Revisiting NASA's Operation IceBridge Snow on Sea Ice Radar Measurements in the Arctic
Bibliographic record
Abstract
Snow on sea ice plays a critical role in modulating ice mass changes in response to anthropogenic warming, with significant implications for ocean mixed layer processes, the surface energy budget, and marine ecosystems. Most importantly, accurate snow depth measurements are essential for deriving reliable sea ice thickness estimates from all altimetry satellites. Operation IceBridge (OIB), which collected snow depth data using the airborne CReSIS FMCW C/S-band snow radar for a decade, remains a pivotal reference for understanding pan-Arctic snow depth changes and validating remote sensing snow retrievals. Despite its importance, significant concerns persist regarding snow retrieval algorithms from snow radar, particularly around algorithm performance and the representation of snow properties.In this study, we revisit OIB snow depth retrieval algorithms by comparing them with underutilized in-situ snow depth measurements from MagnaProbe surveys conducted near Eureka, Canada, in 2016. To enhance the spatial representation of the in-situ data, we employ Kriging interpolation methods. Additionally, we make use of the co-collected conical laser scanner data. A detailed comparison of retrieval algorithms - focusing on the detection of the air-snow and snow-ice interfaces as well as the derived snow depth - reveals that the Continuous Wavelet Transform (CWT) algorithm performs best for the 2-8 GHz snow radar version, yielding a correlation of R=0.72 over undeformed sea ice. However, the CWT algorithm predominantly detects snow depths within the 80-90% quantile of the in-situ distribution within the radar footprint. This bias is attributed to the air-snow interface being identified as the first rise above the radar noise floor, which typically corresponds to the highest snow elevations within the footprint. Finally, we compare a subset of newly derived snow depth data from OIB including highly-valuable uncertainties with existing datasets, highlighting potential improvements.Looking ahead, we propose a framework to enhance snow depth retrieval algorithms, offering robust pathways for validating and improving satellite-based snow datasets. This approach holds significant promise for advancing the accuracy of snow depth measurements critical to polar science in the future campaigns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".