Uncertainty-Aware End-to-End AI Weather Forecasting: Disentangling Observation and Model Contributions
End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numerical weather prediction pipeline, including data assimilation, at a fraction of its cost. These systems are deterministic and issue no uncertainty. Here we render the Aardvark Weather model probabilistic by attaching one stochastic mechanism to each component: learned, input-dependent noise at the observation encoder, capturing aleatoric uncertainty inherited from the observing system, and Monte Carlo dropout in the processor, capturing epistemic uncertainty in the learned dynamics. The resulting nested ensemble attributes forecast spread to...
| Authors | Rodrigo Almeida, Noelia Otero, Jost Arndt et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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