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Abstract Very Low Frequency (VLF, 3–30 kHz) waves propagate long distances in the waveguide formed by the Earth and the lower ionosphere. External sources such as solar flares and lightning discharges perturb the upper waveguide boundary and thereby modify the waves propagating within it. Therefore, studying the propagation of VLF waves within the waveguide enables us to probe the ionospheric response to external forcing. However, the wave propagation also depends on the lower waveguide boundary property, that is, the path conductivity. We tackle two main questions: how accurate should the path conductivity description be to obtain a given accuracy on the ionospheric electron density? Are the currently available ground‐conductivity maps accurate enough? The impact of the ground conductivity values and their spatial extension on VLF wave propagation is studied through modeling with the Longwave Mode Propagator code. First, we show that knowledge of the path conductivity value should be more accurate as the ground conductivity decreases, in particular in regions where S/m. Second, we find that wave propagation is strongly sensitive to the spatial extension of ground conductivity path segments: segments of a few tens of km should be included in the path description to maintain below 50% the error on the derived electron density due to the path description. These results highlight the need for an update of the ground conductivity maps, to get better spatial resolution, more accurate values, and an estimate of the time‐variability of each region. 

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 Balloon Array for RBSP Relativistic Electron Losses mission and backscattered electron energy and pitch angle measurements from the Electron Loss and Fields Investigation with a Spatio-Temporal Ambiguity-Resolving CubeSat mission. The model results and solution techniques are developed into a Python package for public use. 

Atomic Force Microscopy (AFM) force-distance (FD) experiments have emerged as an attractive alternative to traditional micro-rheology measurement techniques owing to their versatility of use in materials of a wide range of mechanical properties. Here, we show that the range of time dependent behaviour which can reliably be resolved from the typical method of FD inversion (fitting constitutive FD relations to FD data) is inherently restricted by the experimental parameters: sampling frequency, experiment length, and strain rate. Specifically, we demonstrate that violating these restrictions can result in errors in the values of the parameters of the complex modulus. In the case of complex materials, such as cells, whose behaviour is not specifically understood a priori , the physical sensibility of these parameters cannot be assessed and may lead to falsely attributing a physical phenomenon to an artifact of the violation of these restrictions. We use arguments from information theory to understand the nature of these inconsistencies as well as devise limits on the range of mechanical parameters which can be reliably obtained from FD experiments. The results further demonstrate that the nature of these restrictions depends on the domain (time or frequency) used in the inversion process, with the time domain being far more restrictive than the frequency domain. Finally, we demonstrate how to use these restrictions to better design FD experiments to target specific timescales of a material's behaviour through our analysis of a polydimethylsiloxane (PDMS) polymer sample. 

Abstract 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 Electron Losses and Fields InvestigatioN (ELFIN) 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, ∼3.3 × 10⁻² 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. 

Abstract 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 intracloud (EIC) lightning discharges—compact intracloud lightning discharges (CIDs) and energetic intracloud 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 data set for researching the lightning context of EICs in a variety of subtropical convective storms. This paper describes the production of an LF‐CID data set in RELAMPAGO and includes a preliminary analysis of CID prevalence. Geolocated lightning events and their corresponding observed waveforms from the RELAMPAGO LF data set are used in the classification of EICs. Height estimates based on skywave reflections are computed, where prefit 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 data. 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. 

Abstract The lightning data products generated by the low‐frequency (LF) radio lightning locating system (LLS) deployed during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observation (RELAMPAGO) field campaign in Argentina provide a valuable data set to research the lightning evolution and characteristics of convective storms that produce high‐impact weather. LF LLS data sets offer a practical range for mesoscale studies, allowing for the observation of lightning characteristics of storms such as mesoscale convective systems 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 cloud‐to‐ground flash rate) or narrow bipolar events which may often be associated with dynamically intense convection. In this article, the geolocation and data processing of the LF data set collected during RELAMPAGO is fully described and its performance characterized, with location accuracy better than 10 km. The detection efficiency (DE) of the data set is compared to that of the Geostationary Lightning Mapper, and spatiotemporal DE losses in the LF data set are discussed. Storm case studies on November 10, 2018, highlight the strengths of the data set, which include robust flash clustering and insightful flash rate and peak current measures, while illustrating how its limitations, including DE losses, can be managed. 

Abstract Both high‐power large aperture 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 Dimant and Oppenheim (2017),https://doi.org/10.1002/2017ja023963depends 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.