tn"),
  sep = "",
  p = 1,
  type = "none",
  season = NULL,
  exogen = NULL,
  lag.max = NULL,
  ic = "AIC",
  activateVARselect = FALSE,
  na.rm = TRUE,
  n_GPCA_iteration = 0,
  n_GPCA_iteration_residuals = n_GPCA_iteration,
  extremes = TRUE,
  nearPD = FALSE
)

Arguments

Value

a varest2 or GPCAvarest2 object representing a VAR model or a GPCA-varest object which also contains the GPCA transformation parameters

Note

It inherits input parameters of VAR, VARselect and addsuffixes. The variable data contains the measured data on which the vector auto-regressive models is estimated. It is a matrix where each row is a realization of the vector random variable. In some application of this package, the random variables may be the daily maximum and minimum temperature anomalies for different stations. Often the the columns of data are called with the IDs of the stations whithout specifying the type of variable (e.g. minimun or maximum temperature anomalies). This means that two or more columns may have the same name. Therefore the function addsuffixes, which is called from this function, adds suitable suffixes to the column names.

Author(s)

Emanuele Cordano, Emanuele Eccel

Examples

set.seed(122)
NSTEP <- 1000
x <- rnorm(NSTEP)
y <- x+rnorm(NSTEP)
z <- c(rnorm(1),y[-1]+rnorm(NSTEP-1))
df <- data.frame(x=x,y=y,z=z)

exogen <- as.data.frame(x+5)
only_var <- VAR(df,type="none")

gpcavar <- getVARmodel(data=df,suffix=NULL,p=3,n_GPCA_iteration=5,
                       n_GPCA_iteration_residuals=5,exogen=exogen)
exogen <- as.data.frame(x+5)

data_for_var <- gpcavar@GPCA_data$final_results

data(collinear_dataset)
gpcavar_coll <- getVARmodel(data=collinear_dataset,suffix=NULL,p=3,n_GPCA_iteration=5,
                       n_GPCA_iteration_residuals=0,exogen=NULL,nearPD=TRUE) ## use nearPD==TRUE

This function makes an inverse Gaussianization procedure besad on PCA iteration ( see inv_GPCA_iteration

Description

This function makes an inverse Gaussianization procedure besad on PCA iteration ( see inv_GPCA_iteration

Usage

inv_GPCA(x = NULL, GPCA_param, type = 3, extremes = TRUE)

Arguments

Value

the non-Gaussian random variable

Note

This function re-iterates the inverse of equation (1) of "PCA Gaussianization for One-Class Remote Sensing Image" by V. Laparra et al., https://ieeexplore.ieee.org/document/5413808/

Author(s)

Emanuele Cordano

See Also

GPCA,GPCA_iteration,inv_GPCA_iteration,inv_GPCA

Examples

library(RMAWGEN)
set.seed(1222)
nIterations <- 30
N <- 20
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

GPCA <- GPCA(df,n=nIterations,extremes=TRUE)

x <- rnorm(N)
y <- x+rnorm(N)
dfn <- data.frame(x=x,y=y)

GPCAn <- GPCA(dfn,n=nIterations,extremes=TRUE)

df_out <- inv_GPCA(GPCA_param=GPCA,extremes=TRUE)
dfn_out <- inv_GPCA(GPCA_param=GPCAn,extremes=TRUE)

This function makes an inverse iteration of PCA-Gaussianization process

Description

This function makes an inverse iteration of PCA-Gaussianization process

Usage

inv_GPCA_iteration(
  x = GPCA_iter_param$x_next,
  GPCA_iter_param,
  type = 3,
  extremes = TRUE
)

Arguments

Value

the non-Gaussian random variable

Note

This function is based on the inverse of the equation (1) of "PCA Gaussianization for One-Class Remote Sensing Image" by V. Laparra et al., https://ieeexplore.ieee.org/document/5413808/

See Also

GPCA,GPCA_iteration,inv_GPCA_iteration,inv_GPCA,GPCA-class for 'GPCA' S3 class

Examples

library(RMAWGEN)
set.seed(1222)
N <- 20
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

GPCA <- GPCA_iteration(df,extremes=TRUE)

x <- rnorm(N)
y <- x+rnorm(N)
dfn <- data.frame(x=x,y=y)

GPCAn <- GPCA_iteration(dfn,extremes=TRUE)

df_out <- inv_GPCA_iteration(GPCA_iter_param=GPCA,extremes=TRUE)
dfn_out <- inv_GPCA_iteration(GPCA_iter_param=GPCAn,extremes=TRUE)

Verifies if 'climate' represents the monthly climatology in one year, i.e 'climate' is monthly.climate type matrix whose rows represent months and each column represents a station. It is also used in setComprehensiveTemperatureGeneratorParameters.

Description

Verifies if 'climate' represents the monthly climatology in one year, i.e 'climate' is monthly.climate type matrix whose rows represent months and each column represents a station. It is also used in setComprehensiveTemperatureGeneratorParameters.

Usage

is.monthly.climate(climate, nstation = 3, nmonth = 12, verbose = TRUE)

Arguments

Value

A logical variable if the matrix 'climate' is monthly.climate type

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

setComprehensiveTemperatureGeneratorParameters

months REPLACEMANT

Description

months REPLACEMANT

Usage

months_f(x, ...)

Arguments

Generates several realizations of a VAR model

Description

Generates several realizations of a VAR model

Usage

newVARmultieventRealization(
  var,
  xprev = rnorm(var@VAR$K * var@VAR$p),
  exogen = NULL,
  nrealization = 10,
  B = t(chol(cov(residuals(var)))),
  extremes = TRUE,
  type = 3,
  noise = NULL
)

Arguments

Value

a matrix of values

Author(s)

Emanuele Cordano, Emanuele Eccel

normality.test method for varest2 object

Description

normality.test method for varest2 object

Usage

normality_test(object, ...)

Arguments

See Also

normality.test

Converts a random variable x extracted by a population represented by the sample data or sample to a normally-distributed variable with assigned mean and standard deviation or vice versa in case inverse is TRUE

Description

Converts a random variable x extracted by a population represented by the sample data or sample to a normally-distributed variable with assigned mean and standard deviation or vice versa in case inverse is TRUE

Usage

normalizeGaussian(
  x = 0,
  data = x,
  cpf = NULL,
  mean = 0,
  sd = 1,
  inverse = FALSE,
  step = NULL,
  prec = 10^-4,
  type = 3,
  extremes = TRUE,
  sample = NULL
)

Arguments

Value

the normalized variable or its inverse

@note This function makes a Marginal Gaussianization. See the R code for further details

Author(s)

Emanuele Cordano, Emanuele Eccel

Converts precipitation values to "Gaussinized" normally-distributed values taking into account the probability of no precipitation occurrences. values or vice versa in case inverse is TRUE

Description

Converts precipitation values to "Gaussinized" normally-distributed values taking into account the probability of no precipitation occurrences. values or vice versa in case inverse is TRUE

Usage

normalizeGaussian_prec(
  x = 0,
  data = x,
  cpf = NULL,
  mean = 0,
  sd = 1,
  inverse = FALSE,
  type = 3,
  extremes = TRUE,
  sample = NULL,
  qnull = 0,
  valmin = 1
)

Arguments

Value

the normalized variable or its inverse

Note

In the version 1.2.5 of RMAWGEN This function is deprecated and not used.

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

normalizeGaussian

Examples

library(RMAWGEN)
NDATA <- 1000
occurrence <- as.logical(runif(NDATA)>0.5)
prec <- rexp(NDATA,rate=1/3)
prec[!occurrence] <- 0
valmin <- 0.5 #0.01
x <- normalizeGaussian_prec(x=prec,valmin=valmin)
prec2 <- normalizeGaussian_prec(x=x,data=prec,valmin=valmin,inverse=TRUE)
qqplot(prec,prec2)

occurrence3 <- as.logical(runif(NDATA)>0.5)
prec3 <- rexp(NDATA,rate=1/3)
prec3[!occurrence3] <- 0
x3 <- normalizeGaussian_prec(x=prec3,valmin=valmin) 

qqplot(x,x3)
abline(0,1)

Converts several samples x random variable extracted by populations represented by the columns of data respectively or sample to a normally-distributed samples with assinged mean and standard deviation or vice versa in case inverse is TRUE

Description

Converts several samples x random variable extracted by populations represented by the columns of data respectively or sample to a normally-distributed samples with assinged mean and standard deviation or vice versa in case inverse is TRUE

Usage

normalizeGaussian_severalstations(
  x,
  data = x,
  cpf = NULL,
  mean = 0,
  sd = 1,
  inverse = FALSE,
  step = NULL,
  prec = 10^-4,
  type = 3,
  extremes = TRUE,
  sample = NULL,
  origin_x = NULL,
  origin_data = origin_x
)

Arguments

Value

a matrix with the normalized variable or its inverse

Note

It applies normalizeGaussian for each column of x and data. See the R code for further details

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

normalizeGaussian

Examples

## Not run: 
library(RMAWGEN)

set.seed(1234)
N <- 30
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

dfg <- normalizeGaussian_severalstations(df,data=df,extremes=TRUE,inverse=FALSE)

dfi <- normalizeGaussian_severalstations(dfg,data=df,extremes=TRUE,inverse=TRUE)

N <- 365*2
origin <- "1981-01-01"
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

dfgm <- normalizeGaussian_severalstations(df,data=df,extremes=TRUE,
inverse=FALSE,origin_x=origin,origin_data=origin,sample="monthly")

dfim <- normalizeGaussian_severalstations(dfg,data=df,extremes=TRUE,
inverse=TRUE,origin_x=origin,origin_data=origin,sample="monthly")


## Compatibility with 'lubridate' package


library(lubridate)


N <- 30
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

dfg <- normalizeGaussian_severalstations(df,data=df,extremes=TRUE,inverse=FALSE)

dfi <- normalizeGaussian_severalstations(dfg,data=df,extremes=TRUE,inverse=TRUE)

N <- 365*2
origin <- "1981-01-01"
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

dfgm <- normalizeGaussian_severalstations(df,data=df,extremes=TRUE,
inverse=FALSE,origin_x=origin,origin_data=origin,sample="monthly")

dfim <- normalizeGaussian_severalstations(dfg,data=df,extremes=TRUE,
inverse=TRUE,origin_x=origin,origin_data=origin,sample="monthly")

## End(Not run)

DEPRECATED Converts several samples x random variable (daily precipitation values) extracted by populations represented by the columns of data respectively or sample to a normally-distributed samples with assinged mean and standard deviation or vice versa in case inverse is TRUE using the function normalizeGaussian_prec

Description

DEPRECATED Converts several samples x random variable (daily precipitation values) extracted by populations represented by the columns of data respectively or sample to a normally-distributed samples with assinged mean and standard deviation or vice versa in case inverse is TRUE using the function normalizeGaussian_prec

Usage

normalizeGaussian_severalstations_prec(
  x,
  data = x,
  cpf = NULL,
  mean = 0,
  sd = 1,
  inverse = FALSE,
  qnull = NULL,
  valmin = 0.5,
  type = 3,
  extremes = TRUE,
  sample = NULL,
  origin_x = NULL,
  origin_data = NULL
)

Arguments

Value

a matrix or a data.frame with the normalized variable or its inverse

Note

In the version 1.2.5 of RMAWGEN This function is deprecated and not used.

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

normalizeGaussian_prec

Plots daily climatology through one year

Description

Plots daily climatology through one year

Usage

plotDailyClimate(
  data,
  title = "Daily_Avereged_Temperture_in_one_year",
  origin = "1961-1-1",
  when = "1979-1-1",
  ylab = "Temperature [degC]",
  xlab = "Time [days]",
  nday = 365,
  bicolor = FALSE,
  col = "black",
  lwd = 1
)

Arguments

Value

a matrix containing the plotted variables

Author(s)

Emanuele Cordano, Emanuele Eccel

It makes a plot by sampling (e.g. monthly) the variables x and y

Description

It makes a plot by sampling (e.g. monthly) the variables x and y

Usage

plot_sample(
  x,
  y = normalizeGaussian_severalstations(x = as.data.frame(x), data = as.data.frame(data),
    origin_x = origin_x, origin_data = origin_data, sample = sample, step = step, prec =
    prec)[, 1],
  xlim = range(x, na.rm = TRUE),
  legend_position = "topleft",
  ylim = range(y, na.rm = TRUE),
  pch = 1,
  col = 1,
  col_max = 0.9,
  col_min = 0.1,
  origin,
  sample = NULL,
  xhist = hist(x, breaks = breaks, plot = FALSE),
  yhist = hist(y, breaks = breaks, plot = FALSE),
  axes = FALSE,
  step = NULL,
  prec = 1e-04,
  breaks = 50,
  origin_x = origin,
  origin_data = origin,
  data = x,
  xlab = "",
  ylab = "",
  color = FALSE,
  gray = TRUE,
  sort = FALSE,
  valmin_x = valmin,
  valmin_y = valmin,
  valmin = -9999,
  abline = c(0, 1),
  ...
)

Arguments

Value

0 in case of success

Note

It makes a plot betwee x and y and shows thair respective probibilty histograms. If y is missing, it is automatically calculated as one-dimensional Gaussianization of x through the function normalizeGaussian_severalstations.

See Also

plot.default,extractmonths, see normalizeGaussian_severalstations

Examples

## Not run: 

library(lubridate)
data(trentino)
plot_sample(x=TEMPERATURE_MIN$T0090,sample="monthly",
 origin="1958-1-1",axes=FALSE,xlab="Tn [ degC]",
 ylab="x")

set.seed(123456)
z <- rexp(10000,rate=0.5)
x <- normalizeGaussian(x=z,data=z)
plot_sample(x=z,xlab="z",ylab="x")

## End(Not run)

print S3 method for GPCA or GPCA_iteration object

Description

print S3 method for GPCA or GPCA_iteration object

Usage

## S3 method for class 'GPCA'
print(x, rmin = 1, rmax = 4, cmin = rmin, cmax = rmax, ...)

## S3 method for class 'GPCAiteration'
print(x, rmin = 1, rmax = 4, cmin = rmin, cmax = rmax, ...)

Arguments

See Also

GPCA,GPCA_iteration

GPCA_iteration

This function creates a Q-Q plot of the lag-lag moving cumulative addition of the values in the samples x,y,z

Description

This function creates a Q-Q plot of the lag-lag moving cumulative addition of the values in the samples x,y,z

Usage

qqplot.lagged(
  x = rnorm(1000),
  y = rnorm(1000),
  z = NULL,
  when = 1:length(x),
  lag = 1,
  pch = 1,
  ...
)

Arguments

Value

the Q-Q plot

See Also

qqplot

Makes a qqplot of measured and simulated data for several stations.

Description

Makes a qqplot of measured and simulated data for several stations.

Usage

qqplotTnTxWGEN(
  measured,
  simulated,
  xlab = "simulated[degC]",
  ylab = "measured[degC]",
  titles = c("Q-Qplot_An._Tx", "Q-Qplot_An._Tn"),
  station = NULL,
  diff = FALSE,
  quantile = 0
)

Arguments

Value

0 in case of success

Note

It uses qqplotWGEN and makes a figure for each pair of columns from measured and simulated. See the R code for further details.

Author(s)

Emanuele Cordano, Emanuele Eccel

Makes four seasonal qqplots (winter, spring, summer and autumn) of measured and simulated data for several stations.

Description

Makes four seasonal qqplots (winter, spring, summer and autumn) of measured and simulated data for several stations.

Usage

qqplotTnTxWGEN_seasonal(
  measured,
  simulated,
  origin = "1961-1-1",
  xlab = "simulated[degC]",
  ylab = "measured[degC]",
  titles = c("Q-Qplot_An._Tx", "Q-Qplot_An._Tn"),
  directorypdf,
  station = NULL
)

Arguments

Value

0 in case of success

Note

Uses qqplotTnTxWGEN for each seasons of collected data and saves the output on pdf files. See the R code for further details.

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

qqplotTnTxWGEN,extractmonths

Makes a qqplot and Wilcoxon test between the two columns of val

Description

Makes a qqplot and Wilcoxon test between the two columns of val

Usage

qqplotWGEN(
  val,
  xlab = "simulated",
  ylab = "measured",
  main = "title",
  ylim = c(min(val), max(val)),
  xlim = c(min(val), max(val)),
  diff = FALSE,
  quantile = 0
)

Arguments

Value

Wilcoxon test between the two columns of 'val'

Author(s)

Emanuele Cordano, Emanuele Eccel

It makes the Q-Q plots observed vs generated time series of daily maximum, minimum temperature and daily thermal range for a list of collected stochastic generations

Description

It makes the Q-Q plots observed vs generated time series of daily maximum, minimum temperature and daily thermal range for a list of collected stochastic generations

Usage

qqplot_RMAWGEN_Tx(
  Tx_mes,
  Tx_gen,
  Tn_gen,
  Tn_mes,
  Tx_spline = NULL,
  Tn_spline = NULL,
  xlab = "observed",
  ylab = "simulated",
  when = 1:nrow(Tx_mes),
  main = names(Tx_gen),
  station,
  pdf = NULL,
  xlim = range(Tx_mes),
  ylim = xlim,
  cex = 0.4,
  cex.main = 1,
  cex.lab = 1,
  cex.axis = 1
)

qqplot_RMAWGEN_Tn(
  Tx_mes,
  Tx_gen,
  Tn_gen,
  Tn_mes,
  Tx_spline = NULL,
  Tn_spline = NULL,
  xlab = "observed",
  ylab = "simulated",
  when = 1:nrow(Tn_mes),
  main = names(Tn_gen),
  station,
  pdf = NULL,
  xlim = range(Tn_mes),
  ylim = xlim,
  cex = 0.4,
  cex.main = 1,
  cex.lab = 1,
  cex.axis = 1
)

qqplot_RMAWGEN_deltaT(
  Tx_mes,
  Tx_gen,
  Tn_gen,
  Tn_mes,
  xlab = "observed",
  ylab = "simulated",
  when = 1:nrow(Tx_mes),
  main = names(Tx_gen),
  station,
  pdf = NULL,
  xlim = range(Tx_mes - Tn_mes),
  ylim = xlim,
  cex = 0.4,
  cex.main = 1,
  cex.lab = 1,
  cex.axis = 1
)

qqplot_RMAWGEN_prec(
  prec_mes,
  prec_gen,
  xlab = "observed",
  ylab = "simulated",
  when = 1:nrow(prec_mes),
  main = names(prec_gen),
  station,
  pdf = NULL,
  xlim = range(prec_mes),
  ylim = xlim,
  cex = 0.4,
  cex.main = 1,
  cex.lab = 1,
  cex.axis = 1,
  lag = 1
)

Arguments

Note

Tx_gen,Tn_gen and main must have an even number of elements.

Author(s)

Emanuele Cordano

Makes a qqplot of measured and simulated data for several stations.

Description

Makes a qqplot of measured and simulated data for several stations.

Usage

qqplotprecWGEN(
  measured,
  simulated,
  xlab = "simulated[mm]",
  ylab = "measured[mm]",
  title = "daily precipitation",
  station = NULL,
  diff = FALSE,
  quantile = 0
)

Arguments

Value

0 in case of success

Note

It uses qqplotWGEN and makes a figure for each pair of columns from measured and simulated. See the R code for further details.

Author(s)

Emanuele Cordano, Emanuele Eccel

Makes four seasonal qqplots (winter, spring, summer and autumn) of measured and simulated data for several stations.

Description

Makes four seasonal qqplots (winter, spring, summer and autumn) of measured and simulated data for several stations.

Usage

qqplotprecWGEN_seasonal(
  measured,
  simulated,
  origin = "1961-1-1",
  xlab = "simulated[mm]",
  ylab = "measured[mm]",
  title = "daily_precipitation",
  directorypdf,
  station = names(simulated)
)

Arguments

Value

0 in case of success

Note

Uses qqplotprecWGEN for each season of collected data and saves the output on pdf files. See the R code for further details.

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

qqplotprecWGEN,extractmonths

Replaces each entry of the rows containing NA values with NA

Description

Replaces each entry of the rows containing NA values with NA

Usage

removeNAs(data)

Arguments

Value

the matrix data with the modified rows of NA values

Note

In getVARmodel, when using VAR or VARselect, all NAs will be removed

See Also

getVARmodel

This function adjusts the monthly mean to a daily weather dataset (e. g. spline-interpolated temperature)

Description

This function adjusts the monthly mean to a daily weather dataset (e. g. spline-interpolated temperature)

Usage

rescaling_monthly(data, val, origin = "1961-1-1")

Arguments

Value

A data frame with data of data rescaled with val for each month

Note

It uses months and julian

Author(s)

Emanuele Cordano

@export

See Also

extractdays

residuals S3 method for varest2 object

Description

residuals S3 method for varest2 object

Usage

## S3 method for class 'varest2'
residuals(object, squared = FALSE, ...)

Arguments

Value

residuals of object as a data frame. In case squared=TRUE , the squared residauls are returned, otherwise simple residuals are returned. The squared residuals can be useful in case of ARCH analysis.

Author(s)

Emanuele Cordano

serial.test function for varest2 object

Description

serial.test function for varest2 object

Usage

serial_test(object, ...)

Arguments

See Also

serial.test

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.

Description

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.

Usage

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
)

Arguments

Value

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.

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

splineInterpolateMonthlytoDailyforSeveralYears,ComprehensiveTemperatureGenerator

Interpolates monthly data to daily data using spline and preserving monthly mean values

Description

Interpolates monthly data to daily data using spline and preserving monthly mean values

Usage

splineInterpolateMonthlytoDaily(
  nday = 365,
  val = as.matrix(cbind(1 * (0.5:11.5) * nday/12, 2 * (0.5:11.5) * nday/12)),
  origin = "1961-1-1",
  first_row = 1,
  last_row = nday,
  no_spline = FALSE,
  no_mean = FALSE
)

Arguments

Value

a matrix or data frame with interpolated daily data

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

spline,splineInterpolateMonthlytoDailyforSeveralYears

Interpolates monthly data to daily data using splineInterpolateMonthlytoDaily for several years

Description

Interpolates monthly data to daily data using splineInterpolateMonthlytoDaily for several years

Usage

splineInterpolateMonthlytoDailyforSeveralYears(
  val,
  start_year = 2010,
  nyear = 1,
  leap = TRUE,
  offset = 2,
  no_spline = FALSE,
  yearly = FALSE
)

Arguments

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

spline,splineInterpolateMonthlytoDaily

Trentino Dataset

Description

It contains the following variables:

TEMPERATURE_MIN

Data frame containing year,month , day and daily minimum temperature in 59 stations in Trentino region

TEMPERATURE_MAX

Data frame containing year,month , day and daily maximum temperature in 59 stations in Trentino region

PRECIPITATION

Data frame containing year,month , day and daily precipitation in 59 stations in Trentino region

STATION_NAMES

Vector containing the names of the meteorological stations

ELEVATION

Vector containing the elevations of the meteorological stations respectively

STATION_LATLON

Matrix containing the latitude and longitude coordinates, respectively, of the meteorological stations

LOCATION

Vector containing the names of the location of each meteorological station

TEMPERATURE_MEASUREMENT_START_DAY

Vector containing the first days referred to midday (expressed as decimal julian day since 1970-1-1 00:00 UTC) of temperature measurement of each meteorological station

TEMPERATURE_MEASUREMENT_END_DAY

Vector containing the last days referred to midday (expressed as decimal julian day since 1-1-1970 00:00 UTC) of temperature measurement of each meteorological station

PRECIPITATION_MEASUREMENT_START_DAY

Vector containing the first days referred to midday (expressed as decimal julian day since 1-1-1970 00:00 UTC) of precipitation measurement of each meteorological station

PRECIPITATION_MEASUREMENT_END_DAY

Vector containing the last days referred to midday (expressed as decimal julian day since 1-1-1970) of precipitation measurement of each meteorological station

Usage

data(trentino)

Format

Data frames and vectors

Details

This dataset stores all information about meteorological stations and instrumental timeseries. The user can easily use the package with his/her own data after replacing the values of such variables.

Source

Original data are provided by Provincia Autonoma di Trento (https://www.meteotrentino.it/), Fondazione Edmund Mach (https://www.fmach.it), Provincia Autonama di Bolzano/Autome Provinz Bozen, ARPA Lombardia, ARPA Veneto (Italy).

This dataset is intended for research purposes only, being distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY.

varest-class

Description

varest S3 class (formal definition) see VAR

Details

The details of the class are reported on VAR documentation in "vars" package

Note

Formal definition with setOldClass for the S3 class varest

Author(s)

Bernhard Pfaff

Examples

showClass("varest")

 

varest2-class

Description

This class derives from a varest S3 class which is a list of objects describing a Vectorial AutoRegressive Model (see VAR)

Details

VAR:

a varest S3 object created by VAR

Note

A varest2 object can be created by new("varest2", ...) or returned by the function getVARmodel

Author(s)

Emanuele Cordano

Examples

showClass("varest2")