The Roles of the Magnetopause and Plasmapause in Storm‐Time ULF Wave Power Enhancements
Bibliographic record
Abstract
Abstract Ultra low frequency (ULF) waves play a crucial role in transporting and coupling energy within the magnetosphere. During geomagnetic storms, dayside magnetospheric ULF wave power is highly variable with strong enhancements that are dominated by elevated solar wind driving. However, the radial distribution of ULF wave power is complex ‐ controlled interdependently by external solar wind driving and the internal magnetospheric structuring. We conducted a statistical analysis of observed storm‐time ULF wave power from the Van Allen Probes spacecraft within 2012–2016. Focusing on the dayside (06 < magnetic local time ≤ 15), we observe large enhancements across 3 < L < 6 and a steep L dependence during the main phase. We consider how accounting for concurrent magnetopause and plasmapause locations may reduce statistical variability and improve parameterization of spatial trends over and above using the L value. Ordering storm time ULF wave power by L provides the weakest dependences from those considered, whereas ordering by distance from the magnetopause is more effective. We also explore dependences on local plasma density and find that spatially localized ULF wave power enhancements are confined within high density patches in the afternoon sector (likely plasmaspheric plumes). The results have critical implications for empirical models of ULF wave power and radial diffusion coefficients. We highlight the necessity of improved characterization of the highly distorted storm‐time cold plasma density distribution, in order to more accurately predict ULF wave power.
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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 | 0.002 |
| 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".