Search Results (17)

Island terrain poses a significant challenge in tropical cyclone (TC) track prediction, particularly during the early development of TCs. Using the Weather Research and Forecasting (WRF) model, China Meteorological Administration (CMA) best-track data, and ERA5 reanalysis, we conducted control (CTRL), terrain-removal (Te0P), terrain-enhancement (Te2P), and no-land-sensible-heat (HFX0) experiments for Typhoon Yagi (2024) near Luzon during 1–3 September. CTRL showed a pronounced northward track bias. Te0P shifted westward and closer to observations, whereas Te2P further amplified the northward bias; HFX0 remained close to CTRL. Terrain-height changes substantially modified the surrounding mid-tropospheric environmental flow and asymmetric horizontal-advection potential-vorticity tendency (PVT_h). Although Te0P produced the closest track, its PVT_h orientation deviated most from ERA5, suggesting that the track improvement partly reflects a compensating reduction in the excessive northward response in CTRL rather than a uniformly more realistic dynamical structure. Land sensible heating mainly affected the boundary layer and nearby circulation, with weaker effects on the broader mid-tropospheric flow and track. These results show that terrain dynamics dominate the simulated track response, while land sensible heat provides secondary modulation, and highlights the need to evaluate track, environmental flow, and PVT_h jointly near complex islands. Full article

Ship weather routing in regional seas under severe seasonal weather systems, such as typhoons, poses a critical operational challenge for maritime safety and efficiency. Traditional single-agent reinforcement learning (RL) methods frequently suffer from training instabilities and erratic trajectory adjustments when exposed to the highly non-stationary, multi-scale dynamics of coupled wind–wave–current fields. To address these limitations, this study proposes a dual-agent hierarchical reinforcement learning framework (HRL-MOO) featuring a built-in dynamic risk-weight adaptation mechanism. The proposed global agent discerns large-scale environmental evolutions and adaptively updates the relative weights of wind-, wave-, and current-induced risks at a strategic level, while the local agent translates this macro-level guidance into short-term tactical heading and speed adjustments within realistic vessel motion boundaries. The framework incorporates bathymetric constraints through a high-resolution navigable domain mask derived from ETOPO topography to guarantee practical navigability. Simulated experiments are executed using hourly environmental reanalysis data and best-track records corresponding to the passage of Typhoon Yagi (2024) through the northern South China Sea and Taiwan Strait. The empirical results demonstrate that the cooperative dual-agent structure establishes a superior global Pareto frontier compared to conventional standard single-agent Proximal Policy Optimization (PPO) and metaheuristic baselines. Under extreme typhoon conditions, the HRL-MOO model effectively decreases the cumulative environment-induced risk from approximately 0.17 to 0.12, improves path smoothness by achieving a higher index of 0.842, and accelerates policy convergence to within 3000 training episodes, all while maintaining a highly efficient voyage distance with minimal detour overhead. Ablation studies further validate that the synergy between macro-level stream field recognition and adaptive multi-objective optimization significantly enhances decision-making stability. This framework offers a robust and interpretable computational tool for autonomous ship weather routing in fast-changing, high-risk ocean environments. Full article

Under climate change, the increasing typhoon intensity poses a severe threat to fishery harbor safety through storm surges and extreme waves. Traditional empirical management approaches fail to capture the complex wave-surge coupling inside harbors, leading to risk blind spots in berth allocation. This study enhances the fishery harbor disaster resilience by employing high-resolution coupled wave-storm surge modeling, taking Xinying Central Fishing Harbor (Hainan, China) during Super Typhoon Yagi (September 2024) as a case study. A Holland typhoon model integrated with ERA5 reanalysis data was used to reconstruct the wind field, which subsequently drove a one-way coupled MIKE 21 FM–SW model to simulate regional tides and deep-water waves. A Boussinesq wave model was then applied to resolve nearshore shallow-water wave transformations inside the harbor. Model validation showed strong agreement with observations: correlation coefficients of 0.97 for tides in Xinying station and 0.95, 0.97, 0.93 for significant wave heights in three buoys around Hainan island, with root-mean-square errors of 0.19 m and 0.67, 0.69, 0.31 m, respectively. The Boussinesq wave simulations revealed detailed spatial distributions of wave heights inside the harbor during the typhoon. Based on these simulations, a dynamic berth zoning strategy was developed, mapping safety zones for different vessel sizes according to wave-height tolerance (e.g., ≤0.6 m for medium-sized trawlers). This framework can provide potential support for decision-making regarding fishing vessel refuge during typhoons, maximizing safe capacity while minimizing capsizing risks. Overall, this study demonstrates a feasible pathway from advanced numerical modeling to practical engineering management, supporting a transition from experience-based to data- and model-driven disaster prevention for coastal fishery harbors. Full article

Typhoons and extreme rainfall significantly exacerbate engineering risks during deep excavation construction. Based on an asymmetric deep excavation project in Hainan under the influence of Super Typhoon Yagi, this study analyzes the evolution of Soil Mixing Wall (SMW) pile deformation and prestressed anchor cable axial forces through field monitoring. PLAXIS 3D 2023.2.0.1059 finite element software is employed to investigate the deformation response of the supporting structure under the coupled effects of excavation and extreme rainfall, revealing the optimal design for embedment depth under such adverse conditions. The results indicate that the presence of existing buildings leads to asymmetric deformation and pronounced corner effects. The synergistic action between the capping beam and the waler transforms the pile displacement profile from a cantilever mode to a bow-shaped distribution. Parametric analysis determines the optimal embedment depth to be 10.6 m and the critical safety embedment depth to be 7.6 m. Under a 400 mm/d typhoon rainfall condition, the maximum horizontal displacement of the supporting structure increases by 1.6–2.0 mm compared to non-rainfall conditions. With a 3.5 m water head, increasing the embedment depth from 6.1 m to 10.6 m reduces the maximum horizontal displacements on the east, south, and west sides by 98%, 42%, and 10%, respectively. This study provides a theoretical basis and practical reference for embedment depth optimization in typhoon-prone regions. Full article

In this study, we employ seven well-established machine learning algorithms for the stochastic simulation of tropical cyclones in the Northwest Pacific, namely Support Vector Machine (SVM), Random Forest (RF), Bayesian Network (BN), Backpropagation Neural Network (BPNN), Wavelet Neural Network (WNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) network. First, based on the CMA (China Meteorological Administration) Tropical Cyclone Best-Track Dataset, we statistically analyze key typhoon parameters within each 5° × 5° grid over the Northwest Pacific. Second, the Random Forest method is applied to rank the importance of feature factors for predicting typhoon translation speed, storm heading, and central pressure in each grid. Third, each algorithm is used to develop prediction models, with hyperparameters optimized via a time-series cross-validation scheme. Fourth, the prediction models are compared to identify the best-performing model for predicting translation speed, storm heading, and central pressure, respectively. The optimal models are then evaluated in terms of computational efficiency and overfitting/underfitting, and validated both against traditional statistical methods and through multi-lead-time (1–72 h) predictions for four independent typhoons: Lekima 2019, Doksuri 2023, Ragasa 2025, and Yagi 2024. The results show that the optimal machine learning models outperform traditional statistical benchmarks, achieve a direct position error of <7 km and R² ≥ 0.979 at 1 h lead time, with track prediction remaining useful up to 48–72 h, while effective intensity prediction does not exceed 24 h. This study provides a robust data-driven framework for short-term typhoon forecasting within stochastic simulation, with future work aiming to extend to long-term predictions. Full article

Typhoon avoidance is critical for ship maneuvering safety under extreme meteo-ocean conditions. This study proposes a data-driven framework that converts AIS trajectories into interpretable course deviation and speed change responses for navigational decision support. After AIS cleaning, temporal resampling, and matching with gridded wind, wave, and current fields, rule-based sliding-window and regression procedures, informed by experienced captains and company staff, automatically generate proxy labels for deviation and speed reduction. Samples are stratified by vessel size to reflect differences in inertia and maneuverability, and XGBoost classifiers are trained with simple resampling to mitigate class imbalance. The framework is demonstrated on a single-event case study of Typhoon Yagi in the South China Sea, covering 8609 vessels and reconstructed sailing fragments. On the test set, the deviation model achieves 89.8% accuracy and high recall for deviation cases, while the speed change model reaches 82% balanced accuracy under the proxy-label setting. Results suggest a scale-dependent response: smaller vessels exhibit more frequent course deviation, whereas larger vessels more often reduce speed under severe wind-wave loading. The framework offers a proof-of-concept approach to derive behavior-based indicators from AIS and environmental data and may support situational assessment under adverse weather. Full article

(1) Background: The accurate remote sensing extraction of mangroves is often impeded by spectral confusion, particularly the misclassification of stagnant water bodies as mangroves in flat coastal regions. (2) Methods: To overcome this challenge, we propose a novel “spectral-spatial-terrain” stepwise correction framework. This approach integrates multi-source data: Sentinel-2 imagery for spectral pre-screening, Gaofen-2 (GF-2) imagery for geometric refinement, and a newly developed Potential Waterlogging Index (PWI), derived from a digital elevation model (DEM), for topographic correction. The framework was applied to evaluate mangrove damage following Typhoon Yagi (2024) in the East Harbour National Nature Reserve. (3) Results: The method achieved high extraction accuracy, with a Kappa coefficient of 0.97. The remote sensing-based damage assessment revealed that 48.2% of the mangrove area was affected, with a significantly higher damage rate of 63.0% observed within the PWI-identified potential waterlogging zones. (4) Conclusions: The high classification accuracy confirms the effectiveness of the proposed framework. More importantly, the spatially consistent damage pattern provides strong ecological evidence supporting the mechanistic rationale behind the terrain-based correction. This study presents a reliable and transferable remote sensing methodology for high-precision, dynamic monitoring and assessment of mangrove ecosystem after disaster. Full article

About 16% of typhoons making landfall in China strike Hainan Island, where near-surface extreme winds in dense urban areas exhibit a strong spatiotemporal heterogeneity that is difficult to capture with current observations and mesoscale models. Focusing on Haikou during Super Typhoon Yagi (2024)—the strongest autumn typhoon to hit China since 1949—we developed a multiscale ERA5–WRF–PALM framework to conduct 30 m resolution large-eddy simulations. PALM results are in reasonable agreement with most of the five automatic weather stations, while performance is weaker at the most sheltered park site. Mean near-surface wind speeds increased by 20–50% relative to normal conditions, showing a coastal–urban gradient: maps of weighted cumulative exposure to strong winds (≥Beaufort force 8) show much longer and more intense events along open coasts than within built-up urban cores. Urban morphology exerted nonlinear effects: wind speeds followed a U-shaped relation with both the open-space ratio and mean building height, with suppression zones at ~0.5–0.7 openness and ~20–40 m height, while clusters of super-tall buildings induced Venturi-like acceleration of 2–3 m s⁻¹. Spatiotemporal analysis revealed banded swaths of high winds, with open areas and islands sustaining longer, broader extremes, and dense street grids experiencing shorter, localized events. Methodologically, this study provides a rare, systematically evaluated application of a multiscale ERA5–WRF–PALM framework to a real typhoon case at 30 m resolution in a tropical coastal city. These findings clarify typhoon–city interactions, quantify morphological regulation of extreme winds, and support risk assessment, urban planning, and wind-resilient design in coastal megacities. Full article

Floating structures are increasingly recognized as crucial infrastructure for deep-sea energy exploitation, offshore communities, and maritime hub facilities in recent years. Understanding their energy consumption characteristics under varying meteorological conditions is essential for ensuring operational efficiency and resilience. This study investigates the influencing factors and variation patterns of energy use in floating structures under normal and typhoon environments. Three representative scenarios with different scales and functions were developed based on a bionic hexagon-shaped floating unit, and their respective energy demands were defined. A systematic sensitivity analysis was conducted using DeST with Typical Meteorological Year data and field observations from Super Typhoon Yagi (No. 2411) at Qionghai Station. Results indicate that, according to sensitivity analysis using the dynamic “intraday fluctuation + daily quantile” threshold, dry-bulb temperature and specific humidity are the dominant factors influencing floating-structure energy consumption, contributing 31.1% and 7.8% increases, respectively—significantly higher than other parameters. Under typhoon conditions, total energy consumption rose slightly relative to the TMY baseline, by 0.12%, 0.49%, and 0.95% across the three scenarios, with diurnal variations within ±5%. This study provides a quantitative basis for optimizing energy storage design and enhancing the resilience of floating structures to extreme meteorological events. Full article

Mangrove ecosystems are vital to coastal carbon cycling, yet their response to extreme climatic events remains underexplored. This study assesses dissolved organic carbon (DOC) dynamics across four ecosystem types—primary mangrove, restored (5-year and 8-year), and bare land—during three typhoons (Maliksi, Yagi, and Trami) that occurred in 2024. DOC concentrations (mol m⁻² s⁻¹) were measured across pre-, during-, and post-event phases and analyzed using boxplots, heatmaps, and ANOVA. Results show that primary mangroves maintained stable DOC levels, indicating strong biogeochemical resilience. Restored plots exhibited phase-dependent DOC variability, with older restoration age linked to improved carbon retention. Bare land showed consistently high DOC release, especially post-event, reflecting vulnerability to hydrological stress. DOC peaks occurred after typhoons, suggesting delayed carbon mobilization via microbial turnover and detrital input. These findings highlight the role of restoration age and vegetation cover in stabilizing coastal carbon under intensifying climatic extremes. Full article

Typhoon disturbances significantly influence forest carbon cycling by altering both physical structures and biogeochemical processes. Typhoon-induced fluctuations in soil respiration can substantially affect the carbon balance in forest ecosystems. In this study, we conducted a comparative investigation of soil respiration in plantations of Casuarina equisetifolia L. that were either affected or unaffected by the severe Typhoon Yagi, which ravaged Hainan Island, China, in 2024. The soil respiration and its components in Casuarina equisetifolia L. plantations in the coastal areas of Hainan, China, as well as their responses to environmental factors before and after typhoon disturbance, were investigated based on total soil respiration rate (R_s), heterotrophic respiration rate (R_h), 5 cm soil temperature (T₅), and 10 cm soil moisture (W₁₀) to support the carbon emission estimation in coastal sandy land plantations. The mean R_s and R_h in the typhoon-disturbed plots were (1.82 ± 0.16) and (1.19 ± 0.26) μmol·m⁻²·s⁻¹, respectively, while those in the control plots were (2.62 ± 1.08) and (1.41 ± 0.23) μmol·m⁻²·s⁻¹, respectively, with statistically significant differences (p < 0.05). In both plots, R_s exhibited a significant positive correlation with T₅ (p < 0.01). The T₅ correlation and Q₁₀ values for soil respiration were significantly higher in the typhoon-disturbed plots than in the control plots (p < 0.05). W₁₀ of the soil exhibited significant negative correlations with R_s and R_h in typhoon disturbance plots (p < 0.05). Consequently, typhoon disturbance markedly inhibited soil respiration and its components in the Casuarina equisetifolia L. plantations, indicating substantial impacts of typhoons on soil respiration processes and carbon cycling within the forest ecosystem. This study provides key parameters and empirical evidence to improve the accuracy of soil carbon emission estimates in Casuarina equisetifolia L. plantations on coastal sandy soils affected by typhoon events. Full article

Typhoon Yagi (2024) was a rapidly moving storm that lasted for eight days and made landfall in three locations, producing heavy rainfall over Hainan and Vietnam. This study aims to investigate the dynamical processes contributing to the heavy rainfall, concentrating on enthalpy flux (EF) and moisture flux (MF). The results indicate that both EF and MF increased significantly during the typhoon’s intensification stage and were high at the time of landfall. Before landfalling at Hainan, latent heat flux (LHF) reached 600 W/m², while sensible heat flux (SHF) was recorded as 80 W/m². Landfall at Hainan resulted in a decrease in LHF and SHF. LHF and SHF subsequently increased to 700 W/m² and 100 W/m², respectively, as noted prior to the landfall in Vietnam. The increased LHF led to higher evaporation, which subsequently elevated moisture flux (MF) following the landfall in Vietnam, while the region’s topography further intensified the rainfall. The mean daily rainfall observed over Philippines is 75 mm on 2 September (landfall and passing through), 100 mm over Hainan (landfall and passing through) on 6 September, and 95 mm at over Vietnam on 7 September (landfall and after), respectively. Heavy rainfall was observed over the land while the typhoon was passing and during the landfall. This research reveals that Typhoon Yagi’s intensity was maintained by a well-organized and extensive circulation system, supported by favorable weather conditions, including high sea surface temperatures (SST) exceeding 30.5 °C, substantial low-level moisture convergence, and elevated EF during the landfall in Vietnam. Full article

All-sky radiance assimilation can increase the utilization of satellite observations in cloudy regions and improve typhoon forecasts. This study focuses on the newly launched FengYun-3F satellite equipped with the Microwave Humidity Sounder II (MWHS-II) and develops an all-sky assimilation capability for its radiance data. A series of assimilation experiments were conducted to evaluate their impacts on the forecast of Typhoon Yagi (2024), demonstrating that all-sky assimilation leads to reductions in track error (23.14%) and improvements in precipitation forecasts (Equitable Threat Score increase of 16.92%) compared to clear-sky assimilation. Furthermore, a detailed comparison of assimilation experiments shows that using only the 183 GHz humidity channels yields limited improvement in tropospheric humidity, whereas assimilating the 118 GHz temperature channels significantly enhances temperature and wind forecasts. Combined assimilation of both frequency bands synergistically maintains accurate track and intensity predictions while further improving precipitation prediction. These findings demonstrate the value of multichannel all-sky assimilation and inform future satellite data assimilation strategies. Full article

This study aimed to compare and evaluate three satellite-based precipitation estimation products: Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-Early Run), Climate Prediction Center MORPHing technique Real Time (CMORPH-RT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain rate Now (PDIR-Now) to identify the optimal integration strategies to improve the extreme rainfall estimation during Super Typhoon Yagi (September, 2024) in Hanoi, Vietnam, using validation data from 25 ground stations. In-depth analysis of three extreme rainfall series during Typhoon Yagi (6–9 September 2024), examining 93 extreme rainfall events at the 95th percentile precipitation threshold (R95p = 21.78 mm/h), combined with statistics at lower percentile thresholds (R1p, R5p, R10p, and R90p) and upper percentile threshold (R99p), revealed IMERG-Early best captured the peak rainfall, CMORPH-RT achieved highest total rainfall accuracy, while PDIR-Now offered the best spatial analysis. However, limitations included time lags, inability to detect rainfall events above R99p (41.69 mm/hour), and low detection rates (8–12%) in areas first impacted by the typhoon. This study identified that integration strategies combining different satellite products based on their strengths at specific time scales showed potential for improved rainfall estimation: PDIR-Now with IMERG-Early (1–3 h) and IMERG-Early with CMORPH-RT (6–12 h). These integration approaches accounted for each product’s unique capabilities in capturing different aspects of extreme rainfall during super typhoon events. Full article

A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As Typhoon Gaemi (05 W) was moving poleward into an unfavorable environment north of 20° N, a sharp westward turn to cross Taiwan was a challenge to forecast. The pre-Yagi (12 W) westward turn across Luzon Island, re-formation, and then extremely rapid intensification prior to striking Hainan Island were challenges to forecast. The slow intensification of Bebinca (14 W) after moving poleward across 20° N into an unfavorable environment was better forecast by the ECEPS than by the Joint Typhoon Warning Center (JTWC), which consistently over-predicted the intensification. An early westward turn south of 20° N by Kong-Rey (23 W) leading to a long westward path along 17° N and then a poleward turn to strike Taiwan were all track forecasting challenges. Four-dimensional COAMPS-TC Dynamic Initialization analyses utilizing high-density Himawari-9 atmospheric motion vectors are proposed to better define the TC intensities, vortex structure, and unfavorable environment for diagnostic studies and as initial conditions for regional model predictions. In Part 2 study of selected 2024 season TCs that started north of 20° N, more challenging track forecasts and slow intensification rates over an unfavorable TC environment will be documented. Full article