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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. 

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. 

Rotation in hurricane flows can highly affect the dynamics and structure of hurricane boundary layers (HBLs). Recent studies (Momen et al. 2021) showed that there is a significant distinction between turbulence characteristics in hurricane and regular atmospheric boundary layers due to the strong rotational effects of hurricane flows. Despite these unique features of HBLs, the current planetary boundary layer (PBL) and turbulence schemes in numerical weather prediction (NWP) models are neither specifically designed nor comprehensively tested for major hurricane flows. In this talk, we will address this knowledge gap by characterizing the role of horizontal and vertical eddy diffusion under different PBL schemes in simulated hurricane intensity, size, and track. To this end, the results of multiple simulated hurricane cases will be presented using the Weather Research and Forecasting (WRF) model. The impacts of changing the grid resolution, horizontal turbulence, PBL scheme, vertical eddy diffusivity, and PBL height on hurricane dynamics and accuracy will be characterized. The results indicate that the current turbulence and PBL schemes in WRF are overly diffusive for simulating major hurricanes (Romdhani et al. 2022; Li et al. 2023) primarily since they do not account for turbulence suppression effects in rotating hurricane flows. We will also show new suites of simulations in which the default horizontal and vertical diffusion in WRF are modulated to determine the impacts of eddy diffusion changes on hurricane dynamics. The results indicate that reducing the default vertical diffusion depth and magnitude led to ~38% and ~24% improvements, on average, in hurricane intensity forecasts compared to the default models in the considered cases (Matak and Momen 2023). Moreover, by decreasing the default horizontal mixing length, we managed to decrease the intensity errors on average between ~8-23% in the WRF’s default models for both low and high resolutions. Figure A displays an example of the simulations in which our new adjustment of the vertical diffusion (reduced diffusion, blue line) agrees better with the observed data (black line) compared to the default WRF results (gray line). The figure also depicts wind speed contours that how this change in vertical diffusion can remarkably influence the structure, size, and intensity of hurricane simulations. The results of this study provide notable insights into the role of turbulent fluxes in simulated hurricanes that can be useful to advance the turbulence and PBL parameterizations of NWP models for accurate tropical cyclone forecasts. References: Li M, Zhang JA, Matak L, Momen M (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 Matak L, Momen M (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, Parlange MB, Giometto MG (2021) Scrambling and reorientation of classical boundary layer turbulence in hurricane winds. Geophys Res Lett 48:.https://doi.org/10.1029/2020GL091695 Romdhani O, Zhang JA, Momen M (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. J Adv Model Earth Syst. https://doi.org/10.1029/2021MS002796 

Abstract Hurricanes have been the most destructive and expensive hydrometeorological event in U.S. history, causing catastrophic winds and floods. Hurricane dynamics can significantly impact the amount and spatial extent of storm precipitation. However, the complex interactions of hurricane intensity and precipitation and the impacts of improving hurricane dynamics on streamflow forecasts are not well established yet. This paper addresses these gaps by comprehensively characterizing the role of vertical diffusion in improving hurricane intensity and streamflow forecasts under different planetary boundary layer, microphysics, and cumulus parameterizations. To this end, the Weather Research and Forecasting (WRF) atmospheric model is coupled with the WRF hydrological (WRF-Hydro) model to simulate four major hurricanes landfalling in three hurricane-prone regions in the United States. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the U.S. Geological Survey (USGS) gauges. Then, 60 coupled hydrometeorological simulations were conducted to evaluate the performance of current weather parameterizations. All schemes were shown to underestimate the observed intensity of the considered major hurricanes since their diffusion is overdissipative for hurricane flow simulations. By reducing the vertical diffusion, hurricane intensity forecasts were improved by ∼39.5% on average compared to the default models. These intensified hurricanes generated more intense and localized precipitation forcing. This enhancement in intensity led to ∼16% and ∼34% improvements in hurricane streamflow bias and correlation forecasts, respectively. The research underscores the role of improved hurricane dynamics in enhancing flood predictions and provides new insights into the impacts of vertical diffusion on hurricane intensity and streamflow forecasts. Significance StatementDespite significant recent improvements, numerical weather prediction models struggle to accurately forecast hurricane intensity and track due to many reasons such as inaccurate physical parameterization for hurricane flows. Furthermore, the performance of existing physics schemes is not well studied for hurricane flood forecasting. This study bridges these knowledge gaps by extensively evaluating different physical parameterizations for hurricane track, intensity, and flood forecasts using an atmospheric model coupled with a hydrological model. Then, a reduced diffusion boundary layer scheme is developed, making remarkable improvements in hurricane intensity forecasts due to the overdissipative nature of the considered schemes for major hurricane simulations. This reduced diffusion model is shown to significantly enhance hurricane flood forecasts, indicating the significance of hurricane dynamics on its induced precipitation. 

Recent studies have shown that climate change and global warming considerably increase the risks of hurricane winds, floods, and storm surges in coastal communities. Turbulent processes in Hurricane Boundary Layers (HBLs) play a major role in hurricane dynamics and intensification. Most of the existing turbulence parameterizations in the current numerical weather prediction (NWP) models rely on the Planetary Boundary Layer (PBL) schemes. Previous studies (Zhang 2010; Momen et al. 2021) showed that there is a significant distinction between turbulence characteristics in HBLs and regular atmospheric boundary layers (ABLs) due to the strong rotational effects of hurricane flows. Nevertheless, such differences are not considered in the current schemes of NWPs, and they are primarily designed and tested for regular ABLs. In this talk, we aim to bridge this knowledge gap by conducting new hurricane simulations using the Weather Research and Forecasting (WRF) model as well as large-eddy simulations. We investigate the role of the PBL parameterizations and momentum roughness length in multiple hurricanes by probing the parameter space of the problem. Our simulations have shown that the most widely used WRF PBL schemes do not capture the hurricane intensification properly and underestimate their intensity. We will present that decreasing the roughness length close to the values of observational estimates and theoretical hurricane intensity models in high wind regimes (≳ 45 m s-1) led to significant improvements in the intensity forecasts of strong hurricanes. Furthermore, by decreasing the existing vertical diffusion values, on average more than 20% improvements in hurricane intensity forecasts were obtained compared to the default runs. Our results provide new insights into the role of turbulence parameterizations in hurricane dynamics and can be employed to improve the accuracy of real hurricane forecasts. The implications of these results and improvements for coastal resiliency and fluid-structure interactions will also be discussed.