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English(EN) TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling

新型轴向状态空间模型提升全球天气预报能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的轴向状态空间模型（TSSM），旨在改进全球站点天气预报。该模型结合了历史天气数据，能够比现有方法更有效地捕捉长期模式和极端事件。TSSM在WEATHER-5K数据集上展现了最先进的性能，在准确性和极端事件指标方面取得了显著的提升，并且即使在数据大量丢失的情况下也能保持高性能。 AI

影响 该模型有望带来更准确、更可靠的天气预报，尤其是在极端事件和长期预报方面。

排序理由 该集群包含一篇详细介绍天气预报新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新型轴向状态空间模型提升全球天气预报能力

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该集群包含一篇详细介绍天气预报新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Songru Yang, Zili Liu, Tao Han, Ben Fei, Fenghua Ling, Lei Bai, Chang Liu, Xiangyang Ji, Zhenwei Shi, Zhengxia Zou ·

    TSSM：用于全球站点天气预报的轴向状态空间模型，具有时变历史建模功能

    arXiv:2607.13101v1 Announce Type: cross Abstract: Global Station Weather Forecasting (GSWF) is pivotal for localized and extreme weather prediction over key regions. Despite efforts to exploit look-back windows, existing methods show limited accuracy gains and struggle with extre…

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