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Abstract Roll vortices are a series of large-scale turbulent eddies that nearly align with the mean wind direction and prevail in the hurricane boundary layer. In this study, the one-way nested WRF-LES model simulation results from Li et al. (J Atmos Sci 78(6):1847–1867,https://doi.org/10.1175/JAS-D-20-0270.1, 2021) are used to examine the structure and generation mechanism of roll vortices and associated coherent turbulence in the hurricane boundary layer during the landfall of Hurricane Harvey from 00 UTC 25 to 18 UTC 27 August 2017. Results indicate that roll vortices prevail in the hurricane boundary layer. The intense roll vortices and associated large turbulent eddies above them (at a height of ~ 200 to 3000 m) accumulate within a hurricane radius of 20–40 km. Their intensity is proportional to hurricane intensity during the simulation period. Before and during hurricane landfall, strong inflow convergence leads to horizontal advection of roll vortices throughout the entire hurricane boundary layer. Combined with the strong wind shear, the strongest roll vortices and associated large turbulent eddies are generated near the eyewall with suitable thermodynamic (Richardson number at around − 0.2 to 0.2) and dynamic conditions (strong negative inflow wind shear). After landfall, the decayed inflow weakens the inflow convergence and quickly reduces the strong roll vortices and associated large turbulent eddies. Diagnosis of vertical turbulent kinetic energy indicates that atmospheric pressure perturbation, caused by horizontal convergence, transfers the horizontal component of turbulence to the vertical component with a mean wavelength of about 1 km. The buoyancy term is weak and negative, and the large turbulent eddies are suppressed. 

Surrogate models have shown improved accuracy in predicting infrastructure responses during dynamic loadings. However, training a surrogate model for complex loading inputs across the entire hazard region remains challenging. This study provides insight into the training of surrogate models to estimate the responses of transmission tower-line structures in a coupled high-dimensional and high-resolution wind field and presents innovative methods for addressing these challenges. Four data- and physics-based spatial-temporal decoupling sampling methods are employed and cross-compared to obtain the most representative in-event wind profiles for training the surrogate model. Long Short-Term Memory (LSTM) is utilised as the surrogate model framework to predict the dynamic responses of the structure during the 2017 Hurricane Harvey. The accuracy and robustness of two transmission tower-line structure configuration surrogate models are validated by comparing the predictions with finite element analyses by using randomly distributed temporal and geospatial wind profiles throughout the hurricane. Finally, a single LSTM surrogate model is developed, trained by applying the full reference wind speed range of Hurricane Harvey for the regional-scale structural performance evaluation of the transmission tower-line system. The results demonstrate that the proposed surrogate model training methodology is general and can be applied to regional-scale structural performance evaluations. 

The extent of loss in a seismic hazard can be moderated with on-time allocation of funds and initiation of recovery tasks. Among various examinations conducted following the hazard, buildings damages are assessed as part of the reconnaissance survey to learn and document the impact of the earthquake on structures. The results of the survey are used in financial aid estimation, which is crucial for the community rapid recovery acts after the hazard. Due to the urgent need for this information, the amount of information gained per unit of time should be optimized. This article aims at answering the question of how to maximize the information gain in the presence of resource constraints by directing the efforts of a reconnaissance surveying team. A data-driven method is proposed that actively learns the patterns of damage and recommends the most informative buildings to be inspected while considering the resource limitations. The framework utilizes an efficient active learning method based on mutual information and developed for Gaussian process regression (GPR) to identify the information-rich cases. To assess the contribution of information gain and resource allocation in the overall outcome of the damage inference, two simulated earthquake testbeds are studied. It is shown that in a co-optimization approach, damage labels of the majority of buildings can be accurately predicted after 1 week of damage inspections. 

Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement. 

This study examines the impacts of assimilating ocean-surface winds derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS) on improving the short-range numerical simulations and forecasts of landfalling hurricanes using the NCEP operational Hurricane Weather Research and Forecasting (HWRF) model. A series of data assimilation experiments are performed using HWRF and a Gridpoint Statistical Interpolation (GSI)-based hybrid 3-dimensional ensemble-variational (3DEnVar) data assimilation system. The influence of CYGNSS data on hurricane forecasts is compared with that of Advanced Scatterometer (ASCAT) wind products that have already been assimilated into the HWRF forecast system in a series of assimilation experiments. The effects of different versions of CYGNSS data (V2.1 vs. V3.0) on hurricane forecasts are evaluated. The results indicate that CYGNSS ocean-surface wind can lead to improved numerical simulations and forecasts of hurricane track and intensity, asymmetric wind structure, and precipitation. The impacts of CYGNSS on hurricane forecasts are comparable and complementary to the operational use of ASCAT satellite data products. The dependence of the relative impacts of different versions of CYGNSS data on optimal thinning distances is evident.