Parametric model for post-processing visibility ensemble forecasts

Abstract. Although, by now, ensemble-based probabilistic forecasting is the most advanced approach to weather prediction, ensemble forecasts still suffer from a lack of calibration and/or display systematic bias, thus requiring some post-processing to improve their forecast skill. Here, we focus on visibility, a weather quantity that plays a crucial role in, for example, aviation and road safety or ship navigation, and we 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 raw ensembles; moreover, the proposed mixture model consistently outperforms the Bayesian model averaging approach used as a reference post-processing technique.