LangPrecip: Language-Guided Precipitation Forecasting
Paper#Weather Forecasting, Multimodal Learning, Natural Language Processing🔬 Research|Analyzed: Jan 3, 2026 20:18•
Published: Dec 26, 2025 12:06
•1 min read
•ArXivAnalysis
This paper introduces LangPrecip, a novel approach to precipitation nowcasting that leverages textual descriptions of weather events to improve forecast accuracy. The use of language as a semantic constraint is a key innovation, addressing the limitations of existing visual-only methods. The paper's contribution lies in its multimodal framework, the introduction of a new dataset (LangPrecip-160k), and the demonstrated performance improvements over existing state-of-the-art methods, particularly in predicting heavy rainfall.
Key Takeaways
- •Proposes LangPrecip, a language-aware multimodal framework for precipitation nowcasting.
- •Utilizes textual descriptions as semantic constraints to improve forecast accuracy.
- •Introduces a new large-scale multimodal dataset, LangPrecip-160k.
- •Demonstrates significant performance improvements over existing methods, especially for heavy rainfall.
Reference / Citation
View Original"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."