Snow depth estimation on leadless landfast ice using Cryo2Ice satellite observations
Bibliographic record
Abstract
Abstract. Observations of snow on Arctic Sea ice are vitally important for sea ice thickness estimation, bio-physical processes, and human activities. While previous studies have combined CryoSat-2- and ICESat-2-derived freeboards to estimate snow depth over Arctic sea ice, these approaches require leads within the ice pack to estimate the freeboard heights above the sea surface. In regions such as the Canadian Arctic Archipelago (CAA), leads are scarce in winter, posing a significant challenge to estimate snow depth from altimeters. This study is the first assessment of the potential for near-coincident ICESat-2 and Cryosat-2 (Cryo2Ice) snow depth retrievals in a leadless region of the CAA including validation with in situ data. In lieu of sea surface height estimates from leads, snow depths are retrieved using the absolute difference in surface heights (ellipsoidal heights) from ICESat-2 and Cryosat-2 after applying an ocean tide correction based on tidal gauges between satellite passes on 29 April 2022. Both the absolute mean snow depths and distributions retrieved from Cryo2Ice were slightly underestimated (2 to 4 cm) when compared to in situ measurements. All four in situ sites had snow with saline basal layers and different levels of roughness and ridging, which significantly impact the accuracy of the Cryo2Ice snow depth retrievals. Differences between Cryo2Ice and in situ snow depth distributions reflect the varying sampling resolutions of the sensors and the in situ measurements. Cryo2Ice tends to miss snow depths greater than 30 cm, especially around ridges. The results suggest that it might be possible to estimate snow depth over landfast sea ice without leads. However, the observed biases of 2–4 cm likely stem from several factors: (1) discrepancies in sampling resolution between ICESat-2 and CryoSat-2; (2) the CryoSat-2 scattering horizon not aligning with the snow–ice interface due to snow salinity, density, and surface roughness; (3) the choice of retracker; and (4) potential errors in the altimeter's tidal corrections. Further investigation is needed to address these issues. Moreover, the proposed methodology for getting snow depth over leadless landfast sea ice needs to be validated using in situ datasets in other landfast sea ice regions in the Arctic.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".