Journal of Geophysical Research: Atmospheres

RESEARCH ARTICLE Drop Size Distribution Characteristics of Seven 10.1029/2017JD027950 in

Key Points: Long Wen1,2,3 , Kun Zhao1,2 , Gang Chen1,2, Mingjun Wang1,2 , Bowen Zhou1,2 , • Raindrops of typhoons in continental 1,2 4 5 6 China are smaller and more spherical Hao Huang , Dongming Hu , Wen-Chau Lee , and Hanfeng Hu

with higher concentration than that of 1 the Pacific and Atlantic Key Laboratory for Mesoscale Severe Weather/MOE and School of Atmospheric Science, Nanjing University, Nanjing, 2 • More accurate precipitation China, State Key Laboratory of Severe Weather and Joint Center for Atmospheric Radar Research, CMA/NJU, Beijing, China, estimation, raindrop size distribution, 3Xichang Satellite Launch Center, Xichang, China, 4Guangzhou Central Meteorological Observatory, Guangzhou, China, and polarimetric radar parameters are 5Earth Observing Laboratory, National Center for Atmospheric Research, Boulder, CO, USA, 6Key Laboratory for obtained for rainfall • Warm rain processes dominate the Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and formation and evolution of typhoon Technology, Nanjing, China rainfall in continental China

Abstract This study is the first attempt to investigate the characteristics of the drop size distribution (DSD) and drop shape relation (DSR) of seven typhoons after making landfall in China. Four typhoons were sampled Correspondence to: K. Zhao, by a C-band polarimetric radar (CPOL) and a two-dimensional video disdrometer (2DVD) in Jiangsu Province [email protected] (East China) while three typhoons were sampled by two 2DVDs in Guangdong Province (south China). Although the DSD and DSR are different in individual typhoons, the computed DSD parameters in these two μ Λ Citation: groups of typhoons possess similar characteristics. The DSR is more spherical, and the shape-slope ( - ) Wen, L., Zhao, K., Chen, G., Wang, M., relation has a significantly lower value of μ for a given Λ than that in typhoons in the area, indicating Zhou, B., Huang, H., et al. (2018). Drop different microphysical processes of typhoons between continental China and other regions (western Pacific size distribution characteristics of seven typhoons in China. Journal of and Atlantic). The convective precipitation of typhoons contains higher raindrop concentration and lower Geophysical Research: Atmospheres, 123, raindrop diameter than that of the maritime convection. A Z (reflectivity factor)-R (rain rate) relationship, 6529–6548. https://doi.org/10.1029/ Z = 147.28R1.38, is derived for typhoons over land in China. The contoured frequency by altitude diagrams of 2017JD027950 CPOL polarimeteric parameters and the vertical distributions of hydrometeors and retrieved DSD

Received 23 OCT 2017 parameters are further investigated to better reveal the microphysical processes of two typhoons (Matmo and Accepted 21 MAY 2018 Soudelor). Despite the differences in DSDs and polarimetric parameters, microphysical characteristics in both Accepted article online 31 MAY 2018 typhoons are similar. The CPOL-derived microphysical properties, in conjunction with high freezing level, Published online 19 JUN 2018 suggest that warm rain accretion processes dominate typhoon rainfall after landfall in China.

1. Introduction The heavy rainfall associated with landfalling typhoons often result in inland flooding and mudslides in the coastal regions. Improving quantitative precipitation estimation (QPE) and quantitative precipitation forecast for landfalling typhoons is crucial for disaster mitigation. Key factors that may help improve typhoon QPE include better understanding of microphysical process that contribute to the development of heavy rainfall (e.g., Chang et al., 2009; Tokay et al., 2008; Zhang et al., 2006). Raindrop size distributions (DSDs) are the fundamental microphysical property of precipitation. Different DSDs in precipitation systems are related to different microphysical processes (e.g., Bringi et al., 2003; Rosenfeld & Ulbrich, 2003). A number of radar QPE algorithms, including Z-R relations and polarimetric radar algorithms were derived from surface-based disdrometers, where Z and R are the radar reflectivity factor and rain rate (e.g., Bringi & Chandrasekar, 2001; Marshall & Palmer, 1948; Rosenfeld et al., 1993; Ryzhkov, Giangrande, & Schuur, 2005; You et al., 2014). However, the diversity of DSDs in different rainfall conditions corresponds to different QPE estimators via the forward scattering simulation of radar measurements (Bringi et al., 2003; Ryzhkov, Giangrande, Melnikov, & Schuur, 2005; Zhang et al., 2001). In addition to the characteristics of the DSDs, the shapes of the raindrops (axis ratio) also vary a lot with different weather systems because of the oscillation and canting effect of raindrops (e.g., Gorgucci et al., 2000). Polarimetric radars make use of this axis ratio to measure the difference in backscatter reflectivity and the propagation phase (Thurai & Bringi, 2005). Drop shapes therefore play a crucial role in retrieving the DSD of the raindrops and the subsequent estimation of rainfall rates from the polarimetric radar fi ©2018. American Geophysical Union. measurements (e.g., Gorgucci et al., 2000). A small error in the axis ratio can lead to signi cant errors in the All Rights Reserved. estimated DSD and rainfall rates (Bringi & Chandrasekar, 2001).

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Disdrometer and/or polarimetric radar observations have been used to investigate DSDs in tropical cyclones (e.g., Chang et al., 2009; Tokay et al., 2008). Through 3 years of JW disdrometer (Joss & Waldvogel, 1969) mea- surements, typhoon DSDs in coastal region in the United States possessed high concentrations of small and midsize drops (Tokay et al., 2008). By combining two-dimensional video disdrometer (2DVD) and C-band polarimetric radar (CPOL) measurements, Chang et al. (2009) analyzed the DSD and the drop shape relation (DSR) of 13 western Pacific typhoons that made landfall in Taiwan. They found that the DSDs characteristics over Taiwan fell in between the continental and maritime clusters illustrated in Figure 11 of Bringi et al. (2003), while the polarimetric radar retrieved DSDs over the ocean were close to the maritime cluster. Moreover, axis ratio of typhoon raindrops greater than 1.5 mm observed by 2DVD tended to be more sphe- rical than that observed in central Florida (Brandes et al., 2002). Previous studies revealed that the DSDs within typhoons varied a lot across different climate regions, over ocean, and over land. China is one of the countries affected by the most serious typhoon disasters in the world. The microphysical characteristics of typhoon over continental China received little attention and appeared only in several case studies (e.g., Chen et al., 2012; Wang et al., 2016). Therefore, more studies of typhoon microphysics in China are desirable and urgent. The DSDs collected by a laser-optical Particle Size Velocity disdrometer in (2009) during its landfall in eastern China (Chen et al., 2012)

showed a smaller mean mass-weighted diameter (Dm) in the convective region than those typhoons in the United States (Tokay et al., 2008) and Taiwan (Chen et al., 2012). However, as the Particle Size Velocity disd- rometer tends to underestimate small raindrops (Tokay et al., 2013; Wen et al., 2017) compared to 2DVD, it

can artificially increase the measured Dm. Recently, the DSDs of a rainband in (2014) after landfall over eastern China have been studied by Wang et al. (2016) using observations from a 2DVD disdrom- eter and an S-band polarimetric radar. Their study indicates that the convection in the rainband generally contains smaller drops and higher number concentrations than the typical maritime convective precipitation. Besides, to our knowledge, the DSR of typhoon over the continental China has not been documented yet. The purpose of this study is twofold: (1) expanding the previous 2DVD database from one to seven typhoons and (2) characterizing typhoon DSR over continental China using 2DVD observations, for improving the under- standing of typhoon microphysics and ultimately more accurate radar QPE and quantitative precipitation forecast. It is understood that data from seven typhoons are not enough to represent general characteristics of typhoon microphysical properties in continental China. The results derived from this limited data set will form the basis for future studies when more data are available in the future. During the 2014–2016 field campaigns of the Observation, Prediction and Analysis of Severe Convection of China project (Xue, 2016), a CPOL and a 2DVD were deployed in Nanjing, Jiangsu Province of (Region 1 in Figure 1), while two 2DVDs were deployed in Guangzou, Guangdong Province in south China (Region 2 in Figure 1). Four typhoons, named Matmo (2014), Soudelor (2015), Meranti (2016), and Megi (2016), were observed in Region 1 by well after they made landfall in China and three typhoons, named Nida (2016), Hato (2017), and Pakhar (2017) were observed in Region 2. Using these unique data sets, we focus mainly on establishing the baseline characteristics of DSD and DSR in typhoon systems after landfall in China. The vertical structure of polarimetric parameters, retrieved DSDs, and the corresponding microphy- sical processes of Typhoon Matmo and Soudelor are investigated as well. Note that CPOL data were not available in typhoons Meranti and Megi. This paper expands the study of Wang et al. (2016) by including 2DVD and/or polarimetric radar data of six typhoons. The paper is organized as follows: section 2 describes data and method that are used to analyze the DSD. The characteristics of typhoon DSDs observed by the 2DVD are discussed in section 3. In section 4, the DSD retrie- vals from the CPOL and the corresponding precipitation vertical structure of typhoons are examined. The conclusions are made in section 5.

2. Data and Methods 2.1. Instruments and Data Set The CPOL (32°12024″N, 118°4302″E) is operated in the volume coverage pattern (VCP) 11 scanning mode with a 0.54° beam width and 75-m radial resolution (Chu et al., 2017). VCP11 consists of 14 elevations between 0.5° and 19.5° (Crum & Alberty, 1993). In addition to VCP11, the CPOL also performed a few vertical pointing scans

at 90° elevation in light rain for the calibration of differential reflectivity ZDR (Bringi & Chandrasekar, 2001).

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Figure 1. Location of the two observational sites in (Region 1) East and (Region 2) south China. The tracks of the seven typhoons from China Meteorological Administrator are presented in different colors. The local topography of the two regions (1 and 2) are presented in the right panels. JN = Jiangning site, CPOL = C-band polarimeteric radar; SD = Shunde site, LM = Longmen site.

Three-third generation 2DVDs are used, with the vertical and horizontal resolutions for raindrop diameter higher than 0.19 mm (Schönhuber et al., 2007). One is deployed at Jiangning site (JN-2DVD; 31°55048″N, 118°53056″E) of East China (about 35 km northwest of the CPOL), and the others is deployed at Shunde site (22°50043″N, 113°14057″E) and Longmen site (23°46052″N, 114°14053″E) in south China. Since the two 2DVDs in south China (Region 2) are the same model, the data will be grouped and labeled SD-2DVD in this paper not to clutter the figures. This instrument has been widely used in the meteorology community for weather polarimetry-related studies. The temporal resolution is 1 min for 2DVDs and about 6 min for CPOL in this study. In situ sounding data were collected as well. Figure 1 shows the location of the observation sites, as well as the tracks of the seven typhoons from the official best track data of the Chinese Meteorological Administration (Ying et al., 2014). After making landfall over southeast coast of China, all seven typhoons kept moving northwest/northward and brought heavy rainfall to the observation sites. The disdrometer data sets used in this study consists of ~161-hr (39-hr) time series of 1-min samples by JN-2DVD (SD-2DVD), while the CPOL data are available only for Typhoons Matmo and Soudelor (about 63 hr in total). In this study, raindrops are considered as small or large if the diameter of a raindrop is less than 1 mm or larger than 4 mm, respectively; otherwise, they are classified as midsize. 2.2. Disdrometer Data Processing Previous studies indicated that the 2DVD could be affected by the error of oversampling small raindrops that results from the wind-caused turbulence near the optical camera and the splash contamination. A fall velocity- based filter to remove the oversampling error was suggested by Kruger and Krajewski (2002) as follows:

jjVmeasured Videal < cVideal (1)

where Vmeasured represents the measured fall velocity and Videal represents fall velocity derived from the formula given in Brandes et al. (2002). The coefficient c is set to 0.4 as recommended in previous works (e.g., Chang et al., 2009; Kruger & Krajewski, 2002; Thurai & Bringi, 2005). As shown in Figure 2, the mean measured fall velocities match well with the empirically derived terminal fall speed, suggesting the reliability of the 2DVD observations. The algorithm eliminates about 2.46% of the total raindrops. Furthermore, rainfall rates of less than 1 mm/hr are also removed to eliminate inadequate DSD cases, following the procedure in Chang et al. (2009).

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When the raindrop size distribution is obtained after applying quality control, the integral rainfall parameters, including rain rate R (mm/hr), liquid water content LWC (g/m3) and the total concentration of raindrops 3 Nt (m ) can be derived from the nth-order weighted moment of the measured DSD and measured fall velocity.

π XL ¼ 6 3 ðÞΔ R 4 Di ViN Di Di (2) 10 i¼1

π XL ¼ 3 ðÞΔ LWC Di N Di Di (3) 6000 i¼1

XL Nt ¼ NðÞDi ΔDi (4) i¼1

Figure 2. Occurrence of velocity-diameter combinations with drop counts where L is the total number of bins, Di (mm) is the equivalent spherical on a log scale from two-dimensional video disdrometer (2DVD) observations. raindrop diameter for size bin i, ΔDi is the corresponding diameter inter- The color shading represents the measured fall velocity of raindrops. Mean val (mm), and Vi (m/s) is the fall speed for the velocity bin i. The and standard deviation of measured fall velocity as a function of diameter are equivalent-volume diameters are sorted into size categories of 0.2 mm. given as well. The black line indicates the Brandes et al. (2002) terminal drop The range in tabulated raindrop diameters is 0.1–8.1 mm (41 bins), velocity and the two dashed lines represent the ±60% filter of drops. and the velocity bin is changed to match the size bin. Vi is obtained 1 3 by averaging measured particle velocities within that size bin. N (Di;mm ·m ) represents the number concentration of drops with diameters in the range from Di 0.5ΔDi to Di + 0.5ΔDi (per unit size interval). The nth-order moment of the DSD is expressed as

Dmax M ¼ ∫ DnNðÞD dD (5) n 0

The mass-weighted mean diameter Dm (mm) equals the ratio of the fourth to the third moment of the size distribution

M4 Dm ¼ (6) M3

1 3 And the generalized intercept parameter Nw (mm ·m ) was computed as 44 LWC N ¼ (7) w πρ 4 w Dm

3 where ρw (assumed to be 10 g/cm ) is the density of water.

The standard deviation of the mass spectrum with respect to Dm is defined as

Dmax 2 3 ∫ ðÞD Dm D NDðÞdD σ2 ¼ 0 (8) m Dmax ∫ D3NDðÞdD 0

The widely used three-parameter gamma model DSD (Ulbrich, 1983) represents the observed raindrop spectra reasonably well. The gamma size distribution is expressed as

μ NDðÞ¼N0D expðÞΛD (9)

1μ 3 1 where D (mm) is the equivalent diameter and N0 (mm ·m ), Λ (mm ), and μ (dimensionless) are the intercept, the slope, and the shape parameters, respectively.

Note that the derived DSD parameters (e.g., D0, Dm, Nt, and Nw) are all calculated directly from measured DSDs, while the three control parameters of gamma DSD model (N0, μ, and Λ) are calculated using the

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M246 (the second, fourth, and six moment of DSDs) truncated moment fitting method (Vivekanandan

et al., 2004). Generally, D0 and Dm represent the mean drop size, while Nt and Nw represent the number concentration of a DSD. Detailed expressions for the computed parameters mentioned above are all given in Wen et al. (2016). Observed DSDs are also used to derive the polarimetric radar parameters (including radar reflectivity in

horizontal polarization ZH (10log10 (Zh)) and differential reflectivity ZDR) for the study of QPE in section 3.3. These variables depend on the DSD and the drop scattering amplitudes and are calculated using the T-matrix (Ishimaru, 1991) scattering techniques described by Zhang et al. (2001).

4 Dmax 4λ 2 Zh;v ¼ ∫ f hh;vvðÞD NDðÞdD (10) 4 2 Dmin π jjKw ¼ Zh ZDR 10 log10 (11) Zv

where λ is the radar wavelength, Kw is the dielectric factor of water, and fhh,vv (D) are the backscattering ampli- tudes of a drop at horizontal or vertical polarization. The polarimetric radar parameters in equations (10) and (11) were directly calculated from the observed DSDs. The raindrops were assumed to follow the newly derived typhoon axis ratio relation in the calculation.

2.3. Polarimetric Radar Data Processing

The CPOL-observed polarimetric radar data, including radar reflectivity (ZH), differential reflectivity (ZDR), cor- relation coefficient (ρhv), and the differential specific phase (KDP), are used in this study. Note that ZDR provides information about the oblateness of particles (specifically, the reflectivity-weighted diameter; Bringi &

Chandrasekar, 2001) and KDP mainly depends on the LWC. The nonmeteorological echoes having ρhv value smaller than 0.85 are excluded. A five-gate median average and a five-gate running mean are performed

for ZH and ZDR observations to reduce the random fluctuations (Schuur et al., 2003). ρhv is biased if signal-to-noise ratio is lower than 20 dB and correction is made for signal-to-noise ratio > 5 dB according to Schuur et al. (2003). Afterward, polarimetric radar data are interpolated onto a 1.0-km horizontally spaced and 0.5-km vertically spaced Cartesian grid using the National Center for Atmospheric Research REORDER software package (more information is available online at http://www.eol.ucar.edu/rsf/UserGuides/ ELDORA/DataAnalysis/reorder/unixreorder.ps). The lowest grid altitude is 0.5 km above ground level.

Previous studies showed that the three parameters (N0, μ, and Λ) in gamma distribution in (2) is not independent of each other (Islam et al., 2012; Testud et al., 2001; Zhang et al., 2001). Hence, an empirical μ-Λ relation (Brandes et al., 2003; Zhang et al., 2001) has been proposed to retrieve the gamma distribution. The shape and slope (μ-Λ) constrained C-G model has been successfully applied in North America (Brandes

et al., 2002) to retrieval DSDs from polarimetric radar variables (Z and ZDR). Previous studies (Brandes et al., 2004; Cao et al., 2008; Zhang et al., 2003) pointed out that the μ-Λ relationship changes across different precipitation systems, climatic regimes. Thus, it needs to be customized in order to be applied for typhoon precipitation in continental China, as will be discussed in section 4.1.

2.4. Classification of Rain Types and Hydrometeors To investigate the characteristics of convective and stratiform rain, a threshold of 10 mm/hr rainfall rate is used to separate them from 2DVD observations in order to compare with previous studies (Bringi et al., 2003; Chang et al., 2009). For polarimetric radar measurements, the precipitation type is identified from radar reflectivity at 2-km height using a robust classification algorithm described in Steiner et al. (1995). This classification is used to compute contoured frequency by altitude diagrams (CFADs) in section 4.3. Moreover, a fuzzy-logic CPOL hydrometeor identification algorithm adapted from Dolan et al. (2013) is employed to further illustrate the polarimetric radar-observed microphysical characteristics. Membership beta

functions are defined for each hydrometeor type and each variable, that is, polarimetric parameters ZH, ZDR, KDP,andρhv, and temperature (T) derived from the nearest (in time) sounding, as given in Table 1 (cited from Table A1 of Dolan et al. (2013)). The hydrometeor with the highest fuzzy logic score was identified as the most probable type at each grid point. The hydrometeor identification algorithm method has been widely used to

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Table 1 Simulated Polarimetric Variable Ranges for Hydrometeors at C-Band and Associated Temperature Ranges (°C)

ZH ZDR KDP ρhv Type Min Max Min Max Min Max Min Max T

Drizzle 27.3 30.8 0.0 0.9 0.0 0.1 0.982 1.018 > 1.0 Rain 20.0 58.0 0.1 4.5 0.0 11.0 0.975 1.025 > 3.0 Ice crystals 24.9 19.3 0.2 5.6 0.0 0.2 0.955 1.005 <0.0 Aggregates 1.1 35.1 0.1 2.1 0.3 0.3 0.86 1.00 51.0 < T < 1.0 Wet snow 2.7 45.3 0.40 2.2 0.2 0.7 0.49 0.99 2.5 < T < 4.5 Vertical ice 26.0 24.0 1.8 0.0 1.5 0.0 0.953 0.996 <0.0 LD graupel 27.8 46.2 0.0 1.8 0.0 0.2 0.975 1.025 <0.0 HD graupel 34.1 54.5 0.40 2.8 0.0 3.8 0.96 1.04 22.5 < T < 17.5 Hail 48.0 76.6 0.4 0.7 2.9 4.1 0.87 1.07 — Big drops 49.3 66.3 2.5 6.3 0.1 6.7 0.96 1.02 > 3.0 Note. Table is cited from Table A1 of Dolan et al. (2013).

identify the hydrometeor type that dominates a given radar sample volume (Dolan et al., 2013; Park et al., 2009; Rowe & Houze, 2014; Vivekanandan et al., 1999). To date, it is still considered as the most reliable method for the identification of hydrometeor types by polarimetric radar despite the intrinsic limitations in certain situations (Barnes & Houze, 2014). Ten hydrometeor types are identified as follows: drizzle, rain, ice crystals, aggregates, wet snow, vertical ice, low-density graupel, high-density graupel, hail, and big drops. The estimated particle types can also be used to infer the underlying ice microphysical processes as sug- gested by Barnes and Houze (2016). Usually, ice crystal refers to pristine ice particles that grow predominantly via vapor deposition, and the vertical aligned ice refers to these same crystals that are reoriented by strong electric fields in the storm. The aggregates suggest the aggregation of pristine ice crystals into larger, tum- bling conglomerates, while graupel and hail categories imply riming, and wet snow usually is an indication of melting. The microphysical processes discussed in section 4.3 are inferred based on the distribution and characteristics of these identified particles.

3. DSD Characteristics 3.1. Overview of Typhoons The time series of typhoon DSDs observed by 2DVDs are presented in Figure 3. The first four typhoons made landfall in Province and were observed by JN-2DVD (Figures 3a–3d) after traveling several hundreds of kilometers from the coast line, while the remaining three typhoons made landfall in Guangdong Province and were observed by SD-2DVD (Figures 3e–3g). Detailed description of DSDs via the passage of typhoon rainfall over 2DVD sites are given below. Note that the division of different stages and/or segments of each typhoon via the passage over the observation sites are determined by screening the time series of composite radar reflectivity mosaic data from Chinese Meteorological Administration. During the passage of Typhoon Matmo over JN site, it can be divided into two segments representing two typhoon rainbands (Figure 3a). The majority of raindrop sizes of Matmo do not exceed 3 mm and the end of both segments possessed high concentration of small drops, which can reach as high as logarithmic 3.7 1 3 (log10 N(D)mm ·m ). The DSDs of showed more variation than that of Typhoon Matmo (Figure 3b). In the beginning, rainfall is quite sporadic accompanied by almost all raindrops smaller than 3 mm with a relatively low concentration. As the concentration of small and midsize drops increases, rainfall intensity increases. The mean rain rate can reach as high as 64 mm/hr and the maximum rain rate is 113.1 mm/hr at 1251 LST. The maximum drop diameter (5.3 mm) appears at 1903 LST with a corresponding rain rate of 70.6 mm/hr. The DSDs of showed slight differences for the number concentration of small drops (Figure 3c). At around 1230 LST on 15 September 2016 and 0230 LST on 16 September 2016, the concentra- 1 3 tion of small and midsize drops is less than 3.2 (log10 mm ·m ) with very light rain rate. Before (and after) this period, the rain rate shows several peak values with the increased concentration of small drops as well as

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Figure 3. The time series of the DSDs observed from (a–d) JN-2DVD in East China and (e–g) SD-2DVD in south China. The color shading represents the DSD in logarithmic units of mm 1 ·m 3. The left y axis indicates the equivalent diameter D (mm) of raindrops, and the right y axis indicates the calculated rain rate R (mm/hr). DSD = drop size distribution.

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1 3 Figure 4. The scatterplot of (a) μ, (b) Dm (mm), (c) Λ (mm ), and (d) log10Nt (m ) versus R (mm/hr) of each 1-min DSDs from typhoon cases observed by JN-2DVD (gray circles) and SD-2DVD (red dots). The probability density functions (PDFs) of the four DSD parameters observed from JN-2DVD and SD-2DVD are given in each panel as well. DSD = drop size distribution.

the maximum drop diameter. Interestingly, the last stage of the above three typhoons are all showed up with a DSDs cluster that only contains a high concentration of small drops, which were referred to as shallow rainfall as documented in Wen et al. (2016). brought the longest rainfall duration to the observation site among the seven typhoons, and its DSD characteristics are somewhat different from that of the others (Figure 3d). At the first ~23 hr, the maximum drop diameter can be as small as 0.5 mm for most of the time, followed by a roughly uniformly distributed raindrop spectrum that lasted ~34 hr. Due to the low concentration of small drops, rainfall intensity in Typhoon Meigi is relatively low. During the northwestward movement of , two rainbands passed over the LM site, with the maximum rain rate not exceeding 73.0 mm/hr and large raindrops rarely exceeding 4 mm (Figure 3e). Similar to previous cases, the increase of concentration of small raindrops and the maximum drop diameter is accompanied by the intermittent peak rain rates in the two rainbands. ’s rainfall begins with light rain that lasts about 7.5 hr (Figure 3f). With the apparent increase in the concentration of small to mid- size drops from about 1230 LST, rainfall intensity increases significantly. After about 8 hr of persistent light 1 3 rain with relative narrow DSDs, the concentration of small drops reaches as high as 5.4 (log10 mm ·m ) with the largest drop diameter over 4.7 mm at around 0100 LST on 24 August 2017. The rain rate increases dramatically to ~171.6 mm/hr at 0030 LST, the highest rain rate among the seven typhoons. Typhoon Pakhar brought sporadic light rain to the SD site until 0640 LST on 27 August 2017 (Figure 3g). After that, rain intensity increases with increasing small drop concentration and large drop diameter. Although the maximum raindrop diameter is only 3.7 mm, the maximum rain rate is about 86.6 mm/hr at 0945 LST benefitting from the high concentration of small drops. Note that due to the signal blockage during heavy rainfall, data collection of the SD-2DVD stopped from about 1000 to 1130 LST and then resumed to operate again. High concentration of small drops appears after 1200 LST as well. 3.2. DSD Parameters Derived From 2DVD Measurements The 1-min DSDs of the seven typhoons derived from 2DVDs measurements are fitted into the gamma dis- tribution form with coefficients μ and Λ via the truncated moment method calculation, while the values of

Dm and Nt are calculated directly from measured DSDs. As shown in Figure 4, the differences in the

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computed parameters of these seven typhoons in south and East China are negligible, indicating their similar DSD characteristics despite the different geographic locations and distances to the coastline. Therefore, the DSD data sets observed by the two 2DVDs are combined for the statistical analyze of typhoons microphysics in China in the following section.

The variability of μ, Λ, Dm, and Nt are distinct when rain rate is less than 10 mm/hr, reflects the complex microphysical processes of stratiform rain; however, the range of variation decreases with the increase of rain rate and became more uniform in higher rain rates. Overall, the values of μ (Figure 4a) and Λ (Figure 4c) decrease with an increase in rain rate. When rain rate is higher than 25 mm/hr, the values of μ and Λ stay near constant at about 1.4 (dimensionless) and 2.1 (mm 1), respectively.

As shown in Figures 4b and 4d, the Dm converges while Nt increases with increasing rain rate when R > 10 mm/hr. Dm stays around about 1.4 mm, while Nt increases significantly with the increase of rain rate Figure 5. Relative contribution of each size classes to the total drop concen- above 25 mm/hr. These suggest that due to more efficient coales- tration N (red) and rain rate R (blue) for the whole data set. t cence and breakup mechanisms, the Dm values become more uniform and Nt values are enhanced with the increase of rain rate (Chen et al., 2013). It is notable that at high rain rates, the DSDs may reach an equilibrium state where coalescence and

breakup of raindrops are in near balance (Hu & Srivastava, 1995). Under the equilibrium condition, Dm remains constant, while any increase in rain rate is mainly due to an increase in number concentration

of raindrops (Bringi & Chandrasekar, 2001). The unique characteristics of the Dm and Nt values in our study is consistent with this theory and further implies that the heavy rainfall of typhoon in continental China is mainly composed of high concentrations of small and midsize drops. Chang et al. (2009) have reached

similar conclusion, but their Dm (Nt) values are higher (lower) and the increasing trend of Nt is less obvious than this study. More evidence can be seen from Figure 5, which shows clearly that the small drops (D < 1 mm) dominate the number concentration (~95.9%) for the whole data set and contribute to about 26.3% of total rainfall. The contribution to total rainfall from the drops at 1–2 mm in diameter is 52.6% and decreases dramatically with the increase of raindrop diameter (18.5% and 2.4% at 2–3and 3–4 mm, respectively). Overall, the small and midsize drops (D < 4 mm) contribute more than 99% to total rainfall, while that of large drops (D > 4 mm) is negligible (0.2%). This is consistent with the conclusion from Figure 4 that the small and midsize drops jointly dominate the typhoon precipitation in China.

The distribution of the Dm-log10Nw pairs derived from the DSDs of seven typhoons shows great variation when the rainfall rate is less than 10 mm/hr in stratiform precipitation (Figure 6a), similar to the stratiform pre- cipitation DSDs in Bringi et al. (2003; shown as a solid pink line in Figure 6a) and Chang et al. (2009). The large

variation of the Dm-log10Nw in stratiform rain, suggested by Bringi et al. (2003), reflects the different micro- physical processes of stratiform rain, ranging from melting of large dry snowflakes (larger Dm and smaller Nw) to melting of tiny graupel or smaller rimed ice particles (smaller Dm and larger Nw). As shown in Figure 6a, nearly 94.4% of stratiform rain area has a value of Dm lower than 1.5 mm, with the mean value of Dm and log10Nw centered at about 1.1 mm and 4.2. This suggests that the rimed ice particles, or the melting of tiny, compact graupel, rather than the melting of large, low-density snowflakes, formed the stratiform precipitation of the typhoons in China.

Bringi et al. (2003) found that the maritime (continental) type of convective systems have Dm ~ 1.5–1.75 mm (2–2.75 mm) and logarithmic Nw ~4–4.5 (3–3.5; the two rectangles in Figure 6a). Based on the DSD observa- tion of two typhoon cases in Taiwan, Chang et al. (2009) found that the DSD characteristics were located

between the maritime and continental convective cluster with a mean Dm of ~2 mm and a logarithmic Nw ~3.8. The convective precipitation of typhoons in this study, however, shows a higher raindrop concentration

and a lower raindrop diameter with a mean Dm of ~1.29 mm and a logarithmic Nw ~4.62, as compared with the maritime convection and convection of typhoons in Taiwan.

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Figure 6. (a) Scatterplot of Dm versus log10Nw for convective (gray plus) and stratiform (gray dot) rain types for the whole data set. The averaged Dm-log10Nw pairs (values) for the convective rain of seven typhoons are given respectively by square box (number) with corresponding color. Averaged convective Dm-log10Nw pairs for the summer rainfall in East China (red triangle) and typhoon rainfall in Taiwan (red circle) are given as well. The dashed magenta line indicates the rainfall rate of 10 mm/hr. Contour lines represent the frequency of occurrences of Dm-log10Nw pairs of the retrieved drop size distributions from CPOL for Typhoons Matmo and Soudelor at 0.5-km height, and the averaged pair is given by cross in red. The two gray rectangles correspond to the maritime and continental convective clusters, and the composite raindrop spectra for (b) the whole data set, (c) convective rain, and (d) rainfall at 40 dBZ. CPOL = C-band polarimeteric radar.

Chang et al. (2009) further proposed that the higher values of Dm and the lower values of Nw could be con- sidered the features of terrain-influenced convective systems during typhoon landfall in Taiwan. The situation could be more complicated during the northwest/northward movement of typhoon systems over the continental China after landfall. As typhoon moved inland and transited into , the com- bination of topography effect, local anthropogenic aerosol effect, and entrainment of cold air from the north

could ultimately affect the microphysical processes within individual typhoon. However, the averaged Dm- log10Nw pairs for convective rain show only slight differences among the seven typhoons and are all clustered at the upper left of the statistical result of 3 years of summer rainfall observed in the JN site (red triangle). These indicate that the DSD of these typhoons consists of higher concentration of smaller drops than that of the average summer rainfall in East China (hereafter, referred to as average summer rainfall). It is worth

mentioning that the mean Dm of 1.67 ± 0.3 mm in Tokay et al. (2008) for Atlantic cyclones is higher than that of ours (1.13 ± 0.24 mm). The interaction between sufficient water vapor brought by typhoon from the ocean and local anthropogenic aerosol in continental China may play an important role in the formation of these characteristics in DSDs. This topic is out of the scope of this study; we leave it for future research.

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Figure 7. Scatterplots of (a, b) D0-log10Nw and (c, d) D0-LWC pairs colored by R and Z classes, respectively. The solid and dashed gray lines represent the separation lines of convective and stratiform rain by Bringi et al. (2009) and Thompson et al. (2015). LWC = liquid water content.

More evidence about the DSDs among the seven typhoons can be seen from the composite raindrop spectra in Figures 6b–6d. The spectra of Typhoons Soudelor and Hato are wider with a larger maximum raindrop dia- meter (5.3 mm) and a higher number concentration within each size bin among the typhoons. In comparison with summer spectrum, the other five typhoons contain lower concentration of raindrops at drop sizes larger than 2 mm. The gap between these curves grows wider with the increase of drop diameter. Similar to that of the whole data sets, the convective spectrums of Typhoons Matmo, Soudelor, and Hato have higher concen- trations at sizes larger than 2 mm when compared to the other four typhoons (Figure 6c). On the other hand, the concentration of drops at sizes less than 2 mm in convective typhoon rainfall is higher than that of the summer rainfall. The differences of raindrop spectra among the typhoons at small drop sizes diminished. The maximum drop diameter in convective rain of Typhoon Megi is 3.9 mm (Figure 6c), while it is 4.9 mm for the whole data set (Figure 6b). This may be attributed to the relatively weak convective rain in Typhoon Megi as shown in Figure 3d. Overall, the concentration of large drops in convective rainfall of typhoons is lower than that of the average summer rainfall. We also recognize that the difference of DSDs in seven typhoons can be partly attributed to limited samples in the 2DVD data set. The 40-dBZ spectra of typhoons show good agreement with each other at drop sizes less than 2.7 mm (Figure 6d). The variations at large drop sizes are narrower than those in Figures 6b and 6c. Typhoon Megi has the highest concentration of large drops, followed by that of average summer rainfall and the other typhoons. Figures 6b–6d suggest that the raindrop spectra of different typhoons converge with increasing rainfall intensity, especially for small and midsize raindrops. Meanwhile, the remaining variation at large drop sizes can be attributed to the insufficient raindrop samples. Overall, despite their individual characteristics, rainfall produced by typhoons in China possesses a higher concentration of small and midsize drops than that of summer rainfall. The differences of DSD spectra among the seven typhoons become narrower with increasing rainfall intensity.

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As presented in Figures 7a and 7b, the Nw values range from logarithmic 2.3 to 5.9, while D0 values range from 0.3 to 2.4 mm during low rain rate (stratiform rain). The distributions of the two parameters become narrower as R and Z increase. More obviously, the convective rain

(R > 10 mm/hr) intensity increases with increasing Nw value, while D0 value rarely exceeds 1.5 mm (>50 mm/hr) and decreases slightly toward about 1.0 mm during heavy rainfall. Similar characteristics can be seen for the distribution of Z during convective rain, where the majority of

D0-log10Nw pairs characterized with Z > 40 dBZ (Figure 7b). These should be attributed to the significant increase of number concentration of small and midsize drops that contribute most to rainfall amounts

(Figure 5). As a result, for a given D0, the LWC (and Z) values increase with rainfall intensity and its distribution become narrower (Figures 7c and 7d). The above analyses further confirmed that the microphysical characteristics of different typhoons during heavy rainfall are similar and have reached the equilibrium state (where the rainfall intensity is dominated by raindrop concentration rather than diameter). Figure 8. Scatterplots of Z-R values and the fitted power law relationship (black line). Referred relationships are given in corresponding colors. Meanwhile, Figure 7 also illustrates that the rain-type classification of 2DVD = two-dimensional video disdrometer; SWM = southwest ; Bringi et al. (2009), by which the D0-log10Nw relation was used, would NEM = northeast monsoon. be more suitable for typhoon rainfall in China than that of Thompson

et al. (2015), which only use a constant log10Nw value for the separation. This is because the variation of DSDs of typhoon precipitation showed

up on both the variety of Nw and D0 values. 3.3. Derived QPE Relationships The widely used Z-R relation is derived for typhoon QPE without the separation of the convective and strati- form rain (Figure 8). Compared to the Z-R relationship derived in previous studies, for example, the standard Z = 300R1.4 is the default relationship used in the Next-Generation Weather Radar of the United States by Fulton et al. (1998), Z = 206.83R1.45 for 13 western Pacific typhoons that made landfall in Taiwan by Chang et al. (2009), Z = 235R1.3 for Typhoon Morakot (2009) during its landfall in eastern China by Chen et al. (2012), Z = 275.25R1.39 and Z = 142.04R1.55 for 11 typhoons in the Bay of Bengal in southeast India during the southwest and northeast monsoon by Radhakrishna and Narayana Rao (2010), or Z = 234R1.3 for 7 typhoons during the passage over Darwin, Australia, by Deo and Walsh (2016; see Table 2), the fitted mean Z-R relationship of typhoon systems in our study has a lower value of A = 147.28 and b = 1.38. As shown in Figure 8a, previous Z-R relationships for typhoon systems over various regions tend to underestimate the rainfall of typhoons in China. The difference was expected with the unique DSDs of the typhoon rainfall in this region. Since Z is the sixth-order moment, and R approximates to the 3.67th-order of DSD under the Rayleigh assumption, the presence of the higher number concentration of small drops in typhoon rainfall in China can result in a higher R for a given Z.

Table 2 Z-R Relation (Z = ARb) Coefficient (A) and Power (b) Over Different Regions Region Studies Ab

Defaulta used in the United States Fulton et al. (1998) 300 1.4 Western Pacific Chang et al. (2009) 206.83 1.45 Eastern China Chen et al. (2012) 235 1.3 BOBb of India Radhakrishna and Narayana Rao (2010), SWMc 275.25 1.39 Radhakrishna and Narayana Rao (2010), NEMd 142.04 1.55 Australia Deo and Walsh (2016) 234 1.3 Continental China This study 147.28 1.38 aThe default relationship used in the Next-Generation Weather Radar of the United States. bBay of Bengal. cThe southwest monsoon. dThe northeast monsoon.

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Figure 9. (a) Log10 [N (De, b/a)], where N (De, b/a) is the number of drops for each corresponding equivalent diameter De and axis ratio (b/a) in typhoon cases. (b) μ-Λ relation for typhoon rainfall derived from two-dimensional video disdrometer (2DVD) observations.

4. Raindrop Size Distribution Retrieval Using CPOL 4.1. DSR and μ-Λ Relation of Typhoons The information of DSR is essential for the retrieval of DSD from polarimetric radar observations. One of the advantages of the 2DVD is the capability of recording the axis ratio of each raindrop. The widely used DSR is proposed by Brandes et al. (2002) by combining the observations of several previous studies and fitting to a fourth-order polynomial, as shown in the dash line of Figure 9a. Chang et al. (2009) further derived the axis ratios of 13 typhoon cases (solid line in Figure 9a) and found that the DSR is more spherical when the diameter is greater than 1.5 mm and under higher horizontal winds compared with that of Brandes et al. (2002). They attributed this unique DSR to the horizontal wind velocity. The higher wind speeds within typhoon systems lead raindrops to have more oscillation and canting effects and thus more spherical. Following the same data process, the axis ratio of each raindrop collected in our study is given in Figure 9a and the fourth-order polynomial DSR fitting is derived as follows (cyan line in Figure 9a):

b ¼2:143 104D4 þ 1:159 103D3 1:868 102D2 þ 2:745 102D þ 0:9946 (12) a

It shows that the maximum sizes of the typhoon raindrops seldom exceeded 4 mm with very low concentra- tion. For raindrops smaller than 1.5 mm, the DSR of typhoons in China is similar to that of Brandes et al. (2002) and Chang et al. (2009). However, the raindrop is more spherical than that of Chang et al. (2009) when the diameter is greater than 1.5 mm. The more spherical particles observed in this study may be related to the high horizontal wind. However, we cannot verify this, because there is no wind measurement above the 2DVD site. Besides DSR information, the constrained-gamma method uses the inherent advantages of polarimetric mea-

surements (ZDR) and an empirically constrained relationship between μ and Λ from disdrometer measure- ments to estimate the DSDs from polarimetric radar observations (Zhang et al., 2001). For fair comparisons with previous studies, the μ-Λ relationship in this study was established by removing any DSDs in which the rain rate was less than 5 mm/hr, which was the same threshold used in Brandes et al. (2004) and Chang et al. (2009). For typhoon systems, Chang et al. (2009) found that the μ-Λ relationship had a smaller slope than that of Brandes et al. (2004); Chen et al. (2012) revealed a similar result. However, the μ-Λ relationship derived in our study has a significantly lower value of μ for a given Λ than that of the previous studies mentioned above (Figure 9b). The differences reveal that the DSD of typhoons in the continental China have much higher con-

centration of small drops, and accordingly a lower value of Dm, as also documented in Wang et al. (2016). This indicating that the microphysical processes of typhoons in continental China is different from the systems observed in Taiwan by Chang et al. (2009).

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Figure 10. The constant altitude plan position indicator (CAPPI) scans of reflectivity and differential reflectivity for Typhoons (a, b) Matmo and (d, e) Soudelor at 1-km height by C-band polarimeteric radar (CPOL) with 150-km range. The black rectangle (160 × 160 km2) in Figure 10a represents the retrieval area of Figure 11, and the red rectangle (30 × 30 km2) represents the study area of contoured frequency by altitude diagram in Figure 12. The two dashed magnate lines represent the two rainbands of Typhoons (R1) Matmo and (R2) Soudelor. The representative skew T-LogP diagram and the vertical wind profile from JN site are given in (c) and (f). The blue and red lines are temperature and dewpoint temperature profiles, respectively. The black curve is the ascending path of a surface-based parcel.

4.2. Spatial Distribution of Radar Observations and Retrieved DSDs With the understanding of typhoon DSD and DSR obtained from surface disdrometer data, more detailed investigation of DSDs in typhoon systems through polarimetric radar becomes practical. Further discussions below are given based on the comparative analysis of two selected typhoon cases, that is, Matmo and Soudelor, for which the CPOL data are available. These polarimetric radar parameters are used to document the structure of typhoon microphysics during the passage over the JN site. Two typhoon rainbands via the passage over JN aloft (labeled as R1 and R2 in Figures 10a and 10d for Typhoons Matmo and Soudelor, respectively) are illustrated using the constant altitude plan position indica-

tor of ZH and ZDR at 1-km altitude. The convection in these typhoon rainbands have the similar polarimetric signatures with those observed in Taiwan (Chang et al., 2009); that is, the maximum ZH is about 50 dBZ (Figures 10a and 10d), and the ZDR are mostly limited below 1.2 dB (Figures 10b and 10e). The moderate ZH and low ZDR suggest that the small and midsize drops are dominant in the typhoon rainband, consistent with the 2DVD observation above. The nearby soundings at the JN site (Figures 10c and 10f) show that both Typhoons Matmo and Soudelor have very moist environment below 400 and 250 hPa with the relative humidity exceeding 80%. At the surface, the air is almost saturated, with the relative humidity higher than 96%, resulting in a very low lifting condensation level at about 72 and 178 m for the two cases, respectively. Such low lifting condensation levels, in conjunction with the high freezing level at about 5–5.5 km, allows for a deep layer of warm clouds, which is favorable for warm rain processes. The convective available potential energy (CAPE) of Typhoon Matmo is moderate with the positive area nearly uniformly distributed aloft,

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Figure 11. Constant altitude plan position indicator (CAPPI) of retrieved log10Nt, Dm, and liquid water content for Typhoons (a–c) Matmo at 1829 LST on 24 July 2014 and (d–f) Soudelor at 1855 LST on 10 August 2015 at 1-km height within the range of the black rectangle in Figure 10a. CPOL = C-band polarimeteric radar.

indicating that the CAPE is in favor of convection but the maximum updraft intensity is limited. The CAPE is slightly lower for Typhoon Soudelor, but the difference in temperature between the environment and the rising air at around 700 to 500 hPa is greater than that in Typhoon Matmo. This means that the rising air in Typhoon Soudelor is more buoyant with respect to its environment than that of Matmo. Accordingly, stronger updrafts and more severe storms are more likely in Typhoon Soudelor. Based on the newly derived axis ratio relation and μ-Λ relation for seven typhoons in China, the CPOL obser- vations of Typhoons Matmo and Soudelor were used for the DSD retrieval to better analyze the structure of

typhoon microphysics. Note that the retrieved ZH and ZDR from 2DVD measurements show good agreements with the time series of CPOL observations at 0.5-km height aloft JN site for the two typhoons (not shown), indicating that the CPOL observations are reliable and certainly adequate for further qualitative analysis.

The contour lines in Figure 6a represent the frequency of occurrences of Dm-log10Nw pairs of the CPOL retrieved DSDs for Typhoons Matmo and Soudelor, with 0.5 and 0.9 in the contour line represent the 50% and 90% occurrence of the pairs, respectively. The distributions from the 2DVD measurements agree well

with that from CPOL in general. For convective rain, the difference of the mean Dm-log10Nw pair from 2DVD (square boxes) and CPOL (red cross) are negligible, which proves the reliability of the retrieved DSDs.

The DSD parameters (log10Nt, Dm, and LWC) retrieved at 1-km height utilizing constrained gamma model is presented in Figure 11 (corresponding to the black box area in Figure 10a). As can be seen, the Dm values show a more inhomogeneous distribution and mostly around 1–1.4 mm for both typhoon cases (Figures 11b

and 11e). The distribution of log10Nt (Figures 11a and 11d) and LWC (Figures 11c and 11f) values, on the other hand, is highly relevant with the variety of reflectivity ZH shown in Figures 10a and 10d. A higher ZH value usually corresponds to a higher Nt (and LWC) and a near-constant Dm, suggesting that the increasing rain

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Figure 12. (a1–a3 and b1–b3): Contoured frequency by altitude diagrams (CFADs) of Z, ZDR, and KDP using the convective data from the polarimeteric radar measurements within the red rectangle in Figure 10a. Contours represent the frequency of occurrence relative to the maximum absolute frequency in the data sample represented in the CFAD, contoured every 10%, with the minimum contour level at 5%. The black contours in the outermost CFAD represent 1% contours. The averaged profiles of the convective-only Z, ZDR, and KDP, and the correspondingly retrieved log10Nt, Dm, and LWC are also given in panels c1–c3. The gray lines represent the level of 0 °C from the sounding data during each typhoon cases. LWC = liquid water content.

rate is mainly due to an increase in number concentration of small/midsize raindrops. This characteristic is consistent with the 2DVD observations.

4.3. Vertical Structure of Typhoon Microphysics

To better understand the vertical structure of typhoon microphysics, the CFADs of ZH, ZDR, and KDP of the two cases are presented using the CPOL-observed convective-only data within the 30 × 30-km area aloft JN site (red box in Figure 10a) during the passage of the two typhoons as shown in Figure 12. For Typhoon Matmo,

the modal distribution of ZH near the ground is between 32 and 41 dBZ, and the height of the model distri- bution of 35 dBZ is about 5-km height (Figure 12a1), meaning that the convection is relatively weak and

mostly generated within warm clouds. The ZDR values are small with the modal distribution between 0.3 and 0.7 dB near the ground (Figure 12a2), and the values of modal KDP are quite small (Figure 12a3). Despite the slightly larger values of modal ZH and ZDR, the CFADs of the polarimetric parameters for Typhoon Soudelor show similar characteristics with that of Typhoon Matmo (Figures 12b1–12b3),

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Figure 13. Normalized frequency of occurrence of the classified hydrometeors in the convective rain of Typhoons (a) Matmo and (b) Soudelor with altitude.

indicating similar warm rain processes during the formation and evolution of typhoon rainfall. Previous

studies (Carr et al., 2017; Wang et al., 2016; Xu et al., 2008) documented that in warm rain events, ZH tends to decrease rapidly with height above the freezing level, indicating limited quantities of supercooled water and/or large frozen hydrometeors. Similar characteristic can be seen for both Typhoons Matmo and Soudelor. It is worth mentioning that, by using observations from a S-band polarimetric radar, Wang et al. (2016) also demonstrated that the warm rain processes are dominant in the heavy rainfall produced in Typhoon Matmo’s rainband R1. The vertical mean profiles of polarimetric parameters during convective rain are further calculated to reveal the evolution of the vertical microphysical structures (Figures 12c1–12c3). Above 9-km height, Typhoon

Soudelor is characterized with larger ZH but lower ZDR and KDP values than those in Matmo (Figure 12c1). These should be attributed to the existence of higher frequency of aggregates with lower density or number concentration, as well as the presence of a small portion of vertical ice above 10-km height, as further seen from the normalized frequency of hydrometeors in convective rain (Figure 13). One can see that the outlier

distribution of ZH can reach as high as 13 km for Typhoon Soudelor while that of Matmo is about 10-km height (Figures 12a1 and 12b1). Higher echo top also indicates more intensive updrafts (Petersen & Rutledge, 2001). With more water vapor transported by relatively strong updrafts to the upper levels in Typhoon Soudelor (characterized with a more moist environment above 400 hPa; Figures 10c and 10f), the ice crystals would grow faster with more irregular shape and larger size and lower density (Wang et al., 2016) because of more efficient aggregation and riming processes, which generally result in the increase

of the proportion of aggregates that featured as smaller ZDR and KDP. Meanwhile, the vertical aligned ice above 10-km height in Typhoon Soudelor also characterized with a smaller ZDR and KDP when compared with ice crystal that dominant the same layer in Typhoon Matmo.

As suggested by Barnes and Houze (2014, 2016), the increase (decrease) of ZH (ZDR and KDP) with decreasing altitudes for the two typhoons between ~9 and ~5 km (Figure 12c) indicates the growing of ice crystals through aggregation, riming, and melting processes, consistent with the downward increase of the fre- quency of aggregates, graupel, and wet snow shown in Figure 13. Benefiting from the mixing of liquid par- ticles by more water vapor within the stronger updraft, ice particles grow larger through aggregation and more graupel exist by riming process, which then melted to wet snow (Figure 13b), resulting in the larger

ZDR and KDP values in Typhoon Soudelor. Larger ice crystals usually melt into larger raindrops. Below the freezing level (~5 km), the melted convective rain of Typhoon Soudelor, which manifest as higher ZH, ZDR, and KDP values, is consequently more intense than that of Matmo. Despite the numerical differences, however, the trends of the mean convective curves are similar between the two typhoons. That is, the

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averaged vertical profiles of ZH, ZDR, and KDP of the two typhoons are all increase (with different growth rate) toward the ground. According to previous studies (Rosenfeld & Ulbrich, 2003; Wang et al., 2016), these signa- tures may indicate the processes of collision coalescence and/or the accretion of cloud water by raindrops. To better reveal the dominant warm rain processes below the freezing level of the two typhoon cases, the

retrieved DSD parameters (i.e., Dm, log10Nt, and LWC) are also given in Figures 12c2 and 12c3. It shows that Dm increases slightly while log10Nt increases more obviously downward to the ground, resulting in dramatic increase of LWC. According to Kumjian and Prat (2014), the collision coalescence processes lead to increasing

ZH, ZDR, and KDP, but they do not change the water content detected by the radar. These suggesting that the increase of drop size and concentrations in typhoon rainfall should be attribute to the accretion of cloud par- ticles. The very moist environment and low cloud base (Figure 10) ensure that this accretion process occurs over a large depth. Moreover, due to the abundant initially melted small droplets right below the freezing level, the number of small to midsize drops increases more significantly from the conversion of cloud water into rainwater (via accretion of cloud water by small raindrops and condensed large cloud drops). As a result,

log10Nt increases more significantly but Dm only shows slight increase while raindrops descend, with the downward increase of LWC value. These characteristics further confirm that the raindrop concentration, rather than the diameter, dominates the intensity of heavy rainfall in typhoons, as concluded in section 3.2. Through the analysis of 49 selected midlatitude warm rain events, Carr et al. (2017) documented that

the majority of warm rain events were characterized by median ZH values ~(35–40) dBZ, ZDR values ~(0.5–1) dB, and KDP values ~0.1–0.5°/km, implying the presence of relatively small drops. The polarimetric parameters in the present study shows similar values with that of Carr et al. (2017), with the corresponding

averaged Dm and log10Nt near the ground is around 1.1–1.2 mm and 3.6–3.8, respectively. This demon- strates, once more, the presence of relatively high concentration of small drops that is associated with warm rain processes in typhoon rainfall (Carr et al., 2017; Ulbrich & Atlas, 2008; Xu et al., 2008). Overall, despite the numerical difference or the differences in the magnitude of changes of the DSD and polarimetric parameters, above analysis implies that similar warm rain processes dominate the formation and evolution of typhoon rainfall in China.

5. Discussion and Conclusion In this paper, the characteristics of rainfall microphysics in seven typhoon systems that made landfall in China are investigated using the data from the observation of three 2DVDs and a CPOL. The vertical structure of polarimetric parameters, retrieved DSDs, and the corresponding microphysical processes of two typhoon cases are further revealed. The main conclusions of this study can be summarized as follows: 1. The differences in the computed DSD parameters of typhoons in south China coastal region and East

China (in land) are negligible. The averaged Dm-log10Nw pair for convective rain showed only slight differ- ences among the seven typhoons. The differences of DSDs become narrower as rainfall intensity increases, indicating microphysical processes of different typhoons are similar and have reached the equi- librium state during heavy rainfall. The increase of raindrop concentration, rather than drop diameter, plays a crucial role in the intensification of heavy rainfall. Overall, the small and midsize drops jointly dom- inate typhoon rainfall in China and show a higher (lower) raindrop concentration (diameter) compared to that of maritime convective (Bringi et al., 2003), typhoons in Taiwan (Chang et al., 2009), Atlantic tropical cyclones (Tokay et al., 2008), and summer rainfall in East China (Wen et al., 2016). 2. Based on the DSD analysis, a new Z = 147.28R1.38 relationship for the unique DSDs of typhoon systems in China is obtained and it improves the accuracy of QPE. The DSR in our study is more spherical than that of Chang et al. (2009) when the diameter is greater than 1.5 mm. Meanwhile, the μ-Λ relationship has a sig- nificantly lower value of μ for a given Λ than their results. This indicates that the microphysical processes of typhoons in continental China is different from the systems observed in Taiwan.

3. The modal distribution of CFADs of ZH, ZDR, and KDP for Typhoons Matmo and Soudelor derived from polarimetric radar data shows similar characteristics with each other, as further revealed from the iden- tified distributions of hydrometeors. For convective rain, differences in numerical values can be seen from the mean vertical profile of retrieved DSD parameters, but the trend of curves as drops descend is consistent. Despite the numerical differences (or the differences in the magnitude of changes) of the

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DSD and polarimetric parameters, the above analysis implies that similar warm rain processes (conver- sion of cloud water into rainwater via accretion process) dominate the formation and evolution of typhoon rainfall in China. Although interesting findings were obtained on the DSD characteristics and vertical profile of precipitation microphysics of typhoons in continental China, the results are not necessarily conclusive due to the still lim- ited typhoon samples. Long-term observations should be continued as more data are collected. The physical/microphysical processes resulting in such unique precipitation microphysics characteristics are worthy of further studies through more observational studies and/or high-resolution numerical simulations.

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