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  "Title": "Wavelet-Based Quantile Mapping for Postprocessing Numerical\nWeather Predictions",
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  "Authors@R": "c(person(given = \"Ze\",\nfamily = \"Jiang\",\nrole = c(\"aut\", \"cre\"),\nemail = \"ze.jiang@unsw.edu.au\",\ncomment = c(ORCID = \"0000-0002-3472-0829\")),\nperson(given = \"Fiona\",\nfamily = \"Johnson\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0001-5708-1807\")))",
  "Author": "Ze Jiang [aut, cre] (<https://orcid.org/0000-0002-3472-0829>),\nFiona Johnson [aut] (<https://orcid.org/0000-0001-5708-1807>)",
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  "Description": "The wavelet-based quantile mapping (WQM) technique is\ndesigned to correct biases in spatio-temporal precipitation\nforecasts across multiple time scales. The WQM method\neffectively enhances forecast accuracy by generating an\nensemble of precipitation forecasts that account for\nuncertainties in the prediction process. For a comprehensive\noverview of the methodologies employed in this package, please\nrefer to Jiang, Z., and Johnson, F. (2023)\n<doi:10.1029/2022EF003350>. The package relies on two packages\nfor continuous wavelet transforms: 'WaveletComp', which can be\ninstalled automatically, and 'wmtsa', which is optional and\navailable from the CRAN archive\n<https://cran.r-project.org/src/contrib/Archive/wmtsa/>. Users\nneed to manually install 'wmtsa' from this archive if they\nprefer to use 'wmtsa' based decomposition.",
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