Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting
arXiv:2606.02661v1 Announce Type: cross Abstract: Accurate precipitation nowcasting is vital for disaster mitigation, but deep learning methods face a key trade-off: regression models produce over-smoothed, spectrally decaying predictions that blur convective details and violate turbulence power laws; diffusion models generate realistic yet unanchored hallucinations lacking physical grounding. We propose Spectral-Decoupled Iterative Refinement (SDIR), a deterministic framework that reformulates nowcasting as progressive frequency-decoupled refinement. SDIR first extracts a stable low-frequency
The paper addresses a known trade-off in deep learning for nowcasting, proposing a novel framework to improve accuracy as AI models become more sophisticated and critical for real-time predictions.
Improved precipitation nowcasting directly impacts disaster mitigation, logistics, agriculture, and general public safety, providing more reliable short-term weather predictions through advanced AI.
The proposed SDIR framework promises to overcome current limitations of deep learning models in weather prediction, offering more precise and physically grounded forecasts of convective details.
- · Weather forecasting agencies
- · Insurance companies
- · Agriculture sector
- · Logistics and transportation
- · Traditional statistical weather models
- · Regions unprepared for rapid weather changes
More accurate and timely warnings for extreme weather events will become possible.
This could lead to optimized resource allocation for disaster response and urban planning.
Enhanced trust in AI-driven weather predictions may accelerate adoption of autonomous systems reliant on precise environmental data.
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Read at arXiv cs.LG