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arXiv · 2605.05912

From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation

Rafael Pablos Sarabia·Joachim Nyborg·Morten Birk·Ira Assent
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Abstract

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. We present DropsToGrid, a Neural Process-based method that generates dense rainfall fields by fusing temporal sequences from noisy, irregularly distributed private weather stations with spatial context from radar. Leveraging multi-scale feature extraction, temporal attention, and multi-modal fusion, the model produces stochastic, continuous rainfall estimates and explicitly quantifies uncertainty. Evaluations on real-world datasets demonstrate that DropsToGrid outperforms both operational and deep learning baselines, generating accurate high-resolution rainfall maps with well-calibrated uncertainty, even when only few stations are available and in cross-regional scenarios.

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Rafael Pablos Sarabia, Joachim Nyborg, Morten Birk, Ira Assent. 2026-05-07. From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation. https://arxiv.org/abs/2605.05912

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