Search Results (11)

Low-level wind shear (LLWS) is a critical issue in aviation meteorology, posing serious risks to flight safety—especially at plateau airports with high elevation and complex terrain. This study investigates a convective wind shear event at Xining Airport on 29 May 2021. Multi-source observations—including the Doppler Wind Lidar (DWL), the Doppler weather radar (DWR), reanalysis datasets, and automated weather observation systems (AWOS)—were integrated to examine the event’s fine-scale structure and temporal evolution. High-resolution simulations were conducted using the Large Eddy Simulation (LES) framework within the Weather Research and Forecasting (WRF) model. Results indicate that the formation of this wind shear was jointly triggered by convective downdrafts and the gust front. A northwesterly flow with peak wind speeds of 18 m/s intruded eastward across the runway, generating multiple radial velocity couplets on the eastern side, closely associated with mesoscale convergence and divergence. A vertical shear layer developed around 700 m above ground level, and the critical wind shear during aircraft go-around was linked to two convergence zones east of the runway. The event lasted about 30 min, producing abrupt changes in wind direction and vertical velocity, potentially causing flight path deviation and landing offset. Analysis of horizontal, vertical, and glide-path wind fields reveals the spatiotemporal evolution of the wind shear and its impact on aviation safety. The WRF-LES accurately captured key features such as wind shifts, speed surges, and vertical disturbances, with strong agreement to observations. The integration of multi-source observations with WRF-LES improves the accuracy and timeliness of wind shear detection and warning, providing valuable scientific support for enhancing safety at plateau airports. Full article

Most damage to buildings across the contiguous United States of America (USA) is caused by gusts in convective events associated with thunderstorms. Design rules for structures to resist these events rely on the integrity of meteorological observations and the methods of assessment. These issues were addressed for the US Automated Surface Observation System (ASOS) in six preliminary studies published in 2022 and 2023, allowing this present study to focus on the analysis and reporting of gust events observed between 2000 and 2023 at 642 well-exposed ASOS stations distributed across the contiguous USA. It has been recently recognized that the response of buildings to convective gusts, which are non-stationary transient events, differs in character from the response to the locally stationary atmospheric boundary gusts, requiring gust events to be classified and assessed by type. This study sorts the mixture of all observed gust events exceeding 20 kn, but excluding contributions from hurricanes and tropical storms, into five classes of valid meteorological types and two classes of invalid artefacts. The valid classes are individually fitted to optimal sub-asymptotic models through extreme value analysis. Classes are recombined into a joint mixture model and compared with current design rules. Full article

Previous studies have demonstrated that cold pools play a pivotal role in the initiation and organization of convection, yet their influence on the evolution of gust fronts (GFs) remains inadequately understood. A destructive wind event associated with a rearward gust front (RGF; 8 grade gale after passing GF) and a prior gust front (PGF; 10 grade gale before passing GF) over the north coast of China on 10 June 2016 was analyzed. Using multiple forms of observation data, as well as the four-dimensional Variational Doppler Radar Data Assimilation System (VDRAS), we found that the depth and intensity of the cold pool in RGF are relatively shallower and weaker, leading to a correspondingly reduced strength in both outflow and convergence. In contrast, the enhanced vertical shear and boundary northeaster inflow of PGF generate intensified and more organized downdrafts, resulting in a deeper cold pool, robust outflow, and convergence. Two schematic models were proposed to explain the discrepancy between GFs and associated cold pools. We further show that there is an internal correlation between meso-γ-scale vortices (MVs) and cold pools, the collision of MVs strengthened low-level convergence and updraft between these two GFs. Moreover, the consolidation of the two cold pools exacerbates low-layer instability and rotation, generating an intense horizontal vorticity that leads to rapid convective storm intensification. These findings offer novel insights into the diversity of GFs and associated cold pools. Full article

This study explores the application of the fully convolutional network (FCN) algorithm to the field of meteorology, specifically for the short-term nowcasting of severe convective weather events such as hail, convective wind gust (CG), thunderstorms, and short-term heavy rain (STHR) in Gansu. The training data come from the European Center for Medium-Range Weather Forecasts (ECMWF) and real-time ground observations. The performance of the proposed FCN model, based on 2017 to 2021 training datasets, demonstrated a high prediction accuracy, with an overall error rate of 16.6%. Furthermore, the model exhibited an error rate of 18.6% across both severe and non-severe weather conditions when tested against the 2022 dataset. Operational deployment in 2023 yielded an average critical success index (CSI) of 24.3%, a probability of detection (POD) of 62.6%, and a false alarm ratio (FAR) of 71.2% for these convective events. It is noteworthy that the predicting performance for STHR was particularly effective with the highest POD and CSI, as well as the lowest FAR. CG and hail predictions had comparable CSI and FAR scores, although the POD for CG surpassed that for hail. The FCN model’s optimal performances in terms of hail prediction occurred at the 4th, 8th, and 10th forecast hours, while for CG, the 6th hour was most accurate, and for STHR, the 2nd and 4th hours were most effective. These findings underscore the FCN model’s ideal suitability for short-term forecasting of severe convective weather, presenting extensive prospects for the automation of meteorological operations in the future. Full article

Gust fronts (GFs) belong to the boundary layer convergence system. A strong GF can cause serious wind disasters, so its automatic monitoring and identification are very helpful but difficult in daily meteorological operations. By collecting convective weather processes in Hubei, Jiangsu, and other regions of China, 1422 GFs from 106 S-band new-generation weather radar (CINRAD/SA) volume scan data are labeled as positive samples by means of human–computer interaction, and the same number of negative samples are randomly tagged from no GF radar data. A deep learning dataset including 2844 labels with a positive and negative sample ratio of 1:1 is constructed, and 80%, 10%, and 10% of the dataset are separated as training, validation, and test sets, respectively. Then, the training dataset is expanded to 273,120 samples by data augmentation technology. Since the height of a GF is generally less than 1.5 km, three deep-learning-based models are trained for GF automatic recognition according to the distance from the radars. Three models (M1, M2, M3) are trained with the data at a 0.5° elevation angle from 65 to 180 km away from the radars, at 0.5° and 1.5° angles from 40 to 65 km, and at 0.5°, 1.5°, and 2.4° angles within 40 km, respectively. The precision, confusion matrix, and its derived indicators including receiver operating characteristic curve (ROC) and the area under ROC (AUC) are used to evaluate the three models by the test set. The results show that the identification precisions of the models are 97.66% (M1), 90% (M2), and 90.43% (M3), respectively. All the hit rates are over 89%, the false positive rates are less than 11%, and the critical success indexes (CSIs) surpass 82%. In addition, all the optimal critical points on the ROC curves are close to (0, 1), and the AUC values are above 0.93. These results suggest that the three models can effectively achieve the automatic discrimination of GFs. Finally, the models are demonstrated by three GF events detected with Qingpu, Nantong, and Cangzhou radars. Full article

This study aims to explore the roles of multiple gust fronts (i.e., outflow boundaries) during a short-lived extreme rainfall that occurred in the Greater Bay Area of South China in the afternoon of 1 August 2021. Through the use of microbarographs and Doppler weather radars, the research highlights how the interactions of five gust fronts, approaching the region from different directions, have contributed to the high precipitation efficiency and damaging surface winds during the event. The close convergence of these gust fronts funneled unstable air masses into the region of interest, priming the mesoscale convective environment. Some isolated convection initiated before the gust fronts’ arrival. Preceding the arrival of these gust fronts, subtle wave-like pressure jumps were identified from the high-frequency (1 Hz) microbarograph observations. The amplitude of the pressure jump is approximately 40 Pa with minimal changes in air temperature. During the early stage of the gust front passages, very high-frequency oscillations in surface pressure are recognized, indicating interaction between the density currents and the low-level troposphere. As suggested through numerical simulations, the subtle pressure jumps are associated with upward displacements of isentropic surfaces aloft, deepening the moist layer and enhancing the lapse rate that are conducive to convective development. The simulated vertical profiles show no evident capping inversion above the dry neutral boundary layer, suggesting that the pressure jumps are likely to be dynamically induced through the collision of the outflows and environmental air masses. The findings of this study suggest the potential application of microbarographs in the nowcasting of the convective development associated with gust fronts. Full article

Ship pilots and maritime safety administration have an urgent need for more accurate and earlier warnings for strong wind gusts. This study firstly investigated the “Oriental Star” cruise ship capsizing event in 2015, one of the deadliest shipwreck events in recent years, and explored all related hydro-meteorological components in a global mesoscale model. It was found that rather than the missing signal in raw surface-wind prediction, the cumulus precipitation variable (CP) increased dramatically during the accident occurrence, which significantly corresponds to a sub-grid strong wind gust. The effective lead time could be extended from 24 h (deterministic model) to 48 h (ensemble model). This finding was then verified in another two recent deadly cruise boat accidents. The introduction of the new variable aims to improve the current maritime safeguard system in predicting sub-grid strong wind gusts for small-sized cruise boats offshore and in inland rivers. Finally, an automatic response system was developed to provide economical convection prediction via INMARSAT email communication, aiming to explore operational severe convective gust early warning and appropriate numerical mesoscale model applications. Full article

Most damage to buildings across the contiguous United States, in terms of number and total cost, is caused by gusts in convective events associated with thunderstorms. Their assessment relies on the integrity of meteorological observations. This study examines the impact on risk due to valid gust observations culled erroneously by the real-time quality control algorithm of the US Automated Surface Observation System (ASOS) after 2013. ASOS data before 2014 are used to simulate the effect of this algorithm at 450 well-exposed stations distributed across the contiguous USA. The peak gust is culled in around 10% of these events causing significant underestimates of extreme gusts. The full ASOS record, 2000–2021, is used to estimate and map the 50-year mean recurrence interval (MRI) gust speeds, the conventional metric for structural design. It is concluded that recovery of erroneously culled observations is not possible, so the only practical option to eliminate underestimation is to ensure that the 50-year MRI gust speed at any given station is not less than the mean for nearby surrounding stations. This also affects stations where values are legitimately lower than their neighbors, which represents the price that must be paid to eliminate unacceptable risk. Full article

The transport sector and road infrastructures are very sensitive to the issues connected to the atmospheric conditions. The latter constitute a source of relevant risk, especially for roads running in mountainous areas, where a wide spectrum of meteorological phenomena, such as rain showers, snow, hail, wind gusts and ice, threatens drivers’ safety. In such contexts, to face out critical situations it is essential to develop a monitoring system that is able to capillary surveil specific sectors or very small basins, providing real time information that may be crucial to preserve lives and assets. In this work, we present the results of the “Campania Region Meteorological Radar Network”, which is focused on the development of X-band radar-based meteorological products that can support highway traffic management and maintenance. The X-band measurements provided by two single-polarization systems, properly integrated with the observations supplied by disdrometers and conventional automatic weather stations, were involved in the following main tasks: (i) the development of a radar composite product; (ii) the devise of a probability of hail index; (iii) the real time discrimination of precipitation type (rain, mixed and snow); (iv) the development of a snowfall rate estimator. The performance of these products was assessed for two case studies, related to a relevant summer hailstorm (which occurred on 1 August 2020) and to a winter precipitation event (which occurred on 13 February 2021). In both cases, the X-band radar-based tools proved to be useful for the stakeholders involved in the management of highway traffic, providing a reliable characterization of precipitation events and of the fast-changing vertical structure of convective cells. Full article

On 23/12/2009, windstorm Xola struck mainland Portugal, causing serious damage in a small area north of Lisbon (Oeste region) and in the south region, inflicting economic losses of over EUR 100 million. In both areas, several power towers, designed to withstand up to 46 m s⁻¹ winds, were destroyed. The causes of these two distinct damaging wind events were investigated. Xola was revealed to have a prominent cloud head and a split cold front structure. In the southern region, the damages were due to downburst winds, associated with a mesovortex, observed in a bow echo line triggered by an upper cold front. The cloud head presented several dry air intrusion signatures, co-located with tops progressively lowering towards the hooked tip. This tip revealed features consistent with the presence of slantwise convection, the descending branches of which may have been strengthened by evaporating cooling. At the reflectivity cloud head tip, a jet streak pattern was identified on weather radar, with Doppler velocities exceeding 55 m s⁻¹, just 400 m above ground. This signature is coherent with the presence of a Sting jet, and this phenomenon was associated with the strongest wind gusts (over 40 m s⁻¹) and the largest damages in the Oeste region. Full article

Lake Victoria in East Africa supports the livelihood of thousands of fishermen and it is estimated that 3000–5000 human deaths occur per year over the lake. It is hypothesized that most of these fatalities are due to localized, severe winds produced by intense thunderstorms over the lake during the rainy season and larger scale, intense winds over the lake during the dry season. The intense winds produce a rough state of the lake (big wave heights) that cause fishing boats to capsize. In this region, weather radars have never been a primary tool for monitoring and nowcasting high impact weather. The Tanzania Meteorological Agency operates an S-band polarimetric radar in Mwanza, Tanzania, along the south shore of Lake Victoria. This radar collects high temporal and spatial resolution data that is now being used to detect and monitor the formation of deep convection over the lake and improve scientific understanding of storm dynamics and intensification. Nocturnal thunderstorms and convection initiation over the lake are well observed by the Mwanza radar and are strongly forced by lake and land breezes and gust fronts. Unexpected is the detection of clear air echo to ranges ≥100 km over the lake that makes it possible to observe low-level winds, gust fronts, and other convergence lines near the surface of the lake. The frequent observation of extensive clear air and low-level convergence lines opens up the opportunity to nowcast strong winds, convection initiation, and subsequent thunderstorm development and incorporate this information into a regional early warning system proposed for Lake Victoria Basin (LVB). Two weather events are presented illustrating distinctly different nocturnal convection initiation over the lake that evolve into intense morning thunderstorms. The evolution of these severe weather events was possible because of the Mwanza radar observations; satellite imagery alone was insufficient to provide prediction of storm initiation, growth, movement, and decay. Full article