Swift: An Autoregressive Consistency Model for Efficient Weather
  Forecasting
Authors: name: Jason Stock, name: Troy Arcomano, name: Rao Kotamarthi

Diffusion models offer a physically grounded framework for probabilistic
weather forecasting, but their typical reliance on slow, iterative solvers
during inference makes them impractical for subseasonal-to-seasonal (S2S)
applications where long lead-times and domain-driven calibration are essential.
To address this, we introduce Swift, a single-step consistency model that, for
the first time, enables autoregressive finetuning of a probability flow model
with a continuous ranked probability score (CRPS) objective. This eliminates
the need for multi-model ensembling or parameter perturbations. Results show
that Swift produces skillful 6-hourly forecasts that remain stable for up to 75
days, running 39times faster than state-of-the-art diffusion baselines while
achieving forecast skill competitive with the numerical-based, operational IFS
ENS. This marks a step toward efficient and reliable ensemble forecasting from
medium-range to seasonal-scales.