Solar Weather Forecasting Gets a Weeks-Long Early Warning System

The problem has vexed heliophysicists for decades: solar flares erupt with little warning, and by the time magnetic disturbances appear on the Sun's surface, the most dangerous eruptions are already hours away. A collaboration between Southwest Research Institute (SwRI) and the National Science Foundation's National Center for Atmospheric Research (NSF-NCAR) has now taken a concrete first step toward changing that timeline from hours to weeks.

The key is a new framework called PINNBARDS - Physics-Informed Neural Network-Based Active Region Distribution Simulator. Published in the Astrophysical Journal, the work connects observations of the Sun's visible surface to the behavior of magnetic structures buried thousands of kilometers beneath it.

The Hidden Engine of Solar Storms

Solar active regions - the tangled magnetic zones responsible for flares and coronal mass ejections - do not appear randomly. They cluster along large-scale, warped magnetic bands called toroidal belts that originate deep inside the Sun, in a thin transition layer known as the tachocline. This layer sits between the uniformly rotating radiative interior and the more turbulent convection zone above it.

The tachocline is the dynamo powering the Sun's magnetic cycle, but it is entirely invisible to conventional telescopes. Most existing forecasting tools work from the opposite direction - they watch for small-scale magnetic signatures on the surface that become detectable only hours before an eruption. That timeline is useful for some applications but inadequate for large infrastructure protection.

"Understanding where and when large, flare-producing active regions on the Sun would emerge is a long-standing problem in heliophysics," said SwRI's Dr. Subhamoy Chatterjee, co-author of the paper. "These regions display tangled magnetic fields and produce explosive solar events, potentially causing hazardous space weather."

Reading the Surface to Reconstruct What Lies Beneath

PINNBARDS takes a fundamentally different approach. Using magnetic measurements from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the team demonstrated that patterns in surface magnetism can be mathematically inverted to reconstruct critical states in the tachocline region below. The surface acts as a readout of the deeper machinery - if you know how to decode it.

The framework merges two technologies rarely combined at this scale: physical solar models and machine learning. The physics component enforces known rules about how magnetic flux tubes rise through the convection zone. The neural network learns the statistical relationships between surface observations and subsurface states. Neither approach works well in isolation here - the physics without data fitting is too approximate, while a pure machine learning model lacks sufficient training data.

"The reconstructed subsurface states from PINNBARDS provide initial conditions for forward simulations of solar magnetic evolution, opening the door to predicting where and when large, flare-producing active regions are likely to emerge weeks in advance," said Dr. Mausumi Dikpati, a senior scientist at NSF-NCAR who led the team.

Why Location Matters as Much as Timing

Extended lead times alone would not make a forecast useful without spatial precision. The latitude and longitude of an emerging active region determine whether the resulting burst of charged particles will travel toward Earth or veer harmlessly into interplanetary space. A flare on the solar limb poses far less risk than one centered directly on the Sun-Earth line.

Current tools can often confirm that a flare is in progress or imminent, but struggle to identify in advance which part of the Sun will be the source. PINNBARDS, by using global magnetic information rather than local surface signatures, could in principle identify which toroidal band is energized and where along that band the instability will surface.

Practical Stakes: GPS, Power Grids, and Human Spaceflight

The consequences of severe space weather events are well documented. The March 1989 geomagnetic storm caused a nine-hour blackout across Quebec, affecting roughly six million people. GPS systems depend on signals that can be severely degraded by ionospheric disturbances following a major solar event.

For human spaceflight, the window for action is narrow. Astronauts aboard the International Space Station, and those traveling to the Moon under NASA's Artemis program, need at minimum 24 to 48 hours of advance warning to shelter in radiation-hardened spacecraft sections. Weeks of lead time would allow mission planners to reschedule spacewalks, adjust launch windows, and pre-position shielding resources.

Power grid operators have long called for days or weeks of warning to take protective actions - including temporarily removing long-distance transmission lines from service or pre-positioning spare transformers, equipment that can cost millions of dollars and take months to manufacture.

First Step, Not a Finished System

The PINNBARDS paper represents a proof-of-concept rather than an operational forecast system. The team demonstrated that surface patterns carry subsurface information and that a physics-informed neural network can extract it - but the framework has not yet been validated against a large archive of real flare events with documented subsurface precursors.

The next step is forward simulation: using the reconstructed tachocline states as starting conditions for models projecting how magnetic structures will evolve over days and weeks. Prediction skill will also depend on how cleanly surface observations encode subsurface dynamics in practice, which may vary across the Sun's 11-year activity cycle.

The research was funded by NASA's Heliophysics Guest Investigator Open program, NSF-NCAR, and Stanford's Consequences of Fields and Flows in the Interior and Exterior of the Sun center.