DIAL: Dynamic and Informed Adjustment of the Native Stochastic Law for Reliable Weather Ensembles
DIAL controls a frozen weather model's inherent randomness during forecasting by learning from past forecasts to adjust noise dynamically. Instead of retraining or post-processing, it predicts where spread needs more or less based on forecast evolution. This method improves multiscale skill and reliability compared to existing methods like learned postprocessing.