  Dealing with meteorological uncertainty poses a major challenge in air
traffic management (ATM). Convective weather (commonly referred to as storms or
thunderstorms) in particular represents a significant safety hazard that is
responsible for one quarter of weather-related ATM delays in the US. With
commercial air traffic on the rise and the risk of potentially critical
capacity bottlenecks looming, it is vital that future trajectory planning tools
are able to account for meteorological uncertainty. We propose an approach to
model the uncertainty inherent to forecasts of convective weather regions using
statistical analysis of state-of-the-art forecast data. The developed
stochastic storm model is tailored for use in an optimal control algorithm that
maximizes the probability of reaching a waypoint while avoiding hazardous storm
regions. Both the aircraft and the thunderstorms are modeled stochastically.
The performance of the approach is illustrated and validated through simulated
case studies based on recent nowcast data and storm observations.
