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Extreme weather events such as hurricanes and heatwaves could cause significant damage to the economy and urban resiliency. Accurate meteorological forecasts of these extreme events could mitigate some aspects of their damage by providing precautionary alerts. The weather forecasts heavily rely on the parameterization of the planetary boundary layer (PBL), which is the lowest layer of the atmosphere that extends up to ~1 km above the surface. In hurricanes, the rotational nature of flows can suppress turbulence; however, such effects are neglected in the conventional PBL schemes, leading to over-diffusive simulations and inaccurate hurricane intensity, size, and track forecasts. In urban areas, complex surface heterogeneities and the Urban Heat Island (UHI) effects are inadequately represented by current PBL models, causing inaccurate forecasts of atmospheric stability, aerosol transport, and wind speeds. To address these issues, the dissertation characterizes the impacts of PBL parameterizations on three problems: hurricane forecasts, air quality forecasts in cities, and wind forecasts in heterogeneous urban areas. To this end, dissertation systematically explored modifications to the existing PBL schemes, urban models, and roughness parameterizations within the Weather Research and Forecasting (WRF) model. More than 500 WRF simulations encompassing major hurricane cases and multiple U.S. cities were performed by varying grid resolutions, eddy diffusivity, UHI magnitudes, and surface roughness configurations. By reducing the vertical diffusion in hurricane simulations, hurricane intensity forecasts improved by ~38% compared to the default PBL schemes in five cases, demonstrating the deficiency of existing parameterizations for rotating cyclonic flows. Our urban simulations also showed that incorporating proper UHI representations in Houston and Dallas led to ~50% and ~12% enhancements in particulate matter and ozone forecasts, respectively, as more realistic nighttime warming prevented excessive aerosol accumulation. Additionally, a novel City-wide Enhanced Directional-Adjusted Roughness (CEDAR) parameterization was introduced that improved surface wind forecasts by ~54% and enhanced the prediction of vertical profiles of winds by ~12%, demonstrating the significance of accounting for upwind surface heterogeneities. The dissertation results collectively highlight that improving PBL processes in weather/climate models can considerably reduce forecasting errors in regular and extreme weather events. Our findings guide the future development of advanced PBL schemes that account for rotation, UHI effects, and surface roughness, thereby improving weather and air quality predictions across diverse environments. The results will be helpful to enhance operational forecasting models, which ultimately could mitigate public health risks, and optimize urban design and hurricane preparedness strategies.
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This content will become publicly available on November 28, 2026
Urban heat forecasting in small cities: evaluation of a high-resolution operational numerical weather prediction model
With rising global temperatures, urban environments are increasingly vulnerable to heat stress, often exacerbated by the Urban Heat Island (UHI) effect. While most UHI research has focused on large metropolitan areas around the world, relatively smaller-sized cities (with a population 100 000–300 000) remain understudied despite their growing exposure to extreme heat and meteorological significance. In particular, urban heat advection (UHA), the transport of heat by mean winds, remains a key but underexplored mechanism in most modeling frameworks. High-resolution numerical weather prediction (NWP) models are essential tools for simulating urban hydrometeorological conditions, yet most prior evaluations have focused on retrospective reanalysis products rather than forecasts. In this study, we assess the performance of a widely used operational weather forecast model, the High-Resolution Rapid Refresh (HRRR), as a representative example of current NWP systems. We investigate its ability to predict spatial and temporal patterns of urban heat and UHA within and around Lubbock, Texas, a small-sized city located in a semi-arid environment in the southwestern US. Using data collected between 1 September 2023, and 31 August 2024 from the Urban Heat Island Experiment in Lubbock, Texas (U-HEAT) network and five West Texas Mesonet stations, we compare 18 h forecasts against in situ observations. HRRR forecasts exhibit a consistent nighttime cold bias at both urban and rural sites, a daytime warm bias at rural locations, and a pervasive dry bias across all seasons. The model also systematically overestimates near-surface wind speeds, further limiting its ability to accurately predict UHA. Although HRRR captures the expected slower nocturnal cooling in urban areas, it does not well capture advective heat transport under most wind regimes. Our findings reveal both systematic biases and urban representation limitations in current high-resolution NWP forecasts. Our forecast–observation comparisons underscore the need for improved urban parameterizations and evaluation frameworks focused on forecast skill, with important implications for heat-risk warning systems and forecasting in small and mid-sized cities.
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- Award ID(s):
- 2327435
- PAR ID:
- 10650733
- Publisher / Repository:
- Copernicus/European Geosciences Union
- Date Published:
- Journal Name:
- Geoscientific Model Development
- Volume:
- 18
- Issue:
- 22
- ISSN:
- 1991-9603
- Page Range / eLocation ID:
- 9237-9256
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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