Development of a Gridded North American Monthly Snow Depth and Snow Water Equivalent Dataset for GCM Validation
Bibliographic record
Abstract
Accurate simulation of large-scale spatial and temporal variations in snow cover is important for global climate models (GCM) as snow influences the climate system through both direct (e.g. albedo) and indirect (e.g. soil moisture) feedbacks. A snow depth analysis scheme developed by Brasnett (1999) and employed operationally at the Canadian Meteorological Center (CMC), was applied to develop a detailed monthly mean snow depth dataset for North America for validating GCM snow cover simulations for the AMIP II (Atmospheric Model Intercomparison Project) period (1979-1996). An extensive database of daily snow depth observations from U.S. cooperative stations and Canadian climate stations was assembled, which provided ~8000 observations/day to the analysis. The first-guess field used a simple accumulation, aging and melt model driven by 6-hourly values of air temperature and precipitation from the European Centre for Medium-range Weather Forecasting (ECMWF) ERA-15 Reanalysis with extensions from the TOGA operational data archive. The snow depth analysis was run at a 1/3º resolution and incorporated the effect of topography to screen out unrepresentative stations. Results from the first run of the analysis revealed several improvements over the existing snow depth climatology of Foster and Davy (1988). An improved snow aging scheme is required to replicate observed snow density information, and provide reliable estimates of snow water equivalent. This will be incorporated in a second analysis run.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".