RGENERATEPREC (version 1.2.9)

PrecipitationAmountModel: Creates a Precipitation Amount Model

Description

Creates a Precipitation Amount Model

Usage

PrecipitationAmountModel(
  x,
  valmin = 1,
  station = names(x),
  sample = "monthly",
  origin = "1961-1-1",
  ...
)

Arguments

x

observed precipitation amount time series (data frame)

valmin

maximum admitted value of precipitation depth

station

string vector containing station identification codes

sample

character string. If it is "monthly" (Default), the corralaton matrix is calculeted per each month.

origin

date of the day referred by he first row of x.

...

further agruments for normalizeGaussian_severalstations

Value

The function returns AN S3 OBJECT ...... the correlation matrix of precipitation amount values (excluding the zeros). In case sample=="monthly" the runction return a MonlthyList S3 object.

See Also

predict.PrecipitationAmountModel,normalizeGaussian_severalstations,generate

Examples

# NOT RUN {
# }
# NOT RUN {
set.seed(1245)

data(trentino)

year_min <- 1961
year_max <- 1990

origin <- paste(year_min,1,1,sep="-")
end <- paste(year_max,12,31,sep="-")

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"))]

precamount <- PrecipitationAmountModel(prec_mes,station=station,origin=origin)

val <- predict(precamount)

prec_gen <- generate(precamount)  




month <- adddate(as.data.frame(residuals(precamount$T0090)),origin=origin)$month
#####plot(month,residuals(precamount$T0090))
plot(factor(month),residuals(precamount$T0090))

qqplot(prec_mes$T0083,prec_gen$T0083)
abline(0,1)


## SINGLE STATION

station <- "T0083"

precamount_single <- PrecipitationAmountModel(prec_mes,station=station,origin=origin)

val_single <- predict(precamount_single)

prec_gen_single <- generate(precamount_single)  



month <- adddate(as.data.frame(residuals(precamount_single[[station[1]]])),origin=origin)$month
plot(factor(month),residuals(precamount_single[[station[1]]]))


### Comparison (Q-Q plot) between multi and single sites. 

qqplot(prec_mes$T0083,prec_gen$T0083,col=1)
abline(0,1)
points(sort(prec_mes$T0083),sort(prec_gen_single$T0083),pch=2,col=2)
legend("bottomright",pch=c(1,2),col=c(1,2),legend=c("Multi Sites","Single Site"))







abline(0,1)

# }
# NOT RUN {
# }