Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting
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 synoptic skeleton, then iteratively refines high-frequency textures under physical constraints, eliminating both blurring and hallucinations. It f...
| Authors | Yunlong Zhou, Chen Zhao, Danyang Peng et al. |
|---|---|
| Primary category | eess.IV |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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