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Abstract This study characterizes the main ionospheric trough (MIT) using a newly implemented detection method applied to ground‐based Global Navigation Satellite System data. The MIT is a region of plasma depletion occurring primarily in the nighttime sub‐auroral F‐region ionosphere. Analysis is based on ground‐based ionosphere total electron content (TEC) measurements from 2012 to 2024 and is applied to both hemispheres. The data are sorted by geomagnetic condition and season. We characterize MIT dynamics and compare the results with previous studies. Detection algorithm limitations, hemispheric asymmetry, trough depth, boundary wall steepness and position are statistically quantified and visualized. Main conclusions include: (a) Automatic trough detection is highest during geomagnetically active winter in the northern hemisphere (NH). (b) This detection method creates synoptic views of the trough which we can use to demonstrate control of sub‐auroral polarization streams (SAPS) over the dusk/afternoon sector and influence of storm onset on the MIT. (c) There is a noticeable morning preference for the southern hemisphere (SH) trough. (d) The dawn‐side SH trough appears equatorward relative to the NH, potentially due to influence from polar convection patterns. The dusk‐side NH trough appears slightly equatorward of the SH trough as a response to SAPS. (e) The deepest trough occurs during dawn hours and demonstrates more consistent longitudinal patterns during quiet local winter. (f) The steepest trough boundary is at the poleward wall with a positive gradient at 12–15 local time in NH summer. Synoptic maps illustrate asymmetries in the trough structure and the influence of density plumes. 

Abstract This study investigates impacts of the May 2024 superstorm on the mid‐latitude Global Positioning System (GPS) scintillation and position errors. Using 1‐Hz GPS receiver data, we identified position errors in PPP mode reaching up to 70 m in the Central United States during the storm main phase on May 10. The PPK solution becomes unstable following the arrival of storm and lasted till the recovery phase, coinciding with reported GPS outages of farming equipment. The large position errors were attributed to strong scintillation and carrier phase cycle slips around the equatorward boundary of the ionosphere trough, where large total electron content (TEC) gradients and irregularities were present. In the Southwestern United States, position errors of 10–20 m were associated with the storm‐enhanced density and equatorial ionization anomaly. Scintillation and cycle slips in this region were minor, and bending of the GPS signal paths (refractive effect) is suggested to cause the position errors. PPP outages were also associated with sudden changes in the geometric distributions of available GPS satellites used in position calculations. On May 11, energetic particle precipitation during substorms led to abrupt jumps in TEC and scintillation, resulting in rapidly evolving position errors of up to 10 m. These findings highlight the critical role of storm‐time plasma transport, precipitation, and irregularity formation in degrading GPS performance. The study underscores the need for accurate ionospheric state specification, improved signal processing technique, real‐time ionospheric corrections, and optimized satellite selection algorithms, to enhance navigation resilience during severe space weather events. 

Data-driven approaches to identify geophysical signals have proven beneficial in high dimensional environments where model-driven methods fall short. GNSS offers a source of unsaturated ground motion observations that are the data currency of ground motion forecasting and rapid seismic hazard assessment and alerting. However, these GNSS-sourced signals are superposed onto hardware-, location- and time-dependent noise signatures influenced by the Earth’s atmosphere, low-cost or spaceborne oscillators, and complex radio frequency environments. Eschewing heuristic or physics based models for a data-driven approach in this context is a step forward in autonomous signal discrimination. However, the performance of a data-driven approach depends upon substantial representative samples with accurate classifications, and more complex algorithm architectures for deeper scientific insights compound this need. The existing catalogs of high-rate (≥1Hz) GNSS ground motions are relatively limited. In this work, we model and evaluate the probabilistic noise of GNSS velocity measurements over a hemispheric network. We generate stochastic noise time series to augment transferred low-noise strong motion signals from within 70 kilometers of strong events (≥ MW 5.0) from an existing inertial catalog. We leverage known signal and noise information to assess feature extraction strategies and quantify augmentation benefits. We find a classifier model trained on this expanded pseudo-synthetic catalog improves generalization compared to a model trained solely on a real-GNSS velocity catalog, and offers a framework for future enhanced data driven approaches. 

Abstract The Antarctic and Arctic regions are Earth's open windows to outer space. They provide unique opportunities for investigating the troposphere–thermosphere–ionosphere–plasmasphere system at high latitudes, which is not as well understood as the mid- and low-latitude regions mainly due to the paucity of experimental observations. In addition, different neutral and ionised atmospheric layers at high latitudes are much more variable compared to lower latitudes, and their variability is due to mechanisms not yet fully understood. Fortunately, in this new millennium the observing infrastructure in Antarctica and the Arctic has been growing, thus providing scientists with new opportunities to advance our knowledge on the polar atmosphere and geospace. This review shows that it is of paramount importance to perform integrated, multi-disciplinary research, making use of long-term multi-instrument observations combined with ad hoc measurement campaigns to improve our capability of investigating atmospheric dynamics in the polar regions from the troposphere up to the plasmasphere, as well as the coupling between atmospheric layers. Starting from the state of the art of understanding the polar atmosphere, our survey outlines the roadmap for enhancing scientific investigation of its physical mechanisms and dynamics through the full exploitation of the available infrastructures for radio-based environmental monitoring.