Waves which couple to energetic electrons are particularly important in space weather, as they drive rapid changes in the topology and intensity of Earth’s outer radiation belt during geomagnetic storms. This includes Ultra Low Frequency (ULF) waves that interact with electrons via radial diffusion which can lead to electron dropouts and rapid acceleration and inward transport of electrons during. In radiation belt simulations, the strength of this interaction is specified by ULF wave radial diffusion coefficients. In this paper we detail the development of new models of electric and magnetic radial diffusion coefficients derived from in-situ observations of the azimuthal electric field and compressional magnetic field. The new models use L* as it accounts for adiabatic changes due to the dynamic magnetic field coupled with an optimized set of four components of solar wind and geomagnetic activity, Bz, V, Pdyn and Sym-H, as independent variables (inputs). These independent variables are known drivers of ULF waves and offer the ability to calculate diffusion coefficients at a higher cadence then existing models based on Kp. We investigate the performance of the new models by characterizing the model residuals as a function of each independent variable and by comparing to existing radial diffusion models during a quiet geomagnetic period and through a geomagnetic storm. We find that the models developed here perform well under varying levels of activity and have a larger slope or steeper gradient as a function of L* as compared to existing models (higher radial diffusion at higher L* values).

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.

Loss mechanisms act independently or in unison to drive rapid loss of electrons in the radiation belts. Electrons may be lost by precipitation into the Earth’s atmosphere, or through the magnetopause into interplanetary space; a process known as magnetopause shadowing. Whilst magnetopause shadowing is known to produce dropouts in electron flux, it is unclear if shadowing continues to remove particles in tandem with electron acceleration processes, limiting the overall flux increase. We investigated the contribution of shadowing to overall radiation belt fluxes throughout a geomagnetic storm starting on the 7 September 2017. We use new, multi-spacecraft phase space density calculations to decipher electron dynamics during each storm phase and identify features of magnetopause shadowing during both the net-loss and the net-acceleration storm phases. We also highlight two distinct types of shadowing; ‘direct’, where electrons are lost as their orbit intersects the magnetopause, and ‘indirect’, where electrons are lost through ULF wave driven radial transport towards the magnetopause boundary.

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.