Based on conventional weather data, automatic weather station data, and the observation of Yueyang Doppler radar, a waterspout occurred in Bianshan waters of East Dongting Lake (Bianshan waterspout for short) on 13 August 2017 is analyzed. Results show that the cold and warm airflows converge in the East Dongting Lake area when the upper East Asian trough forces the cold air southward, and the subtropical high guides the southwestern warm moist flow northward. The quasi stationary front over the north central Hunan Province is northeast to southwest, forming an "S" curve, which is favorable for the convergence of frontal instability energy to the East Dongting Lake area. The special geographical environment is easy to trigger canyon effect, which often leads to increased wind speed and humidity. The strong divergence in front of the upper trough, the deep low-pressure shear from northeast to southwest in the middle and lower layers, strong cyclonic convergence in the boundary layer, and the special topography jointly form a strong convergent upwelling flow field. When three meso-gamma-scale low eddies on the ground move northward to Bianshan waters, influenced by combined effects of the above flow field and the front and back vortices, the second vortex strengthens rapidly and forms a waterspout. The meteorological factors such as wind speed, wind direction, air pressure and visibility recorded by the lighthouse automatic meteorological station in the lake center change significantly when the waterspout passes, while precipitation is only 0.2 mm. Yueyang Doppler radar shows that the centroid of heavy precipitation is low to the north of strong convergence zone, where shear of strong wind is moderate and the radial wind speed over the shear is low. Yueyang Doppler radar wind profiles show that mesocyclone at the height of 0.6 km and the convergent flow fields near the ground at the height of 0.3 km are superimposed when the waterspout formed at 0905 BT. Waterspouts in the southern convergence zone have no storm tracking information, mesocyclones or tornado-type vortices. However, heavy precipitation accompanied by strong subsidence and convergence at the middle and low altitudes often produce both rising and subsidence currents, which are obviously unfavorable for the formation and development of waterspouts that need huge upward pumping. Comparing and analyzing waterspout processes of the Shengjin Lake in Anhui Province and the Dongting Lake in Hunan Province, it is concluded that the funnel-shaped strong lift suction caused by large-scale divergence at high altitude and the deep low-pressure shear from northeast to southwest in the middle and lower layers, and the intense convergence of cyclones and surface cyclones in the boundary layer are the main causes of the waterspout formation.

In order to obtain more detailed small-scale weather system data, Meteorological Observation Center of China Meteorological Administration (CMA) designed and developed X-band array weather radar (AWR), cooperating with relevant manufacturers. In March of 2018, the first prototypeis deployed at Changsha Airport for field experiments. Combing advantages of networked radars and a distributed phased array technology, the AWR has a highly coordinated scanning mode and high spatial and temporal resolutions to acquire fine echo intensity and wind field data. Compared with conventional parabolic antenna weather radars, a phased array antenna has wider beams and stronger side lobes, so that more ground clutter will appear in radar echoes. If the ground clutter cannot be effectively detected and removed, the accuracy of radar products will be affected seriously.Data collected by the X-band AWR at Changsha Airport are used to study the ground clutter detection algorithm for the AWR. According to the research progress all over the world, characteristic parameters of reflectivity factors, radial velocity and velocity spectrum width are extracted. In addition, time variability of reflectivity factor (T_VR), a new parameter, is added due to the high temporal and spatial resolution of the AWR. Based on analyzing statistical characteristics of each feature parameter, membership functions are determined. The contribution of T_VR to the clutter detection algorithm and the performance of the algorithm on different weather conditions are analyzed. Results show that the accuracy of ground clutter detection for Changsha Airport AWR can be maximally increased by 4% by adding T_VR, and the error rate of detecting the precipitation echo as clutter echo can be decreased by 2%. The accuracy of the proposed ground clutter detection algorithm reaches 96% in the detection of ground clutter when no precipitation processes happen. In the precipitation weather, the accuracy is 92%, and the error rate of detecting the precipitation echo as clutter echo is about 10%. The algorithm can basically detect and remove the ground clutter echoes from precipitation echoes.

Most of recent hydrometeor classification schemes for dual-polarimtric radar are based on fuzzy logic. Due to the lack of true value of hydrometeors, it is difficult to verify whether classifications are right or not. Therefore, three methods are proposed, cumulative value test, algorithm sensitivity test and hydrometeors' distribution test. Cumulative value test is used to inspect the ability of classifying and distinguishing. When input data contains system deviation and noise, the output classification would also contain system deviation and noise. Hence, a method is proposed to test the sensitivity of system deviation and noise. Hydrometeor distribution test is to analyze whether the hydrometeor distribution is temporal and spatial continuous. Through these tests the reliability and stability of the algorithm are analyzed, and key factors which affect the classification can be found out. Using observations of precipitation processes in Guangzhou in spring and summer from 2016 to 2017, the efficiency of S-band dual-polarization doppler radar is examined. Main results show that the classifying hydrometeor relies on the membership function. Using cumulative value test, some hydrometeors are found out with inappropriate membership function. These membership functions are not consistent with real characteristics of hydrometeors, which are ground clutter (including that due to anomalous propagation), biological scatters, dry aggregated snow, crystals of carious orientations, light and moderate rain, and a mixture of rain and hail. The way to modify these membership functions is based on hydrometeor statistic characteristics. Another insufficiency of membership function is to distinguish similar hydrometeors, such as crystals of various orientations with dry aggregated snow and heavy rain with mixture of rain and hail. The method to modify membership function's distinguishing ability is increasing parametric weights which has stronger discriminating ability. To ensure every hydrometeor has more than 90% stable results, the error of Z_h is between -0.5 dB and +0.5 dB, that of Z_DR is between -0.1 dB and +0.1 dB, that of ρ_hv is less than 0.02, and that of K_DP is between -0.3 dB and +0.9 dB. Moreover, data quality of Z_h and Z_DR is more important than other parameters, system deviation is more influencing than noise. After hydrometeor distribution test, it is found that heavy rain may be misclassified as mixture of rain and hail. The spatial consistency correction method is to check whether the distribution of rain and hail mixture in a certain space is continuous.

Wind profile radar(WPR), taking atmospheric turbulence of clear air as main detecting object, is the main reference tool currently for short-time forecast because of its high spatial and temporal resolution. In the past few decades, WPR spectral data processing mainly focuses on the wind spectrum estimation. In recent years, with the use of WPR data expansion, there are increasingly high demand for WPR data accuracy, but because of ground clutter and external noise, flying objects, the presence of disturbances such as precipitation and limitations of Fourier Transform method itself, there are often multiple overlapped peaks, which makes it difficult to judge the spectral meaning, resulting in large error detection products. WPR has a large dynamic reception range, so it can receive the echo of scattering of atmosphere turbulence and precipitation particles simultaneously. However, the superimposed spectrum of atmosphere and precipitation cannot be separated effectively. In the meantime, the wind field calculation is based on the hypothesis of local uniform and isotropy, which cannot be met during precipitation with great spatial variability and leads to data of WPR serious deficiency or distortion. It's of great importance to establish an effective spectral extraction programs under different weather conditions to improve the accuracy of spectral estimation for wind field data after the inversion, thereby enhancing the wind profile accuracy of radar detection.A method of WPR-HW is developed for the case of precipitation according to the principle of WPR detection and the feature of spectrum, and then the effectiveness of the method is tested using ECMWF ERA Interim data. 10 precipitation cases in Tianjin are investigated to verify the significance of wind field data processed by WPR-HW in severe convection prediction. Results show that the WPR-HW has significant advantage compared with the recent WIND method (the universal method of wind field inversion from WPR) in integrity and reliability. For the wind field data in 10 precipitation cases, the leakage rate of WIND is 25.4% while that of WPR-HW is 0. The root mean square error in wind speed of WPR-HW is 1.6 m·s^-1 while that of WIND is 2.3 m·s^-1. The RMSE in wind direction of WPR-HW is 22° while that of WIND is 45°. The wind field processed by WPR-HW is able to make up for the deficiency and distortion of WPR data effectively in precipitation, which thus benefit to improve the timeliness and accuracy in strong convective weather forecasting.

Aerosol particles can scatter and absorb solar radiation and affect microphysical processes of clouds to change the earth's radiation budget. It is reported that aerosol particles not only have an impact on climate change, but also cause polluted environment and affect human health. Ground-based measurement networks such as AERONET and SKYNET are very useful and accurate ways to monitor the spatio-temporal distribution of aerosols using the sun-sky radiometric technique. Aerosol optical properties retrieved by a PREDE skyradiometer are used to analyze the variation of aerosol in Beijing from October 2018 to September 2019. Results show that aerosol optical depth at 500 nm is high from February to July, the highest value is 0.71 in June, the highest single scattering albedo is 0.96 in August and the lowest value is 0.89 in May, Ångström exponent in summer (1.11) is higher than that in spring (0.89), and the volume size distribution pattern shows typical bimodal in every month. According to the Chinese National Secondary Standards for PM_2.5, pollution days are picked. It is found that pollution days only account for 17%, of which 62% are light pollution days. The statistical result of air quality in Beijing is good from October 2018 to September 2019. Aerosol optical properties and PM_2.5 under pollution and clean weather conditions in Beijing are discussed. The value of PM_2.5 under pollution weather condition is 2.27 times larger than that under clean weather condition, values of aerosol optical depth at 500 nm are 0.85 and 0.49 under pollution and clean weather conditions, respectively. Values of single scattering albedo are 0.96 and 0.92 under pollution and clean weather conditions, respectively. The value of Ångström exponent under pollution weather condition (1.02) is larger than that under clean weather condition (0.91) in winter while the value of Ångström exponent under pollution weather condition (0.87) is smaller than that under clean weather condition (0.90) in spring. Skyradiometer retrieved data, combined with lidar measurement and meteorological data are used to analyze a serious pollution event in winter over Beijing. The result suggests that poor meteorological conditions (low wind speed and high relative humidity), the hygroscopic growth of aerosol, aerosol secondary transformation, local emissions and regional transportation lead to this serious pollution event.