MeteorPred：面向极端天气事件预测的气象多模态大模型与数据集
MeteorPred: A Meteorological Multimodal Large Model and Dataset for Severe Weather Event Prediction
把 4D 气象场（时间×气压层×空间）当作视觉式高维输入，用三个即插即用的自适应融合模块分别沿时序、垂直层、空间维度做动态特征提取与整合，这套「多维张量输入 + 分维融合」的接口设计可直接迁移到视频、多帧深度、气象/遥感等时空视觉任务的多模态大模型改造中。
原文依据 incorporating three plug-and-play adaptive fusion modules that enable dynamic feature extraction and integration across temporal sequences, vertical pressure layers, and spatial dimensions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论对极端天气事件进行及时、准确的预报，对于早期预警以及约束下游分析与决策至关重要。由于极端天气事件预测仍依赖主观且耗时的人工判读，端到端的「AI 气象站」系统正在兴起，但面临三大挑战：（1）极端天气事件样本稀缺；（2）高维气象数据与文本预警之间存在不完美的对齐；（3）现有多模态语言模型无法有效处理高维气象输入，也难以捕捉其复杂的时空依赖关系。为应对这些挑战，我们提出 MP-Bench，这是首个面向极端天气事件预测的大规模多模态数据集，包含 421,363 对多年原始气象数据与对应文本描述，覆盖广泛的极端天气场景。在该数据集之上，我们开发了气象多模态大模型（MMLM），可直接接收 4D 气象输入。此外，该模型针对 4D 气象数据流的独特特性进行设计，引入三个即插即用的自适应融合模块，可在时序序列、垂直气压层和空间维度上实现动态的特征提取与整合。在 MP-Bench 上的大量实验表明，MMLM 在多项任务上均取得强劲性能，展现出对极端天气的有效理解，是迈向自动化、AI 驱动的极端天气事件预报系统的关键一步。
英文原文摘要 1584 字符
Timely and accurate forecasts of severe weather events are essential for early warning and for constraining downstream analysis and decision-making. Since severe weather events prediction still depends on subjective, time-consuming expert interpretation, end-to-end "AI weather station" systems are emerging but face three major challenges: (1) scarcity of severe weather event samples; (2) imperfect alignment between high-dimensional meteorological data and textual warnings; (3) current multimodal language models cannot effectively process high-dimensional meteorological inputs or capture their complex spatiotemporal dependencies. To address these challenges, we introduce MP-Bench, the first large-scale multimodal dataset for severe weather events prediction, comprising 421,363 pairs of raw multi-year meteorological data and corresponding text caption, covering a wide range of severe weather scenarios. On top of this dataset, we develop a Meteorology Multimodal Large Model (MMLM) that directly ingests 4D meteorological inputs. In addition, it is designed to accommodate the unique characteristics of 4D meteorological data flow, incorporating three plug-and-play adaptive fusion modules that enable dynamic feature extraction and integration across temporal sequences, vertical pressure layers, and spatial dimensions. Extensive experiments on MP-Bench show that MMLM achieves strong performance across multiple tasks, demonstrating effective severe weather understanding and representing a key step toward automated, AI-driven severe weather events forecasting systems.