Swift: An Autoregressive Consistency Model for Efficient Weather
  Forecasting
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.
68dd57baae4a047656f86bb7
Jason Stock
claimed_verified
2025-10-02T03:08:13.420Z
6309242c1a8d77fa3190be5e
https://cdn-avatars.huggingface.co/v1/production/uploads/6309242c1a8d77fa3190be5e/8lIcbT6UACrP0_WpCuV5p.jpeg
Jason Stock
user
stockeh
68dd57baae4a047656f86bb8
Troy Arcomano
68dd57baae4a047656f86bb9
Rao Kotamarthi
Swift: An Autoregressive Consistency Model for Efficient Weather
  Forecasting
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.
Swift, a single-step consistency model, enables efficient and skillful probabilistic weather forecasting by autoregressive finetuning of a probability flow model with CRPS, outperforming diffusion models and competitive with IFS ENS.
diffusion models consistency model autoregressive finetuning probability flow model continuous ranked probability score CRPS ensemble forecasting medium-range seasonal-scales