Description Usage Arguments See Also Examples
Generetion of daily precipitation amount taking into accont
a previous generation of precipitation occurence with
generate
1 | generatePrecipitationAmount(x, origin = "1961-1-1", par = NULL, ...)
|
x |
object of precipitation quantiles returned by
|
origin |
character string containing the date
releted to the first row of |
par |
|
... |
further arguments |
qqfit
,fitdistrForPrecipitation
,generate
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ## # Not Run in the examples, uncomment to run the following lines
#library(RMRAINGEN)
#
#set.seed(1233)
#data(trentino)
#
#year_min <- 1987
#year_max <- 1990
#
#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]
#
#fit <- fitdistrForPrecipitation(data=prec_mes,dname="exp",start=NULL,sample=NULL)
#
#origin <- paste(year_min,1,1,sep="-")
#
### Fitting of Probability Distribution of Precipitation Amount
#fit_monthly <- fitdistrForPrecipitation(data=prec_mes,dname="gamma",
# start=NULL,sample="monthly",origin=origin)
#
### Estimate coefficients for Precipitation Occurence Modeling
### (using generate.YuleWalkerCoefficientBlockmatrices)
#coeff_monthly <- CoeffYWeq(data=prec_mes,p=1,tolerance=0.001,sample="monthly",origin=origin)
#
#generation_monthly <- generate(coeff_monthly,year_min=year_min,year_max=year_max,
# names=names(prec_mes),precipitation.indicator=TRUE)
#
#prec_gen <- generatePrecipitationAmount(x=generation_monthly,origin=origin,par=fit_monthly)
#
### Estimate coefficients for Precipitation Occurence Modeling (using generate.CCGammaObject)
#CCGamma_monthly <- CCGamma(data=prec_mes,lag=0,tolerance=0.001,only.matrix=FALSE,
# sample="monthly",origin=origin)
#
#generation_monthly_2 <- generate(CCGamma_monthly,year_min=year_min,year_max=year_max,
# names=names(prec_mes),precipitation.indicator=TRUE)
#
#prec_gen_2 <- generatePrecipitationAmount(x=generation_monthly_2,origin=origin,par=fit_monthly)
#
### Check Q-Q plots between observations and generations
#idst <- names(prec_mes)[2]
#month <- 6 ## June
#momth <- c(12,1,2) # Winter
#
#prec_mes <- adddate(prec_mes,origin=origin)
#prec_gen <- adddate(prec_gen,origin=origin)
#prec_gen_2 <- adddate(prec_gen_2,origin=origin)
#
#
#qqplot(prec_mes[prec_mes$month %in% month,idst],prec_gen[prec_gen$month %in% month,idst])
#abline(0,1)
#qqplot(prec_mes[prec_mes$month %in% month,idst],prec_gen_2[prec_gen_2$month %in% month,idst])
#abline(0,1)
#
### Not Run in the examples, uncomment to run the following line
### CCGamma_monthly_gen <- CCGamma(data=prec_gen,lag=0,tolerance=0.001,
### only.matrix=FALSE,sample="monthly",origin=origin)
### CCGamma_monthly_gen_2 <- CCGamma(data=prec_gen_2,lag=0,tolerance=0.001,
### only.matrix=FALSE,sample="monthly",origin=origin)
#
#
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