Subkilometer Numerical Weather Prediction in an Urban Coastal Area: A Case Study over the Vancouver Metropolitan Area
Bibliographic record
Abstract
Abstract Numerical weather prediction is moving toward the representation of finescale processes such as the interactions between the sea-breeze flow and urban processes. This study investigates the ability and necessity of using kilometer- to subkilometer-scale numerical simulations with the Canadian urban modeling system over the complex urban coastal area of Vancouver, British Columbia, Canada, during a sea-breeze event. Observations over the densely urbanized areas, collected from the Environmental Prediction in Canadian Cities (EPiCC) network and from satellite imagery, are used to evaluate several aspects of the urban boundary layer features simulated in three model configurations with different grid spacings (2.5 km, 1 km, and 250 m). In agreement with the observations, results from the numerical experiments with 1-km and 250-m grid spacings suggest that two sea-breeze flows converge over the residential areas of Vancouver. The resulting convergence line oscillates around the hill ridge, depending on thermal contrast and flow strength. This propagation mode impacts the growing urban boundary layer, with the presence of subsidence and entrainment events. Urban-induced circulation is superimposed with the sea-breeze circulation and realistically slows down the propagation of the sea-breeze front to the south. A clear improvement is obtained for numerical experiments with 1-km instead of 2.5-km grid spacing. The use of subkilometer grid spacing provides a more detailed representation of the surface thermal forcing and of local circulations, with results more sensitive to the airflow variability and, thus, to the location of measurement sites. Joint analyses of kilometer- and subkilometer-scale numerical experiments are thus recommended for different environmental applications.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".