Snow Depth Estimation on Lead-less 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 as well as for understanding bio-physical processes and human-activities. This study is the first assessment of the potential for near-coincident ICESat-2 and Cryosat-2 (Cryo2Ice) snow depth retrievals in a lead-less region of the Canadian Arctic Archipelago. Snow depths are retrieved using the absolute difference in surface height from a near-coincident ICESat-2 and Cryosat-2 after applying an ocean tide correction between satellite passes 77 minutes apart. Both the absolute mean snow depths and snow depth distributions retrieved from Cryo2Ice compare favourably to in-situ measurements. All four in-situ sites had snow with saline basal layers and different levels of roughness/ridging. The retrieved Cryo2Ice snow depths were underestimated by an average of 20.7 % which is slightly higher than the tidal adjustment applied. Differences in the Cryo2Ice and in-situ snow depth distributions reflected the different sampling resolutions between the sensors and the in-situ measurements, with more heavily ridged areas producing larger mean underestimation of the snow depth. Results suggest the possibility of estimating snow depth over lead-less landfast sea ice but attributing 2–3 cm biases to differences in sampling resolution, snow salinity, density, surface roughness and/or errors in altimeter’s tidal corrections require further investigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 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".