Search Results (1,951)

The data-scarce Varzob River basin, Tajikistan, shows significant cold-season warming, an earlier spring runoff shift, and a sharp rise in avalanche frequency. We analyse long-term runoff (1940–2018), meteorological records (2000–2024), avalanche observations (2019–2026), field snow surveys (2025–2026), and satellite/UAV imagery (2024–2025). Annual runoff shows a 6.7% higher mean in 1991–2018 than in 1940–1990, but the long-term trend is not significant (p = 0.23). However, the centre of mass of spring runoff shifted significantly earlier by 3.7 days (p < 0.001). Cold-season temperature increased significantly (p = 0.016), while wind speed showed no significant trend (p = 0.061). Snow water equivalent at seven elevations (1930–2955 m) ranges from 200 to 440 mm, and melt-freeze crusts indicate a snowpack prone to wet-slab avalanches. Avalanche frequency increased from 81 events in 2019 to 430 in 2025 and 560 (partial) in 2026, coinciding with a ~70% higher snow water equivalent in 2026. Mapped avalanche paths terminate less than 50 m from the Varzob River, suggesting a potential, though unquantified, contribution of avalanche snow to spring runoff. The integration of long-term hydrology, high-resolution meteorology, field surveys, and remote sensing offers a replicable framework for cryospheric-hydrological studies in data-scarce mountain basins. Full article

With the increasing frequency of extreme weather and rapid growth of civil aviation, severe convective weather (thunderstorms, short-term heavy precipitation, and strong winds) poses growing threats to flight safety. This study proposes a multi-label CNN-ConvLSTM framework that fuses airport Doppler radar echoes, Himawari-8 satellite imagery, surface observations, and radar optical flow features to nowcast multiple severe convective events within the next 30 min. The model uses 2D-CNN for spatial extraction, ConvLSTM for temporal dynamics, and a weighted joint loss (Focal Loss and Dice Loss) to address class imbalance. Trained on 396 samples (positive-to-negative ratio 1:2.5) from 83 events at Guanghan Airport (2021–2024), incorporating optical flow features significantly boosted performance: macro-F1 increased from 0.719 to 0.792, and Threat Score (TS) from 0.567 to 0.705. Notably, false negatives for minority classes dropped sharply, with strong winds F1-score rising from 0.15 to 1.00. Ablation analysis showed optical flow as the top contributor (Mean Decrease in TS ≈ 0.5). Through multi-modal fusion and motion enhancement, this interpretable model provides high-precision nowcasting for airport severe convective weather, offering substantial value for aviation safety. Full article

Transmission lines in Southwestern China are highly exposed to compound hazards induced by extreme winds and ice and snow conditions. This study assesses future changes in extreme wind hazards and their spatial overlap with baseline ice susceptibility under the SSP2-4.5 emission scenario, using high-resolution dynamically downscaled climate projections. Compared to the historical period (1995–2014), the results indicate a marked intensification of extreme spring wind events over northwestern Southwestern China and the transitional zone between the Sichuan Basin and the Hengduan Mountains during 2041–2060. The occurrence frequency of wind speeds exceeding historical 50-year return levels is projected to increase by 5–10 times in complex terrain, particularly along the Golmud–Qaidam belt. The Comprehensive Extreme Wind Index (CEWI) identifies the Golmud–Wulanwusu–Qaidam river basin belt as the region of highest wind hazard amplification. Meanwhile, analysis of historical observations reveals that icing-prone conditions occur on more than 25 days each spring in the Nyenchentanglha Mountains and southeastern Tibetan Plateau valleys, establishing a baseline map of ice susceptibility. Due to methodological limitations in projecting future icing, this susceptibility map is used as a static indicator of ice-prone areas. By superimposing projected wind intensification onto the baseline ice susceptibility map, four relative hazard exposure categories are delineated. Regions of highest potential exposure are concentrated in the Bayan Har Mountains and portions of the western Hengduan Mountains, whereas northwestern basins are dominated by high wind risk alone. These results reveal pronounced spatial heterogeneity in the relative amplification of compound hazards under future warming and provide a scenario-informed scientific basis for prioritizing regions in disaster risk reduction and resilient planning of transmission infrastructure in mountainous regions. Full article

Overhead distribution systems may experience concurrent wind and rainfall loading during typhoon events, but most existing studies still emphasize individual components, single-hazard descriptions, or network-level consequences. To address this gap, this paper develops a probabilistic assessment framework for distribution pole–line segments exposed to compound typhoon wind–rain hazards. A three-dimensional finite-element model of a representative segment with three poles, two spans, and three-phase conductors is constructed, and uncertainties in structural properties and loading-related coefficients are incorporated explicitly. Correlated turbulent wind histories are synthesized using the Davenport spectrum and harmonic superposition method, whereas rainfall actions are represented through an impact-based raindrop spectrum formulation. Nonlinear dynamic analyses are performed for multiple combinations of basic wind speed and rainfall intensity, and the resulting peak conductor tension and pole-base bending moment are used as engineering demand parameters. Logarithmic probabilistic demand models are then fitted to derive failure-probability surfaces for the conductor, the pole, and the pole–line segment. Segment failure is defined through the maximum normalized demand among the central pole and the six connected conductors, thereby extending the assessment from component-level failure to local segment-level risk. The results show that basic wind speed governs the overall evolution of failure probability, whereas rainfall acts as a secondary but non-negligible amplifying factor that shifts the probability transition zone toward lower wind-speed levels. For the adopted configuration, the segment-level failure probability is governed mainly by pole response. Additional model checks and event-based comparisons support the consistency of the proposed segment-level probability formulation. The proposed methodology can support risk screening, warning-threshold setting, and maintenance decision making for overhead distribution systems subjected to compound meteorological hazards. Full article

Conventional structural design frameworks assess natural hazards as statistically independent phenomena, a practice that can lead to significant underestimation of risk for structures subjected to sequential or concurrent hazards. The generation of probabilistic fragility functions under such cascading loads, particularly for post-fire seismic events, presents a computational barrier for standard non-linear dynamic analysis. To address this barrier, this study introduces a comprehensive computational framework centered on a physics-constrained neural network (PCNN) to serve as a high-fidelity surrogate model. The framework first uses a non-linear 12-degree-of-freedom structural model to generate a baseline dataset of collapse times under post-fire, concurrent wind-earthquake loading via the computationally efficient endurance time (ET) method, confirming that wind effects are negligible under ambient conditions and that the framework correctly identifies this hazard hierarchy without prior labeling, while fire and seismic parameters dominate. This dataset is subsequently used to train the PCNN, which is validated to achieve exceptional predictive accuracy ($R^2=$ 0.991), performing on par with a state-of-the-art Random Forest model while enforcing physical constraints. A feature importance analysis confirmed that structural collapse is dominated by fire intensity (≈55%) and initial structural period (≈45%). The validated PCNN is then applied to demonstrate the framework’s capability, rapidly generating fragility curves that quantify the catastrophic effect of fire on seismic resilience. This analysis reveals that a severe 800 °C localized fire reduces the structure’s median collapse capacity by 94.7%, thereby establishing the proposed framework as a successful template for tackling complex, non-linear problems in multi-hazard engineering. Full article

Large-span stadium roofs in coastal regions are highly vulnerable to typhoon-induced wind damage, and their post-event performance depends on both structural safety and functionality recovery. This study proposes a probabilistic framework to assess typhoon-induced damage, functionality degradation, recovery, and resilience of a large-span stadium roof system in Shenzhen, China. Progressive damage to the roof cover and the roof-supporting structure is evaluated by combining wind tunnel pressure data and structural analysis. The results show that the roof cover shows greater vulnerability than the supporting structure, with slight damage emerging at around 30 m/s, whereas structural damage requires higher wind speeds. A functionality-based recovery model is further developed by considering repair preparation, repair duration, and repair sequence constraints. The building generally exhibits a high resilience level, with a mean resilience index of 0.9550 and a median of 0.9589. The initial overall building functionality loss increases from about 7% under TY conditions to 20% under STY and 60% under Super TY, while the recovery duration increases by about 2–3 times and 5–6 times relative to the TY case, respectively. The proposed framework provides a practical basis for resilience-oriented performance assessment of large-span roof structures under typhoon hazards. Full article

Space weather events triggered by solar activity impact critical technologies like the Global Navigation Satellite System (GNSS) by causing atmospheric imbalances that alter ionospheric electron density. This study investigates the upper atmosphere response to the severe geomagnetic storms of October 2024, focusing on the coupling and compositional exchange between the ionosphere and thermosphere. Data were analysed from two mid-latitude African stations, Rabat (RABT) and Hermanus (HNUS), using GNSS-Total Electron Content (TEC) measurements alongside thermospheric circulation observations from NASA-GOLD and solar wind indices from OMNIWeb. The October 2024 storm, which reached a minimum Dst of −333 nT, drove a negative ionospheric storm phase marked by TEC depletions exceeding 50 TECU. This response was driven by storm-time thermospheric upwelling of N₂-rich air, which lowered the O/N₂ ratio and accelerated plasma loss via charge-exchange reactions. Furthermore, a distinct hemispheric asymmetry was observed, as the equatorward thermospheric circulation in the Northern Hemisphere arrived before that of the Southern Hemisphere. Direct post-processing of the Earth-Centred Earth-Fixed (ECEF) coordinates using RTKLIB single-point position revealed that, while positioning accuracy significantly degraded at HNUS with errors increasing by up to 270%, it counterintuitively improved at RABT, where errors reached their minimum during the main and early recovery phases of the storm. These findings highlight that the technological impact of severe space weather is determined not just by storm magnitude but by the specific sign and spatial structure of the regional ionospheric response. Full article

The Artificial Intelligence Forecasting System (AIFS), recently released by the European Centre for Medium-Range Weather Forecasts (ECMWF), represents a major shift in global weather prediction by replacing traditional physically based approaches with machine-learning methods. This study evaluates the impact of using AIFS as initial and lateral boundary conditions for the Weather Research and Forecasting (WRF) model, in contrast to the well-established physically based GFS. The aim of this work is to analyze the sensitivity of these different modelling configurations during three high-impact storms that affected Spain in 2025 and the effects of replacing GFS for AIFS as lateral and boundary conditions for WRF over the accuracy of operational forecasts. The analysis focuses on maximum wind gusts, accumulated precipitation, and the generation of meteorological warnings. Results show that AIFS substantially underestimates wind gusts with mean bias values between −13 and −25 km/h, and its forecasts differ markedly from those of GFS. When coupled with WRF, however, both AIFS-WRF and GFS-WRF produce similar results, with a general tendency to overestimate gusts, with mean bias values between 4 and 15 km/h. In all cases, WRF adds value, improving the representation of wind-related variables compared with the raw global model outputs. For accumulated precipitation, both WRF configurations reproduce the main rainfall patterns associated with the storms. AIFS-WRF shows a stronger tendency to overestimate precipitation, with RMSE values of 64, 23, and 12 mm for the different high-impact storms considered, although it also achieves the highest correlations. Finally, the analysis of meteorological warnings indicates that AIFS alone generates almost no wind gusts alerts. Once coupled with WRF, both configurations generate warnings in the regions where the most severe conditions occurred. Overall, while the added value of mesoscale models such as WRF is well established and confirmed here, the AI-based AIFS does not show clear advantages in comparison with traditional global models for these high-impact events being analyzed. Full article

Natural disasters and extreme meteorological events are primary causes of unplanned outages in modern power systems. Existing early warning methods suffer from insufficient non-linear feature extraction, severe class imbalance, and limited minority-class recall under single-classifier architectures. This paper proposes a seven-class meteorological fault early warning framework that integrates a sparse autoencoder (SAE), a G1–entropy composite weighting scheme, SMOTE oversampling, and a soft-voting BP–XGBoost ensemble. A leakage-free experimental protocol confines SMOTE exclusively to the training partition, eliminating data contamination from evaluation. Validated on 1955 fault records from a regional grid in East China covering 110 kV, 220 kV, and 500 kV voltage levels (2013–2022), the proposed framework achieved 96.42% accuracy and a 97.46% macro F1-score on the held-out test set, outperforming SVM (72.68%), Random Forest (89.31%), LSTM (81.47%), 1D-CNN (85.38%), and LightGBM (92.15%). Ablation experiments confirmed that SMOTE and G1–entropy weighting contributed macro F1 gains of 8.34 and 6.91 percentage points, respectively, while removing the XGBoost branch degraded accuracy by 28.25%. Temporal validation on 2019–2022 records yielded 91.57% accuracy, confirming temporal generalization. Error analysis further revealed that bidirectional misclassification between lightning damage and wind damage, rooted in shared atmospheric instability signatures, constitutes the dominant residual error source, providing theoretical guidance for future threshold optimization strategies. Full article

Natural disasters represent a critical intersection of environmental degradation, climate change, and societal vulnerability, posing a severe threat to sustainable development. Building a resilient communication infrastructure is therefore paramount for environmental sustainability and community survival. This paper addresses the shortcomings of traditional systems—such as high latency, limited coverage, and unreliable infrastructure—by proposing a novel integrated disaster management system built on Internet Radio technology. The framework combines a robust early warning system with an efficient emergency information broadcaster, offering global reach, real-time capabilities, and significantly reduced resource requirements. Its low-power consumption and minimal physical infrastructure make it an environmentally sustainable and cost-effective solution, aligning with goals for reducing the ecological footprint of critical services. A comprehensive 6-month case study for the Dodecanese Islands, Greece—with focused implementation on Symi Island—was conducted to validate the system. IoT-based meteorological stations and machine learning models (Random Forest) achieved a temperature prediction RMSE of 1.5 °C (a 35% improvement over traditional models), a wind velocity RMSE of 3.1 km/h, and an F1-Score of 0.80 for rainfall prediction. The integrated system demonstrated end-to-end latency of 10–25 s (210× faster than traditional systems), 98% coverage, 94% user comprehension, and a 70% reduction in operational costs. System-wide testing confirmed an alert accuracy of 92%, a false alarm rate of 12%, and a missed event rate of 10%, all within acceptable thresholds. The system achieved 99.2% overall uptime with redundant components ensuring continuous operation. Comparative analysis shows the proposed system outperforms traditional Greek EWS by 210× in latency, improves coverage by 327%, and reduces costs by 70% while maintaining three UN SDG alignments. The research fills a critical gap by integrating sustainable communication technology with modern predictive analytics, offering a replicable model for island communities worldwide. Full article

Solar radio bursts at very low frequencies are key phenomena in the Sun–Earth space environment, providing crucial diagnostics of the acceleration and propagation of solar wind, coronal mass ejection (CME), and non-thermal energetic particles and serving as important indicators for space weather forecasting. To meet the demand for rapid screening of burst events in large-scale observational datasets, we present an end-to-end automatic detection and evaluation framework tailored for Type III bursts, built upon long-term radio dynamic spectra from STEREO-A/SWAVES. We formulate radio burst detection as a one-dimensional interval localization task along the time axis and, in view of the scarcity of annotated samples, cast it as a few-shot object detection task. Building upon the Faster R-CNN architecture with a ResNet50-FPN backbone, we propose the Meta-FSOD framework, which adopts an episodic training paradigm to construct support–query episode pairs. The framework incorporates a metric-guided prototype learning branch to semantically align and calibrate region-of-interest (RoI) features via class prototypes, and integrates a dynamic Beta-Gating mechanism coupled with Soft-NMS to effectively suppress false positives while preserving high-recall performance. Experimental results demonstrate that, despite being trained on a significantly smaller dataset than comparable studies, Meta-FSOD achieves competitive performance, closely matching that of conventional supervised model. The proposed framework exhibits strong cross-temporal generalization capabilities and holds considerable potential for engineering applications in deep space exploration missions. Full article

Identifying the sources of Ultra-High Energy Cosmic Rays (UHECRs, $E>{10}^{18}$ eV) remains a fundamental challenge in astrophysics due to the significant deflections of charged particles by Galactic and extragalactic magnetic fields. Until now, dozens of events with energies over ${10}^{20}$ eV—Extreme Energy Cosmic Rays (EECRs)—were detected by the Pierre Auger Observatory and Telescope Array, but none of them showed a statistically significant association with potential sources. In this study, we investigate potential sources of EECRs with arrival directions from Local Void region. Since the energy loss lengths of such EECRs are of order of 20–40 Mpc, i.e., smaller than the Local Void extension (∼60 Mpc), potential sources should be predominantly Galactic ones. Since the most promising UHECR accelerators are mildly relativistic shocks, we consider Galactic microquasars, magnetars, and pulsar wind nebulae as potential sources of EECRs in the Local Void sky region. Using event-by-event reconstruction of trajectories of detected EECRs via CRPropa backtracking in the Galactic magnetic field, we find the potential Galactic sources and corresponding charges Z for some of the detected EECRs. The most promising coincidence is found between the EECR event triplet detected by PAO and TA and SGR 1900+14, a Galactic magnetar exhibiting high-energy flaring activity, with the inferred propagation time delay being consistent with the characteristic age of the magnetar. Full article

For the environment and the daily life of urban settlements, in the context of contemporary challenges, severe meteorological events rank second worldwide. Therefore, these events tend to become a real threat to human society and to specific economic activities. The main objective of this study is to analyze the spatio-temporal evolution of severe meteorological events in urban environments and to assess their relationship with atmospheric circulation regimes and urban thermal conditions. The analysis focuses on five types of severe events (significant atmospheric precipitation, hail, strong winds, tornadic structures, and cloud-to-ground lightning) recorded in 11 cities located in the historical region of Muntenia, Romania, over the period 2014–2024. The methodological framework is based on three complementary components. First, a new database was developed by integrating information from multiple sources, including the National Meteorological Administration (ANM), the European Severe Storms Laboratory (ESSL), international databases, and validated media reports, with spatio-temporal filtering and aggregation into synoptic episodes. Second, atmospheric circulation regimes were identified using ECMWF ERA5 reanalysis data, based on geopotential height anomalies at the 500 hPa level, allowing the classification of large-scale synoptic patterns. Third, urban thermal conditions were assessed using the ECMWF CERRA regional reanalysis dataset, which provides high-resolution air temperature data, enabling the analysis of urban–peri-urban thermal contrasts and the estimation of the urban heat island effect. The results highlight a total of 997 severe meteorological events, of which 253 (25.6%) were recorded in the analyzed urban areas, 85 (15.9%) in other towns, and 583 (58.5%) in rural areas. The analysis reveals pronounced interannual and intraseasonal variability, as well as distinct spatial clustering patterns, particularly in urban and peri-urban zones. Among the circulation regimes, the Zonal Regime exhibits the highest event rate, suggesting increased favorability for severe weather occurrence, while other regimes show weaker or even inhibitory effects. In addition, most severe events were associated with positive urban–peri-urban temperature contrasts, indicating an active contribution of the urban heat island effect. By combining observational data, synoptic-scale analysis, and urban-scale thermal assessment, this study provides an integrated regional perspective on severe meteorological events and contributes to the enrichment of data sources in the region, while improving the understanding of their dynamics in urban environments affected by data limitations. Full article

High-Frequency (HF) radar remote sensing offers a unique capability to detect mesoscale air-sea interactions under strong monsoon conditions. This study leveraged HF radar-derived surface currents, buoy observations, and reanalysis data to systematically investigate the driving mechanism of the sea–land breeze (SLB) on surface currents in the Taiwan Strait during the strong winter monsoon. To address the challenge of extracting weak signals from a dominant background flow, we employed the Separation of the Regional Wind Field (SRWF) method and the complex demodulation spectrum shifting technique. The results demonstrate that HF radar observations confirm the presence of regular SLB activity even under the strong monsoon, with its intensity modulated by the land–sea temperature difference influenced by cloud cover. Spatial correlation analysis reveals that the SLB significantly drives diurnal variations in the surface current, with its impact extending up to 110 km offshore and a maximum amplitude of approximately 2.2 cm/s. Additionally, the analysis reveals that the duration of SLB events critically influences the current response: events lasting 7 days produce a stronger and more spatially coherent correlation with the diurnal currents than shorter 5-day events. Furthermore, harmonic analysis indicates that the SLB’s energy primarily affects the non-tidal residual current, with no significant impact on the principal diurnal tidal constituents ($O_1$, $K_1$). This work not only quantifies the SLB-current coupling during sustained SLB events in a strong monsoon regime but, more importantly, demonstrates the capability of HF radar remote sensing for resolving weak signals in complex, high-energy environments, providing a robust methodological framework and valuable insights for regional marine environmental forecasting. Full article

Wind energy is a cornerstone of the global transition to renewable and sustainable energy systems. However, the same meteorological processes that generate this clean energy can also produce extreme wind events that threaten the structural integrity and operational reliability of wind turbines and power grids. Therefore, accurately predicting extreme wind speeds is a critical link between promoting clean energy and ensuring infrastructure resilience. Traditional models often struggle to capture the multimodal characteristics of extreme wind speeds under complex meteorological conditions due to fixed distribution assumptions or unstable training of mixture models, leading to estimation biases that undermine planning reliability and may result in catastrophic turbine failures or overly conservative designs. To address these issues—particularly weight imbalance and overfitting–this study proposes an enhanced regularized extreme value mixture model (ERDC-EVMM). This method integrates elastic network regularization and Kullback–Leibler divergence constraints within a Mixture of Experts framework, and employs K-means initialization and momentum-based training to enhance convergence stability. Validated using daily extreme wind speed sequences from coastal and inland wind farms, the model outperforms standard GEV and mixture models in terms of goodness-of-fit, percentile accuracy, and return period estimates, while achieving a convergence speed that is more than 30% faster (82 iterations). By balancing accuracy and training stability, the ERDC-EVMM model provides a reliable statistical tool for extreme wind speed forecasting, supporting the safe expansion of wind energy infrastructure and the design of climate-resilient communities. Full article