(2010) in that the model’s maximum intensity parameter is set to be a harmonic function of the azimuthal angle. In particular, our asymmetric wind field model is an extension of the mean field model of Holland et al. This article extends on the previous work by modeling wind field asymmetries as a function of environmental inputs, specifically, by parametric incorporation of asymmetries into established models for mean field estimation. The relationships of asymmetries to environmental inputs were studied in Xie et al. Not accounting for this inherent asymmetry can negatively affect hurricane risk assessment of aboveground infrastructure components such as electric distribution lines, whose failure probabilities depend on local surface wind intensities ( Zhou et al. In reality, however, hurricane wind fields tend to be asymmetric. Well-known gradient wind field models are estimated from deterministic axisymmetric models, which we refer to as mean field (MF) models. This article focuses on constructing a physically informed model of the hurricane gradient wind field one can then use a boundary layer model for the gradient-to-surface wind conversion. Thus, a reliable model of the whole surface wind field is desirable. For risk assessment of large-scale infrastructure systems, the impact of spatially heterogeneous wind velocities must be suitably captured. Wind field models include those proposed by Vickery et al. 2004) or FAST intensity simulator ( Emanuel 2017). Maximum wind intensity estimates along the tracks are provided by models such as the Coupled Hurricane Intensity Prediction System (CHIPS) ( Emanuel et al. 2006) and use historical hurricane “best track” data as input. Well-known synthetic-track-generation models combine both physical and statistical modeling approaches ( Casson and Coles 2000 Vickery et al. This simulation-based approach relies on capturing two aspects of hurricane structure: 1) track, or trajectory of the storm from its formation over the ocean to dissipation over land and 2) wind velocities at various points along the track, modeled by either a single intensity measurement (e.g., maximum sustained 10 m wind speed) or a surface wind field. Outputs of these models are used toward quantifying the probability distributions of damage to various infrastructures.Ī common approach to risk assessment is to simulate an ensemble of storms, in order to capture the stochastic nature of hurricane arrival, landfall location, and intensity. Hurricane risk assessment typically involves modeling of storm tracks, wind fields, and occurrence frequency ( Watson and Johnson 2004). Such a procedure becomes especially important in light of projected changes in the frequency of high-intensity hurricanes ( Bender et al. A standardized risk assessment procedure is needed to assess the vulnerability of infrastructure systems to hurricanes. ![]() Hurricanes are a major natural hazard to built infrastructure such as buildings, transportation systems, and electric power networks ( Campbell and Lowry 2012 Ouyang 2014) and often lead to large socioeconomic losses. ![]() The article concludes with brief remarks on how the CNLS-estimated model can be applied for simulating wind fields in a statistically generated ensemble. Overall, the CNLS estimation method can handle the inherently nonlinear wind field model in a flexible manner thus, it is well suited to capture the radial variability in the hurricane wind field’s asymmetry. In addition, inclusion of the wavenumber-1 asymmetry resulting from translation results in a greater decrease in modeling error than does inclusion of the wavenumber-1 shear-induced asymmetry. Adding the translation vector to the wind field model with wavenumber-1 asymmetries further improves the model’s estimation performance. There are statistically significant wavenumber-1 asymmetries in the wind field resulting from both storm translation and wind shear. Model parameters are estimated by solving a constrained, nonlinear least squares (CNLS) problem that minimizes the sum of squared residuals between wind field intensities of historical storms and model-estimated winds. The amplitudes and phases of the asymmetries are parametric functions of the storm-translation speed and wind shear. The model incorporates low-wavenumber asymmetries into the maximum wind intensity parameter of the Holland et al. This article presents an azimuthally asymmetric gradient hurricane wind field model that can be coupled with hurricane-track models for engineering wind risk assessments.
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