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Geomagnetically induced currents (GICs) pose a significant space weather hazard, driven by geomagnetic field variation due to the coupling of the solar wind to the magnetosphere-ionosphere system. Extensive research has been dedicated to understanding ground-level geomagnetic field perturbations as a GIC proxy. Still, the non-uniform aspect of geomagnetic fluctuations make it difficult to fully characterize the ground-level magnetic field across large regions of the globe. Here, we focus on localized geomagnetic disturbances (LGMDs) in the North American region and specify the degree to which these disturbances are localized. Employing the electrodynamics-informed Spherical Elementary Current Systems (SECS) method, we spatially interpolate magnetic field perturbations between ground-based magnetometer stations. In this way, we represent the ground magnetic field as a series of heatmaps at high temporal and spatial resolution. We leverage heatmaps from storm time during solar cycle 24 to automatically identify LGMDs. We build a statistical picture of the frequency with which LGMDs occur, their scale sizes, and their latitude-longitude aspect ratios. Additionally, we use an information theory approach to quantify the dependence of these three attributes on the phase of the solar cycle. We find no clear influence of the solar cycle on any of the three attributes. We offer some avenues toward explaining why LGMDs might behave broadly the same whether they arise during solar maximum or solar minimum. 

Forecasting ground magnetic field perturbations has been a long-standing goal of the space weather community. The availability of ground magnetic field data and its potential to be used in geomagnetically induced current studies, such as risk assessment, have resulted in several forecasting efforts over the past few decades. One particular community effort was the Geospace Environment Modeling (GEM) challenge of ground magnetic field perturbations that evaluated the predictive capacity of several empirical and first principles models at both mid- and high-latitudes in order to choose an operative model. In this work, we use three different deep learning models-a feed-forward neural network, a long short-term memory recurrent network and a convolutional neural network-to forecast the horizontal component of the ground magnetic field rate of change ( dB H / dt ) over 6 different ground magnetometer stations and to compare as directly as possible with the original GEM challenge. We find that, in general, the models are able to perform at similar levels to those obtained in the original challenge, although the performance depends heavily on the particular storm being evaluated. We then discuss the limitations of such a comparison on the basis that the original challenge was not designed with machine learning algorithms in mind.