A Hybrid Machine Learning and Fuzzy Logic Approach to Cit Diagnostic Development

Convectively-induced turbulence (CIT) is one of several threats that requires aircraft to avoid thunderstorms in order to mitigate the risk of passenger injury or aircraft damage. Current Federal Aviation Administration (FAA) thunderstorm avoidance guidelines proscribe flight within 20 nautical miles of a thunderstorm, above thunderstorm tops or beneath anvils. In practice, interpretation of these guidelines is subjective and limited by available weather information, and the guidelines may make large regions of airspace unavailable to aircraft on days of widespread convection. An automated turbulence product that makes use of radar, lightning, satellite, numerical weather model and convective nowcast data to objectively diagnose the likelihood of turbulence in the near-storm environment could provide valuable strategic and tactical decision support to pilots, dispatchers and air traffic controllers.