PrecipitationOccurrenceMultiSiteModel function

Precipitation Occurrence Multi-Site Model

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

PrecipitationOccurrenceMultiSiteModel(
  x,
  exogen = NULL,
  station = names(x),
  origin = origin,
  valmin = 0.5,
  multisite_type = "wilks",
  tolerance_wilks = 0.001,
  p = 2,
  ...
)

Arguments

• x: data frame (each column is a site) of variable utilized for the auto-regression of its occurrence, e.g. daily precipitaton
• exogen: exogenous predictors
• station: character string vectors containing the codes of the station used for model calibration
• origin: character string (yyyy-dd-mm) indicating the date of the first row of "x".
• valmin: minimum admitted value for daily precipitation amount
• multisite_type: string indicating the utilized approach for spatial multi-site dependence description. Default is "wilks".
• tolerance_wilks: see tolerance used by omega_inv through CCGamma
• p: auto-regression order
• ...: further arguments

Returns

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

... PrecipitationOccurrenceModel S3 class objects for each analyzed site. The name is the site (or station) code

ccgama CCGammaObjectListPerEachMonth object, i.e. matices of Gaussian Inter-Site Correlation returned by CCGamma;

type string indicating the utilized approach for spatial multi-site dependence description, only "wilks" type is implemented;

station character string vectors containing the codes of the station used in PrecipitationMultiSiteOccurrenceModel.

Examples

library(RGENERATEPREC)

data(trentino)

year_min <- 1961
year_max <- 1990
origin <- paste(year_min,1,1,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"))]
station <- station[1:2] # to save example elapsed time!!
exogen <- Tx_mes-Tn_mes
months <- factor(prec_mes$month)

#' ### Not Run!! 
# The following lines are commented to save example elapsed time!!
model_multisite <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes,exogen=exogen,
origin=origin,multisite_type="wilks")

### Not Run!! 
#  The following lines are commented to save example elapsed time!!
model_multisite_logit <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes,exogen=exogen,
origin=origin,multisite_type="logit")

See Also

PrecipitationOccurrenceModel,CCGamma