OMAN: NASA's COFFIES team has developed a machine-learning model to predict active regions on the Sun up to 12 hours before they appear, potentially offering critical insights into space weather. This advancement is crucial as solar activity can cause severe disruptions to Earth's satellites, radio communications, and pose risks to astronauts. The COFFIES initiative, part of NASA's DRIVE Science Center, utilizes data from the Solar Dynamics Observatory and the advanced computational resources of NASA Ames. By analyzing acoustic wave fluctuations, the model identifies subtle changes in the Sun's surface that precede the formation of active regions, which often lead to solar storms. "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," Alexander Kosovichev said. The ability to forecast solar storms will enhance preparedness and mitigation strategies for space-related technologies and missions. This AI-driven approach could mark a significant leap in understanding and anticipating space weather events, safeguarding both terrestrial and extraterrestrial operations.
