Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power-grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long- and short-period characteristics?” is addressed. The study focuses on the long-term variability of measured GIC, and establishes the extent to which the variability relates to quiet-time geomagnetic activity. GIC quiet-day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of Thermosphere-Ionosphere- Electrodynamics General Circulation Model (TIE-GCM)-simulated neutral wind and height-integrated current density. The results show strong evidence that the middle-latitude nodes routinely respond to the tidal-driven Sq variation, with a local time and seasonal dependence on the the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC-based significance score computed in this study provides power utilities with a node-specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.

The ionosphere contains many small-scale electron density variations that are under represented in smooth physics-based or climatological models. This can negatively impact the results of Observation System Simulation Experiments, which use a truth model to simulate data. This paper addresses this problem by using ionosonde data to study ionospheric variability and build a new truth model with empirically-driven variations. The variations are studied for their amplitude, horizontal and vertical size, and temporal extent. Results are presented for different local times, seasons, and at two different points in the solar cycle. We find that these departures from a smooth background are often as large as 25\% and are most prevalent near 250 km in altitude. They have horizontal spatial extents that vary from a few hundred to a few thousand kilometers, and typically have the largest horizontal extent at high altitudes. Their vertical extents follow the same pattern of being larger at high altitudes, but they only vary from 10s of km up to 200 km in vertical size. Temporally, these variations can last for a few hours. The procedure for using these spatial and temporal distributions to add empirically-driven variance to a smooth truth model is outlined. This process is used to make a truth model with representative variations, which is compared to ionosonde data as well as GPS Total Electron Content (TEC) data that was not used to inform the model. The new model resembles the data much better than the smooth models traditionally used.

The growing scale of Earth and space science challenges dictate new modes of discovery–discovery that embraces cross-disciplinary interactions and links between communities, between data, between technologies. Nowhere is the challenge more pressing than in the field of Heliophysics where solar energy is generated, propagated through interplanetary space, interacts with the Earth’s space environment, and poses immediate threat to our technological infrastructure and human-natural systems (i.e., space weather). We will present a new project within the National Science Foundation Convergence Accelerator program that represents this new mode of discovery “The Convergence Hub for the Exploration of Space Science (CHESS).” Our approach is to semantically link Heliophysics data through a Knowedge Graph/Network (KG). The presentation and discussion will focus on: - What is a knowledge graph (KG)? - In what ways are KGs poised to transform Earth and space science? - The Convergence Hub for the Exploration of Space Science (CHESS) project and bridging to metadata and knowledge architecture efforts in Heliophysics We will highlight linkages to the NSF EarthCube program and ongoing efforts in the geoinformatics and data science communities across e.g., NSF, NOAA, and NASA.

The growing depth and breadth of available data that span the solar-terrestrial environment place us at a tipping point – the potential of these data is immense but realizing that potential requires a new representation. A new network-based approach to represent data collected by power utilities along with information from the solar-terrestrial connection is used. The progress is generated as part of a new project within the National Science Foundation Convergence Accelerator program: “The Convergence Hub for the Exploration of Space Science (CHESS).” Results are shared from current data provided through the Electric Power Research Institute (EPRI) SUNBURST project linked to magnetometer data from the Super Magnetometer Initiative. These data are transformed into a network with GIC measurements and magnetometers as the nodes in order to answer a long-standing question: “How much more likely are deviations from the average current when there is active space weather”? To answer this question, periods of active space weather are identified in the magnetometer data, and these are compared to times of DC transients in the GIC data. The probability of a these transients is found to be , on average, 1.6 times higher during periods of active space weather than during quiet times. The most indicative magnetometers of these DC transients are often not the closest to where the GICs are measured.