Timestep-Conditioned Transformers for Global Weather Forecasting

TL;DR - GEM-3 is a ~134M-parameter probabilistic global weather model whose autoregressive timestep can be chosen at inference time from a single set of weights, removing the usual fixed-timestep trade-off between sub-daily detail and error accumulation. It matters because one model can serve both short-range and extended-range forecasting without retraining specialists.

• Fixed timesteps force a trade-off: short steps (1–6h) resolve diurnal dynamics but accumulate more error over a horizon, while 24h steps reduce accumulation but lose sub-daily usability.
• Explicit multi-timestep inference lets users configure the step at run time; mixed-timestep training also consistently improved rollout stability versus timestep-specialist models.
• Architecture is a lightweight neighborhood-attention transformer on an equirectangular grid, extending the earlier GEM-2 with further architectural changes.
• Claimed outcome is near-SOTA medium-range probabilistic skill plus stable extended-range rollouts, efficient training/inference, and decision-relevant diagnostics; no specific metrics are given in the provided abstract.