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Tropical cyclones, characterized by their destructive effects, pose significant threats to coastal regions worldwide. This review provides a comprehensive exploration of tropical cyclones, delving into their definitions, regional variations in nomenclature (hurricanes, typhoons, and tropical cyclones), and categorization based on intensity and core structural elements such as the eye, eyewall, and rainbands. Globally, the review meticulously analyzes the profound impacts of tropical cyclones, spanning environmental, social, and economic dimensions, and highlights the disproportionate vulnerability of coastal populations. A thorough literature review summarizes models, exploring their evolution and effectiveness in predicting cyclone behavior and impacts. Additionally, the review discusses emerging advancements in modeling techniques, including numerical simulations and machine learning algorithms, and their potential to enhance forecasting accuracy and risk assessment. Concluding with a critical discussion of current challenges, such as data limitations, model uncertainties, and the influence of climate change, the review underscores the pressing need for interdisciplinary collaborations and innovative solutions to mitigate the increasing risks posed by tropical cyclones in a changing climate.
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Yu Chang*
Institute for Ocean Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
Kam Tim Tse*
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
*Address all correspondence to: changy23@mails.tsinghua.edu.cn and timkttse@ust.hk
1. Introduction
A tropical cyclone serves as the overarching descriptor for a non-frontal synoptic scale low-pressure system situated over tropical or sub-tropical waters. These meteorological phenomena exhibit organized convection, characterized by intense thunderstorm activity, and feature a well-defined cyclonic surface wind circulation [1]. The term “tropical cyclone” encompasses a broad category of weather systems, ranging from tropical depressions to hurricanes, and is rooted in the unique atmospheric conditions prevalent in tropical and sub-tropical regions. These dynamic and often powerful systems derive their energy from warm ocean waters, manifesting in a symphony of atmospheric processes that include convective updrafts, latent heat release, and the characteristic rotation around a central low-pressure center. During their formative stage, tropical cyclones characterized by maximum winds of 17 m/s or lower are referred to as tropical depressions. As these weather systems intensify and attain wind speeds ranging from 18 to 32 m/s, they are designated as tropical storms. Notably, tropical cyclones with maximum winds reaching 33 m/s or higher assume distinct regional names [2].
The terms “hurricane” and “typhoon” are regionally specific names for strong tropical cyclones. When tropical cyclones attain maximum sustained surface winds of 33 m/s (64 kt, 74 mph), they are designated by different names depending on their geographical location. In the North Atlantic Ocean, the Northeast Pacific Ocean east of the dateline, or the South Pacific Ocean east of 160E, these weather systems are termed “hurricanes.” Across the Northwest Pacific Ocean west of the dateline, they adopt the name “typhoon.” In the Southwest Indian Ocean, the term “tropical cyclone” is employed to describe storms with winds reaching this threshold. This regional nomenclature reflects the diverse terminology used globally to identify and characterize these meteorological phenomena based on their specific locations and the associated oceanic regions [3].
Tropical cyclones can exhibit considerable variability in their intensity, size, boundary layer composition, spiral band formation, eye characteristics, and overall symmetry, both between individual storms and over time [4]. The structure of a tropical cyclone is characterized by distinct features that contribute to its intensity and behavior. It can be visualized as a layered system of clouds and weather patterns.
Figure 1 illustrates a top view and a cross-section view of a TC. The size of the storm can be as wide as 300 km [5]. At the center of a mature TC lies the eye, where air descends instead of rising. This descending air suppresses cloud formation, resulting in a clear and calm center. The eye is typically circular and can range in diameter from 30 to 65 kilometers (19–40 miles), although sizes as small as 3 kilometers (1.9 miles) and as large as 370 kilometers (230 miles) have been observed [6, 7].
Surrounding the eye is the “eyewall,” which forms the cloudy outer edge and expands outward with height. Within the eyewall, the most intense weather conditions occur. This includes the highest wind speeds, most rapid ascent of air, tallest clouds, and heaviest precipitation. Consequently, the heaviest wind damage typically occurs when the eyewall of a tropical cyclone makes landfall. This is where most of the convection, or upward movement of air, takes place. This scattering is likely caused by sea spray generated by turbulent ocean surface waves due to strong winds. The eyewall can vary in steepness, ranging from almost vertical to about 45 degrees. Beyond the eyewall is a region with reduced precipitation surrounded by another ring of heavy rain known as the “outer eyewall.” This layered structure of clouds and precipitation patterns defines the complex and dynamic nature of a tropical cyclone.
Associated winds in tropical cyclones exhibit distinct patterns and characteristics essential for understanding the dynamics of TCs. Typically, the strongest winds are located slightly outside the eye wall base, where they circulate counterclockwise in the northern hemisphere. This region, known as the radius of maximum winds, experiences the highest wind speeds, which can extend up to 100 kilometers from the center of the storm. Observations from drop-sondes reveal variations in wind profiles, with maximum wind speeds observed near the eyewall at altitudes ranging from 300 to 800 meters above the ground. However, in storms with larger radii, the strongest winds may occur at higher altitudes, typically between 1 and 2 kilometers above the ground [8].
Rainbands are important weather patterns in TCs, usually arranged in bands around the center of the storm. These rainbands often hover into the center of the storm, and the rainbands are sometimes stationary relative to the center, while in other cases they rotate around the center. This rotating band of clouds is usually associated with a pronounced swing in the storm’s path. When a TC makes landfall, surface friction increases, leading to increased convergence of airflow toward the eyewall and the vertical movement of air that occurs there. The increased convergence and rise of moist air lead to the heavy rainfall associated with TCs, which can reach extremely high levels in a short period of time.
Tropical cyclones are classified into categories to communicate their potential impact and guide preparedness efforts. Tropical cyclones are categorized based on the wind speeds surrounding their circulation center and are assigned a rank. The choice of scale for a specific cyclone depends on its location, such as the utilization of the Saffir–Simpson hurricane wind scale and Australian tropical cyclone intensity scales in the western hemisphere. Regardless of the scale, all of them evaluate tropical cyclones by their maximum sustained winds. These winds are determined through observation, measurement, or estimation using various techniques, typically over a time span ranging from 1 to 10 minutes.
2.1 Saffir-Simpson hurricane wind scale (SSHWS)
The Saffir-Simpson Hurricane Wind Scale, originally developed by wind engineer Herb Saffir and meteorologist Bob Simpson, emerges as a pivotal tool for alerting the public about the possible impacts of various intensity hurricanes. The maximum sustained surface wind speed (peak 1-minute wind at the standard meteorological observation height of 10 m over unobstructed exposure) associated with the cyclone is the determining factor in the scale that determines a hurricane’s category. With its five categories ranging from the minimal impact of Category 1 to the catastrophic consequences of Category 5, the scale provides a clear and concise framework for evaluating the severity of an impending storm. Categories 3–5 are specifically designated as major hurricanes. This widely recognized global system establishes a practical guide for meteorologists, emergency officials, and the public, offering a standardized means of communication crucial for effective preparedness, response, and mitigation efforts. The scale does not include hurricane-related damage from storm surges, rainfall-induced floods, etc. It should also be noted that the extent of damage caused by these winds can vary depending on factors such as local building codes in effect, the duration of high winds, changes in wind direction, and the age of structures. The criteria for each category are delineated in Table 1 [9].
Category
Sustained wind speed (mph)
Damage
Example and year
1
74–95
Very dangerous winds will produce some damage
Humberto 2007
2
96–110
Extremely dangerous winds will cause extensive damage
Ike 2008
3
111–129
Devastating damage will occur
Alicia 1983
4
130–156
Catastrophic damage will occur
Harvey 2017
5
>156
Catastrophic damage will occur
Andrew 1992
Table 1.
Criteria for Saffir-Simpson hurricane wind scale.
2.2 Hong Kong tropical cyclone classification
In Hong Kong, tropical cyclones are classified based on the World Meteorological Organization’s guidelines, specifically focusing on their maximum sustained wind speeds near the center. The local classification system in Hong Kong, implemented since 2009, is distinctive as it defines classifications in terms of wind speeds averaged over a 10-minute period. This system introduces six categories, each delineating different levels of cyclonic intensity as in Table 2.1
Tropical cyclone classification
Maximum sustained winds near the center (km/h)
Tropical depression (TD)
<63
Tropical storm (TS)
63–87
Severe tropical storm (STS)
88–117
Typhoon (T)
118–149
Severe typhoon (ST)
150–184
Super typhoon (SuperT)
185 or above
Table 2.
Tropical cyclone classification in Hong Kong.
2.3 Other criterions for tropical cyclone classification
The Beaufort scale, officially known as the Beaufort wind force scale, employed by meteorological agencies worldwide, evaluates wind speeds based on observational criteria related to the appearance of the sea surface and object behavior. Wind speed on the Beaufort scale is based on the empirical relationship between the equivalent wind speed at 10 meters above the sea surface and Beaufort scale number B [10]. Extending from 0 (calm) to 12 (hurricane force), each level on the Beaufort scale is accompanied by specific descriptions of wind speed effects. The Beaufort scale was extended in 1946 when forces 13–17, with wind speeds more than 83mph, were added [11], which only applies to tropical cyclones. Internationally, the World Meteorological Organization (WMO) defined the Beaufort scale only up to force 12 and there was no recommendation on the use of the extended scale,2 which is only used in China.
The China Meteorological Administration (CMA), the Hong Kong Observatory (HKO), the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA), and the Japan Meteorological Agency (JMA) have established specific classifications for typhoons intended for domestic use.3 The JMA employs a three-tier categorization system based on 10-minute maximum wind speeds, ranging from 33 m/s (64kt) to 44 m/s (85kt). A typhoon is categorized as a very strong typhoon when its maximum wind speeds fall within the range of 44 m/s (85kt) to 54 m/s (105kt). A violent typhoon, on the other hand, is characterized by maximum wind speeds of 54 m/s (105kt) or higher.4
The Indian Meteorological Department (IMD), for example, Regional Specialized Meteorological Center (RSMC), utilizing a 3-minute averaging for the sustained wind, employs seven different classifications to grade systems.5 In the southern hemisphere, a tropical cyclone is defined as having a clear organization of wind circulation with sustained wind speeds exceeding 34 knots (63 km/h or 39 mph) near the center for 10 continuous minutes. Once identified as a tropical cyclone, all centers proceed to name the system using the Australian tropical cyclone intensity scale. This scale, based on maximum mean wind speed and typical strongest gust, categorizes systems into five classes from 1 (weakest) to 5 (strongest).6
Meteorological agencies employ various scales to assess and categorize natural phenomena, providing crucial information for understanding and responding to potential threats. The Saffir-Simpson Hurricane Wind Scale is highly recognized internationally and is commonly referenced in media reports, public communications, and official statements during hurricane events. This scale is extensively used by the National Hurricane Center (NHC) in the Atlantic basin and the eastern North Pacific, as well as by other meteorological agencies globally.
3. Impacts of tropical cyclones on a global perspective
Tropical cyclones are powerful and devastating weather phenomena that have significant impacts on various parts of the world. In this section, we discuss the impact of typhoons on sustainability, looking at it from the economic, social, and environmental perspectives, through which, we can gain a clearer understanding of the challenges and impacts of TC events on various aspects of society. The assessment of the projected response of tropical cyclones to anthropogenic warming indicates that climate change will likely lead to change in the intensity and frequency of tropical cyclones, affecting different regions in unique ways [12, 13, 14, 15, 16]. The distribution of TCs on the planet has undergone clear changes since at least the 1970s. This is evident in a noticeable shift toward higher latitudes in the areas where TCs reach their peak intensity. Specifically, the latitudes where TCs are most intense have been moving toward the poles at a rate of approximately 0.5 degrees of latitude per decade [17]. With these changes, TCs have far-reaching impacts on a global scale, affecting different regions in diverse ways. In response to the environmental, social, and economic impacts caused by these storms, it is imperative to implement disaster preparedness, mitigation measures, and sustainable development strategies to strengthen resilience to disasters.
Figure 2 showcases the devastating impacts of tropical cyclones on various regions around the world, (a) depicts the catastrophic flooding that engulfed New Orleans, following the landfall of Hurricane Katrina. The storm surge breached levees, leading to extensive inundation and widespread destruction of property and infrastructure. (b) captures the consequence of Typhoon Haiyan on Tacloban City in the Philippines. The storm, accompanied by powerful earthquakes, caused widespread devastation, leaving behind a landscape of destruction and ruins. (c) lists the most economically costing tropical cyclones in the past half-century occurred in 2017. The combined economic damage from these storms amounted to roughly 225 billion U.S. dollars. Notably, Hurricane Katrina, recorded in 2005, remains the largest tropical cyclone in terms of economic losses since 1970, with damages exceeding 163 billion dollars.
Tropical cyclone activity impacts humans and the environment through strong winds, heavy rainfall, and storm surges, often resulting in landslides and floods [18, 19, 20]. Tropical cyclones have the potential to cause severe morbidity and mortality. It is estimated that these disasters have resulted in 1.9 million deaths over the past two centuries [21, 22]. The impact on health can be direct (due to adverse weather conditions) or indirect (due to infrastructure damage, environmental destruction, population displacement, and economic hardships). Historically, developing countries in the Asia-Pacific region have experienced the highest absolute and proportional mortality rates from tropical cyclones and other natural disasters [22]. The lack of advanced infrastructure and resources for effective early warning systems in developing countries, as compared to developed countries, exacerbates the severity of cyclone impacts, leading to increased social and economic burdens [20].
In addition to environmental impacts, the social and economic consequences of tropical cyclones are also substantial, with implications for loss of life, displacement, infrastructure damage, and economic burdens. Empirical data suggests that the effects of tropical cyclones can lead to a decrease in economic growth within the affected country for over a decade [23, 24].
Numerous research efforts have concentrated on the socio-economic impacts incurred by countries susceptible to tropical cyclones, including the United States, the Philippines, and China [25, 26, 27, 28, 29, 30]. These studies have devised multiple damage functions specifically for tropical cyclones, aiming to analyze how the extent of damage resulting from these cyclones changes with fluctuations in global mean temperature and socio-economic advancement.
In recent years, the urgency of understanding and effectively mitigating the impacts of tropical cyclones has become increasingly evident. As the global community places greater emphasis on sustainable development and urban resilience [31, 32], the study of TCs and their associated boundary layer dynamics (TCBL) has garnered significant attention. The escalating trend of urbanization, characterized by the proliferation of high-rise structures and densely populated areas [33], underscores the critical need to comprehend the behavior of TCs within the context of a changing climate.
The rise in urbanization has led to the emergence of new challenges in disaster management and urban sustainability. The increasing frequency and intensity of TCs, exacerbated by climate change, pose significant threats to urban environments worldwide. As TCs evolve in response to changing climatic conditions, it becomes imperative to develop models that can accurately predict their behavior and associated impacts.
The important role of TC wind field modeling in disaster management and risk assessment provides critical support for building safer and sustainable urban environments. The application of wind field models can help to optimize economic losses and thus control or reduce the impacts caused by disasters. By collecting and analyzing disaster data, we can target redesign.
Tuan et al. [34] discussed the examination of the costs and benefits of implementing typhoon-resilient housing measures in Vietnam, which aims to assess whether using typhoon-resilient housing has a positive economic return, indicating a focus on redesigning buildings with typhoon-resilient features. In addition, the study identified positive returns while justifying the need to pursue policies related to this type of design. Pantua et al. [35] evaluated the structural integrity of current roofing designs in response to extreme environmental events, such as typhoons. The results highlight structural weaknesses in the current design, considering factors such as wind angle, structural frame, and materials. Then they specifically focused on the optimization of photovoltaic (PV) rooftop installations. It highlights the vulnerability of solar installations to typhoon-force winds and proposes a framework that integrates fluid–structure interaction modeling and building energy simulation to evaluate the structural and energy performance of roof-mounted solar panels [36, 37]. Mata et al. [38] highlighted the need to find suitable building orientation and roof angles for single-family residential houses using simulations, especially in countries highly prone to typhoons like the Philippines. The objective is to optimize these factors to minimize drag forces during typhoon conditions, thereby enhancing the resilience of buildings against such extreme weather events.
Such designs can reduce the damage to buildings and infrastructure caused by disasters and lower the cost of repair and reconstruction. The application of wind field modeling can enhance personal safety and security, especially through the connection with storm warning systems [39]. Early warning systems can provide timely alerts so that people have enough time to take action to avoid disaster areas or take other countermeasures, thus minimizing casualties and property damage. City governments should invest in effective and timely early warning systems and integrate climate change adaptation and resilience into planning and policy development [40, 41]. Wind field models can also be coupled with other hazards such as typhoon-induced rainfall and secondary hazards such as floods and mudslides [42, 43]. By establishing an integrated coastal hazard coupling field, we can provide comprehensive disaster data support and provide a scientific basis for disaster prevention and risk assessment in coastal areas.
A model focusing on the boundary layer within a tropical cyclone serves as a valuable tool for examining the mechanisms behind primary and secondary air circulations within the TC, which are directly linked to much of the associated damages [44]. Therefore, it holds significant importance to develop models and subsequently predict the wind field within the tropical cyclone boundary layer (TCBL). This is particularly crucial for forecasting wind speeds at specific locations of interest, and these models find practical applications in various real-world scenarios, including disaster preparedness and urban planning [45, 46].
4.1 Foundation of the models
The TCBL models constitute a crucial tool utilized for understanding and predicting the behavior of these meteorological phenomena. They leverage a combination of direct observations garnered from within the TCBL itself and a deep understanding of the intricate mechanisms underpinning energy and momentum exchanges occurring within this layer. The TCBL models rely on both direct observations collected within the TCBL and knowledge of the mechanisms governing energy and momentum exchanges within this layer [47]. With observational insights alongside theoretical constructs, the TCBL models strive to furnish a comprehensive depiction of the evolving structure and intensity of tropical cyclones, thereby enhancing our ability to anticipate their impacts and facilitate more effective mitigation and response strategies.
4.1.1 Observation data
Regarding observation, data is primarily gathered through ground soundings, meteorological observation towers, and radar systems. Ground sounding stands out as the predominant technique for uncovering wind flow characteristics within the TCBL. Additionally, surveillance aircraft deploy drop-down sounding instruments, known as Global Positioning System (GPS) dropsondes, equipped with GPS technology to track their positions as they descend through the TCBL. These GPS dropsondes offer invaluable direct measurements of wind speeds and other meteorological variables, allowing for a comprehensive understanding of the thermodynamic and dynamical structures within the TCBL [48, 49]. Since their inception, advancements in measurement techniques and data processing of GPS dropsondes have significantly enhanced their capabilities, leading to substantial contributions to wind profile modeling within the TCBL [50, 51, 52].
4.1.2 Theoretical foundation
The original Navier-Stokes equation is modified through the introduction of the balance between the gravity and static pressure of the air. When the mean wind velocities replace the instantaneous wind velocities, the turbulent exchange coefficient of K is used to show the turbulent mixing effect due to the shear of mean wind velocities. Considering the horizontal mixing of the turbulence is generally less strong than the vertical mixing by at least an order of magnitude, only the vertical mixing coefficient is commonly presented in the simplified Navier-Stokes equation. In addition, the variations in mean vertical wind velocities are significantly less than those in horizontal wind velocities. Consequently, the transportation equation of vertical winds is generally neglected. The azimuthal gradients for the variations in horizontal wind velocities are generally neglected considering the azimuthal size of the TC.
It is noted that both the original and the simplified Navier-Stokes equation are nonlinear in nature due to the connective term. Consequently, the analytical solutions are not available and numerical treatments are required to solve the governing equation for the sake of modeling the wind field inside the TCBL. According to whether employing the numeric treatment of linearization, the modeling of the TCBL wind field is categorized into the linear models and nonlinear simulation.
4.2 Research progress of TCBL models
In the realm of meteorological research, understanding and predicting the behavior of TCs is a paramount challenge with far-reaching implications for disaster preparedness, response, and risk mitigation. Various TCBL wind field models are all based on the same set of governing equations, which is mentioned in Section 4.2.1, depicting the dynamics or thermodynamics of the TCBL, and hence the differences among the models are mainly attributed to the modifications/simplifications made to the original set of equations. Figure 3 gives an overview of TCBL models available.
Figure 3.
Categories of TCBL wind field models.
Since the analytical solutions could be derived for the governing equation of the TCBL wind field once the nonlinear terms are linearized. The linear models of the TCBL wind field are the mainstream tools for the investigation of the meteorology corresponding to a TC in the era with weak computational capacity.
4.2.1 Semi-empirical linear models
Initially, the slab model is introduced, offering a simplified yet insightful representation of the main vortex of a tropical cyclone in a two-dimensional plane. The general idea of the slab model is to average the vertical variations in the TCBL wind field, and hence to focus on the main circulation of the TC wind flow in the horizontal plane. Specifically, the TCBL is assumed as a slab at the constant depth, which leads to the analytical solution to the governing equation under an axis-symmetric pressure distribution.
Smith [53] proposed a pioneering description of the TCBL wind field, modeling it as a steady, axis-symmetric potential vortex, simplifying the analytical solutions but lacking realism due to an inadequate understanding of TCBL turbulence. Subsequent refinements by Bode and Smith [54] and Smith [55] improved the model’s accuracy, predicting spatial oscillations in vertical wind velocity at the TCBL top.
While the symmetric slab model provides satisfactory surface wind estimates, due to insufficient knowledge of turbulent structures at the time, a more realistic understanding of the turbulent layer was not possible. Recent studies [56, 57] identified inconsistencies in predictions due to neglect of vertical momentum exchanges and terrain characteristics.
Recognizing the limitations of two-dimensional modeling, recent advancements focus on height-resolving models to incorporate three-dimensional TCBL wind field dynamics. Meng et al. [58] proposed an analytical model considering underlying terrains, showing promise in predicting TCBL wind fields under complex conditions. Subsequent enhancements by Huang and Xu [59] and Snaiki and Wu [60] incorporated temperature variations and increasingly emerged effects of climate change in the prediction of extreme wind speeds, respectively.
Batts et al. [61] proposed a linear model within the slab category to estimate maximum surface wind speed based on gradient-level winds, applied in Monte-Carlo simulations for U.S. coastal hazard assessment. Shapiro [62] employed a slab boundary layer model of constant depth, analyzing the steady flow under a specified translating symmetric vortex in gradient balance. To validate the model’s findings, comparisons are made with observations from Hurricanes Frederic (1979) and Allen (1980), as well as other relevant observational and theoretical data, which reveals that the simple boundary layer formulation accurately captures the qualitative features of the wind field observed in Hurricane Frederic. The asymmetric slab model, widely used in wind engineering, assesses TC hazards with acceptable computational burden. For instance, Vickery et al. [63] applied Shapiro’s model to estimate extreme TC wind speeds along the U.S. coast with specific recurrence periods, favoring it over Batts’ model. However, recent studies [56, 57] found inconsistency between slab model predictions and observational data on radial TCBL wind variations, due to deliberate neglect of vertical momentum exchanges. While Shapiro’s model provides computational efficiency for risk analysis of large insurance portfolios, its simplifications and assumptions may introduce limitations in accurately estimating hurricane-related losses [64, 65].
The average-depth slab model for the TC boundary layer is a simplified yet insightful approach used to understand and predict the dynamics of the boundary layer within TCs, however, all of the above are modeled in two dimensions; the actual boundary layer wind field is three-dimensional [60, 66, 67, 68], and not taking height into account may lead to inaccurate results.
The analytical model by Meng et al. [58] for calculating the wind field in a moving typhoon boundary layer incorporates two distinct layers: An upper inviscid layer and a lower friction layer. By integrating these layers, the model accurately predicts wind speeds and directions, including variations observed from elevated vantage points during typhoons. Moreover, it accounts for rapid wind speed changes caused by surface features. Although the model obtained an analytical solution and was verified [59, 68], the structural characteristics of the TCBL wind field are not reliably estimated because the gradient balance is not always satisfied for real TCs. Huang and Xu [59] improved upon Meng’s model by considering temperature effects and removing the assumption of a constant central pressure difference with height. Through a decomposition method, it provides more accurate wind speed and direction predictions, validated against field measurements and compared to other models like Meng’s and Shapiro’s. Results show the refined model offers better accuracy in wind speed, direction, and profile estimations, as confirmed by spatial wind speed distribution and mean wind profile analyses.
As a milestone in the development of the height-resolving model, Kepert [69] proposed a linear model with an analytical solution available by neglecting the vertical advection, and the horizontal advection is linearized. This model uses a linear analytical model and a numerical model to describe the boundary layer flow, highlighting the role of vertical diffusion, vertical advection, and horizontal advection in maintaining inflow against outward acceleration. Therefore, the vertical turbulent diffusivity and surface drag coefficient are important as they parameterize the momentum exchanges from the bottom boundary upwards. Subsequent scholars have studied the parameterization of coefficients [52, 66, 70, 71, 72].
A key shortcoming with current height-resolving models is that the super-gradient winds predicted by the model are generally weaker than the observations because it disregards the vertical advection in the TCBL [73]. Yang et al. [68] proposed a height-resolving model that simultaneously considers horizontal and vertical advections, whose better performance confirmed the importance of a height-resolving model to include the vertical advection.
Since climate change cannot be ignored and the effects on TCs have been observed, the new generation of mods is now moving toward considering the effects. Fang et al. [70] incorporate various elements to develop a typhoon velocity field model suitable for the gradient layer and boundary layer, considering multi-field parameters correlation and terrain effects. Overall, the model demonstrates the ability to illustrate specific wind environment characteristics with satisfactory precision, including non-exponential wind profiles in typhoon boundary layers. Kim and Lee [74], on a Monte Carlo Simulation approach, incorporate various components such as genesis, intensity, tracks, and wind field to estimate extreme wind speeds of future typhoons. The method considers climatological factors, and it incorporates sea surface temperature (SST) and oceanic occupation ratio (OOR) for intensity modeling, and a modified Batts’ model for wind field estimation.
4.2.2 Numerical simulation method
As computational capacity increased, the linear model’s limitations led to the rise of numerically solving the nonlinear governing equation to estimate the TCBL wind field, becoming mainstream. Numerical weather prediction packages are commonly employed to depict TC structures, including the TCBL wind field. Additionally, the direct solution of the nonlinear partial differential equation governing TCBL dynamics is reported in the literature.
Examples of TCBL wind models include the Weather Research and Forecasting (WRF) model, which is widely used for weather and climate research. Researchers utilize WRF simulations of historical TCs, known as hindcasting, to analyze TCBL wind fields [75, 76, 77, 78, 79]. For instance, Nolan, McNoldy, and Yunge [80] explored the impact of different planetary boundary layer schemes on TCBL numerical simulations by comparing WRF-simulated tracks, intensities, and sizes of a historical TC with observational data. They generated two WRF simulations with different boundary layer parameterizations and verified their accuracy against observed best track records. Another example is the Large Eddy Simulation (LES) model, which resolves the large-scale turbulent structures in the atmosphere and is used to study boundary layer dynamics. Zhu [79] introduced a LES framework within the WRF model, revealing TCBL boundary layer processes and coherent large eddy circulations during TC landfall.
While numerical weather prediction software can offer detailed representations of the TCBL wind field based on large-scale distributions of meteorological variables like wind velocities, air pressures, and temperatures, its use in TCBL investigations is constrained by boundary and initial condition preparation. Moreover, detailed TCBL wind field structures provided by numerical weather simulations come at the cost of significant computational resources, making it challenging to use them in Monte Carlo simulations to quantitatively assess TC hazards. Three-dimensional numerical models like WRF need continual adjustments based on an evolving understanding of meteorological processes within the TCBL. However, progress in TC meteorology research is slow and limited, hindering accurate simulation of TCBL wind fields with models like WRF ([81]; Wang and Wu [82]). Consequently, simulations of TCBL wind fields with the WRF model may suffer from inaccuracies in TC intensity and size.
In addition to the numerical weather prediction, directly solving the nonlinear governing equation of the TCBL wind field is suggested using general-purpose computational fluid dynamic tools. For example, Ma and Sun [83] showed the structure of the TCBL wind field via the help from the general computational fluid dynamics software of OpenFOAM. However, the numerical models combining the numerical weather prediction tool and computational fluid dynamics code still have difficulties in predicting winds in the strong convective inner core region. Recent modeling has focused on how to integrate various models, to study the impact of climate change on the dynamics of TCBL wind fields. Through Monte Carlo simulations incorporating climate change scenarios, the study reveals potential changes in wind profiles, suggesting that existing wind profile models may be overly conservative when evaluating structural safety under the influence of climate change, especially in tall buildings, when compared to current standards [72].
As various models play critical roles in TC forecasting, it is essential to assess their performance and accuracy in capturing different aspects of TC dynamics is very important, thus facilitating improvements in forecasting accuracy and resilience-building efforts. The wind fields simulated by the WRF model show an overall good agreement with observations, while noting discrepancies such as smaller simulated inflow angles compared to observations and mixed results in comparisons with Doppler radar wind profiles [80, 84]. Models include the OpenFOAM code lacks the possibility to describe the mesoscale process of TCs and their boundary layer processes [85, 86, 87]. Therefore, to replicate the actual meteorological system of a TC, many studies coupled WRF tool and computational fluid dynamics codes due to the limitations of mesoscale WRF tool and small-scale computational fluid dynamics code [88, 89, 90, 91]. Computational fluid dynamics code coupled with WRF can have the highest accuracy in estimating the wind field among other mesoscale meteorology models [92]. The accuracy of such numerical models has improved significantly in recent years [93], making their wind fields more suitable for operational use.
In recent years, machine learning (ML) holds promise for parameterizing boundary layer simulations due to the highly nonlinear nature of TCBL processes, which traditional parametrization struggles to address. ML algorithms, characterized by high dimensions and nonlinearities, offer a potential solution to the challenges in TCBL parameterization [94]. In the realm of numerical modeling, the most active research on ML application pertains to convective parameterization in atmospheric circulation models within climate research [95, 96] and unified physics parameterization [97, 98]. O'Gorman and Dwyer [96] discuss the use of ML to develop new parameterizations for moist convection directly from high-resolution model output, which is a significant aspect of convective parameterization within climate research. The study focuses on understanding the behavior of these ML-based parameterizations when integrated into a general circulation model (GCM) and their effectiveness in simulating climate change or extreme events. Han et al. [95] The study discussed focuses on developing a new moist physics parameterization scheme using deep learning techniques, specifically a residual convolutional neural network. This scheme aims to address biases in simulated precipitation and atmospheric circulation caused by current moist physics parameterization schemes in GCMs. It demonstrates superior performance in accurately reproducing simulations and predicting convective events in various atmospheric conditions.
The application of ML in boundary layer parameterization remains relatively limited. For example, McGibbon and Bretherton [99] describe the training of an artificial neural network to reproduce thermodynamic tendencies and boundary layer properties from high-resolution reanalysis data over a specific region. It demonstrates the use of ML techniques to emulate and parameterize boundary layer processes, which is a crucial aspect of atmospheric modeling. Wang et al. [100] describe the development of an emulator using deep neural networks for a planetary boundary layer parameterization in the WRF climate model. Parameterizations are crucial for representing diurnal variations in the atmospheric boundary layer. The research indicates that neural networks can successfully simulate vertical profiles within the boundary layer and produce accurate predictions of wind speed, temperature, and water vapor profiles. Chen et al. [101] discuss using ML for both pure data-driven models and for enhancing numerical models by incorporating ML techniques. The passage also highlights successful cases of ML methods in various aspects of tropical cyclone forecasting, such as genesis forecasts, track forecasts, intensity forecasts, extreme weather forecasts, and storm surge forecasts. This implies that ML is being applied to improve the accuracy of numerical forecast models, which includes parameterization of boundary layer processes within the context of tropical cyclones.
The limited utilization of ML in TCBL parameterization is primarily attributed to factors such as the intricate nature of TCBL processes, insufficient training data, challenges in interpretability of ML models, as well as difficulties in integrating ML techniques with current parameterization frameworks, underscoring the critical importance of ensuring compatibility and maintaining model accuracy during integration efforts.
Artificial intelligence (AI) has great potential in integrating with climate change models. In the parameterization of climate models, traditional physical parameterization methods may have limitations in accurately describing complex nonlinear processes in the climate system. ML techniques, on the other hand, are able to discover patterns in the climate system by learning from massive amounts of data, and also taking climate change into account, thus providing more accurate parameterization schemes.
This review has delved into the multiple realms of tropical cyclones, providing an in-depth exploration of their characteristics, classification systems, and the extensive impacts of tropical cyclones on a global scale, emphasizing their implications for sustainability and the evolving landscape of climate change. This discussion underscored the urgent need for comprehensive research and action to mitigate the adverse effects of TCs.
Tropical cyclone modeling represents a frontier in meteorological research, where the quest for accuracy and reliability is constantly evolving. Current approaches to hurricane and typhoon modeling face many challenges and also show vast frontiers, including the impacts of climate change and the integration of AI technologies.
Traditional linear models offer quick calculations and reveal certain physical mechanisms, yet their comprehensiveness and reliability fall short compared to nonlinear simulations. Nonlinear methods, while slower, provide indispensable accuracy in predicting real tropical cyclone trajectories. However, their computational demands remain a barrier, limiting applicability to TCs with specific boundaries and initial conditions. The integration of AI and ML presents a transformative avenue in tropical cyclone modeling. While wind field models have limitations, AI-enhanced simulations have the potential to revolutionize TC prediction by significantly reducing computational efforts. Nonetheless, challenges persist, including the need for extensive training data and overcoming the limitations of existing AI models in producing accurate predictions.
5.2 Challenges and future directions
The complexities of physical processes within TCs pose formidable challenges for both AI and numerical models. Boundary layer wind field modeling encounters obstacles such as insufficient data quality and quantity, the intricate influence of boundary layer turbulence, and the generalization capability of AI models across diverse scenarios.
To address these challenges, a balanced approach that integrates AI and numerical modeling is paramount. This entails leveraging the strengths of each approach, incorporating uncertainty analysis, enhancing observational data quality, and regularly updating models to adapt to changing meteorological conditions and geographic environments. By embracing this holistic strategy, the future of hurricane and typhoon modeling holds promise for more accurate predictions and improved resilience in the face of these powerful natural phenomena.
As we navigate the complex terrain of hurricane and typhoon modeling, it becomes evident that collaboration, innovation, and adaptability are key to advancing our understanding and preparedness in a changing climate landscape.
For the practical application of TC models, they can be integrated into a coupled multi-hazard spatial field model to support disaster warning and risk assessment of coastal infrastructure. Such an integrated model can provide multifaceted hazard data, including TC wind field information, to provide a more comprehensive information base for relevant agencies and decision makers.
In addition, the application of TC modeling can be extended to the development of advanced warning systems. By forecasting TC risks in advance, the system can provide reasonable countermeasures for coastal residents and infrastructure, such as advising residents in high-rise buildings to take measures such as evacuating, moving to a safer floor, or closing the windows, in order to minimize potential casualties and property damage. TC models can also be used to study vulnerability and analyze the resilience of building structures and coastal infrastructure. By simulating the performance of buildings and infrastructures under the influence of typhoons, potential weak points can be identified and recommendations for strengthening and improvement can be made to enhance their resilience.
Through concerted efforts and interdisciplinary collaboration, we can forge a path toward more effective modeling techniques and enhanced resilience to tropical cyclones worldwide.
References
1.Holland GJ. Ready reckoner. Global Guide to Tropical Cyclone Forecasting. 1993;9(9):26
2.Emanuel K. Tropical cyclones. Annual Review of Earth and Planetary Sciences. 2003;31(1):75-104
3.Neumann CJ. Global Overview: Global Guide to Tropical Cyclone Forecasting. Geneva, Switzerland: World Meteorological Organization (WMO); 1993
4.Chan JC, Kepert JD. Global Perspectives on Tropical Cyclones: From Science to Mitigation. Singapore: World Scientific Publishing Co. Pte Ltd.; 2010
5.Emanuel K. Divine Wind: The History and Science of Hurricanes. New York, United States: Oxford University Press; 2005
6.Annamalai H, Slingo J, Sperber K, Hodges K. The mean evolution and variability of the Asian summer monsoon: Comparison of ECMWF and NCEP–NCAR reanalyses. Monthly Weather Review. 1999;127(6):1157-1186
7.Pasch RJ, Pasch RJ, Blake ES, Cobb HD III, David P. Tropical Cyclone Report Hurricane Wilma 15-25 October 2005. Miami, USA: Roberts National Hurricane Center; 2006
8.Kepert JD. Observed boundary layer wind structure and balance in the hurricane core. Part I: Hurricane Georges. Journal of the Atmospheric Sciences. 2006;63(9):2169-2193
9.Taylor HT, Ward B, Willis M, Zaleski W. The Saffir-Simpson Hurricane Wind Scale. Washington, DC, USA: Atmospheric Administration; 2010
10.Beer T. Environmental Oceanography. Boca Raton, USA: CRC Press; 2017
11.Saucier WJ. Principles of Meteorological Analysis. Vol. 438. Chicago, IL: University of Chicago Press; 1955
12.Knutson T, Camargo SJ, Chan JC, Emanuel K, Ho C-H, Kossin J, et al. Tropical cyclones and climate change assessment: Part II: Projected response to anthropogenic warming. Bulletin of the American Meteorological Society. 2020;101(3):E303-E322
13.Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D, Skea J, Shukla PR. Global Warming of 1.5 C: IPCC Special Report on Impacts of Global Warming of 1.5 C above Pre-Industrial Levels in Context of Strengthening Response to Climate Change, Sustainable Development, and Efforts to Eradicate Poverty. New York, United States: Cambridge University Press; 2022. DOI: 10.1017/9781009157940
14.Mendelsohn R, Emanuel K, Chonabayashi S, Bakkensen L. The impact of climate change on global tropical cyclone damage. Nature Climate Change. 2012;2(3):205-209. DOI: 10.1038/nclimate1357
15.Middelanis R, Willner SN, Otto C, Levermann A. Economic losses from hurricanes cannot be nationally offset under unabated warming. Environmental Research Letters. 2022;17(10):104013. DOI: 10.1088/1748-9326/ac90d8
16.Neukom R, Steiger N, Gómez-Navarro JJ, Wang J, Werner JP. No evidence for globally coherent warm and cold periods over the preindustrial common era. Nature. 2019;571(7766):550-554. DOI: 10.1038/s41586-019-1401-2
17.Studholme J, Fedorov AV, Gulev SK, Emanuel K, Hodges K. Poleward expansion of tropical cyclone latitudes in warming climates. Nature Geoscience. 2022;15(1):14-28
18.Keim ME. Cyclones, tsunamis and human health. Oceanography. 2006;19(2):40-49
19.Noji EK. Analysis of medical needs during disasters caused by tropical cyclones: Anticipated injury patterns. The Journal of Tropical Medicine and Hygiene. 1993;96(6):370-376
20.Shultz JM, Russell J, Espinel Z. Epidemiology of tropical cyclones: The dynamics of disaster, disease, and development. Epidemiologic Reviews. 2005;27(1):21-35. DOI: 10.1093/epirev/mxi011
21.Nicholls RJ, Mimura N, Topping J. Climate change in south and south-East Asia: Some implications for coastal areas. Journal of Global Environmental Engineering. 1995;1:137-154
22.Prevention, U. N. D. P. B. F. C. Reducing Disaster Risk: A Challenge for Development-a Global Report. New York, United States: United Nations; 2004
23.Berlemann M, Wenzel D. Hurricanes, economic growth and transmission channels: Empirical evidence for countries on differing levels of development. World Development. 2018;105:231-247
24.Krichene H, Geiger T, Frieler K, Willner S, Sauer I, Otto C. Long-term impacts of tropical cyclones and fluvial floods on economic growth–empirical evidence on transmission channels at different levels of development. World Development. 2021;144:105475
25.Bakkensen LA, Mendelsohn RO. Risk and adaptation: Evidence from global hurricane damages and fatalities. Journal of the Association of Environmental and Resource Economists. 2016;3(3):555-587
26.Baldwin JW, Lee C-Y, Walsh BJ, Camargo SJ, Sobel AH. Vulnerability in a tropical cyclone risk model: Philippines case study. In: Weather, Climate, and Society. Boston, USA: AMS Publications; 2023
27.Cinco TA, de Guzman RG, Ortiz AMD, Delfino RJP, Lasco RD, Hilario FD, et al. Observed trends and impacts of tropical cyclones in the Philippines. International Journal of Climatology. 2016;36(14):4638-4650
28.Elliott RJ, Strobl E, Sun P. The local impact of typhoons on economic activity in China: A view from outer space. Journal of Urban Economics. 2015;88:50-66
29.Emanuel K. Global warming effects on US hurricane damage. Weather, Climate, and Society. 2011;3(4):261-268
30.Schmidt S, Kemfert C, Höppe P. The impact of socio-economics and climate change on tropical cyclone losses in the USA. Regional Environmental Change. 2010;10:13-26
31.Agbedahin AV. Sustainable development, education for sustainable development, and the 2030 agenda for sustainable development: Emergence, efficacy, eminence, and future. Sustainable Development. 2019;27(4):669-680
32.Holden E, Linnerud K, Banister D. The imperatives of sustainable development. Sustainable Development. 2017;25(3):213-226
33.Ali MM, Al-Kodmany K. Tall buildings and urban habitat of the 21st century: A global perspective. Buildings. 2012;2(4):384-423
34.Tuan TH, Tran P, Hawley K, Khan F, Moench M. Quantitative cost-benefit analysis for typhoon resilient housing in Danang city, Vietnam. Urban Climate. 2015;12:85-103
35.Pantua CAJ, Calautit JK, Wu Y. A novel fluid-structure interaction modelling and optimisation of roofing designs of buildings for typhoon resilience. In: IOP Conference Series: Materials Science and Engineering. Vol. 556. Chengdu, China: IOP Publishing Ltd.; 2019
36.Pantua CAJ, Calautit JK, Wu Y. A fluid-structure interaction (FSI) and energy generation modelling for roof mounted renewable energy installations in buildings for extreme weather and typhoon resilience. Renewable Energy. 2020;160:770-787
37.Pantua CAJ, Calautit JK, Wu Y. Sustainability and structural resilience of building integrated photovoltaics subjected to typhoon strength winds. Applied Energy. 2021;301:117437
38.Mata JL, Orejudos JN, Opon JG, Guirnaldo SA. Optimizing building orientation and roof angle of a typhoon-resilient single-family house using genetic algorithm and computational fluid dynamics. Buildings. 2022;13(1):107
39.Tan C, Fang W. Mapping the wind hazard of global tropical cyclones with parametric wind field models by considering the effects of local factors. International Journal of Disaster Risk Science. 2018;9:86-99
40.Nhuan MT, Tue NT, Quy TD. Enhancing resilience to climate change and disasters for sustainable development: Case study of Vietnam coastal urban areas. In: Resilient Asia: Fusion of Traditional and Modern Systems for a Sustainable Future. Tokyo, Japan: Springer; 2018. pp. 63-79
41.Sajjad M, Li Y, Li Y, Chan JC, Khalid S. Integrating typhoon destructive potential and social-ecological systems toward resilient coastal communities. Earth's Future. 2019;7(7):805-818
42.Kim J-S, Chen A, Lee J, Moon I-J, Moon Y-I. Statistical prediction of typhoon-induced rainfall over China using historical rainfall, tracks, and intensity of typhoon in the Western North Pacific. Remote Sensing. 2020;12(24):4133
43.Yin J, Lin N, Yang Y, Pringle WJ, Tan J, Westerink JJ, et al. Hazard assessment for typhoon-induced coastal flooding and inundation in Shanghai, China. Journal of Geophysical Research: Oceans. 2021;126(7):e2021JC017319
44.Li T-H, Wang Y. The role of boundary layer dynamics in tropical cyclone intensification. Part I: Sensitivity to surface drag coefficient. Journal of the Meteorological Society of Japan. Ser. II. 2021;99(2):537-554. DOI: 10.2151/jmsj.2021-027
45.Kijewski-Correa T, Taflanidis A, Vardeman C, Sweet J, Zhang J, Snaiki R, et al. Geospatial environments for hurricane risk assessment: Applications to situational awareness and resilience planning in New Jersey. Frontiers in Built Environment. 2020;6:549106
46.Powell MD, Houston SH. Hurricane Andrew's landfall in South Florida. Part II: Surface wind fields and potential real-time applications. Weather and Forecasting. 1996;11(3):329-349
47.Emanuel K. 100 years of progress in tropical cyclone research. Meteorological Monographs. 2018;59:15.11-15.68. DOI: 10.1175/AMSMONOGRAPHS-D-18-0016.1
48.Franklin JL, Black ML, Valde K. GPS dropwindsonde wind profiles in hurricanes and their operational implications. Weather and Forecasting. 2003;18(1):32-44. DOI: 10.1175/1520-0434(2003)018< 0032:GDWPIH>2.0.CO;2
49.Hock TF, Franklin JL. The ncar gps dropwindsonde. Bulletin of the American Meteorological Society. 1999;80(3):407-420. DOI: 10.1175/1520-0477(1999)080< 0407:TNGD>2.0.CO;2
50.Powell MD, Vickery PJ, Reinhold TA. Reduced drag coefficient for high wind speeds in tropical cyclones. Nature. 2003;422(6929):279-283. DOI: 10.1038/nature01481
51.Uhlhorn EW, Black PG, Franklin JL, Goodberlet M, Carswell J, Goldstein AS. Hurricane surface wind measurements from an operational stepped frequency microwave radiometer. Monthly Weather Review. 2007;135(9):3070-3085. DOI: 10.1175/MWR3454.1
52.Vickery PJ, Wadhera D, Powell MD, Chen Y. A hurricane boundary layer and wind field model for use in engineering applications. Journal of Applied Meteorology and Climatology. 2009;48(2):381-405. DOI: 10.1175/2008JAMC1841.1
53.Smith RK. The surface boundary layer of a hurricane. Tellus. 1968;20(3):473-484. DOI: 10.1111/j.2153-3490.1968.tb00388.x
54.Bode L, Smith RK. A parameterization of the boundary layer of a tropical cyclone. Boundary-Layer Meteorology. 1975;8:3-19. DOI: 10.1007/BF02579390
55.Smith RK. A simple model of the hurricane boundary layer. Quarterly Journal of the Royal Meteorological Society: A Journal of the Atmospheric Sciences, Applied Meteorology and Physical Oceanography. 2003;129(589):1007-1027. DOI: 10.1256/qj.01.197
56.Kepert JD. Slab-and height-resolving models of the tropical cyclone boundary layer. Part I: Comparing the simulations. Quarterly Journal of the Royal Meteorological Society. 2010;136(652):1686-1699. DOI: 10.1002/qj.667
57.Khare S, Bonazzi A, West N, Bellone E, Jewson S. On the modelling of over-ocean hurricane surface winds and their uncertainty. Quarterly Journal of the Royal Meteorological Society: A Journal of the Atmospheric Sciences, Applied Meteorology and Physical Oceanography. 2009;135(642):1350-1365. DOI: 10.1002/qj.442
58.Meng Y, Matsui M, Hibi K. An analytical model for simulation of the wind field in a typhoon boundary layer. Journal of Wind Engineering and Industrial Aerodynamics. 1995;56(2):291-310. DOI: 10.1016/0167-6105(94)00014-5
59.Huang W, Xu YL. A refined model for typhoon wind field simulation in boundary layer. Advances in Structural Engineering. 2012;15(1):77-89. DOI: 10.1260/1369-4332.15.1.77
60.Snaiki R, Wu T. Modeling tropical cyclone boundary layer: Height-resolving pressure and wind fields. Journal of Wind Engineering and Industrial Aerodynamics. 2017b;170:18-27. DOI: 10.1016/j.jweia.2017.08.005
61.Batts ME, Simiu E, Russell LR. Hurricane wind speeds in the United States. Journal of the Structural Division. 1980;106(10):2001-2016. DOI: 10.1061/JSDEAG.000554
62.Shapiro LJ. The asymmetric boundary layer flow under a translating hurricane. Journal of Atmospheric Sciences. 1983;40(8):1984-1998. DOI: 10.1175/1520-0469(1983)040< 1984:TABLFU>2.0.CO;2
63.Vickery PJ, Skerlj P, Steckley A, Twisdale L. Hurricane wind field model for use in hurricane simulations. Journal of Structural Engineering. 2000;126(10):1203-1221. DOI: 10.1061/(ASCE)0733-9445(2000)126:10(1203)
64.Daneshvaran S, Yao T, Morden R, Wen Y, Zadeh M. A parametric model for efficient hurricane risk analysis of large portfolios. In: International Conference of Structural Safety and Reliability (ICOSSAR). Rotterdam, The Netherlands: Balkema; 1997
65.Vickery PJ, Twisdale LA. Wind-field and filling models for hurricane wind-speed predictions. Journal of Structural Engineering. 1995;121(11):1700-1709. DOI: 10.1061/(ASCE)0733-9445(1995)121:11(1700)
66.Li W, Hu Z, Pei Z, Li S, Chan PW. A discussion on influences of turbulent diffusivity and surface drag parameterizations using a linear model of the tropical cyclone boundary layer wind field. Atmospheric Research. 2020;237:104847 %@ 100169-104847 %@ 108095. DOI: 10.1016/j.atmosres.2020.104847
67.Snaiki R, Wu T. A linear height-resolving wind field model for tropical cyclone boundary layer. Journal of Wind Engineering and Industrial Aerodynamics. 2017a;171:248-260. DOI: 10.1016/j.jweia.2017.10.008
68.Yang J, Chen Y, Zhou H, Duan Z. A height-resolving tropical cyclone boundary layer model with vertical advection process. Natural Hazards. 2021;107:723-749. DOI: 10.1007/s11069-021-04603-1
69.Kepert J. The dynamics of boundary layer jets within the tropical cyclone core. Part I: Linear theory. Journal of the Atmospheric Sciences;58(17):2469-2484. DOI: 10.1175/1520-0469(2001)058< 2469:TDOBLJ>2.0.CO;2
70.Fang G, Zhao L, Cao S, Ge Y, Pang W. A novel analytical model for wind field simulation under typhoon boundary layer considering multi-field correlation and height-dependency. Journal of Wind Engineering and Industrial Aerodynamics. 2018;175:77-89 %@ 0167-6105. DOI: 10.1016/j.jweia.2018.01.019
71.Hong X, Hong HP, Li J. Solution and validation of a three dimensional tropical cyclone boundary layer wind field model. Journal of Wind Engineering and Industrial Aerodynamics. 2019;193:103973 %@ 100167-106105. DOI: 10.1016/j.jweia.2019.103973
72.Wang J, Tse TK, Li S, Fung JC. A model of the sea–land transition of the mean wind profile in the tropical cyclone boundary layer considering climate changes. International Journal of Disaster Risk Science. 2023;14:1-15. DOI: 10.1007/ s13753-023-00488-9
73.Kepert J, Wang Y. The dynamics of boundary layer jets within the tropical cyclone core. Part II: Nonlinear enhancement. Journal of the Atmospheric Sciences. 2001;58(17):2485-2501 %@ 0022-4928. DOI: 10.1175/1520-0469(2001)058< 2485:TDOBLJ>2.0.CO;2
74.Kim GY, Lee S. Prediction of extreme wind by stochastic typhoon model considering climate change. Journal of Wind Engineering and Industrial Aerodynamics. 2019;192:17-30
75.Davis C, Wang W, Chen SS, Chen Y, Corbosiero K, DeMaria M, et al. Prediction of landfalling hurricanes with the advanced hurricane WRF model. Monthly Weather Review. 2008;136(6):1990-2005. DOI: 10.1175/2007MWR2085.1
76.Done J, Davis CA, Weisman M. The next generation of NWP: Explicit forecasts of convection using the weather research and forecasting (WRF) model. Atmospheric Science Letters. 2004;5(6):110-117. DOI: org/10.1002/asl.72
77.Jiménez PA, González-Rouco JF, García-Bustamante E, Navarro J, Montávez JP, De Arellano JV-G, et al. Surface wind regionalization over complex terrain: Evaluation and analysis of a high-resolution WRF simulation. Journal of Applied Meteorology and Climatology. 2010;49(2):268-287. DOI: 10.1175/2009JAMC2175.1
78.Lo JCF, Yang ZL, Pielke RA Sr. Assessment of three dynamical climate downscaling methods using the weather research and forecasting (WRF) model. Journal of Geophysical Research: Atmospheres. 2008;113(D9). DOI: 10.1029/2007JD009216
79.Zhu P. A multiple scale modeling system for coastal hurricane wind damage mitigation. Natural Hazards. 2008;47:577-591. DOI: 10.1007/s11069-008-9240-8
80.Nolan DS, McNoldy BD, Yunge J. Evaluation of the surface wind field over land in WRF simulations of hurricane Wilma (2005). Part I: Model initialization and simulation validation. Monthly Weather Review. 2021a;149(3):679-695. DOI: 10.1175/MWR-D-20-0199.1
81.Marks FD, Shay LK, PDT-5. Landfalling tropical cyclones: Forecast problems and associated research opportunities. Bulletin of the American Meteorological Society. 1998;79(2):305-323
82.Wang Y-Q , Wu C-C. Current understanding of tropical cyclone structure and intensity changes–a review. Meteorology and Atmospheric Physics. 2004;87(4):257-278
83.Ma T, Sun C. Large eddy simulation of hurricane boundary layer turbulence and its application for power transmission system. Journal of Wind Engineering and Industrial Aerodynamics. 2021;210:104520
84.Nolan DS, McNoldy BD, Yunge J, Masters FJ, Giammanco IM. Evaluation of the surface wind field over land in WRF simulations of hurricane Wilma (2005). Part II: Surface winds, inflow angles, and boundary layer profiles. Monthly Weather Review. 2021b;149(3):697-713
86.Hristov Y, Oxley G, Žagar M. Improvement of AEP predictions using diurnal CFD modelling with site-specific stability weightings provided from mesoscale simulation. Journal of Physics: Conference Series. 2014;524(1):012116
87.Luo X, Cao Y. Simulation of the wind fields over complex terrain with coupling of CFD and WRF. Journal of Computational Methods in Sciences and Engineering. 2021;21(5):1155-1166. DOI: 10.3233/JCM-204759
88.Huang M, Wang Y, Lou W, Cao S. Multi-scale simulation of time-varying wind fields for Hangzhou Jiubao bridge during typhoon Chan-hom. Journal of Wind Engineering and Industrial Aerodynamics. 2018;179:419-437. DOI: 10.1016/j.jweia.2018.06.020
89.Mughal MO, Lynch M, Yu F, Sutton J. Forecasting and verification of winds in an east African complex terrain using coupled mesoscale-and micro-scale models. Journal of Wind Engineering and Industrial Aerodynamics. 2018;176:13-20. DOI: 10.1016/j.jweia.2018.03.006
90.Nakayama H, Takemi T, Nagai H. Coupling of WRF and building-resolving urban CFD models for analysis of strong winds over an urban area. In: Proceedings of the 14th Conference on Mesoscale Processes. Los Angeles, CA, USA: American Meteorological Society; 2011
91.Talbot C, Bou-Zeid E, Smith J. Nested mesoscale large-eddy simulations with WRF: Performance in real test cases. Journal of Hydrometeorology. 2012;13(5):1421-1441. DOI: 10.1175/JHM-D-11-048.1
92.Mohan M, Sati AP. WRF model performance analysis for a suite of simulation design. Atmospheric Research. 2016;169:280-291. DOI: 10.1016/j.atmosres.2015.10.013
93.Duan YH, Fang J, Cheng ZQ, Xu J, Li QQ, Zhan RF. Advances and trends in tropical cyclone research and forecasting: An overview of the ninth World Meteorological Organization International Workshop on tropical cyclones (IWTC-9). Acta Meteorologica Sinica. 2020:537-550
94.Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge, USA: MIT Press; 2016
95.Han Y, Zhang GJ, Huang X, Wang Y. A moist physics parameterization based on deep learning. Journal of Advances in Modeling Earth Systems. 2020;12(9):e2020MS002076
96.O’Gorman PA, Dwyer JG. Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events. Journal of Advances in Modeling Earth Systems. 2018;10(10):2548-2563
97.Brenowitz ND, Bretherton CS. Spatially extended tests of a neural network parametrization trained by coarse-graining. Journal of Advances in Modeling Earth Systems. 2019;11(8):2728-2744
98.Gentine P, Pritchard M, Rasp S, Reinaudi G, Yacalis G. Could machine learning break the convection parameterization deadlock? Geophysical Research Letters. 2018;45(11):5742-5751
99.McGibbon J, Bretherton CS. Single-column emulation of reanalysis of the Northeast Pacific marine boundary layer. Geophysical Research Letters. 2019;46(16):10053-10060
100.Wang J, Balaprakash P, Kotamarthi R. Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model. Geoscientific Model Development. 2019;12(10):4261-4274
101.Chen R, Zhang W, Wang X. Machine learning in tropical cyclone forecast modeling: A review. Atmosphere. 2020;11(7):676
Notes
CHAN, C. Tropical Cyclone Classification. |Hong Kong Observatory(HKO)|Educational Resources. https://www.hko.gov.hk/en/education/tropical-cyclone/classification-naming-characteristics/00145-tropical-cyclone-classification.html.
RA V Tropical Cyclone Committee (2023). Tropical Cyclone Operational Plan for the South-East Indian Ocean and the Southern Pacific Ocean 2023 (Report). World Meteorological Organization. https://community.wmo.int/en/tropical-cyclone-operational-plans.
Written By
Jiayao Wang, Yu Chang and Kam Tim Tse
Submitted: 13 February 2024Reviewed: 13 February 2024Published: 17 July 2024
Open access peer-reviewed chapter - ONLINE FIRST
