Microscale Numerical Prediction over Montreal with the Canadian External Urban Modeling System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Canadian urban and land surface external modeling system (known as urban GEM-SURF) has been developed to provide surface and near-surface meteorological variables to improve numerical weather prediction and to become a tool for environmental applications. The system is based on the Town Energy Balance model for the built-up covers and on the Interactions between the Surface, Biosphere, and Atmosphere land surface model for the natural covers. It 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 September 2008. The numerical results were first evaluated against local observations of the surface energy budgets, air temperature, and humidity taken at the Environmental Prediction in Canadian Cities (EPiCC) field experiment tower sites. As compared with the regional deterministic 15-km model, important improvements have been 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 from this system and from two systems operational at the Meteorological Service of Canada were compared, that is, the 15-km regional deterministic model and the so-called limited-area model with 2.5-km grid size. Comparison of urban GEM-SURF outputs with remotely sensed observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) reveals relatively good agreement for urban and natural areas.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it