Evaluating the PALM-4U representation of outdoor urban thermal exposure in a humid continental climate using MaRTy
Bibliographic record
Abstract
Keywords: Microscale modeling, large-eddy simulations, biometeorology, urban climate, UTCIHuman heat stress and the risk of outdoor thermal discomfort under extreme heat conditions are increasing even in traditionally cold climates due to climate change. Modeling micro-scale urban climate to explore outdoor thermal exposure is a highly topical challenge in modern climate research. PALM-4U is a large-eddy simulation (LES) based high-resolution micrometeorological model that includes a complex radiation scheme and an integrated biometeorology module to assess thermal conditions in complex urban environments. To test the performance of PALM-4U, we compared the model with in-situ measured mean radiant temperature (MRT) and universal thermal climate index (UTCI). Model evaluation was performed using the mobile instrument platform MaRTy. Air temperature, relative humidity, wind speed, and longwave and shortwave radiant flux densities in a 6-directional setup were recorded by the MaRTy cart and compared to PALM-4U. The in-situ measured data were collected across 23 locations in Guelph, Ontario, Canada, during multiple times of day and seasons in 2020 and 2021. Data were collected and aggregated for evaluation of PALM-4U in 10-minute and 1-hour intervals. We found that PALM-4U performed better in simulating UTCI than MRT in summer, especially during periods when the midday surface temperature is high. Furthermore, the MRT simulation result deviates from the measured values under certain shade types. The performance under vegetation is slightly better compared to under engineered shade. This work aids in the improvement of PALM-4U under various urban morphologies and advances the microclimatic modeling research of the field.
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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) | 0.001 | 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 | 0.001 |
| 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".