Urban climate simulation for extreme heat events – A comparison between WRF and GEM
Bibliographic record
Abstract
The Urban Heat Island (UHI) effect and extreme heat events (EHEs) pose significant challenges to urban areas under a changing climate, emphasizing the need for accurate urban climate modeling to guide mitigation strategies. This study evaluates the Weather Research and Forecasting (WRF) and Surface Prediction System (SPS) of the Global Environmental Multiscale (GEM) model during the 2018 Montreal heatwave. Both publicly available models are compared to assess their performance in modeling extreme heat events. WRF incorporates a Building Effect Parameterization (BEP) and the Building Energy Model (BEM), while GEM's Surface Prediction System (SPS) integrates Town Energy Balance (TEB) for urban surfaces. SPS achieved strong accuracy for wind speed and near-surface air temperature predictions (MAE: 1.1682 m/s, 1.6328 °C), while WRF demonstrated good results in land surface temperature estimates (spatially averaged differences <2 °C from MODIS observations). For relative humidity, SPS showed lower error values (STDE: 7.51–10.74 %) compared to WRF (STDE: 10.64–13.37 %), with both models exhibiting negative bias in humidity predictions. Analysis of diurnal urban heat island intensity showed WRF effectively captured overall patterns across different urban morphologies, while SPS performed well during transition periods. These findings highlight the importance of meteorological models' cross-comparison to address model-specific uncertainties and provide comprehensive insights into extreme heat events in urban settings, emphasizing the value of considering different urban meteorological models for resilient urban planning. • WRF and SPS models evaluated for simulation of an extreme heat event. • SPS predicts air temperature (MAE: 1.6328 °C) and wind speed (MAE: 1.1682 m/s) with high accuracy. • WRF excels in urban land surface temperature prediction, outperforming SPS in dense areas. • A multi-model approach can address uncertainties in urban climate simulations. • Both models demonstrate effective UHI modeling to aid urban planning and heat mitigation strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".