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Although by now the ensemble-based probabilistic forecasting is the mostadvanced approach to weather prediction, ensemble forecasts still might sufferfrom lack of calibration and/or display systematic bias, thus require somepost-processing to improve their forecast skill. Here we focus on visibility,which quantity plays a crucial role e.g. in aviation and road safety or in shipnavigation, and propose a parametric model where the predictive distribution isa mixture of a gamma and a truncated normal distribution, both right censoredat the maximal reported visibility value. The new model is evaluated in twocase studies based on visibility ensemble forecasts of the European Centre forMedium-Range Weather Forecasts covering two distinct domains in Central andWestern Europe and two different time periods. The results of the case studiesindicate that post-processed forecasts are substantially superior to the rawensemble; moreover, the proposed mixture model consistently outperforms theBayesian model averaging approach used as reference post-processing technique.

Parametric model for post-processing visibility ensemble forecasts