ECMWF's Global Snow Analysis: Assessment and Revision Based on Satellite Observations
Bibliographic record
Abstract
Snow water equivalent and snow extent are key parameters for the earth's energy and water budget. In this study, the current operational snow-depth analysis (2D spatial Cressman interpolation) at the European Centre for Medium-Range Weather Forecasts (ECMWF), which relies on real-time observations of snow depth, the short-range forecast, and snow-depth climatic data, is presented. The operational product is compared with satellite-derived snow cover. It is found that the total area of grid boxes affected by snow is approximately 10% larger in the analysis than in the National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) snow-extent product. The differences are persistent in time and space and cover the entire Northern Hemisphere. They comprise areas with intermittent and/or patchy snow cover, for example, the Tibetan Plateau, the edges of snow fields, and areas with a low density of observations, which are difficult to capture in the current operational analysis. A modified snow analysis is presented, in which the operational NESDIS snow product is incorporated. The current analysis and the revised analysis are compared with high-resolution snow-cover datasets derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and independent ground-based snow-depth observations from the Meteorological Service of Canada. Using the NOAA/NESDIS snow-extent dataset in the operational analysis leads to a more realistic description of the actual snow extent.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".