PrecipitationOccurrenceModel: Precipitation Occurrence Model

In ecor/RGENERATEPREC: Tools to Generate Daily-Precipitation Time Series

Precipitation Occurrence Model

Description

This functions creates a stochastic Occurrence Model for the variable x (PrecipitationOccurrenceModel S3 object) through a calibration from observed data.

Usage

PrecipitationOccurrenceModel(
  x,
  exogen = NULL,
  p = 1,
  monthly.factor = NULL,
  valmin = 0.5,
  id.name = NULL,
  ...
)

Arguments

x	variable utilized for the auto-regression of its occurrence, e.g. daily precipitaton
exogen	exogenous predictors
p	auto-regression order
monthly.factor	vector of factors indicating the month of the days
valmin	minimum admitted value for daily precipitation amount
id.name	identification name of the station
...	further arguments

Value

The function returns a PrecipitationOccurrenceModel-class S3 object containing the following elements:

predictor data frame containg the endogenous and exogenous predictors of the logistic regression model;

glm the genaralized liner model using for the logistic regression;

p auto-regression order

valmin minimum admitted value for daily precipitation amount

See Also

glm

Examples

library(RGENERATEPREC)

data(trentino)

year_min <- 1961
year_max <- 1990

period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max
period_temp <- TEMPERATURE_MAX$year>=year_min & TEMPERATURE_MAX$year<=year_max

prec_mes <- PRECIPITATION[period,]
Tx_mes <- TEMPERATURE_MAX[period_temp,]
Tn_mes <- TEMPERATURE_MIN[period_temp,]
accepted <- array(TRUE,length(names(prec_mes)))
names(accepted) <- names(prec_mes)
for (it in names(prec_mes)) {
	acc <- TRUE
	acc <- (length(which(!is.na(Tx_mes[,it])))==length(Tx_mes[,it]))
	acc <- (length(which(!is.na(Tn_mes[,it])))==length(Tn_mes[,it])) & acc
	accepted[it]  <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it])) & acc
	
}

valmin <- 1.0
prec_mes <- prec_mes[,accepted]



Tx_mes <- Tx_mes[,accepted]
Tn_mes <- Tn_mes[,accepted]
prec_occurrence_mes <- prec_mes>=valmin

station <- names(prec_mes)[!(names(prec_mes) %in% c("day","month","year"))]
it <- station[2]
vect <- Tx_mes[,it]-Tn_mes[,it]
months <- factor(prec_mes$month)
model <- PrecipitationOccurrenceModel(x=prec_mes[,it],exogen=vect,monthly.factor=months)

probs <- predict(model$glm,type="response")





plot(months[-1],probs)

newdata <- model$predictor[2000:2007,]
probs0 <- predict(model,newdata=newdata)

ecor/RGENERATEPREC documentation built on Feb. 3, 2023, 10:38 a.m.