NASA team predicts sunspot emergence with AI
A multidisciplinary team within NASA's COFFIES center developed a novel machine learning model that predicts the emergence of solar active regions up to 12 hours before surface appearance. The approach uses Solar Dynamics Observatory observations, NASA Ames supercomputing, and a sliding-window transformer architecture to detect slight reductions in acoustic power and magnetic field signals as regions rise beneath the solar surface. The technique can predict approximate emergence locations rather than counting visible sunspots, and the method was published in a peer-reviewed journal, though the model still requires wider validation before operational use by NASA and NOAA forecasting teams.
AI model predicts active regions up to twelve hours before surface emergence.
Context
Space weather forecasting relies on tracking visible sunspots now. The COFFIES team…
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