Impact of Snow Properties on Ka- and Ku-band Winter and Melt Season Microwave Signatures of Arctic Sea Ice
Bibliographic record
Abstract
We present the baseline and detailed assessment of Ka- and Ku-band microwave signatures of winter (Legs 1 and 2) and melt season (Leg 4) snow-covered sea ice, acquired during the 2019-2020 MOSAiC International Arctic Drift Expedition. The microwave signatures were acquired using a surface-based, fully-polarimetric, Ku- and Ka-band radar (KuKa radar), acquired coincident with in situ meteorological and snow/sea ice geophysical property measurements. The KuKa radar mimicked the center frequencies of presently operational Ku- and Ka-band satellite radar altimeter and scatterometer missions. Preliminary observations, supported by microwave backscatter modeling indicates dominant Ka-band snow surface scattering and its strong sensitivity due to snow surface roughness and its changes, induced by snow accumulation, wind-driven redistribution/erosion. For Ku-band, winter backscatter signatures originate from the snow/sea ice interface. We also showcase the winter backscatter sensitivity through its impact during the November 2019 warm storm. During advanced melt, the Ka- and Ku-band signatures demonstrates sensitivity to snow surface melt/refreeze diurnal cycling, caused by fluctuations in liquid water content. During the melt cycle, scattering loss and absorption dominated both frequencies, while refrozen snow surface scattering dominated the refreeze cycle (observed during morning and evening scans). Observations from the KuKa radar will in turn provide critical understanding of snow/sea ice geophysical processes over the annual cycle, that will improve the accuracy of satellite-based retrievals of snow/sea ice critical state variables such as snow depth, sea ice thickness, freeze-up and melt-onset timings etc, from operational and forthcoming missions such as AltiKa, CryoSat-2, Sentinel-3, ScatSat-1, CRISTAL etc.
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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".