Unveiling North–South stripe patterns in the GRACE gravity field using dimensionality reduction
Bibliographic record
Abstract
SUMMARY The spherical harmonic coefficient Level-2 products of the Gravity Recovery and Climate Experiment (GRACE) mission are affected by north–south stripe noise. Toward this end, we proposed a new filter named Variational Mode Decomposition spatial (VMDS) filter that transforms an equivalent water height (EWH) map derived from GRACE Level-2 product into a 1-D sequence, which is then filtered by using variational mode decomposition. This approach overcomes the limitations of the singular spectrum analysis spatial (SSAS) filter, which performs well in the medium-frequency band but omits the high-frequency NSS noise. Integrating the strengths of both, we developed a combined filter termed SV by placing the VMDS filter behind the SSAS filter. A closed-loop simulation demonstrates the better ability of SV to suppress NSS noise and preserve signal at the grid scale compared to the SSAS filter. In the real-world scenario, the SV solution achieves a noise level (46.68 mm of EWH) below that for SSAS and DDK7 solutions (53.52 and 53.68 mm of EWH, respectively) over the ocean at low latitudes. Moreover, the well-documented water level of Lake Victoria and the well-modelled coseismic gravity change of the Mw9.2 2004 Sumatra-Andaman earthquake demonstrate that the SV filter efficiently preserves localized mass evolutions while suppressing north–south stripe noise. Such short-wavelength signals are usually missed in highly filtered spherical harmonics (e.g. DDK5 and DDK6) solutions or are significantly inconsistent for various mass concentration solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".