The Coastal area of Bangladesh remains exceptionally vulnerable to cyclonic disruptions owing to its unique geographical and socio-economic characteristics. The present study evaluates the household-level satisfaction on the accessibility of cyclone shelters at three districts located in the exposed coastal area of the country. Developing a Satisfaction Index (SI) namely, Cyclone Shelter Satisfaction Index (CSSI) on a 5-point rating scale, the level of satisfaction was quantitatively assessed and the qualitative insights were identified by focus group discussions. To derive the satisfaction index, a semi-structured questionnaire survey was completed, taking responses from 162 respondents, ensuring spatial and socio-economic representation throughout the selected administrative unions of the three districts. The overall satisfaction scores of 2.77 out of 5-points suggest a moderate level of community satisfaction on the operational readiness of the cyclone shelters in the area. Amtali Upazila recorded the lowest satisfaction score (i.e. 2.57), whereas Char Fasson Upazila achieved the highest score (i.e. 2.95). The study identified the inter-regional disparities, which highlight the pressing need for targeted infrastructure enhancements, strategic planning, and increased community engagement. To enhance the role of the cyclone shelters on disaster preparedness, this study emphasizes the critical need for infrastructural enhancement, better road networks and increased coverage area of the cyclone shelters. By addressing these critical gaps through evidence-based, resilience-oriented planning on the cyclone shelters, authorities can significantly improve household safety and evacuation efficacy during cyclonic events. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 14(2), 2025, P 117-140

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.

The 2-1-1 information and referral system connects vulnerable populations to essential services during public health emergencies. However, there is limited evidence examining the specific needs of unhoused populations during these events. This study examines associations between temperature variations, tropical cyclones, and service utilization patterns among unhoused populations using 2-1-1 call data from southern Louisiana (2014-2023). Analyzing over 20,000 calls, predominantly in urban areas (65%), we employed negative binomial regression, distributed lag non-linear modeling (DLNM), and natural language processing to uncover patterns in help-seeking behavior during temperature variations and tropical cyclones. Negative binomial models demonstrate declines in 2-1-1 utilization bothduring cyclone events (IRR: 0.77, CI: 0.60-0.99)and in the recovery period (IRR: 0.86, CI: 0.76-0.97). Significant associations were observedacross the temperature distribution with both the coldest (D1, IRR: = 1.22, 95% CI = 1.08-1.38) and warmest (D10, IRR = 1.21, 95% CI = 1.06-1.38) temperature deciles, indicating increased call volume at the coldest and warmest temperatures compared to moderate temperatures. DLNM results corroborated a U-shaped association at temperature extremes, though effects were predominantly significant only at the coldest temperatures. Computational text analysis of call narratives revealed that mental health mentions increased significantly with temperature (from 19.4% in coldest conditions to 30.5% in warmest conditions), but decreased during cyclone periods (from 29.1% in non-cyclone conditions to 15.5-20.2% during various cyclone phases). While 2-1-1 data captures only those with phone access and service awareness, potentially underrepresenting the most marginalized, these findings provide actionable insights into help-seeking patterns among service-connected (e.g., 2-1-1) unhoused populationsduring and following extreme weather events.

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

Abstract As the dominant atmospheric circulation pattern over the western Tibetan Plateau (TP), the Western Tibetan Vortex (WTV) exerts substantial control on springtime 2 m surface air temperature ( T 2m ). However, its underlying radiative processes remain unclear. This study integrates GEWEX satellite observations with ERA5 and MERRA‐2 reanalysis, applying surface energy balance diagnostics to quantify the WTV's radiative forcing on T 2m variability. We find the WTV explains ∼66% of T 2m variance ( R = 0.81) across the western TP and the adjacent Southwest Asia. Downward shortwave radiation (DSW) emerges as the primarily radiative factor modulated by the WTV via cloud radiative forcing (CRF) processes. Specifically, anticyclonic WTV events reduce cloudiness, generating positive CRF anomalies that enhancing surface DSW and cause warming. Conversely, cyclonic events increase cloudiness, producing negative CRF anomalies that diminish DSW and induce cooling. These findings advance understanding of the radiative processes by which the upper circulations modulate the surface climate over the TP.

In tropical marine ecosystems, cyanobacteria are key components of phytoplankton communities, yet their diversity and spatio-temporal dynamics in tropical lagoons remain poorly documented. Here, we use the Nouméa lagoon (New Caledonia) as a model system to genetically characterize cyanobacterial communities in a subtropical lagoon subjected to both natural and anthropogenic pressures. Using metabarcoding, we investigated seasonal and spatial variations along environmental gradients, spanning from estuarine zones to oligotrophic reef areas. A single Synechococcus ASV dominated most communities, particularly in nutrient-enriched coastal waters. Beyond this dominant lineage, we uncovered a diverse “minor cyanosphere,” which comprised Synechococcus subclusters 5.1, 5.2, and 5.3, several Prochlorococcus ecotypes, an as-yet-uncultivated group of picocyanobacteria, and various diazotrophic cyanobacteria. This assemblage exhibited spatial structuring, suggesting environmental filtering and thereby underscoring its potential as a bioindicator of water masses in lagoon systems. We also assessed the impact of Cyclone Uesi (February 2020), which triggered shifts in community composition. Floodwaters resulted in an atypical surge of fast-growing estuarine Synechococcus strains, which were subsequently transported to offshore reef zones, as confirmed by buoy tracking. These findings suggest that increasingly frequent cyclones could influence plankton dynamics and, consequently, reef health under climate change scenarios. Overall, this study highlights the complex structuring of cyanobacterial communities in the Nouméa lagoon, shaped by spatial gradients, seasonal changes, and episodic extreme disturbances, and emphasizes the ecological significance of reef passages, which help maintain the trophic gradient within the lagoon following cyclonic events.

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 The central United States is a favorable region for cyclogenesis east of the Rocky Mountains. This region’s distinctive location in the center of the continental United States and juxtaposition with complex topography contribute to unique forecast challenges, including uncertainties in the prediction of extratropical cyclone (ETC) intensity and location, as well as concomitant areas of heavy precipitation and strong winds. Consequently, this study utilizes machine learning techniques to investigate the variability of cold-season (October–May) ETC characteristics across the central United States. ETCs are identified within the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) during 1980–2021 using a method for ETC detection adopted from previously published work. This ETC dataset subsequently facilitates an analysis of the monthly and seasonal frequency of central U.S. ETCs, for which an increase in cold-season ETC frequency is found for both winter and springtime ETCs. A self-organizing map (SOM) is trained on mean sea level pressure anomalies from ERA5 during ETC events and used to examine the variability in large-scale weather regimes conducive to central U.S. ETCs. These regimes are categorized into five clusters: 1) the Mississippi River anticyclone, 2) the Canadian anticyclone, 3) the Northern Plains cyclone, 4) the Central Plains cyclone, and 5) the Southern Plains cyclone clusters. One main finding is a significant increase of ETCs that are categorized into the Canadian anticyclone cluster, indicating that a subset of weaker central U.S. ETCs are becoming more common. The extent to which the characteristics of near-ETC environments (e.g., moisture sources, upper-level jet stream structure, quasigeostrophic ascent, and precipitation) vary as a function of each SOM cluster is also investigated. Significance Statement Extratropical cyclones frequently produce strong precipitation and winds across the central United States, resulting in profound societal impacts. This study investigates the various atmospheric environments (e.g., moisture sources, upper-level jet stream structure, and precipitation) that accompany cyclone development in this region. Machine learning methods are used to identify distinct categories of cyclone events, and the different upper- and lower-level environments associated with these cyclone categories are explored. We find that while central U.S. ETCs have been increasing during 1980–2021, weaker ETCs associated with higher pressures and less moisture are particularly becoming more common.

Bi-level framework for cost effective robustness and responsiveness to enhance infrastructure resilience of transmission network against cyclones

Chilika Lagoon, the largest brackish water lagoon in India and a Ramsar site, exhibits a highly dynamic ecology primarily governed by shifts in its sea mouth, which regulate freshwater–seawater exchange and influence sedimentation and faunal diversity. This study assessed ecological variability in the Chilika Lagoon over the last 200 years using a foraminifera record retrieved from a 72 cm sediment core recovered from the seaward region of the lagoon. The sediment deposition rate was ascertained using the 210 Pb value. Principal component analysis of foraminifera abundance data, added with cluster analysis, suggests the prevalence of four major ecological phases in the lagoon over the last 200 years. Between 1820 and 1940 ce , the lagoon exhibited a highly restricted ecosystem, dominated solely by Ammonia beccarii, due to limited seawater inflow caused by the progressive northward migration of the sea mouth. Between 1940 and 1975 ce , a moderately brackish environment prevailed, as evidenced by the increased abundance of agglutinated foraminifera taxa, which are indicative of suboxic bottom waters resulting from periodic tidal restriction and sediment accumulation. Between 1980 and 2000 ce , the lagoon’s sea mouth was nearly choked, and lagoon ecology was under threat, marked by the near absence of foraminifera, except for episodic seawater ingress linked to cyclonic events. The opening of the artificial sea mouth and its subsequent maintenance after 2000 ce improved the lagoon ecology, marked by an increase in foraminifera abundance and diversity. This is characterised by high salinity, low organic matter, and stable oxygen levels, as observed in modern conditions. Cyclones and stronger storms have played a significant role in leading to higher sea water ingress and periodically shifting the sea mouth before 2000 ce .

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.

Tropical cyclones have catastrophic impacts on life and infrastructure in many places on the earth. Accurate prediction is therefore crucial for cyclone-prone regions like Bangladesh. This study employs an ensemble learning approach to predict tropical cyclones using relationships between El Niño Southern Oscillation (ENSO) indices and cyclone occurrences. Cyclone data from 1977 to 2022 reveal severe class imbalance, with only 26 cyclone events in 540 months. A voting ensemble model with 17 Random Forest classifiers was developed through random under-sampling, each trained with a Random Forest classifier. Predictions were aggregated using majority voting to improve robustness. Model performance was evaluated with accuracy, precision, recall, and negative predictive value (NPV), supplemented with Wilson Score Intervals and a sensitivity analysis across probability thresholds from 10–90%. Results show that the ensemble model achieved 75% overall accuracy, with high recall (80%) for cyclone months but relatively low precision (22%), favoring sensitivity to minimize missed events. Correlation analysis reveals strong seasonal associations between cyclone activity and ENSO indices, particularly ONI and Niño 3.4 SST during monsoon and post-monsoon periods. These findings demonstrate that ENSO-based ensemble learning can capture non-linear climate–cyclone relationships and enhance early warning capabilities. The proposed framework provides a data-driven tool that can support meteorological agencies and disaster managers in reducing cyclone-related impacts on vulnerable coastal communities.

Sources, transport pathways, seasonal and diurnal variations of atmospheric gaseous elemental mercury on the Russian coast of the sea of Japan: Findings from 2021 to 2022 observations.

Malawi’s Elephant Marsh, a RAMSAR site, is under intense pressure from climate change and human activities. This study utilized remote sensing and machine learning to analyze land use and land cover changes in the Elephant Marsh from 2009 to 2024, revealing a dynamic and surprising ecological trajectory. Contrary to dominant narratives of continuous degradation, our analysis shows two distinct phases. Phase 1 (2009–2018) saw clear environmental decline: water depletion, bareland expansion, and vegetation loss, largely due to unsustainable practices and drought. Yet, Phase 2 (2018–2024) demonstrated a dramatic recovery, characterized by robust water regeneration and vegetation increase, primarily driven by significant cyclonic events from 2021 onwards. This research fundamentally challenges the idea of linear wetland degradation, highlighting how high-magnitude, episodic weather events can temporarily override persistent anthropogenic pressures and catalyze rapid ecosystem recovery. While this short-term recovery is notable, it also contrasts with regional patterns of long-term wetland shrinkage, suggesting a nuanced relationship between immediate climatic impacts and broader environmental trends. Our findings advocate for adaptive environmental management that recognizes the episodic climatic events’ due role in both degrading and restoring these vital ecosystems.

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.

Abstract The Nusantara Capital City (NCC) is susceptible to extreme rainfall influenced by large scale atmospheric systems, including tropical cyclones (TCs) from the Western Pacific Ocean. This study examines TC contributions to rainfall in NCC from 2000 to 2024 using IBTrACS cyclone track data, IMERG daily rainfall, and ERA5 atmospheric reanalysis. The objective synoptic analysis technique was used to isolate TC induced rainfall based on a 1100 km radius and a ±3 day window from peak cyclone intensity. Results show that non-TC rainfall dominates accounting for over 88% of NCC annual precipitation, while indirect TC influence contributes less than 12%. Dynamically, TC related rainfall occurs under specific atmospheric conditions, sea surface temperatures above 27 °C, high relative humidity above 85%, and strong positive vorticity which are generally absent during non-TC periods. Trend analysis indicates an increase in total annual rainfall in NCC primarily driven by an increase in non-TC rainfall of 10 mm/year, while TC related rainfall shows a decreasing trend of 5 mm/year. These findings underscore that long-term rainfall patterns in NCC are mostly shaped by regional weather variability rather than TC, highlighting the importance of adaptive climate strategies that focus on dominant local rainfall drivers and hydrometeorological risks.

Abstract The scoria cones called Formica Leo located at the base of the Piton de la Fournaise terminal cone have been chosen for its significant positive Self‐Potential (SP) anomalies associated with hydrothermal uprising fluids to monitor SP signal and study its dynamics in relation with huge and extreme rainfall events. A SP monitoring device was installed crossing the SE crater of Formica Leo with 64 electrodes placed each meter. A rain gauge has been also installed at 1.5 m depth in order to measure the effective rainwater infiltration. Measurements were acquired each hour. A total of 13,848 data of difference of potential were analyzed for each one of the 64 SP sensors between 1 September 2020 and 31 March 2022. During this period, 2 cyclonic events impact La Réunion Island. The analysis of 12 major raining events allows to discriminate 3 kinds of SP signals related with different intensity range of rainfall. A linear relationship has been also evidenced between the maximum amplitude variation of the SP signal and the cumulative effective infiltration. The physical meaning of this correlation has been proposed in a schematic model. It takes into account the characteristics of the SP signal variations considering the difference in time transfer between the scoria cone and the surrounding lava flow, and the consequence in inducing a hydric forcing inside the scoria cone, responsible for pressuring the hydrothermal fluids.

In preparation for the natural hazardous wind events such as tornados and tropical cyclones, open-air conveyor belts used by Australian mining industry for overland bulk materials transportation are physically restrained by tying them to their structural frames with rigid steel tie-downs over set regular intervals. This study examined a restrained section of a commercial type of conveyor belt focussing on discerning the most accurate approach for optimising the belt restraint interval value. Structural integrity of the belt was analysed with strength of materials, numerical modelling, and aerodynamic methods for a range of airflow angles and for airflow velocities of up to and including category 5 cyclonic events. It was found that strength of materials approach could be used as a rough approximation only, with somewhat fine-tuned results provided by the numerical methods and the conventional aerodynamic techniques. The most accurate results were obtained from introducing dynamic component of the conveyor belt’s elasticity into the aerodynamic flexural–torsional flutter treatment of the belt, with this approach delivering an over 30% higher minimal value of the length of the restrained section of the belt at design safety factor of 1.5. This larger restrained section length will lead to a corresponding reduction of the number of belt tie-downs and this, in turn, will translate into strong operational efficiencies while also delivering significant improvements of personnel safety. Additionally, the results of our work improve understanding of the behavior of open-air overland conveyor belts under the irregular aerodynamic loading conditions of natural hazardous wind events.

Tropical cyclones (TC) can produce waves and water levels that markedly reshape sand cay shorelines. TC Jasper (December 2023) passed near Low Island (Low Isles, Northern Great Barrier Reef [GBF]) as a category 2 storm. Using a combination of remote sensing and ground surveys, we compare detailed, high-resolution digital terrain models created before and after TC Jasper to quantify sediment redistribution around the cay during and after the event. During TC Jasper, net transport of 8,870 m3 occurred to elongate the spits at the eastern and western ends of the cay, but the sediment volume of the cay did not significantly change. Following TC Jasper, the shoreline at Low Island returned to its modal seasonal state within six months. This accords with historical accounts of seasonal shifts in shoreline configuration driven by prevailing wind and wave regimes, as well as the relatively rapid readjustment to a modal form following episodic extreme events. Overall, the documented changes to Low Island following cyclonic events highlight the complex interplay between episodic disturbances and longer-term geomorphic stability, emphasising the importance of ongoing research into these interactions as higher-intensity cyclones increase in frequency due to climate change.

ABSTRACT Winter Migratory Bagnet fishery (WBNF) in Indian Sundarbans contributes significantly to local food security and livelihoods, yet they are increasingly threatened by rising sea levels, declining fish stocks, and recurring cyclones. The main objective of the study is assessing its vulnerabilities and focusing on the adaptive response strategies employed by stakeholders to mitigate the challenges to sustain fisheries and fisher livelihoods. Baseline data were collected from 2019 to 2024 to assess species diversity and fishery dynamics. The findings reveal that WBNF not only provides a steady source of fish protein and income for thousands of households but also serves as a governance model for similar ecosystems, showcasing how community‐based practices, traditional knowledge, and adaptive management can sustain fisheries under ecological and socio‐economic pressures. Research further evaluates the vulnerability of Bagnet fisheries in response to current cyclonic events and climate change, which impacts reduced catches and also the damage to fishing crafts and gears. The Livelihood Vulnerability Score for winter bagnet fisheries in the Sundarbans ranged from 0.253 at Kalisthan to 0.402 at Fraserganj, indicating an overall high‐risk situation faced by fishing communities. Several environmental and socio‐economic drivers such as sea level rise, dependence on middlemen, decrease in species richness, decrease in Catch Per Unit Effort, shift in fish spawning season, changes in rainfall patterns, different adaptation strategies, and so on. influencing the sustainability of these fisheries were also identified and documented for the first time. To address these challenges, the study highlights adaptive management approaches, including stronger community participation, livelihood diversification, and institutional support.