Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013
Bibliographic record
Abstract
Abstract. This study presents an algorithm for detecting winter melt events in seasonal snow cover based on temporal variations in the brightness temperature difference between 19 and 37 GHz from satellite passive microwave measurements. An advantage of the passive microwave approach is that it is based on the physical presence of liquid water in the snowpack, which may not be the case with melt events inferred from surface air temperature data. The algorithm is validated using in situ observations from weather stations, snowpit surveys, and a surface-based passive microwave radiometer. The results of running the algorithm over the pan-Arctic region (north of 50º N) for the 1988–2013 period show that winter melt days are relatively rare averaging less than 7 melt days per winter over most areas, with higher numbers of melt days (around two weeks per winter) occurring in more temperate regions of the Arctic (e.g. central Quebec and Labrador, southern Alaska, and Scandinavia). The observed spatial pattern was similar to winter melt events inferred with surface air temperatures from ERA-interim and MERRA reanalysis datasets. There was little evidence of trends in winter melt frequency except decreases over northern Europe attributed to a shortening of the duration of the winter period. The frequency of winter melt events is shown to be strongly correlated to the duration of winter period. This must be taken into account when analyzing trends to avoid generating false increasing trends from shifts in the timing of the snow cover season.
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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 | 0.002 |
| 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) | 0.000 | 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".