Validation of the Meteorological Outputs of the Canadian Regional Climate Model Using a Kriging Method: Application to Southern Quebec
Bibliographic record
Abstract
To verify the accuracy of Regional Climate Model (RCM) simulated meteorological outputs, field observations can be used as a reference. However, the spatial scale difference between both types of data must be taken into account to allow a fair comparison. In this study, a kriging-based method is developed to compare simulated total precipitation, daily maximum and minimum temperature data from a Canadian Regional Climate Model run (CRCM4.1.1 with CLASS2.7, pilot ERA40d, Quebec domain) with in situ data from meteorological stations located in six watersheds in southern Quebec for the 1968-1999 period. Anisotropy and spatial trend are analysed for both observed and simulated data and then, the data are standardized to comparable resolutions using the kriging method. Analyses show that general spatial trends are well simulated but there are notable small scale differences. Watershed averages calculated using simulated data show that, overall, minimum temperatures are colder (with the exception of fall) and maximum temperatures are warmer than those observed. For total precipitation, winter and fall are simulated accurately (differences <10%) while differences during the spring-summer period varied between 17 and 33%. Precipitation levels in the two largest watersheds (St. François and Chaudière) are generally well estimated by the CRCM 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".