A new study suggests that artificial intelligence (AI) could have predicted the powerful solar storm that struck Earth in May 2024, providing crucial early warnings that traditional forecasting methods failed to deliver. Researchers from the University of Genoa, led by Sabrina Guastavino, claim that by training AI on historical solar activity, it is possible to identify patterns that precede coronal mass ejections (CMEs) and improve space weather predictions.
AI’s Role in Predicting Solar Storms
Solar activity follows an 11-year cycle of peaks and lows, driven by changes in the Sun’s magnetic field. The solar storm in May 2024 was triggered by the highly active sunspot region AR13664, which unleashed a series of CMEs that impacted Earth’s magnetic field. These events caused disruptions to communication networks, GPS navigation, and power grids.
Despite advancements in observational technology, traditional solar forecasting methods still struggle with accuracy and timeliness. Current models rely on human analysis of solar images and space weather data, often leading to uncertainty in both flare forecasting and CME travel time predictions. According to the study, the average uncertainty in predicting a CME’s arrival on Earth is around 12 hours.
The AI model developed by the researchers aims to address these challenges by analyzing vast amounts of solar data, including images, magnetic field measurements, and solar wind patterns. It looks for subtle changes in the Sun’s atmosphere that indicate an imminent CME, allowing for more precise predictions.
“The May 2024 event also underscores the broader implications of AI-driven reverse engineering for space weather science,” the study states. “The ability to predict CME travel times with such precision suggests that AI can serve as a diagnostic tool for testing and refining existing models of CME propagation.”
Understanding Coronal Mass Ejections
Coronal mass ejections are massive bursts of plasma and magnetic fields ejected from the Sun’s corona. These high-speed clouds of electrified gas can travel at hundreds of miles per second, interacting with Earth’s magnetic field upon arrival. Strong CMEs can trigger geomagnetic storms, which can affect satellite operations, disrupt power grids, and interfere with communication systems.
According to the Indian Space Research Organisation (ISRO), the geomagnetic storm in May 2024 was the most intense since 2003, causing widespread disruptions to GPS and radio communication. The event served as a reminder of the potential dangers posed by solar storms and the need for improved forecasting methods.
Solar Cycle 25 and Future Predictions
The Sun is currently in Solar Cycle 25, which is expected to reach its peak around July 2025. Scientists initially predicted that this cycle would have a moderate level of activity, with around 115 sunspots at its maximum. However, the Sun has shown more activity than anticipated, with stronger and more frequent solar flares and CMEs.
Despite having more observational data than ever before, scientists are still uncertain about why the Sun’s activity has exceeded expectations. Further research is needed to refine prediction models and improve our understanding of the Sun’s internal processes. AI-driven forecasting could play a key role in this effort by providing early warnings and reducing the impact of future solar storms on Earth’s technological infrastructure.
As AI continues to advance, it may become an essential tool in space weather forecasting, helping governments, space agencies, and industries prepare for potentially disruptive solar events.
