LangPrecip: Language-Aware AI for Precipitation Nowcasting
A new framework called LangPrecip has been created by researchers to enhance short-term precipitation forecasting through a language-aware multimodal nowcasting approach. This innovative method treats nowcasting as a trajectory generation challenge constrained by semantics, utilizing the Rectified Flow paradigm to effectively merge radar and textual data in latent space. The team also unveiled a comprehensive dataset, LangPrecip-160k, which includes 160,000 radar sequence and motion description pairs. Testing on Swedish and MRMS datasets revealed significant advancements compared to existing leading techniques. This research tackles the challenges posed by the uncertainty and under-constrained aspects of spatiotemporal forecasting, particularly for swiftly changing and extreme weather conditions.
Key facts
- LangPrecip is a language-aware multimodal nowcasting framework.
- It treats meteorological text as a semantic motion constraint on precipitation evolution.
- The method uses the Rectified Flow paradigm for trajectory generation.
- LangPrecip-160k dataset includes 160k paired radar sequences and motion descriptions.
- Experiments were conducted on Swedish and MRMS datasets.
- The framework shows consistent improvements over state-of-the-art methods.
- It addresses uncertainty in short-term precipitation nowcasting.
- The work focuses on rapidly evolving and extreme weather events.
Entities
Institutions
- arXiv
Locations
- Sweden
- United States