Sensitivity of mesoscale model urban boundary layer meteorology to the scale of urban representation
Bibliographic record
Abstract
Abstract. Mesoscale modeling of the urban boundary layer requires careful parameterization of the surface due to its heterogeneous morphology. Model estimated meteorological quantities, including the surface energy budget and canopy layer variables, will respond accordingly to the scale of representation. This study examines the sensitivity of the surface energy balance, canopy layer and boundary layer meteorology to the scale of urban surface representation in a real urban area (Detroit-Windsor (USA-Canada)) during several dry, cloud-free summer periods. The model used is the Weather Research and Forecasting (WRF) model with its coupled single-layer urban canopy model. Some model verification is presented using measurements from the Border Air Quality and Meteorology Study (BAQS-Met) 2007 field campaign and additional sources. Case studies span from "neighborhood" (10 s ~308 m) to very coarse (120 s ~3.7 km) resolution. Small changes in scale can affect the classification of the surface, affecting both the local and grid-average meteorology. Results indicate high sensitivity in turbulent latent heat flux from the natural surface and sensible heat flux from the urban canopy. Small scale change is also shown to delay timing of a lake-breeze front passage and can affect the timing of local transition in static stability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".