The northeast monsoon prevailing over southeastern China in late seasons, generally from October to March, frequently generates water level anomalies upstream of the Taiwan Strait (TWS) that reach the coastal waters of Guangdong in South China, and, with compounding astronomical high tides, elevate coastal flood risk over the region. The risk of coastal flooding or sea inundation is further heightened when monsoon forcing co-occurs with storm surge brought by late-season tropical cyclones (TCs). This study integrates tide gauge observations from Hong Kong (HK) and its vicinity together with Delft3D Flexible Mesh simulations to diagnose a tide-modulated anomaly wave mechanism. Observations show that anomalies originating in or near TWS arrive in HK with station-dependent phasing. These water level anomalies exhibit a characteristic ~6 h periodicity west of the Taiwan Shoal, and display peaks that systematically align with the astronomical high tide. Time–frequency analysis reveals a wave period transformation from ~12 h north of Dongshandao over the coast of southeastern China to ~6 h west of the Taiwan Shoal. We test the hypothesis that wind-forced water anomalies generated in or near TWS undergo shoal-modulated nonlinear tide–wind interaction and tidal-current advection that transform their dominant period and phase-lock them to the tide, producing four anomaly peaks per day downstream and station-dependent phasing in HK. Hindcasts of the November 2024 monsoon episode reproduce the observed timing, periodicity, and spatial transition, while constituent experiments demonstrate that semi-diurnal forcing entering via the TWS is the primary driver of the ~6 h signal, with the Taiwan Shoal acting as the modulation locus. Accurate water level forecasts for the Guangdong coast, therefore, need to incorporate upstream wind forcing over the TWS and bathymetric controls around the Taiwan Shoal, with practical implications for compound flood risk during spring tides and co-occurring monsoon and/or TC events.

This study investigates the impact of marine heatwaves (MHW) on tropical cyclones (TCs) affecting the Korean Peninsula. TC events occurring between June and October from 1982 to 2023 were analyzed. The categorization of MHW TCs or non-MHW TCs was determined based on whether they passed through MHW-affected ocean regions prior to reaching the Korean Peninsula. The results of this study demonstrate that MHW TCs exhibit stronger maximum sustained winds when approaching the Korean Peninsula than non-MHW TCs. This intensification effect was statistically significant only for strong TCs with maximum wind speeds exceeding 33 ms -1 . Analysis of rainfall shows that MHW TCs produce significantly more precipitation, with peak rainfall rates nearly eight times higher than those of non-MHW TCs. Our analysis of the physical mechanisms involved shows that high sea surface temperatures during MHWs increase surface saturation specific humidity, resulting in enhanced air-sea moisture disequilibrium. Combined with strong low-level winds in intense TCs, this leads to increased ocean-to-atmosphere latent heat flux and strengthened moisture flux convergence near the TC core. These processes sustain deep convection and reinforce TC intensity and rainfall. These findings suggest that MHWs can substantially amplify the wind and rainfall hazards posed by strong TCs approaching the Korean Peninsula. This indicates an increased risk in future climate warming scenarios characterized by more frequent and intense MHWs.

Abstract Full impact assessment of tropical cyclones each year requires a comprehensive sociodemographic analysis. We evaluated the sociodemographic characteristics of tropical cyclone-impacted regions during the 2024 calendar year in the recent historical context of 1980–2024. In 2024, tropical cyclone-force winds affected an estimated 429 902 820 people (5.5% of global population), the 12th highest since 1980, in disproportionately more deprived areas. Hurricane-force winds affected an estimated 59 672 600 people (0.8%), the 10th highest since 1980, in disproportionately less deprived areas. Our findings provide a global context for tropical cyclones to better guide resilience and recovery efforts.

Further study of the winds recorded on a bridge in Hong Kong for two tropical cyclone events in 2023

The rainy season in South China is predominantly influenced by monsoon and tropical cyclone (TC) weather systems. However, due to the lack of long-term observational data, the microphysical characteristics of raindrop size distributions (DSDs) associated with these systems remain insufficiently understood. This study utilizes five years (2016–2020) of 2DVD observations to analyze the differences in DSD characteristics of monsoon and TC rainfall. The results indicate that monsoon rainfall exhibits higher concentrations of medium to large raindrops (diameter > 2.1 mm), along with larger mean rainfall rates ( R ) and mass-weighted mean diameters ( D m ), but lower normalized intercept parameters ( N w ) compared to TC rainfall. Distinct differences in the mean normalized DSDs are observed between monsoon and TC rainfall. Convective rain under both systems shows the characteristics of maritime convective rain. Additionally, the relationships between kinetic energy (KE) and R , KE and D m , as well as radar reflectivity ( Z ) and R , differ between monsoon and TC rainfall. TC events have relatively humid atmospheric conditions and higher wind speeds, resulting in the presence of a large number of small raindrops. These findings enhance understanding of the DSD characteristics under different synoptic regimes and provide valuable implications for improving rainfall KE estimation, DSD retrieval, and quantitative precipitation estimation over South China. • Monsoon rainfall shows larger drops; tropical cyclone rainfall shows higher concentrations in South China. • Both monsoon and tropical cyclone convective rainfall show maritime convective rainfall features. • Raindrop size distributions vary in response to differing dynamic and thermodynamic conditions.

ABSTRACT Tropical cyclones (TC) intensity is classified by the maximum sustained wind (MSW), a critical metric for monitoring storm evolution. Satellite-based scatterometers provide essential measurements of 10-metre wind speed (U10) by inverting ocean surface roughness derived from active microwave radar signals. However, reduced accuracy in high wind speed ranges ( > 15 ms−1) and an upper accuracy limit of about 35 ms−1 pose significant challenges for MSW extraction, particularly for intense TCs. This study evaluates the effectiveness of MSW estimation using scatterometer data from MetOp-A, MetOp-B, ERS-2, and OceanSat-2 satellites, analysing 1848 TC events across all intensity stages. A new approach was designed to identify the MSW within the radius of maximum wind as reported in the best-track data. Results demonstrate strong overall agreement between scatterometer-derived MSW and the best-track reports. Detailed analysis indicates robust correlation for tropical storms (R = 0.58–0.76), and moderate agreement for category 1–2 TCs (R = 0.25–0.45). However, performance degrades significantly for major TCs (categories 3–5), primarily due to limitations in high wind speed estimation. This study highlights that improving scatterometer-derived wind speed accuracy at extreme ranges could substantially enhance MSW retrieval in TCs, offering valuable opportunities to advance cyclone monitoring capabilities.

Tropical cyclones (TCs) have ranked as the deadliest and most financially crippling natural disasters in the United States. It is imperative to assess potential shifts in TC intensity within the paradigm of an evolving climate. In this study we apply a fixed-constraint storyline approach that holds storm tracks and initial conditions constant to probe future TC intensity in the North Atlantic Basin. First, we simulate 618 historical TC events using the Risk Analysis Framework for Tropical Cyclones (RAFT)’s deep-learning intensity model. Next, we apply warming signals derived from eight CMIP6 climate scenarios and rerun each event to explore how intensities respond across scenarios. Finally, we develop an interactive dashboard that allows users to explore individual storm simulations and the scenario-modified environmental drivers. Together, this dataset and tool provide a clear, illustrative way to investigate how TC intensity responds to changes in air-sea state.

Abstract Surface ozone pollution poses serious health risks worldwide while individual tropical cyclone (TC) can affect surface ozone through air transport and changes in meteorology influencing ozone formation. However, general variations in surface ozone under the influence of all TCs, as well as the corresponding health risks remain unclear. Using a High-resolution Air Quality Reanalysis Dataset over China (CAQRA), this study examines spatial-temporal variations of ozone and associated health risks during TC events from 2013 to 2019. Results show three distinct regional responses. In eastern coastal China, ozone decreases and reaches its minimum at the moment nearest to the TC center (0 h), due to strong winds and moist inflow. In southeastern and northeastern China, ozone rises slightly and then declines to its minimum about 34 h after 0 h, likely resulting from the changes of temperature and relative humidity (RH) and a lagging effect of strong winds. In central and southwestern China, ozone increases due to the high temperature and low RH near the peripheral circulation of TCs. Health impact assessments indicate the highest ozone-related risks concentrate in the eastern and southern China. These findings highlight the importance of incorporating the air pollution impacts induced by TCs into public health risk assessments.

Global Navigation Satellite System Reflectometry (GNSS-R) provides all-weather, high-resolution ocean wind speed monitoring that offers additional benefits for forecasting tropical cyclones and severe weather events. However, existing GNSS-R wind retrieval models often lack interpretability and suffer accuracy degradation during high wind conditions. To address these limitations, we leverage a mathematical equivalence between Transformers and graph neural networks (GNNs) on complete graphs, which provides a physically grounded interpretation of self-attention as spatiotemporal influence propagation in GNSS-R data. In our model, each GNSS-R footprint is treated as a graph node whose multi-head self-attention weights quantify localized interactions across space and time. This aligns physical influence propagation with the computational efficiency of GPU-accelerated Transformers. Multi-head attention disentangles processes at multiple scales—capturing local (25–100 km), mesoscale (100 km–500 km), and synoptic (>500 km) circulation patterns. When applied to Level 1 Version 3.2 data (2023–2024) from four Asian sea regions, our Transformer–GNN achieves an overall wind speed RMSE reduction of 32% (to 1.35 m s−1 from 1.98 m s−1) and substantial gains in high-wind regimes (winds >25 m s−1: 3.2 m s−1 RMSE). The model is trained on ERA5 reanalysis 10 m equivalent-neutral wind fields, which serve as the primary reference dataset, with independent validation performed against Stepped Frequency Microwave Radiometer (SFMR) aircraft observations during tropical cyclone events and moored buoy measurements where spatiotemporally coincident data are available. Interpretability analysis with SHAP reveals condition-dependent feature attributions and suggests coupling mechanisms between ocean surface currents and wind fields. These results demonstrate that our model advances both predictive accuracy and interpretability in GNSS-R wind retrieval. With operationally viable inference performance, our framework offers a promising approach toward interpretable, physics-aware Earth system AI applications.

ObjectiveTo characterise and quantify the mortality risks for a range of causes after tropical cyclones in nine countries and territories.DesignTwo stage, time series study.SettingNine countries or territories (Australia, Brazil, Canada, South Korea, Mexico, New Zealand, the Philippines, Taiwan, and Thailand), covering tropical, subtropical, and extra-tropical regions.ParticipantsGeneral populations living in regions with tropical cyclones in the nine countries or territories, 2000-19.Main outcomes measuresExcess mortality risk of cardiovascular diseases, respiratory diseases, infectious diseases, injuries, neuropsychiatric disorders, renal diseases, digestive diseases, diabetes, and neoplasms as the leading cause of death. Wind speed and rainfall profiles were quantified with a physics based tropical cyclone field model.Results14.8 million deaths and 217 tropical cyclone events in communities from the nine countries or territories were included in the analysis. Mortality risks from various causes consistently increased after tropical cyclones, with peaks occurring within the first two weeks after the cyclone, followed by a rapid decline. During the first two weeks after a tropical cyclone, the highest increases were seen in mortality from renal diseases and injuries, with a cumulative relative risk of 1.92 (95% confidence interval (CI) 1.63 to 2.26) and 1.21 (1.12 to 1.30), respectively, for each additional tropical cyclone day. Relatively more modest risks were found for mortality from diabetes (cumulative relative risk 1.15, 95% CI 1.08 to 1.21), neuropsychiatric disorders (1.12, 1.05 to 1.19), infectious diseases (1.11, 1.05 to 1.17), digestive diseases (1.06, 1.02 to 1.09), respiratory diseases (1.04, 1.00 to 1.08), cardiovascular diseases (1.02, 1.01 to 1.04), and neoplasms (1.02, 1.00 to 1.04). Mortality risks were substantially higher in communities with greater levels of deprivation and in those with historically fewer tropical cyclones, especially for renal, infectious, and digestive diseases, as well as for diabetes. Rainfall related to tropical cyclones had a more consistent increasing exposure-response relation with mortality risks, particularly for deaths related to respiratory, cardiovascular, and infectious diseases.ConclusionsAfter tropical cyclones, mortality risk increased variably for different causes, populations, and regions. Integrating epidemiological evidence into the development of management systems for climate extremes is urgently needed, particularly in regions with higher levels of deprivation and in those with historically fewer tropical cyclones. These measures are necessary to improve the adaptive capacity in responding to the growing risks and shifting activity of tropical cyclones in a warming climate.

Impact of tropical cyclones on the suspended sediment transport in the Bohai Sea since the 21st century.

Abstract. Tropical cyclones (TC) in the western North Pacific, known as typhoons, cause significant socioeconomic damage in East and Southeast Asian countries. The city of Shanghai in China is highly vulnerable to TC damages. For example, Typhoons Bebinca and Pulasan recently (September 2024) struck the city, resulting in widespread flooding, power outages, and the evacuation of more than half a million residents, while also breaking local rainfall records. Despite these threats, there is limited knowledge about the variability and mechanisms of TC activities in this region under climate change and urbanization. Here, we use the Weather Research and Forecasting (WRF) convection-permitting model to simulate five TC events that made landfall along the southeastern coast of China and severely impacted Shanghai between 2018 and 2022. Different scenarios are conducted, including considering a future expansion of Shanghai's urban area and increases in sea surface temperature (SST) by 1, 2, and 3 °C. The results indicate that while SST warming significantly shifts TC tracks away from the city, the local risk continues to increase due to substantial enhancement of rainfall intensity and wind velocity. Warmer SST increases air temperature and decreases sea level pressure, thereby facilitating the formation and development of TC sizes and intensities. Furthermore, we find a consistent southward shift of the TC tracks that can be linked to the Fujiwhara effect, a phenomenon that occurs when two typhoons interact, causing a mutual counterclockwise rotation. Compared to SST changes, urbanization has a limited influence on TC tracks and structures. The increase in surface roughness due to urban expansion reduces wind velocity but enhances the rainfall intensity within Shanghai, further exacerbating local risk. These findings could improve our understanding of typhoon variability under the combined effects of urbanization and climate change, as well as the risks they pose to Shanghai and other megacities in TC-prone regions.

Abstract The National Weather Service and other weather industry partners increasingly use Hurricane Threats and Impacts (HTI) graphics to communicate the breadth of hazards and impacts associated with tropical cyclones. However, HTI graphics are often poorly designed and difficult to access, inhibiting their wider use during high-impact tropical cyclone events. In this study, we evaluated HTI graphics with broadcast meteorologists and emergency managers to understand how these professional users use and access HTI information. Additionally, we examine how these users perceive and use a prototype redesigned HTI graphic, created by our research team as part of a novel user-driven iterative design process that incorporates feedback from numerous stakeholders, best practices in visual design, and our team’s various interdisciplinary expertise. We demonstrate that professional users find HTI usable and actionable for their decision-making during tropical threats. Further, we find that these users appreciated the prototype redesigned HTI intuitively for its more “modern” feel, ease of use, and broader accessibility. However, these professional users also disagreed over aspects of the prototype HTI graphic’s design, such as the colors used, hazards represented, and the amount of text included in the graphic, suggesting that there is no “one-size-fits-all” approach to designing effective tropical cyclone risk visualizations. We thus suggest continued investment in user-driven iterative design approaches to understand and meet the needs of the weather industry’s various stakeholders when developing new or existing graphical products. Significance Statement The National Weather Service creates Hurricane Threats and Impacts (HTI) graphics to show the areas at risk from different hurricane hazards. However, these graphics can be difficult to understand and access. We interviewed broadcast meteorologists and emergency managers to understand how they use these graphics and how the graphics could be improved. We also showed them a new version of the HTI that we created, using public feedback and good design practices. Most professionals liked the updated graphic for being modern and user-friendly. However, opinions varied on specific features like colors and text, showing that one design may not work for everyone. Our research shows the importance of soliciting user feedback to create more effective tools for communicating hurricane risks.

Abstract Near-inertial internal waves (NIWs) play a crucial role in upper-ocean turbulent mixing through shear instabilities. Tropical cyclones (TCs), with their intense variable winds, contribute significantly to NIWs, yet their global impact has been poorly quantified due to observational and methodological challenges. In this study, we developed a novel data-driven model that integrates hourly satellite-tracked surface drifter data and refined TC records to provide a high-resolution global estimate of TC-induced near-inertial wind power (NIWP), representing a major energy source for the generation of NIWs. Our results show that TCs contribute 0.019–0.024 TW annually to global NIWP, with nearly 45% of this in low-latitude oceans. Moreover, a notable NIW-induced enhancement in thermocline mixing during TC events is identified in subtropical regions, particularly in the northwestern Pacific, based on fine-scale parameterization. Our findings uncover the episodic yet potentially important role of TCs in powering NIWs, with implications for ocean turbulent mixing.

Abstract Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon’s rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We develop a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900 000 synthetic TC events, accounting for projected changes in TC behavior and sea levels. The derived historical 100 year surge (the event with a 1% yearly exceedance probability) agrees well with historical observations and other modeling techniques. When coupled with an inundation model, we find that heightened TC intensities and sea levels by the end of the century result in a 50% increase in population at risk. Key findings include markedly heightened risk in Florida, and critical thresholds identified in Georgia and South Carolina.

Abstract Multiple tropical cyclone (TC) events (MTCEs) can cause disproportionate damages beyond the cumulative impacts of individual TCs, yet their physical processes and driving mechanisms remain poorly understood. This study focuses on spatial diversity in MTCE occurrence and their associated physical processes over the western North Pacific (WNP). Based on spatial features, MTCEs are objectively classified into three clusters: eastern induced (EI) cluster, western induced in the nearshore (WI-N) cluster, and western induced in the open sea (WI-O) cluster. The EI cluster is driven by the strengthened South China Sea summer monsoon, with TCs forming within the monsoon trough and confluence regions. The WI-N cluster primarily arises from the interaction between the monsoon westerlies and easterlies associated with an anomalous anticyclone. The WI-N cluster is characterized by tropical wave trains, potentially linked to TC-induced Rossby wave dispersion and easterly waves. Dynamic genesis potential analysis reveals that enhanced midlevel vertical motion dominates the dynamic factors controlling the MTCE formation across the WNP. Meanwhile, barotropic energy conversions, arising from the convergence and meridional shear of large-scale zonal winds, serve as the primary sources of eddy kinetic energy for MTCE formation. Upper-level baroclinic energy conversions also play a significant role, especially for the WI-N and WI-O clusters. Time decomposition reveals that factors across multiple time scales, including the quasi-biweekly oscillation, intraseasonal oscillations, and low-frequency variability, contribute to WNP-MTCE formation. Our findings offer a comprehensive view to better understand the spatial diversity of MTCE over the WNP.

Abstract. In the early 1990s, the insurance industry pioneered the use of risk models to extrapolate tropical cyclone (TC) occurrence and severity metrics beyond historical records. These probabilistic models rely on past data and statistical modeling techniques to approximate landfall risk distributions. By design, such models are best fit to portray risk under conditions consistent with our historical experience. This poses a problem when trying to infer risk under a rapidly changing climate or in regions where we do not have a good record of historical experience. We here propose a solution to these challenges by rethinking the way TC risk models are built, putting more emphasis on the role played by climate physics in conditioning the risk distributions. The Unified Tropical Cyclone (UTC) modeling framework explicitly connects global climate data to TC activity and event behaviors, leveraging both planetary-scale signals and regional environment conditions to simulate synthetic TC events globally. In this study, we describe the UTC framework and highlight the role played by climate drivers in conditioning TC risk distributions. We then show that, when driven by climate data representative of historical conditions, the UTC is able to simulate a global view of risk consistent with historical experience. Additionally, the value of the UTC in quantifying the role of climate variability in TC risk is illustrated using the 1980–2022 period as a benchmark.

Abstract The growing number of extreme weather events has contributed to an increasing number and severity of power outages. However, the complex interplay of extreme weather events and their compounding effects on power outage characteristics (e.g. event duration) has not been comprehensively explored. Power outage data is often not publicly available, especially at high spatial resolution. Identifying outages related to weather events can also be challenging, as various weather variables can trigger or modulate power outages when they occur, in isolation or combined. Here, we use county-level power outage data from EAGLE-I for the state of Florida from 2015 to 2022 to identify moderate and major weather-related outages and analyze their characteristics. We show that total outage counts were higher in metro areas than in non-metro areas. However, the percentage of weather-related power outages was higher in non-metro areas than in metro areas. The spatial variation of grid reliability indicators derived from all weather-related events follows similar patterns as derived when just focusing on tropical cyclone events, highlighting the importance of these types of extremes in creating prolonged outages. Considering six relevant weather variables, we identify univariate and compound events (i.e. when more than one weather variable was extreme at the time of the outage). Univariate events have a relatively homogenous pattern across the state of Florida, while compound events have more localized hotspots. The average duration of the outages also increases when moving from univariate to multivariate events. Our results shed light on the relative importance of different weather variables (in isolation or combination) in creating power outages with different characteristics across Florida. Identifying such relationships is an important step toward understanding how power outage frequency and/or severity may change when certain extreme weather events become more frequent and/or intense.

This paper presents an example of investigations on the responses of extreme tropical-cyclone-induced wave and storm surge to projected future sea level rise in shelf–bay–channel multi-scale domains. A tide–surge coupling hydrodynamics model and a surface wave model, both using the same computational grid with a high spatial resolution, are developed individually and applied to respectively simulate the storm surge and typhoon wave in a shelf–bay–channel multi-scale domain refining from South China Sea to narrow coastal channels around Macao in the west of Pearl River Estuary. The computational grid has a spatial resolution gradually refining from about 14 km at the open boundary to a highest value of 30 m in the coastal channels around Macao. The Super Typhoon Hato (No. 1713), which has caused catastrophic losses, is considered as an example of extreme tropical cyclone events. The models are well validated and applied to simulate the changes of Hato-induced wave and storm surge in four pre-defined sea-level-rise scenarios. In the four scenarios of sea level rise, the peak height of Hato-induced wave enlarges linearly with the rising sea level in most of the multi-scale domains, notably along the shelf open coasts and bay outlet. In contrast, the peak surge varies insignificantly along the bay outlet and even decreases remarkably in a quadratic function within the narrow channels. These findings suggest that the contribution of waves to extreme sea levels during tropical cyclones may intensify if the mean sea level rises and it varies in shelf–bay–channel multi-scale domains, which should be carefully taken into consideration in coastal hazard assessments.

Extreme surface winds and wave heights of tropical cyclones (TCs)—pose serious threats to coastal community, infrastructure and environments. In recent decades, progress in numerical wave modeling has significantly enhanced the ability to reconstruct and predict wave behavior. This review offers an in-depth overview of TC-related wave modeling utilizing different computational schemes, with a special attention to WAVEWATCH III (WW3) and Simulating Waves Nearshore (SWAN). Due to the complex air–sea interactions during TCs, it is challenging to obtain accurate wind input data and optimize the parameterizations. Substantial spatial and temporal variations in water levels and current patterns occurs when coastal circulation is modulated by varying underwater topography. To explore their influence on waves, this study employs a coupled SWAN and Finite-Volume Community Ocean Model (FVCOM) modeling approach. Additionally, the interplay between wave and sea surface temperature (SST) is investigated by incorporating four key wave-induced forcing through breaking and non-breaking waves, radiation stress, and Stokes drift from WW3 into the Stony Brook Parallel Ocean Model (sbPOM). 20 TC events were analyzed to evaluate the performance of the selected parameterizations of external forcings in WW3 and SWAN. Among different nonlinear wave interaction schemes, Generalized Multiple Discrete Interaction Approximation (GMD) Discrete Interaction Approximation (DIA) and the computationally expensive Wave-Ray Tracing (WRT) A refined drag coefficient (Cd) equation, applied within an upgraded ST6 configuration, reduce significant wave height (SWH) prediction errors and the root mean square error (RMSE) for both SWAN and WW3 wave models. Surface currents and sea level variations notably altered the wave energy and wave height distributions, especially in the area with strong TC-induced oceanic current. Finally, coupling four wave-induced forcings into sbPOM enhanced SST simulation by refining heat flux estimates and promoting vertical mixing. Validation against Argo data showed that the updated sbPOM model achieved an RMSE as low as 1.39 m, with correlation coefficients nearing 0.9881.