On-orbit Calibration of GRACE and GRACE-FO accelerometers
Bibliographic record
Abstract
The onboard GRACE and GRACE-FO accelerometers measure the non-gravitational accelerations of the spacecraft that are indispensable for the modelling of the Earth’s gravity field since they are subtracted from the GPS POD total accelerations to isolate the pure gravitational accelerations. The accelerometers, like any other instrument, must be calibrated very accurately to serve their purpose but the calibration cannot be done as per standard metrological methods, in a laboratory environment due to the influence of the gravitational signal which is almost 10 orders of magnitude larger. Many researchers have proposed different approaches for the estimation of the bias and the scale factor of the accelerometers, most of which are based on the physical models of the non-gravitational accelerations The drawback of these methods is the dependency of the calibration on the theoretical models and specifically on the drag models that exhibit the highest uncertainty due to the complexity of the upper atmosphere. An alternative, on-orbit calibration of the accelerometers is proposed that is commensurate with standard metrological methods and thus it is based only on the satellite measurements. The idea behind this method lies in a time-reversal method widely used in radar applications for the detection and measurement of known but distorted pulses hidden in the scattered signal from the reflectors. By analogy to radar applications, the total accelerations estimated from GPS through numerical double POD differentiation, correspond to the ‘transmitted signal’ (calibrated) and the accelerometer measurements (uncalibrated) comprise the ‘return signal’ (scattered signal). The key to this method is that the penumbra transitions are present in both signals and play the role of the known calibration pulse. Examples of 30 daily scale factors are calculated for different operational periods during lower and higher solar activity of GRACE and GRACE-FO and the accelerometer bias is computed by a daily polynomial fit. The results show a robust estimation of the scale factor and bias of the accelerometers. The thermal sensitivity of the accelerometer and its correlation with the β’ angle is investigated.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".