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.
Recent improvements to the quality control of radar data for the opera data centre
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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.