A plug-and-play generative framework for multi-satellite precipitation estimation

2026-05-14

AI Summary

This study addresses the limitations of traditional multi-source satellite precipitation estimation methods, which suffer from low computational efficiency, and the inflexibility of existing deep learning models in incorporating new sensor data. To overcome these challenges, the authors propose PRISMA, a novel and extensible generative framework for precipitation retrieval. PRISMA decouples modeling into an unconditional precipitation prior and independently trained sensor-conditional branches, enabling plug-and-play integration of new sensors without retraining the backbone network. By fusing infrared and microwave observations, the method achieves a 40.3% improvement in critical success index and a 22.6% reduction in root-mean-square error over microwave-covered regions. In typhoon case studies, absolute errors in storm core areas are reduced by up to 42.3%, with each inference requiring only approximately 37 seconds.

Abstract

Reliable precipitation monitoring is essential for disaster risk reduction, water resources management, and agricultural decision-making. Multi-source satellite observations, particularly the combination of geostationary infrared and passive microwave measurements, have become a primary means of precipitation detection. Traditional multi-source satellite precipitation estimation methods remain computationally inefficient, and many deep learning methods lack the flexibility to incorporate new sensors without retraining the full model. Here we introduce PRISMA (Precipitation Inference from Satellite Modalities via generAtive modeling), a plug-and-play latent generative framework for multi-sensor precipitation estimation. PRISMA learns an unconditional precipitation prior from IMERG Final fields and constrains it through independently trained, sensor-specific conditional branches, allowing new observation sources to be incorporated without retraining the generative backbone. Applied to FY-4B AGRI infrared and GPM GMI microwave observations, PRISMA improves Critical Success Index by up to 40.3% and reduces root-mean-square error by 22.6% relative to infrared-only estimation within microwave swaths, while also improving probabilistic skill and maintaining an average inference time of about 37 s. Independent rain-gauge validation across China confirms consistent gains, and typhoon case studies show that microwave conditioning restores eyewall and spiral rainband structures, reducing storm-core mean absolute error by up to 42.3%. PRISMA thus provides an extensible and efficient framework for multi-sensor precipitation estimation.

Problem

• multi-satellite precipitation estimation
• computational inefficiency
• model retraining
• sensor flexibility
• precipitation monitoring

Innovation

• plug-and-play generative model
• multi-sensor precipitation estimation
• latent generative framework
• conditional sensor branches
• satellite data fusion