Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall
arXiv:2606.09959v1 Announce Type: new Abstract: Precipitation nowcasting is increasingly being approached with deep learning models that learn directly from recent radar observations. Although such models can efficiently capture short-term precipitation motion, they often lack broader contextual information about the meteorological conditions under which rainfall develops. This paper investigates whether lightweight temporal context can improve radar-based nowcasting, particularly for high-intensity rainfall. We propose the Time-Aware Small-Attention U-Net (TA-SmaAt-UNet), which extends the co
Deep learning models are increasingly applied to meteorological problems, with ongoing research focusing on improving accuracy and contextual understanding for critical weather prediction.
Improved precipitation nowcasting, particularly for high-intensity rainfall, has significant implications for disaster preparedness, infrastructure management, and resource allocation.
The integration of lightweight temporal context into deep learning models can lead to more accurate and timely warnings for severe weather events.
- · Meteorological agencies
- · Insurance companies
- · Urban planners
- · Agricultural sector
- · Regions unprepared for intense rainfall
- · Traditional nowcasting models
More precise short-term weather forecasts for precipitation, especially for extreme events, become available.
Better preparation and mitigation strategies for floods and intense rainfall events can be implemented, reducing economic damage and loss of life.
The application of AI in environmental monitoring and disaster response could accelerate, leading to broader intelligence-driven climate adaptation strategies.
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Read at arXiv cs.LG