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.

Rational Identification of Lightning Derivative Disasters by Strong Convective Weather Monitoring Data in Southern China——A Case Study in Guangzhou Development Region

Analytic method and identification direction for rational identification of lightning derivative disasters by strong convective weather monitoring data in southern China were introduced. Taking identification cases of lightning disaster in Guangzhou Development Region as the background,according to the characteristics in the region that large high-precision enterprises were more,lightning derivative disasters occurred frequently in thunderstorm season,and the actual situation that time of the affected enterprise applying for lightning disaster scene identification lagged,combining Technical Specifications of Lightning Disaster Investigation（ QX / T103-2009）,qualitative analysis method of lightning derivative disaster was put forward under the weather condition of strong convection in southern China by using weather monitoring data（ Doppler sounding radar data,lightning positioning monitoring data,atmospheric electric field data,rainfall data,wind direction and force）,and was optimized by technical means（ ＂ metallographic method＂ and ＂ remanence law＂）. The research could put forward efficient and convenient analytical thinking and method for lightning derivative disaster,and further optimize accuracy and credibility of lightning disaster investigation.

Terminal Traffic Flow Prediction Method Under Convective Weather Using Deep Learning Approaches

In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather,a multi-input deep learning(MICL)model is proposed.On the basis of previous studies,this paper expands the set of weather characteristics affecting the traffic flow in the terminal area,including weather forecast data and Meteorological Report of Aerodrome Conditions(METAR)data.The terminal airspace is divided into smaller areas based on function and the weather severity index(WSI)characteristics extracted from weather forecast data are established to better quantify the impact of weather.MICL model preserves the advantages of the convolution neural network(CNN)and the long short-term memory(LSTM)model,and adopts two channels to input WSI and METAR information,respectively,which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal area.Multi-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective weather.The results show that the MICL model has excellent performance in mean squared error(MSE),root MSE(RMSE),mean absolute error(MAE)and other performance indicators compared with the existing machine learning models or deep learning models,such as Knearest neighbor(KNN),support vector regression(SVR),CNN and LSTM.In the forecast period ranging from 30 min to 6 h,the MICL model has the best prediction accuracy and stability.

An Algorithm on Convective Weather Potential in the Early Rainy Season over the Pearl River Delta in China

This paper describes the procedure and methodology to formulate the convective weather potential （CWP） algorithm. The data used in the development of the algorithm are the radar echoes at 0.5° elevation from Guangzhou Doppler Radar Station, surface observations from automatic weather stations （AWS） and outputs of numeric weather prediction （NWP） models. The procedure to develop the CWP algorithm consists of two steps： （1） identification of thunderstorm cells in accordance with specified statistical criteria; and （2） development of the algorithm based on multiple linear regression. The thunderstorm cells were automatically identified by radar echoes with intensity greater than or equal to 50 dB（Z） and of an area over 64 square kilometers. These cells are generally related to severe convective weather occurrences such as thunderstorm wind gusts, hail and tornados. In the development of the CWP algorithm, both echo- and environment-based predictors are used. The predictand is the probability of a thunderstorm cell to generate severe convective weather events. The predictor-predictand relationship is established through a stepwise multiple linear regression approach. Verification with an independent dataset shows that the CWP algorithm is skillful in detecting thunderstorm-related severe convective weather occurrences in the Pearl River Delta （PRD） region of South China. An example of a nowcasting case for a thunderstorm process is illustrated.

Experiments in Forecasting Mesoscale Convective Weather over Changjiang Delta

The real time operational severe convective weather forecast experiment carried out during May to July in 1990 over the Changjiang Delta is briefly described. The heavy rainfall and severe conveetive weather forecast worksheets for the Changjiang Delta have been proposed and used in the daily forecasting. Results show that the ability of 0-12h convective weather prediction has been improved significantly after the development of the forecast methods and the establishment of a mesoscale forecast base at Shanghai Meteorological Center during 1986 to 1990.Three cases of convective weather systems (meso-alpha, meso-beta, meso-gamma) during the experiment period are described and discussed.

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.

Convective Weather Avoidance Prediction in Enroute Airspace Based on Support Vector Machine

With the rapid growth of global air traffic,flight delays are increasingly serious.Convective weather is one of the influential causes for flight delays,which has affected the sustainable development of civil aviation industry and became a social problem.If it can be predicted that whether a weather-related flight diverts,participants in air traffic activities can coordinate the scheduling,and flight delays can be reduced greatly.In this paper,the weather avoidance prediction model(WAPM)is proposed to find the relationship between weather and flight trajectories,and predict whether a future flight diverts based on historical flight data.First,given the large amount of weather data,the principal component analysis is used to reduce the ten dimensional weather indicators to extract 90%information.Second,the support vector machine is adopted to predict whether the flight diverts by determining the hyperparameters c and γ of the radial basis function.Finally,the performance of the proposed model is evaluated by prediction accuracy,precision,recall and F1,and compared with the methods of the k nearest neighbor(kNN),the logistic regression(LR),the random forest(RF)and the deep neural networks(DNNs).WAPM’s accuracy is 5.22%,2.63%,2.26%and 1.03%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s precision is 6.79%,5.19%,4.37%and 3.21%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s recall is 4.05%,1.05%,0.04%greater than those of kNN,LR,and RF,respectively,and 1.38%lower than that of the DNNs;and F1 of WAPM is 5.28%,1.69%,1.98%and 0.68%greater than those of kNN,LR,RF and DNNs,respectively.

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.

Analysis of Heavy Precipitation Process in North China from August 23 to 24, 2020

In order to better understand the formation mechanism of rainstorm in China and promote disaster prevention and reduction, based on the meteorological data of National Meteorological Information Center and Japan Meteorological Agency, this paper draws the isobaric surface map of 850 hPa and 500 hPa, relative humidity and precipitation distribution map. In this study, synoptic methods were used to analyze the heavy precipitation process in North China from August 23th to 24th, 2020. The results show that 1) The formation of short-term heavy precipitation requires sufficient water vapor and very strong upward movement;2) the heavy precipitation in August 23th to 24th 2020 in North China was influenced by the upper-level trough line, cold vortex and cold front, which made the warm and cold air strongly converge over North China, resulting in strong convective weather;3) the heavy rainfall over North China was also influenced by Typhoon Bawei, which caused maximum precipitation and air humidity.

State of China’s climate in 2021

This report is a summary of China’s climate,as well as major weather and climate events,during 2021.In 2021,the mean temperature in China was 10.5°C,which was 1.0°C above normal(1981–2010 average)and broke the highest record since 1951.The annual rainfall in China was 672.1 mm,which was 6.7%above normal.Also,the annual rainfall in northern China was 40.2%above normal,which ranked second highest since 1961.The rainstorm intensity in the rainy season was strong and featured significant extremes,and disasters caused by rainstorms and flooding were more serious than the average in the past decade.In particular,the extremely strong rainstorm in Henan during July and autumn caused flooding in the middle and lower reaches of the Yellow River with severe consequences.Heatwaves occurred more frequently than normal,and their durations in southern China were longer than normal in summer and autumn.Phased drought was obvious,and caused serious impacts in South China.The number of generated and landfalling typhoons was lower than normal;however,Typhoon In-fa broke the record for the longest overland duration,held since 1949,and affected a wide area.Severe convective weather and extreme windy weather occurred frequently,causing serious impacts.The number of cold waves was more than normal,which caused wide-ranging extremely low temperatures in many places.Sandstorms appeared earlier than normal in 2021,and the number of strong dust storm processes was more than normal.

Analysis on the Meso-scale Characteristics of a Hail Process in Linyi Area

[Objective] The research aimed to study the meso-scale characteristics of a hail process in Linyi area. [Method] By comprehensively using MICAPS conventional observation data, automatic encryption ground station, MM5 model product and Doppler weather radar data, a strong convective hail weather process which happened in Shandong Peninsula and southeast of Shandong on May 30, 2010 was analyzed. The circulation background and physical mechanism of strong convection weather occurrence, the features of meso- and micro-scale systems were discussed. Some occurrence and development rules of such weather were found. [Result] The strong convective weather was mainly affected by the cold vortex and translot. The high-altitude northwest airflow, low-level southwest airflow, dry and cold air at the high layer, warm and wet air at the low layer, forward-tilting trough caused the strong convective weather. The radar echo analysis showed that the radar echo in the process belonged to the typical multi-monomer windstorm echo, and the strong echo zone was in the forefront of echo. When the convection development was the strongest, the echo intensity reached 65 dBz, and the echo top height surpassed 11 km. As the development of windstorm monomer, the big-value zone of vertical liquid water content product had the jumping formation and disappearance. Moreover, there was obvious weak echo zone. The windstorm monomer moved to the southeast direction as the precipitation system. In the right front of monomer moving direction, there was hook echo feature. The evolution characteristics of radial speed field at the different elevation angles before and after the hail weather occurrence were analyzed. It was found that the radial speed field had some premonitory variations before the hail weather occurrence. Doppler radar product was used to improve the initial field of MM5 model, which could improve the forecast effect in the certain degree and the accuracy of short-time forecast and nowcasting. [Conclusion] The research accumulated the experience for the short-term forecast and nowcasting work of strong convective weather in future.

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.