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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 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. 

Gigantic jets (GJs) are a type of transient luminous event (TLE) which also includes sprites, elves, halos, and blue jets [Pasko2010, doi: 10.1029/2009JA014860]. However, GJs are unique in that they directly couple electric charge reservoirs in the troposphere (i.e. thunderclouds) with the lower ionosphere, allowing significant amounts of charge (100s of C) to flow between these regions. We do not understand how this affects the ionosphere and global electric circuit. Past observations are very limited, resulting from ground-based cameras getting lucky enough to capture an event while looking over a distant thunderstorm [Liu et al. 2015, doi: 10.1038/ncomms6995]. Additionally, GJ-producing storms are normally accompanied by substantial areas of stratiformclouds obscuring the view, and they tend to occur more often over the ocean. To solve this problem of limited detection capability, we have developed a pipeline that utilizes machine learning and sensor fusion of multiple sensing modalities (optical, VLF, ELF). Our pipeline can detect GJs over nearly a hemisphere and operate 24/7, potentially revolutionizing how GJs are detected and paving the way for other TLE and unique lightning event detection. Our pipeline begins by performing detection with data from the Geostationary Lightning Mapper (GLM), which is a staring optical imager in geostationary orbit that detects the 777.4 nm (OI) triplet from lightning leaders [Goodman et al. 2013, doi: 10.1016/j.atmosres.2013.01.006]. Gigantic jets have unique signatures in the GLM data from past studies [Boggs et al. 2019, doi: 10.1029/2019GL082278]. We have developed a supervised, ensemble machine learning classifier that detects potential gigantic jets in the GLM data. Considering we have an imbalanced dataset, we use data imbalance techniques such as Synthetic Minority Oversampling Technique (SMOTE) when training the classifier. Next, we combine data from multiple sensing modalities to vet the candidate GJs from the classifier in multiple stages. The first stage filters the candidate GJs to the stereo GLM region [Mach and Virts, 2021, doi: 10.1175/JTECH-D-21-0078.1], and calculates the stereo altitudes for all the events. GJs have stereo altitude sources consistently between 15-25 km altitude from the leader escaping the cloud top [Boggs et al. 2022, doi: 10.1126/sciadv.abl8731]. Next, we match the events spatiotemporally to GLD360 data to remove cloud-to-ground events. Subsequently, we use a statistical GOES ABI model (developed at GTRI) to filter out events that have differing parent storms from our truth database. Finally, we use a multi-parameter extremely low frequency (ELF) vetting model (developed by Duke) to filter out the remaining non-GJ events. After a few complete detection and vetting cycles, we have found tens of new events with a high degree of confidence. With further development of our pipeline and deployment to the entire GLM field-of-view (not limited to stereo region), we anticipate hundreds of new detections per year, significantly more than ground-based cameras, allowing for comprehensive studies relating gigantic jets to the other atmospheric phenomena 

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 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. 

Gigantic jets are a type of transient luminous event (TLE, Pasko 2010, doi: 10.1029/2009JA014860) that escape the cloud top of a thunderstorm and propagate up to the lower ionosphere (80-100 km altitude), transferring tens to hundreds of Coulombs of charge. Due to rarity of observations, it is still not understood how they affect the lower ionosphere and what storm systems produce them. In this presentation we will provide an overview and present preliminary results from a multi-institutional collaborative project, which seeks to detect gigantic jets over hemispheric scales using a combination of orbital and ground-based sensors and machine learning. Our pipeline has the potential to detect significantly more gigantic jets (thousands) than current methods, which relies on using ground-based cameras. We will build a large database of gigantic jet detections, and correlate the events with a Very Low Frequency (VLF) remote sensing network (Cohen et al. 2009, doi: 10.1109/TGRS.2009.2028334) to understand how they perturb the lower ionosphere – in addition to other meteorological datasets. Our detection methodology primarily uses the Geostationary Lightning Mapper (GLM), which is a staring optical imager in geostationary orbit that detects the 777.4 nm (OI) triplet commonly emitted by lightning (Goodman et al. 2013, doi: 10.1016/j.atmosres.2013.01.006). Gigantic jets have been shown to have unique signatures in the GLM data from past studies (Boggs et al. 2019, doi: 10.1029/2019GL082278; Boggs et al. 2022, doi: 10.1126/sciadv.abl8731). Thus far, we have built a preliminary, supervised machine learning model that detects potential gigantic jets using GLM, and begun development on a series of vetting techniques to confirm the detections as real gigantic jets. The vetting techniques use a combination of low frequency (LF) and extremely low frequency (ELF) sferic data, in combination with stereo GLM measurements that provide optical source altitude. In addition, we will soon be able to calculate optical stereo sources with GLM on GOES-16 and the newly launched Lightning Imager on EUMETSAT, significantly expanding the stereo region of detection. When our detection methodology grows in maturity, we will deploy it to all past GLM data (2018-present) and share the database publicly, allowing other researchers to use this data for their own research.