Abstract
Accurately predicting the horizontal component of ground magnetic field perturbation (dB H ), a key quantity for calculating the geomagnetically induced currents (GICs), is crucial for assessing the space weather impact of geomagnetic disturbances. The current operational first-principles Michigan Geospace model provides effective forecasts of dB H , but requires significant computational resources to achieve real-time speeds. Existing data-driven methods tend to underpredict dB H and lack uncertainty quantification, which is either overlooked or treated as secondary. In this work, we introduce GeoDGP, a novel and efficient data-driven model based on the deep Gaussian process. GeoDGP provides global probabilistic forecasts of dB H with a lead time of at least 1 hr, at 1-min time cadence, and at arbitrary spatial locations. The model takes solar wind measurements, the Dst index, and the prediction location in solar magnetic coordinate system as inputs, and is trained on 28 years of data from SuperMAG global magnetometer stations. Additionally, GeoDGP is also trained to predict the north (dB N ) and east (dB E ) components of perturbations. We evaluate GeoDGP's performance at over 200 stations worldwide during 24 geomagnetic storms, including the Gannon extreme storm of May 2024. Comparisons with the first-principles Michigan Geospace model and the data-driven DAGGER model revealed that GeoDGP significantly outperforms both across multiple performance metrics. Plain Language Summary Ground magnetic field perturbations are crucial for predicting geomagnetically induced currents, which can harm power grids, communication systems, and other ground infrastructure. Accurately predicting these perturbations with high temporal and spatial resolution is critical but remains a significant challenge in space weather forecasting. In this work, we introduce GeoDGP, an advanced data-driven model that provides reliable forecasts of these perturbations at least 1 hr ahead, at 1 min intervals, and can be used for any location. GeoDGP's performance is evaluated across a wide range of geomagnetic storms using data from over 200 magnetometer stations worldwide. The results show that GeoDGP significantly outperforms leading existing first-principle and data-driven models.
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