Investigating the Impacts of a G4-Level Geomagnetic Storm on Airborne GNSS Performance Using Mass ADS-B Data in Southern Canada
Bibliographic record
Abstract
Geomagnetic storms can significantly degrade global navigation satellite system (GNSS) performance, threatening aviation navigation systems that rely on it. The raw observations and performance metrics output by airborne GNSS receivers directly reflect of the geomagnetic storm impacts. However, acquiring extensive airborne GNSS observations from actual flights remains challenging, thereby limiting comprehensive analyses. By contrast, automatic dependent surveillance-broadcast (ADS-B) data, which derives aircraft locations from airborne GNSS receivers, is more readily accessible. This study thoroughly analyzes the effects of a G4-level geomagnetic storm occurred in April 2023 on airborne GNSS performance by exploiting over 30 million ADS-B messages collected from southern Canada. The ground-based GNSS observations from seven monitoring stations and geomagnetic field data from ten ground magnetometers are also processed. A parameter estimation method is proposed to derive the average navigation accuracy of airborne GNSS receivers using ADS-B parameters. The results reveal that although the geomagnetic storm has a negligible effect on GNSS continuity and integrity, it significantly decreases accuracy. Specifically, the estimated horizontal figures of merit (HFOM) increased by 219% during the storm. Correlation and causality analyses indicate that the ground GNSS positioning errors and the ionospheric index are moderately correlated with airborne GNSS accuracy, with correlation coefficients reaching 0.64 and 0.40, respectively. Furthermore, geomagnetic and ionospheric variations are found to have causal relationships with airborne GNSS accuracy. This study validates the feasibility of using ADS-B data to assess airborne GNSS receiver performance under geomagnetic storms and enhances better understanding ionospheric and geomagnetic disturbance impacts on aviation navigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".