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English(EN) GraphCast-Mamba: Learning Non-Markovian Corrections for Weather Forecasting

GraphCast-Mamba 通过 Mamba 修正分支增强天气预报 · 跟踪 2 个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 GraphCast-Mamba，这是一种新颖的天气预报方法，通过整合一个可训练的 Mamba 修正分支来增强现有模型。该新分支增强了一个预训练的 GraphCast 主干，使其能够从标准短输入窗口之外的时间上下文中学习。通过保留时间记忆状态，GraphCast-Mamba 显著提高了预报准确性，在十天预报的各种气象变量上，损失减少了 14% 以上，均方根误差降低了高达 14.60%。 AI

影响 通过整合时间记忆来提高天气预报的准确性，有可能带来更可靠的远程预测。

排序理由 该集群描述了一篇详细介绍天气预报新模型架构的新研究论文。

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GraphCast-Mamba 通过 Mamba 修正分支增强天气预报 · 跟踪 2 个来源

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该集群描述了一篇详细介绍天气预报新模型架构的新研究论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ilya Vilkoviskiy, Liang-Hong Mo, Suozhi Huang, Dmitry A. Abanin ·

    GraphCast-Mamba：学习非马尔可夫修正以进行天气预报

    arXiv:2610.28512v1 Announce Type: new Abstract: Unresolved atmospheric processes can introduce history dependence beyond the short input windows used by many weather forecasting models. We introduce GraphCast--Mamba, which augments a frozen, pretrained $1^\circ$ GraphCast backbon…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    GraphCast-Mamba：学习非马尔可夫修正以进行天气预报

    Unresolved atmospheric processes can introduce history dependence beyond the short input windows used by many weather forecasting models. We introduce GraphCast--Mamba, which augments a frozen, pretrained $1^\circ$ GraphCast backbone with a trainable Mamba correction branch combi…

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