SWOT in the Cryosphere: Promise, Progress, and Challenges
Bibliographic record
Abstract
The SWOT mission launched in December 2022 with the goals of delivering the first global inventory of Earth's surface water, high-resolution ocean topography, and temporal variability of water bodies. Although SWOT’s primary objectives targeted terrestrial hydrology and oceanography, SWOT’s ~78° latitude orbital turnaround results in up to sub-weekly observations, unaffected by clouds, in many critical polar regions. SWOT may, therefore, also make significant contributions to cryospheric science. To capitalize on this potential, a dedicated Cryosphere Working Group was formed from the 2024 Science Team to explore and expand SWOT’s cryospheric applications. The group’s early efforts yielded the first high resolution (HR) tasking over Antarctica, adding to HR acquisitions for other key Arctic regions. Other promising advancements include a demonstration that KaRIn backscatter enables robust discrimination between sea ice and icebergs, a long-standing challenge for automated classifiers, and that the combination of sea surface height anomaly (SSHA) and backscatter supports a novel SWOT-based classification of sea ice and leads. A preliminary examination of SWOT LR data against ICESat-2 provides confidence in the feasibility of retrieving the first truly two-dimensional estimates of sea ice freeboard. SSHA observations of the Antarctic coastal margin provide, for the first time, the potential to observe variability of major surface currents across multiple timescales. HR data also provide the first frequent repeats of the 3-D structure of rifts on some Antarctic ice shelves. Additionally, the initial assessment of SWOT observations offers valuable insights into the mission’s potential for monitoring freshwater ice. Preliminary comparisons of HR PIXC data with two days of concurrent DEMs collected over river ice show ice surface elevation differences of ~25 cm. For each of these applications, however, significant challenges remain. Errors in geoid, mean sea surface, and tide corrections, and ongoing issues with crossover corrections hinder the retrieval of accurate coastal SSHA values. For ice shelves, our initial studies of HR data reveal many regions where measured elevations experience jumps, some of which may be attributed to errors in the underlying digital elevation model (DEM) from changing ice shelf fronts and rifts, and from large voids in the v1.1 100m REMA DEM. Artifacts in both HR and LR products, combined with complications introduced by onboard and ground-based processing pipelines, present further obstacles to retrieving reliable surface heights in polar regions. Ongoing work aims to resolve these issues — taking advantage of collaborations with other working groups — and establish SWOT as a powerful tool for observing cryosphere processes.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".