Altimetry for estimating snow depth on sea ice: surface and satellite observations from the Canadian Arctic
Bibliographic record
Abstract
Snow plays a vital role in near-shore landfast sea ice physical and biological processes. It needs to be monitored to understand sea ice processes and also to estimate sea ice thickness. However, snow depth remains difficult to estimate directly from space which forces the sea ice thickness products to use snow depth from model outputs or out-of-date climatology. Satellite altimetry based dual-radar and coincident laser/radar have been considered for providing regular estimates of snow depth on sea ice. In addition to the limitations associated with the functioning of individual altimeters, snow on landfast sea ice presents its challenges especially due to the lack of leads. While past studies have mostly focused on approaches suited to pack ice in the Central Arctic, this study aims to provide critical observations from landfast first-year sea ice in the Canadian Arctic. Surface-base altimeter retrievals from snow on sea ice at Churchill, Manitoba are compared to snow on lake ice demonstrating that the position of the Ku-band main scattering horizon is impacted by the presence of brine in the snowpack. The satellite-level study conducted at Dease Strait near Cambridge Bay is the first to assess the possibility of using coincident laser/radar altimeters (Cryo2Ice) for estimating snow depth on land-fast and lead-less sea ice at the Canadian Arctic Archipelago. The retrieved Cryo2Ice snow depths were underestimated by an average of 20.7 % which is slightly higher than the tidal adjustment applied. However, snow geophysical properties and surface roughness are seen to significantly bias Cryo2Ice retrievals. Both surface and satellite-based studies point towards the position of the Ku-band main scattering horizon being closer to the air-snow interface as opposed to the generally assumed snow-ice interface. Therefore, findings from this study may be useful for monitoring snow depth on land-fast sea ice using currently available and future satellite altimeters whereby snow depth and sea ice satellite products may be more useful in a landfast sea ice context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".