Polar Topside Ionosphere During Geomagnetic Storms: Comparison of ISIS‐II With TDIM
Bibliographic record
Abstract
Abstract Space weather deposits energy into the high polar latitudes, primarily via Joule heating that is associated with the Poynting flux electromagnetic energy flow between the magnetosphere and ionosphere. One way to observe this energy flow is to look at the ionospheric electron density profile (EDP), especially that of the topside. The altitude location of the ionospheric peak provides additional information on the net field‐aligned vertical transport at high latitudes. To date, there have been few studies in which physics‐based ionospheric model storm simulations have been compared with topside EDPs. A rich database of high‐latitude topside ionograms obtained from polar orbiting satellites of the International Satellites for Ionospheric Studies (ISIS) program exists but has not been utilized in comparisons with physics‐based models. Of specific importance is that the Alouette/ISIS topside EDPs spanned the timeframe from 1962 to 1983, a period that experienced very large geomagnetic storms. We use a physics‐based ionospheric model, the Utah State University Time Dependent Ionospheric Model (TDIM), to simulate ionospheric EDPs for quiet and storm high‐latitude passes of ISIS‐II for two geomagnetic storms. This initial study finds that under quiet conditions there is good agreement between model and observations. During disturbed conditions, however, a large difference is seen between model and observations. The model limitation is probably associated with the inability of its topside boundary to replicate strong outflow conditions. As a result, modeling of the ionospheric outflows needs to be extended well into the magnetosphere, thereby moving the upper boundary much higher and requiring the use of polar wind models.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".