{ "_id": "6a115138acfb0bcc41cea6e7", "Package": "WQM", "Type": "Package", "Title": "Wavelet-Based Quantile Mapping for Postprocessing Numerical\nWeather Predictions", "Version": "0.1.4", "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] (),\nFiona Johnson [aut] ()", "Maintainer": "Ze Jiang ", "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. 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. 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