Abstract
** The latest version of this paper has been published in the Journal of Advances in Modeling Earth Systems (JAMES) from AGU. Please refer to the latest version on JAMES and cite as:
Li, P., & Sharma, A. (2024). Hyper-local temperature prediction using detailed urban climate informatics.
Journal of Advances in Modeling Earth Systems, 16, e2023MS003943.
https://doi.org/10.1029/2023MS003943 Modeling urban microclimate accurately is challenging due to 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 techniques offers the
computational strength to handle the massive volume of data. In this
study, we proposed a machine learning approach to estimate point-scale
street-level air temperature from the urban-resolving mesoscale climate
model and a suite of hyper-resolution urban informatics, including
three-dimensional urban morphology, parcel-level land use inventory, and
a dense weather observation network. We implemented this approach in the
City of Chicago as a case study. 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 application potentials to the other areas. This study
also aims to gain insights into next-gen urban climate modeling and
guide city observation efforts to build the strength for the holistic
understanding of urban microclimate dynamics.