Bibliographic record
Abstract
Wind transport alters the snow topography and microstructure on sea ice through snow redistribution controlled by deposition and erosion. The impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution on Arctic sea ice from Ka- and Ku-band radar signatures. Measurements were obtained during two wind events in November 2019 during the MOSAiC expedition. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coincident with surface height changes measured from a terrestrial laser scanner are observed. At both frequencies, snow redistribution events increased the dominance of the air/snow interface at nadir as the dominant radar scattering surface, due to wind densifying the snow surface and uppermost layers. The radar waveform data also detect the presence of previous air/snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air/snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite radar altimeters. The relative scattering contribution of the air/snow interface decreases, and the snow/sea ice interface increases with increasing incidence angles. Relative to pre-wind conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Binned backscatter within 5° azimuth bins reveals substantial backscatter variability in the radar footprint at all incidence angles and polarizations. The sensitivity of the co-polarized phase difference is linked to changes in snow settling and temperature-gradient induced grain metamorphism, demonstrating the potential of the radar to discriminate between newly deposited and older snow on sea ice. Our results reveal the importance of wind, through its geophysical impact on Ka- and Ku-band radar signatures of snow on sea ice and has implications for reliable interpretation of airborne 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.393 | 0.235 |
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".