Analysis of Ionospheric Response to the May 2024 Geomagnetic Superstorm Using Commercial Radio Occultation Satellites and Ground‐Based Instruments
Bibliographic record
Abstract
Abstract This study investigates the ionospheric response to the May 2024 geomagnetic storm using electron density and scintillation data from space‐based Global Navigation Satellite System (GNSS) radio occultation (RO) satellites and ground‐based GNSS receivers. Electron density profiles retrieved from commercial RO observations revealed a distinct depletion in the F‐region and an enhancement in the E‐region at high latitudes during the storm, particularly during the recovery phase. These findings were supported and corroborated by observations from collocated digital ionosondes and incoherent scatter radar. A collocation analysis between scintillation indices retrieved from RO data and ground‐based ionospheric scintillation monitoring receivers demonstrated temporal and spatial agreement between the two data sets during the storm. RO‐derived scintillation indices exhibited clear altitude dependence, with strong scintillation in both the F‐ and E‐regions during the initial and main phases of the storm, and increased E‐region scintillation accompanied by suppressed F‐region scintillation during the recovery phase. The variations were consistent with background electron density conditions at different altitudes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".