First Arctic-wide assessment of SWOT swath altimetry with ICESat-2 over sea ice
Bibliographic record
Abstract
Abstract. This study presents an Arctic-wide assessment of the Surface Water and Ocean Topography (SWOT) mission's swath observations of sea surface height. SWOT provides measurements in two-dimensional swaths and enables pixel-based height information with a resolution of 250 metres up to a latitudinal limit of 78° N. Although SWOT doesn’t cover the central Arctic, it provides insights into SSH at an unprecedented spatial and temporal resolution. The quality of these innovative observations in such a challenging environment is evaluated through comparison with data from ICESat-2. Approximately one year of sea level anomaly data, collected between March 2023 and April 2024, is used at around 550 regionally distributed crossover locations, with measurements taken within 30 minutes. Sentinel-1 SAR imagery supports the comparisons if available. Visual comparisons of SWOT and ICESat-2 with Sentinel-1 grey-scale values reveal clear coherence. However, small-scale surface features aren’t captured by SWOT as equally as by ICESat-2. The data shows absolute water level differences of about 5 cm, despite prior harmonisation of references and corrections. Differences of up to 50 cm can occur when comparing left- and right-hand SWOT swaths, mainly during winter and in areas with long sea ice coverage. This may be due to issues with the height correction from the crossover calibration. Quantitative point-by-point comparisons show mean standard deviations of about 8 cm for all surface types and 6 cm if restricted to ICESat-2-detected leads. Higher deviations are found during the early melting period between May and June, in the Canadian Archipelago and the Greenland Sea.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".