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Acting NASA administrator Sean Duffy says the agency will 'move aside' from climate sciences to focus on exploring moon and Mars
Pragmata rethinks sci-fi shooter combat, and it owes a lot to Snake (preview)
NASA and IBM built an AI to predict solar flares before they hit Earth
NASA and IBM built an AI to predict solar flares before they hit Earth
South Korea's K-RadCube radiation satellite will hitch a ride on NASA's Artemis 2 moon mission
Bring NASA Science into Your Library!
2 min read
Bring NASA Science into Your Library!Calling all librarians! NASA sponsors dozens of research projects that need help from you and the people in your community. These projects invite everyone who’s interested to collaborate with scientists, investigating mysteries from how star systems form to how our planet sustains life. You can help by making observations with your cell phone or by studying fresh data on your laptop from spacecraft like the James Webb Space Telescope. You might discover a near-Earth asteroid or a new food option for astronauts. Participants learn new skills and meet scientists and other people around the world with similar interests.
Interested in sharing these opportunities with your patrons? Join us on August 26, 2025 at 1 p.m. EST for a 1-hour online information session. A librarian and a participatory science professional will provide you with a NASA Citizen Science Librarian Starter Kit and answer all your questions. The kit includes everything you need to host a NASA Science Program for patrons of all ages.
- Editable poster to advertise event
- Event prep guide (for the host and for the space)
- Community connection ideas
- Editable event agenda
- Handout for participants
Scan the QR code above or go to https://shorturl.at/tKfTt to register for the session.
Kara Reiman, Librarian and Educator (Left) and Sarah Kirn, Participatory Science Strategist, NASA (Right) Share Details Last Updated Aug 20, 2025 Related Terms Explore More 2 min read A Gigantic Jet Caught on Camera: A Spritacular Moment for NASA Astronaut Nicole Ayers!
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Steve Platnick Steps Down from NASA After 34 Years of Service
Dr. Steven “Steve” Platnick has taken the NASA Deferred Resignation Program (DRP). His last work day was August 8, 2025. Steve spent more than three decades at, or associated with, NASA. While he began his civil servant career at NASA’s Goddard Space Flight Center (GSFC) in 2002, his Goddard association went back to 1993, first as a contractor and then as one of the earliest employees of the Joint Center for Earth Systems Technology (JCET), a cooperative agreement between the University of Maryland, Baltimore County (UMBC) and GSFC’s Earth Science Division. At JCET Steve helped lead the development of the Atmosphere Physics Track curricula. Previously, he held a National Research Council (NRC) post-doctoral fellow at NASA’s Ames Research Center. Along with his research work on cloud remote sensing from satellite and airborne sensors, Steve served as the Deputy Director for Atmospheres in GSFC’s Earth Sciences Division from January 2015–July 2024.
Dr. Steve Platnick Image credit: NASA
During his time at NASA, Steve played an integral role in the sustainability and advancement of NASA’s Earth Observing System platforms and data. In 2008, he took over as the Earth Observing System (EOS) Senior Project Scientist from Michael King. In this role, he led the EOS Project Science Office, which included support for related EOS facility airborne sensors, ground networks, and calibration labs. The office also supported The Earth Observer newsletter, the NASA Earth Observatory, and other outreach and exhibit activities on behalf of NASA Headquarters’ Earth Science Division and Science Mission Directorate (further details below). From January 2003– February 2010, Steve served as the Aqua Deputy Project Scientist.
Improving Imager Cloud Algorithms
Steve was actively involved in the Moderate Resolution Imaging Spectroradiometer (MODIS) Science Team serving as the Lead for the MODIS Atmosphere Discipline Team (cloud, aerosol and clear sky products) since 2008 and as the NASA Suomi National Polar-orbiting Partnership (Suomi NPP)/JPSS Atmosphere Discipline Lead/co-Lead from 2012–2020. His research team enhanced, maintained, and evaluated MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) cloud algorithms that included Level-2 (L2) Cloud Optical/Microphysical Properties components (MOD06 and MYD06 for MODIS on Terra and Aqua, respectively) and the Atmosphere Discipline Team Level-3 (L3) spatial/temporal products (MOD08, MYD08). The L2 cloud algorithms were developed to retrieve thermodynamic phase, optical thickness, effective particle radius, and derived water path for liquid and ice clouds, among other associated datasets. Working closely with longtime University of Wisconsin-Madison colleagues, the team also developed the CLDPROP continuity products designed to bridge the MODIS and VIIRS cloud data records by addressing differences in the spectral coverage between the two sensors; this product is currently in production for VIIRS on Suomi NPP and NOAA-20, as well as MODIS Aqua. The team also ported their CLDPROP code to Geostationary Operational Environmental Satellites (GOES) R-series Advanced Baseline Imager (ABI) and sister sensors as a research demonstration effort.
Steve’s working group participation included the Global Energy and Water Exchanges (GEWEX) Cloud Assessment Working Group (2008–present); the International Cloud Working Group (ICWG), which is part of the Coordination Group for Meteorological Satellites (CGMS), and its original incarnation, the Cloud Retrieval Evaluation Working (CREW) since 2009; and the NASA Observations for Modeling Intercomparison Studies (obs4MIPs) Working Group (2011–2013). Other notable roles included Deputy Chair of the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Science Definition Team (2011–2012) and membership in the Advanced Composition Explorer (ACE) Science Definition Team (2009–2011), the ABI Cloud Team (2005–2009), and the Climate Absolute Radiance and Refractivity Observatory (CLARREO) Mission Concept Team (2010–2011).
Steve has participated in numerous major airborne field campaigns over his career. His key ER-2 flight scientist and/or science team management roles included the Monterey Area Ship Track experiment (MAST,1994), First (International Satellite Cloud Climatology Project (ISCCP) Regional Experiment – Arctic Cloud Experiment [FIRE-ACE, 1998], Southern Africa Fire-Atmosphere Research Initiative (SAFARI-2000), Cirrus Regional Study of Tropical Anvils and Cirrus Layers – Florida Area Cirrus Experiment (CRYSTAL-FACE, 2002), and Tropical Composition, Cloud and Climate Coupling (TC4, 2007).
Supporting Earth Science Communications
Through his EOS Project Science Office role, Steve has been supportive of the activities of NASA’s Science Support Office (SSO) and personally participated in many NASA Science exhibits at both national and international scientific conferences, including serving as a Hyperwall presenter numerous times.
For The Earth Observer newsletter publication team in particular, Steve replaced Michael King as Acting EOS Senior Project Scientist in June 2008, taking over the authorship of “The Editor’s Corner” beginning with the May–June 2008 issue [Volume 20, Issue 3]. The Acting label was removed beginning with the January–February 2010 issue [Volume 22, Issue 1]. Steve has been a champion of continuing to retain a historical record of NASA science team meetings to maintain a chronology of advances made by different groups within the NASA Earth Science community. He was supportive of the Executive Editor’s efforts to create a series called “Perspectives on EOS,” which ran from 2008–2011 and told the stories of the early years of the EOS Program from the point of view of those who lived them. He also supported the development of articles to commemorate the 25th and 30th anniversary of The Earth Observer. Later, Steve helped guide the transition of the newsletter from a print publication – the November–December 2022 issue was the last printed issue – to fully online by July 2024, a few months after the publication’s 35th anniversary. The Earth Observer team will miss Steve’s keen insight, historical perspective, and encouragement that he has shown through his leadership for the past 85 issues of print and online publications.
A Career Recognized through Awards and Honors
Throughout his career, Steve has amassed numerous honors, including the Goddard William Nordberg Memorial Award for Earth Science in 2023 and the Verner E. Suomi Award from the American Meteorological Society (AMS) in 2016. He was named an AMS Fellow that same year. He received two NASA Agency Honor Awards – the Exceptional Achievement Medal in 2008 and the Exceptional Service Medal in 2015.
Steve received his bachelor’s degree and master’s degree in electrical engineering from Duke University and the University of California, Berkeley, respectively. He earned a Ph.D. in atmospheric sciences from the University of Arizona.
Steve Platnick Steps Down from NASA After 34 Years of Service
Dr. Steven “Steve” Platnick has taken the NASA Deferred Resignation Program (DRP). His last work day was August 8, 2025. Steve spent more than three decades at, or associated with, NASA. While he began his civil servant career at NASA’s Goddard Space Flight Center (GSFC) in 2002, his Goddard association went back to 1993, first as a contractor and then as one of the earliest employees of the Joint Center for Earth Systems Technology (JCET), a cooperative agreement between the University of Maryland, Baltimore County (UMBC) and GSFC’s Earth Science Division. At JCET Steve helped lead the development of the Atmosphere Physics Track curricula. Previously, he held a National Research Council (NRC) post-doctoral fellow at NASA’s Ames Research Center. Along with his research work on cloud remote sensing from satellite and airborne sensors, Steve served as the Deputy Director for Atmospheres in GSFC’s Earth Sciences Division from January 2015–July 2024.
Dr. Steve Platnick Image credit: NASADuring his time at NASA, Steve played an integral role in the sustainability and advancement of NASA’s Earth Observing System platforms and data. In 2008, he took over as the Earth Observing System (EOS) Senior Project Scientist from Michael King. In this role, he led the EOS Project Science Office, which included support for related EOS facility airborne sensors, ground networks, and calibration labs. The office also supported The Earth Observer newsletter, the NASA Earth Observatory, and other outreach and exhibit activities on behalf of NASA Headquarters’ Earth Science Division and Science Mission Directorate (further details below). From January 2003– February 2010, Steve served as the Aqua Deputy Project Scientist.
Improving Imager Cloud Algorithms
Steve was actively involved in the Moderate Resolution Imaging Spectroradiometer (MODIS) Science Team serving as the Lead for the MODIS Atmosphere Discipline Team (cloud, aerosol and clear sky products) since 2008 and as the NASA Suomi National Polar-orbiting Partnership (Suomi NPP)/JPSS Atmosphere Discipline Lead/co-Lead from 2012–2020. His research team enhanced, maintained, and evaluated MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) cloud algorithms that included Level-2 (L2) Cloud Optical/Microphysical Properties components (MOD06 and MYD06 for MODIS on Terra and Aqua, respectively) and the Atmosphere Discipline Team Level-3 (L3) spatial/temporal products (MOD08, MYD08). The L2 cloud algorithms were developed to retrieve thermodynamic phase, optical thickness, effective particle radius, and derived water path for liquid and ice clouds, among other associated datasets. Working closely with longtime University of Wisconsin-Madison colleagues, the team also developed the CLDPROP continuity products designed to bridge the MODIS and VIIRS cloud data records by addressing differences in the spectral coverage between the two sensors; this product is currently in production for VIIRS on Suomi NPP and NOAA-20, as well as MODIS Aqua. The team also ported their CLDPROP code to Geostationary Operational Environmental Satellites (GOES) R-series Advanced Baseline Imager (ABI) and sister sensors as a research demonstration effort.
Steve’s working group participation included the Global Energy and Water Exchanges (GEWEX) Cloud Assessment Working Group (2008–present); the International Cloud Working Group (ICWG), which is part of the Coordination Group for Meteorological Satellites (CGMS), and its original incarnation, the Cloud Retrieval Evaluation Working (CREW) since 2009; and the NASA Observations for Modeling Intercomparison Studies (obs4MIPs) Working Group (2011–2013). Other notable roles included Deputy Chair of the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Science Definition Team (2011–2012) and membership in the Advanced Composition Explorer (ACE) Science Definition Team (2009–2011), the ABI Cloud Team (2005–2009), and the Climate Absolute Radiance and Refractivity Observatory (CLARREO) Mission Concept Team (2010–2011).
Steve has participated in numerous major airborne field campaigns over his career. His key ER-2 flight scientist and/or science team management roles included the Monterey Area Ship Track experiment (MAST,1994), First (International Satellite Cloud Climatology Project (ISCCP) Regional Experiment – Arctic Cloud Experiment [FIRE-ACE, 1998], Southern Africa Fire-Atmosphere Research Initiative (SAFARI-2000), Cirrus Regional Study of Tropical Anvils and Cirrus Layers – Florida Area Cirrus Experiment (CRYSTAL-FACE, 2002), and Tropical Composition, Cloud and Climate Coupling (TC4, 2007).
Supporting Earth Science Communications
Through his EOS Project Science Office role, Steve has been supportive of the activities of NASA’s Science Support Office (SSO) and personally participated in many NASA Science exhibits at both national and international scientific conferences, including serving as a Hyperwall presenter numerous times.
For The Earth Observer newsletter publication team in particular, Steve replaced Michael King as Acting EOS Senior Project Scientist in June 2008, taking over the authorship of “The Editor’s Corner” beginning with the May–June 2008 issue [Volume 20, Issue 3]. The Acting label was removed beginning with the January–February 2010 issue [Volume 22, Issue 1]. Steve has been a champion of continuing to retain a historical record of NASA science team meetings to maintain a chronology of advances made by different groups within the NASA Earth Science community. He was supportive of the Executive Editor’s efforts to create a series called “Perspectives on EOS,” which ran from 2008–2011 and told the stories of the early years of the EOS Program from the point of view of those who lived them. He also supported the development of articles to commemorate the 25th and 30th anniversary of The Earth Observer. Later, Steve helped guide the transition of the newsletter from a print publication – the November–December 2022 issue was the last printed issue – to fully online by July 2024, a few months after the publication’s 35th anniversary. The Earth Observer team will miss Steve’s keen insight, historical perspective, and encouragement that he has shown through his leadership for the past 85 issues of print and online publications.
A Career Recognized through Awards and Honors
Throughout his career, Steve has amassed numerous honors, including the Goddard William Nordberg Memorial Award for Earth Science in 2023 and the Verner E. Suomi Award from the American Meteorological Society (AMS) in 2016. He was named an AMS Fellow that same year. He received two NASA Agency Honor Awards – the Exceptional Achievement Medal in 2008 and the Exceptional Service Medal in 2015.
Steve received his bachelor’s degree and master’s degree in electrical engineering from Duke University and the University of California, Berkeley, respectively. He earned a Ph.D. in atmospheric sciences from the University of Arizona.
NASA, IBM’s ‘Hot’ New AI Model Unlocks Secrets of Sun
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
Editor’s Note: This article was updated Aug. 20, 2025, to correct the number of years of training data used and the model accuracy. The original article said the model was trained on 14 years of Solar Dynamics Observatory data and surpassed existing benchmarks by 15%; the model was actually trained on 9 years of data and surpassed existing benchmarks by 16%.
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 9 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 16%. 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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NASA, IBM’s ‘Hot’ New AI Model Unlocks Secrets of Sun
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/SDOEditor’s Note: This article was updated Aug. 20, 2025, to correct the number of years of training data used and the model accuracy. The original article said the model was trained on 14 years of Solar Dynamics Observatory data and surpassed existing benchmarks by 15%; the model was actually trained on 9 years of data and surpassed existing benchmarks by 16%.
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 9 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 16%. 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/SDOThe 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 TeamSolar 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 TeamSurya’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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