WQM: Wavelet-Based Quantile Mapping for Postprocessing NumericalWeather Predictions
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 [aut, cre], Fiona Johnson [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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