This paper augments the Aardvark Weather deterministic end-to-end AI forecasting model with probabilistic capability by adding learned, input-dependent noise at the observation encoder and Monte Carlo dropout in the processor. The dual-component approach attributes forecast uncertainty to either observational limitations or model epistemic uncertainty via variance decomposition, producing improved mean forecasts and calibrated ensemble predictions while keeping the uncertainty sources transparent.
