On the evaluation of different WRF urban canopy schemes for the study of precipitation related to urban heat island in Kuala Lumpur
Bibliographic record
Abstract
Cities can have a significant impact on local microclimate. Higher temperatures that often characterise urban fabric can influence other meteorological parameters, such as precipitation. In this study, we investigated how the urban heat island (UHI) of Kuala Lumpur impacts rainfall through a set of sensitivity studies performed with the Weather Research and Forecasting (WRF) model. Many studies have already pointed out that the UHI can increase local rainfall, but they disregarded the city heterogeneity to large extent. Here, we investigated the effect of the city on precipitation incorporating different representations of the urban landscape. We performed three simulations with different urban land cover: 1) without city (control experiment) 2) with the urban terrain represented homogeneously and 3) with the urban land represented heterogeneously with the surface classification in the 11 categories of the Local Climate Zone (LCZ) system. We observed that the consideration of the city of Kuala Lumpur in the simulations results in a localised increase in mean annual precipitation and mean intense precipitation within the boundaries of the urban area. However, in the case of the homogeneous representation of the city, the increase is more pronounced than in the case of the heterogeneously represented city. In the former case, the increases also occur over a larger area and the impacts propagate more strongly into the upper layers of the atmosphere. Thus, a more realistic representation of the city and its heterogeneities limits the urban-induced effects on precipitation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".