Nasa and IBM apply artificial intelligence to tackle solar digital disruption
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Solar storms and flares can have a big impact on digital society, which is why IBM and Nasa are forecasting solar weather with AI
IBM and Nasa have made available on Hugging Face an open source artificial intelligence (AI) model called Surya, trained to understand and predict how solar activity affects Earth and space-based technology.
Surya applies AI to solar image interpretation and space weather forecasting research. According to IBM, it can be used to help protect GPS navigation, power grids and telecommunications from the Sun’s ever-changing nature.
Digital technology that powers modern society is vulnerable to space weather, and a systemic risk scenario created by Lloyd’s showed that the global economy could be exposed to losses of $2.4tn over a five-year period. A hypothetical solar storm could lead to $17bn of economic damage.
Solar flares and coronal mass ejections can damage satellites, spacecraft and/or astronauts that are stationed beyond Earth. They may cause satellite hardware to fail, damaging solar panels and circuits. Solar weather can also impact airline travel, due to navigational errors and potential risk of radiation for airline crew and passengers. Disruption to GPS due to solar weather could impact agriculture, leading to lower food production.
Surya is a 366 million-parameter foundation model for heliophysics – the study of the Sun and its effect on the Solar System. It was pre-trained using data from the Atmospheric Imaging Assembly and Helioseismic and Magnetic Imager instruments on Nasa’s Solar Dynamic Observatory (SDO) mission, which was launched in 2010.
It uses self-supervised learning to identify patterns in unlabelled solar data, which eliminates the need for experts to categorise thousands of complex solar events manually and was trained on nine years of high-resolution solar observations from Nasa’s SDO. These solar images are 10 times larger than typical AI training data. Surya required a custom technology architecture to handle the massive scale of the dataset while maintaining efficiency.
In a paper discussing the model, researchers from IBM and Nasa said Surya learned general-purpose solar representations that capture both “the fine-scale variability of magnetic fields and the large-scale dynamics of the solar atmosphere”. They claimed the pre-training strategy enabled the model to perform zero-shot forecasting of solar activity, representing a shift from narrowly focused, task-specific models to a more versatile and scalable approach for heliophysics.
Traditional solar weather prediction relies on partial satellite views of the Sun’s surface, making accurate forecasting extremely difficult. Surya addresses this typical limitation by training on the largest curated heliophysics dataset, which IBM and Nasa said was designed to help researchers better study and evaluate critical space weather prediction tasks.
“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’s headquarters in Washington.
“By developing a foundation model trained on Nasa’s heliophysics data, we’re making it easier to analyse the complexities of the Sun’s behaviour 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.”
By releasing Surya on Hugging Face, IBM and Nasa said they were “democratising access to advanced tools for understanding and forecasting solar weather and scientific exploration”, encouraging the development of specialised applications for different regions and industries.
Juan Bernabe-Moreno, director of IBM Research Europe for UK and Ireland, said: “This AI model gives us unprecedented capability to anticipate what’s coming, and is not just a technological achievement, but a critical step toward protecting our technological civilisation from the star that sustains us.”
