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arXiv Computer Vision By Yucheng Wang, Haonan Guo, Tianhao Zhao, Di Wang, Yu Wu, Zengmao Wang, Bo Du

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

Read the original on arXiv Computer Vision →

The paper introduces DIAL, a method that dynamically adjusts the native stochasticity of a frozen weather forecasting model to improve ensemble reliability across variables, scales, and lead times. DIAL learns from past forecasts to predict necessary spread adjustments and applies small noise updates during each forecast step, avoiding retraining or full backpropagation. Experiments on FourCastNet 3 show that DIAL outperforms post‑processing and reward‑based baselines, achieving an 8.27% improvement in multiscale CRPS over the unmodified model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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