Forecasting the solar storm threat

A proposed permanent network of electromagnetic monitoring stations across the continental US, operating in tandem with a machine learning (ML) algorithm, could facilitate accurate predictions of geomagnetic disturbances (GMDs). If realized, this predictive system could help grid operators avert disruption and reduce the likelihood of damage to their — and their customers’ — infrastructure, including data centers.

Geomagnetic disturbances, also referred to as “geomagnetic storms” or “geomagnetic EMP”, occur when violent solar events interact with Earth’s atmosphere and magnetic field. Solar events that cause geomagnetic EMP (such as coronal mass ejection, or solar flares) occur frequently but chaotically, and are often directed away from Earth. The only long-term available predictions are probabilistic, and imprecise: for example, an extreme geomagnetic EMP typically occurs once every 25 years. When a solar event occurs, the US Space Weather Prediction Center (SWPC) can give hours’ to days’ notice of when it is expected to reach Earth. At present, these warnings lack practical information regarding the intensity and the location of such EMPs’ effects on power infrastructure and customer equipment (such as data centers).