Abstract The WIVERN (Wind Velocity Radar Nephoscope) mission significantly enhances the global tropical cyclone observing system. Operating from a 500 km near‐polar orbit, the 3 m diameter conically scanning antenna provides an 800 km swath. The radar operated at 94 GHz (3 mm wavelength) provides high‐resolution observations with a vertical resolution of 600 m and horizontal resolution finer than 1 km. With quasi‐daily global coverage, WIVERN measures in‐cloud tropical cyclone winds from 1 km above the surface to the upper troposphere. Simulations of the Weather Research and Forecasting model with 1.5 km grid spacing were carried out for Hurricane Milton (2024) to serve as a testbed to demonstrate the potential capabilities of the WIVERN mission and its associated data products. The high‐resolution simulation successfully reproduces the hurricane's trajectory and intensification, capturing a remarkable 78‐knot increase in maximum sustained wind speed during the 24‐hr period from 7 October to 8 October. End‐to‐end simulations demonstrate that WIVERN: (a) can provide a three‐dimensional view of the horizontal wind inside cyclones, in particular capturing the vertical wind shear, the upper level divergence and the in‐cloud circulations inside the anvil produced by the hurricane convective towers, and some of the inflow and outflows in the lower layers of the atmosphere; (b) in presence of close‐in‐time overpasses, has the potential to detect the intensification of cyclone by estimating the maximum winds in the inner core; (c) can profile the tropical cyclone ice mass as a function of the distance from the eye, which will help shed light on the anvil formation and dissipation mechanisms.

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.

Abstract Potential intensity (PI) is a key indicator for tropical cyclone (TC) activity, yet it exhibits considerable variability across global climate model (GCM) simulations, even with identical sea surface temperatures (SSTs). We show that the spread in PI across GCMs is primarily driven by differences in outflow temperature, a consequence of different upper atmospheric temperatures. To explore the impacts of these biases on TC activity, we conduct several idealized experiments with altered temperature profiles. In these experiments, global TC frequency and accumulated cyclone energy change by ∼35% and hurricane frequency by ∼80%. There are smaller but still significant impacts on lifetime maximum intensity. These findings highlight an underappreciated role of upper atmospheric model biases in modulating TC activity in GCMs, how future changes in TC activity may be influenced by responses of upper atmospheric temperature to anthropogenic emissions, and that TCs are more directly influenced by PI than SST alone.

Abstract Global tropical cyclone (TC) activity is influenced by the tropical Pacific mean state, which is modulated by the El Niño–Southern Oscillation (ENSO) phenomenon at interannual time scales. In recent decades, observations show a La Niña–like trend in the tropical Pacific, while most global climate models simulate a more El Niño–like trend. The cause of this inconsistency remains under debate, but it is likely quite consequential for global TC activity. This study examines the response of global TC activity to sea surface temperature (SST) warming patterns using historical simulations from the High Resolution Model Intercomparison Project (HighResMIP): atmosphere-only simulations forced by observed SSTs, which exhibit a La Niña–like SST trend in the tropical Pacific; and coupled simulations, showing a weaker La Niña–like SST trend. TC activity is compared between the two sets of simulations using multiple statistics of TC activity, along with TC-relevant large-scale environmental fields. The TC genesis index (TCGI) is used to quantify the contributions of each environmental field to changes in TC activity. Results show that the forced global TC response is sensitive to the tropical Pacific mean state and varies by basin. In SST-forced simulations, TC activity’s trend patterns resemble the TC anomalies during La Niña events. In those simulations, thermodynamic environmental fields, namely, potential intensity and column-relative humidity, induce zonal variations in TC frequency, while dynamical fields, namely, vertical wind shear and absolute vorticity, induce meridional variations in TC frequency. In contrast, coupled simulations show no consistent TC response across HighResMIP models. Significance Statement Global tropical cyclone (TC) activity is influenced by the sea surface temperature pattern in the tropical Pacific. In recent decades, observations show strengthening zonal (west minus east) and meridional (off-equator minus equator) sea surface temperature gradients, whereas most climate models simulate the opposite trends. This study examines how global TC activity responds to the sea surface warming patterns using climate model simulations. Results show that TC activity is sensitive to changes in the tropical Pacific mean state, with basin-dependent responses. In models forced with strengthening gradients, TC activity changes resemble those during La Niña events, featuring anomalously positive zonal and meridional gradients in sea surface temperature. However, no consistent signals for TC activity have been found in models with weakening gradients.

Changes in tropical cyclone (TC) frequency in a warming climate remain highly uncertain, with model simulations often yielding conflicting results. Here, through an analysis of 26 CMIP6 models under the SSP5-8.5 scenario, we project a decline in global TC frequency of 2–10% by the late 21st century. This decline is driven by an El Niño-like warming pattern, marked by amplified warming in the equatorial central-eastern Pacific, equatorial Atlantic, and North Indian Ocean. These non-uniform sea surface temperature (SST) trends weaken zonal SST gradients, suppressing the Walker circulation, and inducing Gill-type atmospheric responses to localized heating, in addition to an equatorward shift in the Intertropical Convergence Zone. These changes reduce upward motion across most basins, suppressing TC genesis. Additionally, radiative forcing reduces interhemispheric temperature contrasts, dampening the cross-equatorial Hadley circulation and further decreasing TC frequency, most notably in the South Indian Ocean. Our findings highlight the critical role of spatially heterogeneous SST warming in modulating TC activity and stress the need to reduce uncertainties in future SST projections. These insights advance the physical understanding of climate-driven TC projections and provide essential guidance for adaptation planning in vulnerable regions.

Abstract We quantify the impact of ocean eddies on the global‐mean tropical‐cyclone intensity in a 1‐year simulation run with a coupled atmosphere‐ocean model with quite‐realistic seasonal climatology that resolves both Tropical cyclones (TCs) and ocean eddies. We find a significantly lower global‐mean intensity and intensification rate for the subset of TCs that spend more time over cold‐core eddies than warm‐core eddies. While not statistically significant, we also find that TCs that encounter more warm than cold‐core eddies display a higher global‐mean intensity and intensification rate. The differing impact of warm‐core and cold‐core ocean eddies are consistent with coherent differences in sea‐surface cooling prior to peak intensity. Our results demonstrate that ocean eddies can have a statistically significant impact on the global‐mean intensity, suggesting that resolving ocean eddies does matter for global TC statistics.

Abstract High-impact weather events can occur downstream from recurving western North Pacific (WNP) tropical cyclones (TC) that undergo extratropical transition and interact strongly with the midlatitude flow. These events are the result of jet streak intensification and downstream wave amplification triggered by a prolonged period of strong negative potential vorticity (PV) advection by the divergent component of the wind in the TC outflow region, here called Rossby wave (RW) forcing. A 46-year global climatology of all TC-related RW forcing episodes during 1979–2024 is obtained to test the hypothesis that RW forcing events can occur outside of the recurving WNP TC context. Using global TC data from the International Best Track Archive for Climate Stewardship (IBTrACS) database, it is first demonstrated that strong RW forcing events associated with recurving TCs are mostly confined to the WNP basin. Downstream response is assessed by determining composite time series of downstream meridional index, 250-hPa wind speed and a proxy for midlatitude cyclones. Removing the requirement for TC recurvature resulted in a ~72% increase in cases but with no significant change in downstream response, suggesting that TC recurvature is not a prerequisite for excitation or amplification of Rossby waves. A similar but smaller downstream response occurred in other TC basins. Spatial composites for recurving TCs revealed that the downstream response reflected the direct impact of TCs migrating into the compositing domain rather than remote downstream Rossby wave amplification triggered by the RW forcing. Spatial composites for non-recurving TCs having little eastward progression revealed an amplifying standing wave pattern downstream from the longitude of the RW forcing.

Abstract Tropical cyclone (TC) rainfall poses a significant threat to coastal regions, particularly in the near‐storm region, where the inner core's strongest convection occurs. Temporal variability in the near‐storm rainfall rate over a TC's lifetime, including rapid increases and decreases in rainfall rate, impacts forecast accuracy and hazard preparedness. Using multiple HighResMIP CMIP6 simulations under the SSP5‐8.5 scenario, we find that near‐storm rainfall rate temporal variability increases under pronounced anthropogenic warming, driven primarily by elevated atmospheric moisture. Analysis of relative rainfall rate changes suggests that this heightened variability aligns with rising near‐storm rainfall rate trends. These findings have critical implications for coastal communities, highlighting not only the increased risk of near‐storm rainfall but also the rapid intensification or decline of these rainfall events.

Tropical cyclones and climate change: An overview for the public health community.

Abstract Tropical cyclones (TCs) in the North Indian Ocean (NIO) constitute only 6% of global TCs but cause over 80% of cyclone-related fatalities. Conventional wind speed-based assessments often underestimate the compound nature of TC hazards. We develop a multivariate framework integrating wind speed and precipitation to characterize TC compound hazards in India (1951–2020). Using multivariate dependence modeling, we estimate joint return periods, conduct district-level exposure characterization, and assess non-stationarity. Advanced metrics, including the Shannon surprise, various statistical distance metrics, and fraction attributable risk, reveal regions with heightened susceptibility and the climate change influence on cyclone risks, especially notable among districts of Odisha, Tamil Nadu, and Andhra Pradesh. This methodologically simple and computationally efficient framework, based on a quasi-Lagrangian perspective, while demonstrated for India, offers a scalable methodology for assessing compound TC hazards to cyclone-prone regions globally. It supports targeted adaptation strategies, contributing to the broader goals of sustainable development and climate resilience.

Tropical cyclones (TCs) are among the most destructive weather phenomena, significantly impacting atmosphere-ocean interactions and coastal regions. Accurate and high-resolution TC wind field datasets are critical for enhancing storm forecasting, disaster risk assessment, and understanding ocean-atmosphere interactions. Existing reanalysis datasets, such as ERA5, typically underestimate peak wind speeds and inadequately capture inner-core structural characteristics of TCs. To address these limitations, we introduce a globally applicable, high-resolution TC wind field dataset reconstructed from ERA5 through an integrated parametric correction approach. This novel methodology combines the Willoughby adjustment for open-ocean TCs, proportional correction methods for landfalling TCs, and a linear interpolation method to ensure smooth transitions in coastal regions. Comprehensive validation against satellite-based measurements (SMAP and WindSat), airborne observations (SFMR), and ground-based meteorological data demonstrates substantial enhancements in the accuracy of reconstructed TC tracks, maximum wind speeds, and radius of maximum wind. The resulting dataset provides significant improvements in representing global TC dynamics, and serving as a valuable resource to advance TC forecasting, climate modeling, and disaster resilience strategies.

China is one of the countries most affected by tropical cyclones (TCs) worldwide. However, it remains unclear whether the significant poleward and coastal shifts in global TCs due to climate warming have affected the lifetime of landfalling TCs in China. Result shows that the lifetime of landfalling TCs in China has significantly decreased by 24% over the past 40 years, which is primarily contributed by the minor TCs. The duration in development stage has significantly shortened for both major and minor TCs. The northwestward migration of genesis locations has led to a decrease in duration of minor TCs, while the increase in the intensification rate has contributed to the shorter major TC durations. Further analysis suggests that these changes in TC lifetime and northwestward shifts are associated with a decrease in the Convective Available Potential Energy and westward extension of the subtropical high. The observed increase in rapid intensifications of major TCs is mainly driven by an increase in favourable thermodynamic environments. These findings clarify the connection between large-scale changes in TC genesis locations and regional characteristics of TC activity.

Abstract In this study, the Bayesian Inference and Dynamic Programming Tracking (BIDTrack) algorithm is developed for enhanced global tropical cyclone (TC) tracking in reanalysis datasets, particularly the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5). BIDTrack addresses challenges like trajectory discontinuities and parameter sensitivity in traditional methods by combining Bayesian inference with dynamic programming. The algorithm is optimized through a Bayesian interval optimization (BIO) process, which refines the parameters to retain cyclone candidates that are statistically significant and physically meaningful. Results indicate a strong spatial correlation between BIDTrack-derived trajectories and International Best Track Archive for Climate Stewardship (IBTrACS) observations, especially in cyclone-prone regions like the North Atlantic and western Pacific. BIDTrack captures both major hurricanes and weak storms, providing a reliable tool for cyclone path reconstruction and climate impact assessments. This research demonstrates BIDTrack’s potential in improving TC tracking and enhancing the understanding of cyclone dynamics in ERA5. Significance Statement Tropical cyclones, such as hurricanes, are powerful storms that pose significant risks to coastal communities. Tracking their paths accurately is crucial for understanding their behavior and mitigating their impacts. In this study, an emerging method, Bayesian Inference and Dynamic Programming Tracking (BIDTrack), is introduced by combining Bayesian inference with dynamic programming to enhance cyclone tracking in the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5). BIDTrack generates cyclone paths with probability estimates, providing a more precise assessment of whether a given track point corresponds to the actual cyclone. This algorithm is effective in tracking both strong hurricanes and weaker storms, making it a valuable tool for researchers and decision-makers interested in cyclone behavior and climate impacts.

Abstract Whether, and to what extent, strong tropical volcanic eruptions (TVEs) affect global tropical cyclone (TC) genesis frequency (TCGF) remains uncertain. Here, we address this issue using both high-resolution reanalysis data and large ensemble simulations. We find a significant increase in TCGF over the Pacific within two years following TVEs, while a statistically insignificant decrease over the South Indian Ocean, leading to an overall increase in global TCGF driven mainly by Pacific basin responses. Strong TVEs suppress precipitation over the Maritime Continent and lower global mean sea surface temperature (SST), while inducing warming in the equatorial central Pacific, resulting in an El Niño-like SST pattern. These anomalies weaken the Walker Circulation and drive low-level westerly wind and upper-level easterly wind anomalies over the tropical Pacific, creating favorable conditions for enhanced Pacific TCGF. These results offer new insights into how TVEs influence TC activity on both global and regional scales.

Abstract We investigate a scaling relationship between global tropical cyclone (TC) frequency and the latitude of the intertropical convergence zone (ITCZ) in simulations performed with a 50‐km‐resolution aquaplanet version of the Geophysical Fluid Dynamics Laboratory Atmosphere Model 4.0. The simulations use fixed, zonally symmetric sea surface temperature distributions, including some with uniform warming and cooling perturbations. We find that TC frequency per unit area is proportional to the Coriolis parameter at the ITCZ, following the same scaling introduced in a previous study. We hypothesize that TCs in these simulations originate as precursor disturbances at the ITCZ and intensify into TCs upon reaching sufficiently warm SSTs. We test this interpretation by tracking TC precursors, with different methods based on precipitation and vorticity, and comparing TC precursor frequency with TC frequency and ITCZ latitude. Both tracking methods show that precursors predominantly originate around the poleward edge of the ITCZ, consistent with our hypothesized TC genesis pathway. We also verify that most TC genesis events are immediately preceded by the occurrence of a precursor in the same area. However, precursor frequency is only weakly correlated with the Coriolis parameter at the ITCZ and precursor frequency. The correlation is stronger for vorticity‐based precursors than for precipitation‐based precursors. These mixed results provide partial, but not complete, support for our hypothesized interpretation. They also illustrate how results can depend on the choice of precursor tracking scheme, underlining a need for improved understanding of how best to define and track TC precursors.

Accurate tropical cyclone (TC) forecasting is critical for disaster prevention. While deep learning shows promise in weather prediction, existing approaches demonstrate limited accuracy in TC track and intensity forecasting, hindered by the lack of open multimodal datasets and insufficient integration of meteorological knowledge. Here we propose TropiCycloneNet containing TCND - a open multimodal TC dataset spanning six major ocean basins with 70 years of multi-source data, and TCNM - an AI-meteorology integrated prediction model including multiple modules such as Generator Chooser Network and Environment-Time Net. Comprehensive evaluations demonstrate that TCNM outperforms both existing deep learning methods and official meteorological forecasts across multiple metrics. This advancement stems from synergistic optimization of our meteorologically-informed architecture and the dataset’s comprehensive spatiotemporal coverage. The released resources and method can attract more researchers to the field, thereby accelerating data-driven tropical cyclone prediction research.

Abstract The intensification rate (IR) equation, derived from a time‐dependent simplified energetically based dynamical system (EBDS), links tropical cyclone (TC) intensity, IR, and their evolutions, suggesting a connection between the timing of TC lifetime maximum intensity (LMI) and the maximum intensification rate (MIR). This study demonstrates that the IR equation predicts MIR typically preceding LMI within 36 hr, as confirmed by numerical simulation and analysis of best track data. It was observed that the majority of global TCs reached LMIs within 36 hr after MIR from 1982 to 2023. This consistency between observational data, numerical simulations and analytical results highlights the EBDS‐based IR equation's ability in capturing the timing relationship between MIR and LMI. This intrinsic link highlights the importance of understanding the timing of MIR in the evolution of TCs.

Abstract Because of the limited length of observed tropical cyclone (TC) data and low confidence in modeling TC genesis frequency, it has been difficult to understand why the genesis frequency of global TCs has remained nearly invariant while regional variations are highly pronounced. Investigating the covariability of TC genesis frequency between different regions may shed light on this question. Here, we identify that TC genesis frequency varies out of phase between the eastern and western regions of the western North Pacific (WNP). Such a seesaw relationship could be explained by the east–west dipole patterns of low-level vorticity and midtropospheric relative humidity in the WNP, associated with variations in atmospheric conditions. Composite analyses and numerical experiments show that a combination of cooling in the WNP and warming in the north Indian Ocean (NIO) exerts a significant influence on the seesaw pattern. Our study adds more evidence to believe on number conservation of worldwide TC genesis. Significance Statement Little had been known about why the genesis frequency of global tropical cyclones has remained nearly invariant in a changing climate. To find out whether there is any covariability of tropical cyclone genesis frequency between different regions may provide a chance to understand such a steady trend. The western North Pacific is the ocean basin where tropical cyclones are the most active in the world. In this study, it is observed that tropical cyclone genesis frequency between the eastern and western regions of the western North Pacific has a negative correlation. It is demonstrated that the zonal contrast of sea surface temperature in the western North Pacific to the north Indian Ocean plays a significant role in driving the seesaw relationship of tropical cyclone genesis frequency by moderating the large-scale atmospheric circulation.

Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. Numerous researchers have explored deep-learning methods to address computational and post-processing issues in operational forecasting. Regrettably, they exhibit subpar long-term forecasting capabilities. We use two strategies to enhance long-term forecasting. (1) By enhancing the matching between TC intensity and spatial information, we can improve long-term forecasting performance. (2) Incorporating physical knowledge and physical constraints can help mitigate the accumulation of forecasting errors. To achieve the above strategies, we propose the VQLTI framework. VQLTI transfers the TC intensity information to a discrete latent space while retaining the spatial information differences, using large-scale spatial meteorological data as conditions. Furthermore, we leverage the forecast from the weather prediction model FengWu to provide additional physical knowledge for VQLTI. Additionally, we calculate the potential intensity (PI) to impose physical constraints on the latent variables. In the global long-term TC intensity forecasting, VQLTI achieves state-of-the-art results for the 24h to 120h, with the MSW (Maximum Sustained Wind) forecast error reduced by 35.65%-42.51% compared to ECMWF-IFS.

Tropical easterly waves (TEWs) are westward-moving waves often within trade winds but occur ubiquitously in the tropics and play a significant role in the genesis of tropical cyclones (TCs). They are well-known as primary precursors of TCs in the Atlantic, yet their global relationship with TCs has been less explored. This study, for the first time, presents the global distribution of TEW activity using a combined thermodynamic and dynamic framework based on 6-hourly Outgoing Longwave Radiation and curvature vorticity. We then demonstrate that TEWs play a dominant role in approximately 22–71% of global TC genesis, with their highest impacts in the North Atlantic (71%) and Western Pacific (54%). We further identify that TEWs, in their general coupling with TC genesis dynamics, act to intensify TC convection and vorticity in all TC main development regions, albeit the vorticity enhancement is relatively weaker in the North Atlantic. To understand the cross-basin differences in this general TEW-TC relationship, we further investigated background conditions for TC genesis in each basin and found an additional dry environment constraint in the Atlantic TC genesis, yet still delineating the critical role of TEWs in TC development.