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By NASA
6 min read
NASA, IBM’s ‘Hot’ New AI Model Unlocks Secrets of Sun
This image from June 20, 2013 shows the bright light of a solar flare and an eruption of solar material shooting through the sun’s atmosphere, called a prominence eruption. Shortly thereafter, this same region of the sun sent a coronal mass ejection out into space — a phenomenon which can cause magnetic storms that degrade communication signals and cause unexpected electrical surges in power grids on Earth. NASA’s new heliophysics AI foundation model, Surya, can help predict these storms. NASA/Goddard/SDO NASA is turning up the heat in solar science with the launch of the Surya Heliophysics Foundational Model, an artificial intelligence (AI) model trained on 14 years of observations from NASA’s Solar Dynamics Observatory.
Developed by NASA in partnership with IBM and others, Surya uses advances in AI to analyze vast amounts of solar data, helping scientists better understand solar eruptions and predict space weather that threatens satellites, power grids, and communication systems. The model can be used to provide early warnings to satellite operators and helps scientists predict how the Sun’s ultraviolet output affects Earth’s upper atmosphere.
Preliminary results show Surya is making strides in solar flare forecasting, a long-standing challenge in heliophysics. Surya, with its ability to generate visual predictions of solar flares two hours into the future, marks a major step towards the use of AI for operational space weather prediction. These initial results surpass existing benchmarks by 15%. By providing open access to the model on HuggingFace and the code on GitHub, NASA encourages the science and applications community to test and explore this AI model for innovative solutions that leverage the unique value of continuous, stable, long-duration datasets from the Solar Dynamics Observatory.
Illustrations of Solar Dynamics Observatory solar imagery used for training Surya: Solar coronal ultraviolet and extreme ultraviolet images from the Atmospheric Imaging Assembly (AIA) and solar surface velocity and magnetic field maps from the Helioseismic and Magnetic Imager (HMI). NASA/SDO The model’s success builds directly on the Solar Dynamics Observatory’s long-term database. Launched in 2010, NASA’s Solar Dynamics Observatory has provided an unbroken, high-resolution record of the Sun for nearly 15 years through capturing images every 12 seconds in multiple wavelengths, plus precise magnetic field measurements. This stable, well-calibrated dataset, spanning an entire solar cycle, is uniquely suited for training AI models like Surya, enabling them to detect subtle patterns in solar behavior that shorter datasets would miss.
Surya’s strength lies in its foundation model architecture, which learns directly from raw solar data. Unlike traditional AI systems that require extensive labeling, Surya can adapt quickly to new tasks and applications. Applications include tracking active regions, forecasting flare activity, predicting solar wind speed, and integrating data from other observatories including the joint NASA-ESA Solar and Heliospheric Observatory mission and NASA’s Parker Solar Probe.
“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.”
These images compare the ground-truth data (right) with model output (center) for solar flares, which are the events behind most space weather. Surya’s prediction is very close to what happened in reality (right). These preliminary results suggest that Surya has learned enough solar physics to predict the structure and evolution of a solar flare by looking at its beginning phase. NASA/SDO/ODSI IMPACT AI Team Solar storms pose significant risks to our technology-dependent society. Powerful solar events energize Earth’s ionosphere, resulting in substantial GPS errors or complete signal loss to satellite communications. They also pose risks to power grids, as geomagnetically induced currents from coronal mass ejections can overload transformers and trigger widespread outages.
In commercial aviation, solar flares can disrupt radio communications and navigation systems while exposing high-altitude flights to increased radiation. The stakes are even higher for human spaceflight. Astronauts bound for the Moon or Mars may need to depend on precise predictions to shelter from intense radiation during solar particle events.
The Sun’s influence extends to the growing number of low Earth orbit satellites, including those that deliver global high-speed internet. As solar activity intensifies, it heats Earth’s upper atmosphere, increasing drag that slows satellites, pulls them from orbit, and causes premature reentry. Satellite operators often struggle to forecast where and when solar flares might affect these satellites.
The “ground truth” solar activity is shown on the top row. The bottom row shows solar activity predicted by Surya. NASA/SDO/ODSI IMPACT AI Team “Our society is built on technologies that are highly susceptible to space weather,” said Joseph Westlake, Heliophysics Division director at NASA Headquarters. “Just as we use meteorology to forecast Earth’s weather, space weather forecasts predict the conditions and events in the space environment that can affect Earth and our technologies. Applying AI to data from our heliophysics missions is a vital step in increasing our space weather defense to protect astronauts and spacecraft, power grids and GPS, and many other systems that power our modern world.”
While Surya is designed to study the Sun, its architecture and methodology are adaptable across scientific domains. From planetary science to Earth observation, the project lays the foundational infrastructure for similar AI efforts in diverse domains.
Surya is part of a broader NASA push to develop open-access, AI-powered science tools. Both the model and training datasets are freely available online to researchers, educators, and students worldwide, lowering barriers to participation and sparking new discoveries.
The process for creating Surya. Foundation models enhance the utility of NASA’s Solar Dynamics Observatory datasets and create a base for building new applications. NASA/ODSI IMPACT AI Team Surya’s training was supported in part by the National Artificial Intelligence Research Resource (NAIRR) Pilot, a National Science Foundation (NSF)-led initiative that provides researchers with access to advanced computing, datasets, and AI tools. The NAIRR Pilot brings together federal and industry resources, such as computing power from NVIDIA, to expand access to the infrastructure needed for cutting-edge AI research.
“This project shows how the NAIRR Pilot is uniting federal and industry AI resources to accelerate scientific breakthroughs,” said Katie Antypas, director of NSF’s Office of Advanced Cyberinfrastructure. “With support from NVIDIA and NSF, we’re not only enabling today’s research, we’re laying the groundwork for a national AI network to drive tomorrow’s discoveries.”
Surya is part of a larger effort championed and supported by NASA’s Office of the Chief Science Data Officer and Heliophysics Division, the NSF , and partnering universities to advance NASA’s scientific missions through innovative data science and AI models. Surya’s AI architecture was jointly developed by the Interagency Implementation and Advanced Concepts Team (IMPACT) under the Office of Data Science and Informatics at NASA’s Marshall Space Flight Center in Huntsville, Alabama; IBM; and a collaborative science team.
The science team, assembled by NASA Headquarters, consisted of experts from the Southwest Research Institute in San Antonio, Texas; the University of Alabama in Huntsville in Huntsville, Alabama; the University of Colorado Boulder in Boulder, Colorado; Georgia State University in Atlanta, Georgia; Princeton University in Princeton, New Jersey; NASA’s SMD’s Heliophysics Division; NASA’s Goddard Space Flight Center in Greenbelt, Maryland; NASA’s Jet Propulsion Laboratory in Pasadena, California; and the SETI Institute in Mountain View, California.
For a behind-the-scenes dive into Surya’s architecture, industry and academic collaborations, challenges behind developing the model, read the blog post on NASA’s Science Data Portal:
https://science.data.nasa.gov/features-events/inside-surya-solar-ai-model
For more information about NASA’s strategy of developing foundation models for science, visit:
https://science.nasa.gov/artificial-intelligence-science
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Last Updated Aug 20, 2025 Related Terms
Science & Research Artificial Intelligence (AI) Heliophysics Solar Dynamics Observatory (SDO) The Sun The Sun & Solar Physics Explore More
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By European Space Agency
Video: 00:01:20 Europe’s first MetOp Second Generation, MetOp-SG-A1, weather satellite – which hosts the Copernicus Sentinel-5 mission – has launched aboard an Ariane 6 rocket from Europe’s Spaceport in French Guiana. The rocket lifted off on 13 August at 02:37 CEST (12 August 21:37 Kourou time).
MetOp-SG-A1 is the first in a series of three successive pairs of satellites. The mission as a whole not only ensures the continued delivery of global observations from polar orbit for weather forecasting and climate analysis for more than 20 years, but also offers enhanced accuracy and resolution compared to the original MetOp mission – along with new measurement capabilities to expand its scientific reach.
This new weather satellite also carries the Copernicus Sentinel-5 mission to deliver daily global data on air pollutants and atmospheric trace gases as well as aerosols and ultraviolet radiation.
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By European Space Agency
As preparations to launch Europe’s first MetOp Second Generation, MetOp-SG-A1 satellite continue on track, the team at Europe’s Spaceport in Kourou, French Guiana, has bid a heartfelt farewell to this precious satellite as it was sealed from view within the Ariane 6 rocket’s fairing.
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By NASA
Curiosity Navigation Curiosity Home Mission Overview Where is Curiosity? Mission Updates Science Overview Instruments Highlights Exploration Goals News and Features Multimedia Curiosity Raw Images Images Videos Audio Mosaics More Resources Mars Missions Mars Sample Return Mars Perseverance Rover Mars Curiosity Rover MAVEN Mars Reconnaissance Orbiter Mars Odyssey More Mars Missions Mars Home 3 min read
Curiosity Blog, Sols 4609–4610: Recharged and Ready To Roll Onwards
NASA’s Mars rover Curiosity acquired this image showing the boxwork hollow where it is investigating, and the boxwork ridge on the far side of the hollow, using its Left Navigation Camera. Curiosity captured the image on July 20, 2025 — Sol 4605, or Martian day 4,605 of the Mars Science Laboratory mission — at 18:51:55 UTC. NASA/JPL-Caltech Written by Catherine O’Connell-Cooper, Planetary Geologist at University of New Brunswick
Earth planning date: Wednesday, July 23, 2025
For today’s planning, we were in the same workspace as the Monday plan — on purpose! We don’t often have a plan without a drive but in order to allow the battery to recover from some power-hungry SAM atmospheric measurements over the weekend and on Monday, we needed to stay put and skip our usual drive. As a result, we gained a bonus planning cycle at this interesting workspace.
We are in one of the “hollows” between the resistant ridges of the “boxwork” terrain, as you can see in the image for this blog. This made for a quieter Operations day for me as the APXS planner. As Deborah noted in Monday’s blog, we have already gotten three APXS and MAHLI measurements in this workspace, so we didn’t acquire more in this plan.
This morning, we focused on documenting some small light-toned, rounded, white pebbles in the workspace (you can see them in the accompanying Navcam image), which look very different from the underlying bedrock. We used our one ChemCam LIBS analysis for the plan on “Yana Qaqa.” Mastcam will image this pebble, another at “Ojos del Salado,” and a really cool-looking target with a dendritic-looking texture at “Punta de Lobos.”
Further afield, Mastcam will image the adjacent boxwork ridge and hollow in our drive direction, and a series of troughs with raised edges to the right of our current workspace. ChemCam will image a long-distance RMI mosaic of “Cueva de los Vencejos y Murciélagos,” which was imaged by Mastcam on Monday, and also acquire some further images of the “Mishe Mokwa” hill.
We had a bumper couple of sols of atmospheric measurements over the weekend and Monday. Now we revert back to our more normal environmental and atmospheric monitoring. These do not get as much attention sometimes as the amazing images we take of the fascinating rocks we see, but have been taking place consistently and continuously since Curiosity’s landing almost 13 years ago now. This plan includes a series of Navcam movies (suprahorizon, dust devil) and a line-of-sight observation of dust, standard REMS and DAN observations, and two Mastcam tau measurements, looking at dust in the atmosphere.
Our 24-meter drive (almost 79 feet) will take us out of this hollow and back up on top of a ridge. From here, we hope to be able to spy the best driving path through the boxwork. The ridges are up to 5 meters in diameter (about 16 feet), so we are cautiously hopeful that we can just trundle along one of the ridges as we investigate this fascinating terrain.
For more Curiosity blog posts, visit MSL Mission Updates
Learn more about Curiosity’s science instruments
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Last Updated Jul 28, 2025 Related Terms
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