 Exploring thermospheric disturbance patterns: A weighted accelerometer 1B dataset of GRACE C
Bibliographic record
Abstract
Satellite measurements are essential for studying Earth’s environment, but they often lack reliable error estimates, making it harder to interpret the data reliably. In this study, we present a new way to calculate realistic covariance matrices directly from raw satellite data, focusing on the accelerometer measurements from the GRACE C satellite from August 2018 to August 2022, covering the solar minimum and the ascending phase of Solar Cycle 25. The ACC1A dataset (10 Hz sampling rate) is used as the primary data source because it provides the raw measurements suitable for generating experimental covariance matrices directly from the original observations. Using autocorrelation analysis of the ACC1A measurements, we build a block-diagonal covariance matrix and integrate it into the data processing through a low-pass Gaussian filter. This method improves the accuracy of the proposed weighted ACW1B dataset by reducing noise, such as sudden spikes due to thruster activations while preserving important signals.Our results show that the accelerometer variances depend on the satellite’s position and time in orbit. We observe clear fluctuations during geomagnetic storms, especially near the equinoxes, and during crossings through Earth’s shadow and terminator. These variances are highest in the radial, and smallest in the cross-track direction, due to the accelerometer’s lower sensitivity in the latter. The new ACW1B dataset also shows a strong link between measurement variances and orbital factors like latitude, local time, and β' angle, making it more suitable for studying satellite-environment interactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".