Document Type
Article
Publication Date
5-1-2025
Abstract
Sporadic-E (Es) 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 Es, 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, link in AFIT Scholar, 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 Es 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.
Source Publication
Space Weather (eISSN 1542-7390)
Recommended Citation
Ellis, J. A., Emmons, D. J., & Cohen, M. B. (2025). Global sporadic-E prediction and climatology using deep learning. Space Weather, 23, e2025SW004366. https://doi.org/10.1029/2025SW004366
Comments
This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License, which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed. CC BY-NC 4.0
Sourced from the published version of record cited on this page. The article is shared on AFIT Scholar as the repository of the employer of co-author Daniel Emmons.
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