Generating a 1000-member Ensemble of Horizontal Vapor Transport Forecasts with Diffusion

Stochastic weather predictions can be immensely beneficial towards making informed decisions for operations that require risk assessment and optimization. The traditional approach towards creating an ensemble often involves making slight perturbations to initial conditions. This study instead uses diffusion, a form of generative artificial intelligence (AI), to make ensemble forecasts consisting of 1000 members from single-member deterministic dynamical forecasts. Simulating a large sample size of possibilities can be particularly useful for evaluating the tail ends of distributions. The diffusion ensemble can make predictions at high speeds and at a low computational expense while improving the skill of medium-range integrated vapor transport (IVT) forecasts. It is also well-calibrated and demonstrated utility in representing real outcomes of extreme destructive events with realistic high-quality images.