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.

翻译：尽管目前基于集合的概率预报是天气预报最先进的方法，但集合预报仍可能存在校准不足和/或系统偏差，因此需要一定的后处理来提升其预报技巧。本文聚焦于能见度——这一在航空安全、道路安全及船舶导航等领域至关重要的气象变量，提出一种参数化模型，其中预测分布由伽马分布与截断正态分布混合构成，两者均在最大报告能见度值处进行右截尾。基于欧洲中期天气预报中心覆盖中欧和西欧两个不同区域及两个不同时段的能见度集合预报，通过两个案例研究对该新模型进行评估。案例结果表明，后处理预报显著优于原始集合；此外，所提出的混合模型持续优于作为参考后处理技术的贝叶斯模型平均方法。