Evaluation of the Town Energy Balance Model in Cold and Snowy Conditions during the Montreal Urban Snow Experiment 2005
Bibliographic record
Abstract
Abstract Using the Montreal Urban Snow Experiment (MUSE) 2005 database, surface radiation and energy exchanges are simulated in offline mode with the Town Energy Balance (TEB) and the Interactions between Soil, Biosphere, and Atmosphere (ISBA) parameterizations over a heavily populated residential area of Montreal, Quebec, Canada, during the winter–spring transition period (from March to April 2005). The comparison of simulations with flux measurements indicates that the system performs well when roads and alleys are snow covered. In contrast, the storage heat flux is largely underestimated in favor of the sensible heat flux at the end of the period when snow is melted. An evaluation and an improvement of TEB’s snow parameterization have also been conducted by using snow property measurements taken during intensive observational periods. Snow density, depth, and albedo are correctly simulated by TEB for alleys where snow cover is relatively homogeneous. Results are not as good for the evolution of snow on roads, which is more challenging because of spatial and temporal variability related to human activity. An analysis of the residual term of the energy budget—including contributions of snowmelt, heat storage, and anthropogenic heat—is performed by using modeling results and observations. It is found that snowmelt and anthropogenic heat fluxes are reasonably well represented by TEB–ISBA, whereas storage heat flux is underestimated.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".