Review of Wind Field Characteristics of Downbursts and Wind Effects on Structures under Their Action

Abstract

Downbursts belong to sudden, local, and strong convection weather, which present significant destruction for structures. At any given time, there are approximately 2000 thunderstorms occurring on the Earth. Many studies have investigated the effects of downbursts on different structures. However, the extensive range of varying wind field parameters and the diverse representations of wind speeds render the study of structural wind effects complex and challenging under downbursts. This study firstly reviews the research of wind field properties of downbursts according to four common approaches, and the major findings, advantages, and disadvantages of which are concluded. Then, failure analysis of transmission line systems under stationary and moving downbursts is explored. The article also reviews the wind pressure on the roof of different kinds of low-rise buildings, and some dominant parameters, namely roof slope, distance of building from downburst center, wind direction angle, and so on, are discussed. Moreover, the wind effects caused by downbursts on high-rise buildings and some specialized structures are also considered because more and more wind hazards are related to downbursts. Finally, the limitations of the current study are pointed out, and recommendations for further research are given for the accurate assessment of the effects of wind on buildings, with a view to providing safer and more economical wind-resistant design solutions for structures.

1. Introduction

A downburst is a near-surface, destructive wind phenomenon caused by the rapid descent of cold air from a thunderstorm. Upon reaching the ground, this cold air spreads radially across the surface [1] (Figure 1). Downbursts are characterized by their high intensity, sudden onset, substantial variability, significant regional impacts, and brief duration. They exhibit a distinctive three-dimensional wind field and a “nose-shaped” wind speed profile, with notable non-stationary and non-Gaussian characteristics [2,3,4,5,6] (Figure 2). These attributes result in considerable differences in their effects on building structures compared to large-scale, steady strong winds. Therefore, it is crucial to account for the impact of extreme wind events, such as downbursts, in the wind-resistant design of buildings.

As an extreme wind load, downbursts pose severe threats to various structures, including transmission lines and towers, roofs, high-rise buildings, and bridges, resulting in significant property damage and potential fatalities [7,8,9] (Figure 3). Based on data from global natural disaster assessment reports [10], the total insured losses from thunderstorm-induced severe winds in the United States between 1980 and 2008 have increased annually, with approximately 36,000 thunderstorm-related wind events observed in 2008 alone, resulting in losses exceeding USD 10 billion. In 2009, a downburst event in Texas, USA, caused the collapse of a football stadium, resulting in USD 4 million in damages [7]. In 2005, a downburst in Qiuyang, Jiangsu, led to the collapse of all ten transmission towers along the 500 kV Renshang 5237 line [8]. In 2015, the “Oriental Star” passenger ship capsized due to a sudden downburst, resulting in 442 fatalities [9]. More recently, on 31 March 2024, a downburst in Nanchang, Jiangxi, blew out the glass windows of a high-rise building, causing three residents to fall from the building and perish, with multiple injuries reported. Worldwide, disasters and substantial losses arise from thunderstorm phenomena such as downbursts. Therefore, elucidating the characteristics of downburst wind fields and their impacts on various structures has become a critical issue in the field of structural wind engineering.

Furthermore, current building codes and standards seldom encompass the impacts of severe winds such as downbursts. Although relevant standards have been issued by the Australian/New Zealand Standard AS/NZS 1170.2 (2011) [11], the American Society of Civil Engineers (ASCE) 7-16 (2009) [12], and the International Organization for Standardization (IOS) (2009) [13], these standards merely provide suggestive conditions without prescribing definitive design values. Consequently, to enhance the resilience of building structures against downbursts and ensure the safety of infrastructure, research into downbursts is of paramount importance.

This paper systematically reviews key research achievements concerning the impact of downbursts on building structures. The review encompasses an analysis of the characteristics of downburst wind fields and their associated effects on transmission line towers, roofs, high-rise buildings, and other structures subjected to such extreme wind events.

2. Characteristics of Downburst Wind Fields

Understanding the characteristics of downburst wind fields is a prerequisite to assessing the effects of wind on structures subjected to downbursts. This section summarizes existing research on downburst wind field characteristics from four aspects: field measurements, wind tunnel tests, numerical simulations, and theoretical analyses.

2.1. Field Measurement Studies

Field measurements are the most direct and effective method for studying downbursts. In 1978, the North Illinois Meteorological Research on Downbursts (NIMROD) project successfully recorded downburst events using Doppler radar and anemometers for the first time [14]. Subsequently, the Joint Airport Weather Studies (JAWS) project in Denver, CO, USA, in 1982 further expanded the scale of downburst field measurement data [15]. Based on these two projects’ data, Fujita [14] conducted an in-depth analysis of downburst flow field characteristics and classified them into macro- and micro-downbursts based on whether their near-surface horizontal influence range exceeded 4 km. In 1984, Wilson [16] analyzed JAWS data and summarized the vertical outflow structure of micro-downbursts. Hjelmfelt [3] further analyzed JAWS data, outlined the lifecycle of the outflow phase of downbursts, and drew a typical downburst wind profile diagram (Figure 4).

Additionally, Fujita [17] examined a severe downburst event with a maximum instantaneous wind speed of 67 m/s observed at the Andrews Air Force Base (AAFB) in the USA, summarizing the wind speed time history at a 4.9 m observation point and initially clarifying the sudden changes in downburst wind speed and direction. Wolfson [18] studied the forcing mechanisms of downbursts based on data from the Federal Aviation Administration’s Lincoln Laboratory Weather Operations Research Program (FLOWS) in 1984. Atkins [19] conducted the Microburst Identification Signature Test (MIST) in northern Alabama, USA, in 1986, analyzing downburst characteristics in humid environments. In 2002, Chen [20] investigated the horizontal and vertical structures and corresponding lifecycles of downbursts observed at Texas Tech University, distinguishing them from large-scale, steady strong winds based on field data. Lombardo et al. [21], in 2009, studied the wind speed profiles and turbulence characteristics of downburst wind fields based on a series of downburst events captured by the Wind Engineering Research Field Laboratory (WERFL) at Texas Tech University, highlighting their differences from large-scale steady strong winds and advocating for considering the non-stationary and non-Gaussian effects of downbursts in wind load calculations.

Since 2009, the Solari [22] team has conducted field measurement projects, “Wind and Ports” (WP) and “Wind, Ports and Sea” (WPS), in European ports to monitor real-time data on wind speed, direction, temperature, and other parameters. These projects aimed to provide early warnings of short-term weather events like downbursts and typhoons. Gaetano et al. [23] used partial data from these projects to propose a preliminary storm classification method and obtained a limited number of thunderstorm wind records. Subsequently, Solari et al. [24,25] further analyzed thunderstorm characteristics and introduced the concept of a thunderstorm response spectrum. Zhang et al. [26,27,28,29] developed an automated method for extracting thunderstorm winds, expanding the database to 277 records and proposing a high-precision wind field model using this database. Roncallo [30] developed a modeling approach for the Evolutionary Power Spectral Density (EPSD) of thunderstorm outflows. Canepa [31] investigated the dynamic response of structures using the novel wind speed decomposition method proposed by Zhang et al., further validating its effectiveness.

In 1999, Choi [4,5] studied and classified the wind speed profiles of 50 thunderstorm events recorded at a 152 m-high observation tower in Singapore, discovering that these profiles were primarily influenced by storm intensity, distance from the storm center, and ground roughness. Subsequently, he analyzed observational data from five meteorological stations in Hong Kong, finding that local thunderstorm winds occurred more frequently and with greater intensity than tropical cyclones and monsoons, emphasizing their importance in structural wind-resistant design.

In 2006, Yu Xiaoding et al. [32] analyzed a downburst event at the junction of Dingyuan and Feidong counties in Anhui Province, conducting preliminary research on the forcing mechanisms of downbursts. In 2015, Li Honghai et al. [33] analyzed the spatiotemporal distribution characteristics of downbursts based on observational data from 707 meteorological stations nationwide from 1971 to 2000. In 2016, Huang Guoqing and Peng Liuliu et al. [34,35], relying on field measurement projects in the Xuanwei wind field in Yunnan, explored the non-stationary and non-Gaussian characteristics of mountain winds. They performed preliminary automatic extraction and classification of recorded wind speed data, further analyzing the wind field characteristics of mountain thunderstorms. In 2019, Yang Qingshan and Zhang Shi et al. [36,37,38], based on measured data from a 325 m-high meteorological tower in Beijing, conducted research on different storm classifications, obtained multiple thunderstorm wind data (Figure 5), established a thunderstorm wind database, and compared it with measured data from Mediterranean port areas. They proposed methods for the occurrence, evolution, and association of thunderstorms with large-scale weather conditions, establishing a high-precision thunderstorm wind field model for urban Beijing. Using this modeling approach, Liu Muguang et al. [39,40] statistically analyzed the time-varying mean and fluctuating wind characteristics of downbursts based on 29 events recorded by a 356 m meteorological gradient tower in Shenzhen. In summary, field measurements can provide the most accurate data [41].

In summary, researchers worldwide have conducted preliminary studies on the identification, classification, and wind field characteristics of downbursts based on wind speed data from various regional field measurement projects.

Table 1. Maximum horizontal velocity of downbursts.

Author	Country	Number of Downburst Events	Maximum Horizontal Velocity	Height of Maximum Horizontal Velocity	Radial Position of Maximum Horizontal Velocity
Wilson [16]	USA	1	26 m/s	600 m	approximately 4 km
Hjelmfelt [3]	USA	11	20–30 m/s	50–100 m	approximately 3–4 km
Chen [20]	USA	2	30.6 m/s	50–100 m	approximately 4–5 km
Lombardo [21]	USA	7	29–36 m/s	10 m	approximately 1.5 km
Solari [24,25]	Europe	141	33.98 m/s	24–26 m	approximately 3–5 km
Zhang [26,27,28,29]	Italy	277	20–30 m/s	24–26 m	approximately 4–5 km
Choi [4,5]	Singapore	Multiple events	26.2–40 m/s	15–20 m	approximately 1–2 km
Yu [32]	China	1	22–24 m/s	500–600 m	approximately 4 km
Huang [34,35]	China	8	22–24 m/s	10–30 m	approximately 4 km
Zhang [36,37,38]	China	Multiple events	20–24 m/s	280 m	approximately 4 km
Liu [39,40]	China	29	37.6 m/s	60–160 m	approximately 1–3 km

summarizes the maximum horizontal velocities of downbursts obtained from field measurements. However, due to downbursts’ small spatial scale and short lifespan, current research methods and technologies are insufficient to fully capture their characteristics. Moreover, given downbursts’ strong locality and the variability in their wind fields across regions, a universal downburst wind field model has yet to be established. Additionally, most current structural design wind speed specifications are based on large-scale, steady strong winds, rarely considering the impact of extreme downburst winds, leading to potentially unsafe structural designs. Therefore, with the increasing availability of measured data, it is urgent to conduct comparative studies on the wind field characteristics of downbursts in multiple countries and regions to establish a universal downburst wind field model.

2.2. Wind Tunnel Experimental Studies

Due to the limited availability of field measurement data and the high time cost associated with downbursts, scholars worldwide have begun utilizing wind tunnel experiments to simulate downbursts and investigate their wind field characteristics. In 1957, Bakke [42] initiated experimental studies on wall jets, primarily focusing on average velocity distributions and growth rates. Subsequently, Poreh [43] further examined the turbulence intensity of wall jets. Lin et al. [44,45] employed wall jets to study the transient characteristics of downbursts, exploring the wind profile properties near the ground through two-dimensional wall jet simulations. Zhong et al. [46,47] used a wall jet apparatus to simulate near-ground downburst wind fields, validating its feasibility. However, because wall jets cannot fully replicate downbursts, researchers developed impinging jet devices to more accurately simulate their formation. Holmes [48] and Cassar [49] conducted physical experiments on downbursts using impinging jet devices, verifying the feasibility of this simulation. Building upon this, Wood [50] studied the wind speed characteristics of downburst wind fields over flat and sloping terrain through a static continuous impinging jet apparatus and established empirical formulas for vertical wind speed profiles based on experimental data. In 2008, Xu and Hangan [51] used an impinging jet test device to study the effects of Reynolds number, scale ratio (H/D ratio), initial outflow parameters, and surface roughness on downburst wind fields. They found that higher Reynolds numbers and smaller H/D ratios increased peak wind velocities, while initial outflow parameters like turbulence intensity affected wind speed distributions. Increased surface roughness raised boundary layer height but decreased maximum wind velocity. In 2009, Xu et al. [52] designed a dedicated impinging jet device for simulating downburst wind fields, investigating parameters such as impinging wind intensity, diameter, jet height, and incidence angle. They discovered that jet height and incidence angle were critical factors affecting impinging wind field characteristics. Subsequently, Li et al. [53] studied how jet incidence angle affects downburst wind fields, finding that a higher angle increases turbulence intensity behind the jet and decreases it in front while also increasing positive pressure below the outflow.

In 2012, Duan et al. [54] used adjustable deflectors, baffles, and roughness elements within an atmospheric boundary layer wind tunnel to simulate downburst wind fields at 1:300 and 1:600 scale ratios. Building upon this, Richter [55] further considered background wind factors in impinging jet experiments, studying the impact of boundary layer winds on downburst wind fields. In 2018, Junayed [56] simulated downbursts using the impinging jet facility at the Wind Engineering, Energy, and Environment (WinDEEE) Dome Laboratory at the University of Western Ontario, investigating the mean and fluctuating wind characteristics of stationary downbursts. Additionally, Zhao et al. [57] employed an actively controlled wind tunnel to simulate the sudden wind speed changes associated with thunderstorm downbursts, enabling the exploration of the aerodynamic properties of structures under such conditions. Yuan et al. [58] designed an actively controlled multi-blade system for simulating downburst wind fields, analyzing characteristics such as mean wind speed and turbulence intensity. They found that blade angle, rotation speed, and arrangement position were key factors influencing wind field properties.

However, the aforementioned studies were all conducted based on stationary downburst wind fields, whereas in real-world environments, downbursts exhibit continuous movement. In light of this, scholars have begun to research moving downbursts. As early as 2002, Chay and Letchford [59,60] used a moving wall jet apparatus to investigate wind pressure characteristics on the surface of a cube under thunderstorm winds, discovering significant differences in pressure distributions compared to uniform and boundary layer flows. Chen et al. [61] designed a jet device with a movable plate to simulate moving downburst wind fields, studying the effects of jet wind speed, nozzle height, and moving speed on storm wind speed distributions. Wang et al. [62] conducted wind tunnel tests using a movable nozzle-based jet impingement device and discovered that mobility significantly enhances the peak wind speed of downbursts while the jet velocity exerts minimal influence on the peak wind speed. Zhang et al. [63] simulated the wind field of moving downbursts using the jet impingement facility at Beijing Jiaotong University (Figure 6), based on which they investigated the wind pressure characteristics on the surface of a light steel industrial building model under various offset distances and moving speeds.

In summary, domestic and foreign researchers have conducted preliminary investigations into the characteristics of downburst wind fields based on wind tunnel experiments such as wall jets and impulse jets. While the existing results provide an essential data foundation for downburst research, discrepancies persist between wind tunnel simulation outcomes and natural downburst phenomena due to limitations in experimental conditions, including constraints on a model scale and simplifications of boundary conditions. Hence, there is an urgent need to further optimize experimental setups and methods, coupled with field measurement data, in order to establish a more precise and universally applicable physical model of downbursts.

2.3. Theoretical Analysis and Research

Currently, numerous scholars worldwide have conducted a series of theoretical studies on downbursts based on field measurement data and wind tunnel experimental data, proposing several downburst wind profile models. In 1988, Oseguera and Bowles [64] established an analytical model (the OB model) for the vertical and radial mean wind profiles of downbursts:

$$V\left(z\right)=\left({\displaystyle \frac{\lambda R^2}{2r}}\right)\left[1-e^{-{\left(r/R\right)}^2}\right]\left(e^{-z/z\ast }-e^{-z/\epsilon }\right)$$

(1)

where $V\left(z\right)$ represents the horizontal wind speed at height z, R is the characteristic radius of the downburst, r is the distance from the storm center, $\epsilon$ are characteristic heights within and outside the boundary layer, respectively, and $z^{\ast }$ is a proportional parameter. $\lambda$ is a scale parameter.

Vicroy [65] refined the OB model by proposing a simplified analytical model (the OBV model) for downbursts:

$${\displaystyle \frac{V\left(z\right)}{V_{\max }}}=1.22\times \left[\exp \left(c_1{\displaystyle \frac{z}{z_{\max }}}\right)-\exp \left(c_2{\displaystyle \frac{z}{z_{\max }}}\right)\right]$$

(2)

where $V\left(z\right)$ represents the horizontal wind speed at height z; $z_{\max }$ denotes the maximum horizontal wind speed and $V_{\max }$ is the height at which this maximum horizontal wind speed occurs. $c_1,c_2$ is the shape function parameter of the downburst wind field. $c_1=-0.15,c_2=-3.2175$.

In 1998, Wood [66] presented an empirical model for the vertical wind profile of horizontal wind speeds during the fully developed stage of a downburst:

$${\displaystyle \frac{V\left(z\right)}{V_{\max }}}=1.55{\left({\displaystyle \frac{z}{\delta }}\right)}^{1/6}\left[1-\mathrm{erf}\left(0.70{\displaystyle \frac{z}{\delta }}\right)\right]$$

(3)

where $V\left(z\right)$ represents the horizontal wind speed at height z, $z_{\max }$ denotes the maximum horizontal wind speed, $\delta$ is the height at which the maximum horizontal wind speed occurs, $\mathrm{erf}$ is the height parameter, and f represents the error function.

In 2000, Holmes and Oliver [2] proposed a model for horizontal wind speeds in a moving downburst:

$$V_{\mathrm{r}}\left(r\right)=\{\begin{array}{l}{V}_{r,\max }\times \left(r/r_{\max }\right),0\le r<r_{\max }\\ V_{r,\max }\times \exp \left(-{\left[\left(r-r_{\max }\right)/R\right]}^2\right),r\ge r_{\max }\end{array}$$

(4)

where $r_{\max }$ represents the radial distance between the location of maximum wind speed and the center of the thunderstorm, $V_{r,\max }$ denotes the maximum radial horizontal wind speed, and $R$ is the radial scale. Figure 7 shows a comparison of typical downburst wind profiles.

In 2009, Qu et al. [67] improved upon the OBV model by considering the movement of the storm center and variations in the intensity of the downdraft, resulting in a model that provides a more accurate description of the actual physical processes of downbursts.

2.4. Numerical Simulation Studies

With advancements in computer technology, Computational Fluid Dynamics (CFD) has emerged as one of the effective methods in structural wind engineering research (Figure 8). Compared to wind tunnel experiments, numerical simulation techniques are more time-efficient and economical. When investigating stationary downbursts, scholars worldwide have employed various turbulence models to analyze the influence of different parameters on the characteristics of downburst wind fields.

Table 2. Typical mean turbulence intensity in different countries.

Author	Country	Mean Turbulence Intensity	Comments/Findings
Solari et al. [24,25,26]	Italy	0.12	The study indicates that the turbulence intensity of thunderstorm outflows is relatively low and shows little variation compared to classical weather events.
Liu et al. [39,40]	China	0.15–0.25	The study shows that as height increases, the turbulence intensity of downbursts decreases, with significant fluctuations in the longitudinal and lateral turbulence intensities.
Lombardo et al. [21]	USA	0.129	The study reveals that the turbulence intensity in thunderstorm events is lower than in traditional weather events, and varying averaging methods may lead to different results, posing challenges for building code design.
Choi [4,5]	Singapore	0.34–0.38	The study shows that the gust factors and turbulence intensities during tropical thunderstorms are much higher than under non-thunderstorm conditions, which is critical for wind load design standards in Singapore.

summarizes the mean turbulence intensity of typical downbursts in various countries.

Selvam and Holmes [68] explored the feasibility of the k-ε turbulence model in simulating two-dimensional axisymmetric impinging jet wind fields. Sengupta and Sarkar [69] simulated impinging jet wind fields using diverse turbulence models, revealing that the realizable k-ε, Large Eddy Simulation (LES), and Reynolds Stress Model (RSM) provided results that better aligned with experimental data. Based on this, Chay [70] used the RNG k-ε model and ARMA to simulate downburst wind fields, aligning with Fujita’s measurements. While effective for simple flows, the k-ε model has limitations with complex wind fields. Consequently, Abd-Elaal et al. [71] employed Detached Eddy Simulation (DES) to investigate the unsteady wind field of downbursts. Their results demonstrated the successful simulation of vortex ring sequences in downbursts, validating DES’s effectiveness in vortex ring modeling. However, DES simulations are computationally expensive, inefficient, and suitable only for smaller Reynolds number jet wind fields. In light of this, Aboshosha and Bitsuamlak [72] utilized LES to explore downburst wind field characteristics under various terrain conditions, providing insights into turbulence characteristics and demonstrating LES’s accuracy and efficiency in complex wind field calculations. Qu Weilian and Ji Baifeng [73] applied various turbulence models to investigate the vertical wind speed profiles of downbursts and their temporal variations, gaining insights into downburst behavior patterns at different stages. Lou Wenjuan et al. [74] proposed a three-dimensional fluctuating wind field simulation method for downbursts using an improved auto-power spectrum function, stochastic flow field generation techniques, and Fast Fourier Transform (FFT), offering a novel technical approach to wind field modeling.

As numerical simulation methods evolve, scholars have begun studying moving downburst wind fields. Orf et al. [75] developed five cold source models to investigate the impact of storm movement speed on downburst wind fields. Their results indicated that storm movement causes asymmetry in the wind field structure and a significant increase in near-ground horizontal wind speed peaks. Mason et al. [76] studied the acceleration effects of storm movement under varying wind field parameters and terrain conditions, further emphasizing the importance of storm dynamics on wind fields. Sengupta et al. [77] analyzed the wind load characteristics of small cubes under different storm movement speeds using LES simulations based on the impinging jet model. They found that drag coefficients varied with storm movement speed effects. Li Jinhua et al. [78] developed a Time-Aggregated Random (TAR) model to simulate non-stationary fluctuating wind speeds in downbursts. Their simulations showed that wind speed correlations weaken with distance and amplitudes increase with rising mean wind speeds, aligning with real wind field characteristics and proving the model’s effectiveness. Fang Zhiyuan et al. [79] numerically modeled moving downbursts and validated the model’s reliability by comparing it with impinging jet test results. They discovered that storm movement significantly influenced wind field velocity and direction, emphasizing the complexity of simulating moving downbursts. Additionally, Mekdes [80] employed a structural smart monitoring system to investigate the dynamic response of structures subjected to downbursts.

In summary, numerous scholars have conducted numerical simulations of downburst wind fields using various turbulence models. The results indicate that DES and LES methods are highly accurate and effective in simulating vortex rings and wind field turbulence characteristics, particularly when considering unsteady downburst wind fields. However, due to the high computational demands and sensitivity to boundary conditions, extreme wind field simulations are still not sufficiently precise. Therefore, there is an urgent need to improve turbulence models and simulation methods to enhance simulation efficiency and accuracy.

Table 3. Research methods and main findings of wind field characteristics of downbursts.

Research Method	Method Overview	Main Findings	Advantages and Disadvantages
Field Measurements [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]	Recording downburst events using Doppler radar and anemometers.	Proposed an automatic identification method for extreme winds like downbursts, explored the vertical and horizontal structure of the wind field.	Can reflect the characteristics of the downburst wind field more accurately and reliably; however, it is limited by the randomness of the events and the limitations of the data.
Wind Tunnel Experiments [37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58]	Simulating near-ground wind fields of downbursts using wall jet and impinging jet devices.	Revealed the velocity distribution of the downburst wind field under different parameters.	Can repeat experiments in a controlled environment, providing detailed wind field data; however, the simulation conditions are limited, making it difficult to fully reproduce the natural conditions of downbursts.
Theoretical Analysis [59,60,61,62]	Using various mathematical and physical methods to propose different wind speed profile models.	Established analytical models of the downburst wind field.	Can provide preliminary predictions and understanding of complex wind field phenomena; however, the simplified models may deviate from actual conditions.
Numerical Simulation [63,64,65,66,67,68,69,70,71,72,73,74]	Using CFD technology to simulate downburst wind fields.	Clarified the vortex structure and turbulence characteristics of downbursts.	Can handle complex boundary conditions and nonlinear flows with low computational cost; however, the research results depend on the accuracy of the simulations.

systematically summarizes the research methods and main findings related to downburst wind field characteristics.

3. Effects of Wind on Transmission Line-Tower Systems under Downbursts

Given the unique wind field characteristics of downbursts, damage incidents involving transmission line-tower systems under their influence occur frequently. To investigate the underlying mechanisms and mitigate disasters, scholars worldwide have conducted a series of research endeavors. Savory [81] studied the response of transmission line-tower systems under downbursts, finding that self-supporting towers with larger spans are more prone to failure than tension-type towers. Shehata and Damatty [82,83] used finite element models to predict the behavior of these systems under downbursts, showing that the models handle nonlinear deformations and complex interactions effectively. They also found that structural performance is influenced by jet diameter and the relative position of the storm center to the tower (Figure 9). Building on this, Damatty et al. [84,85,86] developed simplified models, identifying the ratio of transmission line span to jet diameter as a key factor affecting critical loads, offering guidance for wind-resistant design. Yang et al. [87] found that inland areas experience relatively uniform wind load distributions on transmission line-tower systems, impacting overall tower force and deformation with minimal local damage, while coastal regions face drastic wind speed variations, leading to localized stress concentrations and potential damage. The findings of these studies are summarized in

Table 4. Summary of structural analysis of different transmission Tower types under downbursts.

Authors	Transmission Tower Type	Finite Element Model/Methods	Conclusions
Savory et al. [81]	Lattice transmission tower.	Dynamic structural analysis with ABAQUS to model wind loading.	microbursts have less impact due to lower intensity.
Shehata et al. [82,83]	Tangent suspension tower (Manitoba).	3D linear elastic frame elements for towers; 2D curved beam elements with non-linearity for conductors.	HIW, such as downbursts, significantly affect transmission towers and should be included in design codes, especially with non-linear effects of conductors.
Damatty et al. [84]	Various transmission towers.	Simplified procedure for estimating longitudinal forces using parametric study and interpolation.	Developed a practical method to estimate the maximum longitudinal force on transmission towers due to downbursts, accounting for variations in the size and location of the downburst.
Damatty et al. [85,86]	Various transmission towers.	New technique for analyzing multi-spanned conductors under HIW.	Proposed a technique significantly faster than FEA, with only minor discrepancies in displacement and reactions, making it highly efficient for parametric studies.
Yang et al. [87]	110 kV inland transmission tower and 500 kV coastal transmission tower.	Elastic beam and link elements, ANSYS software for structural analysis.	The study found that inland towers face higher wind loads under downburst conditions compared to normal wind, leading to potential failure in upper sections of the tower.

below.

However, previous studies did not consider storm movement. Darwish [88,89] incorporated storm movement characteristics into the analysis using the vector superposition method. He also combined turbulence signals with mean wind speed components from CFD analysis, finding that downbursts cause significant tower displacement but do not greatly alter vibration frequency or amplitude. Aboshosha and Damatty [90] used dynamic and pseudo-static analysis methods, concluding that the pseudo-static approach is more reasonable for high wind speed effects. Elawadyd et al. [91] validated the pseudo-static method through wind tunnel tests, while Wu et al. [92] found that moving downbursts lead to unbalanced wind loads on transmission line-tower systems

Wang et al. [93] developed a thunderstorm-induced impulsive wind load model for transmission line-tower systems through the Fast Fourier Transform (FFT) algorithm and the harmonic superposition method, which effectively simulates the horizontal fluctuating wind speed of impulsive winds. Wang et al. [94] explored the failure modes of transmission line towers by establishing a finite element model. Li et al. [95] summarized the research progress in analytical models, dynamic characteristics, and wind-induced dynamic responses of transmission line-tower systems, emphasizing the wind effects under downbursts and highlighting their sensitivity and vulnerability to such extreme weather conditions. Qu et al. [96] discussed the disastrous effects and failure characteristics of downbursts on transmission line towers, as well as their implications for wind-resistant design. Liu et al. [97] discovered that compared to conventional Class B wind fields, the displacement responses of transmission line towers induced by thunderstorm impulsive winds are more significant under equivalent wind speeds. Wei [98] investigated the collapse moment of transmission line towers under downbursts using the energy method and analyzed the failure mechanism by studying the stress and spatial distribution of plastically deformed members. Bi [99] focused on analyzing the local large deformations and collapse processes of transmission line towers under downbursts, with particular attention to axial strains and displacement jumps at the intersections between the upper-middle and lower-middle sections of the towers, as well as their failure characteristics and weak points. Wang et al. [100,101,102] studied the wind-induced vibration responses of transmission line towers in both frequency and time domains under three conditions: average wind, static force, and transient dynamics of moving downbursts. They preliminarily revealed the distribution of equivalent static wind loads for the fluctuating wind-induced vibration responses under the most unfavorable wind profiles, providing an effective exploration method for assessing design wind loads on transmission line towers under downbursts.

In summary, although preliminary studies have been conducted by domestic and international scholars on the wind effects of transmission line towers under downbursts, the current research mainly concentrates on static wind load effects. Comparatively, research on the complex dynamic responses, particularly nonlinear dynamic effects, induced by downbursts is relatively scarce. Furthermore, existing wind tunnel tests are mostly based on simplified open and flat terrains, neglecting the influence of complex terrains such as mountains and valleys on the wind effects of transmission line towers. Therefore, there is an urgent need to conduct research on the complex dynamic responses induced by downbursts and the wind effects of transmission line towers under other terrain conditions.

4. Effects of Wind on Building Roofs under Downbursts

Downbursts exert significant pressure on building roofs due to their intense winds, particularly posing risks of deformation or direct damage to the roof structures of low-rise buildings. Consequently, scholars have conducted a series of studies on common low-rise buildings with flat and pitched roofs, focusing primarily on wind load distribution, flow field characteristics, dynamic responses, and structural stability in downburst environments. These efforts aim to provide theoretical foundations and technical support for the wind-resistant design of low-rise buildings. Figure 10 is a wind pressure cloud diagram for a factory building with a sloping roof. Matthew [103], Jubayer [104], Chen [105], Chen Bo [106], Asano [107], and others have investigated the wind pressure characteristics of flat roofs under downbursts, discovering that wind pressure distribution is closely related to the distance from the downburst center. As the building structure moves away from the downburst center, the pressure on the roof gradually shifts from positive to negative. Furthermore, wind pressure distribution is influenced not only by the relative position of the building structure and the downburst center but also significantly by the building orientation. In downburst wind fields, the wind pressure distribution pattern on flat roofs differs markedly from that in atmospheric boundary layer wind fields, exhibiting stronger local effects and more complex time-varying characteristics.

Zhang [108], Jesson [109], Wang Zhisong [110,111], Ji Baifeng [112], and others have comprehensively analyzed the wind pressure distribution characteristics on the surfaces of pitched roof buildings, considering factors such as roof pitch, the distance between the building and the thunderstorm center, and wind direction angle. Their results indicate that the wind pressure distribution of buildings is closely related to their distance from the thunderstorm center. Notably, when a building is located within the diameter of the thunderstorm center, its windward side experiences positive wind pressure, while the roof, leeward side, and sides are subjected to negative pressure. As the building moves away from the thunderstorm center, the positive pressure on the windward side gradually decreases, while the negative pressure on the leeward and side surfaces first increases and then decreases. Additionally, roof pitch significantly affects the distribution of wind pressure coefficients, with steeper roofs generating higher positive pressures on the windward side and larger negative pressures on the leeward side and other areas. Variations in wind direction angle further exacerbate the asymmetry in wind pressure distribution between the windward and leeward sides, thereby influencing the overall wind load characteristics of the building.

Table 5. Summary of effects of wind on typical low-rise buildings under downburst conditions.

Researcher	Roof Type	Research Content	Main Conclusions
Matthew [103]	Flat Roof	Studied the wind load characteristics on low-rise buildings under downburst using LES technology.	Transient lift and drag coefficients are significantly affected during downburst events; flow field characteristics such as circulation and separation vortices have an important impact on building surface wind pressure.
Jubayer [104]	Flat Roof	Studied the wind pressure distributions on a low-rise building in a laboratory-simulated downburst.	The maximum pulsating wind pressure occurred at the foot of the roof.
Chen Yong [105]	Flat Roof	Studied the dynamic response of flat roofs under moving downbursts using the DSHM model combined with CFD technology.	Wind pressure coefficient decreases with increasing jet velocity and increases with the first natural frequency of the roof.
Chen Bo [106]	Flat Roof	Studied the wind load distribution on flat roofs under downburst using CFD numerical simulation technology.	The wind pressure distribution on flat roofs is closely related to the distance from the downburst center; as the distance increases, the roof pressure changes from positive to negative.
Asano [107]	Flat Roof	studied the wind pressure distribution characteristics of low-rise buildings Using a moving downburst simulator.	Pulsed jet with or without moving produces larger negative pressures onthe roof and larger positive pressures on the wall than the turbulentboundary layer.
Zhang [108]	Sloped Roof	Simulated downburst using an impinging jet device to study the wind load characteristics on low-rise buildings with different geometries.	Low-slope double-pitched and conical roofs generate higher lift in the downburst center area compared to flat roofs and high-slope double-pitched roofs.
Jesson [109]	Sloped Roof	Studied the wind pressure coefficients on low-rise buildings with different wall heights under downburst using a transient wind simulator.	Low-rise buildings under downburst experience positive pressure on the windward side and suction on the roof, leeward side, and sides; the wind pressure distribution is closely related to building height and wind direction.
Wang Zhisong [110,111]	Sloped Roof	Simulated downburst using an impinging jet device to study the wind pressure distribution on low-rise buildings at different radial distances and other parameters.	When the building’s radial distance is greater than the nozzle diameter, the roof wind pressure decreases with increasing radial distance; the absolute values of wind pressure on the leeward and side surfaces first increase and then decrease with increasing radial distance.
Ji Bofeng [112]	Sloped Roof	Studied the effect of different radial distances and wind directions on the surface wind pressure of double-pitched roofs through wind tunnel experiments.	When the roof slope is large, significant positive pressure is generated on the windward side, while the leeward side and other areas experience greater negative pressure; changes in wind direction further increase the uneven distribution of wind pressure.

summarizes the key findings of these studies.

Among low-rise buildings, large-span roofs have become a primary research focus in structural wind engineering due to their lightweight, large scale, and high sensitivity to wind loads. Figure 11 is the wind pressure cloud map of the large-span cable dome structure. Xie Zhuangning et al. [113] studied the surface wind pressure distribution patterns of large-span flat roofs under downbursts and found that the roof surface is primarily subjected to negative pressure, with the highest pressures occurring near the windward eaves and ridges. This research provides an essential reference for designing large-span flat roofs to resist downbursts. Pan Feng et al. [114] investigated the multi-modal random wind-induced responses of large-span roofs under downbursts, revealing the non-uniform distribution of load wind effect coefficients and displacement wind effect coefficients on the roofs, offering insights for assessing the impact wind-induced responses of large-span roofs. Wang Zhisong et al. [115] analyzed the variation patterns of extreme wind pressures on large-span roofs at different radial distances through rigid model wind tunnel pressure tests. Additionally, scholars have conducted studies on other types of large-span roofs. Chen Yong et al. [116,117] studied wind pressure on large-span roofs with varying rise-to-span ratios under downbursts, finding that larger ratios increase positive pressure on the windward side and negative pressure on the leeward side, raising the risk of roof damage. Yao Yong et al. [118] found that in flat terrain, wind pressure on double-sided spherical roofs shifts from positive to negative with distance, while in sloping terrain, it remains negative and increases. Chu Yunpeng et al. [119,120] discovered that wind pressures on circular and diamond-shaped roofs initially increase and then decrease with radial distance, with diamond-shaped roofs experiencing slightly higher pressures due to their shape.

In summary, scholars, both domestically and internationally, have conducted partial research on the effects of wind on low-rise building roofs under downburst conditions through various methods such as wind tunnel tests and numerical simulations. This research primarily focuses on different types of common roof structures, including flat roofs and gable roofs, while considering multiple factors such as building shapes, roof slopes, distances between buildings and the thunderstorm center, and the incoming flow direction. Preliminary analyses have been performed on characteristics such as wind pressure distribution and dynamic responses on roof surfaces. However, due to the non-stationarity of downburst wind fields and the diversity of roof structures, there is an urgent need to develop more accurate wind load models and design methodologies to ensure the safety and reliability of roof structures under extreme wind loads.

5. Wind Effect of Tall Buildings under Downburst

Scholars from both domestic and international communities have conducted in-depth investigations into the impacts of downbursts on tall buildings through wind tunnel tests, numerical simulations, and other methodologies, with a particular emphasis on dynamic response and wind pressure distribution characteristics.

In terms of dynamic response, Chen et al. [121] examined the along-wind response of tall buildings during thunderstorms, introducing the concept of Maximum Dynamic Magnification Factor, which they found crucial for measuring and comparing the effects of non-stationary downbursts on tall buildings. Following this, Kim et al. [122,123] further compared the impacts of downbursts with those of large-scale, steady strong winds on tall buildings, revealing that downbursts induce more intense wind-induced vibration responses, manifesting as pronounced vibrations and displacement variations in structures. Le et al. [124,125] employed the wavelet-Galerkin method to investigate the coupled dynamic response of tall buildings subjected to thunderstorm winds, developing a computational model capable of simulating the instantaneous dynamic response of tall buildings during thunderstorms.

Regarding wind pressure distribution, Ji et al. [126] analyzed and found that downbursts significantly increase wind pressure on the surfaces of tall buildings, particularly on the windward and lateral faces, while also revealing the non-uniformity of wind pressure distribution. Wang and their colleagues [127,128] first simulated downburst wind fields using a stationary impulsive jet apparatus, analyzing the time-domain and frequency-domain responses of local and overall wind loads on tall buildings at different radial positions. Subsequently, incorporating the factor of hillside terrain, they unveiled the influence patterns of slope angles on the temporal variation of building drag coefficients. Huang et al. [129] analyzed the effects of both stationary and moving downbursts on tall buildings through numerical simulations, discovering that while moving downbursts enhance wind speeds in the direction of motion, they reduce speeds in the opposite direction, significantly increasing the wind pressure coefficients on building surfaces. Fang et al. [130], by conducting pressure measurement tests on tall building models with varying depth-to-width ratios under thunderstorm impulsive winds, analyzed the amplitude characteristics of wind pressure under downbursts. They found that the wind pressure on the windward, lateral, and leeward faces of buildings is significantly influenced by depth-to-width ratio and radial position and differs markedly from that induced by large-scale, steady strong winds. Consequently, they recommended appropriately amplifying wind load values in certain areas during the design of tall buildings, considering thunderstorm-impulsive winds. Additionally, Miguel [131] analyzed wind loads on tall buildings subjected to downbursts, combining experimental and theoretical studies to propose a risk assessment method for tall buildings.

In summary, existing research has confirmed that downbursts significantly increase wind loads on the windward and lateral faces of tall buildings and has preliminarily established models for wind pressure distribution and wind-induced structural responses. However, due to the non-stationarity of downburst wind fields and the wind pressure interference between building groups, current studies are still limited in their ability to deeply analyze the interference effects and the influence laws of complex terrain on building wind effects. Therefore, further exploration of the effects of wind on tall buildings in different downburst scenarios and complex environments is needed to enhance the adaptability and safety of building designs.

6. Wind Effect of Other Structures under Downburst

In addition to the extensive analysis of conventional structures mentioned above, scholars have also conducted relevant research on other specialized structures. Wang et al. [132] proposed an improved hybrid model and verified its capability to predict the influence of downbursts on lattice tower structures with relatively high accuracy. Li et al. [133], through numerical simulations and wind tunnel tests, revealed the structural response characteristics of an extra-large cooling tower under downburst conditions, clarified failure modes under various wind speeds and directions, and analyzed its collapse mechanism and failure criteria. Han et al. [134] employed both experiments and numerical simulations to investigate the impact of downbursts on the wind pressure distribution around cooling towers, discovering an instantaneous maximum wind pressure in the windward region at the lower area of the tower, which is 65.22% higher than that under normal wind conditions.

Liu et al. [135], based on field measurement data, analyzed the characteristics of downburst wind fields and their influence on wind loads on tall masts, finding that the peak wind pressure on the windward side of tall structures was significantly higher than observations under normal wind conditions. Yang and Li et al. [136] utilized wind tunnel tests to assess the safety performance of high-speed trains operating under downburst conditions, discovering that track types and jet angles significantly impacted the aerodynamic wind loads on trains.

In summary, existing research has preliminarily analyzed the wind effects of various specialized structures, including cooling towers and masts, under downburst conditions. However, due to the complexity of downburst wind fields, which exhibit non-stationary and non-Gaussian characteristics, further research is urgently needed with the establishment of high-precision wind field models. This research should deepen the understanding of wind loads and wind-induced vibration responses of specialized structures under complex wind field conditions, develop more accurate wind-resistant design and optimization methods, and ultimately enhance the safety and reliability of structures under extreme wind conditions.

7. Conclusions and Recommendations for Future Work

As extreme wind with short duration, high impulsiveness, and peak wind speeds, downbursts pose significant threats to the safety of building structures. Currently, the influence of downburst wind loads is not considered in domestic building structural design codes, leading to potentially dangerous design outcomes. Therefore, intensifying research into the characteristics of downburst wind fields and the effects of wind on building structures under their influence is crucial for enhancing the wind resistance reliability of building structures. Further research in the future can apply deep learning and intelligent optimization technology to the field of extreme winds to explore its wind field characteristics.

Scholars worldwide have conducted preliminary studies on the characteristics of downburst wind fields using field measurements, wind tunnel tests, and numerical simulations. However, there are still limitations in understanding the similarities and differences in wind field characteristics across different regions, the non-stationary features of wind fields, and the analysis of downbursts under complex terrain conditions. Therefore, it is necessary to apply intelligent technology to the traditional wind engineering field, conduct corresponding statistical modeling, realize intelligent identification of multiple types of storms, and establish a more accurate and universal theoretical model of the downburst wind field.

Research on the wind effects of transmission line towers and various roof structures under downburst conditions has revealed the complexity and diversity of their wind loads. Nevertheless, most studies focus on the impact of stationary downbursts, with relatively few investigations into the wind pressure characteristics under moving downbursts, particularly in flat terrain. Consequently, there is an urgent need for more in-depth research into the dynamic behavior of roofs and transmission lines-towers under extreme wind conditions and in complex terrain. The adoption of intelligent structural health monitoring methods can be utilized to ascertain their damage conditions, thereby enhancing their wind resistance capabilities.

Although progress has been made in studying the wind effects caused by downbursts on high-rise buildings and other building structures, research on the interference effects between adjacent buildings and the coupling of wind and structures remains inadequate. Therefore, it is necessary to comprehensively consider these factors and conduct more comprehensive wind resistance research. Dynamic tracking of wind resistance of building structures is carried out, and then big data analysis is performed to achieve wind effect prediction, so as to improve the overall safety and sustainability of building structures.