Sporadic E Modeling Using Convolutional Neural Networks and Radio Occultations

Sporadic E (Es_ manifests itself as regions of enhanced ionization, occurring primarily between altitudes of 90-130 km above Earth's surface. These irregularly ionized layers can reflect or degrade radio waves propagating through the ionosphere and impact applications such as satellite and high frequency (HF) communications, Global Navigation Satellite System (GNSS) navigation and positioning, and over-the-horizon radar (OTHR). In order to effectively operate in these complex electromagnetic environments, a global understanding and accurate characterization of Es is critical. This work develops models to detect and characterize Es using convolutional neural networks. In training the networks, data from six GNSS radio occultation (RO) missions over the years of 2008 to 2022 are used as the input features while ionosondes are used as the target variables. The learned model can then be applied to any GNSS-RO data to produce a global prediction of sporadic E.