Cross-comparison at cross-over between SWOT LR SSH products and Sentinel-3 marine altimetry products
Bibliographic record
Abstract
SWOT is an Earth-Observation science mission dedicated to measuring surface water and ocean topography and jointly developed by NASA and CNES, in partnership with the Canadian Space Agency and UK Space Agency. The primary payload on board SWOT is a Ka-Band swath radar altimeter (KaRIn) which is covering the ocean in Low-Rate (LR) operation mode. A Ku-band Poseidon-Class nadir radar altimeter and a microwave radiometer is also embarked to support and complement the KaRIn swath measurements. Over the ocean, the SWOT Science Team provides the user community with the LR L2 SSH (Sea Surface Height) forward-processed products via the NASA and CNES distribution centres. These products are delivered on a 2x2 km geographically fixed grid with a short latency (usually few days) and they are now in version D, spanning the time-frame 28-April-2025 onward. In this work, we assess the performance and data quality of this SWOT L2 D SSH dataset at cross-over points with the Copernicus Sentinel-3 altimeter marine data in term of variance and bias at the cross-over location. The cross-over time difference will be chosen as short as possible (< 2 hours) to limit the impact of the natural ocean variability and both satellites S3A/B will be used to improve the geographic coverage. Also, special case of collinear tracks between Sentinel-3 and SWOT will be analysed, if any. The Sentinel-3 marine altimetry dataset to be used is an operational non-time-critical (NTC) dataset from baseline collection BC006.01 that was updated on 2024-12-04 to latest GDR-G standards. In the cross-comparison exercise, we take care that the same “standards” are used in both datasets. The objective is to identify any residual long wavelength error or systematic sea-state bias dependency error which may be still persisting after the KaRIn cross-over calibration application.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".