Convective precipitation embedded in stratiform clouds on the northern slope of the central Tianshan Mountains: macro- and microphysical insights from multi-source observations

Abstract

Introduction:

Heavy rainfall in mountainous regions, such as the northern slope of the central Tianshan Mountains, is characterized by high intensity, short duration, and strong locality, often triggering secondary disasters like floods and landslides. While convective clouds are the main contributors to summer precipitation, observations in mountainous areas are scarce. This study addresses the knowledge gap by providing a detailed, multi-sensor analysis of the macro- and microphysical evolution of convective cells embedded within a stratiform cloud system. Based on multi-source observational data from a Ka-band millimeter-wave cloud radar (MCR), micro-rain radar (MRR), raindrop spectrometer (RDS), C-band radar (CBR), automatic weather stations (AWS), and FY-4A satellite products at the Tianshan Orographic Cloud Seeding Demonstration Base, this study analyzes a convective–stratiform mixed cloud precipitation event that occurred on 14 August 2023.

Methods:

Precipitation types were classified according to radar reflectivity, with convective precipitation defined as reflectivity ≥40 dBZ and stratiform as < 40 dBZ. The justification for this threshold is elaborated in Section 3, considering local orographic effects and verification against raindrop size distribution data. The two convective precipitation periods during the event were identified as T1 (16:30–18:10) and T2 (20:40–21:30). Data from the MCR, MRR, and RDS underwent quality control and correction procedures, including removal of outliers based on raindrop physical limits and correction for raindrop shape deformation. Key microphysical parameters, including raindrop size distribution (DSD), rainfall rate (R), radar reflectivity factor (Z), liquid water content (LWC), mass-weighted mean diameter (Dm), and normalized intercept parameter (Nw), were calculated from the processed DSD data.

Results:

The results show that the precipitation event was significantly influenced by solar radiative heating and orographic lifting, with heavy precipitation primarily occurring in the evening. The CBR and FY-4A satellite observations showed good consistency, with the convective periods T1 and T2 corresponding to peaks in both radar reflectivity (exceeding 40 dBZ) and lower cloud-top brightness temperatures. The merger of two convective cells during T2 led to explosive growth in raindrop size parameters, with maximum diameter, median diameter, and reflectivity factor reaching 2.36 mm, 1.17 mm, and 25.43 dBZ, respectively, indicating that larger drops contributed more to precipitation in this phase. The MCR observed vertically coherent echo columns extending up to 4.7 km above ground during T2, indicating strong updrafts and active ice-phase processes, with glaciated cloud tops suggested by filamentary structures.

Discussion:

This study demonstrates the effectiveness of multi-source ground-based and satellite observations in revealing the macro- and microphysical structure of convective precipitation embedded in stratiform clouds in an arid mountainous region. Future research should focus on establishing a long-term climatology of such events and employing high-resolution numerical models to elucidate the dynamical triggers of convective initiation and cell merger.

1 Introduction

Convective clouds are cumuliform cloud systems generated by thermal or dynamical forcing in an unstable atmospheric stratification. They are characterized by short lifetimes, strong locality, and pronounced spatiotemporal variability (Jia and Yao, 2016). As the main contributor to global precipitation, convective clouds account for more than 60% of total global rainfall (Xu et al., 2015). In mountainous areas in particular, the high intensity and short duration of convective clouds often trigger secondary disasters such as floods and landslides (Li et al., 2019), which not only severely threaten ecological balance and human living environments, but also pose challenges to socio-economic development and regional sustainability (Yang et al., 2014). At the same time, the natural precipitation efficiency of convective clouds is usually lower than that of stratiform clouds or convective–stratiform mixed clouds, implying considerable potential for artificial precipitation enhancement (Hu et al., 2005). As a result, convective clouds have become a key target for cloud water resource development.

In terms of observation techniques, geostationary meteorological satellites play an important role in real-time monitoring of convective clouds (Setvak et al., 2003). For example, Jiang and Fan (2002) used GMS data to reveal the spatial–temporal distribution of convective systems over the Tibetan Plateau. However, their relatively low spatial and spectral resolutions limit the capture of fine-scale microphysical structures. Polar-orbiting satellites provide higher spatial resolution (Peng et al., 2014; Rosenfeld, 1998), but their low temporal resolution limits monitoring of rapidly evolving systems. Aircraft in-cloud observations can obtain cloud microphysical parameters directly (Feng et al., 2012; Chang, 2019), but they are costly and complex. In comparison, ground-based remote-sensing instruments such as millimeter-wave cloud radars (MCR) and Doppler weather radars offer real-time monitoring and can reveal internal cloud structure in detail (Zhang, 2019; Zhu, 2017).

Given these limitations, joint multi-source observations have become an important approach. Atlas (1954) and Sauvageot (1987)combined radar with in situ spectra to retrieve cloud parameters. Frisch (1995) and Rémillard (2013) used MCR and radiometer data to retrieve microphysical properties of stratocumulus. Liu et al. (2015) analyzed cloud diurnal variations over the Tibetan Plateau using a Ka-band MCR, radar, lidar, and disdrometers. Xu et al. (2001) showed that convective clouds over the central Tibetan Plateau are narrow, deep columnar cells. Wang Lei et al. (2026) conducted comprehensive observations of convective precipitation over Mount Qomolangma, revealing isolated cells, small scales, and weak intensity. However, most studies have focused on the Tibetan Plateau, while relatively few have examined Xinjiang, which features a distinct “three mountains and two basins” topography.

A significant knowledge gap remains regarding the detailed microphysical and dynamical evolution of convective cells embedded in stratiform clouds in arid mountainous regions like the northern slope of the central Tianshan Mountains. Baiyanggou, the study area, is located on this slope, with an average elevation of 2,252 m. Summer precipitation accounts for >50% of the annual total, and convective precipitation is the dominant form (Wan et al., 2012; Zhe, 2013). Urumqi is a water-scarce city (Jiang et al., 2014), and maximizing precipitation from this region is critical. However, due to the scarcity of meteorological stations in mountainous areas, previous observations have been severely limited.

Therefore, the primary objectives of this study are: (1) to characterize the macro- and microphysical structure and evolution of summer convective clouds embedded in stratiform clouds over this region using a comprehensive set of multi-source observations; (2) to quantify the differences in raindrop size distribution (DSD) parameters between different convective periods (T1 and T2) and explain the microphysical processes responsible, particularly the effect of convective cell merger on the DSD; and (3) to evaluate the utility and complementarity of different ground-based remote sensing instruments (MCR, MRR, CBR) and satellite (FY-4A) for monitoring orographic convective precipitation in an arid environment. To achieve these objectives, we analyze a convective–stratiform mixed cloud event on 14 August 2023, providing scientific guidance for cloud seeding, disaster prevention, and sustainable development.

2 Data

The Tianshan Orographic Cloud Seeding (Rain/Snow) Demonstration Base is located on the northern slope of the central Tianshan Mountains (87.13°E, 43.29°N; elevation 2 331 m). Built in 2019 under the “Northwest China Weather Modification Capacity-Building Project”. The instruments are maintained and calibrated according to standard operating procedures provided by the manufacturer (YLU1-A33 for MCR, MRR-2, etc). Regular maintenance includes weekly system health checks, monthly cleaning of optics and radomes, and annual factory calibration to ensure data accuracy. The base is equipped with a Ka-band MCR, MRR, laser RDS, and AWS (Figure 1).

FIGURE 1

On 14 August 2023, a convective–stratiform mixed cloud precipitation event occurred. Two convective precipitation periods occurred: 16:30–18:10 and 20:40–21:30 (Beijing time). These periods of convective bubble formation are defined as Period 1 (T1) and Period 2 (T2). The technical parameters of each observational instrument are listed in Table 1.

To ensure comparability across instruments with different temporal resolutions (MCR: 1 min, MRR: 10 s, RDS: 1 min, CBR: 6 min), all data were aggregated to a uniform 1-min resolution by averaging the higher-resolution data (e.g., MRR) within each 1-min interval. For the lower-resolution CBR, data (6 min), linear interpolation was applied to estimate reflectivity at 1-min intervals for consistent time series comparison with other datasets.

3 Methods

There are many ways to classify summer rainfall. Gamache et al. (1982) classified precipitation using a reflectivity threshold of 38 dBZ. However, due to differences in radar wavelength, topography, and regional precipitation characteristics, this threshold requires adjustment. In this study, we follow a similar approach but adopt a threshold of ≥40 dBZ for convective precipitation, based on the following justifications: First, the higher elevation of the study area (approx. 2,300 m) results in radar beams sampling a shallower, potentially more convective portion of clouds, which can lead to higher reflectivities for similar precipitation rates compared to sea level. Second, analysis of collocated CBR, MRR, and RDS data from this event (Figure not shown) indicated that using 38 dBZ classified some periods with low rain rates (<2 mm/h) and small drop sizes (<1 mm Dm) as “convective,” whereas the ≥40 dBZ threshold more accurately identified the intense, short-duration peaks in rainfall rate and Dm that are characteristic of convective cores. Therefore, this locally validated threshold provides a more reliable separation of convective and stratiform precipitation for the Tianshan region.

To obtain more accurate and reliable raindrop spectra, quality control was applied to the raw data collected by the RDS. First, based on the microphysical properties of falling raindrops, natural raindrops rarely exceed 8 mm in diameter; therefore, data beyond this range were discarded. Such outliers may stem from superposition of multiple drops within the sampling area, leading to overestimation of particle size (Carey and Rutledge, 1996). Considering that the signal-to-noise ratio for droplets smaller than 0.2 mm is low and uncertainty is high, such records were also removed (Marzuki et al., 2013). Additionally, samples whose rainfall rate derived from the DSD exceeded 200 mm·h^-1 were excluded.

During the fall of raindrops, their upper and lower parts do not fall at the same speed, causing deformation and a transition from spherical to oblate shapes. As a result, measured diameters are generally overestimated (Löffler-Mang and Joss, 2000). Therefore, the raw diameters must be corrected for shape deformation (Blanchard and Spencer, 1970). This study adopts the deformation correction model by Battaglia et al. (2010): when the drop diameter is less than 1 mm, deformation is neglected; for diameters between 1 and 5 mm, the correction formula is , where is the raw instrument output; when the diameter exceeds 5 mm, is used as an approximation. Furthermore, quality control was performed using the terminal velocity–diameter relationship: samples whose measured fall velocity deviated from the classical empirical relation by more than ±60% were removed (Chen et al., 2013).

The DSD parameters are calculated from the observed DSD using the following relation:

N(D_i) represents the number concentration of raindrops per unit volume and per unit size interval; is the size interval of the -th diameter bin; is the total number of bins. Previous studies (Han et al., 2021) show that the maximum diameter observed in fitted DSDs usually does not exceed 8 mm, and the concentration of larger drops is very low and can be statistically neglected. Thus, is set to 25 size bins in this study. is the number of raindrops in the -th diameter bin and -th velocity bin; and are the effective sampling area (54 cm²) and sampling time (60 s); is the mean fall velocity in the -th velocity bin.

From the observed DSD, rainfall rate (mm·h^-1), radar reflectivity factor (mm⁶·m^-3), and liquid water content (g·m^-3) can be calculated using standard DSD moments:where is the water density (g·m^-3) and is the equivalent diameter in the -th size bin. To further describe DSD characteristics, the mass-weighted mean diameter (mm) and normalized intercept parameter (mm^-1·m^-3) are introduced and defined by:

4 Research results

4.1 Weather situation and synoptic background

Influenced by a West Siberian trough, from the afternoon of 14 August to the early morning of 15 August 2023, a convective–stratiform mixed cloud precipitation event occurred over the southern mountainous area of Urumqi on the northern slope of the central Tianshan Mountains. The total precipitation amounts were 16.1 mm at XQ station and 13.4 mm at HX station. During the entire precipitation event, hourly rainfall at both stations exhibited a bimodal distribution, with peak times coinciding. The first peak occurred at 18:00, with rainfall rates of 2.7 mm·h^-1 and 3.5 mm·h^-1, respectively. The second peak, which also marked the maximum hourly rainfall rate, occurred at 21:00, with values of 5.5 mm·h^-1 and 4.5 mm·h^-1 (Figure 2), corresponding to the convective bubble periods T1 and T2.

FIGURE 2

At 08:00 on 14 August 2023, the large-scale circulation pattern shows that the South Asian high at 100 hPa exhibited a double-center structure, forming a trough over 60–80°E. Southwesterly flow ahead of the trough led to upper-level divergence, which, together with strong suction by a 200 hPa jet (wind speed exceeding 50 m·s^-1), provided upper-level dynamical forcing for evening convection over the Tianshan region. At 500 hPa, the combined effect of the Iranian subtropical high and the Ural ridge allowed the system to extend northward, causing a Central Asian low vortex to stall north of Lake Balkhash. A short-wave trough on its southern flank moved eastward, enhancing baroclinicity along the Tianshan region. Temperature fields show that over the central Tianshan region, there existed a pronounced vertical temperature gradient between 500 hPa (−12 to −8 °C) and 700 hPa (4 °C–8 °C), with ∂T/∂z > 7 °C·km^-1, favoring the accumulation of convective available potential energy. At 700 hPa, a warm center over southern Xinjiang (16 °C) and a cold center over northern Xinjiang (≤−4 °C) formed a strong horizontal temperature gradient. Combined with orographic lifting over the Tianshan Mountains, this provided thermal forcing for frontal-type convection. In the 850–700 hPa layer, northwesterly flow transported cold, dry air from Central Asia southward, where it interacted with warm, moist low-level air over the southern slopes of the Tianshan Mountains to form conditionally unstable stratification. Under the joint effect of diurnal solar heating and orographic lifting, short-duration heavy precipitation events were triggered in the mid-mountain belt of the northern slope of the Tianshan Mountains during the afternoon and evening (T1 and T2), with the main precipitation area located over the southern mountainous region of Urumqi.

4.2 Macroscopic characteristics of the cloud system

Regarding Figure 3, the TBB values for T1 and T2 are correctly noted as −32 °C and −40 °C, respectively. The statement that T2 had a colder TBB and thus a more vigorous cloud system is directly supported by the satellite imagery provided. For instance, at 21:00 (Figure 3f) the cold cloud shield is more expansive and has a lower TBB minimum than at 17:30 (Figure 3b).

FIGURE 3

As shown in Figure 4, convective precipitation during T1 mainly occurred over XQ and areas to its north, while HX was located near the edge of strong echoes. During T1, radar reflectivity exceeded 40 dBZ, with a maximum of 45.8 dBZ. The evolution of these strong reflectivity cores, interpreted as regions of active convection, was tracked using the CBR.

FIGURE 4

Around 20:40, two shallow convective cores developed. Reflectivity increased to 25–30 dBZ, with isolated 40 dBZ echoes. These newly formed convective cells were horizontally small (diameters about 5–8 km) and structurally loose. Around 21:00, these convective cores merged with a 30 dBZ echo system moving eastward. Their merger induced vertical development, rapidly expanding the horizontal scale of the new cluster of strong echoes to 20–30 km and increasing the core strength to 43.3 dBZ. T1 and T2 together present a complete evolution of convective cells embedded in stratiform clouds—from triggering and development to re-transition into stratiform precipitation.

4.3 Analysis of cloud microphysical processes

At the Tianshan Orographic Cloud Seeding Demonstration Base, the Ka-band MCR, MRR, and RDS observed a long-lasting stratiform precipitation process embedded with short-duration convective precipitation. The CBR captured a large convective–stratiform mixed cloud system moving from southwest to northeast across the southern mountainous area of Urumqi. The temporal variations of cloud radar and MRR reflectivity and velocity are generally consistent at the site, although the MRR, due to its limited detection height, cannot provide cloud-top information.

Figure 5 shows temporal evolution of reflectivity, radial velocity, and spectral width from the Ka-band MCR. As seen in Figure 5a, during the precipitation event on 14 August, the cloud system was primarily a single-layer cloud with embedded short-lived convective clouds, with reflectivity ranging from −40 to 21 dBZ. The high-reflectivity regions mainly corresponded to convective precipitation periods T1 and T2, with maxima of 21 dBZ and 20.5 dBZ, respectively (Figure 5a). Vertically, cloud tops reached their highest levels during T1 and T2, at 10.89 km and 10.59 km above ground level, respectively. Between 20:20 and 20:40, the vertical structure of reflectivity became more coherent, with upward-extending echo columns exceeding 20 dBZ and reaching up to 4.7 km above ground, indicating strong updrafts. Figure 5b shows large areas of positive radial velocity above 4 km, especially near 20:40 when the area with positive velocities increased significantly and maximum radial velocity reached 2 m·s^-1, further confirming enhanced upper-level upward motion. Throughout the event, the cloud radar also detected filamentary structures near cloud top, suggesting that this portion of the cloud was already glaciated (Huang et al., 2017).

FIGURE 5

In convective cloud precipitation, the attenuation of MRR reflectivity in the lower layer can be neglected (White et al., 2002). Figure 6a shows the temporal–vertical evolution of the MRR reflectivity factor from 14:00 to 23:00 on 14 August 2023 over the entire 3.0 km observation layer above ground level. The rainfall event occurred from the afternoon into the early morning of the next day. As can be seen from Figure 6a, before surface rainfall onset, the reflectivity factor decreased with decreasing height. During this stage, surface relative humidity was relatively low, evaporation exerted a strong influence on falling raindrops, and no rainfall was recorded at the ground. During the two periods 16:30–18:10 and 20:00–22:00, the reflectivity factor increased sharply and continued to grow with decreasing height, with values generally exceeding 40 dBZ below 1.5 km. This is broadly consistent with the analysis based on the CBR. Figures 6b,c show that at 0.5 km above ground, the reflectivity factor during period T1 ranges from 26.1 dBZ to 39.2 dBZ, with a mean of 35.2 dBZ, whereas during period T2 it ranges from 14.9 dBZ to 39.9 dBZ, with a mean of 28.4 dBZ. Overall, below 1.0 km, the reflectivity factor in T1 is larger than in T2. During T1, the mean reflectivity factor increases from the lower to the mid-level, then decreases and increases again toward higher levels, with relatively large variability. During T2, with increasing height, the mean reflectivity factor shows a slight increase in the mid-level and then gradually decreases, indicating that in the mature stage of the convective cloud, the strongest core is located in the middle or upper part of the cloud.

FIGURE 6

The raindrop size distribution of precipitation is one of its important microphysical properties (Zhang et al., 2013; Jiang et al., 2023), and it reflects the evolution of precipitating cloud systems under the combined influence of cloud dynamics, thermodynamics, and microphysical processes during their formation (Pruppacher and Klett, 2010; Mudiar et al., 2018; Han et al., 2021). According to the RDS data in Figure 7, the maximum particle diameter , liquid water content (LWC), normalized intercept parameter , mass-weighted mean diameter , reflectivity factor , and rainfall rate all exhibit a fluctuating multi-peaked structure throughout the precipitation event, with generally consistent variation trends. During the convective precipitation periods T1 and T2, as the cloud system develops, , LWC, , , and all reach their peak values. The difference is that in the early development stage of the cloud system (T1), the hourly rainfall rate and liquid water content are larger and higher, at 1.44 mm·h^-1 and 0.10 g·m^-3, respectively. After the cloud system stabilizes and redevelops into a mature stage in period T2, the maximum raindrop diameter, median diameter, and reflectivity factor increase explosively, doubling within the 1-h period from 20:00 to 21:00 and exceeding the values in T1, reaching 25.43 dBZ, 1.17 mm, and 2.36 mm, respectively, indicating that large drops contribute more to precipitation during this stage. The normalized intercept parameter (which approximately represents changes in particle number concentration) is largest at the beginning of the precipitation and gradually decreases as the rainfall proceeds.

FIGURE 7

5 Conclusion and discussion

5.1 Conclusion

5.2 Discussion

This study utilized multi-source observations from the Tianshan region to systematically analyze the macro- and microphysical structure and evolution of convective precipitation embedded within a mixed convective-stratiform cloud event that occurred on 14 August 2023, on the northern slope of the central Tianshan Mountains. The novelty of this work lies in its high-resolution, multi-sensor perspective on the life cycle of embedded convection in an arid mountain region, particularly the quantitative documentation of DSD evolution before and after a convective core merger event.

The results indicate that the precipitation process was jointly influenced by solar radiative heating and orographic lifting, with the most intense precipitation occurring in the evening when thermal conditions were most favorable. The reflectivity factor from the C-band radar and the cloud-top brightness temperature from the FY-4A satellite showed consistent high-value correspondence during the strong convective periods T1 and T2, demonstrating the complementarity and reliability of multi-source observations in monitoring convective clouds over complex terrain.

The merger of two convective cells during T2 led to explosive growth in raindrop size parameters, including the maximum diameter, mass-weighted mean diameter, and radar reflectivity factor, indicating that larger drops contributed more significantly to precipitation during this mature phase. This microphysical shift suggests enhanced collision-coalescence processes and possibly the onset of active ice-phase aloft, as supported by the filamentary cloud-top structures observed by the Ka-band cloud radar, which are indicative of cloud glaciation (Huang et al., 2017). The observed microphysical progression from a high-concentration, small-drop regime in T1 to a lower-concentration, large-drop regime in T2 following core merger provides new observational evidence that cell interaction can rapidly alter the precipitation DSD. The consistent detection of intermittent bright bands near the 0 °C level by both the cloud radar and the micro-rain radar further implies the presence of melting particles, highlighting the mixed-phase nature of even the convective cores in this region, a characteristic noted in previous studies of plateau convection (Zhang et al., 2019).

Vertical observations from the Ka-band cloud radar revealed coherent echo columns extending up to 4.7 km above ground level during T2, accompanied by notable positive radial velocities, signaling the presence of strong, organized updrafts. Such structures are conducive to transporting supercooled liquid water to higher altitudes where riming and deposition can occur, thereby supporting ice-phase growth and subsequent surface precipitation after melting. This vertical dynamical insight, which could not be captured by the height-limited micro-rain radar, underscores the unique value of millimeter-wave cloud radar in diagnosing the vertical extent and microphysical stratification of convective systems, especially in regions where direct in-cloud measurements are scarce (Liu et al., 2015).

The microphysical progression observed—from a regime dominated by numerous smaller droplets with higher liquid water content in T1 to one characterized by fewer but larger drops in T2—carries direct implications for weather modification strategies. The period following convective cell merger, with its evident supercooled water and active ice processes, appears particularly promising for glaciogenic seeding operations aimed at enhancing precipitation efficiency (Hu et al., 2005). The high temporal resolution of the disdrometer and micro-rain radar offers potential for real-time monitoring of raindrop size distribution evolution, which could inform the timing and assessment of seeding activities.

However, this study has limitations. The lack of in situ aircraft observations prevents direct validation of inferred ice-phase processes and hydrometeor types. Furthermore, this is a single case study; the generalizability of our findings to other convective events in the region requires confirmation through a long-term climatological analysis spanning multiple seasons. Future research should incorporate dual-polarization radar to better discriminate hydrometeor types, along with high-resolution numerical modeling to simulate the observed cell merger mechanism and explore its sensitivity to aerosols and seeding agents. Building a climatology of such events across seasons will also be crucial for refining regional precipitation parameterizations and improving quantitative precipitation estimation in mountainous areas.

In conclusion, this integrated multi-sensor analysis advances our understanding of the microphysical and dynamical evolution of embedded convection within stratiform environments in an arid mountainous region. The findings highlight the importance of cell interaction and mixed-phase processes in shaping precipitation characteristics and provide a scientific foundation for optimizing cloud seeding protocols, enhancing short-term convective forecasting, and supporting water resource management in water-sensitive regions like northern Xinjiang.

References

• 1

  AtlasD. (1954). The estimation of cloud parameters by radar. J. Atmos. Sci.11 (4), 309–317. 10.1175/1520-0469(1954)011<0309:teocpb>2.0.co;2

  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  BattagliaA.RustemeierE.TokayA.BlahakU.SimmerC. (2010). PARSIVEL snow observations: a critical assessment. J. Atmos. Ocean. Technol.27 (2), 333–344. 10.1175/2009JTECHA1332.1

  • CrossRef
  • Google Scholar

• 3

  BlanchardD. C.SpencerA. T. (1970). Experiments on the generation of raindrop-size distributions by drop breakup. J. Atmos. Sci.27 (1), 101–108. 10.1175/1520-0469(1970)027<0101:EOTGOR>2.0.CO;2

  • CrossRef
  • Google Scholar

• 4

  CareyL. D.RutledgeS. A. (1996). A multiparameter radar case study of the microphysical and kinematic evolution of a lightning producing storm. Meteorol. Atmos. Phys.59 (1), 33–64. 10.1007/BF0103200010.1007/BF01032000

  • CrossRef
  • Google Scholar

• 5

  ChangW. (2019). Aircraft observation study on cloud microphysical characteristics and precipitation formation mechanisms over the naqu region of the Tibetan Plateau in summer. Chin. Acad. Meteorological Sci.

  • Google Scholar
  • View reference in article

• 6

  ChenB. J.YangJ.PuP. R. (2013). Statistical characteristics of raindrop size distribution in the Meiyu season observed in eastern China. J. Meteorol. Soc. Jpn.91 (2), 215–227. 10.2151/jmsj.2013-208

  • CrossRef
  • Google Scholar

• 7

  FengQ. J.LiP. R.JinL. J.HouT. J.WangG. W. (2012). Analysis on microphysical characteristic of stratocumulus precipitation in Shanxi province in summer. Plateau Meteorol.31 (2), 538–546.

  • Google Scholar
  • View reference in article

• 8

  FrichA. S.FairallC. W.SniderJ. B. (1995). Measurement of stratus cloud and drizzle parameters in ASTEX with a Ka-Band doppler radar and a microwave radiometer. J. Atmos. Sci.52 (16), 2788–2799.

  • Google Scholar
  • View reference in article

• 9

  GamacheJ. F.HouzeR. A. (1982). Mesoscale air motions associated with a tropical squall line. Mon. Weather Rev.110 (2), 118–136. 10.1175/1520-0493(1982)110<0118:MAMAWA>2.0.CO;2

  • CrossRef
  • Google Scholar
  • View reference in article

• 10

  HanY.GuoJ. P.YunY. X.LiJ.GuoX. R.LY. M.et al (2021). Regional variability of summertime raindrop size distribution from a network of disdrometers in beijing. Atmos. Res.257, 105591. 10.1016/j.atmosres.2021.105591

  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  HuW.ShenY. Y.ZengG. P. (2005). A study on artificial precipitation enhancement technology for summer convective clouds in southern China. J. Appl. Meteorological Sci.16 (3), 413–416. 10.3969/j.issn.1001-7313.2005.03.016

  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  HuangY. M.ZhouY. Q.YangM. (2017). Using 3 mm cloud radar data to analyze frontal mixed cloud vertical structure and supercooled water. Plateau Meteor36 (1), 219–228. 10.7522/j.issn.1000-0534.2015.00119

  • CrossRef
  • Google Scholar

• 13

  JiaS.YaoZ. Y. (2016). Case study on the convective clouds seeding effects in Yangtze-Huaihe region. Meteorol. Mon.42 (2), 238–245. 10.7519/j.issn.1000-0526.2016.02.012

  • CrossRef
  • Google Scholar

• 14

  JiangJ. X.FanM. Z. (2002). Convective clouds and mesoscale convective systems over the Tibetan Plateau in summer. Chin. J. Atmos. Sci.26 (2), 263–270.

  • Google Scholar

• 15

  JiangH. M.LiuC. Y.GongH. R. (2014). Variation features of precipitation in Urumqi region in recent 40 years. J. Anhui Agric. Sci.42 (34), 12215–12218+12238. 10.3969/j.issn.0517-6611.2014.34.076

  • CrossRef
  • Google Scholar

• 16

  JiangY. F.YangL. M.ZengY.TongZ. P.ChenP. (2023). Characteristics of raindrop size distribution for stratiform and convective precipitation in summer in Zhaosu. Desert Oasis Meteorology17 (3), 8–16. 10.12057/j.issn.1002-0799.2023.03.002

  • CrossRef
  • Google Scholar

• 17

  LiJ. G.JiangC. L.ZhangY. H.YangL. M.SunM. J. (2019). Spatiotemporal distribution characteristics of MαCS in summer of Tianshan Mountains. Plateau Meteorol.38 (3), 604–616. 10.7522/j.issn.1000-0534.2018.00151

  • CrossRef
  • Google Scholar

• 18

  LiuL. P.ZhangJ. F.RuanZ.CuiZ. H.HuZ. Q.WuS. H.et al (2015). The preliminary analyses of the cloud properties over the Tibetan Plateau from the field experiments in clouds precipitation with the various radars. Acta Meteorol. Sin.73 (4), 635–647. 10.11676/qxxb2015.041

  • CrossRef
  • Google Scholar

• 19

  Löffler-MangM.JossJ. (2000). An optical disdrometer for measuring size and velocity of hydrometeors. J. Atmos. Ocean. Technol.17 (2), 130–139. 10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2

  • CrossRef
  • Google Scholar

• 20

  MarzukiM.HashiguchiH.YamamotoM. K.MoriS.YamanakaM. D. (2013). Regional variability of raindrop size distribution over Indonesia. Ann. Geophys.31 (11), 1941–1948. 10.5194/angeo-31-1941-2013

  • CrossRef
  • Google Scholar

• 21

  MudiarD.PawarS. D.HazraA.KonwerM.GopalakrishnanV.SrivastavaM. K.et al (2018). Quantification of observed electrical effect on the raindrop size distribution in tropical clouds. J. Geophys. Res. Atmos.123 (9), 4527–4544. 10.1029/2017JD028205

  • CrossRef
  • Google Scholar

• 22

  PengJ.ZhangH.LiZ. Q. (2014). Temporal and spatial variations of global deep cloud systems based on CloudSat and CALIPSO satellite observations. Adv. Atmos.31 (3), 593–603. 10.1007/s00376-013-3055-6

  • CrossRef
  • Google Scholar

• 23

  PruppacherH. R.KlettJ. D. (2010). Microphysics of Clouds and Precipitation. 2nd ed.Dordrecht: Springer.

  • Google Scholar

• 24

  RémillardJ.KolliasP.SzyrmerW. (2013). Radar-radiometer retrievals of cloud number concentration and dispersion parameter in nondrizzling marine stratocumulus. Atmos. Meas. Tech.6 (7), 1817–1828. 10.5194/amt-6-1817-2013

  • CrossRef
  • Google Scholar

• 25

  RosenfeldD.LenskyI. M. (1998). Satellite-based insights into precipitation formation processes in continental and maritime convective clouds. Bull. Amer. Meteor. Soc.79 (11), 2457–2476. 10.1175/1520-0477(1998)079<2457:SBIIPF>2.0.CO;2

  • CrossRef
  • Google Scholar

• 26

  SauvageotH.OmarJ. (1987). Radar reflectivity of cumulus clouds. J. Atmos. and Ocean. Technol.4 (2), 264–272. 10.1175/1520-0426(1987)004<0264:RROCC>2.0.CO;2

  • CrossRef
  • Google Scholar

• 27

  SetvakM.RabinR. M.DoswellC. A.LevizzaniV. (2003). Satellite observations of convective storm tops in the 1.6, 3.7 and 3.9 μm spectral bands. Atmos Res.67-68, 607–627. 10.1016/S0169-8095(03)00076-0

  • CrossRef
  • Google Scholar

• 28

  WanY.CaoX.CuiY. L.GongH. R.JiaJ. (2012). Analysis of climate change tendency in Northern Piedmont of middle Tianshan Mountain over recent 30 years. J. Arid Land Resour. Environ.35 (3), 73–79.

  • Google Scholar

• 29

  WangH.ZhangD. G.WangW. Q.WangJ.LiY.WangS. (2022). Microphysical characteristics of stratiform precipitation with embedded convection based on multisource data. Chin. J. Atmos. Sci.46 (4), 886–902. 10.3878/j.issn.1006-9895.2107.21043

  • CrossRef
  • Google Scholar

• 30

  WangL.LiuP.LiY. Q. (2026). Comprehensive observation and characteristic analysis of convective cloud precipitation in Mount Oomolangma region (MOR). Meteor Mon.52 (3), 273–286. 10.7519/j.issn.1000-0526.2025.091301

  • CrossRef
  • Google Scholar

• 31

  WhiteA. B.GottasD. J.StremE. T.RalphF. M.NeimanP. J. (2002). An automated brightband height detection algoritm for use with doppler radar spectral moments. J. Atmos. Ocean. Technol.19 (5), 687–697. 10.1175/1520-0426(2002)019<0687:aabhda>2.0.co;2

  • CrossRef
  • Google Scholar

• 32

  XuH. B. (2015). The Questions and Answers: Science and Technology in Weather Modification. China Meteorological Press, 87.

  • Google Scholar

• 33

  XuX. D.ZhouM. Y.ChenJ. Y.et al (2001). Comprehensive physical patterns of the dynamic and thermal structure of the earth-atmosphere system over the Tibetan Plateau. Sci. China Earth Sci.31 (5), 428–441. 10.3969/j.issn.1674-7240.2001.05.011

  • CrossRef
  • Google Scholar

• 34

  YangL. M.YangQ.YangL. (2014). Characteristics of the atmospheric moisture cycle over the tian Shan Mountains. Clim. Environ. Res.19 (1), 107–116. 10.3878/j.issn.1006-9585.2013.12171

  • CrossRef
  • Google Scholar

• 35

  ZhangY.NiuS. J.JiaX. C. (2013). Spectrum evolution in the raindrop falling process: numerical simulation. Chin. J. Atmos. Sci.36 (6), 699–707. 10.13878/j.cnki.dqkxxb.2013.06.006

  • CrossRef
  • Google Scholar

• 36

  ZhangT.ZhengJ. F.LiuY. X. (2019). A study on the vertical structure and microphysical characteristic of convective cloud and precipitation over Tibetan Plateau by using Ka-band cloud radar. J. Infrared Millim. Waves. 10.11972/j.issn.1001-9014.2019.06.015

  • CrossRef
  • Google Scholar

• 37

  ZheY. Y. (2013). Characteristics of Runoff and its Response to Climate Change at the Alpine Area of Urumqi River in the Tianshan Mountains. Northwest Normal University.

  • Google Scholar

• 38

  ZhuS. C.YuanY.WuL. L.YaoY. Q.WuY. (2017). Analysis on characteristics and vertical structure of convective clouds over the area between Yangtze River and Huaihe River. Meteorol. Mon.43 (6), 696–704. 10.7519/j.issn.1000-0526.2017.06.006

  • CrossRef
  • Google Scholar