Investigation of the Urban Microclimate Variations Based on the Measured Weather Data: a Case Study of Education City and Lusail City of Qatar
Bibliographic record
Abstract
Abstract Due to significant cooling requirements, urban buildings in hot climatic regions require careful analysis of Urban Heat Island (UHI) and Urban Wind Sheltering (UWS) effects for accurate design of cooling systems. Most studies considered microclimatic data from a nearby weather station, estimated by remote sensing or CFD simulation, all of which carry numerous variables and could yield erroneous results. The most reliable source would be to use data directly measured by weather stations in the study area. To this end, this study investigates the UHI and UWS effects in Qatar based on the comparative analysis of hourly microclimatic data obtained from two urban weather stations in Lusail City and Education City and five established airport weather stations across Qatar for the entirety of 2022. From analyzing the difference in temperature (UHI intensity) and wind speed (UWS intensity) of urban and rural areas, a clear increase in temperature and a sharp decrease in wind speed is observed in both studied urban areas. According to the results, the highest UHI intensity recorded was 16.8°C on the 16th of May (summer) at 1 pm, and the UWS intensity was −65.9 m/s on the 17th of March (winter) at 4 pm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".