Global‐scale observations of ionospheric convection variation in response to sudden increases in the solar wind dynamic pressure
Bibliographic record
Abstract
We have used a superposed epoch analysis to study 205 sudden commencement (SC) events detected with ground‐based magnetometers between the years 2000 and 2007. The strength of the SC events was clearly correlated to the magnitude of the jump in the solar wind dynamic pressure, regardless of whether or not the SC events were followed by a magnetic storm. Data from the Super Dual Auroral Radar Network (SuperDARN) demonstrated that both the ionospheric plasma drift speed and the number of echoes increased in the noon sector in response to the increase in solar wind dynamic pressure. In contrast, the number of SuperDARN echoes in the midnight sector decreased as the solar wind dynamic pressure increased, even though the average drift speed in the midnight sector also increased. We also uncovered that the ionosphere and ring current evolve differently in response to the pressure pulses. The SYM‐H index, which represents changes in both the magnetopause and ring currents, responded immediately and either rapidly returned to pre‐SC values or progressed into the main phase of a geomagnetic storm. In contrast, the ionospheric convection data were affected for a much longer time. The implication is that the ring current reacts to a sudden compression of the magnetosphere on a time scale of 10 min, while the convection pattern itself is affected for as long as the increase in solar wind dynamic pressure is sustained, or until a geomagnetic storm was triggered, as is the case in the sudden storm commencement (SSC) subset of events.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".