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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 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. 

Abstract Lightning induced perturbations of the lower ionosphere are investigated with very low frequency (VLF) remote sensing on a unique overlapping propagation path geometry. The signals from two VLF transmitters (at different frequencies) are observed at a single receiver after propagation through a common channel in the Earth‐ionosphere waveguide. This measurement diversity allows for greater certainty in quantification of perturbations to the ionosphericDregion. Changes in amplitude and phase are modeled with the Long Wave Propagation Capability (LWPC) software package to quantify changes in reference height and steepness of the two parameterDregion electron density model. Since the nighttimeDregion profile prior to the perturbation is found to strongly affect the resulting quantification, and is highly variable and generally unknown at nighttime, an error minimization method for identifying the most likely ionospheric disturbance independent of the ambient profile is used. Analysis of 12 large lightning perturbations resulting from discharges with peak currents greater than 160 kA shows that the ionospheric reference height can change by 2–8 km. We investigate both early/fast events (direct ionization and heating from lightning) and lightning‐induced electron precipitation (LEP) events, induced by lightning hundreds of kilometer away. LEP events increaseDregion electron density while early/fast events can lead to a increase or decrease in electron density. Multi‐point observations along a VLF propagation path are needed to further improve ionospheric perturbation quantification with VLF remote sensing. 

Stony coral tissue loss disease (SCTLD) was first observed in St. Thomas, U.S. Virgin Islands (USVI) in January 2019. This disease affects at least 20 scleractinian coral species; however, it is not well understood how reef diversity affects its spread or its impacts on reef ecosystems. With a large number of susceptible species, SCTLD may not follow the diversity-disease hypothesis, which proposes that high species diversity is negatively correlated with disease prevalence. Instead, SCTLD may have a higher prevalence and a greater impact on reefs with higher coral diversity. To test this, in 2020 we resampled 54 sites around St. Thomas previously surveyed in 2017 or 2019 by the National Oceanic and Atmospheric Administration National Coral Reef Monitoring Program. These sites represented a variety of species diversity values [categorized into poor (<12 spp. rich.) and rich (12 spp. rich.)] in multiple disease zones (Endemic: disease present  9 months; Epidemic: disease present 2–6 months; Control and Emergent: disease present no disease/<2 months). We hypothesized that, contrary to the diversity-disease hypothesis, sites with high species diversity (as measured by species richness or Simpson’s index) would have higher disease prevalence within the epidemic zone, and that high species diversity sites would have a greater impact from disease within the endemic zone. Results indicated a significant positive relationship between disease prevalence and diversity in the epidemic zone, and a similar trend in the endemic zones. Additionally, a negative relationship was seen between pre-outbreak diversity and loss of diversity and coral cover within the endemic zone. This supports the hypothesis that higher diversity predicts greater disease impact and suggests that SCTLD does not follow the diversity-disease hypothesis. Within the epidemic zone, the species with the highest SCTLD prevalence were Dendrogyra cylindrus, Colpophyllia natans, and Meandrina meandrites, while in the endemic zone, Diploria labyrinthiformis, Pseudodiploria strigosa, Montastraea cavernosa, and Siderastrea siderea had the highest SCTLD prevalence. Understanding the relationship between species diversity and SCTLD will help managers predict the most vulnerable reefs, which should be prioritized within the USVI and greater Caribbean region.