用气象文本约束降水预测，提升极端天气预报精度
LangPrecip: Language-Aware Multimodal Precipitation Nowcasting
- 将气象描述文本作为运动约束，融合雷达数据生成降水轨迹
- 在80分钟预报中，重降雨CSI指标提升超60%和19%
- 适合需要高精度短时天气预报的气象与应急部门
短时降水预报是典型的不确定且约束不足的时空预测问题，尤其对快速演变的极端天气。现有生成方法主要依赖视觉条件，导致未来运动信息弱且模糊。本文提出语言感知的多模态预报框架LangPrecip，将气象文本视为降水演化的语义运动约束。在修正流范式下，将预报建模为语义约束的轨迹生成问题，实现了文本与雷达信息在隐空间中的高效、物理一致融合。我们进一步构建了包含16万对雷达序列与运动描述的大规模多模态数据集LangPrecip-160k。在瑞典和MRMS数据集上的实验表明，相比现有最优方法，80分钟预报时重降雨CSI分别提升超过60%和19%。
原文摘要 · Abstract (English)
Short-term precipitation nowcasting is an inherently uncertain and under-constrained spatiotemporal forecasting problem, especially for rapidly evolving and extreme weather events. Existing generative approaches rely primarily on visual conditioning, leaving future motion weakly constrained and ambiguous. We propose a language-aware multimodal nowcasting framework(LangPrecip) that treats meteorological text as a semantic motion constraint on precipitation evolution. By formulating nowcasting as a semantically constrained trajectory generation problem under the Rectified Flow paradigm, our method enables efficient and physically consistent integration of textual and radar information in latent space.We further introduce LangPrecip-160k, a large-scale multimodal dataset with 160k paired radar sequences and motion descriptions. Experiments on Swedish and MRMS datasets show consistent improvements over state-of-the-art methods, achieving over 60 \% and 19\% gains in heavy-rainfall CSI at an 80-minute lead time.
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