ULF Wave Driven Radial Diffusion During Geomagnetic Storms: A Statistical Analysis of Van Allen Probes Observations
Bibliographic record
Abstract
Abstract The impact of radial diffusion in storm time radiation belt dynamics is well‐debated. In this study we quantify the changes and variability in radial diffusion coefficients during geomagnetic storms. A statistical analysis of Van Allen Probes data (2012–2019) is conducted to obtain measurements of the magnetic and electric power spectral densities for Ultra Low Frequency (ULF) waves, and corresponding radial diffusion coefficients. The results show global wave power enhancements occur during the storm main phase, and continue into the recovery phase. Local time asymmetries show sources of wave power are both external solar wind driving and internal sources from coupling with ring current ions and substorms. Wave power enhancements are also observed at low L values (L < 4). The accessibility of wave power to low L is attributed to a depression of the Alfvén continuum. The increased wave power drives enhancements in both the magnetic and electric field diffusion coefficients by more than an order of magnitude. Significant variability in diffusion coefficients is observed, with values ranging over several orders of magnitude. A comparison to the Kp parameterized empirical model of Ozeke et al. (2014) is conducted and indicates important differences during storm times. Although the electric field diffusion coefficient is relatively well described by the empirical model, the magnetic field diffusion coefficient is approximately ∼10 times larger than predicted. We discuss how differences could be attributed to data set limitations and assumptions. Alternative storm‐time radial diffusion coefficients are provided as a function of L* and storm phase.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".