**5. Conclusions**

To investigate the microphysical properties of surface precipitation and improve the accuracy of radar QPE, 14-month continuous PARSIVEL<sup>2</sup> measurements during 2017–2018 in Beijing, China, were analyzed in this study. After quality control, a total of 25,499 1-min DSD spectra were obtained, corresponding to 1013.78 mm of total rainfall. The major rainy periods were from June to August, which contributed to 81.5% of rainfall amount and 78.3% of total DSD samples. The least contribution of rainfall was from September. In October, the precipitation tends to be steady with relatively long time but low intensity.

DSD dataset was classified as stratiform and convective rain types using the BR09 C−S scheme [50] in log10 *Nw*–*D*<sup>0</sup> domain. A large number of samples were identified as stratiform, which accounted for less than half of the total rainfall amount. The mean integral rainfall parameters, such as *R*, . log10 *Nw* / , *D*<sup>0</sup>, and three X-band dual-polarization variables, were higher in convective rain than stratiform rain. The occurrence of DSDs concentrated with *D*<sup>0</sup> and log10 *Nw* in the ranges of 0.8–1.1 mm and 3.2–4.1, respectively, which corresponds to *W* about 0.02–0.11 g m<sup>−</sup>3. The increases of *R* and *ZH* were proportional to the increases of log10 *Nw* and *<sup>D</sup>*0, and BR09 line was equivalent to *<sup>R</sup>* <sup>=</sup> 8.6 mm h−<sup>1</sup> and *ZH* = 36.8 dBZ. The comparation with other C−S classification schemes showed the similar distribution in log10 *Nw*–*D*<sup>0</sup> domain, but the detailed characteristics of DSDs among different schemes were different, with larger discrepancies in convective rain than stratiform rain. The different predominant microphysical processes in Beijing and other climate regions result in different DSD distributions in log10 *Nw*–*D*<sup>0</sup> domain, especially for convective rain. Compared to the warm rain characterized by a collision-coalescence process in Eastern and Southern China during the Asian Summer Monsoon Season, as well as in tropical, oceanic regions, the precipitation in Beijing is dominated more by mixed phase precipitation microphysical processes. The melting large ice-phase hydrometers increased *D*<sup>0</sup> but decreased *Nw* compared to other climate regions. For stratiform rain, the mean values of log10 *Nw* and *D*<sup>0</sup> correspond to the high occurance ranges. For convective rain, three groups were separated, which showed distinct seasonal variations. The mean values of log10 *Nw*–*D*<sup>0</sup> pairs from May to August (Group 1) clustered together while those from April (Group 2) and September-October (Group 3) were distributed on the two sides of Group 1 above the BR09 line. Group 2 tends to contain more warm rain processes, while Group 3 was dominated by intense ice-based processes, such as aggregation and riming that sharply decrease the number of small size hydrometers but slowly increase the particle size. This finding provides additional insight to precipitation microphysics in midlatitude Asian (northern China) and further appends the archievements of Dolan et al. [46].

In addition, dual-polarization radar variables were computed from the DSD dataset using the *T*-matrix scattering method and the radar-based QPE estimators were derived through nonlinear regression analysis. The estimated rainfall products using radar rainfall relations were also independently verified using collocated rain gauge measurements. It was concluded that for single-polarization variable, the fitted *Zh*–*R* relationship, *Zh* = 265.14*R*1.399, was almost coincident with the operational WSR-88D rainfall estimator [60], *Zh* = 300*R*1.4; for dual-polarization radar applications, *R*dpr(*Zh*,*Z*DR) performed the best for hourly rainfall estimation, while *R*dpr(*K*DP,*Z*DR) performed the best at high rainfall intensities. In addition, a blended algorithm is derived based on the architecture of DROPS2 [5] to enhance radar rainfall estimation. It was shown that *R*dpr(DROPS2–X) performed better than any individual QPE estimators at hourly scale. Future work will focus on the large scale application of *R*dpr(DROPS2–X) for the X-band dual-polarization radar network being deployed in Beijing.

**Author Contributions:** L.J. designed this research and drafted the manuscript; H.C. supervised the analysis and edited the manuscript; L.L. conducted the field experiment; B.C. and G.Z. reviewed the manuscript; M.C. and X.X. provided the financial supports.

**Funding:** This research was funded by the National Key R&D Program of China (No. 2018YFC1506801), the National Natural Science Foundation of China (Nos. 41505117, 41775132, 41605022), the Ministry of Science and Technology of China (Nos. IUMKY201904, IUMKY201729), the Beijing Natural Science Foundation of China (Nos. 8162018, 8184072), and the Beijing Municipal Science and Technology Commission (No. Z161100004516018). B. Chen was also supported by the Key Laboratory of Meteorology and Ecological Environment of Hebei Province and the Weather Modification Office of Hebei Province (hbrywcsy-2017-04).

**Acknowledgments:** The authors would like to express their gratitude to the four anonymous reviewers for their comments that improved this manuscript. We thank Rui Qin for his assistance in plotting Figure 1.

**Conflicts of Interest:** The authors declare no conflict of interest.
