UrbanTALES: A Comprehensive LES Dataset for Urban Canopy Layer Turbulence Analyses and Parameterization Development

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Authors

Negin Nazarian, Jiachen Lu, Mathew Lipson, Sijie Liu, Melissa Anne Hart, Scott Krayenhoff, Lewis Blunn, Alberto Martilli

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 UrbanTALES, based on state-of-the-art Large Eddy Simulations (LES) 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 [4-50m]. 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: a) revisit neighborhood-scale urban canopy parameterizations in various climate models; and b) 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.

DOI

https://doi.org/10.31223/X58X3B

Subjects

Physical Sciences and Mathematics

Keywords

Dates

Published: 2025-01-14 11:15

Last Updated: 2025-01-14 19:13

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
provided in the manuscript