English(EN) DIAL: Dynamic and Informed Adjustment of the Native Stochastic Law for Reliable Weather Ensembles

新的DIAL方法通过调整模型随机性来提高天气预报的可靠性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了一种名为动态和知情调整原生随机定律（DIAL）的新颖方法，以提高天气集合预报的可靠性。DIAL通过在预报过程中动态调整冻结天气模型的随机性来工作，而不是重新训练模型或依赖后处理。这种方法学习根据过去的预报性能和对准确性及可靠性的潜在未来影响来预测和修改模型的原生随机性。在FourCastNet 3（FCN3）模型上的评估表明，DIAL显著提高了尺度解析的可靠性，与未修改的FCN3相比，多尺度连续排名概率得分（CRPS）提高了8.27%。 AI

影响这种方法可以提高人工智能驱动的天气预测系统的准确性和可靠性。

排序理由该集群包含一篇详细介绍改进天气预报模型新方法的论文。[lever_c_demoted from research: ic=1 ai=0.7]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Yucheng Wang, Haonan Guo, Tianhao Zhao, Di Wang, Yu Wu, Zengmao Wang, Bo Du · 2026-10-16 04:00

DIAL：动态和有信息量的调整原生随机定律以获得可靠的天气集合

arXiv:2610.21639v1 Announce Type: new Abstract: Ensemble weather forecasts can look reliable in global averages while hiding too little spread in some cases and too much in others. These errors vary across variables, spatial scales, lead times, and weather states. Adapting random…

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DIAL：动态和有信息量的调整原生随机定律以获得可靠的天气集合

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