Learn R Programming

RMAWGEN (version 1.3.9.3)

Multi-Site Auto-Regressive Weather GENerator

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.

Copy Link

Version

Install

install.packages('RMAWGEN')

Monthly Downloads

547

Version

1.3.9.3

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/ecor/RMAWGEN

Homepage

https://ecor.github.io/RMAWGEN/

Maintainer

Emanuele Cordano

Last Published

April 12th, 2025

Functions in RMAWGEN (1.3.9.3)

TemperatureEndDay

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

RMAWGEN-package

R - Multi-site Autoregressive WEather Generator

Gets the toponym where a meteorological station is located

arch.test function for varest2 object

PrecipitationStartDay

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

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

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

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

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

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

counts NAs in each row of data

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

is.monthly.climate

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.

months REPLACEMANT

forecastResidual

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

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

generateTemperatureTimeseries

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

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

inv_GPCA_iteration

This function makes an inverse iteration of PCA-Gaussianization process

continuity_ratio

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

collinear_dataset

Collinear Dataset

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

normality.test method for varest2 object

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

newVARmultieventRealization

Generates several realizations of a VAR model

extractTxFromAnomalies

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

extractTnFromAnomalies

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

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

normalizeGaussian

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

normalizeGaussian_prec

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

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

serial.test function for varest2 object

normalizeGaussian_severalstations

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

print S3 method for GPCA or GPCA_iteration object

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

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

plotDailyClimate

Plots daily climatology through one year

normalizeGaussian_severalstations_prec

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

Replaces each entry of the rows containing NA values with NA

rescaling_monthly

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

residuals.varest2

residuals S3 method for varest2 object

qqplotTnTxWGEN_seasonal

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

setComprehensiveTemperatureGeneratorParameters

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.

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

qqplot_RMAWGEN_Tx

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

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

splineInterpolateMonthlytoDaily

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

splineInterpolateMonthlytoDailyforSeveralYears

Interpolates monthly data to daily data using splineInterpolateMonthlytoDaily for several years

qqplotprecWGEN_seasonal

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

Trentino Dataset

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

ComprehensivePrecipitationGenerator

The comprehensive Precipitation Generator

NewVAReventRealization

Generates a new realization of a VAR model

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

This function makes an iteration of PCA-Gaussianization process

ComprehensiveTemperatureGenerator

The Comprehensive Temperature Generator

GPCAvarest2-class

GPCAvarest2-class

PrecipitationEndDay

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

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

TemperatureStartDay

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

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

GPCAiteration-class

GPCAiteration-class

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