To be able to produce accurate and reliable predictions of visibility has crucial importance in aviation meteorology, as well as in water- and road transportation. Nowadays, several meteorological services provide ensemble forecasts of visibility; however, the skill, and reliability of visibility predictions are far reduced compared to other variables, such as temperature or wind speed. Hence, some form of calibration is strongly advised, which usually means estimation of the predictive distribution of the weather quantity at hand either by parametric or non-parametric approaches, including also machine learning-based techniques. As visibility observations - according to the suggestion of the World Meteorological Organization - are usually reported in discrete values, the predictive distribution for this particular variable is a discrete probability law, hence calibration can be reduced to a classification problem. Based on visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts covering two slightly overlapping domains in Central and Western Europe and two different time periods, we investigate the predictive performance of locally, semi-locally and regionally trained proportional odds logistic regression (POLR) and multilayer perceptron (MLP) neural network classifiers. We show that while climatological forecasts outperform the raw ensemble by a wide margin, post-processing results in further substantial improvement in forecast skill and in general, POLR models are superior to their MLP counterparts.

翻译：为了生成准确可靠的能见度预测，在航空气象学以及水路和道路运输领域具有至关重要的意义。当前，多个气象服务机构提供能见度的集合预报，然而，与温度或风速等其他变量相比，能见度预报的技巧和可靠性明显较低。因此，强烈建议采用某种形式的校准，这通常意味着通过参数或非参数方法（包括基于机器学习的技术）来估计当前气象变量的预测分布。根据世界气象组织的建议，能见度观测通常以离散值报告，因此针对这一特定变量的预测分布是一种离散概率律，从而使校准可简化为分类问题。基于欧洲中期天气预报中心覆盖中欧和西欧两个略有重叠区域以及两个不同时间段的能见度集合预报，我们研究了本地、半本地和区域训练的比例优势逻辑回归（POLR）与多层感知机（MLP）神经网络分类器的预测性能。结果表明，尽管气候预报显著优于原始集合，但后处理进一步大幅提升了预报技巧，且总体而言，POLR模型的性能优于对应的MLP模型。