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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. 

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. 

Hurricanes have unique dynamics when compared to regular Atmospheric Boundary Layers (ABLs). Strong winds and elevated surface waves differentiate the air-sea interactions in Hurricane Boundary Layers (HBLs) from classic marine ABLs. Although significant progress has been made in modeling hurricanes, our understanding of the turbulence dynamics of HBLs is still limited due to the lack of sufficient measurement data and high-resolution simulations. Our objective in this work is to address this knowledge gap using high-resolution Large-Eddy Simulations (LESs) that explicitly resolve hurricane turbulence (Momen et al. 2021; Sabet et al. 2022). In this presentation, we will characterize the role of surface waves in HBL mean and turbulence dynamics with the help of multiple unique LES runs in the parameter space of the problem. First, we will show the impacts of surface waves on HBL dynamics using wave-resolving LESs. It was found that the ocean waves can significantly modulate the surface layer dynamics of HBLs as shown in the attached figure. The steep waves in hurricanes were found to remarkably influence the HBL turbulence up to ~800 m away from the surface. The impacts of waves on turbulent eddies are high near the surface (up to ~100 m) as shown in the 3D spatial correlation of the attached figure. Typical low wave ages enhance surface drag and decrease the HBL wind, while higher wave ages can intensify the local surface winds. Moreover, the Turbulent Kinetic Energy (TKE) is increased by the enhanced drag of young waves, while older higher speed waves can decrease the TKE compared to the flat non-wavy case. We also found that higher wave heights, which are more prevalent in hurricanes, magnify these effects. The implications of these results on surface layer parameterizations in large-scale hurricane forecasts will also be briefly discussed using the Weather Research and Forecasting (WRF) model. We will present that the current aerodynamic roughness length parameterizations in WRF overestimate the observational estimates and theoretical hurricane intensity models for high wind regimes over the ocean (≳ 45 m/s). By adjusting the roughness length values in WRF, we were able to improve the intensity forecasts of five strong hurricane cases (category 3-5) by more than 20% on average compared to the default models (Li et al. 2023). These insights and findings can be useful for improving hurricane forecasts in numerical weather prediction models, eventually aiding in disaster preparedness efforts. References: Li, M., J. A. Zhang, L. Matak, and M. Momen, 2023: The impacts of adjusting momentum roughness length on strong and weak hurricanes forecasts: a comprehensive analysis of weather simulations and observations. Mon Weather Rev, https://doi.org/10.1175/MWR-D-22-0191.1. Momen, M., M. B. Parlange, and M. G. Giometto, 2021: Scrambling and reorientation of classical boundary layer turbulence in hurricane winds. Geophys Res Lett, 48, https://doi.org/https://doi.org/10.1029/2020GL091695. Sabet, F., Y. R. Yi, L. Thomas, and M. Momen, 2022: Characterizing mean and turbulent structures of hurricane winds via large-eddy simulations. Proceedings of the Summer Program 2022, Stanford, Center for Turbulence Research, Stanford University, 311–321. 

Forecasting hurricanes is critically important for mitigating their devastating impacts caused by wind damage, storm surges, and flooding. Despite remarkable advancements in numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, accurate hurricane forecasts remain challenging likely due to inaccurate physical parameterizations of complex dynamics of these storms. One major issue of these models is related to their Planetary Boundary Layer (PBL) schemes, which are not typically designed for hurricane flows with strong rotation. Previous studies have shown that the existing PBL schemes of hurricane simulations are often overly dissipative, leading to underestimations of the storm intensity (Matak and Momen 2023; Romdhani et al. 2022). Our recent research (Khondaker and Momen 2024) demonstrated that reducing diffusion in these models improved the hurricane’s intensity and size forecasts by more than ~30% on average in four considered major hurricanes. This reduced diffusion is due to the strong rotational nature of hurricanes, which suppresses turbulence and produces smaller eddies compared to regular PBLs (Momen et al. 2021). While prior studies showed that decreasing the vertical diffusion significantly improves major hurricane intensity forecasts, they mostly relied on simplified and often invariable adjustments of vertical diffusion such as multiplying it by a constant coefficient. The objective of this study is to address this issue by introducing a rotation-based variable adjustment of diffusion to account for the strong rotational nature of tropical cyclone (TC) dynamics. To this end, we will present multiple real strong and weak hurricane simulations using the Advanced Research WRF (ARW) model in the US. We modified the vertical eddy diffusivity based on the relative vorticity to accommodate the rotational dynamics of TCs in PBL schemes. While the default model significantly underpredicts hurricane intensity, our new adjustments outperform the default schemes for these strong hurricanes (see, e.g., attached fig. a), with notable improvements in track and minimum sea level pressure accuracy. This modification also remarkably increases the inflow in hurricanes compared to default models and leads to the intensification of the TC vortex (see, e.g., attached fig. b,c). Our newly adjusted model matched more closely with dropsonde, and satellite observations compared to the default WRF’s PBL schemes. These modifications to the PBL schemes make them more physics-based adjustments compared to previous treatments, offering valuable insights for improving hurricane forecasts in NWP models. References: Khondaker, M. H., and M. Momen, 2024: Improving hurricane intensity and streamflow forecasts in coupled hydro-meteorological simulations by analyzing precipitation and boundary layer schemes. J Hydrometeorol, https://doi.org/10.1175/JHM-D-23-0153.1. Matak, L., and M. Momen, 2023: The Role of Vertical Diffusion Parameterizations in the Dynamics and Accuracy of Simulated Intensifying Hurricanes. Boundary Layer Meteorology, https://doi.org/10.1007/s10546-023-00818-w. Momen, M., M. B. Parlange, and M. G. Giometto, 2021: Scrambling and Reorientation of Classical Atmospheric Boundary Layer Turbulence in Hurricane Winds. Geophysical Research Letters, 48, https://doi.org/10.1029/2020GL091695. Romdhani, O., J. A. Zhang, and M. Momen, 2022: Characterizing the Impacts of Turbulence Closures on Real Hurricane Forecasts: A Comprehensive Joint Assessment of Grid Resolution, Horizontal Turbulence Models, and Horizontal Mixing Length. Journal of Advanced Modeling Earth System, 14, https://doi.org/10.1029/2021ms002796. 

Hurricanes have been the most expensive weather disaster in US history, causing over $1 trillion in damage since 1980. Despite significant progress in modeling hurricanes, our understanding of the turbulence dynamics of Hurricane Boundary Layers (HBLs) is still limited due to lack of sufficient measurement data and high-resolution simulations. The objective of this study is to address this knowledge gap using high-resolution Large-Eddy Simulations (LESs) of HBLs. In this presentation, we will characterize the role of rotation and surface waves on HBL mean and turbulence dynamics with the help of more than 40 unique LES runs in the parameter space of the problem. First, we will show that strong rotation in HBLs alters the turbulence structures by breaking down the large eddies into smaller eddies at the same elevation (Momen et al. 2021). The differences between regular Atmospheric Boundary Layers (ABLs) and HBLs will then be presented by contrasting comparative cases with and without rotational effects (Sabet et al. 2022). Next, the impacts of surface waves on HBL dynamics will be shown using wave-resolving LESs. It was found that surface waves significantly modulate the surface layer dynamics of HBLs compared to regular flat simulations as shown in the attached figure. Typical low wave ages enhance surface drag and decrease the HBL wind, while higher wave ages can intensify the local surface winds. Moreover, the Turbulent Kinetic Energy (TKE) is increased by the enhanced drag of young waves, while older higher speed waves can decrease the TKE compared to the flat case. We also found that higher wave heights, which are more prevalent in hurricanes, magnify these effects. This presentation will show that rotational and surface wave effects are two important factors that need to be simultaneously considered for the correct prediction of HBL winds. These insights can be useful for improving hurricane forecasts in numerical weather prediction models, ultimately aiding in disaster preparedness and mitigation efforts. References: Momen M, Parlange MB, Giometto MG (2021) Scrambling and reorientation of classical boundary layer turbulence in hurricane winds. Geo Res Let 48. Sabet F, Yi YR, Thomas L, Momen M (2022) Characterizing mean and turbulent structures of hurricane winds. Cen for Turb Res, Stanford, pp 311–321. 

Obtaining accurate and dense three-dimensional estimates of turbulent wall-bounded flows is notoriously challenging, and this limitation negatively impacts geophysical and engineering applications, such as weather forecasting, climate predictions, air quality monitoring, and flow control. This study introduces a physics-informed variational autoencoder model that reconstructs realizable three-dimensional turbulent velocity fields from two-dimensional planar measurements thereof. Physics knowledge is introduced as soft and hard constraints in the loss term and network architecture, respectively, to enhance model robustness and leverage inductive biases alongside observational ones. The performance of the proposed framework is examined in a turbulent open-channel flow application at friction Reynolds number Reτ=250. The model excels in precisely reconstructing the dynamic flow patterns at any given time and location, including turbulent coherent structures, while also providing accurate time- and spatially-averaged flow statistics. The model outperforms state-of-the-art classical approaches for flow reconstruction such as the linear stochastic estimation method. Physical constraints provide a modest but discernible improvement in the prediction of small-scale flow structures and maintain better consistency with the fundamental equations governing the system when compared to a purely data-driven approach. 

The atmospheric boundary layer (ABL) is a highly turbulent geophysical flow, which has chaotic and often too complex dynamics to unravel from limited data. Characterizing coherent turbulence structures in complex ABL flows under various atmospheric regimes is not systematically well established yet. This study aims to bridge this gap using large eddy simulations (LESs), Koopman theory, and unsupervised classification techniques. To this end, eight LESs of different convective, neutral, and unsteady ABLs are conducted. As the ratio of buoyancy to shear production increases, the turbulence structures change from roll vortices to convective cells. The quadrant analysis indicated that as this ratio increases, the sweep and ejection events decrease, and inward/outward interactions increase. The Koopman mode decomposition (KMD) is then used to characterize their turbulence structures. Our results showed that KMD can reveal non-trivial modes of highly turbulent ABL flows (e.g., transverse to the mean flow direction) and can reconstruct the primary dynamics of ABLs even under unsteady conditions with only ∼5% of the modes. We attributed the detected modes to the imposed pressure gradient (shear), Coriolis (inertial oscillations), and buoyancy (convection) forces by conducting novel timescale and quadrant analyses. We then applied the convolutional neural network combined with the K-means clustering to group the Koopman modes. This approach is displacement and rotation invariant, which allows efficiently reducing the number of modes that describe the overall ABL dynamics. Our results provide new insights into the dynamics of ABLs and present a systematic data-driven method to characterize their complex spatiotemporal patterns.