5 papers
LangRetrieval: Language-Guided Self-Evolving Satellite-to-Radar Retrieval via CSI-Driven Reward
Chunlei Shi, Junming Hou, Yi-Lin Wei +5
Satellite-to-radar (S2R) retrieval estimates ground radar precipitation from geostationary satellite observations, providing a critical solution for precipitation monitoring in rad…
MambaRain: Multi-Scale Mamba-Attention Framework for 0-3 Hour Precipitation Nowcasting
Chunlei Shi, Cui Wu, Xiang Xu +10
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 f…
VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting
Chunlei Shi, Hao Li, Yufeng Zhu +6
Precipitation nowcasting is a vital spatio-temporal prediction task for meteorological applications but faces challenges due to the chaotic property of precipitation systems. Exist…
PixelFlowCast: Latent-Free Precipitation Nowcasting via Pixel Mean Flows
Yufeng Zhu, Chunlei Shi, Yongchao Feng +1
Precipitation nowcasting aims to forecast short-term radar echo sequences for extreme weather warning, where both prediction fidelity and inference efficiency are critical for real…
WaveC2R: Wavelet-Driven Coarse-to-Refined Hierarchical Learning for Radar Retrieval
Chunlei Shi, Han Xu, Yinghao Li +4
Satellite-based radar retrieval methods are widely employed to fill coverage gaps in ground-based radar systems, especially in remote areas affected by terrain blockage and limited…