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This paper realized the interpolation and three-dimensional mosaic of Doppler weather radar data. Currently, the Adaptive Barnes interpolation method is generally recognized as an effective algorithm for weather radar. The smoothing scheme of this algorithm are improved to keep good characteristics of the raw volume data. Compared with the commonly used interpolation methods, the improved smoothing scheme of the Adaptive Barnes interpolation can get better CAPPI data without over-smoothing effect.

For to know the performance of dual linear polarization SC radar and its consistency with CINRAD-SA detection data, the echo reflectance data of two kinds of radar are compared and analyzed. Using reflectivity data of two radars at the same time in the same rainfall event, two radar data were interpolated into a 3d grid at the same height to compare their intersecting areas by using the method of region lattice point comparison. Study on the scatter characteristics and probability distribution of two radar reflectivity values in intersecting regions. The results show that echo reflectivity characteristics of the perpendicular bisector in two radars have good consistency, but the echo data value detected by dual linear polarization SC radar is larger than that of SA radar; Under 25 dB, the detection value of dual-line polarization SC radar is greater than that of SA radar; The echo reflectivity data of SC radar are less discrete. The rainfall calculated by using the Z-R relation, R(KDP) and R(ZH, ZDR) method of the dual-line polarization SC radar is all higher than the observed values of the ground rain gauge, and the difference between the observed values of the rainfall calculated by the R(KDP) method and the rain gauge is relatively small. In order to improve the effect of quantitative precipitation estimation, further quality control of polarization parameters and echo data is needed.

In this paper, we analyzed the precipitation data from 2016 to 2017 obtained by the new-generation weather radar and 256 ground automatic rainfall stations in Leshan and applied the dynamic grading Z-I relationship method of the quantitative precipitation estimation of Leshan area, while the method was based on constant altitude plan position indicator (CAPPI) products. The results show the dynamic grading Z-I relationship method is better than the fixed Z-I relationship method of estimation of heavy rain intensity (above 40 mm/h) and the overall estimation effect in the rain intensity and hourly cumulative rainfall. But there still exists an underestimation of heavy precipitation.

Doppler Weather Radar (DWR) observations of a severe hailstorm were assimilated in the Weather Research and Forecasting (WRF) model, the three dimensional variational (3D-Var) assimilation is the more common approach. The hailstorm occurred in Bayannur, Inner Mongolia, China, during August 9-10, 2019. The processes of formation, development, and dissipation of the hail-cloud were analyzed, simulated, and discussed in this study. The simulation and assimilation results were verified against X-band dual-linear polarization radar observations. The Morrison and Milbrandt parameterization scheme is applied to simulate synoptic process. In this paper, comparative experiments are set to analysis the impact with and without assimilation of doppler weather radar data on hail simulation:1) control experiment (CTL): without data assimilation; 2) assimilate reflectivity and radial velocity together. The result shows that the Morrison and Milbrandt parameterization scheme can simulate the hail weather process well; at the time when hail occurred, there were strong vertical wind shear, small precipitation amount, low freezing layer, the potential temperature decreases with the height, and the radar echo intensity was around 60dBZ. The hail simulation is improved significantly after assimilation of doppler radar reflectivity and radial velocity

—This paper uses Haikou ’s dual linearly polarized SA bond radar (Z9898) and Dongfang’s SA band new generation Doppler weather radar (Z9072) to compare and analyze a precipitation process that occurred in Hainan Province on July 2, 2019, and carefully compared the relationship between the reflectivity factors of the two radars to study the detection ability of dual polarization radars. Considering the consistency of radar observations, this study selected the reflectivity factors near the mid-perpendicular line of the two radars for comparison, and used the bilinear interpolation method to interpolate the radar reflectivity volume scan data in the spherical coordinate system to the grid under coordinate system to obtain the CAPPI (Constant Altitude Plan Position Indicator), and compare the reflectivity factors of the same height, and then draw and analyze a scatter plot of the reflectivity factors. At the same time, two The probability distribution of the reflectance factors of the two radars in the selected area is compared, and the mean, standard deviation and correlation coefficient of the observation data of the two radars in the area are counted. Finally, the rainfall estimation is performed according to the Z-R relationship. Experiments show that for areas of the same distance from two radars, the observation data of dual-linear polarization radar is significantly larger than that of CINRAD-SA radar; and through statistical analysis, the average value of dual-polarization radar is 7.7 dBZ greater than CINRAD-SA and the standard deviation is 0.32 lower, indicating dual-linear polarization radar performance is better than the CINRAD-SA radar; finally, the rainfall calculation result of the dual linear polarization radar is greater than that of the CINRAD-SA radar.

In this study, the Weather Research and Forecasting (WRF) model is used to simulate a severe convective process in Hainan, China on September 16, 2019. For the purpose of improving precipitation simulation and forecast, the WRF model’s three-dimensional Variational (3DVAR) assimilation system is used to assimilate CINRAD/SA Weather radar data. The assimilation results and precipitation forecast are compared and analyzed in order to study the influence of weather radar data assimilation on precipitation prediction. Four experiments of assimilation are carried out: 1) Control experiment: no assimilation of any data; 2) Only radar reflectivity data is assimilated; 3) Assimilating the radar radial velocity data; 4) Simultaneously assimilating the radar reflectivity and radial velocity data. The results show that the assimilation of the reflectivity data from the weather radar can effectively adjust the spatial distribution and magnitude of water vapor and temperature field in the background field. Assimilating the radar radial velocity data mainly improves the wind field of initial field. The assimilation of reflectivity can better adjust the precipitation forecast compared with the assimilation of radial velocity. Assimilating both reflectivity and radial velocity at the same time shows the best positive effect on precipitation forecast.

The reflectivity observation was analyzed to study the reliability of three-dimensional gridding algorithm during a precipitation process in Hainan on July 2, 2019, which acquired from CINRAD/SA weather radars located in Haikou and Dongfang. In this study, according to the nearest neighbor method in radial and the vertical linear interpolation method in azimuth, the volume-scanned reflectivity data of radar at Haikou in the spherical coordinate were interpolated into the longitude and latitude grids in the cartesian coordinate. The three-dimensional gridding reflectivity was verified by comparing its structural characteristics with the original volume-scanned reflectivity. The statistical errors between the three-dimensional gridding reflectivity and the volumescanned reflectivity observed from radar at Dongfang during the same period of time and in the common detection area were analyzed. The results show that the three-dimensional gridding reflectivity has a good continuity in space, and also retains the structural features of the original volume-scanned reflectivity data, and it is in good spatial consistency with the reflectivity observed from the weather radar in Dongfang city.

In order to understand of physics process in the hailstorm, a hail event on August 9, 2019 had been analyzed by using the data collected of two X-band dual polarization radars located in Wuyuan County Meteorological Bureau and Ulansuhai Meteorological Observation Station in Bayan Nur, Inner Mongolia Autonomous Region. Hailstorm information recorded by 58 hail pads which were deployed in the experiment region were also used for the validation of the radar observation. The volume scan data of the Wuyuan radar is meshed by three-dimensional coordinates of longitude, latitude and altitude by vertical interpolation. In the common observation area, through comparing and validating the values of the polarization parameters on the crossover line to analyze the vertical structure characteristics of hail cloud. The results showed that the characteristics of hail observation data of the two radars in the same observation area are similar, Combining with two radar data can more completely obtain the hail information. The paper can provide relevant basis for the X-band dual polarization radar to the artificial hail suppression work in the Bayan Nur area.

Dual-polarization Doppler weather radar variables such as differential reflectivity (ZDR), differential propagation phase shift (ΦDP), specific differential phase (KDP), and the co-polar correlation coefficient (ρHV), which are closely related to the characteristic of hydrometeors. The precision of these variables determines the accuracy for quantitative precipitation estimation (QPE) and hydrometeor classification. By analyzing the relationship between the polarimetric radar variables and the signal-to-noise ratio (SNR), it is found that when the SNR decreases to a certain threshold, polarimetric variables become more sensitive to the decrease in the SNR, and the precision of these variables become deteriorated with the decrease of the SNR. This threshold usually is closely related to the radar performance, for the X-band dual-polarization radar used in this paper, the SNR threshold is about 20 dB. The results of this study can provide some reference for radar system performance evaluation and data quality control.

Weather radar is a traditional remote sensing tool for precipitation observation and analysis. Compared with millimeter wave radar, it is less affected by attenuation, but at the same time, it has lower spatial and temporal resolution normally. Cloud radar has shorter wavelength and can detect basic information of cloud, such as cloud height, thickness and other parameters. It provides a fantastic technique to study fine structure for clouds. But due to the attenuation effect it is rarely used to analyze precipitation. In this paper, A cloud radar and a C-band weather radar nearby are used to analyze two precipitation processes. The Z-I relationship can be obtained by combining ground automatic precipitation observation at the same site of the cloud radar. By a primitive research in this paper, the characteristics of stratiform precipitation was obtained by analyzing the reflectivity vertical profiles, layer mean reflectivity and standard deviation from cloud radar and C-band radar, and it shows that some useful information can be retrieved using cloud radar observation even under precipitation conditions. So with the higher spatial and temporal resolution, more research work will be focused on the fine vertical structure characteristics retrieval from cloud radar observation in the future.

A historical severe rainfall case in the metropolitan area of Beijing was studied using a C-band dual-polarization radar system. The QPE results from the evaluation was then validated using 5-min resolution gauge observation from an operational national weather station where is in the severe rainfall area. Results show that the rainfall estimation is more consistent with the surface rain gauge observation compared with the S-band single polarization observation and the algorithm of R(KDP): Brandes (2002) is the best for this case.

With the successful implementation of distributed collaborative adaptive sensing (DCAS) paradigm in CASA project, weather radar network has shown great advantages over conventional stand-alone weather radar, especially in urban environment. In order to better understand the initiation, development and cloud microphysics of thunderstorm in rainy season near the plateau area, this paper attempts to design a radar network near Chengdu Metropolitan area in western Sichuan Basin, China. This radar network, as a core part of the mesoscale meteorological observation system for Sichuan Basin, is an ideal research platform to investigate the local precipitation microphysics. It can also serve as a semi-operational network for local weather monitoring and emergency management.