Seasonal changes in snow properties from passive and active microwave satellite observations: A conceptual model.
Bibliographic record
Abstract
Snowmelt processes on sea ice are the key drivers determining the seasonal sea-ice energy and mass budgets. Around Antarctica, snowmelt on pack ice is weak and very different than in the Arctic, with most snow surviving the summer. It is therefore important to understand the mechanisms that drive snowmelt, both at different times of the year and in different regions around Antarctica. \nDoing so, we compile time series of snowmelt onset dates on perennial Antarctic sea ice from 1992 to 2014 using active microwave observations from European Remote Sensing Satellite (ERS-1/2), Quick Scatterometer (QSCAT) and Advanced Scatterometer (ASCAT) radar scatterometers. Describing snow melt processes, we define two transition stages: A weak backscatter rise indicating the initial warming and metamorphism of the snowpack (pre-melt), followed by a rapid rise indicating the onset of thaw-freeze cycles in the interior snowpack (snowmelt). We compare these with pan-Antarctic temporary snowmelt onset dates in the uppermost snowpack retrieved from diurnal variations in the brightness temperatures from passive microwave (PMW) observations. \nResults show that QSCAT Ku-band (13.4 GHz signal frequency) derived pre-melt and snowmelt onset dates are earlier by 25 and 11 days, respectively, than ERS and ASCAT C-band (5.6 GHz) derived dates. Snowmelt onset dates from the shortwave PMW observations (37 GHz) are later by 13 and 5 days than those from the scatterometers, respectively. \nBased on the observed successive timing of melt events retrieved from different sensors and microwave bands, we developed a conceptual model of the temporal evolution of snow temperature and metamorphism and their effect on different microwave wavelengths during the spring/summer transition. These results suggest that future multi-frequency microwave satellite missions could be used to resolve melt processes throughout the vertical snow column. \nOverall, results show that the magnitude and timing of seasonal and diurnal variations in Antarctic snow on sea ice are highly dependent on latitude, with earlier and more frequent snowmelt in the north. All retrieved melt onset dates show large interannual variability but no significant decadal trends.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".