A new guidance on measuring, modelling and monitoring the canopy layer urban heat island
Bibliographic record
Abstract
Urbanization influences the local climate by changing the natural surface energy balance affecting the regional temperature field. One of the best-known and widely studied phenomenon is the canopy layer urban heat island (CL-UHI) which is found in cities of all sizes. Specifically, night-time temperatures are often higher in urban areas than in the surrounding rural areas. The CL-UHI characteristics differ between cities, within a city and with time of the day and the season. Climate change induced warming in cities is similar to that experienced in rural areas, but modified by the CL-UHI. The CL-UHI is an additional heat burden on top of background anthropogenic warming, and therefore an increasing focus of urban planners. Given this development, and in response to the request of the 18th World Meteorological Congress (Resolutions 32 and 61), experts from WMO GAW (Global Atmosphere Watch) Urban Research Meteorology and Environment (GURME) initiated in 2020 an expert team inviting more than 30 world-wide experts to contribute to a guidance on measuring, modelling and monitoring the CL-UHI [1]. Topics include a clear definition of the CL-UHI and clarifications of what it is not, causes of the CL-UHI (e.g. meteorological and morphological influences), methods to assess the CL-UHI intensity (measurements, modelling approaches) as well as CL-UHI application examples. The guidance also explains why the CL-UHI mitigation is only part of an answer to reduce urban heat problems. The guidance will serve as a useful reference for meteorologists, climatologists, meteorological administrative staff, and others interested in the CL-UHI. [1] WMO (2023): Guidance on Measuring, Modelling and Monitoring the Canopy Layer Urban Heat Island (CL-UHI). K.H. Schlünzen, S. Grimmond, A. Baklanov (edts.), World Meteorological Organisation, WEATHER CLIMATE WATER. 2023 edition. WMO-No. 1292, pp.88. https://library.wmo.int/doc_num.php?explnum_id=11537 last used 11.04.2023
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".