DIAL: Dynamic and Informed Adjustment of the Native Stochastic Law for Reliable Weather Ensembles

Key points

• Ensemble weather forecasts can look reliable in global averages while hiding too little spread in some cases and too much in others.1
• Adapting randomness during forecasting can correct these errors, but training fixes stochastic behavior beforehand and output calibration acts afterward.1
• DIAL learns from past forecasts to predict whether each variable and scale needs more or less spread.1
• DIAL provides better scale-resolved reliability than learned postprocessing and reward-based control baselines.1

Sources (1)

• [1]DIAL: Dynamic and Informed Adjustment of the Native Stochastic Law for Reliable Weather Ensembles
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 10:21 AM

  We therefore introduce Dynamic and Informed Adjustment of the Native Stochastic Law (DIAL), which controls a frozen model's native stochasticity.

  Ensemble weather forecasts can look reliable in global averages while hiding too little spread in some cases and too much in others.

Extractive summary: sentences quoted from the sources.