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 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.

We present observations and modeling of Low Frequency (LF; 30-300 kHz) and Medium Frequency (MF; 300-3000 kHz) signals during 21-August-2017 “Great American Solar Eclipse” using Nationwide Differential GPS (NDGPS) transmitters as a signal of opportunity. Apparent forward and back scattering from the eclipse totality spot is presented for the first time. The effect of the solar eclipse on the D-region electron density is investigated using FDTD modeling. The waveguide parameters of the totality spot are estimated to be h’ = 80 +/- 3 km and β = 0.9 +/- 0.1 km. The transition from an obscured ionosphere to a fully eclipsed ionosphere may be slow, 10s of seconds, but the transition from a fully eclipsed ionosphere to obscured likely occurred quite fast, less then a second, when the Sun’s influence reappeared.

The first demonstration of rocket exhaust driven amplification (REDA) of whistler mode waves occurred on 26 May 2020 by transferring energy from pickup ions in a rocket exhaust plume to EM waves. The source of coherent VLF waves was the Navy NML Transmitter at 25.2 kHz located in LaMoure, North Dakota. The topside ionosphere at 480 km altitude became an amplifying medium with a 60 second firing of the Cygnus BT-4 engine. The rocket engine injected exhaust as a neutral cloud moving perpendicular to field lines that connected the NML transmitter to the VLF Radio Receiver Instrument (RRI) on e-POP/SWARM-E. Charge exchange between the ambient O+ ions and the hypersonic water molecules in the exhaust produced H2O+ ions in a ring-beam velocity distribution. The 25.2 kHz VLF signal from NML was amplified by 30 dB for a period 77 seconds as observed by the RRI. Simultaneously, preexisting coherent ELF waves at 300 Hz were amplified by 50 dB during and after the Cygnus burn. Extremely strong coherent emissions and quasi-periodic bursts in the 300 to 310 Hz frequency range lasted for 200 seconds after the release. The excitation of an ELF whistler cavity may have lasted even longer, but the orbit of the SWARM-E/e-POP moved the RRI sensor away from the wave emission region. The amplified 300 Hz ELF waves may have gained even more energy by cyclotron resonance with radiation belt electrons as they were ducted between geomagnetic-conjugate hemispheres.

Observations of radio waves in the Extremely Low Frequency and Very Low Frequency band (ELF/VLF, 0.3-30 kHz) have a host of geophysical uses, including lightning detection and characterization, D-region ionosphere remote sensing, detection of solar flares and geomagnetic storms, gravity waves, gamma-ray burst detection, observations of whistlers, chorus and hiss, to infer wave-particle interactions in the magnetosphere, plasmaspheric state. It’s been looked at for earthquake forecasting and also has commercial uses like submarine communications and subterranean prospecting. For many years ELF/VLF data have been collected at various locations and by various groups around the world for a variety of scientific purposes, but most of this data is not available publicly. We introduce the World Archive of Low-frequency Data and Observations (WALDO), a repository of ELF/VLF data from recordings taken over the past two decades by Stanford University and subsequently by Georgia Tech and University of Colorado Denver. The locations of the recordings are all around the world, including Alaska, Antarctica, Australia, and many low and mid latitude stations. Some sites were more consistent than others but there’s a lot of untapped value in this dataset. Funding for these recordings came from many years of funding from NSF, NASA, DoD, and others, on various basic science projects, and we feel a responsibility to make sure the datasets are now preserved. We are in the process of transferring many 100s of TBs of data and sharing every raw bit for anyone to download and analyze. This includes both “broadband” data that includes the entire spectrum from 500 Hz – 50 kHz, and “narrowband” data corresponding to amplitudes and phases of specific transmitting beacons. We are also including automatically generated summary plots, and a host of basic analysis tools to allow anyone to download and analyze the data. We will announce and present WALDO, update its status and timeline for full deployment, and detail some of the uses of ELF/VLF data, with the goal of enabling its use by anyone interested. We will not be finished by the Fall meeting (ripping 80,000 DVDs can take a while) but whatever we finished will be public and hopefully we will be far along by then. Finally, we will have the answer to the age-old question…”Where’s WALDO?”

A new model of high-latitude convection derived using machine learning (ML) is presented. The ML algorithm random forests regression was applied to a database of velocity observations from the Super Dual Auroral Radar Network (SuperDARN). The features used to train the model were the IMF components Bx, By, and Bz; the solar wind velocity, vsw; the auroral indicies, Au and Al; and the geomagnetic index, SYM-H. The SuperDARN velocities were separated into north-south, and east-west components and sorted into a magnetic local time - magnetic latitude grid that ran from 55° to the magnetic pole with a bin size of 2° in latitude, and 1-hour in MLT. Separate models were created for each velocity component in each bin of the grid. It is found that even though the models in each bin are independent of one another a coherent convection pattern is formed when the models are viewed in aggregate. The resulting convection pattern responds to changes in the auroral indicies by expanding and contracting in a way that is consistent with expectations for a substorm cycle. Further it is found that the mean-squared difference between predictions of the model and observed values of the velocity are substantially lower than the same quantity calculated for an existing climatology that was not formed with ML techniques

Lightning and transient luminous events (TLEs) emit a short burst (~1 ms) of broadband electromagnetic waves, whose frequencies can range from a few Hz to the optical band, but the bulk of their energy is radiated as longwaves (<500 kHz). These longwave radio signals are named radio atmospherics, or colloquially sferics. Due to their low frequency, sferics can propagate in the Earth-ionosphere waveguide at global distances with relatively low attenuation (~3 dB per 1000 km). This allows a sparse network of longwave receiver stations, placed hundreds of kilometers apart, to geolocate lightning strikes at a global scale. Hardware performance of the receivers at these stations significantly impacts the data quality and determines the detection efficiency and location accuracy of the lightning detection network. In this work, we present a low-frequency remote sensing instrument for lightning geolocation in the form of an ultra-sensitive broadband electric field receiver. It is capable of detecting extremely weak sferics, enabled by its ideal sensitivity of 1 nV/(m√Hz), or 0.003 fT/√Hz. We present this receiver’s antenna-amplifier co-design and the design considerations to achieve this low sensitivity. We then report its performance characteristics, validated both theoretically and empirically. Finally, we present some of the novel applications of this device in the scope of lightning geolocation and remote sensing.

We present a method of characterizing the horizontal and vertical electron density roughness of the D-region ionosphere using Nationwide Differential GPS (NDGPS) transmitters as Low Frequency (LF; 30-300 kHz) and Medium Frequency (MF; 300-3000 kHz) signals of opportunity. The horizontal roughness is characterized using an amplitude cross-correlation method, which yields the correlation length scale metric. The vertical roughness is characterized using a differential phase height, which is needed to mitigate the effects of transmitter phase instability. The ranges and typical values of roughness metrics are investigated using data from several field campaign measurements. Finally, the roughness metrics for an NDGPS transmitter and VLF transmitter are compared. It is found that the roughness detected by the VLF transmitter is significantly smoother and demonstrates the utility of this method to complement traditional VLF measurements.