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  "Title": "Stochastic Precipitation Downscaling with the RainFARM Method",
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  "Authors@R": "person(\"Jost\", \"von Hardenberg\", email = \"j.vonhardenberg@isac.cnr.it\",\nrole = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0002-5312-8070\"))",
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  "Description": "An implementation of the RainFARM (Rainfall Filtered\nAutoregressive Model) stochastic precipitation downscaling\nmethod (Rebora et al. (2006) <doi:10.1175/JHM517.1>). Adapted\nfor climate downscaling according to D'Onofrio et al. (2018)\n<doi:10.1175/JHM-D-13-096.1> and for complex topography as in\nTerzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>. The\nRainFARM method is based on the extrapolation to small scales\nof the Fourier spectrum of a large-scale precipitation field,\nusing a fixed logarithmic slope and random phases at small\nscales, followed by a nonlinear transformation of the resulting\nlinearly correlated stochastic field. RainFARM allows to\ngenerate ensembles of spatially downscaled precipitation fields\nwhich conserve precipitation at large scales and whose\nstatistical properties are consistent with the small-scale\nstatistics of observed precipitation, based only on knowledge\nof the large-scale precipitation field.",
  "License": "Apache License 2.0",
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  "Repository": "https://jhardenberg.r-universe.dev",
  "Date/Publication": "2022-03-19 00:15:01 UTC",
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