AI Breakthrough Extends Solar Storm Warning Window from Hours to Weeks
A new artificial intelligence tool called PINNBARDS has cracked a decades-old problem in space weather forecasting: predicting where and when the Sun's most dangerous active regions will emerge—weeks before they strike.
Developed jointly by Southwest Research Institute (SwRI) and NSF-NCAR, PINNBARDS represents a fundamental shift in how scientists approach solar prediction. Rather than waiting for magnetic storms to form on the Sun's visible surface, the tool looks beneath it, using neural networks to connect what we observe on the solar surface to the turbulent magnetic dynamics churning in the layers below. The result: decision-makers now have weeks instead of hours to protect satellites, power grids, and astronauts from incoming solar flares and coronal mass ejections (CMEs).
The Problem: The Sun's Invisible Warnings
The Sun's magnetic field is less a unified force and more a tangled knot of energy. Beneath the photosphere, complex magnetic loops twist and warp. Occasionally, they destabilize explosively, creating active regions (ARs)—zones of intense magnetic activity capable of hurling radiation and energetic particles toward Earth at near-light speeds.
For heliophysicists, predicting when and where these regions will erupt has been the fundamental unsolved problem. "Understanding where and when large, flare-producing active regions on the sun would emerge is a long-standing problem in heliophysics," explained Dr. Subhamoy Chatterjee, co-author of the research. Traditional forecasting tools have been constrained to short-term predictions—typically hours at best. That narrow window gives operators precious little time to prepare. A grid operator cannot simply shut down a continental power system on six hours' notice. Satellite operators cannot maneuver high-value assets with minimal lead time.
How PINNBARDS Works: Physics Meets Machine Learning
PINNBARDS—a Physics-Informed Neural Network-Based AR Distribution Simulator—takes a different approach. It ingests data from NASA's Solar Dynamics Observatory and its Helioseismic and Magnetic Imager (HMI), which measures the Sun's surface magnetic field with unprecedented precision. But instead of stopping at the surface, PINNBARDS uses AI to infer the subsurface magnetic state—essentially reading the Sun's "pulse" at depths of thousands of kilometers below what we can directly observe.
These reconstructed subsurface conditions become initial conditions for forward simulations of solar magnetic evolution. The model can then project forward, identifying which regions of subsurface magnetic twist are likely to erupt into dangerous active regions. "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, the project's lead scientist.
The leverage is enormous. Where hours allow reactive measures, weeks allow strategic preparation—rerouting spacecraft, increasing shielding, adjusting satellite orbits, or implementing protective measures in power grid operations.
Implications for Critical Infrastructure and Deep Space Exploration
The stakes extend far beyond the grid. A severe solar storm can knock out communication satellites, disable GPS networks, and cascade failures across dependent infrastructure. For space agencies planning extended missions to Mars and beyond, solar forecasting is now a mission-critical capability. Astronauts in deep space lack Earth's magnetic field for protection; advance warning of solar events becomes the difference between safe passage and potentially fatal radiation exposure.
The research, published in the Astrophysical Journal, marks the beginning of operational deployment. Researchers are working to integrate PINNBARDS into real-time forecasting systems operated by NOAA's Space Weather Prediction Center and international partners.
What's Next
The immediate priority is validating PINNBARDS' forecasts against historical solar data and live observations. Early results have been promising, but operational systems demand proof of concept. As the model matures, researchers expect even longer prediction windows and finer granularity—eventually, perhaps, forecasting specific flares rather than just emergence windows for active regions.
