Package 'WQM'

Title: Wavelet-Based Quantile Mapping for Postprocessing Numerical Weather Predictions
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.
Authors: Ze Jiang [aut, cre] , Fiona Johnson [aut]
Maintainer: Ze Jiang <[email protected]>
License: GPL (>= 3)
Version: 0.1.4
Built: 2024-12-11 06:46:14 UTC
Source: CRAN

Help Index

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

data

a list of input dataset
subset

a index of number denoting the subset for calibration
variable

a character string denoting the type of variable.
theta

threshold of rainfall.
QM

a character string denoting the qm method used.
number_sim

The total number of realizations.
wavelet

a character string denoting the wavelet filter to use in calculating the CWT.
dt

sampling resolution in the time domain.
dj

sampling resolution in the frequency domain.
method

Shuffling method, M1: non-shuffling and M2: shuffling. M2 by default.
block

Block size.
seed

Seed for shuffling process.
PR.cal

Logical value for phase randomization of calibration.
do.plot

Logical value for ploting.
...

Additional arguments for QDM.

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

n

sample size.
dt

sampling resolution in the time domain.
dj

sampling resolution in the frequency domain.

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

x.wave

input complex matrix.
dt

sampling resolution in the time domain.
dj

sampling resolution in the frequency domain.
flag.wav

String for two different CWT packages.
scale

Wavelet scales.

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

x

input time series.
do.plot

Logical value of plot.

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)

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)

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

modulus

Modulus of complex values.
phases

Argument of complex values.
noise_mat

Complex matrix from random time series.
method

Shuffling method, M1: non-shuffling and M2: shuffling. M2 by default.
size

Block size.
seed

Seed for shuffling process.

Value

A new complex matrix

Verification Rank and Histogram

Description

Verification Rank and Histogram

Usage

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

Arguments

forecasts

A matrix of ensemble forecasts, in which the rows corresponds to locations and times and the columns correspond to the individual ensemble members.
observations

A vector of observations corresponding to the locations and times of the forecasts.
do.plot

Logical value of plot.

Value

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

References

ensembleBMA::verifRankHist

Sample data: Rainfall forecasts data

Description

A dataset containing 2 stations including observation and raw forecasts.

Usage

data(sample)