Search Results (1,311)

Satellite Precipitation Products (SPPs) play a crucial role in hydrological modeling, particularly in data-scarce and climate-sensitive basins such as the Magat River Basin (MRB), Philippines—one of Southeast Asia’s most typhoon-prone and infrastructure-critical watersheds. This study presents the first full-cycle evaluation of nine widely used multi-source precipitation products (2000–2024), integrating raw validation against rain gauge observations, bias correction using quantile mapping, and post-correction re-ranking through an Entropy Weight Method–TOPSIS multi-criteria decision analysis (MCDA). Before correction, SM2RAIN-ASCAT demonstrated the strongest statistical performance, while CHIRPS and ClimGridPh-RR exhibited robust detection skills and spatial consistency. Following bias correction, substantial improvements were observed across all products, with CHIRPS markedly reducing systematic errors and ClimGridPh-RR showing enhanced correlation and volume reliability. Biases were decreased significantly, highlighting the effectiveness of quantile mapping in improving both seasonal and annual precipitation estimates. Beyond conventional validation, this framework explicitly aligns SPP evaluation with four critical hydrological applications: flood detection, drought monitoring, sediment yield modeling, and water balance estimation. The analysis revealed that SM2RAIN-ASCAT is most suitable for monitoring seasonal drought and dry periods, CHIRPS excels in detecting high-intensity and erosive rainfall events, and ClimGridPh-RR offers the most consistent long-term volume-based estimates. By integrating validation, correction, and application-specific ranking, this study provides a replicable blueprint for operational SPP assessment in monsoon-dominated, data-limited basins. The findings underscore the importance of tailoring product selection to hydrological purposes, supporting improved flood early warning, drought preparedness, sediment management, and water resources governance under intensifying climatic extremes. Full article

This study employs an improved fractional-order grey multivariable convolution model (FGMC(1,N,2r)) to predict abalone aquaculture output in Fujian, Shandong, and Guangdong. By integrating fractional-order accumulation (r₁, r₂) with a particle-swarm-optimization (PSO) algorithm, the model addresses limitations of handling multivariable interactions and sequence heterogeneity within small-sample regional datasets. Grey relational analysis (GRA) first identified key factors exhibiting the strongest associations with production: abalone production in Fujian and Shandong is predominantly influenced by funding for aquatic-technology extension (GRA degrees of 0.9156 and 0.8357, respectively), while in Guangdong, production was most strongly associated with import volume (GRA degree of 0.9312). Validation confirms that FGMC(1,N,2r) achieves superior predictive accuracy, with mean absolute percentage errors (MAPE) of 0.51% in Fujian, 3.51% in Shandong, and 2.12% in Guangdong, significantly outperforming benchmark models. Prediction of abalone production for 2024–2028 project sustained growth across Fujian, Shandong, and Guangdong. However, risks associated with typhoon disasters (X₆ and import dependency (X₅) require attention. The study demonstrates that the FGMC(1,N,2r) model achieves high predictive accuracy for regional aquaculture output. It identifies the primary drivers of abalone production: technology-extension funding in Fujian and Shandong, and import volume in Guangdong. These findings support the formulation of region-specific strategies, such as enhancing technological investment in Fujian and Shandong, and strengthening seed supply chains while reducing import dependency in Guangdong. Furthermore, by identifying vulnerabilities such as typhoon disasters and import reliance, the study underscores the need for resilient infrastructure and diversified seed sources, thereby providing a robust scientific basis for production optimization and policy guidance towards sustainable and environmentally sound aquaculture development. Full article

Against the backdrop of global climate change, extreme weather events are increasingly challenging the safe and stable operation of power distribution networks. These events can cause sudden load fluctuations, equipment failures, and disruptions in power transfer. To address these, this paper proposes an optimal control strategy for distribution network power transfer, integrating Long Short-Term Memory (LSTM) networks and dynamic optimization models. By fusing meteorological data with grid characteristics, the LSTM model predicts load demand and fault probability, capturing complex system behaviors under extreme conditions. Combined with Mixed-Integer Linear Programming (MILP), a decision-making model is developed, and a deep-reinforcement-learning-based algorithm handles uncertainties in weather, load, and equipment faults, enabling accurate control. Validation on a 33-bus system shows the method enhances reliability under extreme weather, providing practical value. Furthermore, typhoons, as extreme weather events, can severely damage infrastructure, disrupt power lines, and affect grid stability. In the 33-bus system, typhoons can cause tower collapses and line failures, impacting power transfer. This paper explores the impact of typhoons on a bus model integrated with renewable energy, proposing optimal control strategies to ensure power supply to critical loads while minimizing equipment damage. Full article

A comprehensive understanding of the evolution and phase transitions of hydrometeors during the development of tropical cyclones (TCs) is essential for advancing research on the mechanisms of TC intensity change. In this study, utilizing the Weather Research and Forecasting numerical model, we simulate the evolution of Super Typhoon Nangka (No. 1511), explore the relationship between the TC intensity variations and the internal hydrometeor distribution, and examine the secondary circulation characteristics. The results indicate that the total content of hydrometeor particles increased during the intensification of Typhoon Nangka. Ice-phase particles expanded outward radially as the typhoon intensified, while liquid-phase particles contracted inward. Ice-phase hydrometeor distributions varied in conjunction with TC intensity variations, whereas liquid-phase hydrometeor variations were closely related to the complex dynamic–thermodynamic–microphysical processes within the typhoon. The spatial pattern of the secondary circulation exhibits high consistency with the distribution of hydrometeor particles. Low-level radial inflow, upper-level radial outflow, and middle-level vertical updrafts played dominant roles in regulating the distribution and transport of particles at different stages. The intensification of Typhoon Nangka was primarily driven by water vapor convergence and the latent heat released by ascending liquid-phase particles near the eyewall, while the stagnation of its intensification was mainly attributed to the resistance exerted by descending ice-phase particles from upper levels and the heat consumption associated with their melting. These findings provide a foundation for better understanding how hydrometeors modulate TC intensity variations and offer valuable insights into energy conversion mechanisms during hydrometeor phase transitions under the influence of secondary circulations. Full article

In recent years, frequent typhoon-induced disasters have significantly increased the risk of power grid outages, posing severe challenges to the secure and stable operation of distribution grids with high penetration of distributed photovoltaic (PV) systems. Furthermore, during post-disaster recovery, the dual uncertainties of distributed PV output and the charging/discharging behavior of flexible resources such as electric vehicles (EVs) complicate the configuration and scheduling of mobile energy storage systems (MESS). To address these challenges, this paper proposes a two-stage robust optimization framework for dynamic recovery of distribution grids: Firstly, a multi-stage decision framework is developed, incorporating MESS site selection, network reconfiguration, and resource scheduling. Secondly, a spatiotemporal coupling model is designed to integrate the dynamic dispatch behavior of MESS with the temporal and spatial evolution of disaster scenarios, enabling dynamic path planning. Finally, a nested column-and-constraint generation (NC&CG) algorithm is employed to address the uncertainties in PV output intervals and EV demand fluctuations. Simulations on the IEEE 33-node system demonstrate that the proposed method improves grid resilience and economic efficiency while reducing operational risks. Full article

This study evaluates the forecast performance of four radiation parameterization schemes—the Rapid Radiative Transfer Model for General Circulation Models (RRTMG), its improved version RRTMG-K, the infrequently applied variant, RRTMG-K_60x, and the neural network emulator, RRTMG-K_NN, within a high-resolution numerical weather prediction (NWP) model. The evaluation uses satellite-derived observations of Outgoing Longwave Radiation (OLR) and Outgoing Shortwave Radiation (OSR) from the Clouds and the Earth’s Radiant Energy System (CERES) over the Korean Peninsula during 2020, including an extreme case study of Typhoon Haishen. Results show that RRTMG-K reduces RMSEs by 4.8% for OLR and 17.5% for OSR relative to RRTMG, primarily due to substantial bias reduction (42.3% for OLR, 60.4% for OSR). The RRTMG-K_NN scheme achieves approximately 60-fold computational speedup while maintaining similar or slightly better accuracy than RRTMG-K; specifically, it reduces OLR errors by 1.2% and OSR errors by 1.6% compared to the infrequently applied RRTMG-K_60x. In contrast, the infrequent application of RRTMG-K (RRTMG-K_60x) slightly increases errors, underscoring the trade-off between computational efficiency and accuracy. These findings demonstrate the value of integrating advanced satellite flux observations and machine learning techniques into the evaluation and optimization of radiation schemes, providing a robust framework for improving cloud–radiation interaction representation in NWP models. Full article

Extreme weather events, such as typhoons, induce strong wave–current interactions that significantly alter nearshore hydrodynamic conditions, particularly in shallow intertidal zones. This study investigates the influence of wind speed and water depth on wave–current coupling under typhoon conditions in Shenhu Bay, southeastern China—a semi-enclosed bay that hosts multiple ancient forest relics within its intertidal zone. A two-tier numerical modeling framework was developed, comprising a regional-scale hydrodynamic model and a localized high-resolution model centered on a protective structure. Validation data were obtained from in situ field observations. Three structural scenarios were tested: fully intact, bottom-blocked, and damaged. Results indicate that wave-induced radiation stress plays a dominant role in enhancing flow velocities when wind speeds exceed 6 m/s, with wave contributions approaching 100% across all water depths. However, the linear relationship between water depth and wave contribution observed under non-typhoon conditions breaks down under typhoon forcing. A critical depth range was identified, within which wave contribution peaked before declining with further increases in depth—highlighting its potential sensitivity to storm energy. Moreover, structural simulations revealed that bottom-blocked devices, although seemingly more enclosed, may be vulnerable to vertical pressure loading due to insufficient water exchange. In contrast, perforated designs facilitate an internal–external hydrodynamic balance, thereby enhancing protective effect. This study provides both theoretical and practical insights into intertidal structure design and paleo-heritage conservation under extreme hydrodynamic stress. Full article

Severe Typhoon Danas over the northern part of the South China Sea and seas near Taiwan in early July 2025 had an erratic path that had not been observed before, according to historical data in the region. Its formation, movement, and intensification posed significant challenges to the timely tropical cyclone (TC) warning services. This paper documents the observational aspect and forecasting aspect of this cyclone. There are key findings: (a) when Danas interacted with the Central Mountain Range of Taiwan, a “secondary cyclone” appeared over the northeastern part of Taiwan, which was observed by both weather radars and meteorological satellite winds, and was simulated to a certain extent by a mesoscale numerical weather prediction (NWP) model; (b) data-driven AI global models performed better than physics-based global NWP models in capturing the formation and the rather erratic track of Danas a couple of days earlier, although AI models generally underestimate the intensity forecasts; and (c) an atmosphere–ocean–wave coupled model was found to perform the best in capturing both the track changes of Danas (because of being driven by an AI global model) and its intensity changes (because of better physical representation of the oceanic impact on the intensity of this TC), whereas AI global models, though with various recent enhancements, still tended to underestimate the strength of Danas. This paper serves as a reference of this rather unusual TC for the weather forecasting services in the region. Full article

Meteorological hazards threaten sustainable development by affecting human safety, economic stability, and food security. Climate change increases extreme weather frequency, underscoring the urgency for comprehensive evaluation frameworks. However, existing frameworks rarely integrate multiple impact dimensions, limiting their practical utility. To address this gap, our core objective is to develop two novel index series, a single-hazard composite impact index (SHCI) and a multi-hazard composite impact index (MHCI), employing entropy weighting to integrate demographic and economic factors, enabling a more holistic assessment of meteorological hazard impacts in China. Analysis of 2004–2021 data on drought, rainstorm and flood (RF), hail and lightning (HL), typhoon, and low-temperature freezing (LTF) revealed decreases in the national MHCI and SHCI. Key results include the following: (1) the relative MHCI decreased by 74.8%, exceeding 61.21% of absolute MHCI; (2) nationally, 2010, 2013, and 2016 had high MHCI values, and Sichuan has the most extreme hazard years (three) among all the provinces; and (3) provincially, Ningxia has the highest absolute and relative MHCI, while SHCIs varied spatially. These findings provide specific references for climate adaptation planning and the optimization of hazard risk reduction strategies. The methodology offers a versatile framework for multi-hazard risk assessment in nations experiencing climatic and demographic transitions. Full article

Landslides pose a significant threat to Taiwan’s mountainous regions, particularly after extreme weather events such as typhoons. This study introduces a machine learning framework for post-disaster land use-land cover (LULC) classification and landslide detection in Nanfeng Village, central Taiwan, following Typhoon Khanun in August 2023. Using high-resolution Pléiades imagery and 22 environmental and spectral factors, a Random Forest classifier was developed. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was systematically evaluated across multiple variants. The Distance_SMOTE method yielded the best results, increasing overall accuracy from 74% to 85% and the Kappa coefficient from 0.69 to 0.82. F1-scores for landslides, roads, and grassland improved markedly, reaching 0.97, 0.85, and 0.78, respectively. The optimized model produced accurate pre- and post-typhoon LULC maps, revealing significant expansion of landslide zones after the event. This study demonstrates the practical value of combining SMOTE-based resampling with Random Forest for rapid, reliable post-disaster assessment, offering actionable insights for disaster response and land management in data-imbalanced conditions. By enabling timely mapping of hazard-affected areas and informing targeted recovery actions, the approach supports disaster risk reduction, sustainable land use planning, and ecosystem restoration. These outcomes contribute to the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by strengthening community resilience, promoting climate adaptation, and protecting terrestrial ecosystems in hazard-prone regions. Full article

In the context of global climate change and accelerated urbanization, coastal cities face severe threats from storm surges, and accurately predicting coastal water level changes during storm surges has become a core technological demand for disaster prevention and reduction. Storm surges are caused by atmospheric pressure and wind conditions, and their destructive power is closely related to the morphology of the coastline. Traditional tide level prediction models often face difficulties in boundary condition parameterization. Tide level changes result from the combined effect of various complex processes. In past prediction studies, harmonic analysis and numerical simulations have dominated, each with their own limitations. Although machine learning applications in tide prediction have garnered attention, issues such as data inconsistency or missing data still exist. The physical–data fusion approach aims to overcome the limitations of single methods but still faces some challenges. This paper proposes a Deep-Numerical-Reinforcement learning fusion prediction model (DNR), which adopts ensemble learning. First, deep learning models and the numerical model Finite-Volume Coastal Ocean Model (FVCOM) are used to predict tide levels at different tide stations, and then a fusion approach based on the improved reinforcement learning model DDPG_dual is applied for model assimilation. This reinforcement learning fusion model includes a module specifically designed to handle tide extreme points. In the case of the Typhoon Mangkhut storm surge, the DNR model achieved the best results for tide level predictions at six tide stations in the South China Sea. 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

The Geostationary Interferometric Infrared Sounder (GIIRS) on board FengYun-4B (FY-4B), a Chinese second-generation hyperspectral infrared, enables the provision of critical data for forecasting high-impact weather events such as typhoons. To evaluate the reliability of FY-4B/GIIRS data, this study conducted three comparative assimilation trials for both Typhoon Gaemi and Typhoon Doksuri, assimilating observations from the Infrared Atmospheric Sounding Interferometer (IASI), Advanced Microwave Sounding Unit-A (AMSU-A), and FY-4B/GIIRS, respectively. Results demonstrate that the assimilation of GIIRS observations yields more stable forecasts of the wind field at 300 hPa and 500 hPa compared to AMSU-A and IASI, with biases within ±6 m/s relative to NCEP FNL data. However, GIIRS assimilation produces systematic underprediction of vertical velocity, whereas AMSU-A forecasts align more closely with reanalysis. For track forecasts, the GIIRS-assimilated trajectory exhibits closer alignment with observations than AMSU-A and IASI experiments, maintaining biases below 50 km throughout 48 h forecast period of Gaemi. This study provides valuable experience for the application of FY-4B/GIIRS data assimilation. Full article

This study examines the upper-ocean response to Typhoon Khanun, which traversed the northern South China Sea in October 2017, by integrating multi-satellite observations with numerical simulations from the Regional Ocean Modeling System (ROMS). For the ROMS simulations, an Arakawa C-grid was adopted with a 4-km horizontal resolution and 40 vertical terrain-following $\sigma$-layers, covering the domain of 105° E to 119° E and 15° N to 23° N. Typhoons significantly influence ocean dynamics, altering sea surface temperature (SST), sea surface salinity (SSS), and ocean currents, thereby modulating air–sea exchange processes and marine ecosystem dynamics. High-resolution satellite datasets, including GHRSSST for SST, SMAP for SSS, GPM IMERG for precipitation, and GLORYS12 for sea surface height, were combined with ROMS simulations configured at a 4-km horizontal resolution with 40 vertical layers to analyze ocean changes from 11 to 18 October 2017. The results show that Typhoon Khanun induced substantial SST cooling, with ROMS simulations indicating a maximum decrease of 1.94 °C and satellite data confirming up to 1.5 °C, primarily on the right side of the storm track due to wind-driven upwelling and vertical mixing. SSS exhibited a complex response: nearshore regions, such as the Beibu Gulf, experienced freshening of up to 0.1 psu driven by intense rainfall, while the right side of the storm track showed a salinity increase of 0.6 psu due to upwelling of saltier deep water. Ocean currents intensified significantly, reaching speeds of 0.5–1 m/s near coastal areas, with pronounced vertical mixing in the upper 70 m driven by Ekman pumping and wave-current interactions. By effectively capturing typhoon-induced oceanic responses, the integration of satellite data and the ROMS model enhances understanding of typhoon–ocean interaction mechanisms, providing a scientific basis for risk assessment and disaster management in typhoon-prone regions. Future research should focus on refining model parameterizations and advancing data assimilation techniques to improve predictions of typhoon–ocean interactions, providing valuable insights for disaster preparedness and environmental management in typhoon-prone regions. Full article

The large-scale integration of wind power into coastal power systems introduces significant challenges to reserve planning, especially under the threat of typhoons, which can cause extensive generation loss and threaten system security. Conventional reserve planning methods often fail to account for such extreme typhoon events. To fill the gap, this paper proposes a novel two-stage reserve planning framework that integrates economic optimization with operational security verification. In the first stage, a diverse set of high-impact typhoon scenarios are generated using a multivariate Markov chain Monte Carlo (MMCMC)–based path reconstruction method, which captures the dynamic evolution of key typhoon characteristics. In the second stage, the economically optimal reserve capacity is identified through cost-benefit analysis and then validated against the typhoon scenarios via N − 1 security verification. A case study on the modified IEEE RTS79 test system indicates that economically optimal reserve may be inadequate for ensuring security under severe typhoon conditions. However, a small increase in reserve capacity can effectively enhance system resilience with minimal additional cost. These results highlight the importance of incorporating typhoon scenario-based security verification into reserve planning especially for high-penetration wind power systems in coastal regions. Full article