A mathematical model for the numerical simulations of traveling ionospheric disturbances/atmospheric gravity waves generated by the Joule heating
Bibliographic record
Abstract
Traveling ionospheric disturbances (TIDs) and atmospheric gravity waves (AGWs) generated by the Joule heating produced from intensified auroral electrojet and/or intense particle precipitation in the auroral and subauroral regions during geomagnetic storms and which propagate downward toward the lower (neutral) atmosphere are numerically simulated using a simple two-dimensional mathematical model for internal gravity waves propagating in the lower atmosphere. An explicit expression for the Joule heating is obtained, and the characteristics of the simulated TIDs/AGWs (e.g., buoyancy frequency, wavenumbers, cutoff wavelength, speed, structures) are also examined and compared with the results obtained from observations. As may be seen in the observations, small-scale TIDs/AGWs with wavelengths shorter than 100 km, medium-scale TIDs/AGWs with wavelengths of several hundred kilometers, and large-scale TIDs/AGWs with wavelengths longer than 1000 km generated by the Joule heating were modeled and numerically simulated. For example, observations have revealed that the Joule heating can generate TID/AGW pairs. The developed numerical model was used to simulate medium-scale TID/AGW wave packet pairs, and the results (wavelength, speed, structure) are in agreement with SuperDARN observations reported by Sofko and Huang, 2000. TIDs/AGWs with shorter horizontal wavelengths are more trapped than those with longer wavelengths (medium- and large-scale TIDs/AGWs). Their cutoff horizontal wavelength was approximated using the linear stability theory of AGWs. The approximated cutoff horizontal wavelength suggests that not all simulated small-scale disturbances with horizontal wavelengths shorter than 100 km are traveling as may be seen in the observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".