Comment on egusphere-2024-1583
Bibliographic record
Abstract
Abstract. Sea ice thickness is essential for climate studies and numerical weather prediction. Radar altimetry has provided sea ice thickness measurement since the launch of ERS-1 and currently through CryoSat-2, Sentinel-3 and Altika but 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, modelling the scattering of the electromagnetic waves with the snowpack and ice is necessary to retrieve the sea ice thickness accurately. The Snow Microwave Radiative Transfer (SMRT) model was used to simulate the low resolution altimeter waveform echo from the snow-covered sea ice, using in-situ measurements as input. Measurement from four field campaigns were used: Cambridge Bay, Eureka Sound and near Alert, Nunavut, Canada in April 2022 in the cold and later winter condition when snow and ice thickness are neat their seasonal maxima prior to melt. In-situ measurements included snow 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 adjust sea ice roughness. The retrieved roughness (root-mean-square height) in Cambridge Bay was 2.1 mm and 1.6 mm in Eureka, which was close to the observed value of 1.4 mm for flat sea ice. In addition, simulations of backscatter in preparation for the European Space Agency's CRISTAL mission demonstrated the dominance of scattering from the snow surface at Ku and Ka-band. However, these findings depend on the parameterisation of the roughness. The scattering from the snow surface dominates when roughness is high, but the interface return dominates if the roughness is low ( < 2.5 mm). This is the first study to consider scattering within the snow and demonstrate the origin of CryoSat-2 signals. This work paved the way to a new 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.007 |
| Insufficient payload (model declined to judge) | 0.347 | 0.262 |
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".