NASA and IBM release Surya, an open-source machine learning model trained on over a decade's worth of NASA solar data to predict solar flares and solar winds
a new foundation model designed to help researchers protect infrastructure through accessible, accurate modeling of space weather. It's going to totally change how we forecast solar storms. See how.🧵 [image] Forums: r/technews : NASA's new AI model can predict when a solar storm may strike r/space : NASA's new AI model can predict when a solar storm may strike
Context & Ripple Effects
NASA and IBM had already collaborated with Hugging Face on an open geospatial foundation model for climate and Earth-science work. Surya extends that public-model approach from observing Earth systems to modeling the solar activity that affects them.
The release arrives as AI forecasting models are being positioned as faster, more precise complements to conventional forecasting systems, including AI weather forecasting work framed as a complement to supercomputers. Its significance is the application of that model pattern to space weather.
First-order effects
- Researchers gain an open-source foundation model trained on more than a decade of NASA solar data, lowering the barrier to testing and adapting machine-learning approaches to solar flares and solar wind.
- NASA and IBM make a shared, reusable research asset available for space-weather modeling rather than limiting the work to a proprietary product or a single forecasting workflow.
Second-order effects
- Space-weather research groups can compare Surya with their existing methods and build specialized tools on top of a common starting point, shifting differentiation toward validation, domain adaptation, and operational use.
- Other AI forecasting efforts—including Aurora's faster, smaller-scale weather forecasts—face a clearer precedent for releasing or supporting reusable models for scientific prediction, even though terrestrial weather and space weather are distinct domains.
Third-order effects
- If open scientific foundation models prove reliable in operational settings, forecasting capability may increasingly be built around shared public models plus specialized downstream systems, rather than wholly bespoke models at each institution.
- That shift would make access to long-lived scientific datasets, evaluation standards, and deployment safeguards central parts of critical-infrastructure AI—not just model training.
The trend: Scientific agencies and technology companies are turning domain-specific foundation models into shared infrastructure for forecasting complex physical systems.