The influence of solar wind variability on magnetospheric ULF wave power
Bibliographic record
Abstract
Abstract. Magnetospheric ultra-low frequency (ULF) oscillations in the Pc 4–5 frequency range play an important role in the dynamics of Earth's radiation belts, both by enhancing the radial diffusion through incoherent interactions and through the coherent drift-resonant interactions with trapped radiation belt electrons. The statistical distributions of magnetospheric ULF wave power are known to be strongly dependent on solar wind parameters such as solar wind speed and interplanetary magnetic field (IMF) orientation. Statistical characterisation of ULF wave power in the magnetosphere traditionally relies on average solar wind–IMF conditions over a specific time period. In this brief report, we perform an alternative characterisation of the solar wind influence on magnetospheric ULF wave activity through the characterisation of the solar wind driver by its variability using the standard deviation of solar wind parameters rather than a simple time average. We present a statistical study of nearly one solar cycle (1996–2004) of geosynchronous observations of magnetic ULF wave power and find that there is significant variation in ULF wave powers as a function of the dynamic properties of the solar wind. In particular, we find that the variability in IMF vector, rather than variabilities in other parameters (solar wind density, bulk velocity and ion temperature), plays the strongest role in controlling geosynchronous ULF power. We conclude that, although time-averaged bulk properties of the solar wind are a key factor in driving ULF powers in the magnetosphere, the solar wind variability can be an important contributor as well. This highlights the potential importance of including solar wind variability especially in studies of ULF wave dynamics in order to assess the efficiency of solar wind–magnetosphere coupling.
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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 | 0.003 |
| 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 | 0.001 | 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".