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This paper aims to explore machine learning techniques for post‐processing high‐resolution Numerical Weather Prediction (NWP) products for the early detection of convection. Data from the Arome Ensemble Prediction System and satellite observations from the Rapidly Developing Thunderstorm (RDT) product by Météo‐France are used to train a recurrent neural network model to predict areas of total convection and moderate convection. The learning task is formulated as a binary classification problem using a long short‐term memory (LSTM) network architecture. Results from the LSTM model are compared with an object‐based probabilistic approach to forecast convection using metrics such as a receiver operating characteristics (ROC) curve, the Brier score and reliability. Results indicate that the LSTM model performs similarly to the object‐based probabilistic benchmark when classifying moderate convection areas and shows improved skill when classifying areas of total convective. Finally, the LSTM model results are presented within an air traffic management context to showcase the potential use of machine learning models within an operational application. [ABSTRACT FROM AUTHOR]

Thunderstorms can be a large source of disruption for European air-traffic management causing a chaotic state of operation within the airspace system. In current practice, air-traffic managers are provided with imprecise forecasts which limit their ability to plan strategically. As a result, weather mitigation is performed using tactical measures with a time horizon of three hours. Increasing the lead time of thunderstorm predictions to the day before operations could help air-traffic managers plan around weather and improve the efficiency of air-traffic-management operations. Emerging techniques based on machine learning have provided promising results, partly attributed to reduced human bias and improved capacity in predicting thunderstorms purely from numerical weather prediction data. In this paper, we expand on our previous work on thunderstorm forecasting, by applying convolutional neural networks (CNNs) to exploit the spatial characteristics embedded in the weather data. The learning task of predicting convection is formulated as a binary-classification problem based on satellite data. The performance of multiple CNN-based architectures, including a fully-convolutional neural network (FCN), a CNN-based encoder–decoder, a U-Net, and a pyramid-scene parsing network (PSPNet) are compared against a multi-layer-perceptron (MLP) network. Our work indicates that CNN-based architectures improve the performance of point-prediction models, with a fully-convolutional neural-network architecture having the best performance. Results show that CNN-based architectures can be used to increase the prediction lead time of thunderstorms. Lastly, a case study illustrating the applications of convection-prediction models in an air-traffic-management setting is presented. • CNNs are applied to numerical weather prediction data to predict thunderstorms. • CNNs outperform MLP architectures in the task of predicting thunderstorms. • ML-based weather forecasts allow for better planning of air traffic operations. [ABSTRACT FROM AUTHOR]

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. • Machine learning algorithms can predict air traffic management behaviour. • Spatial–temporal integration of historical air traffic and weather data. • Weather data can predict traffic entry count and regulated entry count. [ABSTRACT FROM AUTHOR]

Convective weather is a major source of disruption to air traffic operations responsible for roughly one third of en-route delay in the network. Understanding how weather impact air traffic flows is an important first step in improving Air Traffic Flow Management operations during convective weather. In this research we exploit advances in machine learning to find the spatial correlations between convective weather and regulated air traffic flows. Our approach utilises historical satellite observations and traffic data from the summer of 2018 and 2019. Three deep learning model architectures are trained to detect traffic hotspots that are subject to regulation due to weather conditions. We also establish a baseline indicator to assess prediction quality. The weather and traffic data are transformed into images using a 2D grid covering a substantial portion of Europe, with a high-resolution spatial resolution. The temporal dimension is considered through hourly or 15-minute time windows, depending on the model architecture. By integrating this information, our models are able to capture relevant correlations effectively. Results indicate a clear correlation between weather and regulated traffic and reveal that our machine learning model is able to learn from the spatial correlations in the data and thus characterised the geographical areas where traffic volumes regulated due to convective weather are expected to appear during Air Traffic Flow Management practice. • Historical satellite observations and traffic data are used to train a CNN to detect ATFM weather hotspots. • Accounting for the spatial distribution of storms enhances the identification of ATFM hotspots during convective weather. • Data driven models can reduce the cognitive workload to solve demand-capacity imbalances. [ABSTRACT FROM AUTHOR]