Probabilistic pilot-behavior models for clear-air turbulence avoidance maneuvers

A probabilistic analysis of how pilots tactically maneuver when encountering severe Clear-Air Turbulence (CAT) was performed to build a set of pilot-behavior models. Given probabilistic estimates of severe CAT from the Numerical Weather Prediction (NWP) Rapid Update Cycle (RUC)-based Graphical Turbulence Guidance (GTG) turbulence forecast model and aircraft trajectory data describing potential turbulence encounters in the National Airspace System (NAS), models are developed to estimate relationships between the type and magnitude of the maneuver versus the probability of the existence of severe CAT in the upcoming sector of airspace or along the upcoming trajectory. The results indicate that pilot responses to CAT depend on several factors including user class, weight class, physical class, aircraft type, as well as airline policies, but all results must necessarily be interpreted as probabilistic in nature. This information is a necessary starting point for increasing the capacity of the NAS while maintaining aviation safety within the Next Generation Air Transportation System (NextGen).