Energetic electron precipitation (EEP) from the radiation belts into Earth’s atmosphere leads to several profound effects (e.g., enhancement of ionospheric conductivity, possible acceleration of ozone destruction processes). An accurate quantification of the energy input and ionization due to EEP is still lacking due to instrument limitations of low-Earth-orbit satellites capable of detecting EEP. The deployment of the ELFIN (Electron Losses and Fields InvestigatioN) CubeSats marks a new era of observations of EEP with an improved pitch-angle (0°–180°) and energy (50 keV–6 MeV) resolution. Here, we focus on the EEP recorded by ELFIN coincident with electromagnetic ion cyclotron (EMIC) waves, which play a major role in radiation belt electron losses. The EMIC-driven EEP (~200 keV – ~2 MeV) exhibits a pitch-angle distribution (PAD) that flattens with increasing energy, indicating more efficient high-energy precipitation. Leveraging the combination of unique electron measurements from ELFIN and a comprehensive ionization model known as Boulder Electron Radiation to Ionization (BERI), we quantify the energy input of EMIC-driven precipitation (on average, ~3.3x10-2 erg/cm2/s), identify its location (any longitude, 50°–70° latitude), and provide the expected range of ion-electron production rate (on average, 100–200 pairs/cm3/s), peaking in the mesosphere – a region often overlooked. Our findings are crucial for improving our understanding of the magnetosphere-ionosphere-atmosphere system as they accurately specify the contribution of EMIC-driven EEP, which serves as a crucial input to state-of-the-art atmospheric models (e.g., WACCM) to quantify the accurate impact of EMIC waves on both the atmospheric chemistry and dynamics.

Calculating meteoroid masses from photometric observations relies on prior knowledge of the luminous efficiency, a parameter that is not well characterized; reported values vary by several orders of magnitude. We present results from an experimental campaign to determine the luminous efficiency as a function of mass, velocity, and composition. Using a linear electrostatic dust accelerator, iron and aluminum microparticles were accelerated to 10+ km/s and ablated, and the light production measured. The luminous efficiency of each event was calculated and functional forms fit for each species. For both materials, the luminous efficiency is lowest at low velocities, rises sharply, then falls as velocity increases. However, the exact shape and magnitude of the curve is not consistent between the materials. The difference between the luminous efficiencies for iron and aluminum, particularly at high velocities, indicates that it is not sufficient to use the same luminous efficiency for all compositions and velocities.

We present a new model designed to simulate the process of energetic particle precipitation, a vital coupling mechanism from Earth’s magnetosphere to its atmosphere. The atmospheric response, namely excess ionization in the upper and middle atmosphere, together with bremsstrahlung X-ray production, is calculated with kinetic particle simulations using the GEANT4 Monte Carlo framework. Mono-energy and mono-pitch angle electron beams are simulated and combined using a Green’s function approach to represent realistic electron spectra and pitch angle distributions. Results from this model include more accurate ionization profiles than previous analytical models, deeper photon penetration into the atmosphere than previous Monte Carlo model predictions, and predictions of backscatter fractions of loss cone electrons up to 40%. The model results are verified by comparison with previous precipitation modeling results, and validated using balloon X-ray measurements from the BARREL mission and backscattered electron energy and pitch angle measurements from the ELFIN CubeSat mission. The model results and solution techniques are developed into a Python package for public use.

This study presents results from magnetic field line conjunctions between the medium-Earth orbiting Demonstration and Science Experiments (DSX) satellite and the low-Earth orbiting VLF Propagation Mapper (VPM) satellite. DSX transmitted at very low frequencies (VLF) towards VPM, which was equipped with a single-axis dipole electric field antenna, when the two spacecraft passed near the same magnetic field line. VPM did not observe DSX signals in any of the 27 attempted conjunction experiments; the goal of this study, therefore, is to explain why DSX signals were not received. Explanations include i) the predicted power at LEO from DSX transmissions was too low for VPM to observe; ii) VPM’s trajectory missed the “spot” of highest intensity due to the focused ray paths reaching LEO; or iii) rays mirrored before reaching VPM. Different combinations of these explanations are found. We present ray-tracing analysis for each conjunction event to predict the distribution of power and wave normal angles in the vicinity of VPM at LEO altitudes. We find that, for low-frequency (below 4kHz) transmissions, nearly all rays mirror before reaching LEO, resulting in low amplitudes at LEO. For mid- and high-frequency transmissions (~8kHz and 28kHz respectively), the power at LEO is above the noise threshold of the VPM receiver (between 0.5µV/m and 1µV/m). We conclude that the antenna efficiency and plasmasphere model are critical in determining the predicted power at LEO, and are also the two most significant sources of uncertainty that could explain the apparent discrepancy between predicted amplitudes and VPM observations.

Severe weather forecasting is an important tool for mitigating damages brought by intense lightning, large hail, heavy precipitation, strong winds, or tornadoes during thunderstorms, yet the reliability of such forecasts suffers from our limited understanding of the severe weather generative processes inside thunderclouds. With an increasing knowledge of the occurrence context of distinct types of lightning within storms, lightning remote sensing may elucidate the kinematic and microphysical environment where severe weather initiates. In particular, distinct energetic intra-cloud (IC) lightning discharges, compact intra-cloud lightning discharges [CID; e.g., Nag and Rakov, 2010] and energetic intra-cloud pulses [EIP; e.g., Lyu et al., 2015], have been shown to have different occurrence contexts, making them strong candidates for thunderstorm remote sensing research. In this study, observations from the RELAMPAGO field campaign in Argentina (November 1 to December 12th 2018) are used to determine lightning flash rates and the prevalence of different energetic lightning types in RELAMPAGO storms, enabling further research on the link between lightning activity and severe weather production inside thunderstorms.Lightning events during RELAMPAGO were observed by a deployed array of four Low-Frequency (LF, ~1-400 kHz) radio receivers. Using time of arrival, magnetic direction finding, and peak amplitude for each observed event at different stations, lightning source locations are estimated using a statistical least squares filter, along with clock and site errors associated with the receivers. Return stroke peak current for each event is also estimated in the filter, using an atmospheric attenuation observation model. The energetic lightning events in the campaign are then classified automatically between cloud-to-ground (CG), IC, CID or EIP, following an improved parametrization scheme originally proposed by Lyu et al. [2015]. In this paper we present the geolocation and classification of RELAMPAGO lightning events, and we also provide an analysis of lightning flash rates during the campaign. A few individual thunderstorm case studies are also discussed, which are augmented by other meteorological data from dual-polarimetric radar, hailpads, and other sources.

Both high-power large aperture (HPLA) radars and smaller meteor radars readily observe the dense head plasma produced as a meteoroid ablates. However, determining the mass of such meteors based on the information returned by the radar is challenging. We present a new method for deriving meteor masses from single-frequency radar measurements, using a physics-based plasma model and finite-difference time-domain (FDTD) simulations. The head plasma model derived in~\citeA{dimopp17} depends on the meteoroids altitude, speed, and size. We use FDTD simulations of a radar pulse interacting with such head plasmas to determine the radar cross section (RCS) that a radar system would observe for a meteor with a given set of physical properties. By performing simulations over the observed parameter space, we construct tables relating meteor size, velocity, and altitude to RCS. We then use these tables to map a set of observations from the MAARSY radar (53.5 MHz) to fully-defined plasma distributions, from which masses are calculated. To validate these results, we repeat the analysis using observations of the same meteors by the EISCAT radar (929 MHz). The resulting masses are strongly linearly correlated; however, the masses derived from EISCAT measurements are on average 1.33 times larger than those derived from MAARSY measurements. Since this method does not require dual-frequency measurements for mass determination, only validation, it can be applied in the future to observations made by many single-frequency radar systems.

The lightning data products generated by the Low-Frequency (LF) radio lightning locating system (LLS) deployed during the RELAMPAGO field campaign in Argentina provide a valuable dataset to research the lightning evolution and characteristics of convective storms that produce high-impact weather. LF LLS datasets offer a practical range for mesoscale studies, allowing for the observation of lightning characteristics of storms such as Mesoscale Convective Systems (MCSs) or large convective lines that travel longer distances which are not necessarily staying in range of regional VHF-based lightning detection systems throughout their lifetime. LF LLSs also provide different information than optical space-borne lightning detectors. Lightning measurements exclusive to LF systems include discharge peak current, lightning polarity, and lightning type classification based on the lightning-emitted radio waveform. Furthermore, these measurements can provide additional information on flash rates (e.g. positive CG flash rate) or Narrow Bipolar Events (NBE) which may often be associated with dynamically intense convection. In this paper, the geolocation and data processing of the LF dataset collected during RELAMPAGO is fully described and its performance characterized, with location accuracy better than 10 km. The detection efficiency (DE) of the dataset is compared to that of the Geostationary Lightning Mapper (GLM), and spatiotemporal DE losses in the LF dataset are discussed. Storm case-studies on November 10, 2018, highlight the strengths of the dataset, which include robust flash clustering and insightful flash rate and peak current measures, while illustrating how its limitations, including DE losses, can be managed.

This study presents analysis of very low frequency (VLF) transmitter signal measurements on the Very-Low-Frequency Propagation Mapper (VPM) CubeSat in low-Earth orbit. Six months of satellite operation provided good data coverage, used to build global statistical maps of VLF power distribution. The power distribution above four powerful transmitters is used as input for ray tracing to study signal propagation to the conjugate hemisphere in two plasmaspheric density models. The ray tracing results are further compared with VPM measurements to determine which model provides better agreement with observations. As ray propagation largely depends on the background plasma density distribution, this indirect method can be used for plasmaspheric density model validation as an alternative to multipoint in situ plasma measurements that may not be readily obtainable. In addition, it can be used to investigate Landau damping and ducted vs. non-ducted propagation of VLF signals.

Quantification of energetic electron precipitation caused by wave-particle interactions is fundamentally important to understand the cycle of particle energization and loss of the radiation belts. One important way to determine how well the wave-particle interaction models predict losses through pitch-angle scattering into the atmospheric loss cone is the direct comparison between the ionization altitude profiles expected in the atmosphere due to the precipitating fluxes and the ionization profiles actually measured with incoherent scatter radars. This paper reports such a comparison using a forward propagation of loss-cone electron fluxes, calculated with the electron pitch angle diffusion model applied to Van Allen Probes measurements, coupled with the Boulder Electron Radiation to Ionization (BERI) model, which propagates the fluxes into the atmosphere. The density profiles measured with the Poker Flat Incoherent Scatter Radar operating in modes especially designed to optimize measurements in the D-region, show multiple instances of quantitative agreement with predicted density profiles from precipitation of electrons caused by wave-particle interactions in the inner magnetosphere. There are two several-minute long intervals of close prediction-observation approximation in the 65-93 km altitude range. These results indicate that the whistler wave-electron interactions models are realistic and produce precipitation fluxes of electrons with energies between 10 keV to >100 keV that are consistent with observations.

A particular strength of lightning remote sensing is the variety of lightning types observed, each with a unique occurrence context and characteristically different emission. Distinct energetic intra-cloud (EIC) lightning discharges – compact intra-cloud lightning discharges (CIDs) and energetic intra-cloud pulses (EIPs) – produce intense RF radiation, suggesting large currents inside the cloud, and they also have different production mechanisms and occurrence contexts. A Low-Frequency (LF) lightning remote sensing instrument array was deployed during the RELAMPAGO field campaign in west central Argentina, designed to investigate convective storms that produce high-impact weather. LF data from the campaign can provide a valuable dataset for researching the lightning context of EICs in a variety of sub-tropical convective storms. This paper describes the production of an LF-CID dataset in RELAMPAGO, and includes a preliminary analysis of CID prevalence. Geolocated lightning events and their corresponding observed waveforms from the RELAMPAGO LF dataset are used in the classification of EICs. Height estimates based on skywave reflections are computed, where pre-fit residual data editing is used to improve robustness against outliers. Even if EIPs occurred within the network, given the low number of very high peak current events and receiver saturation, automatic classification of EIPs may not be feasible using this dataset. The classification of CIDs, on the other hand, is straightforward and their properties, for both positive and negative polarity, are investigated. A few RELAMPAGO case studies are also presented, where high variability of CID prevalence in ordinary storms and high-altitude positive CIDs, possibly in overshooting tops, are observed.

The Very Low Frequency (VLF) Propagation Mapper (VPM) is a 6U CubeSat designed to measure VLF radio waves in Low-Earth Orbit. The science goals of the VPM mission are to measure VLF signals broadcast by the DSX mission, and to study natural and anthropogenic signals (from lightning and VLF transmitters) in the near-Earth space environment. The primary payload consists of an electric field dipole antenna deployed to 2 meters in length, and a magnetic search coil deployed 50 cm from the spacecraft. Signals from these two sensors are conditioned by analog electronics, sampled, and then processed digitally into downloadable data products. The VPM mission was launched in January 2020; science operations began in March 2020 and continued through September, when contact with the spacecraft was lost. This paper describes the mission goals and instrument designs in detail, as well as some examples of the VPM dataset.