Empirical Relationship for Geomagnetically Induced Currents (GIC) and Solar Wind Conditions
Geomagnetically induced currents (GICs) during extreme space weather events represent a critical hazard to modern infrastructure, including electrical power grids, telecommunication systems, pipelines, and railways. This study presents empirical models designed to provide rapid, site-specific estimates of the maximum GIC, $|\text{GIC}|_{\text{max}}$, expected during a storm. Utilizing data from 67 sites across nine major geomagnetic storms ($K_p \ge 8$) occurring between 2020 and 2025, we performed multivariate linear and quantile regressions incorporating solar wind parameters and local scaling factors. The solar wind parameters address storm intensity and are the minimum interplanetary magnetic field $z$-component, $(B_{z}^{\tiny{\text{IMF}}})_{\text{min}}$, and the maximum magnitude of the solar wind velocity $x$-component, $(V_{x})_{\text{max}}$. The scaling factors account for local differences due to geomagnetic latitude ($α$) and ground conductivity ($β$). A 6-parameter model was identified via best subset selection and the corrected Akaike Information Criterion (AICc) as the optimal predictor for mean $|\text{GIC}|_{\text{max}}$, yielding a Pearson correlation of $r = 0.73$ and a coefficient of determination of $R^2 = 0.54$. To examine peak GICs during storms, a 7-parameter quantile regression model was developed to estimate the upper 80% bound of $|\text{GIC}|_{\text{max}}$. Statistical analysis of 39 severe storms since 1995 shows that the $|\text{GIC}|_{\text{max}}$ upper bounds are consistent with a lognormal distribution. These relationships offer a computationally efficient alternative to complex physics-based simulations, enabling space weather practitioners to estimate site-specific GICs in high-level analyses.