Arctic‐scale assessment of satellite passive microwave‐derived snow depth on sea ice using Operation IceBridge airborne data
Bibliographic record
Abstract
Snow depth on sea ice (SD) is a key geophysical variable, knowledge of which is critical for calculating the energy and mass balance budgets. Moreover, accurate knowledge of the SD distribution is important to retrieve sea‐ice thicknesses from altimetry data. So far, only space‐based microwave radiometers (e.g., Advanced Microwave Scanning Radiometer for Earth Observing System; AMSR‐E) provide operational SD on seasonal sea‐ice retrievals. A thorough assessment of these retrievals is needed on a large scale and on a variety of sea‐ice types. Our study presents such an assessment on Arctic sea ice using NASA's airborne Operation IceBridge (OIB) SDs, retrieved from radar measurements. Between 2009 and 2011, ∼610 12.5 km satellite grid cells were covered by seasonal sea ice where both satellite SD retrievals and OIB data were available. Using all the available data, the difference between the AMSR‐E product and the averaged OIB snow‐radar‐derived SD is 0.00±0.07 m. Satellite‐derived SD was accurate in the Beaufort Sea and the Canadian Archipelago but underestimated (∼0.07 m) in the Nares Strait. The RMSE between the two products ranges between 0.03 and 0.15 m. The RMSE is less than 0.06 m over a shallow snow cover (<0.20 m), in areas where satellite‐retrieved ice concentrations are higher than 90%, surface smooth, and ice thicker than ∼0.5 m. Locally the AMSR‐E algorithm can significantly underestimate SD. Several regions where the retrievals were less accurate (error >0.10 m) have been identified and related to the presence of either low ice concentration or significant fraction of multiyear ice within the grid cell that has not been flagged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".