Evaluation of a High-Resolution Operational Numerical Weather Prediction Product in Capturing Urban Heat Dynamics in a Small City
Yuqi Huang is currently a Ph.D. student in the School of Meteorology at the University of Oklahoma (OU), advised by Prof. Chenghao Wang. Before coming to OU, Yuqi completed his master’s degree in civil engineering at Beijing Normal University, China. His previous research focused on understanding and modeling the physical, hydrological, and ecological processes of inland water bodies and the response of aquatic ecosystems to climate change. His Ph.D. research topic is urban climate and hydrometeorology in which he seeks to understand and improve the predictive capability of urban hydrometeorological and climate simulations across multiple spatial scales.
Abstract
With rising global temperatures, urban environments are facing escalating heat stress, often worsened by the Urban Heat Island (UHI) effect. Previous research has predominantly focused on characterizing the Urban Heat Island (UHI) effect in major metropolitan areas across the United States, often neglecting the extreme heat conditions in smaller cities. However, smaller urban areas are also critical for understanding UHI-induced meteorological impacts, as several atmospheric processes, such as pollutant dispersion, are directly or indirectly influenced by UHI. A key knowledge gap in UHI research is the role of urban heat advection (UHA)—the transport of heat by mean winds—in shaping spatial temperature distributions within and around the cities. Current numerical weather prediction models, such as the High-Resolution Rapid Refresh (HRRR) model, face challenges in accurately quantifying and predicting UHI dynamics under varying meteorological and seasonal conditions. This study investigates the spatial variability of urban heat and UHA, while assessing the performance of the HRRR model in simulating urban heat features in and around Lubbock, Texas—a small-sized city located in a semi-arid environment in the southwest U.S. Observational data were collected between July 1, 2023, and June 30, 2024, using 23 data loggers from the Urban Heat Island Experiment in Lubbock, Texas (U-HEAT) Micronet, along with five stations from the West Texas Mesonet. The results reveal a pronounced cold bias in nighttime 2-m air temperature and a warm bias during daytime at urban sites. Furthermore, the HRRR model was unable to capture UHA effects under any meteorological conditions. Analysis of the model’s performance suggests that prediction errors stem from both urban influences and inherent systematic biases. The findings of this study are expected to enhance operational urban heat forecasting, meanwhile highlighting the importance of improved urban planning and risk management strategies for mitigating UHI effects in smaller cities.