Global ionospheric and thermospheric response to the 5 April 2010 geomagnetic storm: An integrated data‐model investigation
Bibliographic record
Abstract
Abstract We present a case study of the 5 April 2010 geomagnetic storm using observations and numerical simulations. The event was driven by a fast‐moving coronal mass ejection and despite being a moderate storm with a minimum Dst near −50 nT, the event exhibited elevated thermospheric density and surges of traveling atmospheric disturbances (TADs) more typically seen during major storms. The Thermosphere‐Ionosphere‐Mesosphere‐Electrodynamics General Circulation Model (TIMEGCM) was used to assess how these features were generated and developed during the storm. The model simulations gave rise to TADs that were highly nonuniform with strong latitude and longitude/local time dependence. The TAD phase speeds ranged from 640 m/s to 780 m/s at 400 km and were ~5% lower at 300 km and approximately 10–15% lower at 200 km. In the lower thermosphere around 100 km, the TAD signatures were nearly unrecognizable due to much stronger influence of upward propagating atmospheric tides. The thermosphere simulation results were compared to observations available from the Gravity Field and Steady‐State Ocean Circulation Explorer (GOCE), CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) satellites. Comparison with GOCE data shows that the TIMEGCM reproduced the cross‐track winds over the polar region very well. The model‐data comparison also revealed some differences, specifically, the simulations underestimated neutral mass density in the upper thermosphere above ~300 km and overestimated the storm recovery tome by 6 h. These discrepancies indicate that some heating or circulation dynamics and potentially cooling processes are not fully represented in the simulations, and also that updates to some parameterization schemes in the TIMEGCM are warranted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".