Altimetric Ku-band Radar Observations of Snow on Sea Ice Simulated with SMRT
Bibliographic record
Abstract
Abstract. Radar altimetry provides sea ice thickness estimates for polar region. However, uncertainty in the scattering horizon used to retrieve sea ice thickness arises from interactions between the emitted signal and snow cover on the ice surface. Therefore, improving our knowledge on electromagnetic waves scattering with the snowpack and ice is necessary to retrieve sea ice thickness accurately. The Snow Microwave Radiative Transfer (SMRT) model was used to simulate the low-resolution altimeter waveform echo from snow-covered sea ice, using in-situ measurements as input. In-situ measurements from four field campaigns in three distinct Canadian Arctic regions includes temperature, salinity, density, specific surface area, microstructure from X-ray tomography and surface roughness measurements using structure from motion photogrammetry. Evaluation of SMRT in altimeter mode was performed against CryoSat-2 waveform data in pseudo-low-resolution mode. Simulated and observed waveforms showed good agreement, although it was necessary to optimize the snow and sea ice roughness. In addition, simulations of backscatter in low-resolution mode in preparation for the European Space Agency’s CRISTAL mission indicated that the dominant return comes from the ice surface at Ku-band and from the snow surface at Ka-band for smooth first-year ice. However, for rougher multi-year ice, the main scattering comes from the snow surface for both Ku and Ka-band. These findings depend on the parameterisation of the roughness. This work offers insight into the dominant surface return for Ku and Ka and paves the way towards a physical retracker using SMRT to retrieve snow depth and sea ice thickness for radar altimeter missions.
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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".