UrbanTALES: A Large-Eddy Simulation Dataset for Urban Canopy Layer Turbulence and Parameterization
Bibliographic record
Abstract
Abstract The urban canopy layer (UCL) exhibits complex, heterogeneous flow patterns shaped by urban geometry. Traditionally, research has relied on microscale simulations over limited and often idealized building arrays, leaving a need for more extensive datasets to capture the dynamics across diverse urban neighborhoods. Responding to this gap, we developed an extensive dataset, known hereafter as Urban Turbulence Analyses from Large-Eddy Simulations (UrbanTALES), based on state-of-the-art large-eddy simulations (LESs) over 538 urban layouts (generated using over 3 000 000 CPU hours and 35 TB of storage) with both idealized and realistic configurations. Realistic urban neighborhood configurations were obtained from major cities worldwide, incorporating wide variations in building plan area densities [0.06–0.64] and height distributions [4–50 m]. Idealized urban arrays, on the other hand, include two commonly studied configurations (aligned and staggered building arrays), featuring both uniform and variable height scenarios along with oblique wind directions. UrbanTALES offers canopy-averaged flow data as well as 2D and 3D flow fields tailored for different applications in urban climate research such as the development and testing of urban canopy models. 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 applications, noting the unique opportunity to use high-fidelity simulated flow in realistic urban neighborhoods to 1) revisit neighborhood-scale urban canopy parameterizations in various climate models and 2) inform in-canopy flow and turbulent analyses in complex urban configurations. UrbanTALES is openly available at https://urbantales.climate-resilientcities.com/ and can be extended to incorporate future LES datasets in the field. Significance Statement The urban canopy layer plays a crucial role in shaping urban climate, yet its complexity has often been oversimplified due to limited datasets. To address this, we developed Urban Turbulence Analyses from Large-Eddy Simulations (UrbanTALES), an open-access dataset based on high-resolution large-eddy simulations (LESs) over 538 urban configurations, including both real-world neighborhoods and idealized building layouts. With detailed flow fields and turbulence statistics, UrbanTALES provides a new foundation for improving urban canopy models, turbulence studies, and informing weather/climate simulations over cities. By making it openly available, we aim to foster collaboration and encourage future extensions that enhance our understanding of urban airflow.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".