Certain kinds of clouds are misbehaving – appearing more often and lower in the sky than they used to. To help identify the factors influencing these changes (e.g. shifts in Earth’s long-term weather patterns), scientists have asked people around the world with cameras to submit fresh images of these clouds as a part of the NASA-supported Space Cloud Watch project. Now, one volunteer has developed a new tool to help other Space Cloud Watch volunteers work more efficiently.
The misbehaving clouds are “noctilucent” or “night-shining” clouds (NLCs). These clouds scatter light from the Sun long after sunset and long before sunrise, giving them a silvery glow. But despite this glow, it can be hard to differentiate NLCs from lower-altitude look-alikes. That confusion has meant extra work for project leaders.
Volunteer Namai Chandra shared, “I noticed that NLC images were being manually verified by the project leaders. It felt like a task well-suited for a human-in-the-loop machine learning pipeline, one that could handle the repetitive screening automatically, while keeping human judgment central for the images that matter most.” In other words, Namai found a way to help observers verify when they are indeed seeing NLCs and when they’re not.
Namai reached out to the Space Cloud Watch scientists Drs. Chihoko Cullens and Brentha Thurairajah, who were delighted with his idea. Namai soon developed a machine learning pipeline, training it on a variety of cloud images, including both the NLCs and the lower altitude look-alikes that are often submitted to Space Cloud Watch. The pipeline combines image pre-screening, cloud classification, and confidence-based review routing. After several rounds of development, testing, and refinement, he released his NLC identification tool to the project. This tool is now being used by cloud contributors who are unsure whether they have observed NLCs, as well as project scientists that want to flag images for review.
Grab a camera and join the Space Cloud Watch project today! If you’ve hesitated to contribute to Space Cloud Watch because you were not certain if what you were seeing was a noctilucent cloud, you now have a way to check before you share – thanks to Namai.
Namai Chandra, Space Cloud Watch volunteer and creator of the Noctilucent Cloud Detector tool.
Photo by Surabhi Chandra.
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Space Cloud Watch
Photograph clouds just after sunset or before dawn to investigate our changing atmosphere.
NASA’s COFFIES Uses AI to Predict Storm-Causing Active Regions on Sun
As humanity looks to the Moon and stars for future exploration, predicting space weather — conditions in space primarily driven by the Sun — is more important than ever.
Now, a team of astrophysicists and data scientists with NASA’s COFFIES (Consequence Of Fields and Flows in the Interior and Exterior of the Sun) has developed a novel machine-learning model capable of predicting the emergence of active regions on the Sun up to 12 hours before they appear.
The Sun is constantly churning. Intense concentrations of localized magnetic fields can suddenly break through the solar surface, forming sunspots. Space weather forecasters then collectively number and track sunspots since they are visible manifestations of active regions, which serve as the main engines behind severe space weather events such as solar flares and coronal mass ejections. These eruptions send waves of high-energy radiation and charged particles across space, creating storms that can threaten astronauts, disable satellites, and disrupt radio communications on Earth.
NASA’s Solar Dynamics Observatory captured this image of a solar flare — seen as the bright flash in the upper right — on June 30, 2026. The image shows a subset of extreme ultraviolet light that highlights the extremely hot material in flares and which is colorized in teal.
NASA’s Goddard Space Flight Center/SDO
By bridging expertise across different scientific institutions, COFFIES, a NASA DRIVE (Diversify, Realize, Integrate, Venture, Educate) Science Center, brought together a team of researchers from New Jersey Institute of Technology (NJIT), Princeton University, and NASA’s Ames Research Center in California’s Silicon Valley. The team turned to advanced artificial intelligence architectures — which dictate how data is processed and used to produce reliable predictions or actions — to capture subtle, time-based pattern changes on the solar surface before an active region took shape. By analyzing data captured by the agency’s Solar Dynamics Observatory and using NASA Ames’ supercomputing resources, this new approach, published in the Journal of Geophysical Research: Machine Learning and Computation, looks at fluctuations in acoustic waves caused by sunspot regions when the regions form beneath the solar surface and begin the journey upward to emerge on the surface.
“We cannot directly see the magnetic structure while it is still rising through the solar interior. Instead, we must look for indirect effects — very small changes in the magnetic field and in the pattern of acoustic waves continually traveling through the Sun,” said Alexander Kosovichev, a COFFIES co-investigator at NJIT. “The developed technique identifies precursors associated with an emerging active region in slight changes of the Sun’s acoustic power — more like a slight change in rhythm within a very noisy orchestra.”
This video is an example of what scientists use when analyzing the solar surface. This particular time frame tracks the magnetic field on the Sun’s surface during the emergence of active region AR11158 in February 2011. The blue square grid highlights a target area on the Sun. The squares on the right side translates the data from the target grid area to show opposing magnetic polarities, indicated by the warm and cool-colored tones. The first column of blocks shows targeted areas at original resolution, the middle column displays data as 2D maps, and the right column plots changes in magnetic polarity over time as 1D curves. By watching these blocks, scientists can see signs of active region emergence, such as drops in acoustic waves and rises in magnetic fields.
NASA’s COFFIES DRIVE Science Center/Irina Kitiashvili and Spiridon Kasapis
To develop current operational forecasts, the National Oceanic and Atmospheric Administration’s Space Weather Prediction Center and the United States Air Force monitor active regions that are already visible on the Sun to analyze the regions’ characteristics and estimate the probability of solar flares.
The COFFIES team aims to revolutionize this process. The AI model the team developed a specialized early detection system to handle very long sequences of data — called sliding-window transformer architecture — to use observations to find tiny reductions in the Sun’s acoustic activity and magnetic field, signals that scientists struggled to capture until now. These reductions form patterns that the AI model uses to predict active regions several hours before they become visible on the solar surface. Instead of looking at all activity on the solar surface at once, like earlier deep learning approaches have done, this new model moves a fixed-size “viewing window” across a long timeline of the Sun’s activity to focus on recent data while remembering overall patterns. This method allows forecasters the ability to predict approximate locations of emerging sunspots, rather than relying on counting already visible sunspots.
This promising AI architecture shows how deep machine learning can contribute to heliophysics — the field studying the nature of the Sun and how it influences the very nature of space and the planets that exist there. While the model is not ready for operational real-time forecasting, the team plans to validate the approach across many more known solar events to fine-tune the model.
NASA’s real-time space weather monitoring
As NASA focuses on sending humans to explore the Moon with the Artemis missions and sending the first crewed missions to Mars, monitoring and forecasting space weather is important for ensuring the safety of our astronauts and the equipment they rely on. This predictive leap from the COFFIES team could prove vital for safeguarding technology and deep-space explorers from the volatile environment of our solar system.
NASA’s Moon to Mars Space Weather Analysis Office monitors space weather 7 days a week. This important work helps decision makers not only protect people and equipment but maintain the services our modern society relies on every day. NASA’s space weather monitoring is also critical for safeguarding astronauts as they journey to the Moon and onward to Mars.
NASA/Lacey Young
Teams across NASA and NOAA collaborate to transition research capabilities into actual 360-degree space weather monitoring operational tools — including NASA’s Space Radiation Analysis Group, Moon to Mars Space Weather Analysis Office (M2M SWAO), and Community Coordinated Modeling Center as well as NOAA’s Space Weather Prediction Center. Sunspot region emergence prediction capabilities, especially of the Sun’s far side, could provide new information that supplements current models used by these teams.
“The COFFIES AI model is exciting to our team because it could provide us with new capabilities towards predicting potential flaring locations ahead of time,” said Michelangelo Romano, M2M SWAO deputy director. “With this heads up, we can provide additional support to NASA missions.”
NASA’s COFFIES is one of three DRIVE Science Centers created to encourage collaborative science by establishing centers that are made of multidisciplinary teams from several institutions across the U.S. These pioneering facilities employ modelers, theoreticians, computer scientists, and observers to study important mysteries of our star and its influence, a branch of science known as heliophysics.
The COFFIES team focuses on the interconnected processes behind the Sun’s activity. Understanding the Sun’s interior and magnetic variability is key to advancing our understanding of the Sun’s 11-year activity cycle and fine-tuning space weather forecasting tools.
France’s Constitutional Council said that the ban was too wide-ranging, and did not take into account the age, maturity or family situation of children.
France’s Constitutional Council said that the ban was too wide-ranging, and did not take into account the age, maturity or family situation of children.
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