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arXiv · 2512.22317

LangPrecip: Language-Aware Multimodal Precipitation Nowcasting

Xudong Ling·Chaorong Li·Tianxi Huang·Qian Dong·Guiduo Duan
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Abstract

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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Xudong Ling, Chaorong Li, Tianxi Huang, Qian Dong, Guiduo Duan. 2026-05-14. LangPrecip: Language-Aware Multimodal Precipitation Nowcasting. https://arxiv.org/abs/2512.22317

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