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  1. Abstract

    After a tropical storm makes landfall, its vortex interacts with the surrounding environment and the underlying surface. It is expected that diurnal variation over land will affect storm structures. However, this has not yet been explored in previous studies. In this paper, numerical simulation of postlandfall Tropical Storm Bill (2015) is conducted using a research version of the NCEP Hurricane Weather Research and Forecasting (HWRF) model. Results indicate that during the storm's interaction with midlatitude westerlies over the Great Plains, the simulated storm with the SLAB land‐surface scheme is stronger, with faster eastward movement and attenuation, and more asymmetric structures than that with the NOAH land‐surface scheme. More symmetric structures correspond with a slower weakening and slower eastward movement of the storm over land. Further diagnoses suggest an obvious response of the storm's asymmetric structures to diurnal effects over land. Surface diabatic heating in the storm environment is important for the storm's symmetric structures and intensity over land. Specifically, during the transition from nighttime to daytime, the evident strengthening of convective instability, atmospheric baroclinicity, and the lateral advection of highair in the storm environment, associated with the rapid increase in surface diabatic heating, are conducive to the development of vertical vorticity and storm‐relative helicity, thus contributing to the maintenance of the storm's symmetric structures and intensity after landfall.

     
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  2. Abstract

    Horizontal boundary layer roll vortices are a series of large-scale turbulent eddies that prevail in a hurricane’s boundary layer. In this paper, a one-way nested sub-kilometer-scale large-eddy simulation (LES) based on the Weather Research and Forecasting (WRF) Model was used to examine the impact of roll vortices on the evolution of Hurricane Harvey around its landfall from 0000 UTC 25 August to 1800 UTC 27 August 2017. The simulation results imply that the turbulence in the LES can be attributed mainly to roll vortices. With the representation of roll vortices, the LES provided a better simulation of hurricane wind vertical structure and precipitation. In contrast, the mesoscale simulation with the YSU PBL scheme overestimated the precipitation for the hurricane over the ocean. Further analysis indicates that the roll vortices introduced a positive vertical flux and thinner inflow layer, whereas a negative flux maintained the maximum tangential wind at around 400 m above ground. During hurricane landfall, the weak negative flux maintained the higher wind in the LES. The overestimated low-level vertical flux in the mesoscale simulation with the YSU scheme led to overestimated hurricane intensity over the ocean and accelerated the decay of the hurricane during landfall. Rainfall analysis reveals that the roll vortices led to a weak updraft and insufficient water vapor supply in the LES. For the simulation with the YSU scheme, the strong updraft combined with surplus water vapor eventually led to unrealistic heavy rainfall for the hurricane over the ocean.

     
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  3. Abstract

    Post‐earthquake reconnaissance survey of structural damage is an effective way of documenting and understanding the impact of earthquakes on structures. This article aims at providing an efficient data‐based framework that reduces the required time for reconnaissance missions and predicts the damage intensities for every building in the affected region. We hypothesize that a joint selection of necessary structural and earthquake parameters along with sparse damage observations are sufficient to train a supervised learning algorithm and accurately infer the damage for other buildings in the region. Gaussian process regression is employed to prove the hypothesis for probabilistic inference of different damage indices. The algorithm performs efficiently by selecting a set of diverse and representative buildings for damage observations using K‐medoids clustering. To validate the hypothesis and the proposed method, the algorithm framework is implemented on two severe earthquake simulation testbeds. The impacts of different building and ground motion variables on the damage inference performance are discussed. Furthermore, the effectiveness of observation sampling by clustering in the post‐earthquake damage inference is compared with random sampling.

     
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  4. The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropriate initial target parameters give the unscented Kalman filter a head start, the uncertainty levels and noise parameters set the rate of convergence in the process. Therefore, due to the coupling effect of these parameters, an inclusive approach is desired to maintain the chance of convergence for expensive experimental tests. In this paper, a framework is proposed that, via a virtual emulation prior to the experiment, determines a set of initial conditions to ensure a successful application of the online parameter identification. A Bayesian optimization method is proposed, which considers the level of confidence in the initial guesses for the target parameters to suggest the appropriate noise covariance matrices. The methodology is validated on a five-story shear frame tested on a shake table. The results indicate that, indeed, a trade-off can be made between the robustness of the online updating and the final parameter accuracy. 
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  5. Regional damage simulation is a promising method to prepare organizations for the unforeseeable impact of a probable seismic natural hazard. Nonlinear time history analysis (NLTHA) of the finite element models (FEM) of the buildings in a region can provide resembling results to the actual buildings’ damages and responses. This approach requires large-scale computational resources, and to improve efficiency, parallel processing and representing building FEM models with lumped mass models are proposed. However, the computing complexity is still far-reaching when high-performance computing is not available. The building inventory of a region consists of numerous similar buildings with a limited number of distinct structures. In this paper, we propose a data-driven method that runs the NLTHA for the distinct structures exclusively and infers the damage and responses of other buildings using a surrogate model. Considering the skewed distribution of the buildings in a region, a novel informative sample selection method is proposed that is designed for bimodal sampling of the input domain. We use the Gaussian process regression as the surrogate model and compare the performance of different sample selection methods. The proposed method is able to approximate the results of the regional damage simulation regarding total economic loss estimation with 98.99% accuracy while reducing the computational demand to about 1/7th of the simulation processing time. 
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