Global Snow Cover Monitoring with Spaceborne K_u-band Scatterometer
Bibliographic record
Abstract
This paper presents a study to demonstrate the potential of a spaceborne u -band scatterometer to monitor global snow cover. Global u -band data were acquired by the National Aeronautics and Space Administration (NASA) Scatterometer (NSCAT) operated on the Advanced Earth Observing Satellite (ADEOS) from September 1996 to June 1997. NSCAT backscatter patterns over the northern hemisphere reveals boundaries between different snow classes, defined by the Cold Regions Research and Engineering Laboratory (CRREL), Hanover, NH, snow classification system at different times of the snow season. We show the evolution of the backscatter signature throughout the entire seasonal snow cycle. Within the snow extent determined by the National Oceanic and Atmospheric Administration (NOAA), Washington, DC, and Climate Prediction Center (CPC), operational snow product, u -band backscatter data expose detailed features and rapid changes as observed in in-situ snow depth data from surface weather stations in U.S., Canada, and Russia. Sensitivity of u -band backscatter to snow conditions is illustrated with the dramatic change over the U.S. northern plains and the Canadian prairie region corresponding to the snow event leading to the 1997 Flood of the Century. We discuss snow field experiments and data analysis plan to understand snow scattering mechanisms, to interpret snow backscatter, and to derive its relationship with snow physical parameters. In view of current and future satellite u -band scatterometers, the development of algorithms for quantitative snow cover monitoring is pertinent.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".