reddPrec (version 2.0.4)

gapFilling: Estimating new values in original missing values data series of daily precipitation

Description

This function uses the neighboring observations to estimate new precipitation values in those days and locations where no records exist.

Usage

gapFilling(
  prec,
  sts,
  dates,
  stmethod = NULL,
  thres = NA,
  neibs = 10,
  coords,
  crs,
  coords_as_preds = TRUE,
  window,
  ncpu = 2
)

Arguments

prec

matrix containing the original (cleaned) precipitation data. Each column represents one station. The names of columns must coincide with the names of the stations.

sts

data.frame. A column "ID" (unique ID of stations) is required. The rest of the columns (all of them) will act as predictors of the model.

dates

vector of class "Date" with all days of observations (yyyy-mm-dd).

stmethod

standardization method. 'quant' or 'ratio', see details.

thres

numeric. Maximum radius (in km) where neighboring stations will be searched. NA value uses the whole spatial domain.

neibs

integer. Number of nearest neighbors to use.

coords

vector of two character elements. Names of the fields in "sts" containing longitude and latitude.

crs

character. Coordinates system in EPSG format (e.g.: "EPSG:4326").

coords_as_preds

logical. If TRUE (default), "coords" are also taken as predictors.

window

odd integer. Length of data considered for standardization

ncpu

number of processor cores used to parallel computing.

Details

After the gap filling, "stmethod" allows for an standardization of the predictions based on the observations. It only works for daily data. For other timescales (monthly, annual) use "stmethod=NULL". The "window" parameter is a daily-moving centered window from which data is collected for each year (i.e. a 15-day window on 16th January will take all predictions from 1st to 30th January of all years to standardize them with their corresponding observations. Only standardized prediction of 16th January is returned. Process is repeated for all days).

Examples

if (FALSE) {
set.seed(123)
prec <- round(matrix(rnorm(30*50, mean = 1.2, sd = 6), 30, 50), 1)
prec[prec<0] <- 0
prec <- apply(prec, 2, FUN = function(x){x[sample(length(x),5)] <- NA; x})
colnames(prec) <- paste0('sts_',1:50)
sts <- data.frame(ID = paste0('sts_',1:50), lon = rnorm(50,0,1), 
                  lat = rnorm(50,40,1), dcoast = rnorm(50,200,50))
filled <- gapFilling(prec, sts, 
                    dates = seq.Date(as.Date('2023-04-01'),
                    as.Date('2023-04-30'),by='day'), 
                    stmethod = "ratio", thres = NA, coords = c('lon','lat'),
                    coords_as_preds = TRUE, crs = 'EPSG:4326', neibs = 10, 
                    window = 11, ncpu = 2)
str(filled)
summary(filled)
}