Spectral Asymmetry of Near-Concentric Traveling Ionospheric Disturbances Due to Doppler-Shifted Atmospheric Gravity Waves
Bibliographic record
Abstract
Atmospheric Gravity Waves (AGWs) excited by meteorological sources are one of the prominent sources of variability in the ionosphere. Partially-concentric Traveling Ionospheric Disturbances (TIDs) associated with AGWs launched by convective storms have been reported in Total Electron Content (TEC) data from distributed networks of Global Navigation Satellite System (GNSS) receivers. In this paper, TEC data from GNSS receivers in the COntiguous United States (CONUS) are presented to examine AGWs in the ionosphere generated by a convective thunderstorm on April 28, 2014 over Mississippi (MS) and Tennessee (TN). Our analysis of the TID perturbations in the TEC data shows zonal asymmetry of the wave frequencies. This spectral asymmetry is examined to determine the effects of the background neutral wind on the intrinsic periods of the underlying AGWs. This work shows that if the relative motion of the TID wavefronts and the background neutral wind is in the opposite direction, the intrinsic periods will decrease and if they both travel in the same direction, the intrinsic periods will increase. Furthermore, our results show that the characteristics of the TIDs observed on April 28, 2014 in the TEC over CONUS are consistent with those of underlying AGWs being excited by a point source, such as a deep convection 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 | 0.000 | 0.000 |
| 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".