PRISMA: A Generative AI Framework for Multi-Satellite Precipitation Estimation
A new framework named PRISMA (Precipitation Inference from Satellite Modalities via generAtive modeling) has been developed by researchers for estimating precipitation using multiple sensors. This plug-and-play latent generative model overcomes the computational limitations of conventional techniques and the rigidity of deep learning models, which necessitate complete retraining to add new sensors. By learning an unconditional precipitation prior from IMERG Final fields, PRISMA utilizes independently trained, sensor-specific branches to integrate new observation sources seamlessly. This advancement is vital for managing water resources, disaster risk reduction, and agricultural planning, as effective precipitation monitoring depends on diverse satellite data, especially the combination of geostationary infrared and passive microwave measurements. Detailed findings are available in a paper on arXiv (2605.14426).
Key facts
- PRISMA is a plug-and-play latent generative framework for multi-sensor precipitation estimation.
- It learns an unconditional precipitation prior from IMERG Final fields.
- It uses independently trained, sensor-specific conditional branches.
- New sensors can be incorporated without retraining the full model.
- The framework addresses computational inefficiency of traditional methods.
- It overcomes inflexibility of deep learning methods that require full retraining.
- Reliable precipitation monitoring is essential for disaster risk reduction, water management, and agriculture.
- Multi-source satellite observations combine geostationary infrared and passive microwave measurements.
Entities
Institutions
- arXiv