Thunderstorm prediction using convolutional neural networks to support air traffic management in Switzerland

Adverse weather conditions substantially affect aviation operations. In 2023, weather accounted for the largest fraction of en-route air traffic delays in the European network [1]. Alongside low visibility and strong winds, thunderstorms are one of the main causes for these delays. This is exacerbated during the summer months when both convective activity and air traffic demands are high. To anticipate the adverse effect of thunderstorms on air traffic, accurate information on the location and timing of convective initiation as well as on the duration of convective activity are required. However, convective developments at time-scales greater than the nowcasting range still pose a great challenge even for convection-resolving numerical weather prediction (NWP) models.

To support air traffic management, MeteoSwiss is developing a range of novel products in close collaboration with the Swiss air navigation service provider and airports. Specifically, machine learning (ML) based approaches to forecast wind, visibility [2], and thunderstorms are developed that exploit the full potential of NWP data and observations. Moreover, solutions to improve the interpretability and usefulness of these probabilistic forecasts are implemented [3].

The presented work provides probabilistic thunderstorm predictions together with an estimation of cloud top height up to 33 hours in advance. The icosahedral non-hydrostatic (ICON) model, which is the newly operational convection-resolving ensemble prediction system at MeteoSwiss, forms the data basis of the approach. Thunderstorm probabilities are derived from relevant NWP parameters using convolutional neural networks (CNNs) as they are highly efficient in identifying spatial relationships in data. We train both a U-Net and fully connected ResNet50 model on ICON re-forecast data from the convective seasons of the previous three years. The objective is defined as a binary classification problem using ground-based lightning observations as the target variable. Predictions are provided in the greater Alpine region and are updated in accordance with the operational NWP system every 3 hours.

We discuss the feature selection procedure for which we use a tree-based ML model to identify the most meaningful model ensemble statistics. We compare the performance in terms of skill and reliability of the CNN approaches with the direct model output of ICON for different lead times. Finally, insights and challenges regarding the use of the new product in operational air traffic flow and capacity management are presented.

[1] https://www.eurocontrol.int/publication/performance-review-report-prr-2023-consultation
[2] abstract Wehrli et al., 2024, submitted to EMS 2024.
[3] abstract Landmann et al., 2024, submitted to EMS 2024.