GRACE-FO and Swarm integrated data analysis reveals ionospheric disturbances on the accelerometer measurements
Bibliographic record
Abstract
Low Earth Orbit (LEO) satellites are subject to numerous disturbances related to the Earth’s upper ionosphere. Perturbations induced by the activity of the electromagnetic field (EM) at the upper ionospheric layers have not been fully understood yet. This study focuses on the disturbances shown on GRACE-FO accelerometer measurements when the EM field was disturbed by an intense geomagnetic storm occurred on August 2018. A thorough analysis of the accelerometer measurements of GRACE-C as well as the magnetic and electric field measurements from Swarm constellation is conducted, to enlighten their impulse-response relationship. We derive the temporal variations of the magnetic field by removing the main static field and we calculate the Poynting vector employing the Swarm magnetic field measurements and electric field data, by implementing rigorous data analyses to analyze the spatiotemporal characteristics of the energy flow of the electromagnetic field. Results show that GRACE-C accelerometer measurements are highly disturbed in the higher latitudes especially near the auroral regions. The signature of the spatial temporal variations of the magnetic field and the Poynting vector demonstrates very similar behaviour with GRACE-C disturbances. Cross wavelet analysis between Poynting vector and GRACE-C accelerometer disturbances shows a very strong coherence. With the two LEO missions, i.e. GRACE-FO and Swarm, orbiting the Earth in very similar orbits, further analysis towards integrating their measurements will enhance our understanding of the interaction of LEO satellites with the space environment and how this interaction is depicted in their measurements.
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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 | 0.000 | 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".