Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice
Bibliographic record
Abstract
Wind-driven redistribution of snow on sea ice alters its \ntopography and microstructure, yet the impact of these processes on radar \nsignatures is poorly understood. Here, we examine the effects of snow \nredistribution over Arctic sea ice on radar waveforms and backscatter \nsignatures obtained from a surface-based, fully polarimetric Ka- and Ku-band \nradar at incidence angles between 0∘ (nadir) and 50∘. \nTwo wind events in November 2019 during the Multidisciplinary drifting Observatory for \nthe Study of Arctic Climate (MOSAiC) expedition are evaluated. During both events, changes in Ka- and \nKu-band radar waveforms and backscatter coefficients at nadir are observed, \ncoincident with surface topography changes measured by a terrestrial laser \nscanner. At both frequencies, redistribution caused snow densification at \nthe surface and the uppermost layers, increasing the scattering at the \nair–snow interface at nadir and its prevalence as the dominant radar scattering surface. The waveform data also detected the presence of previous \nair–snow interfaces, buried beneath newly deposited snow. The additional \nscattering from previous air–snow interfaces could therefore affect the \nrange retrieved from Ka- and Ku-band satellite altimeters. With increasing \nincidence angles, the relative scattering contribution of the air–snow \ninterface decreases, and the snow–sea ice interface scattering increases. \nRelative to pre-wind event conditions, azimuthally averaged backscatter at \nnadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Results show substantial backscatter variability within the scan \narea at all incidence angles and polarizations, in response to increasing \nwind speed and changes in wind direction. Our results show that snow \nredistribution and wind compaction need to be accounted for to interpret \nairborne and satellite radar measurements of snow-covered sea 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".