AI RESEARCH
From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation
arXiv CS.LG
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ArXi:2605.05912v1 Announce Type: new High-resolution rainfall observations are crucial for weather forecasting, water management, and hazard mitigation. Traditional operational measurements are often biased and low-resolution, limiting their ability to capture local rainfall. Accurate high-resolution rainfall maps require integrating sparse surface observations, yet existing deep learning densification methods are hindered by rainfall's skewed, localized nature, noise, and limited spatio-temporal fusion.