EARSeL eProceedings x, issue/year 1 Mapping Daily Snow Cover Extent over Land Surfaces using NOAA AVHRR Imagery
Bibliographic record
Abstract
The Global Climate Observing System (GCOS) has identified snow cover, mapped on a daily basis at 1km resolution or better, as an essential climate variable. GCOS specifically highlighted the need to produce historical snow cover maps from NOAA AVHRR sensors. We present an algo-rithm, SnowCover, for mapping snow cover from polar orbiting optical sensors with frequent (~daily) repeat passes. The algorithm is based on a new time series filter, adaptive to local cloud conditions, and pixel wise calibration of snow and snow free end members. SnowCover was ap-plied to a 1km resolution climate data archive of NOAA AVHRR data over the Western Arctic to produce daily snow cover maps from 1982 to present with, on average, 90 % temporal coverage. Comparison of the maps with snow cover estimates derived from 83 in-situ long term snow depth sites in Canada indicate year round agreement rates above of 90 % at the 50%ile (85 % at the 95%ile). Agreement rates during the spring melt transition increases to 87 % at 50%ile indicating that the temporal filtering can preserve this phenomenon.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.149 | 0.072 |
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".