The 2022 Starlink Geomagnetic Storms: Global Thermospheric Response to a High‐Latitude Ionospheric Driver
Bibliographic record
Abstract
Abstract In this study, we present ionospheric observations of field‐aligned currents from AMPERE and the ESA Swarm A satellite, in conjunction with high‐resolution thermospheric density measurements from accelerometers on board Swarm C and GRACE‐FO, for the third and 4 February 2022 geomagnetic storms that led to the loss of 38 Starlink internet satellites. We study the global storm time response of the thermospheric density enhancements, including their decay and latitudinal distribution. We find that the thermospheric density enhances globally in response to high‐latitude energy input from the magnetosphere‐solar wind system and takes at least a full day to recover to pre‐storm density levels. We also find that the greatest density perturbations occur at polar latitudes consistent with the magnetosphere‐ionosphere dayside cusp, and that there appeared to be a saturation of the thermospheric density during the geomagnetic storm on the fourth. Our results highlight the critical importance of high‐latitude ionospheric observations when diagnosing potentially hazardous conditions for low‐Earth‐orbit satellites.
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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 | 0.000 | 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) | 0.001 | 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".