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Microphysical Characteristics of Precipitation during Pre-monsoon, Monsoon, and Post-monsoon Periods over the South China Sea

Fund Project:

Chinese Beijige Open Research Fund for the Nanjing Joint Center of Atmospheric Research (Grant No. NJCAR 2018ZD03), the National Key Research and Development Program of China (2018YFC1507304), and the National Natural Science Foundation of China (Grant Nos. 41575024 and 41865009)


doi: 10.1007/s00376-019-8225-8

  • Raindrop size distribution (RSD) characteristics over the South China Sea (SCS) are examined with onboard Parsivel disdrometer measurements collected during marine surveys from 2012 to 2016. The observed rainfall is divided into pre-monsoon, monsoon, and post-monsoon periods based on the different large-scale circumstances. In addition to disdrometer data, sounding observation, FY-2E satellite, SPRINTARS (Spectral Radiation-Transport Model for Aerosol Species), and NCEP reanalysis datasets are used to illustrate the dynamical and microphysical characteristics associated with the rainfall in different periods. Significant variations have been observed in respect of raindrops among the three periods. Intercomparison reveals that small drops (D < 1 mm) are prevalent during pre-monsoon precipitation, whereas medium drops (1−3 mm) are predominant in monsoon precipitation. Overall, the post-monsoon precipitation is characterized by the least concentration of raindrops among the three periods. But, several large raindrops could also occur due to severe convective precipitation events in this period. Classification of the precipitation into stratiform and convective regimes shows that the lg(Nw) value of convective rainfall is the largest (smallest) in the pre-monsoon (post-monsoon) period, whereas the Dm value is the smallest (largest) in the pre-monsoon (post-monsoon) period. An inversion relationship between the coefficient A and the exponential b of the ZR relationships for precipitation during the three periods is found. Empirical relations between Dm and the radar reflectivity factors at Ku and Ka bands are also derived to improve the rainfall retrieval algorithms over the SCS. Furthermore, the possible causative mechanisms for the significant RSD variability in different periods are also discussed with respect to warm and cold rain processes, raindrop evaporation, convective activities, and other meteorological factors.
    摘要: 雨滴谱作为降水最基本的微物理特征量之一,在揭示降水微结构特征、改进数值模式参数化方案和提高遥感估测降水精度等方面有着广泛的应用。海洋作为全球降水最为重要的源和汇,是地球水循环最重要的组成部分。因此,对海洋降水的观测研究具有重要价值。南海夏季风是连接南亚季风和东亚季风的纽带,对我国夏季降水的分布起决定性作用。同时,南海地区的降水对东亚地区的大气环流和能量收支也有着重要的影响,研究南海夏季风降水结构特征具有重要意义。本文利用海洋调查过程中收集的降水观测资料,对比研究了南海海域夏季风前期、季风期和季风后期降水的微物理特征差异,结果表明:南海季风前期降水小个雨滴(D < 1 mm)浓度最高,季风期降水中含有更多的中端粒子(1-3 mm),季风后期降水粒子浓度最小;不同类型降水相比较,季风前期的对流降水lgNw(Dm)最大(小),季风后期的lgNw(Dm)最小(大);三个时期降水的Z=ARb关系中,系数A从季风前期到季风后期逐渐递增,指数b从季风前期到季风后期逐渐递减;为提高卫星遥感南海海域降水精度,对Dm–Ze 和 Nw–Dm等参数之间的关系进行了拟合。本文研究可为建立雨滴谱函数关系、改进数值模式预报效果和提高遥感反演降水精度提供参考。
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  • Figure 1.  (a) Field view of the Parsivel instrument installed on the roof of the boat. (b) Locations of the rainfall events used to construct the raindrop dataset. The superimposed rectangles in (b) correspond to region 01 (12°−19°N, 111°−114°E), region 02 (20°−22°N, 113°−120°E), and region 03 (18°−21°N, 112°−116°E).

    Figure 2.  Spectra for the pre-monsoon (PRM), monsoon (MON), and post-monsoon (POM) precipitation periods.

    Figure 3.  Averaged RSDs for each of the six class intervals in the pre-monsoon (green), monsoon (red) and post-monsoon (blue) periods.

    Figure 4.  Variations of mean raindrop concentration with rain type for (a) stratiform and (b) convective precipitation. PRM, MON, and POM stand for the pre-monsoon, monsoon, and post-monsoon periods, respectively, while-Str and -Con denote the results from stratiform and convective precipitation, respectively.

    Figure 5.  Average value of lg(NW) versus average Dm (along with ±σ standard deviation bars) for stratiform and convective rainfall during the three periods. The two outlined squares represent (left) the maritime and (right) continental types of convective systems reported by Bringi et al. (2003). The dashed line is the result for their stratiform rain. The suffixes -Str and -Con denote the results from stratiform and convective precipitation, respectively. The blue triangles indicate results observed in other geographic locations.

    Figure 6.  (a) Scatterplots of the ZR values for precipitation during different monsoon periods and the fitted power-law relationships, Z = ARb, where A is the coefficient and b is the exponent, derived from the least-squares method. (b) Scatterplots and the fitted power-law relationships of the total stratiform (black dashed line) and convective (red solid line) rain types observed in the SCS. The relation for mei-yu convective rain, Z = 368R1.21 (Chen et al., 2013); the standard NEXRAD ZR relationship, Z = 300R1.40 (Fulton et al., 1998); and the tropical ZR relationship, Z = 250R1.20 (Rosenfeld et al., 1993) are also shown (green, blue and yellow lines, respectively).

    Figure 7.  Scatterplots of (a) Dm (units: mm) and the Ku-band effective radar reflectivity factor ZKu (units: dBZ), and (b) Dm (units: mm) and the Ka-band effective radar reflectivity factor ZKa (units: dBZ). The fitted curves of the pre-monsoon, monsoon, and post-monsoon periods are represented by solid, dashed, and dash-dotted lines, respectively.

    Figure 8.  Scatterplots of lg(Nw) (units: mm−1 m−3) and Dm (units: mm) in the three monsoon periods. The fitted curves of the pre-monsoon, monsoon, and post-monsoon periods are represented by solid, dashed, and dash-dotted lines, respectively.

    Figure 9.  Average heights of the LCL, 0°C isotherm, and cloud top (CTH). The green circles indicate the results of the pre-monsoon period; the red rectangles indicate the results of the monsoon period; and the blue triangles indicate the results of the post-monsoon period. The error bars represent ±3σ, where σ is standard deviation.

    Figure 10.  Histograms of regional convective available potential energy (CAPE).

    Figure 11.  Emagrams from the average radiosonde measurements during (a) pre-monsoon period, (b) monsoon period, and (c) post-monsoon period.

    Figure 12.  PDF of TBB during the rainy days.

    Figure 13.  Distribution of (a) cloud-top temperature and (b) ice crystal effective radius on 19 October 2012.

    Table 1.  Averaged microphysical parameters of RSD for the pre-monsoon (PRM), monsoon (MON), and post-monsoon (POM) periods. These parameters include the rain rate R, radar reflectivity Z, average diameter Da, mass-weighted mean diameter Dm, median diameter D0, average maximum diameter Dmax, total number concentration of raindrops Nt, and normalized intercept parameter lg(Nw).

    Period No. samples (min−1) R
    (mm h−1)
    Z (dBZ) Da (mm) Dm (mm) D0 (mm) Dmax (mm) Nt (m−3) lg(Nw)
    (mm−1 m−3)
    N1 / Nt
    (%)
    R1 / R
    (%)
    Z1 / Z
    (%)
    PRM 220 5.42 32.31 0.89 1.40 0.93 2.40 904.29 3.80 81.11 7.86 1.44
    MON 293 12.25 38.61 0.92 1.75 1.47 3.16 834.67 3.72 71.93 5.12 0.65
    POM 282 4.86 32.27 0.84 1.45 1.07 2.54 631.02 3.66 75.65 8.53 0.94
    All 795 8.39 35.21 0.88 1.55 1.23 2.82 754.19 3.71 76.74 6.29 0.83
    DownLoad: CSV

    Table 2.  Averaged microphysical parameters for each of six rain-rate intervals of precipitation in the pre-monsoon, monsoon, and post-monsoon period. Except for the parameters in Table 1, the RSD parameters including intercept parameter lg(N0), shape parameter μ, and slope parameter λ are also presented in Table 2.

    Class
    (mm h−1)
    No. samples (min−1) R
    (mm h−1)
    Nt
    (m−3)
    Da (mm) Dm (mm) lg(Nw)
    (mm−1 m−3)
    lg(N0)
    (m−3 m−1 − μ)
    μ λ (mm−1)
    PRM R ≤ 2 91 1.10 396.67 0.74 1.12 3.60 8.12 9.97 13.18
    2< R ≤5 43 3.16 455.40 0.81 1.43 3.63 4.97 4.54 5.81
    5 < R ≤ 10 35 7.02 626.00 0.84 1.56 3.68 4.60 3.55 4.57
    10 < R ≤ 20 28 13.79 1759.2 0.85 1.72 3.98 4.77 2.92 4.24
    20 < R ≤ 40 18 31.16 2317.8 0.88 2.05 3.96 4.36 2.19 3.09
    R > 40 5 50.09 3242.2 0.90 2.08 4.13 4.21 1.70 2.75
    MON R ≤ 2 70 0.89 850.90 0.67 1.16 3.81 6.88 6.88 10.43
    2 < R ≤ 5 45 3.89 777.16 0.81 1.45 3.79 5.06 3.90 5.67
    5 < R ≤ 10 84 7.75 848.16 0.94 1.69 3.68 4.69 4.05 4.86
    10 < R ≤ 20 55 14.03 946.87 0.98 1.91 3.69 4.48 3.63 4.17
    20 < R ≤ 40 28 26.91 1065.9 1.06 2.17 3.71 4.25 3.83 3.67
    R > 40 11 52.39 1881.1 0.95 2.47 3.73 3.97 2.81 2.76
    POM R ≤ 2 103 1.05 543.95 0.73 1.20 3.60 5.79 5.33 8.36
    2 < R ≤ 5 72 3.09 714.25 0.86 1.51 3.62 5.62 6.22 7.47
    5 < R ≤ 10 55 7.41 720.28 0.95 1.74 3.64 4.93 5.65 5.64
    10 < R ≤ 20 27 13.82 667.83 1.00 2.09 3.52 4.25 4.37 4.15
    20 < R ≤ 40 19 31.60 701.80 1.23 2.70 3.35 3.64 4.78 3.37
    R > 40 6 41.66 390.74 1.45 3.05 3.20 3.10 6.42 3.24
    DownLoad: CSV

    Table 3.  Averaged microphysical parameters for convective and stratiform rain types. The stratiform and convective precipitation types are denoted as “Str” and “Con”, respectively.

    Rain types M R (mm h−1) Nt (m−3) Da (mm) Dm (mm) lg(Nw) (mm−1 m−3) lg(N0) (m−3 m−1 − μ) μ λ (mm−1)
    PRM-Str 170 3.00 836.42 0.90 1.43 3.65 5.27 4.72 6.60
    MON-Str 188 6.07 639.51 0.84 1.51 3.59 4.63 3.36 5.01
    POM-Str 203 2.83 479.37 0.78 1.45 3.52 4.77 3.81 5.90
    PRM-Con 50 17.35 1575.6 0.94 1.70 4.02 5.03 3.86 4.84
    MON-Con 105 18.49 940.02 1.00 1.90 3.77 4.76 4.55 4.75
    POM-Con 79 17.20 635.30 1.06 2.00 3.61 4.91 6.62 5.74
    DownLoad: CSV

    Table 4.  Results of the ZR and R(ZH, ZDR) rainfall estimation methods. ZH denotes the horizontal polarization and ZDR represents the differential reflectivity. NB is short for the normalized mean bias and NSE is short for the normalized standard error.

    Rainfall estimation methods
    R(ZH, ZDR) ZR
    a b c NB (%) NES (%) A b NB (%) NES (%)
    PRM 0.009 40 0.730 −1.092 −5.92 34.6 466 1.33 31.4 73.2
    MON 0.004 46 0.864 −1.197 −3.16 32.1 346 1.41 12.2 70.5
    POM 0.002 39 0.916 −1.245 −3.97 29.9 339 1.46 31.8 67.6
    DownLoad: CSV

    Table 5.  Second-degree polynomial relations of DmZKu, DmZKa, and lg(Nw)−Dm for the three monsoon periods.

    Relation Data a b c
    Dm = ${{aZ_{\rm{Ku}}^{2}}} $ + bZKu + c PRM 0.000 417 3 0.0201 0.5178
    MON 0.001 232 2 −0.0304 1.1780
    POM 0.001 574 1 −0.0288 0.9831
    Dm = ${{aZ_{\rm{Ka}}^{2}}} $ + bZKa + c PRM 0.000 309 2 0.0316 0.3581
    MON 0.001 161 3 −0.0140 0.9082
    POM 0.001 353 1 −0.0073 0.7637
    lg(Nw) =${{aD_{\rm{m}}^{2}}} $ + bDm + c PRM 0.0878 −1.1521 4.8647
    MON 0.1178 −1.1023 4.9553
    POM 0.0652 −0.9211 4.5127
    DownLoad: CSV
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Manuscript received: 22 October 2018
Manuscript revised: 07 May 2019
Manuscript accepted: 03 June 2019
通讯作者: 陈斌, bchen63@163.com
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Microphysical Characteristics of Precipitation during Pre-monsoon, Monsoon, and Post-monsoon Periods over the South China Sea

    Corresponding author: Yun ZHANG, zhangyun.1207@163.com
  • 1. College of Meteorology and Oceanography, National University of Defense and Technology, Nanjing 211101, China
  • 2. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 3. Hainan Province Weather Modification Center, Haikou 570100, China

Abstract: Raindrop size distribution (RSD) characteristics over the South China Sea (SCS) are examined with onboard Parsivel disdrometer measurements collected during marine surveys from 2012 to 2016. The observed rainfall is divided into pre-monsoon, monsoon, and post-monsoon periods based on the different large-scale circumstances. In addition to disdrometer data, sounding observation, FY-2E satellite, SPRINTARS (Spectral Radiation-Transport Model for Aerosol Species), and NCEP reanalysis datasets are used to illustrate the dynamical and microphysical characteristics associated with the rainfall in different periods. Significant variations have been observed in respect of raindrops among the three periods. Intercomparison reveals that small drops (D < 1 mm) are prevalent during pre-monsoon precipitation, whereas medium drops (1−3 mm) are predominant in monsoon precipitation. Overall, the post-monsoon precipitation is characterized by the least concentration of raindrops among the three periods. But, several large raindrops could also occur due to severe convective precipitation events in this period. Classification of the precipitation into stratiform and convective regimes shows that the lg(Nw) value of convective rainfall is the largest (smallest) in the pre-monsoon (post-monsoon) period, whereas the Dm value is the smallest (largest) in the pre-monsoon (post-monsoon) period. An inversion relationship between the coefficient A and the exponential b of the ZR relationships for precipitation during the three periods is found. Empirical relations between Dm and the radar reflectivity factors at Ku and Ka bands are also derived to improve the rainfall retrieval algorithms over the SCS. Furthermore, the possible causative mechanisms for the significant RSD variability in different periods are also discussed with respect to warm and cold rain processes, raindrop evaporation, convective activities, and other meteorological factors.

摘要: 雨滴谱作为降水最基本的微物理特征量之一,在揭示降水微结构特征、改进数值模式参数化方案和提高遥感估测降水精度等方面有着广泛的应用。海洋作为全球降水最为重要的源和汇,是地球水循环最重要的组成部分。因此,对海洋降水的观测研究具有重要价值。南海夏季风是连接南亚季风和东亚季风的纽带,对我国夏季降水的分布起决定性作用。同时,南海地区的降水对东亚地区的大气环流和能量收支也有着重要的影响,研究南海夏季风降水结构特征具有重要意义。本文利用海洋调查过程中收集的降水观测资料,对比研究了南海海域夏季风前期、季风期和季风后期降水的微物理特征差异,结果表明:南海季风前期降水小个雨滴(D < 1 mm)浓度最高,季风期降水中含有更多的中端粒子(1-3 mm),季风后期降水粒子浓度最小;不同类型降水相比较,季风前期的对流降水lgNw(Dm)最大(小),季风后期的lgNw(Dm)最小(大);三个时期降水的Z=ARb关系中,系数A从季风前期到季风后期逐渐递增,指数b从季风前期到季风后期逐渐递减;为提高卫星遥感南海海域降水精度,对Dm–Ze 和 Nw–Dm等参数之间的关系进行了拟合。本文研究可为建立雨滴谱函数关系、改进数值模式预报效果和提高遥感反演降水精度提供参考。

    • As one of the most fundamental microphysical features of precipitation, the raindrop size distribution (RSD) is of great significance to exploring microphysical processes (Hu and Srivastava, 1995; Rosenfeld and Ulbrich, 2003; Niu et al., 2010; Konwar et al., 2014; Zhang et al., 2017; Li et al., 2018), evaluating cloud water conditions (Wu and Liu, 2017), improving the accuracy of precipitation estimation from radar and satellite data (Yuter and Houze, 1997; Lee and Zawadzki, 2005; Chapon et al., 2008; Liao et al., 2014; Huang et al., 2018), and optimizing microphysics parameterization schemes in numerical models (Milbrandt and Yau, 2005; Williams et al., 2014; McFarquhar et al., 2015).

      The broad applications of the RSD necessitates its widespread investigation around the world (Sauvageot et al., 1995; Bringi et al., 2003; Radhakrishna et al., 2009; Tenório et al., 2012; Jayalakshmi and Reddy, 2014; Gatlin et al., 2015; Suh et al., 2016; Wen et al., 2017; Seela et al., 2018; Wen et al., 2019; Zhang et al., 2019). The RSD characteristics over a wide range of continental locations have been extensively studied for many years. In recent years, there has also been an increasing interest in studying the RSD of marine precipitation (Ushiyama et al., 2009; Gatlin et al., 2015; Thompson et al., 2015; Krishna et al., 2016; Seela et al., 2017). The microphysical characteristics of clouds over the ocean and land are quite different from each other in terms of cloud droplets, updraft velocities, and subcloud processes, which may result in significant differences in the RSD characteristics (Rosenfeld and Ulbrich, 2003). Based on both direct disdrometer observations and indirect dual-polarized radar retrievals from a wide range of climatic regimes, Bringi et al. (2003) identified two categories—namely, a “maritime-like” cluster and a “continental-like” cluster—for convective rainfall. The “maritime-like” cluster is characterized by a high concentration of small drops, while the “continental-like” cluster is characterized by a low concentration of large drops. Ulbrich and Atlas (2007) demonstrated a higher median volume diameter (D0) but lower normalized intercept parameter (Nw) in continental storms than in maritime storms. Tenório et al. (2012) indicated that small raindrops (D ≤ 2 mm) are more dominant in maritime rain than in continental rain in northern Brazil. A comparison study of RSD between the equatorial Indian (Gan Island) and West Pacific (Manus Island) oceans was performed by Thompson et al. (2015), and they reported higher Nw values and more small-to-medium drop diameters at open ocean locations than at continental locations. Recently, the RSD characteristics of rainfall in Taiwan and Palau were analyzed by Seela et al. (2017). They found higher Dm but lower lgNw values in Taiwan than in Palau for rainfall with the same rain rate.

      The Asian monsoon has long been known to play significant roles in the global climate system, the global tropical seasonal circulations, and interannual changes (Webster et al., 1998). The South Asian (Indian) monsoon and the East Asian monsoon are two subsystems of the Asian monsoon, both of which are closely linked but have their own characteristics. The RSD varies not only between land and sea, but also between different monsoon regions (Kouz et al., 2006). In the past several years, the characteristics of clouds and precipitation during different onset periods of the South Asian monsoon have been extensively studied at several Indian stations (Rao et al., 2001; Reddy and Kozu, 2003; Chakravarty et al., 2013; Konwar et al., 2014; Das et al., 2017). For instance, Reddy and Kozu (2003) reported that raindrops in southwestern monsoon precipitation are usually larger than those in northeastern monsoon precipitation at Gadanki. Radhakrishna et al. (2009) found higher concentrations of small drops in northeastern monsoon than that in southwestern monsoon. Moreover, the RSDs in three different periods, i.e., the pre-monsoon, monsoon, and post-monsoon periods, have also been studied using data measured at two stations in India (Chakravarty and Raj, 2013; Chakravarty et al., 2013). The results showed that, for a given rain rate, large raindrops are dominant in the post-monsoon season, but small- and medium-sized drops are more dominant in the monsoon season at Pune. However, at Kolkata, large raindrops are dominant in the pre-monsoon season, whereas small drops are more dominant in the monsoon season.

      The onset and movement of the East Asian summer monsoon usually leads to the establishment of a quasi-stationary subtropical front, i.e., the mei-yu (or baiu) front, in East Asia. The mei-yu front brings persistent heavy rainfall along the Yangtze−Huaihe River basin and over the southern part of Japan. Although the RSD characteristics during the baiu period have been previously studied in Japan (Bringi et al., 2006; Oue et al., 2010), few studies have focused on the RSD during the mei-yu season in China. For instance, Chen et al. (2013) revealed that lower concentrations of raindrops with a high median diameter of precipitation can be observed in China than in Japan. Wen et al. (2016) noticed significant variation in the RSD before, during, and after the mei-yu season. They demonstrated that the average Dm decreased from the pre- to the post-mei-yu period, while the Nw value of precipitation in the post-mei-yu period was larger than that in the other two periods. More recently, Wen et al. (2019) investigated the seasonal variations of RSD in East China. They observed a relatively larger raindrop diameter and the highest concentration of raindrops during the summer season than the other three seasons.

      The South China Sea (SCS) summer monsoon is the link between the South Asian summer monsoon system and the East Asian monsoon. The establishment of the SCS summer monsoon is anterior to the South Asian and East Asian monsoon, which is also an indicator of the northward movement of the Asian monsoon (Chen and Chen, 1995; Lau and Yang, 1997; Jiang and Qian, 2000). The characteristics and mechanisms of the SCS summer monsoon eruption and its influence on the East Asian rain band have been studied comprehensively since the South China Sea Monsoon Experiment in 1998 (Wang et al., 2001; Lin et al., 2004; Wang and Qian, 2009). The thermodynamic environment of the SCS is quite different before and after the onset of the SCS monsoon, which may lead to considerable variations in the spatial and temporal characteristics of cloud and precipitation microphysics (Chang and Chen, 1995; Li et al., 2009, 2013). Jiang and Qian (2000) demonstrated that the rainy season in the SCS mainly occurs in the north and central parts of 10°N. Chen et al. (2005) noticed both a spatial and seasonal variation of rainfall distribution in the SCS. Furthermore, the specific area of precipitation before and after the SCS summer monsoon were also found to be very different (Song et al., 2010). With the onset of the monsoon period, convective rain became deeper and the bright band altitude of stratiform cloud became higher (Song et al., 2010). However, due to the lack of observational datasets, up to now, little is known about the characteristics of RSD during the different periods of the SCS monsoon season over the SCS.

      Based on the onboard Particle Size Velocity (Parsivel) disdrometer dataset collected during three marine surveys conducted in 2012, 2014, and 2016, we investigate the characteristics of the RSD during pre-monsoon, monsoon and post-monsoon periods over the SCS in this paper. Brief descriptions of the instruments, data and methods used in the present study are provided in section 2. Section 3 presents the properties of RSD in different monsoon periods. As an application of the RSD research, several relations used in quantitative precipitation estimation (QPE) are also reported in section 4. Possible mechanisms for the seasonal variability of RSD are discussed in section 5. The major conclusions are summarized in section 6.

    2.   Materials and methods
    • The RSD dataset presented in this study was collected through a Parsivel laser particle spectrometer manufactured by OTT Messtechnik, Germany (Löffler-Mang and Joss, 2000). This instrument has been proven to be a rational scientific instrument for measuring the general strength of precipitation whilst simultaneously measuring the fall speed and size of precipitation particles (Tokay et al., 2013). In 2011, OTT launched a new generation of Parsivel instruments—namely, Parsivel2. With a new laser sensor, the OTT Parsivel2 achieves more accurate measurements of raindrop size and rainfall velocity, far exceeding the original version (Tokay et al., 2014). Figure 1a shows the installation of the instrument. The locations of the observational rainfall events used to construct the raindrop dataset are depicted with three rectangles in Fig. 1b. The first period of observation was performed in region 01 (12°−19°N, 111°−114°E) from 1 September to 31 October 2012; the second period of observation was performed in region 02 (20°−22°N, 113°−120°E) from 3 May to 20 July 2014; and the third period of observation was in region 03 (18°−21°N, 112°−116°E) from 1 April to 20 June 2016.

      Figure 1.  (a) Field view of the Parsivel instrument installed on the roof of the boat. (b) Locations of the rainfall events used to construct the raindrop dataset. The superimposed rectangles in (b) correspond to region 01 (12°−19°N, 111°−114°E), region 02 (20°−22°N, 113°−120°E), and region 03 (18°−21°N, 112°−116°E).

      Along with the disdrometer observations, sounding observations (Vaisala RS92-GDP) were also conducted twice daily [0800 LST (Local Standard Time) and 2000 LST] during the voyages. Additionally, we also used FY-2E satellite data downloaded from the China National Satellite Meteorological Center (NSMC), reanalysis data from NCEP−NCAR, and cloud data from SPRINTARS (Spectral Radiation-Transport Model for Aerosol Species) (Takemura et al., 2005). FY-2E is a geostationary meteorological satellite located approximately 35 800 km above the equator at a longitude of 105°E and is operated by the China Meteorological Administration. Black body temperature (TBB) data products of FY-2E available at a spatial resolution of 0.1° × 0.1° and a 1-h temporal resolution were used. The NCEP−NCAR reanalysis data are recorded four times a day at a spatial resolution of 1° × 1°. Based on these datasets, this study attempts to understand the dynamic and thermodynamic characteristics of clouds associated with the rainfall over the SCS.

      Based on the SCS monsoon index published by the China National Climate Center (https://cmdp.ncc-cma.net/Monitoring/monsoon.php), the data were arranged into three different periods—namely, the pre-monsoon (March to May), monsoon (June to September), and post-monsoon (October to December) periods. In the pre-monsoon period, the direction of the zonal wind over the SCS starts to change and the water content also increases. Accompanying the onset of summer monsoon over the SCS, the warm and wet southwesterly flow in the low latitudes strengthens and the convective activity intensifies remarkably over the SCS. During the post-monsoon period, the dry and cold air from the north moves southward to the coastal areas of South China and the northern part of the SCS, resulting in significant changes in the thermal properties over the SCS. These thermodynamic differences are expected to further affect the microphysical characteristics of precipitation.

    • Some measurement error sources, such as strong winds, fall margins, and splashing effects, can induce the misclassification of raindrops by the Parsivel disdrometer. Therefore, quality control procedures were applied to the initial raindrop spectrum data. First, samples with one-minute raindrop values less than 10 or a rain rate (R) value less than 0.1 mm h−1 were considered to be instrument noise and excluded from the samples (Tokay et al., 2013). Second, raindrops with diameters greater than 6 mm or fall speeds 60% greater than or less than the Beard (Beard, 1976) empirical speed−diameter relationship for rain were eliminated. Third, the effective sampling cross section of the Parsivel was defined as

      where Di is the ith size, L represents the beam length (180 mm) and W represents the width (30 mm). In this paper, the sampling area is adjusted according to Eq. (1) when calculating the RSD quantities containing S.

    • The Parsivel disdrometer only provides the number of drops (ni,j) in the ith size (Di) and jth velocity (Vj) bins. The number concentration of raindrops per unit volume per unit size interval N(Di) (m−3 mm−1) can be calculated by

      where ΔDi is the width of the ith size bin and Vj is the measured velocity of the jth velocity bin. Δt is the sampling duration, which was set to 1 min in this study.

      Several bulk parameters related to the RSD, including the radar reflectivity factor Z (mm6 m−3), the rain rate R (mm h−1), and the total concentration of raindrops Nt (m−3), are usually utilized to describe the microphysical characteristics of precipitation, and these parameters can be calculated by the following equations:

      The first two size bins were not used because of their low signal-to-noise ratios. Characteristic diameters, including the mean diameter Da (mm), the median volume diameter D0 (mm), and the mass-weighted mean diameter Dm (mm) were also used to describe the properties of RSD, and these parameters can be expressed by:

      The normalized intercept parameter Nw (mm−1 m−3), which is related to the rainwater content W and Dm, can be calculated as:

      where ρw is the density of water (1 g cm−3). The observed RSD was fitted using the gamma function (Ulbrich, 1983), which can be expressed as

      where N0 (m−3 m−1 − μ), μ and λ (mm−1) represent the intercept parameter, the shape factor, and the slope parameter, respectively. The moment method with the second, fourth, and sixth moments was chosen to calculate these three parameters (Cao et al., 2010).

      The nth-order moment is defined as:

      where $ \eta = M_4^2/\left( {{M_2}{M_6}} \right)$.

    3.   Results
    • Table 1 shows several averaged RSD parameters for the three periods, including the rain rate R, radar reflectivity Z, average diameter Da, mass-weighted mean diameter Dm, median diameter D0, average maximum diameter Dmax, total number concentration of raindrops Nt, and normalized intercept parameter Nw. In addition, the contribution of raindrops with diameters less than 1 mm to Nt (N1 / Nt), R (R1 / R) and Z (Z1 / Z) is also given, which can be expressed by:

      Period No. samples (min−1) R
      (mm h−1)
      Z (dBZ) Da (mm) Dm (mm) D0 (mm) Dmax (mm) Nt (m−3) lg(Nw)
      (mm−1 m−3)
      N1 / Nt
      (%)
      R1 / R
      (%)
      Z1 / Z
      (%)
      PRM 220 5.42 32.31 0.89 1.40 0.93 2.40 904.29 3.80 81.11 7.86 1.44
      MON 293 12.25 38.61 0.92 1.75 1.47 3.16 834.67 3.72 71.93 5.12 0.65
      POM 282 4.86 32.27 0.84 1.45 1.07 2.54 631.02 3.66 75.65 8.53 0.94
      All 795 8.39 35.21 0.88 1.55 1.23 2.82 754.19 3.71 76.74 6.29 0.83

      Table 1.  Averaged microphysical parameters of RSD for the pre-monsoon (PRM), monsoon (MON), and post-monsoon (POM) periods. These parameters include the rain rate R, radar reflectivity Z, average diameter Da, mass-weighted mean diameter Dm, median diameter D0, average maximum diameter Dmax, total number concentration of raindrops Nt, and normalized intercept parameter lg(Nw).

      According to Table 1, the total average number concentration of raindrops and the percentage of raindrops with diameters less than 1 mm in the pre-monsoon period is 904.29 m−3 and 81.11%, respectively. This suggests that small raindrops (D ≤ 1 mm) are prevalent in the pre-monsoon period. The average diameter Da, mass-weighted mean diameter Dm, and average maximum diameter Dmax in the monsoon period are the largest in the three periods. Thus, medium- and large-sized drops are expected to be prevalent during the monsoon period. This feature can also be deduced from the smallest contribution of raindrops with diameters less than 1 mm in the monsoon period. The post-monsoon period has the lowest average drop concentration Nt and the smallest average diameter Da. This indicates the significant contribution of small raindrops to precipitation in this period, which can also be deduced from the largest R1 / R ratio (8.53%). In addition, the larger mass-weighted mean diameter Dm and median diameter D0 in the post-monsoon period than those in the pre-monsoon period suggests that there may also be some big raindrops in this period.

      The median volume diameter D0 in the monsoon period is 1.47 mm, which is very close to the value of 1.5 mm measured during the Australian monsoon season as reported by Penide et al. (2013). This similarity may be related to the influence of both of them by the warm and humid air currents from the ocean. Compared with the precipitation of the pre-monsoon, summer monsoon, and post-monsoon seasons observed at the tropical coastal station of Thiruvananthapuram (Sreekanth et al., 2017), the total concentration of raindrops Nt and the mass-weighted mean diameter Dm during the same season over the SCS are both much larger. The average maximum diameter Dmax of precipitation in the pre-monsoon period is close to that observed in Taiwan (Seela et al., 2017), but the mean Dmax in the other two seasons is both larger. The Nw values of precipitation in the pre-monsoon and monsoon periods are very close to the values during autumn and winter in East China, whereas they are smaller than those in summer and spring (Wen et al., 2019). The Dm values in the SCS are much larger than those in East China, which indicates there are fewer small drops but more large drops observed in precipitation over the SCS. The average Nt of precipitation over the SCS is higher than the summer precipitation over northern China, whereas the average Dm over northern China is much larger (Wen et al., 2017). Considering the also smaller median diameter D0 over the SCS, we infer that small drops are more abundant over the SCS than over northern China in the summer season. This might be attributable to the sufficient water vapor supply in the SCS summer monsoon period over the SCS.

    • Figure 2 presents the average raindrop spectra for the three different monsoon periods. The concentration of small drops (D ≤ 1 mm) is the highest in the pre-monsoon period, and the concentration of medium-sized raindrops (1−3 mm) is the highest during the monsoon period. The post-monsoon period has the smallest concentration of raindrops, with a diameter below 3 mm. However, the concentration of raindrops with diameters greater than 4 mm is higher during the post-monsoon period than those during the other two periods. This further confirms the presence of several big raindrops in the post-monsoon period.

      Figure 2.  Spectra for the pre-monsoon (PRM), monsoon (MON), and post-monsoon (POM) precipitation periods.

      The concentration of drops observed during the SCS summer monsoon period is higher than that during the South India summer monsoon season as reported by Chakravarty et al. (2013). Compared to the raindrop spectrum reported by Wen et al. (2016), also using a Parsivel disdrometer, the concentration of raindrops with diameters greater than 3 mm is higher in the SCS summer monsoon period.

      To further discern the RSD differences among the three periods, the observed RSDs were further divided into six classes with respect to rain rate (mm h−1)—namely, R ≤ 2 mm h−1, 2 < R ≤ 5 mm h−1, 5 < R ≤ 10 mm h−1, 10 < R ≤ 20 mm h−1, 20 < R ≤ 40 mm h−1, and R > 40 mm h−1.

      Table 2 shows the statistical values of RSD parameters in the six rain-rate classes. With an increase in rain rate, there is an increase in Nt and Da for precipitation in the pre-monsoon and monsoon periods. The mean Dm values all increase with the rain rate in the three periods. The lg(Nw) observed in the pre-monsoon period tends to increase, whereas it tends to decrease in the post-monsoon period. From low-intensity precipitation to high-intensity precipitation, no significant change in lg(Nw) values is observed in the monsoon period. Overall, the values of RSD parameters N0 and λ tend to decrease with the increasing rain rate during the three periods. For the shape parameter μ, it decreases with the rain rate increasing in the pre-monsoon period. However, there is no orderliness to the change of parameter μ with rain rate in the monsoon and post-monsoon periods.

      Class
      (mm h−1)
      No. samples (min−1) R
      (mm h−1)
      Nt
      (m−3)
      Da (mm) Dm (mm) lg(Nw)
      (mm−1 m−3)
      lg(N0)
      (m−3 m−1 − μ)
      μ λ (mm−1)
      PRM R ≤ 2 91 1.10 396.67 0.74 1.12 3.60 8.12 9.97 13.18
      2< R ≤5 43 3.16 455.40 0.81 1.43 3.63 4.97 4.54 5.81
      5 < R ≤ 10 35 7.02 626.00 0.84 1.56 3.68 4.60 3.55 4.57
      10 < R ≤ 20 28 13.79 1759.2 0.85 1.72 3.98 4.77 2.92 4.24
      20 < R ≤ 40 18 31.16 2317.8 0.88 2.05 3.96 4.36 2.19 3.09
      R > 40 5 50.09 3242.2 0.90 2.08 4.13 4.21 1.70 2.75
      MON R ≤ 2 70 0.89 850.90 0.67 1.16 3.81 6.88 6.88 10.43
      2 < R ≤ 5 45 3.89 777.16 0.81 1.45 3.79 5.06 3.90 5.67
      5 < R ≤ 10 84 7.75 848.16 0.94 1.69 3.68 4.69 4.05 4.86
      10 < R ≤ 20 55 14.03 946.87 0.98 1.91 3.69 4.48 3.63 4.17
      20 < R ≤ 40 28 26.91 1065.9 1.06 2.17 3.71 4.25 3.83 3.67
      R > 40 11 52.39 1881.1 0.95 2.47 3.73 3.97 2.81 2.76
      POM R ≤ 2 103 1.05 543.95 0.73 1.20 3.60 5.79 5.33 8.36
      2 < R ≤ 5 72 3.09 714.25 0.86 1.51 3.62 5.62 6.22 7.47
      5 < R ≤ 10 55 7.41 720.28 0.95 1.74 3.64 4.93 5.65 5.64
      10 < R ≤ 20 27 13.82 667.83 1.00 2.09 3.52 4.25 4.37 4.15
      20 < R ≤ 40 19 31.60 701.80 1.23 2.70 3.35 3.64 4.78 3.37
      R > 40 6 41.66 390.74 1.45 3.05 3.20 3.10 6.42 3.24

      Table 2.  Averaged microphysical parameters for each of six rain-rate intervals of precipitation in the pre-monsoon, monsoon, and post-monsoon period. Except for the parameters in Table 1, the RSD parameters including intercept parameter lg(N0), shape parameter μ, and slope parameter λ are also presented in Table 2.

      As shown in Fig. 3, with the rain rate increasing, the width of the RSD spectra in different monsoon periods broadens. The concentration of drops with diameter less than 2 mm increases in the pre-monsoon period but decreases in the post-monsoon period. For R < 10 mm h−1, the disagreement among the three curves diminishes with the rain rate increasing. For R ≥ 10 mm h−1, the disagreement among the three curves increases with the rain rate increasing. It is clear that there is a sharp increase in the small raindrop concentration during the pre-monsoon period. The concentration of large (small) drops is the highest (lowest) during the post-monsoon period. This pattern is consistent with the largest mean Dm for the same rain rate classes in the post-monsoon period and the highest Nw in the pre-monsoon period for R ≥ 10 mm h−1, as shown in Table 2.

      Figure 3.  Averaged RSDs for each of the six class intervals in the pre-monsoon (green), monsoon (red) and post-monsoon (blue) periods.

      The value of rain rate is usually proposed to be a discriminator for types of precipitation. Tokay and Short (1996) took R = 10 mm h−1 as a critical value for the classification of rain types. Thus, the previous analysis might indicate the differences in RSD for precipitation with different types, which will be analyzed further below.

    • In the present study, the classification criteria of stratiform and convective precipitation based on rain rate and its standard deviation by Bringi et al. (2003) were adopted. For 10 consecutive 1-min RSD samples, samples with R ≥ 0.5 mm h−1 and σR ≤ 1.5 mm h−1 were classified as stratiform rain and samples with R ≥ 5.0 mm h−1 and σR ≥ 1.5 mm h−1 were convective rain.

      Figure 4 compares the RSDs of stratiform and convective rainfall in the three periods. As depicted in Fig. 4a, the stratiform rainfall in the pre-monsoon period is characterized by a high concentration of small raindrops with diameter below 1 mm. The stratiform rainfall in the monsoon period has the highest concentration of raindrops with diameter between 1 mm and 5 mm. The concentration of raindrops in different particle sizes during the post-monsoon period is almost the minimum. In Fig. 4b, for convective rainfall, the highest concentration of drops with diameter less than 2 mm occurs in the pre-monsoon period. The concentration of drops with diameter greater than 4 mm for convective rainfall is the highest in the post-monsoon period. Relatively, the drops with diameter between 2 mm and 4 mm in monsoon precipitation are much more abundant.

      Figure 4.  Variations of mean raindrop concentration with rain type for (a) stratiform and (b) convective precipitation. PRM, MON, and POM stand for the pre-monsoon, monsoon, and post-monsoon periods, respectively, while-Str and -Con denote the results from stratiform and convective precipitation, respectively.

      Table 3 shows the statistical values of RSD parameters for stratiform and convective rainfall during the three periods. For stratiform rainfall, the mean Nt, mean Da, and mean lg(Nw) are all the largest in the pre-monsoon period. The mean Dm is the largest in the monsoon period, followed by the post-monsoon period. The three gamma shape parameters of stratiform rainfall are all the largest in the pre-monsoon period, but are the smallest in the monsoon period. In comparison with the stratiform precipitation of the summer and winter seasons observed over northern Taiwan (Seela et al., 2018), larger Dm but smaller lg(Nw) are observed over the SCS. However, their mean gamma distribution parameters, μ and λ, are very close. The stratiform precipitation during the three periods over the SCS has a higher number concentration of raindrops Nt and larger mass-weighted mean diameter Dm than that over northern China (Wen et al., 2017). This suggests that RSDs for stratiform precipitation over the SCS have a higher concentration of bigger-sized drops than those over northern China.

      Rain types M R (mm h−1) Nt (m−3) Da (mm) Dm (mm) lg(Nw) (mm−1 m−3) lg(N0) (m−3 m−1 − μ) μ λ (mm−1)
      PRM-Str 170 3.00 836.42 0.90 1.43 3.65 5.27 4.72 6.60
      MON-Str 188 6.07 639.51 0.84 1.51 3.59 4.63 3.36 5.01
      POM-Str 203 2.83 479.37 0.78 1.45 3.52 4.77 3.81 5.90
      PRM-Con 50 17.35 1575.6 0.94 1.70 4.02 5.03 3.86 4.84
      MON-Con 105 18.49 940.02 1.00 1.90 3.77 4.76 4.55 4.75
      POM-Con 79 17.20 635.30 1.06 2.00 3.61 4.91 6.62 5.74

      Table 3.  Averaged microphysical parameters for convective and stratiform rain types. The stratiform and convective precipitation types are denoted as “Str” and “Con”, respectively.

      For convective rainfall, the largest (smallest) Nt and lg(Nw) have also been found in the pre-monsoon (post-monsoon) period. However, the average Da and Dm of post-monsoon convective rainfall are the largest, which indicates the occurrence of large raindrops in this period. The slope parameter λ is also much larger in the post-monsoon period than in the other two periods. The average number concentration of raindrops Nt for convective precipitation in the post-monsoon period is very close to the value observed in a coastal city of South China, Yangjiang (Tang et al., 2014). However, the mean Dm [lg(Nw)] of precipitation during the post-monsoon period is larger (smaller) than that in Yangjiang. The difference may be related to the existence of large raindrops in the post-monsoon period over the SCS.

    • To further reveal the characteristics of RSD, the distributions of Dm and lg(Nw) are analyzed in this section. The mean lg(Nw) versus Dm values, with standard deviation, for the stratiform and convective rain types are depicted in Fig. 5. The maritime-like and continental-like convective clusters reported by Bringi et al. (2003) are also shown in the figure. In addition, Fig. 5 also provides comparative results from other studied regions. From Fig. 5, we can see that the mean Dm and Nw of stratiform rainfall in the three periods are near the right-hand side of the “stratiform line” given by Bringi et al. (2003). Relatively, the Dm (Nw) of stratiform rainfall in the monsoon (pre-monsoon) period is slightly larger.

      Figure 5.  Average value of lg(NW) versus average Dm (along with ±σ standard deviation bars) for stratiform and convective rainfall during the three periods. The two outlined squares represent (left) the maritime and (right) continental types of convective systems reported by Bringi et al. (2003). The dashed line is the result for their stratiform rain. The suffixes -Str and -Con denote the results from stratiform and convective precipitation, respectively. The blue triangles indicate results observed in other geographic locations.

      Convective precipitation in the pre-monsoon period is very close to the “maritime cluster”, whereas convective precipitation in the post-monsoon period is closer to the “continental cluster”. The convective monsoon precipitation is not similar to either maritime or continental convective precipitation. The figure shows a clear shift in the RSD from the “maritime” to the “continental” cluster from the pre-monsoon to post-monsoon period.

      Figure 5 also provides comparative results from other studied regions. The stratiform and convective precipitation over the SCS show larger Dm but smaller Nw in comparison with those observed in East China (Wen et al., 2016) and Taiwan and Palau (Seela et al., 2017). The average Nw (Dm) of stratiform precipitation over the SCS is slightly smaller (much larger) than that over northern China reported by Wen et al. (2017). The average Dm and Nw of stratiform precipitation over the SCS are both much larger those in the South Korean coastal region (Suh et al., 2016) (not shown in the figure). However, the average Dm and Nw of convective precipitation observed in the South Korean coastal region are just located between the values of the pre-monsoon convective precipitation and monsoon convective precipitation. This similarity may be related to the fact that all the three observation areas are located in coastal areas, where the precipitation is affected by warm and humid air currents from the sea.

    4.   QPE
    • One of the key RSD applications is QPE, because it can provide proper relations for rainfall retrieval algorithms (Lee and Zawadzki, 2005; Chapon et al., 2008; Chen et al., 2017). In this section, the power-law relationship Z = ARb, widely used in radar meteorology, is firstly studied based on the observed datasets. Then, several empirical relationships used in polarimetric radar are also derived. Finally, the relationships of DmZku, DmZka, and lg(Nw)−Dm, used in the construction of the dual-frequency precipitation radar (DPR) rainfall retrieval algorithm, are also fitted, to improve the inversion accuracy of marine precipitation with GPM.

    • Figure 6a presents scatterplots of radar reflectivity factor Z and rain rate R, as well as the results of ZR fittings by using the least-squares method. The results show that the coefficient A is the largest (smallest) and the exponent b is the smallest (largest) in the pre-monsoon (post-monsoon) period. There is a clear inverse relationship between the coefficient A and exponent b in the three periods. Testud et al. (2001) noted that the ZR relationship could be approximately set to Z$ N_{\rm{w}}^{ - 0.5}{R^{1.5}}$, which means that the coefficient A is proportional to $ N_{\rm{w}}^{ - 0.5}$ if exponent b is close to 1.5. The changes in coefficient A during the three periods are shown to be consistent with the previous statistical results of lg(Nw) (as in Table 1). Uijlenhoet et al. (2003) proposed that the exponent b in the ZR relation can reflect the different mechanisms of the formations of precipitation particles. Thus, the difference in the values of exponent b infers that the microphysical processes occurring during different monsoon periods are different (Seela et al., 2017). Comparing the current results with those obtained in Taiwan and Palau, the coefficient A over the SCS is slightly higher. The exponent b during the pre-monsoon period is very close to that in Taiwan and Palau (Seela et al., 2017). The coefficient A and exponent b in the pre-monsoon period are also close to the values of southwest monsoon precipitation at Gadanki, reported by Rao et al. (2001). Compared with the values of southwest monsoon precipitation observed at another tropical station, Kadapa, which is located in the southern part of India (Jayalakshmi and Reddy, 2014), the coefficient A of precipitation during the three periods over the SCS is all larger. The exponent b in the pre-monsoon period is slightly smaller than that at Kadapa, while the exponent b in the monsoon period and post-monsoon period are both slightly larger. These comparisons further illustrate the need to study the localized ZR relationships.

      Figure 6.  (a) Scatterplots of the ZR values for precipitation during different monsoon periods and the fitted power-law relationships, Z = ARb, where A is the coefficient and b is the exponent, derived from the least-squares method. (b) Scatterplots and the fitted power-law relationships of the total stratiform (black dashed line) and convective (red solid line) rain types observed in the SCS. The relation for mei-yu convective rain, Z = 368R1.21 (Chen et al., 2013); the standard NEXRAD ZR relationship, Z = 300R1.40 (Fulton et al., 1998); and the tropical ZR relationship, Z = 250R1.20 (Rosenfeld et al., 1993) are also shown (green, blue and yellow lines, respectively).

      The NEXRAD weather radar quantifies the default equation for the precipitation using Z = 300R1.40. However, Rosenfeld et al. (1993) recommend Z = 250R1.20 to estimate the precipitation in the tropics. Scatterplots between Z and R for the total stratiform and convective precipitation over the SCS are presented in Fig. 6b with cross symbols and gray dots, respectively. The results of correlation fitting are also given in this figure. The coefficient A is larger and exponent b is smaller for stratiform precipitation than convective precipitation, which indicates the need for different relationships to estimate different types of precipitation. The ZR relationship for stratiform precipitation over the SCS has a larger coefficient A but smaller exponent b than those observed at Kadapa (Jayalakshmi and Reddy, 2014) and Gadanki (Rao et al., 2001). This difference may be related to the higher Dm but lower Nw of stratiform precipitation over the SCS than those at the two Indian stations.

      The A and b values of the ZR relationship for convective precipitation over the SCS are 384 and 1.38, respectively, which are similar to those of the standard relationship, Z = 300R1.40, but differ from those of the standard tropical relationship, Z = 250R1.20. The tropical relation underestimates the intense precipitation over the SCS. The coefficient A of precipitation over the SCS is very close to that observed during the mei-yu season, whereas the exponent b in the SCS is much bigger. Compared to the ZR relationship derived from summer monsoon at Zhuhai (Z = 498R1.3), the convective precipitation over the SCS shows a smaller coefficient A (Zhang et al., 2019). But, their exponent b is much closer, which may result from the fact that they are both affected by the warm and wet currents from the tropical ocean region. Compared with the convective precipitation observed in Taiwan and Palau (Seela et al., 2017), the coefficient A in the SCS is also higher, but their exponent b values are very close, which may be related to the fact that they are all in the western Pacific region. The coefficient A and exponent b for convective precipitation in the SCS are both larger than those during southwest monsoon convective precipitation at Kadapa (Jayalakshmi and Reddy, 2014). These results reconfirm previous opinion that there is a need to adopt modified ZR relations in the estimation of rainfall in different regions (Seela et al., 2018).

    • In addition to conventional weather radar, polarimetric radar has also been used in rainfall estimation for several years (Zhang et al., 2001; Cao et al., 2010; You et al., 2014). Cao and Zhang (2009) demonstrated that polarimetric measurements, such as differential reflectivity ZDR and specific differential phase Kdp, can provide valuable information caused by DSD variability, hail contamination, beam blockage, and other sources of error, which is conducive to resolving problems. To improve precipitation estimation with polarimetric radar, several empirical relationships, such as R(ZH, ZDR), R(Kdp), R(ZH), and R(ZDR, Kdp), also needed to be constructed (Zhang et al., 2017).

      In this paper, the widely used R(ZH, ZDR) relationship was derived, which can be expressed as follows:

      where a is the coefficient. b and c are the exponent for the horizontal polarization ZH (mm6 m−3) and the differential reflectivity ZDR (dBZ), respectively.The two parameters were calculated from the observed DSDs using the T-matrix scattering technique described by Zhang et al. (2001), as follows:

      where λ is the radar wavelength, Kw is the water dielectric factor, and fhh,vv(D) is the backscattering amplitudes of a raindrop for horizontally and vertically polarized waves, respectively. The frequency of the radar’s electromagnetic wave is assumed to be 2.85 GHz (S-band). The raindrop temperature was assumed to be 20°C in this study, and the raindrops were also assumed to follow the axis ratio relation proposed by Beard and Chuang (1987):

      In addition, for comparison purposes, we defined two parameters—the normalized mean bias (NB) and normalized standard error (NSE), which can be expressed by:

      where RDSD and Rradar are 1-min precipitation values from the DSD data and the calculated radar parameters based on the T-matrix simulation, respectively.

      The results are shown in Table 4. As shown in Table 4, the coefficient a in R(ZH, ZDR) decreases from the pre-monsoon to the post-monsoon period, whereas exponents b and c in R(ZH, ZDR) increase from the pre-monsoon to the post-monsoon period. It also clearly shows that both the NB errors and the NES errors are much smaller for precipitation estimated with R(ZH, ZDR) relationships than ZR relationships, during the same period. The results indicate that the R(ZH, ZDR) estimator has an advantage over the ZR relation, which reconfirm previous opinion (Cao et al., 2010; Tang et al., 2014; Zhang et al., 2017).

      Rainfall estimation methods
      R(ZH, ZDR) ZR
      a b c NB (%) NES (%) A b NB (%) NES (%)
      PRM 0.009 40 0.730 −1.092 −5.92 34.6 466 1.33 31.4 73.2
      MON 0.004 46 0.864 −1.197 −3.16 32.1 346 1.41 12.2 70.5
      POM 0.002 39 0.916 −1.245 −3.97 29.9 339 1.46 31.8 67.6

      Table 4.  Results of the ZR and R(ZH, ZDR) rainfall estimation methods. ZH denotes the horizontal polarization and ZDR represents the differential reflectivity. NB is short for the normalized mean bias and NSE is short for the normalized standard error.

    • Nowadays, the GPM Core Observatory satellite serves as an optimal platform to estimate precipitation globally. However, more ground validation needs to be performed to improve the rainfall retrieval algorithm.

      The DPR on board GPM is the key instrument. It estimates the RSD from the attenuation difference between the Ku-band precipitation radar (13.6 GHz) and the Ka-band precipitation radar (35.5 GHz) (Schwaller and Morris, 2011). The normalized gamma distribution described in Eq. (1) is widely used in the GPM DPR rainfall retrieval algorithms (Grecu et al., 2016). Accordingly, the DSD can be reconstructed from Nw and Dm given a μ value. The radar reflectivity at the Ku band or Ka band can be used to derive the parameters Nw and Dm. The DFR (dB) takes the following form:

      The effective radar reflectivity factors at Ku-band (ZKu) and Ka-band (ZKa) frequencies can be calculated by

      where Ze is the effective radar reflectivity factor at a specific wavelength λ, which can be derived from the onboard disdrometer observations. σb(Di, λ) is the backscattering cross section of a raindrop with diameter Di, which is directly calculated according to Mie theory. ${{k_{\rm{w}}^{2}}} $ is the dielectric factor, which is related to the complex refractive index of region and is conventionally taken to be 0.93.

      As pointed out by Chen et al. (2017), DFR is commonly used for the retrieval of Dm. As shown by the scatterplots of DmZKu and DmZKa for the three periods in Fig. 7, the values of Dm increase with ZKu or ZKa increasing. There is a high correlation between Dm and ZKu (ZKa). As in Chen et al. (2017), we also derived second-degree polynomial relations between Dm and ZKu (ZKa). The relevant fitting results are presented in Table 5. As shown in Fig. 7, the values of Dm in the monsoon period are the largest for ZKu (ZKa) below 15 dBZ and those in the post-monsoon period are the largest for ZKu (ZKa) above 25 dBZ.

      Figure 7.  Scatterplots of (a) Dm (units: mm) and the Ku-band effective radar reflectivity factor ZKu (units: dBZ), and (b) Dm (units: mm) and the Ka-band effective radar reflectivity factor ZKa (units: dBZ). The fitted curves of the pre-monsoon, monsoon, and post-monsoon periods are represented by solid, dashed, and dash-dotted lines, respectively.

      Relation Data a b c
      Dm = ${{aZ_{\rm{Ku}}^{2}}} $ + bZKu + c PRM 0.000 417 3 0.0201 0.5178
      MON 0.001 232 2 −0.0304 1.1780
      POM 0.001 574 1 −0.0288 0.9831
      Dm = ${{aZ_{\rm{Ka}}^{2}}} $ + bZKa + c PRM 0.000 309 2 0.0316 0.3581
      MON 0.001 161 3 −0.0140 0.9082
      POM 0.001 353 1 −0.0073 0.7637
      lg(Nw) =${{aD_{\rm{m}}^{2}}} $ + bDm + c PRM 0.0878 −1.1521 4.8647
      MON 0.1178 −1.1023 4.9553
      POM 0.0652 −0.9211 4.5127

      Table 5.  Second-degree polynomial relations of DmZKu, DmZKa, and lg(Nw)−Dm for the three monsoon periods.

      Figure 8 presents scatterplots of the two parameters lg(Nw) and Dm in a normalized gamma distribution. It clearly shows that the lg(Nw) decreases with the increase of Dm. In order to reduce the parameters in the normalized gamma distribution, we further derived an empirical polynomial relation between lg(Nw) and Dm. The fitting results indicate that the lg(Nw) decreases most obviously with Dm in the monsoon period. Comparing the current results with those for the Tibetan Plateau reported by Chen et al. (2017), the coefficients of the second-degree polynomial relation over the ocean are smaller.

      Figure 8.  Scatterplots of lg(Nw) (units: mm−1 m−3) and Dm (units: mm) in the three monsoon periods. The fitted curves of the pre-monsoon, monsoon, and post-monsoon periods are represented by solid, dashed, and dash-dotted lines, respectively.

    5.   Discussion
    • Several studies have shown that the differences in raindrop spectrum characteristics are not random behavior, but rather are closely related to the precipitation of clouds and environmental factors (Rao et al., 2009). In this section, the causative mechanisms for the observed seasonal variation of RSD are discussed.

      Rosenfeld and Ulbrich (2003) demonstrated that warm and cold rain processes are quite different from each other in terms of updraft, evaporation, particle formation, and growth, thereby leading to further differences in RSD characteristics. In warm rain, active coalescence is the main process that widens the drop size distribution. However, most raindrops in cold rain originate from the melting of ice hydrometeors such as graupel or hail. The melted hydrometeors continue to grow by accretion of cloud droplets. Thus, higher concentrations of large drops and lower concentrations of small drops are extensively observed during the process of cold rain.

      To roughly classify types of cloud related to observed rainfall, we firstly estimated the lifting condensation level (LCL) height, 0°C isotherm, and cloud top. In this paper, the LCL height is regarded as approximately equivalent to the cloud-base height. Sounding data were adopted to estimate the LCL height and 0°C isotherm. For the cloud-top height, we first estimated the cloud-top temperature based on the daily datasets from SPRINTARS, and then roughly converted the temperature to the equivalent height according to the sounding data.

      The LCL was calculated from the empirical formulae given by Barnes (1968):

      where T and Td are the surface temperature and surface dewpoint temperature, respectively; p is the surface pressure (in hPa); Tlcl and plcl are the temperature and pressure at the LCL height, respectively; a = 1 / 273 and t = TlclT (in °C). We estimate that, in the pre-monsoon, monsoon, and post-monsoon periods, the average LCL height is 1125 m, 1141 m, and 1185 m, respectively, while the average height of the 0°C isotherm is 5087 m, 5146 m, and 5158 m, respectively, and the average height of the cloud top is 6793 m, 12 611 m, and 9893 m, respectively. The results are also presented in Fig. 9.

      Figure 9.  Average heights of the LCL, 0°C isotherm, and cloud top (CTH). The green circles indicate the results of the pre-monsoon period; the red rectangles indicate the results of the monsoon period; and the blue triangles indicate the results of the post-monsoon period. The error bars represent ±3σ, where σ is standard deviation.

      In Fig. 9, the cloud-base height is slightly higher in the post-monsoon period than in the other two periods. The cloud-top height is the highest in the monsoon period, which indicates the prominent presence of the cold-rain process in this period. The distance between the LCL height and 0°C isotherm is the warm cloud depth, and the distance between the 0°C isotherm and cloud top is the cold-cloud depth. The warm-rain process dominates the pre-monsoon rainfall, contributing to a high “maritime-like” highest concentration of small drops owing to collision and coalescence during this period (see Table 1 and Fig. 2). The increase in rainfall intensity during this period is likely attributable to a greater increase in the number concentration of small drops in this period than in the other two periods (Table 2 and Fig. 3). The RSD characteristics may be attributable to the abundant water vapor supply during this period (not shown here).

      The cold-rain process dominates the monsoon rainfall as the cloud-top height can reach up to 12 km, which can also be understood from the distribution of regional convective available potential energy (CAPE), as shown in Fig. 10. When dealing with the NCEP datasets, we chose (15°−20°N, 112°−118°E) for the pre-monsoon period, (20°−22°N, 112°−120°E) for the monsoon period, and (10°−15°N, 110°−115°E) for the post-monsoon period, and only datasets with their times close to the precipitation episodes were selected. As can be seen, the CAPE values are the highest during monsoon period, followed by the post-monsoon period, and those in the pre-monsoon period tend to be small. The highest CAPE value indicates obvious convective activity with more vigorous updrafts and downdrafts can happen in the monsoon period (Seela et al., 2018). The convective activity is also associated with the occurrence of intense rainfall, as noted in Table 2.

      Figure 10.  Histograms of regional convective available potential energy (CAPE).

      The strong convective activity during the SCS monsoon period extends the clouds to deeper altitudes, which results in a cold-rain process with faster growth of ice crystals above the melting layer. Meanwhile, the strong updrafts carry the small drops to higher altitudes and thereby allow the growth of medium-sized drops at the expense of suspended small drops at higher altitudes. These processes promote bigger drops to precipitate locally. Thus, higher concentrations of large drops and lower concentrations of small drops are expected to be observed during the monsoon period than the pre-monsoon period. As shown in Fig. 3, large drops are extensively observed for the precipitation with high rain rate in the monsoon period. Moreover, medium-sized drops (1−3 mm) are also predominant in monsoon precipitation. The active coalescence of small raindrops may account for the increase in the concentration of medium-sized drops, as the intense convective activity with high updraft velocity can lengthen the time for the interaction between particles. Meanwhile, the breakup of big raindrops can also increase the high concentration of medium-sized drops obviously. We analyzed the average wind profiles based on sounding data and found that the horizontal wind during the monsoon period is usually strong (Fig. 11). The intense wind variation in the monsoon period may result in a significant breakup of large drops during this period of precipitation.

      Figure 11.  Emagrams from the average radiosonde measurements during (a) pre-monsoon period, (b) monsoon period, and (c) post-monsoon period.

      The post-monsoon precipitation is characterized by small concentrations of small- and medium-sized drops. The presence of evaporation can result in a greater reduction in the number of small drops than that of large drops (Rosenfeld and Ulbrich, 2003). Evaporation depends on temperature and, more importantly, on humidity (Radhakrishna et al., 2009). As shown in Fig. 11, the difference between temperature and the dewpoint in the post-monsoon period is much larger than in the other two periods in the low atmosphere, which means that the atmospheric condition is usually dry during the post-monsoon period. Thus, we speculate that evaporation may account for the lack of small drops in the post-monsoon precipitation.

      The post-monsoon precipitation is also characterized by the presence of several large drops, with diameters above 4 mm (as shown in Fig. 2). Based on the cloud-top height and CAPE depicted in Figs. 9 and 10, intense convective activity may also develop in the post-monsoon period. The probability density function (PDF) of TBB < −5°C for the rainy days during the three periods is depicted in Fig. 12. The regions were chosen as the same as those for CAPE. TBB can be used to evaluate the intensity of convective activity, and TBB ≤ −32°C (241.15K) is usually considered as the threshold to distinguish the development of convection (Maddox, 1980). The PDF of TBB in Fig. 12 suggests that convective activities occur more frequently during the monsoon period. Meanwhile, several convective activities also develop during the post-monsoon period.

      Figure 12.  PDF of TBB during the rainy days.

      According to the general understanding of midlatitude precipitation, convective activity is rare in post-monsoon periods. However, the existence of large raindrops during the SCS post-monsoon period indicates it can also occur in this period. This phenomenon deserves further study. Therefore, we checked the daily raindrop data carefully and selected the heaviest precipitation event during the observation period in 2012 (i.e., 19 October 2012). Further analysis of the RSD showed that there were indeed some large raindrops with their diameter exceeding 4 mm during the precipitation on that day. Thus, we downloaded the SPRINTARS datasets on that day to explain why there are some big raindrops in post-monsoon precipitation.

      Figure 13 shows the distribution of cloud-top temperature and ice crystal effective radius. These two figures were downloaded from the SPRINTARS website. As shown with the black rectangular box in the two figures, the minimal cloud-top temperature is lower than 220 K and the maximal ice crystal effective radius is bigger than 40 μm. Although the height of the 0°C isotherm is very close during the three periods (Fig. 9), due to the low-pressure and dry atmospheric environment, the graupel particles in the post-monsoon period are more likely to fall with the process of melting taking place slowly, ultimately resulting in big raindrops. Thus, the occurrence of large raindrops in post-monsoon precipitation is closely related to this convective precipitation system. In addition, the strong convective activity extends the clouds to deeper altitudes and the medium-sized drops grow at the expense of suspended small drops at higher altitudes, which also results in the absence of small raindrops in the post-monsoon period.

      Figure 13.  Distribution of (a) cloud-top temperature and (b) ice crystal effective radius on 19 October 2012.

      From the above discussion, it is clear that the microphysical and dynamic processes (like evaporation, convection, and collision−coalescence) are very different during different periods over the SCS, which further modify the RSD obviously. As a result, small drops are predominant in the pre-monsoon period, whereas medium-sized drops are present in larger numbers in the monsoon period. The concentration of raindrops in the post-monsoon period is much smaller than in the other two periods.

    6.   Conclusions
    • In this paper, an effort has been made to study the RSD of precipitation during different periods of the SCS summer monsoon based on a raindrop dataset collected by an onboard Parsivel disdrometer. In addition to disdrometer measurements, sounding, reanalysis (NCEP−NCAR), and remote sensing (FY-2E and SPRINTARS) data were used to illustrate the possible dynamic and microphysical characteristics associated with the rainfall in different periods. This study provides a unique opportunity in understanding the seasonal microphysical characteristics of precipitation over the SCS. The major conclusions are as follows:

      (1) Small drops are more prevalent during the pre-monsoon period, whereas medium-sized drops are more abundant during the monsoon period. The post-monsoon period has the smallest concentration of raindrops.

      (2) Evaluation of RSD with the rain rate shows that RSDs tend to broaden with the rain rate increasing. The mean Dm values increase with the rain rate in the three periods. For the same rain-rate class, the mean Dm in the post-monsoon period is the largest. With the rain rate increasing, the lg(Nw) observed in the pre-monsoon period tends to increase, whereas it tends to decrease in the post-monsoon period. The values of the RSD parameters N0 and λ tend to decrease with increasing rain rate over the SCS.

      (3) Evaluation of RSD by rain type shows that a higher concentration of small drops can be found in stratiform rainfall during the pre-monsoon period and a higher concentration of medium-sized and large drops can be found in the monsoon period. The mean Dm and lgNw of stratiform rainfall in the three periods are near the right-hand side of the “stratiform line” given by Bringi et al. (2003). There is a clear shift in the RSD of convective rainfall from a “maritime-like” to “continental-like” cluster from the pre-monsoon to the post-monsoon period. The largest lgNw and smallest Dm for convective rainfall were observed during the pre-monsoon period, and the smallest lgNw and largest Dm for convective rainfall were observed during the post-monsoon period.

      (4) The ZR relationships for precipitation during the three periods are Z = 466R 1.33, Z = 346R 1.41 and Z = 339R 1.46, respectively. An inverse relationship has been found for the coefficient A and exponential b of the ZR relationships for the three periods of precipitation. Polarimetric radar parameters were also used for rainfall estimations. The R(ZH, ZDR) relationships for the pre-monsoon, monsoon, and post-monsoon precipitation periods were R = 0.940 × 10−2${{Z_{\rm{H}}^{0.730}}}$$ {{Z_{\rm{DR}}^{-1.092}}}$, R = 0.446 × 10−2${{Z_{\rm{H}}^{0.864}}}{{Z_{\rm{DR}}^{-1.197}}}$ and R = 0.239 × 10−2${{Z_{\rm{H}}^{0.916}}}{{Z_{\rm{DR}}^{-1.245}}} $, respectively. The rainfall estimations using the R(ZH, ZDR) estimator are more accurate than those using a single-parameter estimator (ZR relation) algorithm. Moreover, empirical relationships between DmZe and NwDm were also derived to improve the GPM DPR rainfall retrieval algorithms over the SCS.

      (5) The causative mechanisms/processes responsible for the observed seasonal RSD variations are discussed. Significant differences in temperature, relatively humidity, CAPE, and cloud structure may account for the differences in the RSD during the three periods.

      In general, an attempt has been made in this paper to understand the RSD characteristics of precipitation in the open-sea region of the SCS under the SCS monsoon pattern. Although our results are not necessarily conclusive owing to the still-limited rainfall samples, the RSD characteristics discovered in this study could serve as a reference for developing and refining the estimation algorithm of precipitation in NASA GPM in the open ocean. More detailed research on cloud and precipitation microstructural characteristics over the SCS will be conducted in the future.

      Acknowledgments. This work was primarily supported by the Chinese Beijige Open Research Fund for the Nanjing Joint Center of Atmospheric Research (Grant No. NJCAR2018ZD03), the National Key Research and Development Program of China (2018YFC1507304), and the National Natural Science Foundation of China (Grant Nos. 41575024 and 41865009). The authors also wanted to acknowledge the editor and anonymous reviewers for their constructive comments and suggestions, which helped to improve the manuscript. The classification of the monsoon period was obtained from https://cmdp.ncc-cma.net/Monitoring/monsoon.php. The FY-2E data from NSMC can be obtained from http://satellite.nsmc.org.cn/PortalSite/Data/Satellite.aspx. The NCEP data (1° × 1°) products can be obtained from https://rda.ucar.edu/datasets/ds083.2/. The data from SPRINTARS are based on an atmosphere−ocean general circulation model, MIROC, and can be obtained from http://sprintars.riam.kyushu-u.ac.jp/archive.html.

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