Unusual SuperDARN Backscatter During the 11 May 2024 Geomagnetic Storm
Bibliographic record
Abstract
Abstract A geomagnetic storm, one of the largest in this solar cycle, was launched on 10 May 2024, producing spectacular auroral displays that could be observed across the continental United States (US) at middle and low latitudes. In this study, we focus on a brief 20‐min interval during the peak of the storm when the Sym‐H index dropped to −500 nT, and the auroral activity specified by the AL and AU indices was elevated. During this interval, the Blackstone (BKS) Super Dual Auroral Radar Network (SuperDARN) radar, observed strong ionospheric backscatter blanketing the near‐ranges across its field‐of‐view. Upon analyzing the elevation and virtual height characteristics of this backscatter we find that: (a) the BKS radar observed F‐region backscatter at unusually close ranges (750 km), and (b) this backscatter was observed over a broad range of elevation angles, including unusual very high ones. It is not physically realistic that all the radio waves, launched over a broad range of elevation angles, refract to become perpendicular to the B‐field. We therefore interpret that a sizable portion of this backscatter is produced by irregularities that are not field‐aligned. These observations show that plasma irregularities generated during strong geomagnetic storms can produce strong and unusual High Frequency (HF) radar backscatter, and significantly impact their operations. Finally, we suggest that the high‐aspect angle backscatter was most likely associated with the non‐linear decay of gradient‐drift modes that had been excited unusually strongly during the event.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".