Evaluation of the Town Energy Balance (TEB) Scheme with Direct Measurements from Dry Districts in Two Cities
Bibliographic record
Abstract
The Town Energy Balance (TEB) model of Masson simulates turbulent fluxes for urban areas. It is forced with atmospheric data and radiation recorded above roof level and incorporates detailed representations of the urban surface (canyon geometry) to simulate energy balances for walls, roads, and roofs. Here the authors evaluate TEB using directly measured surface temperatures and local-scale energy balance and radiation fluxes for two ''simple'' urban sites: a downtown area within the historic core of Mexico City, Mexico (stone buildings five to six stories in height), and a light industrial site in Vancouver, British Columbia, Canada (flat-roofed, single-story warehouses). At both sites, vegetation cover is less than 5%, which permits direct evaluation of TEB in the absence of a coupled vegetation scheme. Following small modifications to TEB, notably to the aerodynamic resistance formulations, the model is shown to perform well overall. In Mexico City, with deep urban canyons and stone walls, almost two-thirds of the net radiation is partitioned into storage heat flux during the day, and this maintains large heat releases and an upward turbulent sensible heat flux at night. TEB simulates all of these features well. At both sites TEB correctly simulates the net radiation, surface temperatures, and the partitioning between the turbulent and storage heat fluxes. The composite wall temperature simulated by TEB is close to the average of the four measured wall temperatures. A sensitivity analysis of model parameters shows TEB is fairly robust; for the conditions considered here, TEB is most sensitive to roof characteristics and incoming solar radiation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".