Paris 2024 Olympics Project results on urban heat modelling: intercomparison and application to the marathon event
Bibliographic record
Abstract
The Paris 2024 Olympics Research Demonstration Project, endorsed by the World Weather Research Programme of WMO, aimed to make progress on future fine-scale weather forecasting systems in cities. The project brought together more than 20 national meteorological centres and laboratories from 10 countries.During the Olympic and Paralympic summer, simulations from 7 hectometric numerical models were conducted daily by the partners. Comparisons were made with weather stations inside and outside the city. The results demonstrated the capacity of the models to reproduce urban effects, but also revealed large variability among models, particularly regarding the extent of the urban heat plume at night. This opens up new scientific questions, which are explored in depth using the crowdsourced netatmo observations, in order to assess the role of the various physical processes in play, such as the competition between hot air advection and local cooling in the suburbs.A decision-making procedure was also established regarding whether or not to hold the Paris 2024 “Marathon for All” in hot weather situations. Throughout the summer of 2022, 100m MesoNH model simulations were conducted over Paris and its inner suburbs, extending to Versailles. Analysis of these simulations by expert forecasters from Météo-France Sports led to proposed scenarios assessing the heat stress conditions runners would face along the marathon route, based on their running speeds. The Paris 2024 marathon organisers were able to take this meteorological information into account when planning the event. A 100m MesoNH simulation was used specifically on the day of the Marathon for All to refine the forecasts of race conditions, and to adapt the safety and assistance arrangements for runners as best possible. This study shows the value of 100-m resolution models for targeted forecasting applications.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".