MambaRain: Multi-Scale Mamba-Attention Framework for 0-3 Hour Precipitation Nowcasting
Chunlei Shi, Cui Wu, Xiang Xu, Hao Li, Ni Fan, Xue Han, Yongchao Feng, Yufeng Zhu, Boyu Liu, Zengliang Zang, Hongbin Wang, Yanlan Yang, Dan Niu

TL;DR
MambaRain is a multi-scale framework combining long-range temporal modeling and spatial attention to improve 0-3 hour precipitation nowcasting accuracy.
Contribution
It introduces a hybrid architecture integrating Mamba's efficient temporal modeling with self-attention for spatial correlation, extending forecasting horizons.
Findings
Outperforms existing methods in 0-3 hour nowcasting.
Achieves significant accuracy gains in 2-3 hour predictions.
Uses spectral loss to reduce blurring artifacts.
Abstract
Accurate precipitation nowcasting over extended horizons (0-3 hours) is essential for disaster mitigation and operational decision-making, yet remains a critical challenge in the field. Existing deterministic approaches are predominantly constrained to shorter prediction windows (0-2 hours), exhibiting severe performance degradation beyond 90 minutes owing to their inherent difficulty in capturing long-range spatiotemporal dependencies from radar-derived observations. To address these fundamental limitations, we propose MambaRain, a novel multi-scale encoder-decoder architecture that synergistically integrates Mamba's linear-complexity long-range temporal modeling with self-attention mechanisms for explicit spatial correlation capture. The core innovation lies in a hybrid design paradigm wherein Mamba blocks leverage selective state space mechanisms to model global temporal dynamics…
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