Prediction and Early Warning Indicators of Short-term Severe Convection Weather in Ulanqab City

Based on the disaster reports,NCEP2.5X2.5 reanalysis data and radiosonde data of 11 national stations in Ulanqab region from June to August during 2012-2017,the weather situation classification and warning indicators of thunderstorm and gale,hail and short-term heavy rainfall were studied.The results show that the cold vortex weather situation was easy to produce hail,and the falling area of severe convection could be found in the downstream of the cold vortex,the intersection area of jet stream at 200 and 500 hPa,and the wet area side of the 700 hPa main line.The cold trough type weather situation was easy to produce thunderstorm and gale,and the falling area of severe convection appeared on the right side of the upper jet stream axis,the left side of the lower jet stream axis,the wet side of the 700 hPa main line,and the east of the shear line at 700 hPa.The weather situation of the low trough and subtropical high type was dominated by short-term rainstorm,and the falling area of severe convection was on the right side of upper jet stream at 200 hPa,the left side of the low southeast jet stream,and the wet side of the 700 hPa main line.The warning index thresholds of the total index,the temperature change at 850-500 hPa with height,the height of 0 and-20℃layer,lifting condensation height,temperature dew point difference and mixing ratio were highly reliable.

A Method Fusing Conventional Wind Field with Cloud Motion Wind and Its Application in Location Forecast of the Severe Convection

Based on the fast algorithm of meteorological satellite guide wind vector tracing, cloud motion wind vector is calculated. According to the different characteristics of cloud motion wind field and sounding wind field, a method which fuses conventional data with unconventional data based on variation principle is presented. The fundamental is constructing a cost function that makes the value approach conventional data and the gradient approach unconventional data. Using this method, the conventional wind and the cloud motion wind are fused. The fused wind field has high resolu- tion. Its wind direction approaches cloud motion wind which indicates move direction of the synoptic system, and its velocity approaches conventional wind which indicates move velocity of the synoptic system. The wind field data are used for short-time forecast of severe convective weather location, which gets a good result.

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.

Advanced Method for Forecasting and Warning of Severe Convective Weather and Local-scale Hazards

Hurricane Ida ferociously affected many south-eastern and eastern parts of the United States,making it one of the strongest hurricanes in recent years.Advanced forecast and warning tool has been used to track the path of the ex-Hurricane,Ida,as it left New Orleans on its way towards the northeast,accurately predicting significant supercell development above New York City on September 01,2021.This advanced method accurately detected the area with the highest possible level of convective instability with 24-h lead time and even Level 5,devised in the categorical outlooks legend of the system.Therefore,an extreme level implied a very high probability of the local-scale hazard occurring above the NYC.Cloud model output fields(updrafts and downdrafts,wind shear,near-surface convergence,the vertical component of relative vorticity)show the rapid development of a strong supercell storm with rotating updrafts and a mesocyclone.The characteristic hook-shaped echo signature visible in the reflectivity patterns indicates a signal for a highly precipitable(HP)supercell with the possibility of tornado initiation.Open boundary conditions represent a good basis for simulating a tornado that evolved from a supercell storm,initialized with initial data obtained from a real-time simulation in the period when the bow echo and tornado-like signature occurred.Тhe modeled results agree well with the observations.

Organizational Modes of Severe Wind-producing Convective Systems over North China

Severe weather reports and composite radar reflectivity data from 2010-14 over North China were used to analyze the distribution of severe convective wind（SCW） events and their organizational modes of radar reflectivity. The six organizational modes for SCW events（and their proportions） were cluster cells（35.4%）, squall lines（18.4%）, nonlinear-shaped systems（17.8%）, broken lines（11.6%）, individual cells（1.2%）, and bow echoes（0.5%）. The peak month for both squall lines and broken lines was June, whereas it was July for the other four modes. The highest numbers of SCW events were over the mountains, which were generally associated with disorganized systems of cluster cells. In contrast, SCW associated with linear systems occurred mainly over the plains, where stations recorded an average of less than one SCW event per year. Regions with a high frequency of SCW associated with nonlinear-shaped systems also experienced many SCW events associated with squall lines. Values of convective available potential energy, precipitable water, 0-3-km shear, and 0-6-km shear, were demonstrably larger over the plains than over the mountains, which had an evident effect on the organizational modes of SCW events. Therefore, topography may be an important factor in the organizational modes for SCW events over North China.

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.

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.

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.