Despite the continuous development of the different operational ensemble prediction systems over the past decades, 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 climatology is substantially superior to the raw ensemble; nevertheless, the forecast skill can be further improved by post-processing, at least for short lead times. Moreover, the proposed mixture model consistently outperforms the Bayesian model averaging approach used as reference post-processing technique.

翻译：尽管过去几十年间各业务集合预报系统持续发展，集合预报仍可能存在校准不足或系统性偏差的问题，因此需要后处理以提升预报技巧。本文聚焦于能见度这一对航空安全、道路安全及船舶导航等具有关键作用的气象要素，提出了一种参数化模型——将预报分布构建为伽马分布与截断正态分布的混合模型，两者均以最大报告能见度值为右删失阈值。基于欧洲中期天气预报中心覆盖中欧与西欧两个不同区域及两个不同时间段的能见度集合预报，通过两项案例研究评估了该新模型。案例研究结果表明，气候态预报显著优于原始集合预报；但通过后处理可进一步提升预报技巧（至少在短期预报时效内有效）。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。