You can now configure forecast timesteps after training instead of before, letting one model handle both detailed short-range and stable long-range weather predictions without retraining.
GEM-3 is a weather forecasting model that solves a key problem: existing models must choose between short timesteps (detailed but error-prone) or long timesteps (stable but missing short-term details). This model lets you pick the timestep at inference time with a single trained model, and training on mixed timesteps makes forecasts more stable across longer periods.