Topside equatorial spread F-related field-aligned Poynting flux: observations and simulations
Bibliographic record
Abstract
Abstract Electric and magnetic field data from the Swarm constellation mission are used to report on the Poynting flux associated with postsunset topside equatorial spread F . A three-dimensional numerical simulation of plasma density irregularities in the F region ionosphere leading to spread F is used to interpret and support the satellite observations. Here, we focus on quasi-static magnetic and electric fields nearby equatorial plasma depletions (EPDs). The observations show a correlation of the Poynting flux with the plasma number density when background densities are larger than $$\mathrm {10^5 ~cm^{-3}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>5</mml:mn> </mml:msup> <mml:mspace/> <mml:msup> <mml:mi>cm</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> —typical of pre-midnight hours. In other words, the Poynting flux increases as EPDs get more depleted. As time passes, both plasma density and Poynting flux decay. For the latter, however, this temporal dependence is evident in the pre-midnight sector only. Concerning spatial variations, the Poynting flux is observed to enhance inside EPDs as a function of magnetic latitude mainly due to the strengthening of field-aligned currents as they flow away from the dip equator. The Poynting flux follows the dynamo theory, wherein the winds in the F region act as the generator at night and the E region conductivity on shared magnetic field lines as the load. That said, the Poynting flux is generally expected to flow along the field lines away from a dynamo source at the dip equator. Nevertheless, observations show unidirectional flows from one magnetic hemisphere to another, suggesting a generator below the satellites’ altitude. The numerical simulations confirm these observations and show that such latitudinal shifts of the generator are due almost entirely to the winds. Graphical Abstract
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".