In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. In this paper we develop regression and classification supervised learning algorithms to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active. Examples using data from the Maastricht Upper Area Control Centre are presented with varying levels of predictive performance by the machine learning algorithms. Data sources include Demand Data Repository from EUROCONTROL and the Rapid Developing Thunderstorm product from EUMETSAT.

近年来，对流天气一直是欧洲领空出现严重延误的原因。由于气候专家预计未来对流天气的频率和强度会增加，因此有必要找到减轻对流天气事件对空域系统影响的解决方案。对历史空中交通和天气数据的分析将为未来如何处理破坏性对流事件提供宝贵的见解。我们提出了一种处理和整合历史交通和天气数据的方法，以便使用机器学习算法来预测恶劣天气期间的网络性能。在本文中，我们开发了回归和分类监督学习算法来预测空域性能特征，例如进入计数、受天气法规影响的航班数量，以及天气法规是否处于活动状态。使用来自马斯特里赫特上区控制中心数据的示例通过机器学习算法呈现出不同级别的预测性能。数据来源包括 EUROCONTROL 的 Demand Data Repository 和 EUMETSAT 的 Rapid Development Thunderstorm 产品。