Gridded North American monthly snow depth and snow water equivalent for GCM evaluation
Bibliographic record
Abstract
Evaluation of snow cover in GCMs has been hampered by a lack of reliable gridded estimates of snow water equivalent (SWE) at continental scales. In order to address this gap, a snow depth analysis scheme developed by Brasnett (1999) and employed operationally at the Canadian Meteorological Centre (CMC), was applied to generate a 0.3° latitude/longitude grid of monthly mean snow depth and corresponding estimated water equivalent for North America to evaluate GCM snow cover simulations for the Atmospheric Model Intercomparison Project II (AMIP II) for the period 1979–96. Approximately 8000 snow depth observations per day were obtained from U.S. cooperative stations and Canadian climate stations for input to the analysis. The first‐guess field used a simple snow 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 Tropical Ocean Global Atmosphere (TOGA) operational data archive. The gridded snow depth and estimated SWE results agree well with available independent in situ and satellite data over mid‐latitudinal regions of the continent, and the snow depth climatology exhibited several improvements over Foster and Davy (1988). The monthly snow depth and estimated SWE climatologies are available for downloading from the Canadian Cryospheric Information Network (http://www.ccin.ca).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".