Search Results (310)

This study investigates tide–surge nonlinear interactions along the southeastern coast of China (SCC) using Typhoon Winnie as a case study. A coupled tide–surge model is established based on the Finite-Volume Community Ocean Model (FVCOM), incorporating realistic bathymetry, tidal constituents, wind fields, and atmospheric pressure. The results show that tide–surge interactions contribute up to 1.8 m to the total water level, with the most pronounced effects occurring in shallow, high-friction coastal regions such as Hangzhou Bay, the Yangtze River Estuary, and the Jiangsu coast. Sensitivity experiments reveal that the quadratic bottom friction term is the dominant mechanism driving the nonlinear interaction, while the advection term plays a secondary role. The interaction intensity is highly sensitive to water depth and topographic slope; reducing water depth generally intensifies the interaction, though the response is non-monotonic in regions with complex bathymetry such as the radial sand ridge field. The phase and period of astronomical tides also exert significant control. Notably, semi-diurnal constituents (e.g., M₂, S₂) dominate the interaction, accounting for up to 80% of the nonlinear effect, whereas diurnal constituents contribute negligibly (less than 0.1 m). Tide–surge coupling significantly affects both the magnitude and timing of extreme water levels, with enhanced interaction occurring during astronomical low tide at some stations (e.g., Dinghai). These findings underscore the necessity of incorporating tide–surge interactions, particularly with accurate bottom friction and semi-diurnal tidal forcing, into storm surge models for improved forecasting and disaster risk assessment along China’s southeastern coast. Full article

This study investigates the forecast bust of Typhoon SHANSHAN (2024) characterized by large track errors using the four major interactive grand global operational ensemble data and the atmospheric reanalysis data. Ensemble space empirical orthogonal function (EOF) analysis is applied to 850, 500, and 300 hPa geopotential heights at three target times to diagnose how synoptic-scale uncertainty contributed to the erroneous motions of SHANSHAN. We align the multi-level EOF bases to a reference-time basis via a weighted Procrustes rotation and evaluate similarity to the atmospheric reanalysis data in the aligned principal component (PC) space, enabling robust, distance-based conditioning of ensemble members. Results show that ensemble spread is consistently larger in the mid-latitudes, with relatively large uncertainty concentrated around the upper-tropospheric trough and lower-tropospheric structure near SHANSHAN. The dominant EOF modes differ by phase of SHANSHAN: lower-tropospheric modes govern the westward-moving stage, whereas mid- and upper-tropospheric modes dominate after recurvature. Selecting members whose EOF-based PC structures most closely match the atmospheric reanalysis effectively suppresses large-error outliers and yields improved conditional track predictions. These findings highlight phase-dependent synoptic controls and demonstrate that adaptive, reference-consistent conditioning can enhance the track guidance of tropical cyclones during difficult forecast situations. Full article

Landfalling typhoons are the dominant trigger of short-duration extreme rainfall along the Zhejiang coast. It is necessary to estimate the recurrence of future typhoon rainfall at the city scale under the global-warming scenarios. Using Super Typhoon Lekima (2019) as a representative high-impact event, this study develops an event-based assessment framework for Taizhou city by combining the Weather Research and Forecast (WRF) model simulation, pseudo-global-warming (PGW) perturbation experiments, and generalized extreme value analysis. The historical simulation is first evaluated against the China Meteorological Administration best track, storm intensity evolution, and station rainfall observations. Future counterparts of the same event are then generated using CMIP6-derived thermodynamic perturbations under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Finally, scenario-dependent rainfall totals are projected onto a historical GEV curve to identify equivalent historical rainfall return periods. Results show that the WRF setup reproduces the main track, intensity tendency, and rainfall timing of Lekima with reasonable fidelity. The ensemble-mean cumulative rainfall over the Taizhou area increases from 204.75 mm in the historical simulation to 335.85, 366.72, 400.79, and 464.08 mm under the four SSPs, respectively. These increases translate into equivalent historical rainfall return periods of 47.40, 84.61, 164.28, and 604.05 years, compared with 5.24 years for the historical case. The results indicate that the moderate thermodynamic rainfall amplification produces a highly nonlinear escalation of event rarity based on historical frequency statistics. This implies that future typhoon rainfall should be interpreted using scenario-aware benchmarks within the historical reference framework. Full article

Research on the differences and correlations of typhoon precipitation along similar trajectories, as well as their underlying causes, remains insufficient. Therefore, this study selects two typhoons with similar tracks but significantly different precipitation characteristics—Chanthu (2114) and Mitag (1918) in the Western Pacific—as research cases. Using the China Meteorological Administration best-track dataset, ERA5 reanalysis data, surface station observations, and GPM IMERG precipitation products, their precipitation features and underlying mechanisms are analyzed. Results show that the area-averaged land precipitation associated with Chanthu (51.9 mm) was nearly twice that of Mitag (27.2 mm). Chanthu produced broader and more persistent rainfall, mainly distributed along the northern side of its track, whereas Mitag exhibited weaker and more scattered precipitation. These differences were primarily related to the combined effects of large-scale circulation, moisture transport, dynamical and thermodynamic structure, and convective instability. During Chanthu, the subtropical high remained stable and the upper-level trough stayed farther north, favoring the maintenance of an organized typhoon structure. Chanthu also featured stronger upper-level divergence, sustained dual-channel moisture transport, a deeper warm-core structure, stronger upward motion, and better-developed convective instability. In contrast, Mitag was affected by the southward extension of the upper-level trough and the eastward retreat of the subtropical high, together with weaker divergence, insufficient moisture supply, a shallower structure, and weaker instability. Overall, precipitation differences between similarly tracked typhoons result from the synergistic effects of multiple environmental and internal factors. These findings improve understanding of typhoon precipitation mechanisms and may provide guidance for forecasting. Full article

Accurate simulation of landfalling typhoons is essential for urban resilience in the densely populated Pearl River Delta. Using Super Typhoon Mangkhut (2018) as a case study, this paper evaluates the Weather Research and Forecasting (WRF) model through a contribution analysis designed to disentangle the roles of surface layer, planetary boundary layer (PBL), urban canopy model (UCM), and eddy-coefficient/diffusion closure parameterizations in wind-hazard prediction. Model results are validated against observations at the Hong Kong Observatory headquarters (HKO) and King’s Park (KP) stations, demonstrating that the hierarchy of physical controls is strongly metric-dependent. Substantial and structured spread is found among the tested configurations. Controlled comparisons show that PBL selection is the primary driver of variability in peak timing and high-wind persistence, whereas surface-layer formulation and diffusion closure exert secondary but systematic influences by shifting distributional centers and reshaping variability and upper tails. Urban canopy effects are comparatively weaker in aggregate but become more apparent during the impact and recovery phases. Overall, the results confirm that no single parameterization is consistently optimal across all metrics and motivate a multi-objective physics-selection strategy, in which multi-physics ensembles are used to better represent uncertainty in wind-event duration and associated loading risks in complex urban environments. 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

Accurate forecasting of typhoon evolution from satellite cloud imagery is critical for disaster preparedness and mitigation, yet remains challenging due to the complex spatiotemporal dynamics of typhoon systems. While deep learning models have shown promise in spatiotemporal sequence prediction, existing approaches often struggle to balance the modeling of large-scale structural evolution with fine-grained local dynamics. In this paper, we propose FusionTyphoonPredictor, a novel dual-branch encoder–decoder framework designed for typhoon cloud image prediction. The model integrates a Global Fusion Module to capture multi-scale spatial interactions using large-kernel attention and multi-scale convolution, and an ST Recurrent Refiner to enhance temporal consistency and local detail through recurrent processing with ConvGRU and residual blocks. Extensive experiments on the Digital Typhoon dataset demonstrate that our approach achieves improved performance compared to existing methods (including PredFormer and PhyDNet) across most metrics and forecasting horizons. Specifically, FusionTyphoonPredictor shows consistent advantages in SSIM, MAE, and MSE, with particular strength in short-term forecasting. Comprehensive ablation studies validate the complementary design of the two branches and confirm the effectiveness of each proposed component. Our work advances typhoon forecasting and has potential for real-time operational deployment. Full article

The accuracy of numerical weather prediction largely depends on the quality of the initial conditions. Global Navigation Satellite System radio occultation (GNSS-RO) observations, with their high vertical resolution, play an important role in reducing initial condition errors. In this study, multiple simulations with different initialization times were conducted during the development of Typhoon BEBINCA using the WRF-GSI assimilation system to evaluate the impact of YunYao GNSS-RO observations on improving extreme weather simulation performance and to investigate the sensitivity of refractivity assimilation to different cloud microphysics parameterization schemes. The results show that assimilating YunYao GNSS-RO data significantly improves the consistency between the model initial fields and observations and enhances the analysis quality in the middle and upper troposphere. Compared with ERA5 reanalysis data, the assimilation experiments better reproduce the spatial and temporal evolution of key atmospheric variables, and the improvements persist from 36 h to 120 h forecast lead time. Statistical results from multiple initializations show that the maximum RMSE reductions exceed 0.2 K for temperature, 0.1 m s⁻¹ for wind speed, and geopotential height shows consistent improvements throughout the entire atmosphere. In addition, the assimilation experiments improve the simulation of Typhoon BEBINCA’s track and intensity. Statistical results from multiple initializations indicate that the 84 h track error is reduced by approximately 30 km on average, and the minimum central pressure bias is also reduced. Sensitivity experiments further show that the WSM6 microphysics scheme performs better in track forecasting, while the Thompson scheme is more suitable for intensity forecasting. Overall, YunYao GNSS-RO assimilation effectively improves typhoon forecast accuracy and demonstrates strong potential for operational applications. Full article

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The model uses pre-typhoon ocean background conditions and ocean states at the typhoon peak moment as inputs, including wind field, sea level anomaly (SLA), mixed layer depth (MLD), and 100 m water temperature. Trained on historical typhoon data and multi-source ocean observations from 2002 to 2018, the model directly predicts SSC during typhoon events from 2019 to 2020. Results show that the model achieves a mean absolute error (MAE) of 0.379 °C, a root mean square error (RMSE) of 0.488 °C, and a bias of 0.087 °C. The model reproduces the typical rightward bias in SSC spatial distribution. Under normal ocean conditions, such as open deep-water areas with moderate stratification and no strong eddy interference, the model performs well, with errors below 0.1 °C at some points. Although some biases exist under complex ocean environments and abrupt changes in typhoon dynamics, the model still captures the overall cooling trend. This study demonstrates the feasibility of machine learning for typhoon–ocean interaction forecasting. The proposed framework can provide technical support for typhoon intensity forecasting, marine disaster warning, and aquaculture risk prevention. Full article

Tidal-scale wave modulation is a critical yet complex process in macro-tidal estuaries. This study investigates semidiurnal wave modulations in the Changjiang River Estuary (CRE) using unique, long-term in situ observations and high-resolution ADCIRC–SWAN coupled simulations. Pronounced semidiurnal signals are identified in significant wave height (H_s), mean wave period, and wave direction. Observational results demonstrate that the modulation intensity is highest in Hangzhou Bay and the CRE mouth, decreasing gradually offshore. A key finding is that semidiurnal H_s maxima systematically coincide with peak flood currents and precede high water by approximately three hours. Long-term records confirm that this modulation persists year-round and intensifies during energetic events such as typhoons. The expression of the tidal signal depends on wave composition: wind-sea-dominated conditions exhibit stronger period modulation, whereas swell-dominated conditions favor coherent H_s modulation as kinematic tidal effects remain more apparent in the absence of strong local wind forcing. Numerical sensitivity experiments demonstrate that tidal currents are the primary driver of the observed wave modulation, while water-level effects are largely confined to shallow shoals. The results highlight that accurately reproducing the observed frequency–directional structure requires the inclusion of current-induced Doppler shifts and refraction. Beyond the classical following-current effects, the analysis suggests that the spatial deceleration of currents along the wave path acts as a kinematic trap that focuses wave action and sustains H_s intensification. This mechanism provides a physically plausible explanation for the observed phase relationship and points to the non-local nature of estuarine wave dynamics, where the wave state appears as an integrated response to cumulative current gradients along the propagation path. These findings emphasize the necessity of incorporating wave–current coupling in future coastal modeling and hazard forecasting. Full article

Typhoon track prediction is an important research direction in weather forecasting. Although deep learning methods have achieved some progress in this field, challenges remain, including insufficient fusion of meteorological features, limited capability in modeling temporal and spatial evolution, and high computational cost of some models. To address these issues, this paper proposes a dual-path, multi-modal typhoon track prediction model that incorporates a gated axial Transformer to enhance the modeling of deep structural features in the meteorological environment. Numerical experimental results show that the proposed model achieves higher prediction accuracy than comparative methods in typhoon track prediction tasks across multiple time scales, demonstrating the effectiveness of the approach. Full article

A vulnerability surface modeling method based on dual intensity metrics is proposed to assess the impact of typhoons and heavy rainfall disasters on the distribution pole-tower conductor system. A three-dimensional finite-element model is developed for a typical “three-pole four-conductor” distribution line, considering the uncertainties in both load-side and structural-side parameters. A spatially coherent turbulent wind field is generated using the Davenport spectrum and harmonic superposition method, while an equivalent rain load is derived based on raindrop spectrum integration. Nonlinear dynamic time-history analysis is then conducted under multiple combinations of basic wind speeds and rainfall intensities, extracting engineering demand parameters such as conductor axial tension and pole-base bending moments. Based on probabilistic demand analysis, the relationship between engineering demand parameters and dual intensity measures is regressed in the logarithmic domain to construct bivariate fragility surfaces for both the conductors and the poles. Critical failure curves are obtained by intersecting the fragility surfaces with the 10% exceedance probability level, enabling rapid classification of structural risk under the joint effects of wind and rain. The results show that the regression model provides a high fit, effectively revealing that wind speed is the dominant control factor, while rainfall intensity serves as a secondary amplifying factor. The resulting critical failure curves can be directly used as operation and maintenance warning thresholds and can be coupled with observed and forecast meteorological data for time-varying risk assessment. These findings provide methodological support and engineering guidance for risk assessment, operation and maintenance decision-making, and resilience enhancement of distribution networks under multi-hazard coupling. Full article

The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain predictable systematic bias components). To address the issue that traditional linear methods struggle to capture the nonlinear relationships between biases and forecast predictors, this study proposes an intelligent bias correction method that integrates ensemble learning and explainable artificial intelligence. First, the entropy reduction method is used to select 69 mid-wave channels. Then, Random Forest, XGBoost, LightGBM, Decision Tree, and Extra Tree are used as base learners to construct a weighted average ensemble model. Training and validation are conducted using high-frequency clear-sky observation data from FY-4A/GIIRS during Typhoon Lekima. The results show that: (1) the ensemble learning correction method outperforms single models and traditional offline methods, with root mean square errors of brightness temperature bias of less than 0.9209 K for the training set and 1.4447 K for the test set; (2) Shapley Additive Explanations (SHAP)-based interpretability analysis reveals the contribution and nonlinear influence mechanisms of factors such as longitude, atmospheric thickness, surface temperature, and total precipitable water on bias correction. This study provides an intelligent bias correction framework with both high precision and explainability, offering a reference for the bias correction and assimilation applications of hyperspectral satellite observations like GIIRS. Full article

Understanding the upper-ocean thermal response during and between typhoons is critical for accurate prediction of typhoon intensity and for evaluating air–sea interactions. Previous studies have primarily focused on ocean cooling induced by individual typhoons and sea surface temperature (SST) recovery after that, yet oceanic thermal rejuvenation within the typhoon Inactivity Duration and its influence on the subsequent typhoon remains insufficiently explored. Using 42 years of typhoon best-track data, satellite observations and reanalysis data, we provide the first systematic quantification of the physical link between Inactivity Duration and subsequent typhoon intensification. Here we found that the intensity of the subsequent typhoon increased with typhoon Inactivity Duration. The subsequent typhoon is 6.34 kt and 7.69 hPa stronger than the previous typhoon for every 10 days of increase in typhoon Inactivity Duration. Upper-ocean thermal condition rejuvenated with time and contributed to subsequent typhoon development, and both SST and ocean heat content (OHC) exhibited significant phase changes from negative after the preceding typhoon to positive prior to the subsequent one, accompanied by a notable shoaling of the mixed layer depth (MLD) and sustained high levels of atmospheric instability. These coordinated environmental changes provide enhanced energy reserves and more favorable thermodynamic conditions for typhoon development after the inactivity period. These findings highlight the importance of considering ocean thermal rejuvenation in forecasting typhoon intensity and provide a quantitative framework for assessing sequential typhoon interactions with the upper ocean, offering theoretical support for improved intensity forecasting. Full article

In recent years, the increasing frequency of strong and super typhoons has been attributed to rising sea surface temperatures due to global warming. This study utilized the Weather Research and Forecasting (WRF) and Simulating WAves Nearshore (SWAN) models to analyze 30 years of wind and wave data for the Yangjiang sea area in China. The accuracy of the numerical simulations was validated using observed data from typhoons Ty201213, Ty201522, Ty201822, and Ty202118, along with wind and wave data from December 2024. This study utilized the P-III distribution to analyze design wind parameters. At a height of 10 m, the 3 s and 10 min mean wind speeds for the 100- and 50-year return periods were 62.21 m/s, 47.85 m/s, 57.99 m/s, and 44.61 m/s, respectively. At hub height (170 m), the corresponding values were 80.27 m/s, 61.75 m/s, 74.84 m/s, and 57.57 m/s. Furthermore, this study successfully applied a 2D-KDE approach to construct a joint probability model and derive environmental contours for extreme environmental assessments. The HS and TP at project point P for the 100- and 50-year return periods are 13.61 m and 15.91 s, as well as 12.39 m and 15.07 s, respectively. Full article