Evaluation of the snow cover variation in the Canadian Regional Climate Model over eastern Canada using passive microwave satellite data
Bibliographic record
Abstract
Abstract Snow cover from a 3 year (1992/93–1994/95) simulation of the Canadian Regional Climate Model (CRCM; operational version 3·5·3) driven by National Centers for Environmental Prediction analysis data over eastern Canada was evaluated using passive‐microwave‐derived snow cover information from the daily Special Sensor Microwave/Imager (SSM/I) data. The presence of snow on the ground was derived from 19 and 37 GHz normalized difference brightness temperature series to which a low‐pass filter was applied to remove day‐to‐day noise, and thresholding was also applied at different levels for taking into account the variation in vegetation density. The thresholds calibrated for four density classes with surface observations show a mean residual underestimation in the SSM/I number of days with snow cover during seasonal transition of −7 days. Compared with the SSM/I‐derived information, the CRCM was found to delay systematically the onset of the snow cover (typically 50 days late) and to ablate snow too quickly during the spring melt period (typically 30 days early). These systematic errors were attributed to the single‐layer force–restore representation of the soil–snow layer and contributed to snow cover extent underestimations in the order of 9% relative to the total area. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".