Package {WQM}

Type:	Package
Title:	Wavelet-Based Quantile Mapping for Postprocessing Numerical Weather Predictions
Version:	0.1.4
Author:	Ze Jiang [aut, cre], Fiona Johnson [aut]
Maintainer:	Ze Jiang <ze.jiang@unsw.edu.au>
Description:	The wavelet-based quantile mapping (WQM) technique is designed to correct biases in spatio-temporal precipitation forecasts across multiple time scales. The WQM method effectively enhances forecast accuracy by generating an ensemble of precipitation forecasts that account for uncertainties in the prediction process. For a comprehensive overview of the methodologies employed in this package, please refer to Jiang, Z., and Johnson, F. (2023) <doi:10.1029/2022EF003350>. The package relies on two packages for continuous wavelet transforms: 'WaveletComp', which can be installed automatically, and 'wmtsa', which is optional and available from the CRAN archive https://cran.r-project.org/src/contrib/Archive/wmtsa/. Users need to manually install 'wmtsa' from this archive if they prefer to use 'wmtsa' based decomposition.
License:	GPL (≥ 3)
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 3.5.0)
Imports:	MBC, WaveletComp, matrixStats, ggplot2
Suggests:	stats, tidyr, dplyr, wmtsa, scales, data.table, graphics, testthat (≥ 3.0.0), knitr, rmarkdown, bookdown
Config/testthat/edition:	3
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-10-07 22:38:30 UTC; z5154277
Repository:	CRAN
Date/Publication:	2024-10-11 08:00:18 UTC

Australia NWP rainfall forecasts at lead 1h over Sydney region

Description

A dataset containing 160 stations including observation and raw forecasts.

Usage

data(NWP.rain)

Verification Rank and Histogram

Description

Verification Rank and Histogram

Usage

RankHist(forecasts, observations, do.plot = FALSE)

Arguments

Value

A vector giving the rank of verifying observations relative to the corresponding ensemble forecasts. The verification rank historgram is plotted.

References

ensembleBMA::verifRankHist

CWT based quantile mapping

Description

CWT based quantile mapping

Usage

bc_cwt(
  data,
  subset,
  variable,
  theta = 0.1,
  QM = c("MBC", "MRS", "QDM"),
  number_sim = 5,
  wavelet = "morlet",
  dt = 1,
  dj = 1,
  method = "M2",
  block = 3,
  seed = NULL,
  PR.cal = FALSE,
  do.plot = FALSE,
  ...
)

Arguments

Value

a list of post-processed data

Function: Total number of decomposition levels

Description

Function: Total number of decomposition levels

Usage

fun_cwt_J(n, dt, dj)

Arguments

Value

the total number of decomposition levels.

Inverse of continuous wavelet transform

Description

Inverse of continuous wavelet transform

Usage

fun_icwt(x.wave, dt, dj, flag.wav = "WaveletComp", scale = NULL)

Arguments

Value

reconstructed time series

References

fun_stoch_sim_wave in PRSim, Brunner and Furrer, 2020.

Examples

set.seed(100)

dt<-1
dj<-1/8
flag.wav <- switch(2, "wmtsa", "WaveletComp")

n <- 100
x <- rnorm(n)
x.wave <- t(WaveletComp::WaveletTransform(x=x)$Wave)
rec <- fun_icwt(x.wave, dt, dj, flag.wav)

x.wt <- WaveletComp::analyze.wavelet(data.frame(x=x),"x",dt=dt,dj=dj)
rec_orig <- WaveletComp::reconstruct(x.wt,only.sig = FALSE, plot.rec = FALSE)$series$x.r

### compare to original series
op <- par(mfrow = c(1, 1), mar=c(3,3,1,1), mgp=c(1, 0.5, 0))
plot(1:n, x, type="l", lwd=5, xlab=NA, ylab=NA)
lines(1:n, rec, col="red",lwd=3)
lines(1:n, rec_orig, col="blue", lwd=1)
legend("topright",legend=c("Raw","Inverse","Inverse_orig"),
       lwd=c(5,3,1),bg="transparent",bty = "n",
       col=c("black","red","blue"),horiz=TRUE)
par(op)

Inverse Fourier transform

Description

Inverse Fourier transform

Usage

fun_ifft(x, do.plot = FALSE)

Arguments

Value

reconstruction time series

References

fun_stoch_sim in PRSim, Brunner and Furrer, 2020.

Examples

x <- rnorm(100)
x.new <- fun_ifft(x, do.plot=TRUE)

Phase randomization and shuffling

Description

Phase randomization and shuffling

Usage

prsim(
  modulus,
  phases,
  noise_mat,
  method = c("M1", "M2")[2],
  size = 3,
  seed = NULL
)

Arguments

Value

A new complex matrix

Sample data: Rainfall forecasts data

Description

A dataset containing 2 stations including observation and raw forecasts.

Usage

data(sample)