This study presents a new back-propagation (BP) method to determine the vertical location of ionospheric irregularities using GNSS Radio Occultation (GNSS-RO) signals. GNSS-RO employs signals from GNSS satellites intercepted by Low Earth Orbit (LEO) satellites to gather data about different atmospheric layers, e.g., the ionosphere, which are crucial for weather prediction and studying ionospheric dynamics. The BP method involves computing diffractive integrals along the LEO path to identify disturbances such as sporadic E-layer clouds and equatorial plasma bubbles (EPBs). By effectively unwinding diffraction and multipath effects, the method pinpoints regions with minimal amplitude disturbance, indicating the location of ionospheric irregularities along the ray path. Beside estimates along the horizontal axis, case studies demonstrate the new method's capabilities in locating and estimating the vertical extent of these irregularities, showing its potential to enhance ionospheric modelling and forecasting. Results achieved show consistency with previous publications on the topic as well as methodologies used to locate ionospheric irregularities, allowing the presented method a better picture of the ionospheric irregularity.

This paper investigates on the detection and localization of ionospheric irregularities using GNSS Radio Occultation (GNSS-RO). We propose a new segmented phase screen (PS) approach to improve vertical and horizontal localization and remove the presence of outliers. The study focused on the May 2024 geomagnetic solar storm is presented, consisting of a comparison of the GNSS-RO back propagation (BP) irregularity positioning against the data of NASA’s Globalscale Observations of the Limb and Disk (GOLD) mission. This study is performed for validation purposes and examines the presence of equatorial plasma bubbles (EPBs) at predicted locations. Experimental RO data from EUMETSAT’s MetOp satellites is used to demonstrate the method’s capability to characterize the distribution of ionospheric irregularities. Results validate the segmented approach's capabilities of detecting irregularity structures and identifying their centroids with improved performance compared with the previous version of the algorithm. 

This study investigates the potential of machine learning techniques for detecting Equatorial Plasma Bubbles (EPBs) using nighttime airglow imagery from the Global-scale Observations of the Limb and Disk (GOLD) mission. EPBs are ionospheric irregularities characterized by significant plasma density depletions that disrupt trans-ionospheric radio wave propagation, affecting satellite navigation and communication systems. We propose and implement a convolutional encoder-decoder neural network specifically designed for precise pixel-level segmentation of EPB structures within GOLD’s 135.6 nm radiance images. The convolutional neural network demonstrated remarkable performance, achieving high precision and recall, successfully detecting prominent EPBs as well as subtle features overlooked in manual annotations. Results also reveal the network’s capability to generalize beyond explicitly labeled data, indicating its robustness in capturing intricate EPB morphologies. Additionally, a preliminary cross-validation was conducted using GNSS Radio Occultation (RO) data, which showed promising correspondence with the EPB locations identified by the machine learning algorithm. This supports the value of integrating deep learning methods with GNSS-RO techniques to achieve comprehensive global detection and validation of EPBs.