Search Results (350)

The alerting of a microburst at the Hong Kong International Airport (HKIA) is currently detection-based. The technical feasibility of forecasting microbursts in an operational environment was examined in this study through four examples: three cases of a band of intense convection and another case of a severe squall line. A Weather Research and Forecasting (WRF) model with a spatial resolution of 40 m was used in the simulation. Data from several weather radars were integrated into the WRF model using a three-dimensional variational method. A forecast time of 8 h was adopted, and the forecast reflectivity and velocity fields were input into an operationally used microburst detection algorithm to forecast the intensity, sign, and location of the microbursts, which were then compared with the actual observations from a terminal Doppler weather radar at the HKIA. The microbursts were simulated with mixed success. In general, the vertical velocity within the convection band was accurately simulated. However, there may be difficulties in forecasting the magnitude of downbursts, and thus, the intensity of the forecast microbursts in comparison with the actual observations. This study is preliminary, and more cases with available flight data will be studied in the future. Full article

The accurate and timely nowcasting of severe weather events such as short-term torrential rainfall is essential for disaster preparedness and early warning systems. Our prior studies have demonstrated a high correlation (0.92) and ~10 min time lag between in-cloud (IC) lightning and ground rainfall. In this study, based on the approach introduced by Shimizu and Uyeda, an automatic method for identifying and tracking convective storm cells, we integrate total lightning data and heavy precipitation data for further improving the prediction accuracy of torrential rainfall. High-resolution 2D weather radar composite precipitation data are collected from XRAIN, operated by MLIT, Japan, and total lightning data (TL, i.e., IC and CG) are collected from the Japanese Total Lightning Network (JTLN). The adapted algorithm is used to track lightning-frequent areas (≥5 and ≥2 pulses per 5 min) as well as heavy (≥50 mm/h) and torrential (≥80 mm/h) precipitation cells. To evaluate the predictive capability of TL, cross-correlation analyses are performed across multiple intensity thresholds and time lags. The results of correlation matrix analysis for identifying the movement of the storm and utilization towards spatiotemporal nowcasting of extreme rainfall is discussed. Full article

This study analyzes the interannual trends in the Total Duration of Thunderstorms (TDT) in the Northern Caspian Region (Atyrau region, Kazakhstan). While TDT has generally increased, consistent with Northern Eurasian trends, a sustained local decrease was observed in the 21st century. We propose that the observed local decrease in TDT may result from the stabilizing influence of the Total Duration of Atmospheric Blocking (TDB), which could counteract thermal convection enhanced by climate warming. To evaluate this relationship, trends in TDT (from nine meteorological stations) and TDB (from ERA5 reanalysis) for May–August were analyzed over 30-year and 10-year periods. The analysis confirmed a stable inverse relationship between TDT and TDB trends, which is particularly strong in June and August. This verifies the dominant role of atmospheric blocking in suppressing convective activity. Given the high spatial coherence of TDB trends, these findings are generalized to the entire Caspian region. The research confirms that rising TDB acts as a crucial regulator of thunderstorm activity. The correlation coefficients between the number of thunderstorms and the recurrence of atmospheric blocking events at all stations range from 0.88 to 0.95. Projections indicate that if current TDB trends persist under ongoing warming, spring–summer TDT will continue to decline. Overall results suggest that if current TDB trends persist under ongoing warming, TDT during the spring–summer period is likely to continue declining. Full article

To address the challenge of rapidly selecting optimal numerical model products for weather forecasting in critical applications such as aviation route planning, this study proposes an enhanced object-based methodology comprising individual object scoring matching and a regional overall forecast selection scheme, building upon previous research. The method focuses on radar reflectivity forecasts within critical areas along air routes. Individual thunderstorm cells are evaluated using weighted scores for multiple parameters, including the Threat Score (TS), center-of-mass position, maximum radar reflectivity intensity, and shape forecasting accuracy. The regional overall score is then calculated by applying different weights to each convective cell within the area. After examining case studies of various convection types and bulk tests from June to September of 2024 and 2025, the results demonstrate that this method effectively selects the optimal convective forecasts from among the numerical models initiated at different times. The methodology shows promising applications in aviation weather forecasting. Different optimal selection schemes yield varying results: for large-scale convective weather, various test schemes generally align with TS score selection; for small-scale convective weather, schemes emphasizing radar reflectivity intensity show better performance; for scattered convection, schemes prioritizing center-of-mass position forecasting demonstrate superior results. These findings provide valuable insights for precision weather forecasting in both aviation and the agricultural–ecological sectors, in which accurate convective weather prediction is crucial for operational safety and resource management. Full article

A thunderstorm is a weather system that can trigger severe natural disasters, characterized by sudden onset, short duration, and significant damage. Accurate forecasting of thunderstorms has long been a challenging task. Data-driven artificial intelligence (AI) technologies have provided new solutions, yet AI-driven thunderstorm forecasting still lacks high-quality thunderstorm training datasets. Leveraging lightning data from the China Meteorological Administration’s Advanced Direction and Time-of-Arrival Detecting (ADTD) network and the three-dimensional Very Low Frequency/Low Frequency (VLF/LF) lightning location data of the Institute of Electrical Engineering, Chinese Academy of Sciences, we have constructed an AI training dataset for thunderstorms over China (AITDTS) through four sequential procedures: rigorous data quality control, multi-source integration, thunderstorm-prone area labeling, and feature extraction. The AITDTS encompasses 85,071 thunderstorm events and 3,973,171 corresponding gridded samples at 10 min temporal resolution and 1 km × 1 km spatial resolution across China during 2016–2023. Each sample includes location labels, 38 radar-derived physical parameters with a 10-min temporal resolution and 62 environmental parameters with an hourly temporal resolution. We further quantified predictor information gain for thunderstorm forecasting: radar echo top/base heights, composite reflectivity, vertical integrated liquid water content and reflectivity at 10 km showed high information gain. Atmospheric instability, dynamic uplifting, moisture conditions and vertical wind shear at 1 km exhibited moderate information gain. The AITDTS can be directly applied to training and evaluation of AI-driven forecasting models, offering critical data for thunderstorm nowcasting. Full article

Deep convection strongly influences regional water cycles over the Tibetan Plateau (TP), often referred to as the “Asian Water Tower.” Using FY-2E thundercloud observations, we examined the deep convection characteristics over the central TP. Deep convective storms over the TP exhibit pronounced spatiotemporal heterogeneity. The frequency distribution of storm areas follows an exponential pattern in all seasons, and the cloud-top black body temperature (TBB) distribution is negatively skewed, with values concentrated between −40 and −36 °C. Deep convection is most active in summer, with storms that are larger and have colder cloud tops. In spring, storms are less frequent but tend to cover larger areas, whereas autumn is dominated by small- to medium-sized systems. Spatially, the southeastern and southwestern TP are high-frequency centers, with storm occurrence 2–3 times higher than in the northern TP. Associations between deep-convection properties and precipitation vary by season and region. In summer, storm-related precipitation is primarily linked to large storm areas, whereas in autumn it is more strongly associated with storms with lower TBB. In the southwestern TP, precipitation intensity is more strongly related to TBB, whereas in the northwestern TP, it is more sensitive to storm area. Topographic slope also modulates both precipitation and storm properties. Most storm precipitation occurs over slopes ≤14°, and heavy precipitation shows a bimodal dependence on slope, with peaks at 3–4° and 11–13°. Gentle slopes favor storm growth and horizontal expansion; as the slope increases, mean TBB increases, and deep convection weakens. Full article

To advance marine thunderstorm forecasting and enhance the operational utility of lightning data, this study developed a novel very low-frequency (VLF) lightning data assimilation scheme for the South China Sea region. The three-dimensional graupel mixing ratio field was successfully inverted from VLF lightning detection data through the application of an empirical formula linking lightning frequency to graupel mass, a database of graupel mixing ratio profiles, and a distance-weighted diffusion scheme. This reconstructed field was then subjected to horizontal diffusion and assimilated into the Weather Research and Forecasting (WRF) model using the Grid Nudging module within the WRF–Four-Dimensional Data Assimilation (WRF-FDDA) system. A quantitative evaluation of 37 nocturnal marine convective cases was conducted using Fengyun-4A(FY-4A) satellite observations. The results demonstrate that the proposed assimilation method significantly enhances short-term (0–6 h) forecast performance. Specifically, the Fractions Skill Score (FSS) derived from the Advanced Geosynchronous Radiation Imager (AGRI) data increased rapidly during the early forecast stage, exceeding a value of 0.9. Meanwhile, the Lightning Mapping Imager Event (LMIE) product evaluation showed a high probability of detection (POD) of 85% for lightning forecasts, with a false alarm ratio (FAR) of only 9%. These findings indicate that the assimilation approach improves the accuracy of capturing the spatial structure and evolution of convective systems. Although the degree of improvement diminished with longer lead times, the results confirm the value of VLF lightning data in initializing convective-scale processes and underscore its practical value in marine nowcasting applications. Full article

Much of the information for studying the processes of lightning discharge initiation and development is provided by studying thundercloud radio emissions in various frequency bands. The High-Frequency (HF) band better corresponds to the characteristic scales of lightning development but has been undeservedly forgotten and is used quite rarely. Based on observations carried out in the Upper Volga region it is shown that the intensity of HF radio emissions from lightning is high enough to be reliably recorded in nearby thunderstorms. It is found that at all stages of lightning development the intensity of its radio emission in the HF band up to 10 MHz exceeds the average background intensity significantly. The amplitudes of lightning pulses exceed the background level more significantly, up to 60 dB and more. The feasibility of using the HF band for lightning observations including tracing the direction of arrival of a radio emission is clearly demonstrated. Full article

This article reinterprets modern realist drama as a site of secular spirituality, where aesthetic form sustains the sacred under conditions of modern secularity. Employing a phenomenological–theological framework, it integrates Charles Taylor’s account of the secular age, Mircea Eliade’s sacred–profane dialectic and hierophany, and René Girard’s anthropology of sacrifice. Through textual and performance-historical analysis of key works—Ibsen’s A Doll’s House (1879) and An Enemy of the People (1882)—together with Chinese modern drama shaped by Ibsenization, including Hu Shi’s translations, Lu Xun’s critiques, and Cao Yu’s Thunderstorm (1934), the article argues that realist theatre fulfils religious functions in secular culture: revelation as truth-telling, confession as critical self-disclosure, and renewal as ethical transformation. In early twentieth-century China, the encounter between Ibsen’s moral realism and indigenous moral traditions generated a distinctive spiritual humanism, in which theatre assumed ritual and didactic functions traditionally associated with religious practices. Full article

The rapid expansion of wind power as a key component of global renewable energy systems has led to the widespread deployment of wind turbines in environments exposed to diverse natural hazards. While hazard effects are often investigated individually, real wind turbine systems frequently experience concurrent or sequential hazards over their operational lifetime, giving rise to interaction effects that are not adequately captured by conventional design approaches. This paper presents Part 2 of a comprehensive review on natural hazards affecting wind turbine performance, combining bibliometric keyword co-occurrence analysis with a critical synthesis of recent technical studies. The review focuses on earthquakes, sea waves, and extreme wind events, while also highlighting other hazard types that have received comparatively limited attention in the literature, examining their effects on wind turbine systems and the mitigation strategies reported to address associated risks. Rather than treating hazards in isolation, their impacts are synthesised through cross-hazard interaction pathways and component-level failure modes. The findings indicate that wind turbine vulnerability under multi-hazard conditions is governed not only by load magnitude but also by hazard-induced changes in system properties and operational state. Key research gaps are identified, emphasising the need for state-aware, mechanism-consistent multi-hazard assessment frameworks to support the resilient design and operation of future wind energy systems. Full article

5G deployment in Malaysia is increasing the need for safe and efficient radio-frequency (RF) scanning at telecommunication towers, but service providers lack a clear, structured way to choose among available methods. This study develops a decision framework using a hybrid Delphi–Analytic Hierarchy Process (AHP) approach. A literature review identified criteria, sub-criteria, and six RF scanning alternatives. Ten experts then participated in three Delphi rounds: Rounds 1 and 2 confirmed five criteria and twenty-five sub-criteria, while Round 3 produced an expert ranking of the six alternatives, with drone-based and human-based scanning as the top priorities. Thirty practitioners subsequently completed AHP pairwise comparisons based on the Delphi-validated hierarchy. The AHP results show that Safety and Environment are the most important criteria, with ‘Fall’ and ‘Thunderstorm’ having the highest global weights. Drone-based scanning ranks highest, followed by human-based and ground-based methods, and the AHP ranking closely matches the expert ranking. The study provides a clear decision method for industry and policymakers to improve worker safety, guide inspection decisions, and strengthen telecommunication infrastructure in line with SDG 8 (Decent Work), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities), and SDG 13 (Climate Action). Full article

We report the results achieved by the Experiment to Study Thunderstorm High-Energy Radiation (ESTHER), a small ground-based project devoted to the investigation of high-energy radiation in thunderstorms, installed on Mt. Etna (Italy), during the first observational campaign of summer 2024. The experimental setup was installed at high altitude, at the Citelli Refuge (1741 m a.s.l.) and at the Etnean Observatory (2818 m a.s.l.), and acquired data for more than 4 months, experiencing 22 days of thunderstorms and recording correlated variations in the gamma-ray background. The most interesting result encountered during these first data takes is the detection of a 6.3 min high-energy event that occurred during an intense thunderstorm, which was recorded at the first installation site, on 22 July 2024. The gamma-ray detection system revealed a high-energy emission consisting of several episodes: an initial weak gamma-ray glowing, a following shallow prolonged emission, and a final intense burst. The last two episodes exhibited a remarkable 511 keV emission, with the last burst releasing more than 12% of its total counts within $511\pm 25$ keV and exhibiting a count rate in that energy range five times higher than that typically encountered in the environmental background. We interpret this emission as the possible result of positron annihilation occurring inside the parent thundercloud. Several lightning discharges took place nearby the installation site, with the closest one occurring at less than 500 m from the detectors, just before the onset of the final burst dominated by positron annihilation. Full article

Doppler weather radars play a pivotal role in meteorology, providing critical data for monitoring severe weather phenomena, such as thunderstorms. However, Doppler velocity measurements are subjected to aliasing errors when the true velocity exceeds the radar’s maximum detection velocity, compromising the accuracy of velocity data. Effective dealiasing techniques are essential to correct these errors and improve data, leading to reliable data assimilation and therefore improved numerical weather prediction (NWP) as well as nowcasting applications. In this study, an attempt is made to present a comparative study of four dealiasing algorithms—convolution-, expansion-, amplitude correction-, and sine-based algorithms—to assess their effectiveness in processing Doppler radar velocity data. The study aims to evaluate these algorithms based on their ability to correct aliasing errors, their computational efficiency, and their practical applicability in real-world meteorological scenarios. Through an experimental evaluation, the performance of each algorithm is analyzed. Results indicate varying degrees of effectiveness among the algorithms, highlighting their respective strengths and limitations in dealing with the velocity aliasing of radar data. It was found that the Amplitude Correction and Convolution algorithms outperformed the others in correcting aliasing. A combined multi-algorithm approach achieved the highest overall accuracy when compared to manually corrected reference data and other algorithms. This research contributes to advancing the understanding of radar data processing techniques and provides insights into optimizing dealiasing strategies for enhanced meteorological forecasting and nowcasting, as well as severe weather prediction. Full article

This study investigates five catastrophic historical floods of the Tripero stream, a small tributary of the Guadiana River that flows through Villafranca de los Barros (Extremadura, Spain), occurring between 1865 and 1952. Despite their devastating impacts on the local population and infrastructure, these events have received little scientific attention. By combining historical documentary evidence with meteorological reanalysis data from the Twentieth Century Reanalysis (20CRv3), this research reconstructs the circumstances and atmospheric mechanisms associated with each event. The results reveal a notable diversity of synoptic configurations, reflecting both seasonal variability and the distinct meteorological origins of the floods. The 1865 and 1876 events were associated with large-scale Atlantic disturbances—the former linked to a cut-off low and moisture transport resembling an atmospheric river, and the latter to a strongly negative North Atlantic Oscillation (NAO) phase and other atmospheric river, producing widespread flooding across southwestern Iberia. In contrast, the floods of 1903, 1949, and 1952 were triggered by intense convective activity, typical of late spring and summer thunderstorms, fueled by local moisture and instability. The combination of historical sources and modern reanalysis provides valuable insights into the climatological context of extreme hydrometeorological events in small Mediterranean basins, contributing to improved understanding of local flood risks in historically understudied regions. Full article

Risk management is vital for financial institutions to evaluate and mitigate potential losses. Thunderstorm count modeling with risk analysis is used by various sectors, such as insurance and utility companies, to forecast storm recurrence, analyze risk, and estimate financial losses based on factors like wind speed, hail size, and tornado potential. This paper introduces a novel discrete distribution, the Beta-Gamma Discrete (BGD) distribution, designed for modeling count data that inherently excludes zero values. Developed through the compounding of a discrete gamma distribution with a beta distribution, the BGD offers significant flexibility in handling overdispersion and complex data characteristics. The study derives key statistical properties of the BGD, including its probability mass function, moments, hazard rate function, moment generating function, and mean residual life. A comprehensive characterization theorem is also established. The model’s practical utility is demonstrated through an application to thunderstorm event data from the Kennedy Space Center (KSC), where the frequency of thunderstorms per event is a critical operational concern. The performance of the BGD is thoroughly assessed against established zero-truncated models—namely, the Zero-Truncated Generalized Poisson (ZTGP), Size-Biased Negative Binomial (SBNB), and Zero-Truncated Generalized Negative Binomial (ZTGNB)—using evaluation criteria such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Chi-square goodness-of-fit, and the Vuong test. The results consistently show that the BGD provides a superior and more accurate fit for the thunderstorm data, thus help NASA and other space agencies for establishing it as a robust and effective tool for modeling positive count data in meteorological and other applied contexts with risk analysis. Full article