Prithvi-Precip: Integrating Satellite Observations into an Atmospheric AI Foundation Model for Precipitation Forecasting

Accurate precipitation forecasting remains one of the most challenging problems in weather prediction. While recent AI weather prediction (AIWP) systems have achieved substantial improvements in medium-range forecasting skill, precipitation often remains a secondary target and is commonly learned from reanalysis datasets that contain considerable uncertainty. In this work, we investigate two complementary strategies for improving AI-based precipitation forecasts. Building on the Prithvi-WxC foundation model, we develop Prithvi-Precip, a global precipitation forecasting system, and examine (1) the impact of training targets derived from satellite-based precipitation estimates rather than reanalysis fields and (2) the direct assimilation of satellite observations into the forecasting model. We systematically evaluate key design choices for finetuning the Prithvi-WxC AI foundation model for precipitation forecasting. We find that autoregressive rollout training produces substantially more accurate forecasts than direct conditioning on forecast lead time. Using independent radar-based precipitation estimates for evaluation, we show that training on satellite-derived precipitation targets yields improved forecast accuracy relative to training on MERRA-2 precipitation fields. Furthermore, direct ingestion of satellite observations provides additional improvements at short lead times, with the largest gains occurring in tropical and subtropical regions. Together, these advances enable Prithvi-Precip to substantially improve upon directly comparable precipitation forecasts from the Goddard Earth Observing System. Our results highlight the potential of improved precipitation targets and the direct integration of satellite observations as promising pathways for advancing medium-range AI precipitation forecasting.