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Abstract Ionosondes are primarily used to measure the electron densities of the ionosphere's E and F‐region via frequency‐range analysis of the probing signal returns. The amplitude of the returning signal has often been ignored, however, and may allow estimates of other propagation effects such as D and E‐region absorption. We introduce a methodology to extract this information from amplitude data and view results in ensemble with Very Low Frequency‐derived, D‐Region absorption estimates. This comparison allows us to infer what portion of High Frequency (HF) attenuation is due to D‐region versus E‐region absorption. The attenuation observed by both methodologies are congruent with each other in the diurnal cycle across HF frequencies between 2.5 and 4.5 MHz. This technique may extend the utility of ionosondes beyond their traditional applications. 

Abstract Sporadic‐E is an ionospheric phenomenon defined by strong layers of plasma which may interfere with radio wave propagation. In this work, we develop deep learning models to improve the understanding of , including the presence, intensity and height of the layers. We developed three separate models. The first, building off earlier work in (J. A. Ellis et al., 2024,https://doi.org/10.1029/2023sw003669), includes only the main features from radio occultation (RO) measurements. The second adds to that time, date, location, geomagnetic and solar indices, solar winds, x‐ray flux, weather and lightning. A third model excludes RO measurements but includes the rest. In training the first two models, the ordinary mode critical frequency (foEs), a measure of intensity, and height (hEs) parameters extracted from ionosondes were used as the “ground truth” target variables. In training the third model, estimated foEs and hEs values from the RO model were added as target variables to augment the data set and produce physically reasonable model predictions globally. We find that the second model performs well with intensity prediction tasks, but struggles with height estimations, which is likely due to the tangent point assumption made during RO signal processing and errors inherent in the ionosonde extracted virtual heights. The third model performed reasonably well considering the lack of in situ RO measurement. The third model performs the best on height predictions, which points to the height being very climatologically driven, whereas the intensity is a more complex interaction of several variables. 

Abstract An internationally collaborative airborne campaign in July 2023 – led by the University of Bergen (Norway) and NASA, with contributions from many other institutions – discovered that thunderstorms near Florida and Central America produce gamma rays far more frequently than previously thought. The campaign was called Airborne Lightning Observatory for Fly’s Eye Geostationary Lightning Mapper (GLM) Simulator (FEGS) and Terrestrial Gamma-ray Flashes (TGFs), which shortens to ALOFT. The campaign employed a unique sampling strategy with NASA’s high-altitude ER-2 aircraft, equipped with gamma-ray and lightning sensors, flying near ground-based lightning sensors. Realtime updates from instruments, downlinked to mission scientists on the ground, enabled immediate return to thunderstorm cells found to be producing gamma rays. This maximized the observations of radiation created by strong electric fields in clouds, and showed how gamma-ray production may be physically linked to thunderstorm lifecycle. ALOFT also sampled storms entirely within the stereo-viewing region of the GLM instruments on GOES-16/18 and performed multiple underflights of the International Space Station Lightning Imaging Sensor (ISS LIS), while using an upgraded FEGS instrument that demonstrated the operational value of observing multiple wavelengths (including ultraviolet) with future spaceborne lightning mappers. In addition, a robust complement of airborne active and passive microwave sensors – including X- and W-band Doppler radars, as well as radiometers spanning 10-684 GHz – sampled some of the most intense convection ever overflown by the ER-2. These observations will benefit planned convection-focused NASA spaceborne missions. ALOFT is an exemplar of a high-risk, high-reward field campaign that achieved results far beyond original expectations. 

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. 

Abstract We examined three observations of green emission events (labeled as event A, B and C, respectively) associated with red sprites as captured by amateurs. In all cases, the green emissions were recorded atop of red sprite. Based on the location of causative strokes and background star fields for events A and B, their altitudes are confined between 88 and 100 km, with the maximum brightness at 90.7 and 95.5 km, respectively. Events B and C were lit up for a second time after the recurrence of a sprite element, extending their duration to approximately 1,084 ms and 732.6 ms, much longer than that (about 500 ms) for event A; the intensity of green emissions was also enhanced due to sprite recurrence. It is inferred that the recurrence of sprite elements could affect the ambient condition by further increasing electron density and strengthening the electric field for the ghost production. 

Abstract Terrestrial Gamma‐ray Flashes (TGFs) are ten‐to‐hundreds of microsecond bursts of gamma‐rays produced when electrons in strong electric fields in thunderclouds are accelerated to relativistic energies. Space instruments have observed TGFs with source photon brightness down to ∼10¹⁷–10¹⁶. Based on space and aircraft observations, TGFs have been considered rare phenomena produced in association with very few lightning discharges. Space observations associated with lightning ground observations in the radio band have indicated that there exists a population of dimmer TGFs. Here we show observations of TGFs from aircraft altitude that were not detected by a space instrument viewing the same area. The TGFs were found through Monte Carlo modeling to be associated with 10¹⁵–10¹²photons at source, which is several orders of magnitude below what can be seen from space. Our results suggest that there exists a significant population of TGFs that are too weak to be observed from space. 

Abstract We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical‐Canadian High Arctic Ionospheric Model (E‐CHAIM). In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite‐years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E‐CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. We discuss precision and accuracy metrics to better understand model strengths and weaknesses. The updated neural network shows improved mid‐latitude performance with absolute errors lower than the IRI by 2.5 × 10⁹to 2.5 × 10¹⁰e⁻/m³, modestly improved performance in disturbed geomagnetic conditions with absolute errors reduced by about 2.5 × 10⁹ e⁻/m³at high Kp compared to the IRI, and high Kp percentage errors reduced by >50% when compared to E‐CHAIM. 

Abstract We demonstrate a methodology for utilizing measurements from very low frequency (VLF, 3−30 kHz) transmitters and lightning emissions to produce 3D lower electron density maps, and apply it to multiple geophysical disturbances. The D‐region lower ionosphere (60−90 km) forms the upper boundary of the Earth‐ionosphere waveguide which allows VLF radio waves to propagate to global distances. Measurements of these signals have, in many prior studies, been used to infer path‐average electron density profiles within the D region. Historically, researchers have focused on either measurements of VLF transmitters or radio atmospherics (sferics) from lightning. In this work, we build on recently published methods for each and present a method to unify the two approaches via tomography. The output of the tomographic inversion produces maps of electron density over a large portion of the United States and Gulf of Mexico. To illustrate the benefits of this unified approach, daytime and nighttime maps are compared between a sferic‐only model and the new approach suggested here. We apply the model to characterize two geophysical disturbances: solar flares and lower ionospheric changes associated with thunderstorms. 

Abstract In this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (E_s), demonstrating an improvement over current methods. This includes a binary classification model to determine ifE_sis present, followed by a regression model to estimate theE_sordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which theE_slayer occurs (hEs). Signal‐to‐noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the height (hEs) values are obtained from the global network of ground‐based Digisonde ionosondes and are used as the “ground truth,” or target variables, during training. After corresponding the two data sets, a total of 36,521 samples are available for training and testing the models. The foEs CNN binary classification model achieved an accuracy of 74% and F1‐score of 0.70. Mean absolute errors (MAE) of 0.63 MHz and 5.81 km along with root‐mean squared errors (RMSE) of 0.95 MHz and 7.89 km were attained for estimating foEs and hEs, respectively, when it was known thatE_swas present. When combining the classification and regression models together for use in practical applications where it is unknown ifE_sis present, an foEs MAE and RMSE of 0.97 and 1.65 MHz, respectively, were realized. We implemented three other techniques for sporadic E characterization, and found that the CNN model appears to perform better. 

Abstract We present a tomographic imaging technique for the D‐region electron density using a set of spatially distributed very low frequency (VLF) remote sensing measurements. The D‐region ionosphere plays a critical role in many long‐range and over‐the‐horizon communication systems; however, it is unreachable by most direct measurement techniques such as balloons and satellites. Fortunately, the D region, combined with Earth's surface, forms what is known as the Earth‐Ionosphere waveguide allowing VLF and low frequency (LF) radio waves to propagate to global distances. By measuring these signals, we can estimate a path measurement of the electron density, which we assume to be a path‐averaged electron density profile of the D region. In this work, we use path‐averaged inferences from lightning‐generated radio atmospherics (sferics) with a tomographic inversion to produce 3D models of electron density over the Southeastern United States and the Gulf of Mexico. The model begins with two‐dimensional great circle path observations, each of which is parameterized so it includes vertical profile information. The tomography is then solved in two dimensions (latitude and longitude) at arbitrarily many altitude slices to construct the 3D electron density. We examine the model's performance in the synthetic case and determine that we have an expected percent error better than 10% within our area of interest. We apply our model to the 2017 “Great American Solar Eclipse” and find a clear relationship between sunlight percentage and electron density at different altitudes.