Snow effects on altimeter waveforms over sea ice in the Weddell Sea — Part I: Radar waveform decomposition
Bibliographic record
Abstract
Snow on sea ice strongly modulates growth, albedo, and air–sea exchange, but it also drives major uncertainties in altimeter-based thickness retrievals. This is critical for Ku-band radar altimeters (e.g., CryoSat-2, CS-2), whose waveforms integrate backscatter from snow-covered sea ice. Contrary to the common assumption that returns originate near the snow–ice interface, complex snow properties (roughness, layering, wetness, ice lenses) can shift effective scattering upward into the snowpack. We analyze Ku-band CS-2 satellite and Ka-band KAREN airborne waveforms over the Weddell Sea to partition contributions from the snow surface, snow volume, and ice surface. Using a physics-based Forward Backscatter Emulation Model (FBEM) and a CNN trained on simulated waveforms, we retrieve geophysical parameters and assess sensitivity to snow conditions. Under typical Antarctic conditions, snow-volume scattering contributes as much as, or more than, the snow–ice interface to CS-2 returns, while Ka-band is dominated by surface/near-surface snow scattering with minimal penetration to the ice surface. Wet snow further amplifies upper-layer backscatter. Sensitivity tests identify volume scattering and ice-surface roughness as primary controls on waveform shape. These results argue for explicit snow-volume terms in waveform models and support dual-frequency strategies relevant to ESA’s upcoming CRISTAL mission. Part I (this study) treats waveform decomposition; Part II evaluates retracking for improved thickness retrievals. • In summer Ku-band, snow volume exceeds interface returns, biasing freeboard. • Physically based FBEM+CNN separates surface, volume, and interface scattering. • Enables snow-aware retrackers for CRISTAL and scale-consistent altimetry processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".