SEEDS
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Google's Scalable Ensemble Envelope Diffusion Sampler (SEEDS) uses diffusion models to generate very large, physically plausible weather-forecast ensembles conditioned on only one or two operational forecasts.
Diffusion-based ensemble: SEEDS learns from historical reanalysis and forecast data, so each generated sample is a coherent "alternative atmosphere" consistent with numerical-weather-prediction physics.
Few inputs, many outputs: A small number of operational NWP forecasts is enough to seed the sampler, which then produces hundreds of ensemble members at a fraction of the compute cost of running NWP that many times.
Uncertainty quantification: The resulting ensembles better capture the tails of the forecast distribution, which is exactly where traditional small ensembles under-sample extreme events.
Operational implications: The approach can complement existing NWP systems by cheaply densifying their ensembles, improving probabilistic forecasts for tropical cyclones, heatwaves, and other high-impact weather.
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