A stochastic model for fog forecasting

Despite significant advancements in parameterizations of boundary layer processes, forecasting, and nowcasting low-level clouds using numerical models remain challenging. The purpose of this study is to test a prototype of a high-resolution stochastic-deterministic model designed to simulate the life cycle of fog cover based on the Ising model from statistical mechanics. The case of advection fog around St. John's Airport in Newfoundland (Canada) has been considered. The model demonstrates promising capabilities in forecasting mean fog cover and replicating the horizontal structure observed in satellite imagery, including bands, rolls, and closed or open cells. We evaluate the model's predictive skill by analyzing its effectiveness in reproducing the evolution of fog cover across three representative cases. A contingency table and associated performance metrics are used to assess its accuracy.