The Canadian external urban and land surface modeling system: Summertime evaluation over the Montreal metropolitan area
Bibliographic record
Abstract
The Canadian urban and land surface external modeling system (urban GEM-SURF) has been developed to improve numerical weather prediction of surface and near-surface meteorological variables and to become a tool for environmental applications. Following the methodology used for complex terrain and natural covers using the Interactions between the Surface, Biosphere, and Atmosphere (ISBA) land surface model (Carrera et al 2010, Bernier et al. 2011), the two-dimensional system is augmented with the Town Energy Balance (TEB) model for the built-up covers (Leroyer et al. 2011). The urban modeling system is driven by coarse resolution forecasts from the 15-km Canadian regional operational model. This new system was tested for a 120-m grid-size computational domain covering the Montreal metropolitan region from 1 May to 30 Sept 2008. The numerical results were first evaluated against local observations of the surface energy budgets, air temperature, and humidity taken at the three tower sites of the Environmental Prediction in Canadian Cities (EPiCC) field experiment. As compared with the 15-km regional model, important improvements are achieved with this system over urban and suburban sites. GEM-SURF’s ability to simulate the Montreal surface urban heat island was also investigated and the radiative surface temperatures were compared between this system and two operational systems at the Meteorological Service of Canada (the 15-km regional model and the so-called limited-area model LAM with 2.5-km grid size). Finally, comparison of urban GEM-SURF outputs against remotely sensed observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) reveals relatively good agreement for urban and natural areas.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".