A Monte Carlo methodology is proposed for simulating air traffic blockage patterns under the impact of convective weather. The simulation utilizes probabilistic convective weather forecasts such as those produced by the 1-6 hour National Convective Weather Forecast. A matrix of random numbers is fed to the simulation process to obtain an instantiation of traffic blockage maps. Gaussian smoothing with varying Full Width at Half Maximum across the grid is employed to model the varying spatial correlation between cells. Special Cellular Automata techniques are employed to model the evolvement, i.e. the trend, growth, and dissipation of convection, between consecutive time intervals. Model parameters are obtained from analyzing historical convective weather data. A software tool is also developed to implement the simulation methodology. The simulation methodology thus provides a means to improve the utilization of short term probabilistic convective weather forecast products, and to improve air traffic efficiency in the large.
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