Representing the effects of building height variability on urban canopy flow
Bibliographic record
Abstract
Abstract We conducted large‐eddy simulations over 98 urban arrays with varying building densities and height distributions. Compared with uniform‐height urban arrays, the influence of height variability on urban flow is pronounced and acts differently in two idealized urban configurations: the low buildings induce higher wind speed and stronger turbulence over staggered arrays but act inversely over aligned building configurations. The flow motions around tall buildings generate strong dispersive fluxes, which are sometimes of similar magnitude to the turbulent momentum flux and responsible for a persistent isolated roughness flow pattern in the upper canopy regardless of the urban density. Tall buildings further contribute disproportionately to the form drag of the urban surface, reaching up to 3.9 times the form drag induced by buildings of height equal to the average building height, in dense layouts. The flow inflection points—that is, the largest wind‐speed gradient that defines the aerodynamic interface between the urban canopy flow and the surface layer flow above—are found to be displaced to the maximum building height if less than 25% of buildings are below the mean building height. These findings provide critical insight for the development of urban canopy models, where the impacts of height variability on flow are often linked to the vertical variation in urban density alone. To address this deficiency, we provide a case study that considers the drag amplification due to the impact of vertical urban structures in the urban canopy model, enabling high‐resolution regional climate models to reproduce urban air flows better.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".