Assessing heat stress with a mesoscale model. An application of WRF-comfort to Madrid
Bibliographic record
Abstract
Heat stress depends on a set of metereological variables, namely, air temperature, wind speed, air humidity and mean radiant temperature. In urban areas, wind speed and mean radiant temperature are strongly spatially hetereogeneous, at scales of few meteres, much smaller than the typical resolution of mesoscale models, which is of the order of one kilometer or several hundreds of meters. This is the main obstacle to produce reliable estimates of heat stress at city scale. In this contribution, we present a methodology, built over a set of microscale simulations, to represent subgrid scale variability of wind speed and mean radiant temperature, and as a consequence heat stress. The scheme is implemented in the multilayer urban canopy parameterization BEP-BEM embedded in the mesoscale model WRF (therefore called WRF-comfort), and it opens the way to the city scale evaluation of the impact of different adaptation/mitigation strategies on heat stress, something that is essential to plan liveable future cities in the context of a changing climate. This is illustrated with a series of simulations for a summertime period over the city of Madrid (Spain). Then main outcome of the study is that the time evolution and spatial variability of UTCI (the Universal Thermal Climate Index, one of the most used heat stress indexes) are strongly affected by the urban morphology, and that the spatial pattern of UTCI at city scale is only partially similar to the one of air temperature, and dissimilar to the one of Land Surface Temperature, as it can be seen from satellite, a variable often used to assess urban overheating.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".