Ilya Vilkoviskiy, Liang-Hong Mo, Suozhi Huang, Dmitry A. Abanin · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28512 · open-access PDF
未解决的大气过程可以引入历史依赖超出许多天气预报模型使用的短输入窗口。我们引入了GraphCast--Mamba,它增加了冻结的,预训练的$1\circ$GraphCast脊椎,具有可训练的Mamba纠正分支,结合了图形消息传输和Mamba层。该分支在连续六小时的预报步骤中保留了时间内记忆状态,学习纠正由时间背景告知,超出GraphCast的双框输入。
Machine translation (Meta M2M100); the English original follows.
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 combining graph message passing and Mamba layers. The branch retains a temporal memory state across successive six-hour forecast steps, learning corrections informed by temporal context beyond GraphCast's two-frame input. Training is confined to the correction branch, using ERA5 data from 2015--2021. After 2,500 optimizer u
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