Organizational Modes and Environmental Conditions of the Severe Convective Weathers Produced by the Mesoscale Convective Systems in South China

Composite radar reflectivity data during April-September 2011-2015 are used to investigate and classify storms in south China(18-27°N;105-120°E). The storms appear most frequently in May. They are either linear;cellular or nonlinear systems, taking up 29.45%, 24.51% and 46.04%, respectively, in terms of morphology. Linear systems are subdivided into six morphologies: trailing stratiform precipitation(TS), bow echoes(BE), leading stratiform precipitation(LS), embedded line(EL), no stratiform precipitation(NS) and parallel stratiform precipitation(PS). The TS and NS modes have the highest frequencies but there are only small samples of LS(0.61%) and PS(0.79%) modes.Severe convective wind(≥17m s-1at surface level) accounts for the highest percentage(35%) of severe convective weather events produced by cellular systems including individual cells(IC) and clusters of cells(CC). Short-duration heavy rainfall(≥50 mm h-1) and severe convective wind are the most common severe weather associated with TS and BE modes. Comparison of environmental physical parameters shows that cellular convection systems tend to occur in the environment with favorable thermal condition, substantial unstable energy and low precipitable water from the surface to300 hPa(PWAT). However, the environmental conditions favoring the initiation of linear systems feature strong vertical wind shear, high PWAT, and intense convective inhibition. The environmental parameters favoring the initiation of nonlinear systems are between those of the other two types of morphology.

Classified Early Warning and Forecast of Severe Convective Weather Based on LightGBM Algorithm

Severe convective weather can lead to a variety of disasters, but they are still difficult to be pre-warned and forecasted in the meteorological operation. This study generates a model based on the light gradient boosting machine (LightGBM) algorithm using C-band radar echo products and ground observations, to identify and classify three major types of severe convective weather (i.e., hail, short-term heavy rain (STHR), convective gust (CG)). The model evaluations show the LightGBM model performs well in the training set (2011-2017) and the testing set (2018) with the overall false identification ratio (FIR) of only 4.9% and 7.0%, respectively. Furthermore, the average probability of detection (POD), critical success index (CSI) and false alarm ratio (FAR) for the three types of severe convective weather in two sample sets are over 85%, 65% and lower than 30%, respectively. The LightGBM model and the storm cell identification and tracking (SCIT) product are then used to forecast the severe convective weather 15 - 60 minutes in advance. The average POD, CSI and FAR for the forecasts of the three types of severe convective weather are 57.4%, 54.7% and 38.4%, respectively, which are significantly higher than those of the manual work. Among the three types of severe convective weather, the STHR has the highest POD and CSI and the lowest FAR, while the skill scores for the hail and CG are similar. Therefore, the LightGBM model constructed in this paper is able to identify, classify and forecast the three major types of severe convective weather automatically with relatively high accuracy, and has a broad application prospect in the future automatic meteorological operation.

Hazard Analysis of Severe Convective Weather in Guangdong Province, China

In the present study,a hazard model of severe convective weather was constructed on the basis of meteorological observational data obtained in Guangdong Province between 2003 and 2015.In the analysis,quality control was first conducted on the severe convective weather data,and the kriging method was then used to interpolate each hazard-formative factor.The weights of which were determined by applying the coefficient of variation method.The results were used to establish the hazard-formative factor model of severe convective weather.The cities showing the greatest hazards for severe convective weather in Guangdong Province include Yangjiang,Dongguan,Foshan,Huizhou,Jiangmen,and Qingyuan.

Analysis on a Severe Convective Weather Process of Guangxi in 2018

Based on conventional meteorological observation data and Doppler radar data,the occurrence and development mechanism of mixed severe convective weather and evolution of convective storm in Guangxi on March 4,2018 were analyzed. The results showed that the dry line was the main trigger mechanism of this severe convective weather. Instable convection stratification of cold advection at middle layer and warm advection at low layer and abundant water vapor from low-level jet provided favorable stratification and water vapor conditions for the occurrence and development of severe convection. Cold trough at middle layer,low pressure and strong vertical wind shear at middle and lower layers may be main factors for the development and maintenance of strong storm system. Squall line developed along ground convergence line,and there was bow echo on reflectivity factor chart. Moving velocity of convective system was quick,and there was gale core and velocity ambiguity on velocity map.

Progress in Severe Convective Weather Forecasting in China since the 1950s

Located in the Asian monsoon region, China frequently experiences severe convective weather(SCW), such as short-duration heavy rainfall(SDHR), thunderstorm high winds, hails, and occasional tornadoes. Progress in SCW forecasting in China is closely related to the construction and development of meteorological observation networks,especially weather radar and meteorological satellite networks. In the late 1950 s, some county-level meteorological bureaus began to conduct empirical hail forecasting based on observations of clouds and surface meteorological variables. It took over half a century to develop a modern comprehensive operational monitoring and warning system for SCW forecast nationwide since the setup of the first weather radar in 1959. The operational SCW forecasting, including real-time monitoring, warnings valid for tens of minutes, watches valid for several hours, and outlooks covering lead times of up to three days, was established in 2009. Operational monitoring and forecasting of thunderstorms,SDHR, thunderstorm high winds, and hails have been carried out. The performance of operational SCW forecasting will be continually improved in the future with the development of convection-resolving numerical models(CRNMs), the upgrade of weather radar networks, the launch of new-generation meteorological satellites, better understanding of meso-γ and microscale SCW systems, and further application of artificial intelligence technology and CRNM predictions.