Simulated Geophysical Noise in Sea Ice Concentration Estimates of Open Water and Snow-Covered Sea Ice
Bibliographic record
Abstract
Sea ice concentration algorithms using brightness temperatures (TB) from satellite microwave radiometers are used to compute sea ice concentration (cice), sea ice extent, and generate sea ice climate data records (CDRs). Therefore, it is important to minimize the sensitivity ofciceestimates to geophysical noise caused by snow/sea ice thermal microwave emission signature variations, and presence of water vapor and clouds in the atmosphere and/or near-surface winds. In this study, we investigate the effect of geophysical noise leading to systematiccicebiases and affectingcicestandard deviations (STD) using simulated top of the atmosphere (TOA)TBs over open water and 100 % sea ice. We consider three case studies for the Arctic and the Antarctic and eight differentcicealgorithms, representing different families of algorithms based on the selection of channels and methodologies. Our simulations show that, over open water and lowcice, algorithms using gradients between V-polarized 19 GHz and 37 GHzTBs shows the lowest sensitivity to the geophysical noise, while the algorithms exclusively using near 90 GHz channels have by far the highest sensitivity. Over sea ice, the atmosphere plays a much smaller role than over open water and theciceSTD for all algorithms is smaller than over open water. The hybrid and low frequency (6 GHz) algorithms have the lowest sensitivity to noise over sea ice, while the polarization type of algorithms have the highest noise levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".