The Intriguing Structure of Stripes in GRACE Geopotential Models
Bibliographic record
Abstract
Geopotential models derived from Gravity Recovery and Climate Experiment (GRACE) mission measurements are significantly obscured by the presence of a systematic artifact, known as longitudinal stripes. Based on our previous work (Peidou and Pagiatakis, 2020) we provide an in-depth analysis of the latitudinal sampling characteristics of GRACE and we reveal the intriguing sampling mechanism that creates sub-Nyquist artifacts (stripes). Because the sub-Nyquist artifacts are poorly understood, we provide a simple simulation example to elucidate the mechanism of the sub-Nyquist artifact generation. Subsequently, we randomly select June 2009 daily GPS precise science orbits for GRACE-A to produce ground tracks to sample the low frequency disturbing potential (geoid) along the parallel of ϕ=10° N. The sampled geoid is then deinterlaced in space to produce a monthly data sequence whose detailed analysis shows that the sub-Nyquist artifacts (stripes) are produced from a critical sampling rate of the low degree gravitational field that is related to the ratio m/n of two mutually prime integers, where m is the number of days it takes to have a nearly repeat orbit and n is the number of complete orbits in one day. We perform extensive analyses of GRACE Level-2 data over a period of eight years to show the variability in the orbital characteristics that are directly linked to the orbit resonances (via integers m and n). It turns out that during short repeat cycle resonances the stripes are amplified. Finally, to minimize the presence of stripes in Level-2 data products, it is recommended that orbits of future missions should be designed to avoid the critical m/n ratios while appropriately monitoring and adjusting them during the mission. For completed missions, or missions that are already active, force modelling the latitudinal low frequency disturbing potential may be a viable and most preferred approach to filtering.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".