Investigating how platform height affects sea ice radar returns with KuKaSim
Bibliographic record
Abstract
Current, and ongoing observations, of Arctic sea ice, indicate a trend towards a younger, thinner and more mobile pack that exhibits significant inter-annual variability. Satellite and airborne radar altimeters have been used extensively to quantify these changes by deriving sea ice freeboard to infer sea ice thickness. Radar returns from altimeters are impacted by both the morphology of snow and ice features on the sea ice surface, in addition to the radar properties of the snowpack, with both contributing to uncertainties in radar-derived sea ice freeboard. Here we make use of airborne lidar data, collected as part of the MOSAiC expedition in the winter of 2019/2020, to investigate the effect of sea ice surface morphology on radar altimeter measurements. We quantify these effects using 'KuKaSim' a forward-modelling approach based on the KuKa instrument deployed at MOSAiC, which allows us to investigate how simulated radar returns vary with radar height. Our results allow us to better constrain the altimetric uncertainty resulting from ice surface morphology, with respect to both radar height and sea ice type, leading to an enhanced understanding of sources of uncertainty in altimeter-derived sea ice thickness products.
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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 | 0.001 |
| 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".