LangPrecip: Language-Aware Multimodal Precipitation Nowcasting
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916012355485696 |
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| author | Ling, Xudong Li, Chaorong Huang, Tianxi Dong, Qian Duan, Guiduo |
| author_facet | Ling, Xudong Li, Chaorong Huang, Tianxi Dong, Qian Duan, Guiduo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22317 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LangPrecip: Language-Aware Multimodal Precipitation Nowcasting Ling, Xudong Li, Chaorong Huang, Tianxi Dong, Qian Duan, Guiduo Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | LangPrecip: Language-Aware Multimodal Precipitation Nowcasting |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.22317 |