| 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
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
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)
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
Sample data: Rainfall forecasts data
Description
A dataset containing 2 stations including observation and raw forecasts.
Usage
data(sample)