Observation data from March to May 2020 of the Ka-band millimeter-wave cloud radar and disdrometer, located in Xinjiang, a typical arid region of China, were used to study the diurnal variation of clouds and precipitation, raindrop size distribution (DSD), and the physical parameters of raindrops. The results showed that there are conspicuous diurnal changes in clouds and precipitation. There is a decreasing trend of the cloud base height (CBH) from 05:00 to 19:00 CST (China Standard Time, UTC +8) and a rising trend of CBHs from 20:00 to 04:00 CST. The cloud top height (CTH) and the cloud thickness show a rising trend from 03:00 to 05:00 CST, 12:00 to 14:00 CST, and 20:00 to 01:00 CST. The diurnal variation of clouds is mainly driven by wind and temperature closely related to the topography of the study area. There are three apparent precipitation periods during the day, namely, 02:00–09:00 CST, 12:00 CST, and 17:00–21:00 CST. The changes in the physical parameters of raindrops are more drastic and evident with a lower CBH, lower CTH, and higher number of cloud layers from 12:00 to 21:00 CST than other times, which are closely related to day-to-day variations of systems moving through, and incoming solar radiation and the mountain–valley wind circulation caused by the trumpet-shaped topography that opens to the west played a secondary role. The DSD is in agreement with a normalized gamma distribution, and the value of the shape factor μ is significantly different from the fixed μ value in the Weather Research and Forecasting (WRF) Model. The rain in arid Xinjiang had a higher concentration of raindrops and a smaller average raindrop diameter than the rain in other humid regions of the Central and Southeast Asian continent. In the $Z-R$ (radar reflectivity–rain rate) relationship, $Z=249R^{1.20}$ is derived for stratiform rain, and it is significantly different from humid regions. Using $Z/D_m$ (mass–weighted mean diameter) and R, a new empirical relationship $Z/D_m=214R^{1.20}$ is established, and improvement is obtained in rain retrieval by using the $Z/D_m-R$ relation relative to the conventional $Z-R$ relation. Additionally, the $N_t-R$, $D_m-R$, $N_w-R$, and $N_t-N_w$ relationships with larger differences from humid regions are established by fitting the power-law equations. These results are useful for improving the data parameters of microphysical processes of WRF and the accuracy of quantitative precipitation estimation in arid regions.

The summer season raindrop size distribution (DSD) characteristics and their important applications, based on a PARSIVEL² disdrometer installed in Zhaosu over the western Tianshan Mountains, China, in 2020–2021 are studied. Our analysis reveals that, for total rainfall and different rainfall types, the DSD in Zhaosu follows the normalized gamma distribution model, and convective rainfall has a higher raindrop concentration than stratiform rainfall at all diameters. For stratiform rainfall, the mean value of mass-weighted mean diameter (D_m) is lower than that of convective DSD, while the mean value of normalized intercept parameter (log₁₀ N_w) is higher than that of convective DSD, and the summer season convective rainfall in Zhaosu is continental convective rainfall according to the conventional classification, which is characterized by relatively larger D_m and lower log₁₀ N_w values. The derived µ–∧ relation in Zhaosu exhibits some differences from those reported in eastern, southern, and northern China and the Tibetan Plateau. Furthermore, derived Z–R relations for stratiform and convective rainfall in Zhaosu are compared with those from other regions. Analysis shows that the empirical relation of Z = 300R^1.4 (widely used), strongly overestimates the R of convective precipitation in Zhaosu. The C-band polarimetric radar rainfall estimation relations are derived, and the R(Z_h,Z_dr) and R(K_dp,Z_dr) relations perform the best in quantitative precipitation estimation. Moreover, the empirical D_m–Z_ku and D_m–Z_ka relations are derived, which are beneficial to the improvement of rainfall retrieval algorithms of the GPM DPR. Lastly, rainfall kinetic energy relations proposed in this study can be used to better assess rainfall erosivity. The empirical relationships of DSD evaluated in this study provide an opportunity to (1) improve rainfall retrieval algorithms for both ground-based and remote sensing radars and to (2) enhance rainfall kinetic energy estimates in rainfall erosivity studies based on disdrometer and GPM DPR.

In order to improve the understanding of the microphysical characteristics of raindrop size distribution (DSD) under different rainfall rates (R) classes, and broaden the knowledge of the impact of radar wavelengths and R classes on the QPE of dual-polarization radars in the Tianshan Mountains, a typical arid area in China, we investigated the microphysical characteristics of DSD across R classes and dual-polarimetric radar QPE relationships across radar wavelengths and R classes, based on the DSD data from a PARSIVEL² disdrometer at Zhaosu in the Tianshan Mountains during the summers of 2020 and 2021. As the R class increased, the DSD became wider and flatter. The mean value of the mass-weighted mean diameters (D_m) increased, while the mean value of logarithm normalized intercept parameters (log₁₀ N_w) decreased after increasing from C1 to C3, as the R class increased. The largest contributions to R and the radar reflectivity factor from large raindrops (diameter > 3 mm) accounted for approximately 50% and 97%, respectively, while 84% of the total raindrops were small raindrops (diameter < 1 mm). Dual-polarization radars—horizontal polarization reflectivity (Z_h), differential reflectivity (Z_dr), and specific differential phase (K_dp)—were retrieved based on the DSD data using the T-matrix scattering method. The DSD-based polarimetric radar QPE relations of a single-parameter (R(Z_h), R(K_dp)), and double-parameters (R(Z_h,Z_dr), R(K_dp,Z_dr)) on the S-, C-, and X-bands were derived and evaluated. Overall, the performance of the R(K_dp) (R(K_dp,Z_dr)) scheme was better than that of R(Z_h) (R(Z_h,Z_dr)) for the QPE in the three bands. Furthermore, we have for the first time confirmed and quantified the performance differences in the QPE relationship of dual-polarization radars under different schemes, radar wavelengths, and R classes in typical arid areas of China. Therefore, selecting an appropriate dual-polarization radar band and QPE scheme for different R classes is necessary to improve the QPE ability compared with an independent scheme under all R classes.

In order to deepen the knowledge of the seasonal variation in total cloud cover (TCC) in Xinjiang, China (XJ), a typical arid region, and to broaden the understanding of the seasonal variation in cloud type (CLT) in the region, we used TCC and CLT datasets from the latest generation of the geostationary satellite Fengyun 4A (FY-4A) from 2018 to 2022 to investigate the seasonal variation characteristics of TCC and CLT in XJ. Meanwhile, to verify the accuracy of TCC from FY-4A, ground observation (GROB) TCC datasets from 105 national meteorological stations (NMSs) in XJ and TCC datasets from ERA5 during the same period were used. In addition, the correlation between TCC from FY-4A and meteorological factors from ERA5 was also analyzed in this study. The TCC from FY-4A, GROB, and ERA5 can all well reflect the significant seasonal variation in TCC in XJ, with the highest (lowest) mean TCC and a distribution pattern of high in the southwest (northwest) and low in the northeast (southeast) in spring (fall) in XJ. Although the mean TCC from FY-4A in all four seasons was lower than that from GROB, the two were comparable in spring (44.09% and 47.32%) and summer (42.88% and 43.17%), while there was a significant difference between the two in fall (27.86% and 40.19%) and winter (30.58% and 46.93%) for 105 NMSs in XJ. The TCC from FY-4A was lower (higher) than that from GROB in spring and summer at most NMSs in northern (southern) XJ, while the TCC from FY-4A was lower than that from GROB for the vast majority of NMSs in fall and winter, especially in northern XJ. The seasonal variation in the spatial distribution of different CLTs (clear, water-type, supercooled-type, mixed-type, ice-type, cirrus-type, and overlap-type) from FY-4A exhibited diverse variation characteristics. Water-type (supercooled-water-type) had a high-frequency center of over 30% in the Tarim Basin (Kunlun Mountains) during summer. Mixed-type (ice-type and cirrus-type) had the highest frequency in winter (spring), while overlap-type had the highest frequency in summer. The correlation between TCC and water vapor conditions (total column vertically integrated water vapor, specific humidity at 250 hPa, 500 hPa, and 700 hPa) was positive in XJ.

Raindrop size distribution (DSD) has an essential effect on rainfall kinetic energy estimation (RKEE) and dual-polarization radar quantitative precipitation estimation (QPE); DSD is a key factor for establishing a dual-polarization radar QPE scheme and RKEE scheme, particularly in mountainous areas. To improve the understanding of seasonal DSD-based RKEE, dual-polarization radar QPE, and the impact of rainfall types and classification methods, we investigated RKEE schemes and dual-polarimetric radar QPE algorithms across seasons and rainfall types based on two classic classification methods (BR09 and BR03) and DSD data from a disdrometer in the Tianshan Mountains during 2020–2022. Two RKEE schemes were established: the rainfall kinetic energy flux–rain rate (KE_time–R) and the rainfall kinetic energy content–mass-weighted mean diameter (KE_mm–D_m). Both showed seasonal variation, whether it was stratiform rainfall or convective rainfall, under BR03 and BR09. Both schemes had excellent performance, especially the KE_mm–D_m relationship across seasons and rainfall types. In addition, four QPE schemes for dual-polarimetric radar—R(K_dp), R(Z_h), R(K_dp,Z_dr), and R(Z_h,Z_dr)—were established, and exhibited characteristics that varied with season and rainfall type. Overall, the performance of the single-parameter algorithms was inferior to that of the double-parameter algorithms, and the performance of the R(Z_h) algorithm was inferior to that of the R(K_dp) algorithm. The results of this study show that it is necessary to consider different rainfall types and seasons, as well as classification methods of rainfall types, when applying RKEE and dual-polarization radar QPE. In this process, choosing a suitable estimator—KE_time(R), KE_mm(D_m), R(K_dp), R(Z_h), R(K_dp,Z_dr), or R(Z_h,Z_dr)—is key to improving the accuracy of estimating the rainfall KE and R.

At present, there is insufficient research on the refinement of the vertical structure of winter snowstorm systems in arid areas, and, compared with the central and eastern China, the observation sites in arid areas of northwestern China are scarce. To deepen the understanding of dynamics and microphysical processes and improve the level of forecasting and warning of snowstorms in northwestern China, the Institute of Desert Meteorology, China Meteorological Administration, Urumqi, carried out the Central Asia Extreme Precipitation Observation Test (CAEPOT) in Yili, Xinjiang, a typical arid region in China in February 2020. This paper uses multiple fine detection datasets obtained from the CAEPOT, including radar wind profiler, ground-based microwave radiometer, and millimeter-wave cloud radar to analyze macroscopic characteristics and microphysical changes of snowstorm system in Xinjiang. Studies have shown that the low trough with sufficient moisture, heat, power conditions, and weakening banded cloud system, which moved eastward from the Aral Sea to the west of Xinjiang during the snowstorm, were the key influencing system of this snowstorm. Before the snowstorm, the vertical shear of the horizontal wind field was severe, which aggravated the instability of the atmosphere, and there was upward motion in the lower atmosphere. A variety of physical quantities related to moisture showed a tendency to increase at the lower level and could be used as an early warning signal for snowstorm about 8 hours in advance, and the cloud and snow particles observed by millimeter-wave cloud radar were simultaneously developing upward and downward from 4km, providing snowstorm warning 12 hours in advance. During the snowstorm, the horizontal wind speed and vertical speed were obviously enhanced, and the physical quantities related to moisture further increased, and, with the blocking and uplifting of the Tianshan Mountains, the snowstorm increased. The particles collided and grew while falling, resulting in a decrease in particle concentration and an increase in particle radius from high altitude to the ground, eventually resulting in near-ground reflectivity factor up to 30dBz. In addition, reflectivity factor, physical quantities related to moisture, wind field, particle concentration, and particle radius all had a good correspondence with the beginning, end, and intensity of snowstorm, so when the physical quantities mentioned above weakened and stopped, snowstorm also weakened and stopped. In a word, this research is an important and meaningful work that provides more backgrounds and references for the forecast and warning of snowstorm in northwestern China.