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By NASA
Explore This Section Perseverance Home Mission Overview Rover Components Mars Rock Samples Where is Perseverance? Ingenuity Mars Helicopter Mission Updates Science Overview Objectives Instruments Highlights Exploration Goals News and Features Multimedia Perseverance Raw Images Images Videos Audio More Resources Mars Missions Mars Sample Return Mars Perseverance Rover Mars Curiosity Rover MAVEN Mars Reconnaissance Orbiter Mars Odyssey More Mars Missions Mars Home This image was taken when Perseverance topped Soroya ridge. Using the Left Navigation Camera (Navcam), the image was acquired on Aug. 17, 2025 (Sol 1597) at the local mean solar time of 13:54:37. NASA/JPL-Caltech Written by Eleanor Moreland, Ph.D. Student Collaborator at Rice University
Perseverance has continued exploring beyond the rim of Jezero crater, spending time last week at Parnasset conducting a mini-campaign on aeolian bedforms. After wrapping up that work, three separate drives brought Perseverance further southeast to an outcrop named Soroya.
Soroya was first picked out from orbital images as a target of interest because, as can be seen in the above image, it appears as a much lighter color compared to the surroundings. In previous landscape images from the surface, Mars 2020 scientists have been able to pick out the light-toned Soryoa outcrop, and they noted it forms a ridge-like structure, protruding above the surface. Soroya was easily identifiable from rover images (below) as Perseverance approached since it indeed rises above the surrounding low-lying terrain.
The Perseverance rover acquired this image looking at Soroya using the onboard Left Navigation Camera (Navcam). This image was acquired on Aug. 15, 2025 (Sol 1595) at the local mean solar time of 16:34:53. NASA/JPL-Caltech From Parnasset to Soroya, the team planned a series of drives so that Perseverance would arrive at Soroya in a great workspace, and the plan was successful. As shown in the first image, we arrived at an area with flat, exposed bedrock – great for proximity science instruments.
The WATSON and SHERLOC ACI cameras plan to acquire many high-resolution images to investigate textures and surface features. For chemistry, SCAM LIBS and ZCAM multispectral activities will give important contextual data for the outcrop while PIXL will acquire a high-resolution chemical map scan of a dust-cleared part of the bedrock. While parked, MEDA will continue monitoring environmental conditions and ZCAM will image the surrounding terrain to inform the next drive location. Take a look at where Perseverance is now – where would you explore next?
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Last Updated Aug 27, 2025 Related Terms
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By NASA
2 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
ResilienX employees Angelo Niforatos, left, and Ryan Pleskach, right, overview the NASA safety tools integrated into the company’s commercial system, July 11, 2025, at the ResilienX Headquarters in Syracuse, New York. Credit: ResilienX A future with advanced air mobility aircraft populating the skies will require the U.S. to implement enhanced preflight planning that can mitigate potential risks well before takeoff – and NASA is working to develop the tools to make that happen.
Preflight planning is critical to ensuring safety in the complex, high-risk environments of the future airspace. Timely, predictive, and up-to-date risk assessment within a single platform makes it much easier for drone or air taxi operators to check flight plans for high-risk concerns.
NASA is working on tools to deliver those services, and in June, the agency and aviation safety company ResilienX Inc. demonstrated how these tools can be integrated into commercial systems.
During a series of tests conducted at ResilienX’s facility in Syracuse, New York, researchers used NASA services that allowed flight operators to submit flight plans prior to departure, obtain risk assessment results, and then decide whether to proceed with flights or change their flight plans and re-assess risks. Allowing operators to perform these tasks quickly reduces the safety risk to flight passengers as well as humans on the ground.
The three NASA-developed services are intended to assess unique risks associated with highly automated aircraft flying at low altitudes over cities.
The partnership was managed under a Phase III NASA Small Business Innovation Research (SBIR) contract, which is an extension of prior work to assess weather-related risks. This collaboration is already leading to direct technology transfer of safety systems into ResilienX’s platform. The partnership is also intended to provide indirect benefits for ResilienX partners and customers, such as the U.S. Air Force and regional operators, helping to advance the overall safety of future airspace operations.
This work is led by NASA’s System-Wide Safety project under the Airspace Operations and Safety program in support of the agency’s Advanced Air Mobility mission. The mission seeks to deliver data, findings, and recommendations to guide the industry’s development of future air taxis and drones.
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Last Updated Aug 22, 2025 EditorDede DiniusContactTeresa Whitingteresa.whiting@nasa.gov Related Terms
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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
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The Research Aircraft for electric Vertical takeoff and landing Enabling techNologies Subscale Wind Tunnel and Flight Test undergoes a free flight test on the City Environment Range Testing for Autonomous Integrated Navigation range at NASA’s Langley Research Center in Hampton, Virginia on April 22, 2025.NASA/Rob Lorkiewicz Flying the friendly skies may one day include time-saving trips in air taxis to get from point A to point B – and NASA researchers are currently working to make that future a reality.
They are using wind tunnel and flight tests to gather data on an electric Vertical takeoff and landing (eVTOL) scaled-down small aircraft that resembles an air taxi that aircraft manufacturers can use for their own designs.
As air taxis take to the skies, engineers need real-world data on air taxi designs to better understand flight dynamics and design better flight control systems. These systems help stabilize and guide the motion of an aircraft while in flight, making sure it flies safely in various conditions.
Currently, most companies developing air taxis keep the information about how their aircraft behaves internal, so NASA is using this small aircraft to produce public, non-proprietary data available to all.
“NASA’s ability to perform high-risk flight research for increasingly automated and autonomous aircraft is really important,” said Siena Whiteside, who leads the Research Aircraft for eVTOL Enabling techNologies (RAVEN) project. “As we investigate these types of vehicles, we need to be able push the aircraft to its limits and understand what happens when an unforeseen event occurs…”
For example, Whiteside said, “…when a motor stops working. NASA is willing to take that risk and publish the data so that everyone can benefit from it.”
Researchers Jody Miller, left, and Brayden Chamberlain, right, stand by a crane that is used for tethered flight testing of the Research Aircraft for electric Vertical takeoff and landing Enabling techNologies Subscale Wind Tunnel and Flight Test at NASA’s Langley Research Center in Hampton, Virginia on Oct. 18, 2024.NASA/Ben Simmons Testing Air Taxi Tech
By using a smaller version of a full-sized aircraft called the RAVEN Subscale Wind Tunnel and Flight Test (RAVEN SWFT) vehicle, NASA is able to conduct its tests in a fast and cost-effective manner.
The small aircraft weighs 38 pounds with a wingspan of six feet and has 24 independently moving components.
Each component, called a “control effector,” can move during flight to change the aircraft’s motion – making it an ideal aircraft for advanced flight controls and autonomous flight research.
The testing is ongoing at NASA’s Langley Research Center in Hampton, Virginia.
Researchers first used the center’s 12-Foot Low-Speed Tunnel in 2024 and have since moved on to flight testing the small aircraft, piloting it remotely from the ground. During initial flight tests, the aircraft flew while tied to a tether. Now, the team performs free flights.
Lessons learned from the aircraft’s behavior in the wind tunnel helped to reduce risks during flight tests. In the wind tunnel, researchers performed tests that closely mirror the motion of real flight.
While the scale aircraft was in motion, researchers collected information about its flight characteristics, greatly accelerating the time from design to flight.
The team also could refine the aircraft’s computer control code in real time and upload software changes to it in under 5 minutes, saving them weeks and increasing the amount of data collected.
Researchers Ben Simmons, left, and Greg Howland, right, upload software changes in real time to the Research Aircraft for electric Vertical takeoff and landing Enabling techNologies Subscale Wind Tunnel and Flight Test at NASA’s Langley Research Center in Hampton, Virginia on Aug. 8, 2024, during testing in the 12-Foot Low-Speed Tunnel.NASA/David C. Bowman Partners in Research
NASA developed the custom flight controls software for RAVEN SWFT using tools from the company MathWorks.
NASA and MathWorks are partners under a Space Act Agreement to accelerate the design and testing of flight control approaches on RAVEN SWFT, which can apply to future novel aircraft.
The work has allowed NASA’s researchers to develop new methods to reduce the time for an aircraft to achieve its first flight and become a finished product.
RAVEN SWFT serves as a steppingstone to support the development of a potential larger, 1,000 pound-class RAVEN aircraft that will resemble an air taxi.
This larger RAVEN aircraft is being designed in collaboration with Georgia Institute of Technology and also would serve as an acoustical research tool, helping engineers understand the noise air taxi-like aircraft create.
The larger aircraft would allow NASA to continue to collect data and share it openly.
By performing flight research and making its data publicly available, NASA aims to advance U.S. leadership in technology development for safe, quiet, and affordable advanced air mobility operations.
Watch this Air Taxi Tests Video
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Last Updated Aug 13, 2025 EditorJim BankeContactDiana Fitzgeralddiana.r.fitzgerald@nasa.govLocationNASA Langley Research Center Related Terms
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By European Space Agency
Image: The development of ESA’s Earth Explorer FLEX mission has recently passed a significant milestone: the mission’s all-important instrument has been joined to its satellite platform. View the full article
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