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Hurricanes are among the costliest natural disasters in the United States and regularly inflict severe damage on urban infrastructure. Accurate forecasts are therefore essential for preparedness and limiting these extreme events' economic toll. Numerical weather‑prediction (NWP) models—such as the Weather Research and Forecasting (WRF) system—are powerful forecasting tools. However, some of their physical parameterizations were neither designed for nor tested with real hurricanes. This thesis addresses that gap by evaluating two key parameterizations in WRF: (i) subgrid‑scale (SGS) turbulence schemes and (ii) surface‑roughness and urban canopy treatments. The first part of the study investigates how SGS eddy‑viscosity choices affect hurricane intensity, turbulence, and wind profiles. Large‑eddy simulations (LES) of five major hurricanes were run with a 1.5‑order, three‑dimensional turbulent‑kinetic‑energy (TKE) SGS scheme. Each storm was simulated under three eddy‑viscosity settings— default, halved, and doubled—yielding 15 cases. A parallel set of 10 cases employed an alternative nonlinear backscatter and anisotropy (NBA) SGS scheme. Two idealized LES runs and one fine-grid (~80 m) nested simulation brought the total to 33. Reducing SGS stress intensified storms by raising boundary‑layer wind speeds and lowering the altitude of peak winds, improving surface‑wind forecasts by ~9 % and minimum sea‑level pressure by ~29 % relative to the default setting. These results reveal that standard SGS models are overly dissipative because they overlook the rotational suppression of turbulence, underscoring the need for SGS schemes tailored to hurricane dynamics. The second part assesses how aerodynamic roughness length (z0) and urban‑canopy schemes shape near‑surface winds over cities. For four land‑falling hurricanes affecting Houston and New Orleans, increasing z0 in the Single‑Layer Urban Canopy Model (SLUCM) reduced modeled wind speeds and cut mean absolute error (MAE) by ~20 %, whereas decreasing z0 introduced large positive biases. Additional experiments compared three urban options—Bulk (no‑urban), SLUCM, and the multi‑layer Building Energy Model (BEM). The Bulk scheme delivered the most accurate surface‑wind forecasts in every nested domain, while SLUCM slightly outperformed BEM in the limited vertical‑profile data. These findings highlight the need to recalibrate urban schemes and surface‑drag parameters when applying WRF to hurricane‑force winds.
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This content will become publicly available on August 22, 2026
Advancing Planetary Boundary Layer Parameterizations for Improved Hurricane and Urban Weather Forecasts
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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- Award ID(s):
- 2228299
- PAR ID:
- 10637854
- Publisher / Repository:
- http://search.proquest.com.ezproxy.lib.uh.edu/pqdtlocal1006646/dissertations-theses/advancing-planetary-boundary-layer/docview/3241723804/sem-2?accountid=7107
- Date Published:
- ISBN:
- 9798291544990
- Format(s):
- Medium: X
- Institution:
- University of Houston
- Sponsoring Org:
- National Science Foundation
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