WQM: Wavelet-Based Quantile Mapping for Postprocessing Numerical
Weather 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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