Global ionospheric and thermospheric response to the 5 April 2010 geomagnetic storm: An integrated data‐model investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it