Abstract

During periods of rapidly changing geomagnetic conditions electric fields form within the Earth's surface and induce currents known as geomagnetically induced currents (GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (|B-N|) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000-2015 and is fine tuned for each station to maximize the effectiveness in predicting |B-N|. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for |B-N|. The neural network models show significant increases over the linear regression models for |B-N| thresholds. We also calculate the Heidke skill scores for d|B-N|/dt by deriving d|B-N|/dt from |B-N| predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict B-N instead of |B-N|, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times.