UrbanTALES: A comprehensive dataset of Urban Turbulent Airflow using systematic Large Eddy Simulations
Bibliographic record
Abstract
The urban canopy layer (UCL) is characterized by a heterogeneous flow pattern that responds to heterogeneous urban geometries. The varying heights and layouts of buildings play a pivotal role in shaping this spatial variability, as they block, divert, and slow wind and determine the exchange of momentum and energy above the urban canopy. When representing these complex dynamics, however, research has conventionally relied on microscale simulations conducted over limited (often idealized) building arrays. Extending the findings to realistic urban neighborhoods and urban parameterizations presents a clear limitation, as evidenced by discrepancies in multi-model comparisons with observational data in cities.More extensive datasets of urban airflow are needed to cover a range of realistic urban neighborhoods and provide a more holistic analysis of turbulent flow in different urban characteristics. Responding to this gap in the field, we developed a historically extensive and comprehensive dataset of Urban Turbulent Airflow based on state-of-the-art  Large Eddy Simulations (UrbanTALES). The dataset includes 400 urban layouts with both idealized and realistic configurations. Realistic urban neighborhoods were obtained from major cities worldwide, incorporating variations in plan area densities [0.0625-0.64] and height distributions [4-70m]. Idealized urban arrays, on the other hand, include two commonly studied configurations (aligned and staggered arrays), featuring both uniform and variable height scenarios along with oblique wind directions. UrbanTALES offers canopy-averaged data as well as 2D and 3D flow fields tailored for different applications in urban climate research. The dataset provides time-averaged wind flow properties, as well as second- and third-order flow moments that are critical for understanding turbulent processes in the UCL. Here, we describe the UrbanTALES dataset and its application, noting the unique opportunity to deploy a comprehensive representation of realistic urban neighborhoods for a) revisiting neighborhood-scale urban canopy parameterizations in various models and b) informing in-canopy flow and turbulent analyses. Furthermore, we discuss the application of this dataset for training Machine Learning algorithms for pedestrian wind speed. 
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| 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 teacher head, 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".