Search Results (153)

Part I of this two-part documentation of the ECMWF ensemble (ECEPS) new tropical cyclone track and intensity forecasting challenges during the 2024 western North Pacific season described four typhoons that started well to the south of an unfavorable external environment north of 20° N. In this Part II, five other 2024 season typhoons that formed and intensified near and north of 20° N are documented. One change is that the Cooperative Institute for Meteorological Satellite Studies ADT + AIDT intensities derived from the Himawari-9 satellite were utilized for initialization and validation of the ECEPS intensity forecasts. Our first objective of providing earlier track and intensity forecast guidance than the Joint Typhoon Warning Center (JTWC) five-day forecasts was achieved for all five typhoons, although the track forecast spread was large for the early forecasts. For Marie (06 W) and Ampil (08 W) that formed near 25° N, 140° E in the middle of the unfavorable external environment, the ECEPS intensity forecasts accurately predicted the ADT + AIDT intensities with the exception that the rapid intensification of Ampil over the Kuroshio ocean current was underpredicted. Shanshan (11 W) was a challenging forecast as it intensified to a typhoon while being quasi-stationary near 17° N, 142° E before turning to the north to cross 20° N into the unfavorable external environment. While the ECEPS provided accurate guidance as to the timing and the longitude of the 20° N crossing, the later recurvature near Japan timing was a day early and 4 degrees longitude to the east. The ECEPS provided early, accurate track forecasts of Jebi’s (19 W) threat to mainland Japan. However, the ECEPS was predicting extratropical transition with V_max ~35 kt when the JTWC was interpreting Jebi’s remnants as a tropical cyclone. The ECEPS predicted well the unusual southward track of Krathon (20 W) out of the unfavorable environment to intensify while quasi-stationary near 18.5° N, 125.6° E. However, the rapid intensification as Krathon moved westward along 20° N was underpredicted. Full article

Boao Jade Beach, on the east coast of Hainan Island, is a typical sandy beach and is one of the areas where typhoons frequently land in Hainan. This study examined wind speed, wind direction, and sediment transport data obtained from field meteorological stations and omnidirectional sand accumulation instruments from 2020 to 2024 to study the coastal aeolian environment and sediment transport distribution characteristics in the region. The findings provide a theoretical basis for comprehensive analyses of the evolution of coastal aeolian landforms and the evaluation and control of coastal aeolian hazards. The research results showed the following: (1) The annual average threshold wind velocity for sand movement in the study area was 6.13 m/s, and the wind speed frequency was 20.97%, mainly dominated by easterly winds (NNE, NE) and southerly winds (S). (2) The annual drift potential (DP) and resultant drift potential (RDP) of Boao Jade Belt Beach from 2020 to 2024 were 125.99 VU and 29.59 VU, respectively, indicating a low-energy wind environment. The yearly index of directional wind variability (RDP/DP) was 0.23, which is classified as a small ratio and indicates blunt bimodal wind conditions. The yearly resultant drift direction (RDD) was 329.41°, corresponding to the NNW direction, indicating that the sand on Boao Jade Belt Beach is generally transported in the southwest direction. (3) When the measured data from the sand accumulation instrument in the study area from 2020 to 2024 were used for a statistical analysis, the results showed that the total sediment transport rate in the study area was 39.97 kg/m·a, with the maximum sediment transport rate in the S direction being 17.74 kg/m·a. These results suggest that, when sand fixation systems are constructed for relevant infrastructure in the region, the direction of protective forests and other engineering measures should be perpendicular to the net direction of sand transport. Full article

Marine atmospheric boundary-layer height (MABLH) is crucial for ocean heat, momentum, and substance transfer, affecting ocean circulation, climate, and ecosystems. Due to the unique geographical location of the South China Sea (SCS), coupled with its complex atmospheric environment and sparse ground-based observation stations, accurately determining the MABLH remains challenging. Coherent Doppler wind lidar (CDWL), as a laser-based active remote sensing technology, provides high-resolution wind profiling by transmitting pulsed laser beams and analyzing backscattered signals from atmospheric aerosols. In this study, we developed a stacking optimal ensemble model (SOEM) to estimate MABLH in the vicinity of the site by integrating CDWL measurements from a representative SCS site with ERA5 (fifth-generation reanalysis dataset from the European Centre for Medium-Range Weather Forecasts) data from December 2019 to May 2021. Based on the categorization of the total cloud cover data into weather conditions such as clear/slightly cloudy, cloudy/transitional, and overcast/rainy, the SOEM demonstrates enhanced performance with an average mean absolute percentage error of 3.7%, significantly lower than the planetary boundary-layer-height products of ERA5. The SOEM outperformed random forest, extreme gradient boosting, and histogram-based gradient boosting models, achieving a robustness coefficient ($R^2$) of 0.95 and the lowest mean absolute error of 32 m under the clear/slightly cloudy condition. The validation conducted in the coastal city of Qingdao further confirmed the superiority of the SOEM in resolving meteorological heterogeneity. The predictions of the SOEM aligned well with CDWL observations during Typhoon Sinlaku (2020), capturing dynamic disturbances in MABLH. Overall, the SOEM provides a precise approach for estimating convective boundary-layer height, supporting marine meteorology, onshore wind power, and coastal protection applications. Full article

When Typhoon Kong-rey hit Taiwan in October 2024, an aircraft attempting to land at Taoyuan International Airport undertook a missed approach and landed successfully on the second attempt. The possible meteorological factors causing this missed approach are studied in this study based on a methodology specifically adopted for Hong Kong International Airport; namely, studying crosswind as derived from aircraft and airport meteorological observations, as well as the low-level turbulence derived from data on the aircraft’s vertical acceleration and high-resolution numerical weather prediction model results. A significant crosswind component and a gusting crosswind are the major reasons for the missed approach. The low-level turbulence appears to have been secondary/minor, as shown by the successful landings of aircraft before and after the event. It is concluded that the methodology supporting airport operations in Hong Kong may be used to explain missed approach cases at other airports under the influence of tropical cyclones. Full article

As China’s largest peninsula, the Shandong Peninsula faces recurrent threats from both tropical and extratropical cyclone-induced storm surges. Understanding the distinct mechanisms governing these surge types is critical for developing targeted coastal hazard mitigation strategies. This investigation employs the FVCOM-SWAVE coupled wave–current model to conduct numerical simulations and comparative analyses of two 2022 surge events, Typhoon Muifa (tropical) and the “221003” extratropical surge. The results demonstrate that hydrodynamic responses exhibit strong dependence on surge-generating meteorological regimes. Tropical surge dynamics correlate closely with typhoon track geometry, intensity gradients, and asymmetric wind field structures, manifesting rightward-biased energy intensification relative to storm motion. Conversely, extratropical surge variations align with evolving wind-pressure configurations during cold air advection, driven by synoptic-scale atmospheric reorganization. The hydrodynamic environmental response in the sea areas surrounding Jiaodong and Laizhou Bay is particularly pronounced, influenced by the intensity of wind stress on the sea surface, as well as the bathymetry and coastal geometry. Full article

Autonomous unmanned surface vehicles (USVs) offer transformative potential for collecting marine meteorological data under extreme weather conditions, yet their capability to provide reliable solar radiation measurements during typhoons remains underexplored. This study evaluates shortwave downward radiation (SWDR) data obtained by a solar-powered USV (developed by IAP/CAS, Beijing, China) that successfully traversed Typhoon Sinlaku (2020), compared with Himawari-8 satellite products. The SUSV acquired 1 min resolution SWDR measurements near the typhoon center, while satellite data were collocated spatially and temporally for validation. Results demonstrate that the USV maintained uninterrupted operation and power supply despite extreme sea states, enabling continuous radiation monitoring. After averaging, high-frequency SWDR data exhibited minimal bias relative to Himawari-8 to mitigate wave-induced attitude effects, with a mean bias error (MBE) of 13.64 W m⁻² under cloudy typhoon conditions. The consistency between platforms confirms the SUSV’s capacity to deliver accurate in situ radiation data where traditional observations are scarce. This work establishes that autonomous SUSVs can critically supplement satellite validation and improve radiative transfer models in typhoon-affected oceans, addressing a key gap in severe weather oceanography. Full article

This study challenges the commonly held belief that typhoons universally improve air quality by dispersing pollutants, offering new insights into their complex effects on PM10 concentrations. Through a comprehensive analysis of long-term data (2001–2021) from seven major South Korean cities, we demonstrate that typhoons can lead to significant increases in PM10 concentrations, particularly before and after their passage, under specific meteorological conditions. Contrary to the prevailing assumption, PM10 levels often rise before typhoons due to atmospheric stagnation, and after typhoons due to subsidence and long-range pollutant transport. Our results indicate that the post-typhoon period is particularly prone to high-PM10 events, with PM10 concentrations increasing by 84.5% in Incheon, 60.8% in Busan, and 62.3% in Gwangju. A case study of Typhoon MITAK revealed that pre-typhoon atmospheric conditions contributed to PM10 concentrations exceeding 81 μg/m³ in Seoul, a level classified as ‘unhealthy’ by Korean air quality standards. These findings challenge existing perceptions and provide essential insights into the complex relationship between typhoons and air pollution. The study underscores the importance of understanding the nuanced dynamics of typhoon-induced air pollution and its implications for air quality management, particularly in the context of ongoing climate change and urbanization. Moreover, the integration of real-time monitoring data into predictive air quality models could enhance the ability to mitigate the adverse effects of typhoon-induced air pollution in vulnerable regions. Full article

The secure operation of the renewable-integrated power system is affected by extreme weather conditions such as typhoons. In order to meet the operational requirements of the system, it is necessary to dynamically evaluate the resilience of the renewable-integrated power systems based on meteorological forecast information to guide operators to make reasonable risk prevention and control decisions. A rapid assessment method for power system resilience is proposed to address the uncertainty of extreme weather caused by typhoons. First, with a focus on the impact of typhoon disasters on power system components, corresponding failure probability models are constructed by taking typhoon meteorological forecast information as input and considering the uncertainty of typhoon meteorological forecast. Error probability circles and average absolute errors of intensity forecasts are included in the sampling of typhoon scenarios. Second, for the resilience assessment process, the impact increment method is used to reduce the dimensionality of multiple fault state analysis in the power system, and resilience indexes are calculated by screening the contingency set based on depth-first traversal through a backtracking algorithm. The weak links in the power system are identified through sensitivity analysis of load loss. Finally, the effectiveness of the proposed method is verified using the modified IEEE RTS-79 power system. Full article

The response of large temporary working platforms for cross-sea bridges under the action of strong wind and waves with large tidal ranges is one of the key issues in offshore engineering. Based on a grand offshore bridge project in Fujian Province of China, on-site monitoring tests were carried out on a temporary working platform. A high-precision and fully automatic monitoring system was adopted to conduct the all-weather and high-frequency monitoring on vibrations, responses, and sea conditions of the platform, enabling us to grasp its structural mechanical characteristic and ensuring the platform safety. The results show that, under the severe sea conditions of typhoons, the stress of the platform structure increases significantly with the increase in the tidal range and reaches its maximum value at the high tide level. The inclination angle changes violently at the high tide level, while the amplitude of inclination angle change is relatively small at the low tide level. The effective value of the platform displacement under the severe sea conditions of typhoon meteorology is much larger than that under normal sea conditions. Compared with the low tide level, the acceleration of the offshore temporary work platform changes more drastically at the high tide level under severe sea conditions. Under severe sea conditions, the tidal level has a significant impact on the frequency corresponding to the peak value of the acceleration power spectrum of the offshore temporary platform. Full article

In recent years, extreme weather events have become more frequent and severe, making it crucial to apply meteorological and climate information services to mitigate the associated losses. However, given limited resources, it is essential to assess the potential value these services can generate while considering uncertainties. Since the impact of disasters and weather prediction accuracy is uncertain, and end-users’ decisions of disaster prevention, resource allocation, and operational planning are costly, the expected returns of acting according to weather forecasting information need to outweigh the cost to make decision-makers act. This study evaluates the direct economic value of meteorological information services for agricultural disaster prevention, with a focus on typhoon preparedness, using the cost-loss model. The results show that the current annual economic value of these services is NTD 77.28 million. Significant benefits can be gained by increasing the proportion of avoidable losses and improving forecast accuracy. A 10% increase in the proportion of avoidable losses, possibly due to the application of innovative technology and the extension of leading time, results in an 8% rise in economic value, while a 50% increase leads to a 38% increase. Moreover, enhancing the forecast accuracy, which is currently at 73.18%, by an additional 50% could boost economic value by up to 34%. From a practical perspective, unless agricultural output is completely protected from weather events (such as indoor horticultural crops), the potential for reducing avoidable losses remains limited. Consequently, the findings underscore the importance of government efforts to promote the establishment of additional weather observation stations in order to improve forecast accuracy, boost farmers’ confidence of application from public meteorological information services, and maximize the impact of meteorological services in reducing agricultural losses and enhancing disaster preparedness. Full article

Ozone (O₃) is a crucial atmospheric component that significantly affects air quality and poses considerable health risks to humans. In the coastal areas of the Yangtze River Delta, typhoons, influenced by the subtropical high-pressure system, can lead to complex ozone pollution situations. This study aimed to explore the causes, sources, and health risks of O₃ pollution during such events. Ground-based data from Jiaxing City’s key ozone precursor (VOCs) composition observations, ERA5 reanalysis data, and models CMAQ-ISAM and PMF were employed. Focusing on the severe ozone pollution event in Jiaxing from 3 to 11 September 2022, the results showed that local ozone production was the main contributor (60.8–81.4%, with an average of 72.3%), while external regional transport was secondary. Concentrations of olefins and aromatic hydrocarbons increased remarkably, playing a vital role in ozone formation. Meteorological conditions, such as reduced cloud cover during typhoon periphery transit, promoted ozone accumulation. By considering the unique respiratory exposure habits of the Chinese population, refined health risk assessments were conducted. Acrolein was found to be the main cause of chronic non-carcinogenic risks (NCRs), with NCR values reaching 1.74 and 2.02 during and after pollution. In lifetime carcinogenic risk (LCR) assessment, the mid-pollution LCR was 1.73 times higher, mainly due to 1,2-dichloroethane and benzene. This study presents a methodology that is readily adaptable to analogous pollution incidents, thereby providing a pragmatic framework to guide actionable local government policy-making aimed at safeguarding public health and mitigating urban ozone pollution. Full article

As global warming continues to intensify, typhoon disasters will more frequently occur in East and Southeast Asia, posing a high risk of causing large-scale power outages in the power system. To investigate the impact of typhoon disasters on high-voltage power grids, a comprehensive risk assessment method that integrates model-driven and data-driven approaches is proposed, which can predict power grid faults in advance and provide support for power grid operators to generate emergency dispatching plans. Firstly, by comparing actual loads with the design strengths of the transmission tower-line system and analyzing the geometric relationship between typhoon wind circles and the system, key variables, such as wind speed, longitude, latitude, and other pertinent factors, are screened. The Spearman correlation coefficient is employed to pinpoint the meteorological variables that exhibit a high degree of relevance, enhancing the accuracy and interpretability of our model. Secondly, addressing the lack of power grid fault samples, three data balancing methods—Borderline-SMOTE, ADASYN, and SMOTE-Tomek—are compared, with Borderline-SMOTE selected for its superior performance in enhancing the sample set. Additionally, a power grid failure risk assessment model is built based on Light Gradient Boosting Machine (LightGBM), and the Borderline-Smoothing Algorithm (BSA) is used for the modeling of power grid faults. The nonlinear mapping relationship between typhoon meteorological data and the power grid equipment failure rate is extracted through deep learning training. Subsequently, the Tree-structured Parzen Estimator (TPE) is leveraged to optimize the hyperparameters of the LightGBM model, thus enhancing its prediction accuracy. Finally, the actual power system data of a province in China under a strong typhoon are assessed, validating the proposed assessment method’s effectiveness. Full article

While extreme oceanic phenomena can often be accurately predicted, sudden abnormal waves along the shore (surge) are often difficult to foresee; therefore, an immediate sensing system was developed to monitor sudden and extreme events to take necessary actions to prevent further risks and damage. Real-time images from coastal surveillance video and meteorological data were used to construct a warning model for incoming waves using long short-term memory (LSTM) machine learning. This model can predict the wave magnitude that will strike the destination area seconds later and issue an alarm before the surge arrives. The warning model was trained and tested using 110 h of historical data to predict the wave magnitude in the destination area 6 s ahead of its arrival. If the forecasting wave magnitude exceeds the threshold value, a warning will be issued, indicating that a surge will strike in 6 s, alerting personnel to take the necessary actions. This configuration had an accuracy of 60% and 88% recall. The proposed prediction model could issue a surge alarm 5 s ahead with an accuracy of 90% and recall of 80%. For surge caused by a typhoon, this approach could offer 10 s of early waring with recall of 76% and an accuracy of 74%. Full article

Climate change has significantly increased multi-hazard disasters caused by typhoons, exposing the limitations of conventional forecasting systems that often neglect regional socio-economic vulnerabilities. This study develops and validates the Typhoon-Ready System (TRS) as an effective disaster-management framework for the Korean Peninsula. The TRS integrates hazard data with socio-economic and environmental vulnerability factors to produce region-specific risk indices. The analysis of four representative typhoons—Lingling (2019), Rusa (2002), Maemi (2003), and Mitak (2019)—demonstrates TRS’s applicability in identifying high-risk zones and supporting disaster preparedness strategies. The TRS framework incorporates indices, such as the Strong Wind Index (SWI), Heavy Rainfall Index (HRI), Storm Surge Index (SSI), and Air Quality Index (AQI), effectively combining meteorological modeling with vulnerability analysis. Results demonstrate that the TRS outperforms traditional systems by accurately identifying high-risk zones and correlating them with observed damage patterns. For example, the TRS successfully pinpointed high wind risks in Seoul and Incheon during Typhoon Lingling and forecasted severe flooding in Gangneung and Samcheok during Typhoon Rusa. By integrating vulnerability factors, including population density, infrastructure aging, and urbanization levels, the TRS provides a more holistic and accurate risk assessment. This research highlights the necessity of a multi-dimensional forecasting approach for enhancing disaster preparedness and resilience against climate change-induced typhoon impacts. Full article

Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average. At the same time, the PWV was inverted by radiosonde data in the study area as a reference to verify the water vapor inversion results of the new model, and it was found that the BIAS of the new model is only 0.8 mm, which has high accuracy. Further, compared with the LSTM model, the new model is more universal when the accuracy is comparable. In addition, in order to evaluate the spatial and temporal variation characteristics of the atmospheric water vapor retrieved by the new model, based on the rainstorm event caused by typhoon in Hong Kong of September 2023, the ERA5 GSMaP rainfall products and inverted PWV information were comprehensively used for analysis. The results show that the PWV increased sharply with the arrival of the typhoon and the occurrence of a rainstorm event. After the rain stopped, the PWV gradually decreased and tended to be stable. The spatial and temporal variation in the PWV have a strong correlation with the occurrence of extreme rainstorm events. This shows that the PWV inverted by the new model can respond well to extreme rainstorm events, which proves the feasibility and reliability of the new model and provides a reference method for meteorological monitoring and weather forecasting. Full article