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

Abstract

Ensemble weather predictions can be critical towards making informed decisions for operations that require risk assessment. The traditional approach towards creating an ensemble often involves making slight perturbations to initial conditions and model numerical solvers and physics to account for forecast uncertainty. 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 West-WRF forecasts over the North Pacific. After it has been trained, the diffusion ensemble can make predictions at high speeds and at a low computational expense while improving the skill of medium-range integrated water vapor transport (IVT). It has high performance when evaluated by binned ranked histograms and outperforms operational ensemble forecasts (GEFS and ECMWF) in Continuous Ranked Probability Score, binned spread to Root Mean Square Error ratio, Reliability diagrams, and Brier Skill Scores. The diffusion ensemble further demonstrated utility in representing real outcomes of extreme destructive events with realistic high-quality images.