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Abstract The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics.
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Ultrafine‐Resolution Urban Climate Modeling: Resolving Processes Across Scales
Abstract Recent advances in urban climate modeling resolution have improved the representation of complex urban environments, with large‐eddy simulation (LES) as a key approach, capturing not only building effects but also urban vegetation and other critical urban processes. Coupling these ultrafine‐resolution (hectometric and finer) approaches with larger‐scale regional and global models provides a promising pathway for cross‐scale urban climate simulations. However, several challenges remain, including the high computational cost that limits most urban LES applications to short‐term, small‐domain simulations, uncertainties in physical parameterizations, and gaps in representing additional urban processes. Addressing these limitations requires advances in computational techniques, numerical schemes, and the integration of diverse observational data. Machine learning presents new opportunities by emulating certain computationally expensive processes, enhancing data assimilation, and improving model accessibility for decision‐making. Future ultrafine‐resolution urban climate modeling should be more end‐user oriented, ensuring that model advancements translate into effective strategies for heat mitigation, disaster risk reduction, and sustainable urban planning.
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- Award ID(s):
- 2327435
- PAR ID:
- 10600122
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Volume:
- 17
- Issue:
- 6
- ISSN:
- 1942-2466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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