L. A. Wilkerson

The May 2024 geomagnetic storm was one of the most severe in the past 20 years. Understanding how large geomagnetic disturbances (GMDs) impact geomagnetically induced currents (GICs) within electrical power grid networks is key to ensuring their resilience. We have assembled and synthesized a large and unique set of GMD‐related data, compared model predictions with measurements, and identified empirical relationships for GICs in the contiguous United States for this storm. Measurement data include GIC data from 47 sites and magnetometer data from 17 sites. Model data include GIC computed by the Tennessee Valley Authority (TVA) power system operators at 4 sites, GIC computed using a reference model at 47 sites, and the difference in the surface magnetic field from a baseline computed at 12 magnetometer sites from three global magnetospheric models—the Multiscale Atmosphere‐Geospace Environment Model (MAGE), Space Weather Modeling Framework (SWMF), and Open Geospace General Circulation Model (OpenGGCM). GIC measured and computed by TVA had a correlation coefficient and a prediction efficiency between 0.4 and 0.7. The horizontal magnetic field perturbation from a baseline, , computed by MAGE, SWMF, and OpenGGCM had a correlation r from 0.21 to 0.65. Two empirical relationships were considered: (a) how the correlation between measured GIC site pairs depended on differences in site separation distance, scaling factor (related to ground conductivity), and geomagnetic latitude; and (b) a regression model for the maximum magnitude at each site given the product of (related to magnetic latitude) and .

The May 2024 geomagnetic storm was one of the most severe in the past 20 years. Understanding how large geomagnetic disturbances (GMDs) impact geomagnetically induced currents (GICs) within electrical power grid networks is key to ensuring their resilience. We have assembled and synthesized a large and unique set of GMD-related data, compared model predictions with measurements, and identified empirical relationships for GICs in the contiguous United States for this storm. Measurement data include GIC data from 47 sites and magnetometer data from 17 sites. Model data include GIC computed by the Tennessee Valley Authority (TVA) power system operators at four sites, GIC computed using a reference model at 47 sites, and the difference in the surface magnetic field from a baseline (∆B) computed at 12 magnetometer sites from three global magnetospheric models — the Multiscale Atmosphere-Geospace Environment Model (MAGE), Space Weather Modeling Framework (SWMF), and Open Geospace General Circulation Model (OpenGGCM). GIC measured and modeled by TVA had a correlation coefficient >0.8 and a prediction efficiency between 0.4 and 0.7. The horizontal magnetic field perturbation from a baseline, ∆BH , computed by MAGE, SWMF, and OpenGGCM had a correlation r from 0.21 to 0.65. Two empirical relationships were considered: (1) how the correlation between measured GIC site pairs depended on differences in site separation distance, β scaling factor (related to ground conductivity), and geomagnetic latitude; and (2) a regression model for the maximum GIC magnitude at each site give the product of α (related to magnetic latitude) and β.