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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.