DropsToGrid is a spatio-temporal neural process that integrates temporal sequences from noisy irregular stations with spatial radar context to produce dense stochastic rainfall fields with calibrated uncertainty, outperforming baselines even with few stations or across regions.
SEVIR : A Storm Event Imagery Dataset for Deep Learning Ap- plications in Radar and Satellite Meteorology
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From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation
DropsToGrid is a spatio-temporal neural process that integrates temporal sequences from noisy irregular stations with spatial radar context to produce dense stochastic rainfall fields with calibrated uncertainty, outperforming baselines even with few stations or across regions.