In the summer of 2016, one phased-array radar and two polarimetric weather radars, representative of advancing radar technology in use in China, jointly collected data in the Foshan area to study severe convective storms in southern China. After an introduction to the technical characteristics and a verification of the radar calibration, the advantages of the abovementioned dual-polarization and phased-array radars are discussed in terms of an observational analysis of a supercell that occurred on 9 May 2016. The polarimetric signatures within the supercell are associated with specific microphysical processes that can reveal different stages of storm evolution. The hydrometeor classification algorithm is a more straightforward and useful method for nowcasting than conventional algorithms, which makes it favorable for further recommendation in China. During the mature and dissipating stages of this supercell, observations of the phased-array radar show detailed changes on short time scales that cannot be observed by parabolic-antenna radars. The initiation and mergers of new convective cells are found in the peak inflow region, and the formation and dissipation of the hook echo are associated with the relative intensities of inflow and outflow. The abovementioned results demonstrate that the phased-array radar and dual-polarization radars recently developed in China are powerful tools to better understand storm evolution for nowcasting and scientific studies.

Heavy rain associated with landfalling typhoons often leads to disasters in South China, which can be reduced by improving the accuracy of radar quantitative precipitation estimation (QPE). At present, raindrop size distribution (DSD)-based nonlinear fitting (QPE_DSD) and traditional neural networks are the main radar QPE algorithms. The former is not sufficient to represent the spatiotemporal variability of DSDs through the generalized Z–R or polarimetric radar rainfall relations that are established using statistical methods since such parametric methods do not consider the spatial distribution of radar observables, and the latter is limited by the number of network layers and availability of data for training the model. In this paper, we propose an alternative approach to dual-polarization radar QPE based on deep learning (QPENet). Three datasets of “dual-polarization radar observations—surface rainfall (DPO—SR)” were constructed using radar observations and corresponding measurements from automatic weather stations (AWS) and used for QPENet_V1, QPENet_V2, and QPENet_V3. In particular, 13 × 13, 25 × 25, and 41 × 41 radar range bins surrounding each AWS location were used in constructing the datasets for QPENet_V1, QPENet_V2, and QPENet_V3, respectively. For training the QPENet models, the radar data and AWS measurements from eleven landfalling typhoons in South China during 2017–2019 were used. For demonstration, an independent typhoon event was randomly selected (i.e., Merbok) to implement the three trained models to produce rainfall estimates. The evaluation results and comparison with traditional QPE_DSD algorithms show that the QPENet model has a better performance than the traditional parametric relations. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h⁻¹), the QPE_DSD model shows a comparable performance to QPENet. Comparing the three versions of the QPENet model, QPENet_V2 has the best overall performance. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h⁻¹), QPENet_V3 performs the best.

Field experiment in South China was undertaken to improve understanding of cloud and precipitation properties. Measurements of the vertical structures of non-precipitating and precipitating clouds were obtained using passive and active remote sensing equipment: a Ka-band cloud radar (CR) system, a C-band frequency modulated continuous wave vertical pointing radar (CVPR), a microwave radiometer and a laser ceilometer (CEIL). CR plays a key role in high-level cloud observation, whereas CVPR is important for observing low- and mid-level clouds and heavy precipitation. CEIL helps us diminish the effects of “clear-sky” in the planetary boundary layer. The experiment took place in Longmen, Guangdong Province, China from May to September of 2016. This study focuses on evaluating the ability of the two radars to deliver consistent observation data and develops an algorithm to merge the CR, CVPR and CEIL data. Cloud echo base, thickness, frequency of observed cloud types and reflectivity vertical distributions are analyzed in the radar data. Comparisons between the collocated data sets show that reflectivity biases between the CR three operating modes are less than 2 dB. The averaged difference between CR and CVPR reflectivity can be reduced with attenuation correction to 3.57 dB from the original 4.82 dB. No systemic biases were observed between velocity data collected in the three CR modes and CVPR. The corrected CR reflectivity and velocity data were then merged with the CVPR data and CEIL data to fill in the gaps during the heavy precipitation periods and reduce the effects of Bragg scattering and fog on cloud observations in the boundary layer. Meanwhile, the merging of velocity data with different Nyquist velocities and resolutions diminishes velocity folding to provide fine-grain information about cloud and precipitation dynamics. The three daily periods in which low-level clouds tended to occur were at sunrise, noon and sunset and large differences in the average reflectivity values were observed. Mid- and high-level clouds tended to occur at 1400 and 1800 BT. Few clouds were found between a height of 3 and 5 km.

The new Chinese Ka-band solid-state transmitter cloud radar (CR) uses four operational modes with different pulse widths and coherent integration and non-coherent integration numbers to meet long-term cloud measurement requirements. The CR and an instrument-equipped aircraft were used to observe clouds and precipitation on the east side of Taihang Mountain in Hebei Province in 2018. To resolve the data quality problems caused by attenuation in the precipitation area; we focused on developing an algorithm for attenuation correction based on rain drop size distribution (DSD) retrieved from the merged Doppler spectral density data of the four operational modes following data quality control (QC). After dealiasing Doppler velocity and removal of range sidelobe artifacts; we merged the four types of Doppler spectral density data. Vertical air speed and DSD are retrieved from the merged Doppler spectral density data. Finally, we conducted attenuation correction of Doppler spectral density data and recalculated Doppler moments such as reflectivity; radial velocity; and spectral width. We evaluated the consistencies of reflectivity spectra from the four operational modes and DSD retrieval performance using airborne in situ observation. We drew three conclusions: First, the four operational modes observed similar reflectivity and velocity for clouds and low-velocity solid hydrometeors; however; three times of coherent integration underestimated Doppler reflectivity spectra for velocities greater than 2 m s⁻¹. Reflectivity spectra were also underestimated for low signal-to-noise ratios in the low-sensitivity operational mode. Second, QC successfully dealiased Doppler velocity and removed range sidelobe artifacts; and merging of the reflectivity spectra mitigated the effects of coherent integration and pulse compression on radar data. Lastly, the CR observed similar DSD and liquid water content vertical profiles to airborne in situ observations. Comparing CR and aircraft data yielded uncertainty due to differences in observation space and temporal and spatial resolutions of the data.

The millimeter-wave cloud radar, ceilometer, and disdrometer have been widely used to observe clouds and precipitation. However, there are some drawbacks when those three instruments are solely employed due to their own limitations, such as the fact that radars usually suffer from signal attenuation and ceilometers/disdrometers cannot provide measurements of the hydrometeors of aloft clouds and precipitation. Thus, in this paper, we developed an integrated technology by combining and utilizing the advantages of three instruments together to investigate the vertical structure and diurnal variation of warm clouds and precipitation, and the raindrop size distribution. Specifically, the technology consists of appropriate data processing, quality control, and retrieval methods. It was implemented to study the warm clouds and precipitation in South China during the pre-flood season of 2016. The results showed that the hydrometeors of warm clouds and precipitation were mainly distributed below 2.5 km and most of the rainfall events were very light with a rain rate less than 1 mm h⁻¹, however, the stronger precipitation primarily contributed the accumulated rain amount. Furthermore, a rising trend of cloud base height from 1000 to 1900 BJT was found. The cloud top height and cloud thickness gradually increased from 1200 BJT to reach a maximum at 1600 BJT (Beijing Standard Time, UTC+8), and then decreased until 2000 BJT. Also, three periods of the apparent rainfall on the ground of the day, namely, 0400–0700 BJT, 1400–1800 BJT, and 2300–2400 BJT were observed. During three periods, the raindrops had wider size spectra, higher number concentrations, larger rain rates, and higher water contents than at other times. The hydrometeor type, size, and concentration were gradually changed in the vertical orientation. The raindrop size distributions of warm precipitation in the air and on the ground were different, which can be expressed by $\gamma$ distributions N(D) = 1.49 × 10⁴D^−0.9484exp(−6.79D) in the air and N(D) = 1.875 × 10³D^0.862exp(−2.444D) on the ground, where D and N(D) denote the diameter and number concentration of the raindrops, respectively.

Cloud radars are widely used in observing clouds and precipitation. However, the raw data products of cloud radars are usually affected by multiple factors, which may lead to misinterpretation of cloud and precipitation processes. In this study, we present a Doppler-spectra-based data processing framework to improve the data quality of a multi-mode pulse-compressed Ka–Ku radar system. Firstly, non-meteorological signal close to the ground was identified with enhanced Doppler spectral ratios between different observing modes. Then, for the Doppler spectrum affected by the range sidelobe due to the implementation of the pulse compression technique, the characteristics of the probability density distribution of the spectral power were used to identify the sidelobe artifacts. Finally, the Doppler spectra observations from different modes were merged via the shift-then-average approach. The new radar moment products were generated based on the merged Doppler spectrum data. The presented spectral processing framework was applied to radar observations of a stratiform precipitation event, and the quantitative evaluation shows good performance of clutter or sidelobe suppression and spectral merging.