Microclimate modeling in the tropics: case study of the outdoor thermal comfort impacts of increased vegetation in an urban park in Singapore
Bibliographic record
Abstract
Urban overheating is a challenge for all cities, leading to increased cooling demand and negative health outcomes. Urban microclimate modeling is a method for exploring the impacts and solutions to urban overheating and have been evaluated and tested in a variety of urban contexts and purposes. Outdoor thermal comfort of parks is a common area of study with models. However, previous studies have primarily focused on mid- and high-latitude cities with temperate or cold climates. In the tropics, the intensity of incoming solar radiation and high water vapor content of the atmosphere create unique conditions that are under-explored in these microclimate models. As such, evaluation of the models in tropical climates is crucial to construct a more complete understanding of how urban parks impact outdoor thermal comfort. Further, a commonly proposed intervention to urban overheating is to increase vegetation. This promotes more shade and evapotranspiration in the city; however, under hot and humid conditions, this may result in decreased outdoor thermal comfort. In this study, we use Bishan Ang Mo Kio Park in Singapore as the case study and the testbed for the theoretical impact of increased vegetation in tropical climates. We run an ENVI-met domain and simulations of the park using previously validated idealized weather typologies for Singapore to assess performance of the model. A second set of simulations are then run with 20% more vegetation in the park to explore the impact of vegetation on outdoor thermal comfort. Results indicate that under the typically hottest conditions of the Singaporean Intermonsoon period, the model performs sufficiently well to assess the impact of increased vegetation. Under increased vegetation, the park experiences up to 3° C of air temperature cooling in small pockets of the park, though with low wind speeds, the advection of cooling is limited.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".