Neural Stochastic Processes for Satellite Precipitation Refinement

Accurate precipitation estimation is critical for flood forecasting, water resource management, and disaster preparedness. Satellite products provide global hourly coverage but contain systematic biases; ground-based gauges are accurate at point locations but too sparse for direct gridded correction. We propose Neural Stochastic Process (NSP), pairing a set-conditioned spatial latent encoder with a Neural SDE transition regularizer to improve spatial refinement with single-hour inference. An Euler-discretized variational model yields an analytic conditional transition KL, used in a regularized training objective without simulating SDE trajectories. We also introduce QPEBench, a benchmark of 43,756 hourly samples over the Contiguous United States (2021–2025), with four aligned data sources and six deterministic metrics. Compared with 17 baselines, NSP achieves the best performance across all six deterministic metrics and surpasses JAXA's operational gauge-calibrated product. NSP also transfers to two regions excluded from training without parameter updates, using sparse local gauges. Controlled experiments show benefits from temporal coupling during training and learned latent transitions during temporary gauge outages. Our project page is available at https://nsp-rlc9n.kinsta.page/.