High-Resolution Modelling of Thermal Exposure during a Hot Spell: A Case Study Using PALM-4U in Prague, Czech Republic
Bibliographic record
Abstract
The modelling of thermal exposure in outdoor urban environments is a highly topical challenge in modern climate research. This paper presents the results derived from a new micrometeorological model that employs an integrated biometeorology module to model Universal Thermal Climate Index (UTCI). This is PALM-4U, which includes an integrated human body-shape parameterization, deployed herein for a pilot domain in Prague, Czech Republic. The results highlight the key role of radiation in the spatiotemporal variability of thermal exposure in moderate-climate urban areas during summer days in terms of the way in which this directly affects thermal comfort through radiant temperature and indirectly through the complexity of turbulence in street canyons. The model simulations suggest that the highest thermal exposure may be expected within street canyons near the irradiated north sides of east–west streets and near streets oriented north–south. Heat exposure in streets increases in proximity to buildings with reflective paints. The lowest heat exposure during the day may be anticipated in tree-shaded courtyards. The cooling effect of trees may range from 4 °C to 9 °C in UTCI, and the cooling effect of grass in comparison with artificial paved surfaces in open public places may be from 2 °C to 5 °C UTCI. In general terms, this study illustrates that the PALM modelling system provides a new perspective on the spatiotemporal differentiation of thermal exposure at the pedestrian level; it may therefore contribute to more climate-sensitive urban planning.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".