Statistical Characterization of Joule Heating Associated With Ionospheric ULF Perturbations Using SuperDARN Data
Bibliographic record
Abstract
Abstract Ultra low frequency (ULF; 1 mHz ‐ several Hz) waves are key to energy transport within the geospace system, yet their contribution to Joule heating in the upper atmosphere remains poorly quantified. This study statistically examines Joule heating associated with ionospheric ULF perturbations using Super Dual Auroral Radar Network (SuperDARN) data spanning middle to polar latitudes. Our analysis utilizes high‐time‐resolution measurements from SuperDARN high‐frequency coherent scatter radars operating in a special mode, sampling three “camping beams” approximately every 18 s. We focus on ULF perturbations within the Pc5 frequency range (1.6–6.7 mHz), estimating Joule heating rates from ionospheric electric fields derived from SuperDARN data and height‐integrated Pedersen conductance from empirical models. The analysis includes statistical characterization of Pc5 wave occurrence, electric fields, Joule heating rates, and azimuthal wave numbers. Our results reveal enhanced electric fields and Joule heating rates in the morning and pre‐midnight sectors, even though Pc5 wave occurrences peak in the afternoon. Joule heating is more pronounced in the high‐latitude morning sector during northward interplanetary magnetic field conditions, attributed to local time asymmetry in Pedersen conductance and Pc5 waves driven by Kelvin‐Helmholtz instability. Pc5 waves observed by multiple camping beams predominantly propagate westward at low azimuthal wave numbers , while high‐m waves propagate mainly eastward. Although Joule heating estimates may be underestimated due to assumptions about empirical conductance models and the underestimation of electric fields resulting from SuperDARN line‐of‐sight velocity measurements, these findings offer valuable insights into ULF wave‐related energy dissipation in the geospace system.
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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 | 0.001 | 0.001 |
| 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".