Neural Stochastic Processes for Satellite Precipitation Refinement

ArXi:2604.10414v1 Announce Type: cross 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. Existing methods fuse these sources by interpolating gauge observations onto the satellite grid, but treat each time step independently and. therefore. discard temporal structure in precipitation fields.