Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions*

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Evaluating Probabilistic Forecasts with scoringRules (2017, background): “For details and examples on choosing appropriate weights, see Scheuerer and Hamill (2015b).”
Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood (2018, background): “…et al. 2005; Vrugt et al. 2006; Hagedorn et al. 2008; Scheuerer 2014; Scheuerer and Büermann 2014; Mohammadi et al. 2015; Feldmann et al. 2015; Scheuerer and Hamill 2015; Scheuerer and Möller 2015; Taillardat et al. 2016; Möller and Groß 2016) and often found it to yield sharper and better…”
Neural networks for post-processing ensemble weather forecasts (2018, background): “However, many applications require accurate models of cross-variable, spatial and temporal dependence structures, and much recent work has been focused on multi-variate post-processing methods (e.g., Schefzik et al., 2013).”
Distributional regression forests for probabilistic precipitation forecasting in complex terrain (2018, background): “Moreover, one could consider a distribution including an additional parameter for capturing skewness (as in Scheuerer and Hamill 2015; Baran and Nemoda 2016).”