Improving Subkilometer Modelling with GEM : case of Paris
Bibliographic record
Abstract
Urban-scale modelling has been performed at ECCC for experimental subkilometer systems for specific events. For example, the model was run realtime with grid spacing down to 250m over Toronto, Canada, in the context of 2015 PanAm sport games, and with grid spacing down to 100m over Paris in the context of 2024 Olympics games WMO-Research and Demonstration Project. The purpose of this study is to continue to explore the benefits and limitations of subkilometer modelling with the Global Environmental Multiscale (GEM) model by taking the opportunity of recent multi-source experimental datasets in the Paris region. For selected case studies of conditions favorable to the presence of strong UHI and thunderstorms, diagnostics are provided on the impact of the resolution on different surface and atmospheric indicators, such as the temperature heterogeneity and UHI intensity, the urban boundary-layer height and the respective contribution of the resolved and subgridscale turbulence. Sensitivity modelling experiments are conducted to progress on the collection of evidence of the importance of some elements of the configuration with 100m grid spacing. First, the robustness of the dynamical core is assessed by varying the time step in order to optimize the computational time. Second, the impact of the shallow convection scheme is investigated. The impact of the method for the description of the urban canopy is then highlighted. Method for the computation of surface energy budget and near-surface diagnostics is revised for dense built-up areas to compensate for the lack of advection in the street. Finally, a more advanced vegetation scheme is tested to improve the city’s surface energy budget but also the surrounding area’s temperature values encountering a persistent warm bias. This study will help to produce multi-purpose reliable weather and environmental urban prediction including air quality and evaluation of urban adaptation scenarios.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".