Snow depth on Arctic sea ice retrieval using a synergy of Sentinel-3's active and passive microwave instruments
Bibliographic record
Abstract
Snow depth remains one of the largest sources of uncertainty in satellite-derived sea ice thickness (SIT). Here, we introduce the novel Nadir Radiometer and Radar Synergy (NaRRS) method that combines data from Sentinel-3’s Microwave Radiometer (MWR) and Synthetic Aperture Radar Altimeter (SRAL) to retrieve Arctic snow depth on sea ice. The resultant snow depths are co-located with SRAL-derived radar freeboard, reducing spatio-temporal mismatches in SIT processing. NaRRS achieves an R2 of 0.72 and RMSE of 0.05 m in cross-validation against Operation IceBridge snow depth data, and better matches IceBird observations than the modified Warren Climatology (mW99). Ice drafts estimated from coupled snow depth and freeboard align with mW99 against Beaufort Sea moorings but reduce bias by up to 50% against Fram Strait moorings. This work provides a proof-of-concept for simultaneous, co-located snow depth and SIT retrievals, paving the way for next-generation satellite missions and retrieval frameworks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".