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.">

WQM: Wavelet-Based Quantile Mapping for Postprocessing Numerical Weather Predictions (original) (raw)

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.

Version: 0.1.4
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
Published: 2024-10-11
DOI: 10.32614/CRAN.package.WQM
Author: Ze Jiang ORCID iD [aut, cre], Fiona Johnson ORCID iD [aut]
Maintainer: Ze Jiang <ze.jiang at unsw.edu.au>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README
CRAN checks: WQM results

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