C and K band microwave penetration into snow on sea ice studied with off-the-shelf tank radars
Bibliographic record
Abstract
Abstract Snow cover on sea ice poses a challenge for radar measurements as microwave penetration into snow is not yet fully understood. In this study, the aim is to investigate microwave penetration into snow on Arctic sea ice using commercial C (6 GHz) and K (26 GHz) band tank radars. Nadir-looking radar measurements collected at nine study locations over first-year and multiyear landfast sea ice in the Lincoln Sea in May 2018 are analysed together with detailed measurements of the physical properties of the snow cover to determine the dominant scattering horizons at both frequencies. They are evaluated for the feasibility to determine snow depth. The results show that in 39% of the measurements and only on first-year ice a major fraction of the C band radar backscatter originated closer to the snow–ice interface potentially enabling snow depth retrieval. At K band, 81% of the radar returns originated from the snow surface. Partly confirming the findings of previous studies, however, the analysis was potentially hampered by relatively warm air temperatures (up to $-0.9^\circ$ C) during the study period as well as stratigraphic features and inconclusive microwave interaction with the saline basal layers found in the snow cover on first-year ice.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".