Efficacy of cool roofs at reducing pedestrian-level air temperature during projected 21st century heatwaves in Atlanta, Detroit, and Phoenix (USA)
Bibliographic record
Abstract
Abstract The air temperature cooling impacts of infrastructure-based adaptation measures in expanding urban areas and under changing climatic conditions are not well understood. We present simulations conducted with the Weather Research and Forecasting (WRF) model, coupled to a multi-layer urban model that explicitly resolves pedestrian-level conditions. Our simulations dynamically downscale global climate projections, account for projected urban growth, and examine cooling impacts of extensive cool roof deployment in Atlanta, Detroit, and Phoenix (USA). The simulations focus on heatwave events that are representative of start-, middle-, and end-of-century climatic conditions. Extensive cool roof implementation is projected to cause a maximum city-averaged daytime air temperature cooling of 0.38 °C in Atlanta; 0.42 °C in Detroit; and 0.66 °C in Phoenix. We propose a means for practitioners to estimate the impact of cool roof treatments on pedestrian-level air temperature, for a chosen roof reflectivity, with a new metric called the Albedo Cooling Effectiveness (ACE). The ACE metric reveals that, on average, cool roofs in Phoenix are 11% more effective at lowering pedestrian-level air temperature than in Atlanta, and 30% more effective than in Detroit. Cool roofs remain similarly effective under future heatwaves relative to contemporary heatwaves for Atlanta and Detroit, with some indication of increased effectiveness under future heatwaves for Phoenix. By highlighting the underlying factors that drive cooling effectiveness in a trio of cities located in different climatic regions, we demonstrate a robust framework for estimating the pedestrian-level cooling impacts associated with reflective roofs without the need for computationally demanding simulations.
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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 | 0.000 |
| 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".