Using Temporal Relationship of Thermospheric Density With Geomagnetic Activity Indices and Joule Heating as Calibration for NRLMSISE‐00 During Geomagnetic Storms
Bibliographic record
Abstract
Abstract The responses of thermospheric densities to geomagnetic activity indices and Joule heating are analyzed during 265 geomagnetic storms and can be used to calibrate the model NRLMSISE‐00 with neutral mass density observed at 400 km based on the CHAMP satellite from 2002 to 2008. In this work, the geomagnetic activities at high and low latitudes are identified by AE indices and Dst indices. During geomagnetic storms, Joule heating and its impacts on the thermospheric density are calculated by the Weimer‐2001 electric potential model and the DMSP spacecraft. The results show that the response of thermospheric density to both AE and Dst index takes a longer time as geomagnetic storms intensify. During weak and moderate storms, density delays AE indices for about 0–1 hr, while it is 2–4 hr for intense storms. In addition, the time differences between Dst indices and AE indices increase as storms intensify. During weak and moderate geomagnetic storms, the difference in the time corresponding to Dst indices with the time when AE indices peak is only 1–2 hr, while it increases to 3–5 hr for the intense storms. Furthermore, the calibration of the NRLMSISE‐00 model results can reproduce the storm‐time thermospheric density well, with the Mean Relative Error (MRE) between density observation and model decreasing from 40% to 10% after the correction.
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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 | 0.000 | 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".