English(EN) WxFM-XL: Adapting Univariate Foundation Models to Multi-Station Weather Forecasting

新型WxFM-XL模型提升多站点天气预报能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了WxFM-XL，这是一种新颖的模型，旨在将单变量时间序列基础模型适配到多站点天气预报。该方法通过整合气象站点之间的空间信息并考虑每个站点特有的不同误差先验，解决了现有模型的局限性。WxFM-XL利用了跨站点误差相关先验图和动态融合机制，该机制整合了空间相关性，在实验中表现优于当前基线方法。 AI

影响该模型可能通过利用先进的时间序列基础模型来提高天气预报的准确性和空间理解能力。

排序理由该条目是一篇研究论文，详细介绍了一种用于特定科学应用的新模型。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xiao Wang, Changjian Chen, Zhuo Tang, Rongwen Li, Hongwu Liu, Kenli Li · 2026-10-08 04:00

WxFM-XL：将单变量基础模型适配到多站点天气预报

arXiv:2610.10057v1 Announce Type: new Abstract: With the rise of univariate time series foundation models (e.g., Sundial, Timer), initial efforts have been made to extend them to multivariate settings. However, these models mainly focus on modeling correlations among variables. W…

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WxFM-XL：将单变量基础模型适配到多站点天气预报

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