At the end of 2017, the Jakarta Tropical Cyclone Warning Center (TCWC) observed the formation of two tropical cyclones in the southern waters of Indonesia, namely Cempaka and Dahlia, which triggered extreme weather events and caused damage and casualties in several regions. This study aims to identify the developmental stages of tropical cyclones Cempaka and Dahlia, from formation to dissipation, and to examine atmospheric conditions before, during, and after the cyclones. The analysis employs the Dvorak technique based on Himawari-8 satellite infrared imagery to monitor cyclone intensity, supported by ECMWF reanalysis data to evaluate atmospheric parameters in the cyclone growth region. The results indicate that Cempaka and Dahlia reached the Tropical Storm (TS) category on 27 November and 1 December 2017, respectively. During the mature stage, atmospheric conditions were characterized by high relative humidity ranging from 90% to 100%, strong cyclonic circulation with negative vorticity values between −10 × 10⁻⁵ s⁻¹ and −50 × 10⁻⁵ s⁻¹, and lower-level convergence indicated by negative divergence values ranging from −10 × 10⁻⁵ s⁻¹ to −20 × 10⁻⁵ s⁻¹. These conditions support the development of convective clouds and the intensification of the cyclonic systems, providing insight into the role of atmospheric dynamics in the growth of tropical cyclones in the vicinity of Indonesia.

Over 30,000 hazardous material pipeline (HMP) failures have caused nearly $11 billion in damages since 1970. Tropical cyclones, which cause more infrastructure damage than all other forms of natural disasters combined, are thought to be under-attributed causes of HMP failures, largely due to historic policy around pipeline failure reporting. This study defines tropical cyclone-associated HMP failure frequency based on spatiotemporal concomitance, while detrending for background HMP failure rates, and explores the relationship between the likelihood and frequency of HMP failures and tropical cyclone intensity using a method that accounts for omitted variable bias associated with unparameterized storm and pipeline characteristics. Though only 4.3% of HMP failures have been formally linked to natural forcings, 32.5% of HMP failures in the eastern United States occur within 60 days of a tropical cyclone. HMP failures 60 days after a tropical cyclone intersection are both significantly more likely, and exponentially more frequent, with increasing tropical cyclone intensity. Since 1975, the annual frequency of tropical cyclone-associated pipeline failures has increased by an order of magnitude. During this same period, the annual lifetime maximum intensity of a tropical cyclone intersecting with HMP infrastructure has increased from a Category 3 to a Category 4 storm on the Saffir-Simpson hurricane scale. Implications for pipeline design, and of accurate natural hazards-related cause attribution on HMP failure incident reports, are discussed.

This study investigates the effectiveness of embedding fiber Bragg grating (FBG) sensors in power transmission towers to assess the remaining service life of the structures following impacts from strong winds and hurricanes. FBG sensors monitor the structural integrity of the tower using online measurement of strain variations at critical structural points. The novelty of this work lies in employing a compact long short-term memory (LSTM) framework to estimate the remaining useful lifetime (RUL) from real-time FBG sensor data under both stable and fluctuating wind conditions. To estimate RUL of the tower, LSTM neural network has been implemented, providing predictive insights for proactive maintenance and risk mitigation. A prototype transmission tower was built and experimentally evaluated in a wind tunnel to assess the effectiveness and performance of the proposed RUL model. To simulate different hurricane categories, the experiment was conducted across wind speeds between 0 and 150 mph. FBG sensors installed at critical locations continuously captured real-time strain data, which was transmitted via a low-power micro FBG interrogator to a computer for input into the RUL prediction model. The proposed three-layer LSTM converges rapidly, reducing training and validation loss by nearly two orders of magnitude within 40 epochs, and achieves robust RUL predictions with an average bias of about 50 s on the test set. To quantify structural health, a mathematical health indicator was formulated based on the observed strain responses. The FBG sensors demonstrated high effectiveness in accurately detecting strain variations and monitoring the tower's dynamic behavior under extreme wind loads. The findings support the implementation of condition-based maintenance strategies, enhance safety assessments, and enable early failure detection. This approach not only improves operational reliability but also facilitates timely intervention and maintenance during critical events. Received: 26 June 2025 | Revised: 12 September 2025 | Accepted: 11 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Yu Shi: Methodology, software, formal analysis, data curation, writing – original draft, writing – review & editing, visualization, supervision, and project administration. Abolghassem Zabihollah: Conceptualization, methodology, software, validation, investigation, resources, data curation, writing – original draft, writing – review & editing, visualization, supervision, and project administration. Yao-Chi Yu: Methodology, software, formal analysis, data curation, and writing – original draft. Arunima Pathak: Investigation, writing – original draft, and visualization. Oluwaseyi Oyetunji: Investigation.

Abstract Accurate modeling of tropical cyclone wind fields is essential for the design, risk assessment, and operational planning of offshore energy infrastructure. While mesoscale simulations are widely used thanks to their computational efficiency, they lack the necessary resolution to capture key features such as wind shear and veer profiles as well as the distribution turbulent kinetic energy (TKE). High-fidelity large-eddy simulation (LES) models on the other hand, can resolve turbulent structures and provide a more accurate representation of the complex wind field, albeit at a higher computational cost. To address this modeling gap, we introduce a two-part generative framework to enhance the resolution and physics-capturing ability of mesoscale simulations. First, a reduced-order model based on Karhunen–Loève (KL) decomposition is used to extract dominant spatial modes from one-dimensional mean wind profiles. A multilayer perceptron (MLP) is trained to map mesoscale mode weights to their LES counterparts, enabling accurate reconstruction of vertical velocity profiles. Second, a conditional Diffusion Denoising Probabilistic Model (DDPM) is developed to super-resolve coarse and low-fidelity mesoscale velocity fields, recovering fine-scale turbulence structures and stress distributions. The framework is evaluated across different tropical cyclone intensity categories defined by the Saffir–Simpson scale and demonstrates strong performance in both interpolation and extrapolation tasks. The generated fields accurately reproduce spatial coherence, stress distributions, and spectral energy characteristics observed in LES data. By bridging the fidelity gap between mesoscale and LES outputs, this approach offers a scalable, data-driven solution for enhancing the representation of tropical cyclone wind fields, enabling more robust offshore energy infrastructure systems design in tropical-cyclone-prone areas.

Abstract. A feasibility assessment of offshore wind in the Gulf of Mexico conducted by the National Renewable Energy Laboratory concluded that hurricane risk was one of the major challenges that would need to be overcome for a mature offshore wind industry to develop in the Gulf of Mexico, as the hurricanes that frequent this area can potentially exceed design limits prescribed by the International Electrotechnical Commission (IEC) wind design standards. To better understand and account for these unique conditions, we target two objectives. The first is to develop a translation between the well-established Saffir–Simpson hurricane scale and the IEC design classes, which are based upon different averaging periods and reference heights and often lead to misinterpretation, speculation, and uncertainty. The conversion of wind speed averaging times between Saffir–Simpson and IEC design standards reflects the behaviour of the sea surface drag coefficient as a function of the mean wind speed, which controls the turbulence characteristics of the hurricane boundary layer near the surface. The second objective is to quantify the hurricane exposure risk for wind turbines at sites potentially impacted by hurricanes in the Gulf of Mexico using probabilistic hurricane track and wind field models. The IEC prescribes the reference wind speeds associated with Class 1A and Typhoon Class limit states to be 50 years, though model results indicate the return periods associated with the IEC Class 1A limit state range from approximately 20 to 45 years, while the return periods associated with the Typhoon Class limit state range from approximately 40 to 110 years. Ultimately, this indicates that the Class 1A limit state may be nonconservative for the entire Gulf of Mexico offshore wind energy area, while the Typhoon Class limit state may be adequate for the design of turbines in some regions of the Gulf of Mexico offshore wind energy area.

Abstract Climate change is expected to increase the severity of hurricanes and tropical storms, posing significant risks to the electricity grid. These include downed power lines, damaged solar panels, and impaired wind turbines from high winds. New York (NY) and New Jersey (NJ) are not spared from these vulnerabilities and must strengthen their infrastructure and mitigate social and technical impacts. Clean energy mandates, such as NJ’s Executive Orders No. 315 and 307 (100% clean energy by 2035 and 11 GW of offshore wind by 2040), and NY’s Executive Order No. 166 (40% emissions reduction by 2030), add urgency to ensuring grid resilience under extreme weather. This study demonstrates the power system cyclone impact model (PCIM), used alongside the GenX electricity system planning tool, to assess grid resilience under hurricane-induced high wind speeds in the NY and NJ region. Results reveal that onshore and offshore wind could contribute additional power during storms, provided transmission and storage systems remain operational. This output helps offset outages elsewhere in the grid across all storm categories. In contrast, solar emerges as a vulnerability due to combined impacts from wind stress and cloud cover, significantly reducing generation during and after storms. Thermal generators show the lowest failure rates, though this may partly reflect current model limitations, as only wind stress and cloud cover are considered, excluding hazards like flooding. Non-served energy costs vary with electricity demand and fluctuations in wind and solar output. July stands out as the most vulnerable month, due to high demand and limited wind generation, leading to higher non-served energy. This research provides a first step toward understanding storm-related grid resilience in NJ and NY. The PCIM is designed to be generalizable, with future work focused on expanding its scope to include additional hazards like storm surge and flooding, and more storm-prone regions.

This study investigates landslide occurrence in Taiwan, a region highly susceptible to landslides due to steep mountains and frequent typhoons (TYPs). The primary objective is to understand how both geomorphological factors and TYP characteristics contribute to landslide occurrence, which is essential for improving hazard prediction and risk management. The research analyzed landslide events that occurred during the TYP seasons of 2019 and 2023. The methodology involved using satellite-derived landslide inventories from SPOT imagery for events larger than 0.1 hectares, tropical cyclone track and intensity data from IBTrACS v4 (classified by Saffir–Simpson Hurricane Scale), and detailed topographic variables (elevation, slope, aspect, Stream Power Index) extracted from a 30 m Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM). Land use and land cover classifications were based on Landsat imagery. To establish a timeline, landslides were matched with TYPs within a ±3-day window, and proximity was analyzed using buffer zones ranging from 50 to 500 km around storm centers. Key findings revealed that landslide susceptibility results from a complex interplay of meteorological, topographic, and land cover factors. The critical controls identified include elevations above 2000 m, slope angles between 30 and 45 degrees, southeast- and south-facing aspects, and low Stream Power Index values typical of headwater and upper slope locations. Landslides were most frequent during Category 3 TYPs and were concentrated 300 to 350 km from storm centers, where optimal rainfall conditions for slope failures exist. Interestingly, despite the stronger storms in 2023, the number of landslides was higher in 2019. This emphasizes the importance of interannual variability and terrain preparedness. These findings support sustainable disaster risk reduction and climate-resilient development, aligning with Sustainable Development Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action). Furthermore, they provide a foundation for improving hazard assessment and risk mitigation in Taiwan and similar mountainous, TYP-prone regions.

Abstract A social media firestorm (SMF) refers to a sudden surge of negative reactions, criticism, or controversy on social media platforms, typically triggered by a specific event, statement, or action. Such firestorms can affect individuals, organizations, or brands, with potential reputational and financial consequences if not addressed appropriately. This paper elaborates on an SMF scale inspired by the Saffir-Simpson hurricane scale, adopting a structured approach to SMF measurement and management. The scale defines three measurable dimensions: width (reach or scope), height (intensity of negative sentiment), and duration of peak activity (the shark-fin shape). To provide preliminary validation, an artificial intelligence-based approach was applied to selected real-world firestorm cases. The findings suggest that the framework represents a first step toward a fully validated scale, offering an initial basis for assessing the potential impact of SMFs and supporting more structured organizational responses to digital crises.

During a hurricane, it is vital that individuals receive communications that are easy to process and provide sufficient information to allow informed hurricane preparedness decisions and prevent loss of life. We study how different hurricane warning scales, the traditional Saffir-Simpson Hurricane Wind Scale (SSHWS) versus the newly developed Tropical Cyclone Severity Scale (TCSS), impact intent to evacuate and understanding of hurricane severity. We use a between-subject design where participants are assigned to either the traditional SSHWS or the new TCSS scale. We collected data in a large-scale (~ 4000 participants) online experiment to examine potential differences in comprehension, risk perception, anticipated evacuation, and preparation decisions among residents in U.S. coastal states prone to hurricanes. We find that participants using the TCSS scale are better at identifying the main hazard of a hurricane. For evacuation, the TCSS leads to significantly higher evacuation intent as opposed to SSHWS in cases where the TCSS is at least two categories higher (due to rainfall or storm surge being the main hazard rather than wind). In addition, the TCSS also seems to have a positive effect on taking appropriate precautionary measures, though not always at our stated significance level. Overall, our results demonstrate that people make better informed and more appropriate decisions with the TCSS as opposed to the currently used SSHWS.Protocol Registration The stage 1 protocol for this Registered Report was accepted in principle on 14 October 2024. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/AYXTK. The approved Stage 1 protocol is available here: https://osf.io/m3swr.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-14170-1.

The city of Acapulco was impacted by two near-consecutive hurricanes. On 25 October 2023, Hurricane Otis made landfall, reaching the highest Category 5 storm on the Saffir–Simpson scale, causing extensive coastal destruction due to extreme winds and waves. Nearly one year later (23 September 2024), Hurricane John—a Category 2 storm—caused severe flooding despite its lower intensity, primarily due to its unusual trajectory and prolonged rainfall. Digital shoreline analysis of PlanetScope images (captured one month before and after Hurricane Otis) revealed that the southern coast of Acapulco, specifically Zona Diamante—where the major seafront hotels are located—experienced substantial shoreline erosion (94 ha) and damage. In the northwestern section of the study area, the Coyuca Bar experienced the most dramatic geomorphological change in surface area. This was primarily due to the complete disappearance of the bar on October 26, which resulted in a shoreline retreat of 85 m immediately after the passage of Hurricane Otis. Sentinel-1 Synthetic Aperture Radar (SAR) showed that Hurricane John inundated 2385 ha, four times greater than Hurricane Otis’s flooding (567 ha). The retrofitted QGIS methodology demonstrated high reliability when compared to limited in situ local reports. Given the increased frequency of intense hurricanes, these methods and findings will be relevant in other coastal areas for monitoring and managing local communities affected by severe climate events.

Tropical cyclones can cause severe damage to coastal communities and the marine industry. However, trends in their intensity remain uncertain due to observational challenges, especially for the more frequent weak tropical cyclones, defined as tropical storms to category-1 tropical cyclones on the Saffir–Simpson scale. Here we develop an inversion model using surface drifter observations to estimate sea-surface wind speed, aiming to reassess the long-term global trend in the intensity of weak tropical cyclones from 1993 to 2022. Our results indicate that the global intensification of weak tropical cyclones has been insignificant, with only a modest upward trend of about 3.4 cm s−1 decade−1. Furthermore, we found that weak tropical cyclones have intensified only in the Southern Hemisphere, rather than across all ocean basins. The present work suggests that global warming is probably having only a limited impact on the evolution of weak tropical cyclones. Weak tropical cyclones intensified very little between 1993 and 2022 in ocean basins, and only notably in the Southern Hemisphere, according to analysis of an inversion model of sea-surface wind speed derived from surface drifter observations.

Abstract. Winter precipitation over Australia's Snowy Mountains provides a crucial water resource in the region. Cloud seeding has been operational to enhance snowfall and water storage. This study presents ensemble simulations to assess cloud seeding impacts across diverse meteorological conditions and evaluate associated model uncertainties. Nine seeding cases from 2016 to 2019 were simulated, with 18 ensemble members varying initialization datasets and model configurations. Two main storm categories were studied (convective vs. stratiform). Results demonstrate that simulated seeding efficacy highly depends on meteorological conditions. Stratiform cases exhibited consistent precipitation enhancement, while convective cases showed reductions and downwind shifts in precipitation. Significantly inter-member variability was also observed. Notably, simulations driven by the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) reanalysis dataset show better representation in supercooled liquid water. Aerosol and planetary boundary layer scheme variations also contributed to ensemble spread. The findings demonstrate the value of ensemble modeling for reliable cloud seeding assessment. Key areas are also identified for future investigations in winter cloud seeding.

Abstract In this work, we investigate the ability of mesoscale and large-eddy simulation (LES) model configurations to predict the mean wind speed profile within the boundary layer of tropical cyclones (TCs). To this end, we perform idealized simulations of five hypothetical intense storms ranging from categories 1 to 5 on the Saffir–Simpson scale and extract time-averaged quantities near the eyewall region. We compare the model-generated data against mean wind speed profiles compiled from dropsondes launched from reconnaissance aircraft operating in the North Atlantic basin. Our analysis shows that mesoscale- and LES-generated mean wind fields display important differences in the boundary layer, including the magnitude of shear as well as the height where their low-level wind speed maxima are located. In addition, a comparison between the two model configurations with the dropsonde data shows that both modeling approaches are unable to capture the typical structure of mean winds in the lower part of the TC boundary layer (10–500 m), calling into question the use of simulations of near-axisymmetric storms for investigating the wind structure of past events. To better understand these differences, we conduct a momentum-budget analysis and show that modeled turbulent fluxes are underestimated in the mesoscale boundary layer parameterization compared to the LES model. Based on the analysis of the horizontal turbulent fluxes and their potential impact on mean flow quantities, a TC-specific boundary layer parameterization may be needed.

Abstract Hurricanes threaten communities in complex and evolving ways due to storm characteristics and geography, as well as demographic and cultural factors. Risks to people in the path of these storms are compounded when wind and water hazards co-occur, such as tornadoes and flash floods, a hazard often referred to as TORFFs. For National Weather Service (NWS) forecasters, messaging these co-occurring threats poses many challenges, including the ongoing assessment and prioritization of which threat is likely to have the greatest impacts and the communication of risks to different publics. In this research, we focus on Hurricane Florence, a category 1 hurricane that produced historic flooding and some wind-related threats, including tornadoes, across the mid-Atlantic coast in September 2018. Through inductive, qualitative analysis of 33 semi-structured interviews with NWS forecasters responsible for issuing alerts during Florence, we examine the intricacies of messaging flood and wind threats as they evolved over the hurricane’s life cycle. Our results show that forecasters aimed to amplify messaging for flood threats over wind threats during Florence. Along with forecast details and expected impacts, motivations for this messaging choice included the potential for flood fatalities and concerns that the public would not understand the severity of compounding hurricane threats. One reason for this disconnect may be the emphasis placed by experts in weather prediction on the Saffir–Simpson hurricane wind scale (SSHWS) as a metric of hurricane severity. Forecaster messaging strategies were informed by these concerns, which may also have implications for how messaging should be shaped in the future.

Post-Traumatic Growth Following a Category-5 Hurricane: An Exploratory Study of Black Communities in Florida, United States

Global warming is a problem that affects humanity; hence, crisis management in the face of natural events is necessary. The aim of the research was to analyze the passage of Hurricane Otis through Acapulco from the theoretical perspective of crisis management, to understand the socio-environmental, economic, and decision-making challenges. For data collection, content analysis and hemerographic review proved useful, complemented by theoretical contrastation. Findings revealed failures in communication by various government actors; the unprecedented growth of Hurricane Otis led to a flawed crisis management. Among the physical, economic, environmental, and social impacts, the latter stands out due to the humanitarian crisis overflow. It is the first time that Acapulco, despite having a tradition in risk management against hydrometeorological events, faces a hurricane of magnitude five on the Saffir-Simpson scale. Ultimately, the city was unprepared to face a category five hydrometeorological event; institutional responses were overwhelmed by the complexity of the crisis, and the community came together to improve its environment and make it habitable again.

Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning

The research presented here tests user comprehension of a new kind of risk-based visualization of hurricane forecasts versus other common representations: a cone of uncertainty (with and without a centerline) and an ensemble representation. Our landfall heat maps use intensity hues and a filled black circle with size representing hurricane category at the time of forecast, rather than representing the hurricane path. In two online studies we compare how different representations of the same scenarios affect viewers’ (a) judgments of the risk that a location is likely to be hit by a hurricane, (b) their likelihood of issuing an evacuation order for that location (if they were to be in a role of issuing evacuation orders), and (c) their confidence in their judgment. Findings contribute to developing a toolkit of visual materials that designers of hurricane visualizations can select from based on empirical evidence of user responses to their elements.

Various studies have given projections for how frequencies of tropical cyclones (TCs) might change under climate change. In this study, we combine a set of such projections with uncertain estimates of frequencies of tropical cyclones in a baseline climate to produce probabilistic projections of tropical cyclone frequencies for the next 50 years. The novel aspect of our projections is the inclusion of baseline uncertainty. We consider frequencies of Saffir-Simpson Hurricane Wind Scale category 0-5 and category 4-5 storms for the six major tropical cyclone basins. We find that in several cases the means and medians of the frequency of category 0-5 storms are projected to decrease, but that increasing uncertainty nevertheless leads to increases in the likelihood of high rates of TC activity in the future. We then show how the variance of the distributions of uncertainty can be decomposed into terms due to baseline uncertainty and climate change uncertainty. We use this decomposition to determine the year in which climate change uncertainty overtakes baseline uncertainty. Over the next 20 years we find that in some basins baseline uncertainty dominates and in other basins climate change uncertainty dominates. We are able to relate these variations between basins to the coefficients of variation of the baseline and climate change inputs. Finally, we quantify how climate change affects estimates of near-term TC frequency, including the extent to which it increases the uncertainty. These results help us understand two of the major sources of uncertainty in estimates of tropical cyclone behaviour now and in the future, and the role climate change is playing in changing that behaviour. They also demonstrate how estimates of TC change can be combined with observations to create projections of future TC climate.

Geomagnetic storms (GMSs) are an important space weather phenomenon that poses serious threats to the advancement of space technology, power transmission lines, oil pipelines, and other infrastructure. This study investigates seasonal patterns of GMSs due to recent reports on the prominence of large storms (Dst ? -50 nT) during equinox conditions. Hourly Dst index data provided by the World Data Center, Kyoto, Japan, for solar cycles 21–24 (1976–2019) were employed. Storm occurrences in each solar cycle considered were identified using the minimum Dst value. The identified storms were categorized and analyzed statistically. Results revealed that storm occurrence varied from month to month, season to season, and solar cycle to solar cycle based on storm categories. Furthermore, the observed seasonal distribution of GMS occurrence decreases in the following order: autumn, spring, winter, and summer. This indicates that equinox conditions are more likely to have GMSs, consistent with the Russell-McPherron effect, compared to solstice conditions. The findings suggest that the distribution and characterization of storm occurrence vary seasonally due to solar activity. The insights on storm occurrence, distribution, and characterization may serve as a guide to space scientists to avert the impacts of GMSs while exploring space.