PrecipitationAmountModel function

Creates a Precipitation Amount Model

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

Returns

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.

Examples

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)

See Also

predict.PrecipitationAmountModel,normalizeGaussian_severalstations,generate