eqc_PrecisionRounding: Precision and rounding patterns of daily precipitation

In reddPrec: Reconstruction of Daily Data - Precipitation

Precision and rounding patterns of daily precipitation

Description

The function determine the level (0, 1 or 2) of the precision and rounding patterns test

Usage

eqc_PrecisionRounding(
  xts_obj,
  lmn_yday = 365 * 80/100,
  ths = list(lv0 = c(percent = 70), lv1to2 = c(percent = 50))
)

Arguments

xts_obj	xts of a single time series
lmn_yday	numeric value of the minimum number of days to be considered a complete year. The default value is 365 * 80 / 100 days
ths	list. List of parameters to define in which level would be the daily precipitation.

Details

Precision and rounding patterns depict inconsistencies in the frequency of decimal values in the time series. As there is no absolute correct frequency of decimals, we decided to measure how similar the decimal patterns are in the time series. A decimal pattern is interpreted as the list of unique decimal values observed, sorted in descending order. In this way, the decimal pattern for each year is computed first, followed by the selection of the most dominating pattern (mode). Based on how much (in percentage) this dominating pattern represents the time series, it is defined: Level 0: coherent precision and rounding pattern (similar decimal pattern in more (or equal) than 70% of the time series). Level 1: a similar decimal pattern in less than 70% but more than (or equal) 50% of the time series. Level 2: different decimal patterns (no dominant pattern). Argument ths present the above default thresholds for the levels definition.

References

Hunziker, S., Gubler, S., Calle, J., Moreno, I., Andrade, M., Velarde, F., ... & Brönnimann, S. (2017). Identifying, attributing, and overcoming common data quality issues of manned station observations. https://doi.org/10.1002/joc.5037

Huerta, A., Serrano-Notivoli, R., & Brönnimann, S. (2024). SC-PREC4SA: A serially complete daily precipitation dataset for South America. https://doi.org/10.31223/X57D8R

Examples

## Not run: 
set.seed(123)

# creating fake daily precipitation data
dates_t <- seq(as.Date("1980-01-01"), as.Date("2015-12-31"), by = "day")
prec <- round(rnorm(length(dates_t), mean = 1.2, sd = 6), 1)
prec[prec<0] <- 0
xts_obj <- xts::xts(prec, dates_t)
names(xts_obj) <- "prec"

# Comparison with visual inspection
eqc_Plot(xts_obj)
eqc_PrecisionRounding(xts_obj)

# it also work if there is some empty data (but not if all is NA)
xts_obj["1990/2010"] <- NA
eqc_Plot(xts_obj)
eqc_PrecisionRounding(xts_obj)

# creating some rounding values
xts_obj["1980/1990"] <- round(xts_obj["1980/1990"], 0) 
eqc_Plot(xts_obj)
eqc_PrecisionRounding(xts_obj)


## End(Not run)

reddPrec documentation built on June 8, 2025, 1:55 p.m.