How do street trees affect urban temperatures and radiation exchange? Observations and numerical evaluation in a highly compact city
Bibliographic record
Abstract
Street trees are an important driver of street microclimate through shading and transpirative cooling, which are key mechanisms for improving thermal comfort in urban areas. Urban canopy models (UCM) with integrated trees are useful tools because they represent the impacts of street trees on neighborhood-scale climate, resolving the interactions between buildings, trees and the atmosphere. In this study, we present the results of a measurement campaign where vehicle transects were completed along two similar parallel streets of Barcelona with different tree densities, recording upward and downward radiation fluxes, air temperature and humidity at street level. These observations are used to evaluate and improve the multi-layer UCM Building Effect Parameterization with Trees (BEP-Tree). Prior simulations of the model revealed insufficient heat exchange between the canyon surfaces and the air at the lowest vertical levels inside the deep canyons, which we solve by including turbulent buoyancy driven wind velocity in the model. Air temperatures are on average 1.3 °C higher in the street with sparser trees when wind direction is perpendicular to the streets. The BEP-Tree simulations demonstrate good agreement with the observations in terms of temperature and radiation, and capture the diurnal evolution of temperature and radiation between the two streets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".