Using deep neural networks for thunderstorm risk prediction.

Airliners, struck by lightnings on average once a year, sometimes sustain structural or electrical damage. Even if these incidents generally do not compromise safety onboard due to existing certifications, they lead to costly downtimes and mandatory maintenance operations for the aviation industry. Anticipating the presence of thunderstorm risk areas could help minimize these impacts. Nowadays, predict the exact location of electrical activity in the atmosphere is a complex task because lightning is a non-linear phenomenon which is related to chaotic stormy environments. Numerous variables influence the initiation of electrical discharges, making their modeling using physical equation very challenging. This motivates the use of neural networks to establish relationships between various atmospheric parameters and electrical activity. In the context of aviation safety, this study focuses on the development of a very short term (less than one hour and every five minutes) thunderstorm risk forecasting method above oceans. The proposed methodology is based on computer vision techniques such as neural networks to generate lightning occurrence’s probability maps in the following hour. An encoder-decoder network named ED-DRAP (Che, H et al. 2022) is employed and adapted to the data. In addition to integrating convolutional operations, it also uses spatial and temporal attention mechanisms to process spatio-temporal sequences. Input data come from NOAA’s geostationary GOES-R satellite, including brightness temperature measured by the Advanced Baseline Imager sensor and past electrical activity detected by the Geostationary Lightning Mapper sensor. Outputs from the Numerical Weather Prediction model, Global Forecasting System, are also employed to complement the information provided by satellite imagery. Finally, the model’s outputs are calibrated to produce lightning risk probability maps which are representative of the physical reality, enabling better risk interpretation.