Abstract:The spatial distributions of severe convective wind (SCW) and nonsevere thunderstorms (NT) over South China, occurring between 08:00 BT and 20:00 BT during spring and summer in 2010-2014, were analyzed by using the observational data from China Meteorological Administration. And then, their environmental characteristics were compared between SCW and NT in spring and summer. It was found that SCW in summer is more frequently than that in spring and that NT in summer is about 3.6 times the counts of NT in spring. SCW events mainly concentrate in the western Guangdong to the Pearl River Delta Region. Compared to NT, SCW is generally associated with stronger baroclinity, instability and stronger dynamic forcing. The precipitable water and averaged relative humidity between 700-500 hPa of SCW tend to be higher than those of NT in spring, while the opposite is the case for the pattern in summer. In conclusion, it is obvious that the dynamic forcing for SCW in spring is much better than these in summer, while the thermal condition is more significant in summer.

Abstract:Thunderstorm gale (TG) monitoring in the severe convective weather is a complex and important task, in which the difficulty is how to distinguish the TG from nonthunderstorm gale (NTG). Based on the multisource data, including radar, satellite, lightning, temperature and dewpoint temperature, this paper proposes a fuzzy logic algorithm to tell them apart, which is proved to be an effective and efficient method. First, get the member functions of the multisource data according to their probability distribution which were extracted from longterm historical data. Second, acquire the weight ratio of each data by calculating the overlap areas of probability distributions. Finally, get the TG probability Q, and choose a threshold of Q to distinguish TG from NTG. In order to evaluate its performance, the algorithm is used to find TGs in the 50873 gale records of China in 2010. The results show that when Q is 0.55, the POD of TG is 0.76, and the FAR of TG is 0.18, and the CSI of TG is about 0.67. Two mixing weather processes, caused by cold air and typhoon, are chosen to evaluate its performance, showing 11 NTGs and 5 TGs are correctly identified. The algorithm would enhance the accuracy and effectiveness of the severe weather monitoring significantly.

Abstract:Using conventional observations, automatic meteorological observation data, NCEP 1°×1° reanalysis data and the data from the satellite and Doppler weather radar, the torrential rainfall event that occurred over South China from 19 to 20 May 2015 was analyzed regarding the synoptic conditions, vertical structure and mesoscale convective activities. The results show that the three rainfall centers during this rainfall process were caused by three different mesoscale convective systems respectively. Firstly, the heavy rainfall over the north of Guangxi was caused by the mesoαscale vortex and shear line in the low level with the obvious frontogenesis and intense uplift. The mesoscale linear convection located behind the cold front lined more intensely, causing the rainfall to maintain a longer time with more total precipitation. The global model shows a good prediction. Secondly, the heavy rainfall over the central and northern Guangdong was triggered by the weak cold air in the boundary layer. The mesoscale linear convection moved southward because of the newborn cells in the warm and wet areas in the south. The linear structure was less intense and moved rapidly, so the total precipitation was less than that in the north of Guangxi, but the local rainfall was more intense. The mesoscale convergence line in the boundary layer and the mesoγscale convective systems played an important role. The mesoscale model can reflect the mesoscale process to some extent, but has more difficulties to forecast it. Thirdly, heavy rainfall over the southeast of Guangdong in the warm sector was triggered by the land and sea topographic uplift, persisting for a long time due to the combination with the MCSB later. The forecast capability of the model is limited. Therefore, the numerical model has different forecasting capabilities according to the different characteristics of the torrential rainfall. Understanding of the characteristics of the heavy rainfall, some corrections can be achieved by subjective forecasting.

Abstract:A haze pollution event hit mideastern China in January 2016. During the haze episode, concentrations of PM2.5 in Wuhan and Nanjing increased by three times in several hours. The meteorological observations and simulated data from an air quality model (CAMx) were used to analyze the transport of PM2.5 in this haze episode. The observations revealed that there were northerly winds in 1000-950 hPa and stable layer in the frontal zone above the two cities during the pollution period. Northerly winds in 1000-950 hPa transported pollutants to the cities, and stable layer in the frontal zone suppressed vertical diffusion of pollutants on the surface. Thus, the transport and the stable layer jointly caused the rapid increase of PM2.5 concentrations in the two cities. The pollution was cleared after the main cold air arrived, especially after the weak wind zone in the frontal zone moved. The simulation data showed that local contribution to the two cities in the severe pollution progress was less than the average value from 15 to 19 January. The transport contribution to PM2.5 in Wuhan and Nanjing was 51% and 58%, respectively. The transport contribution rate was not very certain, because the model underestimated the transport of PM2.5.

Abstract:Trajectory clustering analysis and potential source contribution function (PSCF) analysis are applied for the characteristics of air pollutant transport in China’s BeijingTianjinHebei Region during winter months, and the construction of transport index is improved. On this basis, a heavy air pollution process is used to investigate the effect of transport index. The results show that the probabilities of heavy pollution weather with air masses coming from south, west, east and northwest direction are descending for Beijing, Tianjin and Shijiazhuang. In the surface level, the enhancement of southwesterly and southeasterly wind becomes the main path of air pollutants to Beijing in most time, and the southeastern source of pollutants are mainly concentrated in the lower atmosphere. The improved transport index has a better ability to describe the relationships between air pollutant transports and local PM2.5 concentration. The interregional transport has an obvious influence on the PM2.5 concentration in Beijing, Tianjin and Shijiazhuang, and shortdistant transport from suburbs and surrounding areas has the most significant impact. Therefore, a reasonable application of transport index is useful in making a preliminary judgment on the causes of heavily polluted weather.

Abstract:In this paper, characteristics of air pollution during winter and summer seasons of 2014 and 2015 were analyzed by using the air quality data and the ground surface observation data of Shanghai, and pollution transport paths were analyzed by cluster analysis method and backward trajectory model as well. The results show that the air quality in Shanghai was good and primary pollutants were PM2.5 and O3 respectively in winter and summer. The northwest transport path corresponding to the highest PM2.5 and O3 concentrations, were 62.8 and 130.2 μg·m-3 respectively, and northwest and southwest transport paths corresponding to higher PM2.5 in winter. Then, the transport index was established by combination of potential source contribution function and pollution sources emission intensity and other factors. In general, the pollutants of southern and middle Jiangsu, northern and middle Zhejiang and southern and middle Anhui had great contribution to the quality of Shanghai in summer, while the transport index areas were expanded in winter, including southern Hebei, eastern and middle Henan, Shandong, Anhui, eastern and middle Hubei, Jiangsu and northern and middle Zhejiang.

Abstract:Rainfall runoff simulation and flood forecasting of large river basins is a complex prediction problem. An integrated hydrometeorological forecast model is developed for flood forecast test of complex river basins. Gridbased quantitative precipitation forecast products of National Meteorological Centre are applied as precipitation of leadtime period, and the Xin′anjiang hydrological model is used for rainfallrunoff process simulation. The MuskingumCunge model, based on diffusion, columbar storage and wedge storage theory, is introduced for channel waterlevel and discharge forecasting. For the test case of the upper reaches of the Lutaizi Station of the Huaihe River in flood season of 2016, the developed hydrometeorological forecast model of complex river basins is applied in flood forecast test. The results show that the developed model can perform well. Compared with no considering the precipitation in leadtime period, the flood forecast leadtime can be increased obviously. The developed model has certain reference significance for flood forecasting over similar basins.

Abstract:The focus of national severe weather forecasting is transforming from shortrange forecast to the pattern of the coexistence of shortrange forecasts and shorttime forecasts. For the requirements of severe weather forecast techniques and service, this paper presents the development of national severe weather integrated platform. Based on the core requirements of meteorological data organization and plotting, the development of data analysis system, meteorological autoplot system and WEB search and display system are developed. The data analysis system, as the core of the whole platform, integrates various data, including mutisource monitoring data and mesoscale NWP model forecast and global NWP model forecast, and develops the dataprocessing technology for severe weather monitoring and nowcasting, shortrange forecast and shorttime forecast. The key technology includes: starting from considering the unstable energy, water vapor, uplift, and vertical wind shear, the classified shortrange analysis and prediction technology summarizes conceptual models for different types of severe weather. The severe weather tendency forecast part guided by the severe weather condition analysis, generates a classified severe weather forecasting model and products. The shortterm forecast test part integrates the applications of mososcale NWP, ensemble prediction, mesoanalyses and sounding analysis, and highlightes the seamless link between the observation in the past 3 h and the prediction of the future 12 h. Based on the existing convective system monitoring technologies, the autoalarm products of radar features, severe weather events, convective indices and alarm signals are developed to improve the convective weather monitoring and nowcasting technology. Meteorological autoplot system includes the functions of data analysis, automatically plotting, etc. In the term of service, the WebGis tech is applied to share the national products to the countylevel weather stations and vice versa.

Abstract:An objective correction method is established by making use of ensemble forecast of 10 m wind speed from 2013 to 2015 and wind speed observation from 88 representative stations. The accuracy and stability of the mode are equal or greater than the average values. Thus fully considering the prediction probability of historical data and collecting the distribution of each member of ensemble forecasts could improve the correction effect. Small error and TS score are significantly improved by correction. Specifically, TS score of 8-9 magnitude of wind speed forecast within 72-120 h lead time increases from 0.04 to 0.44, which can effectively promote the forecasting ability of medium and longterm timeliness and high volume of wind speed. The corrected forecasts could bring good effectiveness for cold air and typhoon weather in the offshore areas of China.

Abstract:The visibility features are analyzed using automatic hourly visibility observations at Tianjin Port from 2009 to 2013. Based on the NCEP reanalysis data and Tianjin Port observations in the autumn and winter from 2006 to 2012, higher impact meteorological factors are given wich influence low visibility at port area through the correlation analysis. Three statistical models to different visibility samples have been established with artificial neurological network method, excluding dust and rain weather. Moreover, the statistical models are linked to output products in WRF and are applied in operational forecasting on visibility at Tianjin Port in autumn and winter through progressive screening method according to the three statistical models. So visibility forecast products of 72 h period are provided every day. The test results show that, for the hourly visibility forecast, the forecasting techniques score (TS) of BPTFP (back propagation three filter product) for less than 10 km visibility is from 10.5% to 35.4%, higher than the WRF. The TS is a comparable level for visibility less than 0.5 km, but WRF model forecast is a little better when it forecasts precipitation. For forecasting 0.5-1 km fog, the TS of WRF is less than 1%, and the BPTFP is from 14% to 21%. The average tests of daily minimum visibility show that the TS of BPTFP is 75%, which is 24% higher than WRF for the fog process of less than 1 km. Moreover, for the fog process from 1 to 10 km, the TS of BPTFP is 60% higher than WRF.

Abstract:With the purpose of improving the certification of meteorological quality for apple, this paper utilizes the survey data as well as meteorological and phenological monitoring data and analyzes the quality of the apples from 31 planting and production bases and 155 orchards in Shannxi by means of grey relational analysis and stepwise regression. The differences in the apply quality among the four ecological areas, namely Yanan area, eastern Weibei area, western Weibei area and Guanzhong area, are also analyzed. In addition, the key phenological period and meteorological factors are studied too. It is found that the fruit shape index and titratable acid content in those ecological areas are at similar levels. There is an obvious difference in the single fruit weight, soluble solid content and fruit coloring degree. The maximum temperature in the first fruit swelling period, the maximum temperature during the growth season and the average temperature in the young fruit period have very significant influences on apple quality. The quality of apples in Shannxi can be enhanced by higher temperature and less rain in the bloom period and young fruit period, low temperature and abundant sunshine in the first swelling period as well as the low temperature and high relative humidity in the coloring period. The quality prediction model built through the multivariable regression can achieve the better fitting effect and thus could be applied to the actual production.

Abstract:Urban flooding has gradually become a major natural disaster in many cities with the development of the urbanization, but the current urban flooding models are mostly based on hydrodynamic methods, requiring a lot of input parameters, so they are inconvenient in popularization and application. In this paper, Shanghai Urban Flooding Assessment Model (SUM) was established aiming at the downtown inside the outer ring with the generalized method. Through inputting the hourly rainfall, hourly continuous simulation to urban flooding was achieved. On this basis, using the disaster situation data and ponding monitoring data, we assessed the simulation results. The results indicate that the SUM can simulate the urban flooding situation in the downtown of Shanghai better. The catchment area increases with the increase of precipitation amount. Analysis of disaster threshold shows that the rainfall causing water logging disaster in Puxi of Shanghai is generally lower than that in Pudong. The disaster rainfall is less relatively in the central part of the city including Huangpu District, Xuhui District, Hongkou District, Zhabei District and some streets in Baoshan District.

Abstract:In winter 2016/2017 (December 2016 to February 2017), East Asian winter monsoon was much weaker than normal, and the Siberian high was weaker, too. Arctic Oscillation (AO) was primarily in a positive phase. For geopotential height anomalies at 500 hPa in Northern Hemisphere, zonal circulation was mainly observed in the midhigh latitudes in Asia, and China was dominated by positive height anomaly. In association with the anomalous circulation, the temperature over China was higher than normal. The average temperature in the winter was -1.5℃, which is 1.9℃ higher than the climate mean (-3.4℃). The winter 2016/2017 was the warmest since 1961. The East Asian winter monsoon and Siberian high in each month of the winter were weaker, and the average temperature in the corresponding month was higher. Actually, the abnormal weaker winter monsoon is correlated with the atmospheric circulation anomaly in the Arctic in summer. The thermal and dynamic conditions of Arctic atmospheric circulation in summer 2016 modulate the effect of autumn Arctic sea ice loss on winter atmospheric variability, and this is unfavorable for the strengthen of the winter monsoon.

Abstract:The main characteristics of the general atmospheric circulation in April 2017 are as follows. Double polar vortex centers existed in the Northern Hemisphere, but weaker than usual. The circulation in Eurasian middlehigh latitude showed a fourwave pattern. The strength of Western Pacific subtropical high is stronger than that in normal years. While the south branch trough was almost equal to normal. The monthly mean temperature was 12.0℃, 1.0℃ higher than normal, and the monthly mean precipitation amount was 44.0 mm, which was 2% less than normal. One nationwide strong cold air process happened in this month. There were more precipitation weather events in southern China in this month, with three regional rainstorm processes seen. In addition, two blowing sand weather events happened in the northern China and hail disaster attacked some places of many provinces.