The implications include both academic research and operational preparedness. The new model will provide tools to help experts plan for solar storms, which can disrupt the Earth's technological infrastructure.
"Think of this as a weather forecast for space," said Juan Bernabe-Moreno, Director of IBM Research Europe, UK and Ireland. "Just as we work to prepare for hazardous weather events, we need to do the same for solar storms. Surya gives us unprecedented capability to anticipate what's coming and is not just a technological achievement, but a critical step toward protecting our technological civilization from the star that sustains us."
Traditional solar weather prediction relies on partial satellite views of the Sun's surface, historically making accurate forecasting extremely difficult. Surya addresses this typical limitation by training on the largest curated high resolution heliophysics dataset. This dataset is designed to help researchers better study and evaluate critical space weather prediction tasks. Examples of these tasks, which Surya has been tested on, include predicting solar flares, the speed of solar winds, solar EUV spectra prediction and the emergence of active regions on the Sun.
In early testing, researchers report achieving a 16 percent improvement in solar flare classification accuracy, which researchers report as a very substantial improvement compared to previous methods. In addition to the binary solar flare classification task, Surya is built to visually predict solar flares for the first time, providing a high resolution image of where the flare is predicted to occur up to two hours out.
The technical challenges were immense. Surya was trained on nine years of high-resolution solar observation data from NASA's Solar Dynamics Observatory. These solar images are 10 times larger than typical AI training data, requiring a custom multi-architecture solution to handle the massive scale while maintaining efficiency. The result is a model with unprecedented spatial resolution that can resolve solar features at scales and contexts not previously captured in large-scale AI training workflows.
"We are advancing data-driven science by embedding NASA's deep scientific expertise into cutting-edge AI models," said Kevin Murphy, chief science data officer at NASA Headquarters in Washington. "By developing a foundation model trained on NASA's heliophysics data, we're making it easier to analyze the complexities of the Sun's behavior with unprecedented speed and precision. This model empowers broader understanding of how solar activity impacts critical systems and technologies that we all rely on here on Earth."
Surya is part of a broader effort at IBM to embrace generative and automated approaches that empower algorithms to be discovered, tested, and evolved at scale. Surya is one example of how IBM is positioning AI not just as a tool, but as a driver of scientific discovery. By releasing Surya on Hugging Face, IBM and NASA are democratizing access to advanced tools for understanding and forecasting solar weather and scientific exploration. Researchers worldwide can now build upon this foundation to develop specialized applications for their regions and industries.
This model is part of a larger collaboration between IBM and NASA to use AI technology to explore our planet and solar system. It joins the Prithvi family of foundation models, which includes a geospatial model and a weather model. Last year, IBM and NASA released the Prithvi weather model on Hugging Face for scientists and the broader community to develop short- and long-term weather and climate projections.
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Interplanetary Causes and Impacts of the 2024 May Superstorm on the Geosphere: An Overview - IOPscience. Interplanetary Causes and Impacts of the 2024 May Superstorm on the Geosphere: An Overview, Hajra, Rajkumar, Tsurutani, Bruce Tsatnam, Lakhina, Gurbax Singh, Lu, Quanming, Du, Aimin. https://iopscience.iop.org/article/10.3847/1538-4357/ad7462/meta