most citedWaveC2R: Wavelet-Driven Coarse-to-Refined Hierarchical Learning for Radar Retrieval

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Learning Continuous Regional Temperature Fields with Lead-Time and Resolution Queries

Chunlei Shi, Jiong Wang, Yi-Lin Wei +4

Accurate regional near-surface temperature forecasting is fundamental to short-range weather services and downstream risk assessment. Existing deep learning-based regional forecast…

cs.MM2026

WaveOp-LiteFM: Lightweight Neural-Operator Flow Matching for Satellite-to-Radar Precipitation Retrieval

Chunlei Shi, Yecheng Zhang, Yufeng Zhu +4

Satellite-to-radar (S2R) retrieval refers to estimating ground-based radar precipitation from geostationary satellite observations, enabling precipitation monitoring in regions wit…

cs.MM2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…