Enabling Physics-Based Deep-Learning Thunderstorm Nowcasting for Critical Decision Making in Innovative Air Mobility

Kurzfassung

Adverse weather events constitute a major contributor to delays of aircraft. Thunderstorms, and accompanying effects, are frequently considered most hazardous to aircraft. Spatiotemporally highly resolved short-term forecasting of thunderstorms is possible from remote sensing data and has previously proven beneficial for commercial aircraft operations. This work demonstrates the potential of thunderstorm nowcasting in the innovative air mobility (IAM), by incorporating a physics-based deep-learning approach in an operational IAM flight planning platform. Operators benefit from more accurate and higher-resolved thunderstorm information, translating into smarter, safer, and more efficient operations and flight planning. Ultimately, a higher number of safe IAM operations can be realized.