  Although by now the ensemble-based probabilistic forecasting is the most
advanced approach to weather prediction, ensemble forecasts still might suffer
from lack of calibration and/or display systematic bias, thus require some
post-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 ship
navigation, and propose a parametric model where the predictive distribution is
a mixture of a gamma and a truncated normal distribution, both right censored
at the maximal reported visibility value. The new model is evaluated in two
case studies based on visibility ensemble forecasts of the European Centre for
Medium-Range Weather Forecasts covering two distinct domains in Central and
Western Europe and two different time periods. The results of the case studies
indicate that post-processed forecasts are substantially superior to the raw
ensemble; moreover, the proposed mixture model consistently outperforms the
Bayesian model averaging approach used as reference post-processing technique.
