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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. ; This work is partially supported through the Spanish Government initiative Proyectos de I+D+I "RETOS INVESTIGACIN" Ministerio de Ciencia, Innovación y Universidades by the project entitled "Management of Meteorological Uncertainty for More Efficient Air Traffic: Meteorological Data Provision and Thunderstorm Avoidance" (RTI2018-098471-B-C32 ).

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. ; This work is partially supported through the Spanish Government initiative Proyectos de I+D+I "RETOS INVESTIGACIN"; Ministerio de Ciencia, Innovación y Universidades by the project entitled "Management of Meteorological Uncertainty for More Efficient Air Traffic: Meteorological Data Provision and Thunderstorm Avoidance" (RTI2018-098471-B-C32 ).

Convective weather is a large source of disruption for air traffic management operations. Being able to predict thunderstorms the day before operations can help traffic managers plan around weather and improve air traffic flow management operations. In this paper, machine learning is applied on data from satellite storm observations and ensemble numerical weather prediction products to detect convective weather 36 h in advance. The learning task is formulated as a binary classification problem and a neural network is trained to predict the occurrence of storms. The neural network results are used to develop a probabilistic based convection indicator capable of outperforming existing convection indicators found in the literature. Lastly, applications of the neural network based indicator in an air traffic management setting are presented. ; financial support of the Spanish Ministry of Science, Innovation and Universities under grant RTI2018-098471-B-C32. The project has received funding from the SESAR Joint Undertaking within the framework of the European Union's Horizon 2020 research and innovation programme under grant agreement No 891965.