Generative AI to Quantify Uncertainty in Weather Forecasting
Google Research introduces SEEDS (Scalable Ensemble Envelope Diffusion Sampler), a generative AI model that efficiently produces weather forecast ensembles using denoising diffusion probabilistic models. Traditional physics-based ensemble forecasting requires hours on supercomputers and typically yields only 10-50 members due to cost. SEEDS conditions on as few as one or two operational forecasts and generates 256 ensemble members in 3 minutes on TPUv3-32, achieving comparable or better skill scores (rank histogram, RMSE, CRPS) while accurately representing tail probabilities for extreme events. For the 2022 European heatwave, SEEDS' 16,384-member ensemble captured observed conditions that the 31-member operational ensemble missed entirely. This hybrid approach can free computational resources to improve physical model resolution or forecast frequency.