摘要：Prediction of geomagnetically induced currents (GICs) plays a critical role in the gestalt of space weather forecasting and risk assessment, giving power companies time to enact mitigation strategies that could avoid a catastrophic collapse of the power grid caused by, for example, the impact of an Earth-directed coronal mass ejection. Sun-to-mud prediction of the surface magnetic field (and/or its time derivative) is a long-standing goal for both first-principles and data-driven models. Here, we quantify the upper limits of peak GIC predictability as a function of how much magnetic field information is accurately predictable. Using the United States as a testbed, our results suggest that accurate characterization of temporal scales up to around 30 mHz keeps relative peak GIC errors below 10 across the regions considered. We also found that forecasting if GIC will exceed a specified threshold over the next 30 min is feasible with an accurate prediction of peak dB/dt magnitude. This is supported by reasonable out-of-sample performance across several forecast metrics (0.4–0.8 POD, POFD 0.05, 1–5 forecast ratio, 0.4–0.8 Heidke skill score), and favorable performance relative to a persistence model across all but the most extreme data intervals. We also find that the subsurface conductivity may influence peak GIC predictability. Overall, our results highlight the range of temporal scales in the surface magnetic field that are important for estimation of peak GIC and, in the context of peak dB/dt magnitude prediction, provide an upper bound on expected GIC predictability across a wide range of magnitudes.
