Ecoroof and WRF Model Evaluation for Canadian Climate Model Development
Bibliographic record
Abstract
Green roofs can mitigate urban heat, reduce building energy use, reduce stormwater runoff and contribute to biodiversity and urban livability. To effectively evaluate their impact, green roofs must be incorporated into urban climate models, where reliable green roof models provide critical surface boundary conditions for city-scale adaptation simulations. Consequently, it is essential to test these models to ensure accurate and dependable results.This research evaluates two green roof models—EnergyPlus’ Ecoroof module and the model embedded in the multi-layer urban scheme within the Weather Research and Forecasting (WRF) model—to determine their effectiveness in simulating the green roof energy balance for Canadian climate conditions. Specifically, we investigate: (1) which model more accurately simulates green roof energy balance and (2) the key parameters driving differences in model outputs. Using observations collected from a green roof test array in London, Ontario, during the summers and autumns of 2014 and 2016, the modeled surface temperature (Tsurf), latent heat flux (Qe) and soil heat flux (Qg) are evaluated through statistical analyses and sensitivity assessments.Preliminary results from the EnergyPlus model for Qe, Qg and Tsurf show overall index of agreement (dr) values of 0.59, 0.69 and 0.78, respectively, with variability between years. The tested periods in 2014 show higher dr ranges for all variables—0.50 to 0.81 for Qe, 0.60 to 0.70 for Qg, and 0.72 to 0.90 for Tsurf —while 2016 exhibits lower dr values, with some periods dropping below 0.50 for Qe and Tsurf. Key parameters influencing the model’s performance include LAI, minimum stomatal resistance, soil specific heat, soil thickness, and soil water content. These findings support integrating green roof models into urban climate frameworks, highlighting their role in heat adaptation and sustainable design.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".