Description
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.
README.md
WQM
Wavelet-based Quantile Mapping
Requirements
Dependencies:
ifultools, wmtsa, splus2R, MBC, ggplot2, WaveletComp
Suggest:
dplyr, tidyr,
matrixStats, data.table
# some package can only be install from source
path_ifultools <- "https://cran.r-project.org/src/contrib/Archive/ifultools/ifultools_2.0-26.tar.gz"
if(!require("ifultools")) install.packages(path_ifultools, depen=T, repos = NULL, type = "source")
path_wmtsa <- "https://cran.r-project.org/src/contrib/Archive/wmtsa/wmtsa_2.0-3.tar.gz"
if(!require("wmtsa")) install.packages(path_wmtsa, depen=T, repos = NULL, type = "source")
Installation
You can install the package via devtools from GitHub with:
devtools::install_github("zejiang-unsw/WQM", dependencies = TRUE)
Citation
Jiang, Z., & Johnson, F. (2023). A New Method for Postprocessing Numerical Weather Predictions Using Quantile Mapping in the Frequency Domain. Monthly weather review, 151(8), 1909-1925. doi:10.1175/mwr-d-22-0217.1