Extreme wind events (e.g., thunderstorm downbursts) often present strong nonstationarities in terms of time-varying mean, amplitude modulation and frequency modulation. The stationary-wind assumption adopted in the traditional analysis framework for wind-induced structural responses may lead to severely biased estimates in nonstationary scenarios. Accordingly, advanced methodologies have recently emerged to investigate nonstationary wind effects on structures. Since the consideration of nonstationary winds and associated structural aerodynamics and dynamics usually involves more time and effort in comparison of stationary case, it is important to first measure the degree of nonstationarity of wind signals for selecting an appropriate analysis framework (stationary or nonstationary). In this study, a Hilbert-wavelet-based nonstationarity index is developed to quantify the nonstationarity of extreme winds. The joint utilization of Hilbert transform and wavelet packet decomposition provides a sharp time-frequency representation of the analyzed signal, and hence lays a solid foundation for the nonstationarity quantification. To highlight that the same wind nonstationarity may present different contributions to dynamic responses of various structures, the structural aerodynamics and dynamics are integrated into the proposed nonstationarity index to form a multi-level quantification framework. Furthermore, the obtained Hilbert-wavelet-based multi-level nonstationarity index of a nonstationary wind signal is first summarized over the time and frequency domains and then normalized by the corresponding nonstationarity index of an impulse signal (treated as the most nonstationary signal) for convenient application and easy interpretation of the quantification results. The effectiveness of the proposed index for multi-level quantification of nonstationarity is well demonstrated with its applications to synthesized signals as well as field-measured downburst winds.

极端风事件（例如，雷暴下击暴流）通常在时变均值、幅度调制和频率调制方面表现出很强的非平稳性。在风致结构响应的传统分析框架中采用的静止风假设可能会导致非平稳情景中的严重偏差估计。因此，最近出现了先进的方法来研究非平稳风对结构的影响。由于与静止情况相比，考虑非平稳风和相关的结构空气动力学和动力学通常需要更多的时间和精力，因此首先测量风信号的非平稳程度以选择合适的分析框架（平稳或非平稳）很重要。在这项研究中，开发了基于希尔伯特小波的非平稳性指数来量化极端风的非平稳性。希尔伯特变换和小波包分解的联合使用为分析信号提供了清晰的时频表示，从而为非平稳量化奠定了坚实的基础。为了强调相同的风非平稳性可能对不同结构的动力响应有不同的贡献，将结构空气动力学和动力学整合到所提出的非平稳性指数中，形成一个多层次的量化框架。此外，获得的非平稳风信号的基于希尔伯特小波的多级非平稳性指数首先在时域和频域上进行汇总，然后通过相应的脉冲信号的非平稳性指数（视为最非平稳的信号）进行归一化，以方便应用和量化结果的简单解释。所提出的非平稳性多级量化指标的有效性通过其对合成信号以及现场测量的下击暴流风的应用得到了很好的证明。