Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE

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Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions* (2015, background): “…12/13/20 05:56 AM UTC A predictor like POPf ,s can provide additional information about whether precipitation occurs or not (Sloughter et al. 2007; Bentzien and Friederichs 2012; Scheuerer 2014) while the consideration of precipitable water as an additional predictor can yield some improvement…”
Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics (2013, methodology): “…While f s is a standard predictor for the location parameter and 1 { f s =0 } (or variants of it) can provide additional information about whether precipitation occurs or not (Sloughter et al., 2007; Bentzien and Friederichs, 2012), MD ( f s ) has not been employed in this context so far.”
Comparison of non-homogeneous regression models for probabilistic wind speed forecasting (2013, methodology): “Bentzien and Friederichs (2012) propose such a mixture model for precipitation using lognormal and gamma mixtures for the bulk of the distribution and an adaptive GPD tail which is able to significantly improve the predictive performance for extreme quantiles, see also Frigessi et al. (2002). Simila”