Snow property controls on modelled Ku-band altimeter estimates of first-year sea ice thickness: Case studies from the Canadian and Norwegian Arctic
Bibliographic record
Abstract
Uncertainty in snow properties impacts the accuracy \nof Arctic sea ice thickness estimates from radar altimetry. On firstyear sea ice (FYI), spatiotemporal variations in snow properties \ncan cause the Ku-band main radar scattering horizon to appear \nabove the snow/sea ice interface. This can increase the estimated \nsea ice freeboard by several centimeters, leading to FYI thickness \noverestimations. This study examines the expected changes in Kuband main scattering horizon and its impact on FYI thickness \nestimates, with variations in snow temperature, salinity and \ndensity derived from 10 naturally occurring Arctic FYI Cases \nencompassing saline/non-saline, warm/cold, simple/complexly \nlayered snow (4 cm to 45 cm) overlying FYI (48 cm to 170 cm). \nUsing a semi-empirical modeling approach, snow properties from \nthese Cases are used to derive layer-wise brine volume and \ndielectric constant estimates, to simulate the Ku-band main \nscattering horizon and delays in radar propagation speed. \nDifferences between modeled and observed FYI thickness are \ncalculated to assess sources of error. Under both cold and warm \nconditions, saline snow covers are shown to shift the main \nscattering horizon above from the snow/sea ice interface, causing \nthickness retrieval errors. Overestimates in FYI thicknesses of up \nto 65% are found for warm, saline snow overlaying thin sea ice. \nOur simulations exhibited a distinct shift in the main scattering \nhorizon when the snow layer densities became greater than 440 \nkg/m3 \n, especially under warmer snow conditions. Our simulations \nsuggest a mean Ku-band propagation delay for snow of 39%, \nwhich is higher than 25%, suggested in previous studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".