1

1 Precipitation Microphysical Processes in the Inner Rainband of Tropical Cyclone

2 Kajiki (2019) over the South China Sea Revealed by Polarimetric Radar

3 Hepeng Zheng1, Yun Zhang*1, Lifeng Zhang1, Hengchi Lei2 and Zuhang Wu1

4 1College of Meteorology and Oceanography, National University of Defense Technology,

5 Nanjing, China

6 2Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

7 ABSTRACT

8 Polarimetric radar and 2-D video disdrometer (2DVD) observations provide new

9 insights into the precipitation microphysical processes and characteristics in the inner

10 rainband of tropical cyclone (TC) Kajiki (2019) in the South China Sea for the first time.

11 The precipitation of Kajiki is dominated by high concentrations and small (<3 mm)

12 raindrops, which contribute more than 98% to the total precipitation. The average mass-

13 weighted mean diameter and logarithmic normalized intercept is (1.49 mm, 4.47 log10

14 mm-1 m-3), indicating a larger mean diameter and a lower concentration compared to the

15 TC landed in eastern China. The ice processes of the inner rainband are dramatically

16 different among different stages. The riming process is dominant during the mature

17 stage, while during the decay stage, the aggregation process is dominant. The vertical

18 profiles of the polarimetric radar variables together with ice and liquid water contents in

19 the convective region indicate that the formation of precipitation is dominated by warm

20 rain processes. Large raindrops collect cloud droplets and other raindrops, causing

21 reflectivity, differential reflectivity, and specific differential phase to increase with

22 decreasing height. That is, accretion and coalescence play a critical role in the formation

*Corresponding author: Yun ZhangEmail: zhangyun.1207@163.com

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23 of heavy rainfall. The melting of different particles generated by the ice process has a

24 great influence on the initial raindrop size distribution (DSD) to further affect the warm

25 rain processes. The aloft DSD above heavy rain with the effect of graupel has a wider

26 spectral width compared with the region without the effect of graupel.

27 Key words: South China Sea; cloud precipitation microphysics; polarimetric radar;

28 tropical cyclone rainband htt

29 https://doi.org/10.1007/s00376-020-0179-3

30 Article Highlights:

31 First report on precipitation microphysics characteristics and processes of tropical

32 cyclone over the South China Sea

33 The riming (aggregation) process is dominant during the mature (decay) stage of the

34 inner rainband

35 The formation of precipitation is dominated by warm rain processes, while the

36 melting of different particles generated by the ice processes has a great influence on

37 the initial raindrop size distribution

38 1. Introduction

39 The accurate predictions of tropical cyclone (TC) track, intensity, and quantitative

40 precipitation estimation (QPE) have great impacts on human social activities. Current TC

41 predictions are highly dependent on the descriptions of microphysical processes by

42 numerical weather prediction (NWP) models, which still have large uncertainties (Brown

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43 et al., 2016; Khain et al., 2016; Li et al., 2020b; Wang et al., 2020). A deeper

44 understanding of the microphysical processes is needed. The spiral rainband is an

45 important part of the TC, which directly affects the structure and intensity of the TC

46 (Willoughby, 1989; Wang, 2009). It is critical to study the microphysical characteristics

47 of the TC rainband to improve the NWP model microphysical parameterization and

48 improve the accuracy of TC predictions.

49 Continuously upgraded and refined observations provide valuable insights into the

50 understanding of the microphysical structure of the TC rainband, such as in situ aircraft

51 (Houze et al., 1992) and satellite observations (Hence and Houze, 2012; Chen et al.,

52 2019a), which can be used to improve microphysical parameterizations (Brown et al.,

53 2017; Murphy et al., 2019). Recently, the widespread use of polarimetric radar (PRD) has

54 brought new opportunities for the observation of microphysical processes in TC

55 convection. Compared with conventional radar variables, reflectivity (ZH), and radial

56 velocity, PRD adds variables, such as differential reflectivity (ZDR), specific differential

57 phase (KDP), and the copular correlation coefficient between horizontal and vertical

58 polarizations ( ), which provide more useful information to characterize bulk HV

59 microphysical characteristics (V. N. Bringi, 2001; Kumjian, 2018). The unique

60 observational advantages make PRD observations widely useful in studying cloud

61 precipitation microphysics, such as hydrometeor classification (Vivekanandan et al.,

62 1999), DSD retrieval (Zhang et al., 2001; Sun et al., 2020), QPE (Ryzhkov et al., 2005;

63 Chen et al., 2019b), microphysical fingerprinting (Kumjian and Ryzhkov, 2010,2012;

64 Kumjian and Prat, 2014), microphysical parameterization checking (Brown et al., 2016;

65 Wang et al., 2020), and analyses of squall lines (Wen et al., 2017; Wu et al., 2018a),

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66 mesoscale convection systems (Chang et al., 2014), and winter storms (Moisseev et al.,

67 2015; Schrom and Kumjian, 2016).

68 Although TCs are mostly developed on the ocean, the current PRD observations for

69 TCs are mainly focused on landing TCs because PRDs are deployed on land. At present,

70 there are very few TCs observed by PRD, which have deepened the understanding of the

71 microphysical structure of TCs. May et al. (2008) first analyzed the hydrometeor type

72 distribution in the eyewall and rainband of TC Ingrid (2005). Didlake and Kumjian

73 (2017) documented the vertical profile of PRD variables in different dynamic regions of

74 the eyewall and inner and outer rainbands of Hurricane Arthur (2014). Didlake and

75 Kumjian (2018) further related the microphysics to the asymmetry dynamic structure of

76 Hurricane Irma (2017). Kalina et al. (2017) examined the ice particle distribution and ice-

77 water path of Hurricane Arthur (2014) and Irene (2011).

78 In China, the polarimetric radar observations of TCs recently emerged (Zhao et al.,

79 2019). These studies mainly focused on TCs landing east of China, including analyses of

80 hydrometeor type distribution, kinematics and microphysical process in the inner and

81 outer rainbands (Wang et al., 2016; Wu et al., 2018b), and the relationship between

82 kinematics and microphysics (Wang et al., 2018). Wen et al. (2018) shows that the

83 microphysical processes of TCs between continental China and other regions (e.g.,

84 western Pacific and Atlantic) are different. Previous studies in China have mostly focused

85 on TCs landing at midlatitudes. During the northward movement, the TC rainband may

86 interact with mid-latitude air masses; thus, even for the same TC, the DSD characteristics

87 of different rainbands may have obvious differences (Bao et al., 2019; Bao et al., 2020).

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88 The mid-latitude landing TC is likely to be affected by the mid-latitude air mass, and it is

89 difficult to represent the natural attributes of TCs.

90 Hainan Island is located in a tropical area, and TCs landing in that area maintain their

91 original attributes. PRD observations in this area can be used to study the precipitation

92 attributes of the TC itself. To our knowledge, few studies focused on the microphysical

93 processes of TCs in the South China Sea (SCS) by PRD (Zhao et al., 2019). This study is

94 the early stage of its kind. The differences in the microphysical processes of the TC

95 rainband in different climate regions still need to be revealed, so more in-depth PRD

96 observations are needed to analyze the development process of different TC rainbands. In

97 addition, ice particles have a significant effect on the precipitation process (Brown et al.,

98 2017), while existing studies seldom discuss the interaction of ice processes and warm

99 rain processes in the TC rainband.

100 In the current study, we document for the first time the microphysical characteristics

101 and processes during the inner rainband life cycle of TC Kajiki (2019) in the SCS based

102 on 2DVD and PRD, as well as the potential impact of ice particles on the aloft DSD, to

103 improve our understanding of the microphysical characteristics and processes in the TC

104 rainband and provide a reference for the design and verification of NWP model

105 microphysical parameterization.

106 The paper is organized as follows. Section 2 describes the data and analysis methods.

107 Section 3.1 discusses the characteristics of Kajiki DSDs observed by 2DVD. Section 3.2

108 presents the synoptic conditions and evolution of the inner rainband of Kajiki. The

109 vertical microphysical structure and processes are examined in Section 3.3. The

110 conclusions and discussion are presented in Section 4.

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111 2. Data and Methods

112 2.1 Observations

113 The observation instruments used in this paper mainly include the polarimetric radar

114 located at Haikou (20.00°N, 110.25°E, the hexagonal star in Fig. 1) and the 2-D video

115 distrometer (2DVD) located at the Tunchang station (32.36°N, 110.10°E, the blue cross

116 in Fig. 1, approximately 70 km southwest of the Haikou radar). The best track from the

117 Chinese Meteorological Administration (Ying et al., 2013) of TC Kajiki (2019) and the

118 location of observation sites are shown in Fig. 1. The Haikou (HK) PRD has a 0.987°

119 beamwidth and a 460 km detection distance with a 250 m range resolution and is

120 operated in the VCP21 (volume coverage pattern) model consisting of 9 elevations (0.5,

121 1.5, 2.4, 3.3, 4.3, 6.0, 9.9, 14.6, and 19.5°). The TC Kajiki inner rainband is ~80-160 km

122 away from the PRD. There is a ~1.3 km beamwidth at 80 km range and a ~2.7 km

123 beamwidth at 160 km range. The sampling volume increases as the beam spreads with

124 increasing range and this beam spreading will lead to degrading vertical resolution at

125 large range (Didlake and Kumjian, 2017).

126 The HK PRD is China’s new generation Doppler weather radar (CINRAD), and the

127 data have undergone effective quality control, including calibration and correction for

128 PRD variables before operational observation. The accuracy of quantitative application of

129 polarimetric data, such as DSD retrieval and hydrometeor classification depends heavily

130 on the accurate estimation of ZDR. This study used the method of Zeyong et al. (2019) to

131 measure the system ZDR bias of the HK PRD, and results show that the system ZDR bias is

132 approximately 0.03 dB, which meets the accuracy requirement of ZDR (<0.2 dB). In

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133 addition, the ZDR calculated by 2DVD are well consistent with the PRD, suggesting that

134 the PRD measured ZDR data are well corrected.

135 This paper uses the algorithms of Schuur (2003) and Park et al. (2009) to perform

136 further data quality control procedures based on the output data of HK radar. First, the

137 nonmeteorological returns, such as ground clutter and biological scatters, are discarded,

138 and ZH and ZDR are smoothed along the radial using a five-gate median average and a

139 five-gate running mean, respectively, to reduce fluctuations (Schuur, 2003). Then, noise

140 corrections of ZDR and are performed by the signal-to-noise ratio at horizontal HV

141 polarization (Schuur, 2003), and KDP is estimated by a least square fit of the filtered

142 differential phase. Finally, ZH and ZDR are corrected for attenuation in heavy rain using

143 filtered differential phase (Park et al., 2009). After quality control, the radar data are

144 interpolated onto a cartesian grid at a 1 km horizontal and 0.5 km vertical resolution

145 using the bilinear interpolation method.

146 Before calculating the raindrop size distribution (DSD), some quality control

147 procedures on 2DVD data are performed. First, raindrops with falling velocities 60%

148 lower or higher than the empirical fall velocity-diameter of Brandes et al. (2002) are

149 removed to eliminate the oversampling error. One minute samples with less than 50 drops

150 or rain rates less than 0.1 mm h-1 are removed (Wang et al., 2016; Bao et al., 2019). After

151 quality control, the 2DVD data from 18:00, September 1, 2019 to 18:00, September 3,

152 2019 are used.

153 2.2 Methods

154 2.2.1 Rain Type Classification

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155 Samples with rain rates (measured by 2DVD) less than 10 mm h-1 are considered

156 stratiform; otherwise, they are convective (Chang et al., 2014; Wang et al., 2016; Wen et

157 al., 2018; Wu et al., 2019). A robust separation algorithm was used to identify

158 precipitation type from PRD measurements based on ZH at a 2 km height (Steiner et al.,

159 1995). The classification results are used to calculate the contoured frequency by altitude

160 diagram (CFAD) proposed by Yuter and Houze (1995) to analyze the vertical structure of

161 the inner rainband of TC Kajiki.

162 2.2.2 DSD Retrieval

163 The Marshall and Palmer (MP) distribution was first used for the natural DSD fit

164 (Marshall and Palmer, 1948), Ulbrich (1983) introduced a new shape parameter and

165 proposed the gamma distribution:

0( ) exp( )N D N D D (1)

166 where N(D) (m-3 mm-1) is the number concentration parameter, is 3 10 ( )N m mm

167 the intercept parameter, is the slope parameter, and D (mm) is the equivalent 1 ( )mm

168 volume diameter. Liquid water content (LWC, g m-3) can be computed by the following

169 equations:

max

min

3 310 ( )6

D

w DLWC D N D dD (2)

170 where Dmax, min is the maximum or minimum diameter of raindrops measured by

171 2DVD, and is the water density (1 g cm-3). The normalized intercept 3 ( )w g cm

172 parameter Nw (m-3 mm-1) and the mass-weighted diameter Dm (mm) are defined as

173 follows (Testud et al., 2001; Bringi et al., 2003):

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max

min

max

min

4

3

( )

( )

D

Dm D

D

D N D dDD

D N D dD

(3)

4 3

4

4 10w

w m

LWCND

(4)

174 To better understand the entire space DSD, it is necessary to retrieve DSD based on

175 PRD. Zhang et al. (2001) provided the constrained gamma (CG) model to retrieve DSD,

176 which depends on the relation. This study derives the relationship based on the -

177 truncated method (Vivekanandan et al., 2004). The distribution of is more scattered -

178 when the rain rate is less than 5 mm h-1; thus this study uses DSDs in which the rain rate

179 is greater than 5 mm h-1 to establish the relation (Chang et al., 2009; Wen et al., -

180 2018):

2=-0.0304 +1.3421 -3.2802 (5)

181 The scattering characteristics of raindrops can be simulated by the T-matrix

182 (Mishchenko et al., 1996). PRD variables can be calculated from DSD using PyTMatrix

183 developed by Leinonen (2014).

184 2.2.3 Classification of Hydrometeor types

185 This paper follows the hydrometeor classification algorithms of Park et al. (2009)

186 and Wang et al. (2018), and finally, the hydrometeor types in this study included eight

187 classes: light rain (LR), moderate rain (MR), heavy rain (HR), ice crystal (CR), dry snow

188 (DS), wet snow (WS), graupel (GR), and rain and hail (RH). The same membership

189 functions as Wang et al. (2018) are used.

190 2.2.4 Liquid and Ice Water Content Estimation

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191 To estimate the LWC and ice water content (IWC) in the ice-water mixed phase,

192 both ZH and ZDR are analyzed to separate water and ice using the difference reflectivity

193 (ZDP) method proposed by Golestani et al. (1989). The relation is first RAIN DPZ Z

194 established based on 2DVD observations and is expressed as follows:

,0.82 12.74 (dBZ)RAIN DPZ Z (6)

195 where , and Zh (mm6 m-3), Zv (mm6 m-3) are the horizontal and =10log( )DP h vZ Z Z

196 vertical polarized reflectivity. Zrain (mm6 m-3) is the estimated rain portion of the total Zh.

197 Then, the estimated ice portion Zice (mm6 m-3) is . Finally, LWC and IWC ice h rainZ Z Z

198 can be derived (Carey and Rutledge, 2000; Wen et al., 2017):

,-3 4/73.44 10 rainLWC Z (7)

,-18

3/7 4/70

5.28 101000 ( )720

ice

iZIWC N

(8)

199 where (= 0.917×103 kg m-3) is the ice density and (= 4×106 m-3) is the i 0N

200 intercept parameter.

201 3 Results

202 3.1 Precipitation characteristics of the inner rainbands in TC Kajiki

203 Previous studies have shown that the microphysical characteristics of TC

204 precipitation change with different climate regimes (Wen et al., 2018). To reveal the

205 unique microphysics of Kajiki over the SCS, Fig. 2a shows the Nw and Dm distributions

206 based on 2DVD observations. The results show that the mean Nw and Dm of convective

207 precipitation in different climate regimes are quite different. The convective precipitation

208 of Kajiki is close to the maritime-like convective precipitation defined by Bringi et al.

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209 (2003), but the average diameter is slightly lower (Dm ~ 1.5 mm). Compared with the

210 results in Bao et al. (2019), our Dm is smaller and Nw is larger. Compared with the TC

211 landing in east China given by Wen et al. (2018) and Wang et al. (2016), our Dm is larger

212 and the Nw is smaller. The difference in Nw and Dm may be attributed to different climatic

213 regions, with the caveat that other factors may be contributing as well (such as different

214 storm regions, different storm strengths, land vs. oceanic processes), which need further

215 studies.

216 Fig. 2b shows that the μ-Λ relationship derived in our study has a lower value of μ

217 for a given Λ than that of the studies of Chang et al. (2009) and Bao et al. (2019), which

218 indicates a smaller Dm compared the previous studies mentioned above. Compared with

219 the landfall typhoon in Southeast China studied by Wen et al. (2018), the value of μ for a

220 given Λ in this study is higher, indicating a smaller Dm, which is in line with the results

221 of Fig. 2a. Therefore, the microphysical processes of tropical typhoons over the South

222 China Sea is different from the systems observed in continental China.

223 Fig. 2c-d show the proportion of raindrops with bins of different sizes in rain rate R

224 and total concentration Nt. The results show that small raindrops (0-1 mm) have the

225 highest contribution to Nt, with an average of ~91.4%, but the contribution to R is only

226 ~24.6%. The contribution to Nt of medium raindrops (1-3 mm) decreases to ~8.5%, but

227 the contribution to rain rate increases to ~73.7%. There are very few raindrops greater

228 than 3 mm, and the contribution to rain rate is rare (~1.7%). Thus, these results indicate

229 that the TC precipitation in the SCS is dominated by small and medium (1-3 mm)

230 raindrops with high concentrations.

231 3.2 Evolution of the Inner Rainband

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232 During the landing of TC Kajiki, a rainband was excited in its northeast quadrant.

233 The life history of the rainband is close to 3 hours (01-04 UTC), accompanied by short-

234 term heavy precipitation, and the maximum rain rate exceeds 100 mm h-1. Kajiki has a

235 minimum pressure of 996 hPa, a maximum wind speed of 15 m s-1, and a moving speed

236 of approximately 22 km h-1 during the maturation of the inner rainband. The

237 environmental conditions are shown in Fig. 3. Kajiki is located on the southwest side of

238 the subtropical high, and it is moving southwestward with the guidance of the airflow at

239 500 hPa. The white square area in Fig. 3 is the inner rainband development area, in which

240 the specific humidity is close to 11 g kg-1 at 700 hPa and 16 g kg-1 at 850 hPa. The

241 specific humidity nearly exceeds 13.5 g kg-1 near the southeast side of the target area,

242 which indicates a sufficient water vapor supply. The sounding observed at the Haikou at

243 0000 UTC 2 September is described in Fig. 3d, which shows that the freezing level is

244 approximately 5.3 km.

245 To analyze the evolution of the rainband microphysics processes, the life of the TC

246 Kajiki rainband is separated into three stages: development (Stage 1, S1), mature (Stage

247 2, S2), and decay (Stage 3, S3) according to the echo structure of the rainband. The

248 distributions of ZH, ZDR, and KDP at 0.5° elevation observed by the HK PRD of the three

249 stages are shown in Fig. 4, where the black contours represent the convective area. The

250 rainband is approximately 55 km away from the eye of TC Kajiki, which can be

251 considered a typical inner TC rainband (Wang, 2009; Houze, 2010).

252 The inner rainband was born at 0116 UTC and gradually developed after 0139 UTC

253 (Fig. 4a). At approximately 0207 UTC, the inner rainband was arranged in a spiral

254 around the TC Kajiki eye (Fig. 4b), which is at the mature stage. The inner rainband

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255 structure became fragmented after 0333 UTC (Fig. 4c), which is at the decay stage. There

256 is usually a large stratiform area near the inner rainband away from the TC eyewall (Yu

257 and Tsai, 2012), while the TC Kajiki inner rainband does not have such a stratiform area,

258 which may be related to the weak strength of Kajiki and lack of dense cloud cover. The

259 convective area is arranged linearly during S2, which is similar to the horizontal structure

260 of the squall line at midlatitudes (Wen et al., 2017).

261 3.3 Microphysical Structure and the Dominant Processes of the Rainband

262 The CFAD is a classic method for the analysis of the precipitation structure (Yuter

263 and Houze, 1995), which is widely used in the analysis of the vertical structure of squall

264 lines, mesoscale convective systems (MCSs), and TC rainbands (Hence and Houze, 2011;

265 Wu et al., 2018a; Wu et al., 2018b). To investigate the vertical structure of the inner

266 rainband of TC Kajiki, the CFAD distribution normalized by the maximum occurrence

267 frequency of ZH (Fig. 5a-c), ZDR (Fig. 5e-g), and KDP (Fig. 5i-k) during three stages of the

268 rainband are shown in Fig. 5. The height of the 30 dBZ echo top is often related to the

269 intensity of rainfall (DeMott and Rutledge, 1998).

270 During S1, the 0.1 occurrence frequency of 30 dBZ extends 7 km (Fig. 5a). The ZH

271 at 2 km is concentrated between 30-42 dBZ (Fig. 5a), which is close to S3 and is smaller

272 than S2. The ZDR below the freezing level (Fig. 5e) is higher than S3 and is close to S2,

273 which indicates a higher mean raindrop diameter during the developing and mature

274 stages. Below the freezing level, the ZDR is close to S2 (Fig. 5h), and the KDP is lower

275 than S2 (Fig. 5l), indicating a similar mean raindrop size and low raindrop concentration

276 compared to S2.

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277 During S2, the peak height of the 30 dBZ rises significantly, the 0.1 occurrence

278 frequency extends to 9 km, the 30 dBZ echo height reaches a maximum of 12 km, and

279 the ZH and KDP below the freezing level become larger, suggesting a stronger rainfall

280 intensity. The ZDR under the freezing level is mainly between 0.5-1 dB, which changes

281 little compared with S1. The main changes in ZDR and KDP from S1 to S2 are distributed

282 above the freezing level: the 0.5 occurrence frequencies of ZDR and KDP become

283 significantly wider (Fig. 5f, j). This is because there are more scatterers above the

284 freezing level, which means a deeper convection and more abundant ice particles

285 accordingly.

286 During S3, ZH is obviously weakened, and the 0.1 occurrence frequency of 30 dBZ is

287 reduced to approximately 6 km. At the same time, the ZDR below the freezing level is

288 significantly decreased, which is distributed at approximately 0.5 dB (Fig. 5g), indicating

289 that the average diameter of raindrops decreases. However, the ZDR and KDP above the

290 freezing level are obviously increased (Fig. 5g, k), which is related to the transform of the

291 dominant ice process from riming to aggregation (Moisseev et al., 2015; Li et al., 2018).

292 To examine the evolution of the microphysical processes, the vertical mean profiles

293 of ZH (Fig. 5d), ZDR (Fig. 5h), KDP (Fig. 5l) and (Fig. 5p) of the three stages of the HV

294 inner rainband are shown in Fig. 5. The occurrence frequency of each particle type is also

295 shown in Fig. 5(m-o). The frequency at each height during each stage is the ratio of the

296 number of specific hydrometeor types to the sum of convective grid points. For example,

297 during S2, if 50 graupels are identified at 5.5 km and 100 convective grids identified at 2

298 km using ZH, then the occurrence frequency for graupel at that height is 50/100=0.5.

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299 Dry snow (DS) and graupel (GR) are the dominant hydrometeor types during S1

300 (Fig. 5m), and with the development of the inner rainband, DS and GR are obviously

301 more widely distributed during S2 (Fig. 5n) with increases in ZH (Fig. 5d), ZDR (Fig. 5h)

302 and KDP (Fig. 5l), which indicates that the ice processes are more active. During S3, the

303 occurrence frequency of GR decreased sharply, and DS and crystals (CR) were dominant

304 above the freezing level (Fig. 5o). By comparing the PRD variables during S2 and S3, the

305 dominant ice processes are obviously different. During S2, ZH is higher, while ZDR and

306 KDP are significantly lower compared with S3, which is related to the heavily rimed

307 processes, because particles tend to be round as riming continues (Li et al., 2018). In

308 addition, the proportion of GR increased significantly (Fig. 5n), also suggesting an active

309 riming process during S2.

310 During S3, the ZH above the freezing level is significantly lower than S2, while ZDR

311 and KDP are higher. Above -20 ℃ , ZDR and KDP gradually increased with decreasing

312 height, which is an indicator of the crystal growth zone (Moisseev et al., 2015), and it can

313 be further verified from the classification result of hydrometeor types (Fig. 5o), which

314 shows that CR increases as height decreases. The ZDR peak is clearly above the -20℃

315 level, while the KDP peak is approximately at -20℃ level. The peak value height of KDP

316 appears to be slightly lower than that of ZDR (Fig. 5h, l). The vertical offset from one

317 another is related to the conversion of the crystal growth zone to the aggregation growth

318 zone (Andrić et al., 2012; Moisseev et al., 2015). As the pristine crystals begin to

319 aggregate, ZDR will decrease because low-density aggregates will start masking the

320 contribution of crystals (Moisseev et al., 2015). ZH increases and KDP decreases because

321 the particles become larger but less dense (Li et al., 2020a). However, at the beginning of

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322 aggregation, many early aggregates are formed, which are still oblate particles and will

323 offset the decreasing impact of low concentration results of aggregation (Moisseev et al.,

324 2015). Besides, the proportion of DS increased significantly (Fig. 5o), indicating an

325 active aggregation process during S3.

326 The above analysis used three typical stages of the inner rainband of TC Kajiki. To

327 obtain a comprehensive understanding of the rainband’s life cycle, the time series of

328 averaged profiles of ZH, ZDR, and KDP are shown in Fig. 6 (first row). The results show

329 that the three stages described above well represent the typical stage of the rainband,

330 which reaches development at S1, mature near S2, and decay after S3. During the decay

331 stage of the rainband, the growth of ZDR and KDP at approximately -20℃ can be seen

332 (Fig. 6b, c), which is consistent with the result of Fig. 6. The relative magnitudes of IWC

333 and LWC retrieved from PRD can be used to measure the contribution of the ice-phase

334 process and warm rain process to precipitation formation (Wang et al., 2016; Wen et al.,

335 2017; Wu et al., 2018b).

336 The evolution of the average profile of IWC and LWC during the life of the rainband

337 is shown in Fig. 6d and 6e. With the development of the rainband, the IWC and LWC

338 both gradually increased and reached peak values near S2. In addition, the LWC

339 increased rapidly towards the ground, and the maximum IWC near the freezing level at

340 S2 was approximately 0.4 g m-3, while the LWC near the surface layer was

341 approximately 3 g m-3 (approximately 7.5 times higher than the IWC), which suggests

342 that heavy rainfall was mainly produced by warm rain processes. Wu et al. (2018b) found

343 that the content of GR in the inner rainband is very low compared with the outer rainband

344 in the TC Nida (2016). However, the content of GR during the development and mature

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345 stages in the inner rainband of Kajiki is very high (Fig. 5m-o, Fig. 6f), and the peak value

346 reaches approximately 0.4, which indicates that nearly 40% of the convective area is

347 covered by graupel, so the effect of GR on the precipitation below should be considered.

348 It is helpful to diagnose the potential ongoing microphysical processes from the

349 changes in ZH and ZDR between different heights (Fig. 7a) (Kumjian and Ryzhkov,

350 2010,2012; Kumjian and Prat, 2014). In addition, the combination of LWC and ZDR (Fig.

351 7d) can further distinguish accretion and autoconversion processes (Wang et al., 2018).

352 Fig. 7 shows the modal distribution (frequency greater than 50%, Hence and Houze,

353 2011) and mean values (cross points) of ΔZDR-ΔZH and ΔZDR-ΔLWC during S1-S3 at 4

354 km to 3 km (blue) and 3 km to 2 km (red). The modal distributions of ΔZDR-ΔZH and

355 ΔZDR-ΔLWC are mostly distributed in the first quadrant (Fig. 7), and all the mean values

356 are located in the first quadrant, which indicates that accretion and coalescence are

357 dominant during the life of the inner rainband of Kajiki. In addition, the changes in the

358 mean values of ΔZDR-ΔZH and ΔZDR-ΔLWC aloft are higher than those at low levels,

359 suggesting a more active accretion aloft, which can also be seen in the model simulation

360 results of Wang et al. (2020), who found that the mass content transfer rates of accretion

361 are the highest near the freezing level and gradually decrease with decreasing height. The

362 modal distributions of ΔZDR-ΔZH and ΔZDR-ΔLWC during S1 and S2 are obviously

363 wider than that of S3, indicating that the rainband has a stronger warm rain process in the

364 development and mature stages.

365 The raindrop size distributions (DSDs) can directly reflect rain-forming physical

366 processes. Rosenfeld and Ulbrich (2003) analyzed the impact of coalescence, breakup,

367 coalescence and breakup combined, accretion, evaporation, and size sorting (acting

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368 alone) on the DSD (in their Fig. 3). Although there is often more than one microphysical

369 process in reality, it provides insight into the various processes of natural DSDs. Fig. 8

370 shows the three parameters of the gamma DSD model retrieved by the CG model. The

371 intercept, shape, and slope parameter both decrease with decreasing height (Fig. 8,

372 columns 1-3). From 4 km to 2 km, Dm gradually increased during S1-S3, and the DSD

373 spectral width broadened, reflecting the coalescence process.

374 During S1, the DSD changes from 4km to 2km show that the concentrations of the

375 medium (0.5-2.2 mm) raindrops decrease while the small (<0.5 mm) and big (>2.2mm)

376 raindrops increase (Fig. 8d). At the developing stage of convective cells, the updrafts

377 often exist (Wang et al., 2018), which can transport medium and small raindrops upward.

378 Besides, the coalescence may occur when the raindrops fall, causing a bin transfer from

379 the middle raindrops to the large raindrops. The above two reasons can lead to a decrease

380 in the concentration of medium raindrops at a low level.

381 The increase in the concentrations of small and big raindrops is related to the

382 autoconversion and accretion. That is, the conversion of cloud droplets to raindrops

383 increases the concentration of small raindrops, and at the same time, some small particles

384 are collected by large raindrops, indicating the accretion and coalescence process

385 (Rosenfeld and Ulbrich, 2003), which is consistent with the conclusion of Fig. 7a, d.

386 During S2, as height decreases, Dm increases from 1.37 to 1.43 mm, and the

387 concentration of both small and large particles increases (Fig. 8h), which embodies the

388 process of autoconversion and accretion as claimed in S1. The DSD during S3 (Fig. 8l)

389 changed significantly (a smaller Dm and DSD spectral width) compared with S1 and S2,

390 and DSD did not change much as the height decreased, indicating that the size of

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391 raindrops tended to be uniform during rainband decay, which is in agreement with the

392 smaller ZDR (Fig. 5h) and larger (Fig. 5p).HV

393 The formation of large raindrops below the freezing level is mainly affected by the

394 melting of DS (Fig. 5o) during S3, while GR during S2. The obvious change of DSD

395 between S2 and S3 at the 4 km level (Fig. 8h, l) indicates that the initial DSD below the

396 freezing level can be affected by the ice-phase particles above. Wang et al. (2018) found

397 that the heavy rainfall in the updraft region is mainly contributed by the warm rain

398 process, while outside the updraft region, the melting of graupel mainly produces heavy

399 precipitation. The inner rainband usually tilts away from the center of the TC affected by

400 the wind field in the TC environment (Yu and Tsai, 2012) and graupel usually appears on

401 the downwind side near the freezing level (Wang et al., 2018), indicating the two heavy

402 rainfall formation mechanisms often work separately.

403 To better understand the vertical structure of the inner rainband, the average of all

404 vertical cross sections VCSs (gray line in Fig. 4b) during S2 are shown in Fig. 9. Judging

405 from the average profiles of ZH, ZDR, and KDP (Fig. 9a-c), the tilt of the rainband is not

406 obvious, and graupel exists almost totally above the heavy rainfall (Fig. 9d). The graupel

407 distribution is different from that of the TC Matmo (2014) inner rainband (Wang et al.,

408 2018). The inner rainband studied by Wang et al. (2018) is located in the region ~120-

409 180 km from the TC center, while ~40-80 km in this study. In addition, the strength of

410 the Matmo (minimum sea level pressure: 992 hPa; maximum surface wind: 20 m s-1) and

411 the Kajiki (996 hPa, 15 m s-1) is also quite different. The difference of the rainband

412 location and storm strength will lead to the difference in the tilted structure of the

413 rainband, and then affect the distribution of graupel. The vertical structure and

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414 hydrometeor type distribution of the Kajiki inner rainband are similar to the squall line

415 studied by Wen et al. (2017), but the intensity of the convection is lower. Therefore,

416 although the formation of heavy rain (HR) is mainly contributed by the warm rain

417 process, due to the less tilted structure of the rainband, the influence of the melting of ice

418 particles, such as graupel and snow above, is worth noting.

419 To further analyze the effect of GR on aloft DSD, this paper selects the mature stage

420 of the rainband of Kajiki to study the region above the HR with the effect of GR and

421 without GR (non-GR), separately. The classification of GR vs. non-GR is based on

422 having at least one GR pixel in a column. Finally, 394 GR and 143 non-GR grid points

423 above the HR were identified. Fig. 10 shows the DSD above the HR and below the GR

424 (Fig. 10a) and non-GR regions (Fig. 10b) during S2. As the height decreases, the Dm and

425 the DSD spectral width gradually increase, indicating the coalescence process, and the

426 concentration of all diameter bins has increased (Fig. 10a-b), suggesting autoconversion

427 and accretion processes. The DSD at 4 km, however, has a clear difference between the

428 GR and non-GR regions.

429 In the GR region, the DSD spectral width is significantly wider and the concentration

430 is higher compared with the non-GR region, while the DSD spectral width is obviously

431 narrower in the non-GR region, which is close to the 4 km DSD shape of S3 (Fig. 8l). In

432 the non-GR region, the ice-phase particles above the freezing level are mainly dominated

433 by DS generated by the aggregation process, which has a lower density compared with

434 GR generated by the riming process. The diameter of raindrops below the freezing level

435 is usually very small, originating from the active autoconversion process, and begins to

436 increase by the coalescence of other cloud drops or raindrops during the descent process.

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437 The generation of large raindrops near the freezing level usually comes from the melting

438 of ice-phase particles above the freezing level.

439 Above the HR region, the Dm and DSD spectral width at 4 km below the GR region

440 are significantly higher than those of the non-GR area, which indicates that the melting of

441 GR produced by the riming process produces larger raindrops compared with DS

442 produced by the aggregation process. Because the accretion efficiency is proportional to

443 the size of raindrops (Brandes et al., 2006), the appearance of large raindrops melted by

444 GR will broaden the DSD spectral width aloft and enhance the coalescence and accretion

445 of the warm rain process, resulting in heavier rainfall.

446 In general, the LWC near the ground is much higher than the IWC near the freezing

447 level, and the formation of HR is mainly affected by the warm rain process. The ice

448 particles, such as GR and DS, generated by the ice processes will also play an important

449 role in the warm rain process by affecting the DSD aloft. Therefore, the two mechanisms

450 of heavy rainfall reported by Wang et al. (2018) work together in the inner rainband of

451 Kajiki. Compared with the DS, the melting of the GR will produce larger raindrops,

452 increase the DSD spectral width aloft, and enhance the warm wain process, such as

453 accretion and coalescence, to form heavy rainfall.

454 4 Conclusions and Discussion

455 In this paper, the ice processes and warm rain processes together with precipitation

456 characteristics of the TC Kajiki (2019) inner rainband over the SCS were examined using

457 PRD and 2DVD. The main findings and conclusions obtained in this article are described

458 as follows:

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459 The TC Kajiki precipitation in the SCS is dominated by high concentration small and

460 medium raindrops (D < 3 mm), which contribute about 98.3% to the total precipitation.

461 The average Dm of convection is 1.49 mm, and the log10Nw is 4.47, with obvious

462 characteristics of maritime convection rainfall (Bringi et al., 2003). Compared with TCs

463 in eastern China (Wang et al., 2016; Wen et al., 2018), the average diameter of raindrops

464 is higher and the concentration is lower, which indicates that the tropical cyclone over the

465 SCS has different microphysical processes compared with other climatic regions.

466 The ice microphysical process in the TC inner rainband has obvious differences in

467 different stages. The content of graupel has a high correlation with the degree of rainband

468 development. The riming process is more active as the inner rainband develops, and the

469 graupel content increases gradually and reaches its maximum during the mature stage.

470 The riming process is dominant during the mature stage, while during the decay stage, the

471 aggregation process is dominant.

472 The freezing level is ~5.3 km, with a high specific humidity (~16 g kg-1) near the

473 ground, which is conducive to the development of warm rain processes. The estimated

474 vertical profiles of IWC and LWC and the distribution of ΔZDR-ΔZH and ΔZDR-ΔLWC

475 show that heavy rainfall originates mainly through warm rain processes, primarily from

476 accretion and coalescence processes. In addition, the concentration also increases at small

477 raindrops during the mature stage of the inner rainband, indicating that the

478 autoconversion process is also important.

479 The inner rainband of Kajiki is more upright compared with the previous study of

480 Wang et al. (2018). Although warm rain processes, such as autoconversion, accretion,

481 and coalescence, play a critical role in the formation of heavy rainfall, the melting of ice

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482 particles also have an impact on the warm rain processes. The formation of the DSD

483 below the freezing level is closely related to the ice particles above. Above the heavy rain

484 region, the DSD near the freezing level with the effect of graupel has a wider spectral

485 width and higher Dm and Nt. Compared with the dry snow formed by aggregation,

486 graupel melting will generate larger raindrops and broaden the DSD aloft, which can

487 enhance the accretion and coalescence processes below the freezing level and promote

488 the formation of heavy rainfall.

489 Recent NWP model and observation results show that there are complex potential

490 connections between the ice and liquid processes (Brown et al., 2017; Li et al., 2020a),

491 while the current study has difficulty quantitatively measuring the promotion of ice

492 processes to warm rain processes. The next step is to further study the transformation of

493 ice particles in the TC convection area near the freezing level to evaluate the effect of the

494 melting of ice particles on the rainfall process below.

495 Acknowledgments

496 This work was primarily supported by the National Key Research and Development

497 Program of China (2018YFC1507304) and the National Natural Science Foundation of

498 China (41865009 and 41975066). Datasets for this research are available at

499 https://pan.baidu.com/s/15VzggfqaIeyOlZKpxkbszQ, password: xnng. We thank

500 http://weather.uwyo.edu/upperair/np.html for providing sounding data. We thank Jussi

501 Leinonen for his software: PyTMatrix (https://github.com/jleinonen/pytmatrix). Thanks

502 also due to the editors and reviewers for their critical and constructive comments.

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692

693 Fig. 1. The location of the Haikou polarimetric radar (HK, red hexagonal star), 2-D video

694 distrometer (2DVD, blue cross, approximately 70 km southwest of the HK radar), along

695 with topography (meter) and the best track (solid green line and red dots) from the China

696 Meteorological Administration (CMA) every 3 h from 2100 UTC 1 September to 0600

697 UTC 2 September 2019.

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698

699 Fig. 2. Raindrop characteristics based on the observation of the 2DVD. (a): Distribution

700 of the normalized intercept parameter log10Nw and mass-weighted diameter Dm for

701 convective (red circles) and stratiform (blue points) TC Kajiki. The black circle indicates

702 the average convective result (along with ± standard deviation). The two outlined

703 rectangles correspond to the maritime (left) and continental (right) convective clusters

704 reported by Bringi et al. (2003). ORR represents the convective outer-rainband rain and

705 CFR represents the coastal-front rain defined by Bao et al. (2019). (b): The relation -

706 for TC Kajiki rainfall. The red solid line is the relation derived by the least square -

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707 method based on the blue scatter points (rain rate, R>5 mm h-1). The green solid line

708 represents Dm contour. (c-d): The contribution of raindrops in different diameter ranges to

709 total concentration Nt and rain rate R in convective (Con. red color) and stratiform (Str.

710 blue color) precipitation. in

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711

712 Fig. 3. (a-c): Environmental conditions of TC Kajiki at 0200 UTC, 2 September 2019,

713 including specific humidity (g kg-1, shaded color), wind fields (m s-1, black arrows), and

714 geopotential height (m, solid black lines) at 500, 700, and 850 hPa from the ERA5

715 dataset. The white boxes in Fig. 3a-c represent the area of the inner rainband system

716 (shown in Fig. 4). (d) Sounding information of Haikou station at 0000 UTC, 2 September

717 2019. The green and red color solid lines represent the dew point temperature and

718 temperature profiles, respectively. The black solid curve is the ascending path of a

719 surface-based parcel.

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720

721 Fig. 4. The reflectivity (ZH, a, b, c), differential reflectivity (ZDR, d, e, f), and specific

722 differential phase (KDP, g, h, i) at 0.5° elevation observed by HK polarimetric radar at

723 three stages: 0139 UTC (S1, first column), 0207 UTC (S2, second column), and 0333

724 UTC (S3, third column) on 2 September 2019. The black contours represent the

725 convective area, the purple dot is the location of the TC eye, the purple pentagram is the

726 location of the 2DVD, and the gray lines in (b) are the locations of the cross sections

727 described in Fig. 9.

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728

729 Fig. 5. (a-c): Contoured frequency by altitude diagrams (CFADs) (contours represent the

730 frequency of occurrence relative to maximum absolute frequency) of ZH in the convective

731 area during three stages of development (S1), mature (S2), and decay (S3). The solid gray

732 line in (a) represents the contours of the 0.01 and 0.5 occurrence frequencies. (d):

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733 Average profiles of ZH from the three stages. (e-h) and (i-l): As in (a-d) but for ZDR and

734 KDP. (m-o) Frequency of each hydrometeor class changing with height during the three

735 stages. (p): Average profiles of from the three stages. The three dotted lines are HV

736 heights of 0, -10, and 20℃.in

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737

738 Fig. 6. (a): Time series of average profiles of ZH, where the three gray solid lines

739 represent the three stages of development (S1), mature (S2), and decay (S3). (b-e): As in

740 (a) but for ZDR, KDP, ice water content (IWC), and liquid water content (LWC). (f): The

741 occurrence frequency of graupel (color shaded) changing with height during the life of

742 the inner rainband. The red line is the sum of the frequencies at all heights.

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743

744 Fig. 7. (a): Joint frequency distribution of ΔZDR-ΔZH during development normalized by

745 the maximum frequency in each region (only the 50% contour lines are shown). The blue

746 (red) color represents the changes from 4 km to 3 km (3 km to 2 km), and the cross point

747 is the location of the mean value. (b-c): As in (a) but for the mature and decay stages. (d-

748 f): As in (a-c) but for ΔZDR-ΔLWC.in pre

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749

750 Fig. 8. (a-c): The boxplot of log10N0, and at 4 km (blue color), 3 km (green color),

751 and 2 km (red color) during S1. (d): The DSD at 4 km (blue color), 3 km (green color),

752 and 2 km (red color) during S1, and Dm and total concentration Nt are shown at the

753 lower-left corner of the picture. (e-h) and (i-l): As in (a-e) but for the mature and decay

754 stages.

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755

756 Fig. 9. Average vertical cross sections of ZH, ZDR, and KDP (a-c). The cross-sectional

757 position is the solid gray line in Fig. 4b, and point A is on the side of the TC eye. (d): The

758 most likely hydrometeor type among all cross sections.in pre

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759

760 Fig. 10. The DSD above heavy rainfall at 4 km, 3 km, and 2 km during S2. (a): The

761 region affected by graupel (394 grid points). (b): The region not affected by graupel (143

762 grid points).

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