reddPrec (version 2.0.3)

qcPrec: Quality Control of daily precipitation observations

Description

This function apply several threshold-based criteria to filter original observations of daily precipitation.

Usage

qcPrec(
  prec,
  sts,
  crs,
  coords,
  coords_as_preds = TRUE,
  neibs = 10,
  thres = NA,
  qc = "all",
  qc3 = 10,
  qc4 = c(0.99, 5),
  qc5 = c(0.01, 0.1, 5),
  ncpu = 1
)

Arguments

prec

matrix containing the original precipitation data. Each column represents one station. The names of columns have to be 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.

crs

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

coords

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

coords_as_preds

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

neibs

integer. Number of nearest neighbors to use.

thres

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

qc

vector of strings with the QC criteria to apply. Default is "all". See details.

qc3

numeric. Indicates the threshold (number of times higher or lower) from which a observation, in comparison with its estimate, should be deleted. Default is 10.

qc4

numeric vector of length 2. Thresholds of wet probability (0 to 1) and magnitude (in the units of input precipitation data) from which a observation of value zero, in comparison with its estimate, should be deleted. Default is c(0.99, 5).

qc5

numeric vector of length 2. Thresholds of dry probability (0 to 1) and magnitude (in the units of input precipitation data) from which a observation higher than a specific value (also in the original units), in comparison with its estimate, should be deleted. Default is c(0.01, 0.1, 5).

ncpu

number of processor cores used to parallel computing.

Details

Parameter "sts" must have an "ID" field containing unique identifiers of the stations.

"qc" can be "all" (all criteria are applied) or a vector of strings (e.g.: c("1","2","4")) indicating the QC criteria to apply to observations: "1" (suspect value): obs==0 & all(neibs>0); "2" (suspect zero): obs>0 & all(neibs==0); "3" (suspect outlier): obs is "qc3" times higher or lower than the estimate; "4" (suspect wet): obs==0 & wet probability > "qc4[1]" & estimate > "qc4[2]"; "5" (suspect dry): obs>"qc5[3]" & dry probability < "qc5[1]" & estimate < "qc5[2]"

Examples

if (FALSE) {
set.seed(123)
prec <- round(matrix(rnorm(30*50, mean = 1.2, sd = 6), 30, 50), 1)
prec[prec<0] <- 0
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))
qcdata <- qcPrec(prec, sts, crs = 'EPSG:4326', coords = c('lon','lat'),
                 coords_as_preds = TRUE, neibs = 10, thres = NA,
                 qc = 'all', qc3 = 10, qc4 = c(0.99, 5), qc5 = c(0.01, 0.1, 5),
                 ncpu=2)
str(qcdata)
}