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DOI:10.2151/jmsj.2020-015

J-STAGE Advance published date: February 1st 2020

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1

Raindrop size distribution characteristics of Indian and Pacific Ocean tropical cyclones

observed at India and Taiwan sites

Jayalakshmi Janapati1, Balaji Kumar Seela1, 2, Pay-Liam Lin1 ,3, 4*, Pao. K. Wang5, 6, Chie-

Huei Tseng7, K. Krishna Reddy8, Hiroyuki Hashiguchi9, Lei Feng7, Subrata Kumar Das10,

and C. K. Unnikrishnan11

1Institute of Atmospheric Physics, Department of Atmospheric Sciences, National Central

University, Zhongli district, Taoyuan City, Taiwan 2Taiwan International Graduate Program, Earth System Science Program, Research Center

for Environmental Changes, Academia Sinica, Taipei City, Taiwan 3Earthquake-Disaster & Risk Evaluation and Management Center, National Central

University, Zhongli district, Taoyuan City, Taiwan. 4Research Center for Hazard Mitigation and Prevention, National Central University, Zhongli

district, Taoyuan City, Taiwan 5Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison,

Madison, Wisconsin, USA, 6Research Center for Environmental Changes, Academia Sinica, Taipei City, Taiwan.

7Taiwan Ocean Research Institute, National Applied Research Laboratories (NARLabs),

Taipei City, Taiwan. 8Semi-arid zonal Atmospheric Research Centre, Department of Physics, Yogi Vemana

University, Kadapa, Andhra Pradesh, India 9Research Institute for Sustainable Humanosphere, Kyoto University, Kyoto, Japan.

10Indian Institute of Tropical Meteorology, Pune, India. 11National Centre for Earth Science Studies, ESSO-MoES, Government of

India,Thiruvananthapuram, India.

*Correspondence to:

Prof. Pay-Liam Lin

Institute of Atmospheric Physics, Department of Atmospheric Sciences

National Central University,

No. 300, Zhongda Rd., Zhongli District, Taoyuan City 32001, Taiwan

Phone: +886-03-426-9075, 03-422-7151 ext. 65509

E-mail: tliam@pblap.atm.ncu.edu.tw

2

Abstract 1

We made an effort to inspect the raindrop size distribution (RSD) characteristics of 2

Indian Ocean and Pacific Ocean tropical cyclones (TCs) using ground-based disdrometer 3

measurements from observational sites in India and Taiwan. Five TCs (2010–2013) from the 4

Indian Ocean and six TCs (2014–2016) from the Pacific Ocean were measured using particle 5

size and velocity disdrometers installed in south India and south Taiwan, respectively. 6

Significant differences between the RSDs of Indian Ocean and Pacific Ocean TCs are noticed. 7

For example, a higher number of small drops is observed in Indian Ocean TCs, whereas Pacific 8

Ocean TCs have more mid-size and large drops. RSDs of Pacific Ocean TCs have higher mass-9

weighted mean diameter and lower normalized intercept parameter than Indian Ocean TCs. 10

RSD values quantified based on rainfall rate and precipitation types also showed similar 11

characteristics between Indian Ocean and Pacific Ocean TCs. The radar reflectivity and rainfall 12

rate (Z-R) relations and shape and slope (μ-Λ) relations of both oceanic (Indian and Pacific) 13

TCs are found to be distinctly different. Possible causes for the dissimilarities in RSD features 14

between Indian Ocean and Pacific Ocean TCs are due to relative differences in water vapor 15

availability and convective activity between TCs in these two oceanic basins. 16

17

Keywords: tropical cyclones (TCs), Raindrop size distribution (RSD), rainfall rate 18

19

20

21

22

23

24

25

3

1. Introduction 26

Tropical cyclones (TCs) are a severe natural hazard that cause significant property 27

damage and loss of life when making landfall, in part due to torrential rainfall. The study of 28

raindrop size distribution (RSD) in TCs can be useful for better understanding cloud 29

microphysics and improving the cloud models (Tokay et al. 2008; Zhang et al. 2006), and 30

assessing rainfall-caused erosivity (Janapati et al. 2019). There have been reports on RSD 31

characteristics of TCs around the globe. Over the Atlantic Ocean, Merceret (1974) found no 32

distinct differences in RSD characteristics between the rainbands and eyewall region of 33

Hurricane Ginger. Additionally, Jorgensen and Willis (1982) did not observed much variation 34

in radar reflectivity and rainfall rate (Z-R) relations between the eyewall and outer rainband 35

regions at 3 km above the surface and below. Using airborne radar and disdrometer 36

measurements, Marks et al. (1993) observed significant differences in the eyewall and outer 37

rainband Z-R relations (eyewall: Z = 253R1.3; outer rainband Z = 341R1.25; total Z = 311R1.27). 38

A clear demarcation in RSD characteristics from before and during the passage of Hurricane 39

Helene (2000) was observed by Ulbrich and Lee (2002), who found that Z-R relations (Z = 40

118R1.48) of TCs differ from those of tropical Z-R (Z = 250R1.2) and default Z-R relations (Z = 41

300R1.4). An analysis of seven Atlantic TCs by Tokay et al. (2008) revealed the presence of 42

more small and mid-size drops and fewer large drops, with a maximum diameter seldom 43

exceeding 4 mm. Chang et al. (2009) explored drop shape and RSD characteristics of typhoon 44

rainfall during landfall over north Taiwan and found a maritime convective type RSD for 45

typhoon systems. They mentioned that typhoon convective systems influenced by Taiwan’s 46

terrain had RSD features of intermediate to maritime and continental clusters. Radhakrishna 47

and Narayana Rao (2010) explored seasonal variations of cyclonic and non-cyclonic RSD 48

characteristics over southern India and perceived large numbers of small and medium drops 49

with an almost absence of large drops in cyclonic precipitation. With the aid of the Particle 50

4

Size and Velocity (Parsivel) disdrometer, Chen et al. (2012) analyzed the RSD characteristics 51

of Typhoon Morakot (2009) and noted substantial differences between precipitation 52

characteristics of the eyewall and outer rainbands. Wind profiler and disdrometer observations 53

from Kim et al. (2013) showed strong and weak bright bands in the rainband and eyewall 54

regions of Typhoon Kompasu, respectively. Further, they noticed a higher mass-weighted 55

mean diameter (Dm) in the outer rainband than in the eyewall region. Differences between 56

cyclonic and northeast monsoon thunderstorm rainfall RSDs was detailed by Kumar and Reddy 57

(2013). Over east India, Bhattacharya et al. (2013) noticed stratiform features before and after 58

Tropical Cyclone Aila in the Bay of Bengal. Kumari et al. (2014) illustrated RSD differences 59

between two TCs that passed over southern India. Over Korea, Suh et al. (2016) analyzed the 60

RSD characteristics of nine rainfall groups and noticed smaller Dm and normalized intercept 61

parameter (Nw) values in typhoon rainfall than in other rainfall categories. Higher 62

concentrations of small drops in TC eyewalls and large drops in outer rainband regions was 63

observed over Darwin, Australia, by Deo and Walsh (2016). Wang et al. (2016) demonstrated 64

the microphysical characteristics in the rainbands of Typhoon Matmo (2014) over eastern 65

China using ground-based radar and disdrometer measurements. Kim and Lee (2017) perceived 66

different microphysical characteristics between stratiform and mixed stratiform-convective 67

regimes of the rainbands of Typhoon Bolaven (2012) over South Korea. Janapati et al. (2017) 68

detected clear differences in RSD characteristics in precipitation of TCs from the Bay of 69

Bengal, before and after landfall. Recently, Wen et al. (2018) investigated the RSD 70

characteristics of seven typhoons observed over China, and noticed higher raindrop 71

concentrations and lower rain drop diameters for typhoon convective precipitation than the 72

maritime convective clusters of Bringi et al. (2003). 73

74

5

Thus far in the literature, TC RSD have been limited to case studies or to particular 75

oceanic regions. Additionally, there have been no comparison studies of RSD characteristics 76

between one oceanic region and another. Hence, this study reports on RSD differences between 77

Indian Ocean and Pacific Ocean TCs using Parsivel disdrometer data from stations in southern 78

India and Taiwan. The remainder of this paper is ordered as follows: Section 2 outlines the data 79

and methodology, Section 3 provides results and discussion, and Section 4 gives a summary. 80

81

2. Data and methodology 82

2.1 Tropical cyclones 83

A total of five Indian Ocean TCs (2010–2013) and six Pacific Ocean TCs (2014–2016) 84

were measured using Parsivel disdrometers at Yogi Vemana University in Kadapa, India 85

(14.4742°N, 78.7098°E, 138 m above sea level) and at Shu-Te University in Kaohsiung, 86

Taiwan (120.3746oE, 22.7621oN, 9 m above sea level). The tracks of these TCs and locations 87

of the disdrometers (indicated by red stars) are shown in Fig. 1. Track information for the 88

Indian Ocean TCs was obtained from the India Meteorological Department (IMD) best track 89

archive (http://www.rsmcnewdelhi.imd.gov.in). Track information for the Pacific Ocean TCs 90

was obtained from the Japan Meteorological Agency (JMA) best track database 91

(https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/besttrack.html). Table 1 lists 92

the Indian Ocean and Pacific Ocean TCs used in this study, with their names, life span, 93

disdrometer measurement periods, total rain accumulations, and rainfall rate statistics 94

(maximum, mean, and standard deviation). Rainfall amounts for a location are considered to 95

be attributed to TCs if that location is within 500 km of the TC center (Deo and Walsh 2016; 96

Jiang and Zipser 2010; Prat and Nelson 2013; Wu et al. 2015). Hence, in this study, the RSD 97

measurements of selected TCs (as listed in Table 1) from Parsivel disdrometers in south Taiwan 98

6

and south India are considered for further analysis if the distance between the disdrometer site 99

and the TC center is 500 km or less (Deo and Walsh 2016). 100

101

2.2 Parsivel disdrometer 102

The Parsivel (Löffler-Mang and Joss 2000; Yuter et al. 2006) is a laser-based 103

disdrometer that can simultaneously measure precipitating particle size (liquid particles: 0.2 to 104

5 mm and solid particles: 0.2 to 25 mm) and fall speed (0.2 to 20 ms−1) and records them in 105

32×32 drop size and fall velocity classes. This instrument has an optical sensor that generates 106

a 650 nm 3 mW laser beam 180 mm long, 30 mm wide, and 1 mm high. A decrease in the laser 107

signal occurs when precipitating particles pass through the light sheet. The signal deviation 108

amplitude is a measure of particle size and the signal deviation duration allows estimation of 109

particle fall velocity. Detailed explanation of the Parsivel disdrometer along with the 110

assumptions used to determine hydrometeor size and velocity can be found in Löffler-Mang 111

and Joss (2000), Battaglia et al. (2010), Jaffrain and Berne (2011), Friedrich et al. (2013b), 112

Tokay et al. (2014) and references within. 113

114

Parsivel experiences some instrumental errors in strong wind, marginal effect, and 115

splashing effect conditions. Particles falling through the edges of the sample area appear as 116

small particles moving faster than the empirical relationship between fall velocity and particle 117

diameter, a phenomenon known as the marginal effect. Raindrops that hit the surface of the 118

Parsivel itself break apart and bounce back into the sampling area, a phenomenon known as 119

the splashing effect. To minimize these measurement errors, quality control procedures are 120

applied to disdrometer data. In this study, the first two size bins of disdrometer data are 121

discarded because of their low signal-to-noise ratio. Further, 1-min samples with fewer than 10 122

drops or rainfall rates less than 0.1 mm h−1 are considered noise and are thus discarded (Tokay 123

7

et al. 2013). Additionally, raindrops with diameters greater than 6 mm and fall speeds 60% 124

above or below the Atlas et al. (1973) empirical fall velocity-diameter relation (Jaffrain and 125

Berne 2011) are discarded (Fig. 2). During the passage of TCs at both observational sites, we 126

did not observe any solid precipitation with either disdrometer. 127

128

The quality-controlled raw spectra are used to estimate raindrop concentration N(Di) (m−3 129

mm−1) using the following equation (Friedrich et al. 2013b; Tokay et al. 2014) . 130

𝑁(𝐷𝑖) = ∑𝑛𝑖𝑗

𝐴𝑒𝑓𝑓 ∆𝑡 𝑉(𝐷𝑖) ∆𝐷𝑖 − − − − − (1)32

𝑗=1 131

To compute N(Di) for each diameter class (i = 1 to 32), number concentrations were summed 132

over all velocity classes (j = 1 to 32) (Friedrich et al. 2013b). Here, nij is the number of drops 133

reckoned in size bin i (i = 1 to 32), velocity bin j (j = 1 to 32), Δt (s) is the sampling time (60 134

seconds), ΔDi (mm) is the width of the ith class diameter, V(Di) (m s−1) = 135

9.65−10.3exp(−0.6*Di) is the fall velocity of the ith size bin drops (Atlas et al. 1973), and Aeff 136

(m2) is the effective sampling area expressed by 𝐴𝑒𝑓𝑓 = 10−6 × 𝐿 (𝐵 −𝐷𝑖

2) (Battaglia et al. 137

2010; Löffler-Mang and Joss 2000; Tokay et al. 2014), in which Di (mm) is the ith bin size drop 138

diameter, L is the length of the Parsivel beam (180 mm), and B is the width of the Parsivel 139

beam (30 mm). 140

The rainfall rate R (mm h−1), sixth moment (Rayleigh) reflectivity/radar reflectivity factor Z 141

(mm6 m−3), liquid water content W (g m−3), and total number concentration (Nt, m−3) are derived 142

(Friedrich et al. 2013a; Friedrich et al. 2013b) by the following expressions. 143

𝑅 (𝑚𝑚 ℎ−1) = 6π × 10−4 ∑ 𝑁(𝐷𝑖) 𝐷𝑖3 𝑉(𝐷𝑖) 𝛥𝐷𝑖 − − − − − (2)

32

𝑖=1

144

𝑍 (𝑑𝐵𝑍) = 10𝑙𝑜𝑔10 ∑ 𝑁(𝐷𝑖) 𝐷𝑖6 ∆𝐷𝑖 − − − − − (3)

32

𝑖=1

145

146

8

𝑊 (𝑔 𝑚−3) =𝜋

6× 10−3𝜌𝑤 ∑ 𝑁(𝐷𝑖) 𝐷𝑖

3 ∆𝐷𝑖

32

𝑖=1

− − − − − (4) 147

148

where, ρw (1 g cm−3) is the density of water. 149

𝑁𝑡 (𝑚−3) = ∑ 𝑁(𝐷𝑖)∆𝐷𝑖 − − − − − (5)

32

𝑖=1

150

151

The nth order moment (in mmn m−3) of the drop size distribution can be expressed as 152

𝑀𝑛 = ∫ 𝐷𝑛𝐷𝑚𝑎𝑥

𝐷𝑚𝑖𝑛

𝑁(𝐷)𝑑𝐷 − − − − − −(6) 153

154

where n = 3 for the third moment, 4 for the fourth moment, and 6 for the sixth moment of the 155

size distribution. 156

The mass-weighted mean diameter (Dm, mm), shape parameter (µ, dimensionless), and slope 157

parameter (Λ, mm−1) are obtained (Bringi et al. 2003; Tokay and Short 1996; Ulbrich 1983) 158

from the third, fourth, and sixth moments of the size distribution as 159

𝐷𝑚 =𝑀4

𝑀3− − − − − −(7) 160

The slope parameter Λ (mm−1) is given (Tokay and Short 1996) by 161

Ʌ =( µ + 4)𝑀3

𝑀4− − − − − −(8) 162

where µ is the shape parameter (dimensionless) and is given (Tokay and Short 1996) by 163

µ =(11𝐺 − 8) + √𝐺(𝐺 + 8)

2(1 − 𝐺)− − − − − −(9) 164

where G is 165

𝐺 =𝑀4

3

𝑀32𝑀6

− − − − − (10) 166

9

The normalized intercept parameter Nw (mm−1 m−3) is defined by Bringi et al. (2003) as 167

𝑁𝑤 =44

𝜋𝜌𝑤(

103𝑊

𝐷𝑚4 ) − − − − − −(11) 168

where W (g m−3) represents the liquid water content for the corresponding size distribution. 169

170

The mass spectrum standard deviation σm (mm) can be expressed in terms of Dm and N(D) 171

(Thurai et al. 2014; Ulbrich 1983; Williams et al. 2014) as 172

𝜎𝑚 = [∑ (𝐷𝑖 − 𝐷𝑚)2 𝑁(𝐷𝑖) 𝐷𝑖

3ΔDi32𝑖=1

∑ 𝑁(𝐷𝑖) 𝐷𝑖3 𝑑𝐷32

𝑖=1

]

12⁄

− − − − − −(12) 173

174

2.3 MODIS and ERA-Interim data 175

Along with the disdrometer measurements, Moderate Resolution Imaging 176

Spectroradiometer (MODIS) and European Centre for Medium-Range Weather Forecasts 177

(ECMWF) Interim Re-Analysis (ERA-Interim) datasets over the observational sites in Taiwan 178

(22.75o–22.875oN, 120.25o–120.375oE) and India (14.375o–14.5oN, 78.625o–78.75oE) were 179

used for the TCs dates listed in Table 1. Convective available potential energy (CAPE, J kg−1: 180

available every three hours) and vertical integral water vapor (W, kg m−2; available every six 181

hours) from ECMWF ERA-Interim (Dee et al. 2011) with a 0.125o × 0.125o grid resolution are 182

used. Cloud top temperatures (CTT, oC) from the MODIS level 3 data product (Platnick 2015) 183

are used. The level 3 daily data product (MOD08_D3) of MODIS consists of 1° × 1° grid 184

average values of atmospheric parameters related to aerosol particle properties, water vapor, 185

and cloud optical and physical properties. Details about the MODIS cloud product algorithms 186

are provided in Platnick et al. (2003) and King et al. (2003). CTT data, which are available at 187

a 1° × 1° grid resolution, are interpolated to a 0.125° × 0.125° grid resolution. 188

189

190

10

3. Results 191

The disdrometers at the observational sites in Taiwan and India recorded a 8392 RSD 192

samples (1-min) for six Pacific Ocean TCs and 3776 RSD samples (1-min) for five Indian 193

Ocean TCs. The variations of mean raindrop concentration (N(D), m−3 mm−1) with drop 194

diameter (D, mm) for Indian Ocean and Pacific Ocean TCs are illustrated in Fig. 3. Throughout 195

this paper, raindrops with diameters of 1–3 mm are considered as mid-size drops, and drops 196

below and above this range are considered, small and large drops, respectively (Janapati et al. 197

2017; Kumar and Reddy 2013; Kumari et al. 2014; Tokay et al. 2008). Figure 3 clearly 198

demonstrates that the raindrop concentration of mid-size and large drops is higher for Pacific 199

Ocean TCs than Indian Ocean TCs. The Pacific Ocean TCs have higher mean rainfall rates (R, 200

mm h−1), mass-weighted mean diameters (Dm, mm), and lower normalized intercept parameters 201

(Nw, mm−1 m−3) than Indian Ocean TCs (Fig. 3). A higher concentration of small drops and 202

lower concentration of mid-size and large drops in Indian Ocean TCs results in lower Dm values 203

than Pacific Ocean TCs. 204

205

Further, to recognize dissimilarities in rain parameters (log10R, log10W, Dm, and 206

log10Nw) of Indian Ocean and Pacific Ocean TCs, the probability distribution functions (PDF) 207

of these parameters are computed and illustrated in Fig. 4. The Pacific Ocean TCs have larger 208

values than Indian Ocean TCs for log10 R > 0.5, where R is in mm h−1, and the Indian Ocean 209

TCs show peak distributions at lower rainfall intensities (log10R = 0) (Fig. 4a). The liquid water 210

content (log10W) PDF shows relatively greater frequency in Indian Ocean TCs for log10W < 211

−0.6 (here W is in g m−3) than in Pacific Ocean TCs (Fig. 4b). A clear variation in PDF 212

distributions of Dm for Indian Ocean and Pacific Ocean TCs can be seen (Fig. 4c). The Indian 213

Ocean and Pacific Ocean TCs have peak PDF distributions of Dm at around 0.8 mm and 1.4 214

mm, respectively. Like Dm, the normalized the intercept parameter (log10Nw) also showed 215

11

distinct differences in PDF distributions between Indian Ocean and Pacific Ocean TCs (Fig. 216

4d). The PDF of log10Nw shows a higher percentage at lower log10Nw values in the Pacific 217

Ocean TCs, and a higher percentage at higher log10Nw values in the Indian Ocean TCs (Fig. 218

4d). Additionally, to show the dissimilarities between Indian Ocean and Pacific Ocean TCs 219

rain parameters, a Student’s t-test is executed for log10R, log10W, Dm, and log10Nw, and the 220

results reject the null hypothesis H0(log10RPacific = log10RIndian; log10WPacific = log10WIndian; 221

Dm_Pacific = Dm_Indian; log10Nw_Pacific = log10Nw_Indian) at significance levels of 0.05 and 0.01. 222

223

3.1 RSD in different rainfall rate classes 224

The mean raindrop concentrations of Indian Ocean and Pacific Ocean TCs are classified 225

into six rainfall rate classes (C1:0.1–1, C2:1–2, C3:2–5, C4:5–10, C5:10–20, C6: >20 mm h−1) 226

and are shown in Fig. 5. The rain statistics of these six rainfall rate classes for Indian Ocean 227

and Pacific Ocean TCs are provided in Table 2. For C1 and C2, there are higher concentrations 228

of mid-size and large drops in Pacific Ocean TCs than in Indian Ocean TCs (Fig. 5a and b). 229

Additionally, raindrops with diameters of > 1.4 mm and 1.6 mm in C3 and C4, respectively, 230

have greater concentrations in Pacific Ocean TCs (Fig. 5c and d). In C5 and C6 (Fig. 5e and f), 231

raindrops larger than 2 mm in diameter are more common in Pacific Ocean TCs than Indian 232

Ocean TCs. From Fig. 5, it can be seen that even after separating the Pacific Ocean and Indian 233

Ocean TCs raindrop spectra into different rainfall rate classes, mid-size and large drops are 234

more common in Pacific Ocean TCs than Indian Ocean TCs. For both oceanic TCs, it can be 235

seen that the concentration of large drops and spectral width increase with increased rainfall 236

rate. 237

238

Variations of Dm and log10Nw values in the six rainfall rate classes of both oceanic TCs 239

are shown in Fig. 6 with a box and whisker plot. It is obvious from Fig. 6a that Pacific Ocean 240

12

TCs have higher Dm values than Indian Ocean TCs. In contrast, Dm values increase with 241

increased rainfall rate classes for the TCs in both oceans. In contrast to Dm, the log10Nw values 242

are higher for Indian Ocean TCs than Pacific Ocean TCs. Higher mid-size and large drop 243

concentrations in Pacific Ocean TCs result in higher Dm values for Pacific Ocean TCs. The 244

mean and standard deviation values of Dm, log10Nw, μ, and Λ for Pacific Ocean and Indian 245

Ocean TCs are provided in Table 3. For both oceanic TCs, increased rainfall rates correlate 246

with increasing Dm values due to the increase in the mid-size and large drop concentration with 247

increased rainfall rate. 248

249

3.2 Dm-R and Nw-R relations 250

The normalized intercept parameter (log10Nw) and mass-weighted mean diameter (Dm) 251

infer the RSD features, and these parameters varied with rainfall intensity for different 252

precipitating cloud systems (Chen et al. 2013; Marzano et al. 2010; Thurai et al. 2010). Fig. 7 253

shows scatter plots of Dm and log10Nw with rainfall rates for both oceanic TCs. An apparent 254

distinction in the distribution of Dm with rainfall rate can be seen between Indian Ocean and 255

Pacific Ocean TCs. The Dm values in Pacific Ocean TCs (Fig. 7a) are distributed between 0.4 256

and 3 mm, with few points around 3–3.5 mm, whereas the Dm values for Indian Ocean TCs are 257

scattered from 0.4 to 2 mm, with few points between 2–2.5 mm. Despite this, as rainfall rate 258

increases, the distribution of Dm narrows for both oceanic TCs, which is consistent with 259

previous studies (Chang et al. 2009; Kumar and Reddy 2013; Wen et al. 2018). Lower variation 260

of Dm with increased rainfall rate could be due to the RSD reaching equilibrium at higher 261

rainfall rates, in which raindrop breakup and coalescence reach a near balance (Hu and 262

Srivastava 1995), and further rainfall rates increases under RSD equilibrium conditions are due 263

to an increase in number concentration (Bringi and Chandrasekar 2001). The power law fitting 264

equations derived for Dm-R and log10Nw-R are also shown in Fig. 7. The Dm-R relations 265

13

obviously show that the Pacific Ocean TCs have higher coefficient and exponent values than 266

Indian Ocean TCs. This implies that for a given rainfall rate, Pacific Ocean TCs have higher 267

Dm values than Indian Ocean TCs, which can be seen in Fig. 6. In contrast, the coefficient and 268

exponent values of log10Nw-R relations are higher for Indian Ocean TCs than Pacific Ocean 269

TCs, indicating the higher concentration of small drops in Indian Ocean TCs. Over east China, 270

Chen et al. (2016) estimated the Dm-R and Nw-R relations for a continental squall line at four 271

different locations, with coefficient and exponential values of Dm-R relations of 1.642–1.725 272

and 0.1–0.13, respectively. The coefficient values of Dm-R relations reported by Chen et al. 273

(2016) are higher than the coefficient values of both oceanic TCs in this present study. In 274

contrast, the coefficient and exponent values of log10Nw-R relations of the continental squall 275

line ranged were 2.843–2.855 and 0.053–0.073, respectively, and their coefficient values were 276

lower than the coefficient values of both oceanic TCs in this study. This clearly demonstrates 277

that both oceanic TCs have higher concentrations of small drops than the continental squall 278

line observed over east China. 279

280

3.3 Shape and slope (μ-Λ) relations 281

The μ-Λ relations provide useful information for understanding RSD characteristics and 282

retrieving RSD parameters from polarimetric radar observations through the constrained-283

gamma method (Brandes et al. 2004; Cao et al. 2008; Zhang et al. 2001; Zhang et al. 2006; 284

Zhang et al. 2003). These relations were found to vary by region and rain type (Seela et al. 285

2018; Tang et al. 2014; Zhang et al. 2003), which necessitates investigation of each region’s 286

representative μ-Λ relations. Fig. 8 shows scatterplots for the μ and Λ values of Indian Ocean 287

and Pacific Ocean TCs. To estimate the μ-Λ relations of Indian Ocean and Pacific Ocean TCs, 288

we adopted criteria similar those of Cao et al. (2008). That is, if the sum of the count of particles 289

from drop channels 3–21 is less than 1000 or the rainfall rate is less than 5 mm h−1, those 290

14

datasets would not be used to derive μ-Λ relations. Because μ and Λ values greater than 20 and 291

20 mm−1, respectively, are ascribed to measurement error rather than storm physics (Zhang et 292

al. 2003), μ-Λ relations are estimated for μ < 20 and Λ < 20 mm−1, and are shown in Fig. 8. For 293

rainfall in Indian Ocean TCs, 50.13% of the data points are associated with μ > 20 and Λ >20 294

mm−1, whereas for Pacific Ocean TCs, 13.32 % of the data points are associated with μ > 20 295

and Λ >20 mm−1. The red and blue solid lines in Fig. 8a and Fig. 8b represent the polynomial 296

least squares fit for the data points of Pacific Ocean and Indian Ocean TCs, respectively. The 297

computed μ-Λ relations of both oceanic TCs are depicted in Fig. 8, and these relations vary 298

greatly between one another. The green solid line in Fig. 8 represents the equation from Zhang 299

et al. (2003). Current μ-Λ relations, as well as previously reported μ-Λ relations of TCs in 300

different parts of the world (Chang et al. 2009; Chen et al. 2012; Chu and Su 2008; Wen et al. 301

2018), are provided in Table 4. 302

303

3.4 The mass-mean diameter and standard deviation of mass spectrum ( Dm-σm) relations 304

Fig. 9 shows scatterplots of mass-weighted mean diameter and the standard deviation 305

of mass spectrum for Indian Ocean and Pacific Ocean TCs. The data points shown in Fig. 9a 306

and b are those that satisfy the quality control procedure of Williams et al. (2014), i.e., each 1-307

min raindrops spectra is considered if 1) there are at least 50 raindrops in at least three different 308

diameter bins, 2) the reflectivity factor is greater than 10 dBZ, and 3) the rainfall rate is greater 309

than 0.1 mm h−1, and σm values corresponding to Dm < 0.5 mm are discarded. After applying 310

this quality control procedure, 3611 and 8297 min of raindrop spectra are observed in Indian 311

Ocean and Pacific Ocean TCs, respectively. If fitting is performed for data with Dm > 0.5 mm, 312

the fitted equation becomes σm = 0.36 Dm1.35 and σm = 0.29 Dm

1.71 for the Pacific Ocean and Indian 313

Ocean TCs, respectively. Using large data sets (24,872 min) of two-dimensional video 314

disdrometer (2DVD) measurements over Huntsville, Alabama, Williams et al. (2014) 315

15

computed the σm-Dm relation as σm = 0.30Dm1.36. A similar relation (σm = 0.266Dm

1.65) was 316

evaluated by Thurai et al. (2014) for 10 months of 2DVD observations over Alabama. Thurai 317

et al. (2017) estimated σm-Dm relations (σm = 0.48Dm0.94) for two precipitation events measured 318

in Colorado and Huntsville, Alabama, using 2DVD. The differences in the Dm-σm relations of 319

Thurai et al. (2017) and our results could be due to Thurai et al. (2017) using a droplet 320

spectrometer and the truncation effect of the Parsivel disdrometer used in this study. 321

322

3.5 RSD in stratiform and Convective precipitation 323

It has been well documented that RSD features significantly change between convective 324

and stratiform precipitation types (Tokay and Short 1996; Ulbrich and Atlas 2007). To classify 325

precipitation into stratiform and convective types, different researchers adopted various 326

classification criteria (Bringi et al. 2003; Das et al. 2017; Krishna et al. 2016; Steiner et al. 327

1995; Tokay and Short 1996). Among previous precipitation classifications studies, Bringi et 328

al. (2003) documented the RSD characteristics of different precipitation types over a wide 329

range of climatic regimes and observed profound variations between stratiform and convective 330

regimes in maritime and continental clusters. The RSDs of Indian Ocean and Pacific Ocean 331

TCs are categorized into stratiform and convective types by adopting the threshold criteria (i.e., 332

standard deviation of rainfall rate) of Bringi et al. (2003). In this study, 10 consecutive 1-min 333

RSD samples were considered to be stratiform type if the mean value of R > 0.5 mm h−1 and 334

the standard deviation of R (σR) < 1.5 mm h−1, and convective type if the mean value of R > 5 335

mm h−1 and the standard deviation of R (σR) >1.5 mm h−1. Samples not meeting these criteria 336

were discarded. With these classification criteria, 66.67% of data points in the Pacific Ocean 337

TCs are stratiform type and 33.33% are convective type. For the Indian Ocean TCs, 82.35 % 338

of the data points are stratiform type and 17.65% are convective type. 339

340

16

The raindrop concentrations of convective and stratiform regimes of Indian Ocean and 341

Pacific Ocean TCs are shown in Fig. 10. For both oceanic TCs, a relatively higher drop 342

concentration can be seen for convective regimes than stratiform regimes (Fig. 10a and b). For 343

both oceanic TCs, the stratiform regimes have nearly exponential distributions, whereas the 344

convective regimes have broad distributions, which might be due to the collisional breakup of 345

large drops in convective rain (Hu and Srivastava 1995). To compare the raindrop 346

concentration of Indian Ocean and Pacific Ocean TCs with respect to precipitation type, Fig. 347

10a and b are re-plotted into Fig. 10c and d. Even within the different precipitation types we 348

see a strong distinction in RSDs, with a higher concentration of mid-size and large drops in 349

Pacific Ocean TCs. It is also worth noting that larger numbers of small drops can be seen in 350

Indian Ocean TCs. 351

352

The distribution of the mean Dm and log10Nw values in the stratiform and convective 353

regimes of Indian Ocean and Pacific Ocean TCs are illustrated in Fig. 11. The gray rectangular 354

boxes in Fig. 11 are the maritime and continental clusters defined by Bringi et al. (2003). For 355

both oceanic TCs, convective regimes have higher mean Dm and log10Nw values than stratiform 356

regimes. In contrast, Pacific Ocean TCs have higher Dm and lower log10Nw values than Indian 357

Ocean TCs in both convective and stratiform precipitations. The Indian Ocean TCs have 358

smaller drop diameter values than the maritime convection of Bringi et al. (2003). The mean 359

and standard deviation values of Dm, log10Nw, μ, and Λ in the stratiform and convective regimes 360

of the Pacific Ocean and Indian Ocean TCs are listed in Table 5. If we compare our Dm and 361

log10Nw distributions with those of the maritime and continental clusters of Bringi et al. (2003), 362

only Pacific Ocean convective precipitation is near the maritime-like clusters, with the rest 363

having lower Dm values than the convective clusters of Bringi et al. (2003). Additionally, 364

Bringi et al. (2003) proposed two different microphysical processes that lead to large Dm and 365

17

log10Nw variations in stratiform rain. Larger Dm and smaller log10Nw values occur due to the 366

melting of large snowflakes and smaller Dm and larger log10Nw values are due to the melting of 367

tiny graupel or smaller rimed ice particles. As shown in Fig. 11, Indian Ocean TCs stratiform 368

precipitations have lower Dm and larger log10Nw values than Pacific Ocean TCs, implying that 369

Indian Ocean TCs stratiform precipitation is associated with melting of tiny graupel or smaller 370

rimed ice particles, whereas Pacific Ocean TCs feature melting of large snowflakes. 371

372

3.6 Radar reflectivity and rainfall rate (Z-R) relations 373

Z-R relations play a vital role in quantitative precipitation estimation from radar 374

measurements, and these relations were found to vary by geographical location and storm type 375

and strongly depend on RSD characteristics (Rosenfeld and Ulbrich 2003; Seela et al. 2017). 376

The uncertainties in estimating rainfall rate from weather radars can be minimized using 377

indigenous Z-R relations rather than default or tropical Z-R relations (Ulbrich and Lee 2002). 378

In Z = ARb relations, the presence of large or small drops can be inferred from the coefficient 379

A and the exponent b representing microphysical processes. If b is greater than unity, then 380

collision-coalescence (size or mixed controlled) is the characteristic feature. If b equals unity, 381

then the collision, coalescence, and breakup processes (number controlled) are associated with 382

homogeneous rainfall (Atlas and Williams 2003; Atlas et al. 1999; Steiner et al. 2004). The Z-383

R relations for Indian Ocean and Pacific Ocean TCs are deduced by applying linear regression 384

to logarithmic values of rainfall rate (R, mm h−1) and radar reflectivity (Z, mm6 m−3), and are 385

provided in Fig. 12. A clear demarcation in the coefficient and exponent values of Z-R relations 386

can be seen between the Indian Ocean and Pacific Ocean TCs. For the observational site in 387

India (Kadapa), Jayalakshmi and Reddy (2014) estimated Z-R relations for seasonal rainfall 388

(southwest monsoon (SW): Z = 300.5R1.375 and northeast (NE) monsoon: Z = 163.324R1.35) as 389

well as for precipitation type (SW stratiform: Z = 334.13R1.424, NE stratiform: Z = 245.35R1.283, 390

18

SW convective: Z = 265.59R1.341, NE convective: Z = 122.41R1.430). The coefficient A in the 391

Z-R relations of Indian Ocean TCs (A = 148.44) is smaller than the seasonal rainfall 392

coefficients, which indicates that the raindrop sizes are relatively smaller in TCs than in 393

seasonal rainfall. Over northern Taiwan, Seela et al. (2018) computed the Z-R relations for 394

summer (stratiform: Z = 276.13R1.41, convective: Z = 237.88R1.41, total: Z = 266.42R1.38) and 395

winter (stratiform: Z = 127.67R1.54, convective: Z = 142.94R1.52, total: Z = 129.76R1.55) rainfall 396

using Joss-Waldvogel disdrometer measurements. For the same observational site over 397

northern Taiwan, Chang et al. (2009) evaluated the Z-R relations (Z = 206.83R1.45) of typhoon 398

rainfall using 2DVD measurements. The current Z-R relations of Pacific Ocean TCs (stratiform 399

Z = 368.28R1.49, convective: Z = 328.73R1.42, total: Z = 346.03R1.42) are different from those of 400

seasonal and typhoon rainfall, and these variations could be due to either the use of different 401

instruments to estimate Z-R relations or Taiwan’s complex orography. The estimated Z-R 402

relations of Indian Ocean and Pacific Ocean TCs will enhance the quantitative precipitation 403

estimation of TCs rainfall for these two oceanic basins. 404

405

4. Discussion 406

To determine the possible rationale for RSD variations between Indian Ocean and 407

Pacific Ocean TCs, the CAPE(J kg−1), water vapor (Kg m−2), vertical profiles of temperature 408

(oC), and relative humidity values from ERA-Interim, and CTT from MODIS are considered 409

for disdrometers’ measurement periods (as listed in Table 1). Figure 13 shows a box and 410

whisker plot of CAPE and water vapor values for the disdrometer observational periods of 411

Indian Ocean and Pacific Ocean TCs. It is apparent that Pacific Ocean TCs have relatively 412

higher water vapor and strong convective activity (higher CAPE) than Indian Ocean TCs. 413

Relatively higher water vapor with vigorous updrafts and downdrafts leads to the growth of 414

cloud particles (both liquid and solid) to a sufficiently larger size (by aggregation, riming, and 415

19

collision-coalescence processes) in Pacific Ocean TCs than in Indian Ocean TCs. The MODIS-416

obtained CTT values are slightly higher for Pacific Ocean TCs than Indian Ocean TCs (Fig. 417

14). Melting of large (small) particles in the Pacific (Indian) TCs results in relatively higher 418

(lower) Dm and lower (higher) log10Nw values in the Pacific (Indian) TCs (Fig. 11). A Student’s 419

t-test is applied to the CAPE, water vapor, and CTT values of the Indian Ocean and Pacific 420

Ocean TCs. The test results showed a significant difference at the 0.01 level in CAPE and 421

column water vapor between Indian Ocean TCs and Pacific Ocean TCs, whereas, there was 422

not a significant difference in CTT. Further mean vertical profiles of temperature and relative 423

humidity for Pacific Ocean and Indian Ocean TCs are shown in Fig. 15. The temperature 424

profiles of Pacific Ocean TCs show relatively higher values than Indian Ocean TCs (Fig. 15a) 425

at all pressure levels. However, the relative humidity profiles showed higher values below 925 426

hPa and above 775 hPa (Fig. 15b) for the Pacific Ocean TCs. Higher temperature and lower 427

relative humidity values can be seen for Pacific Ocean TCs between 775 and 925 hPa (~2.2–428

0.76 km), which shows that it is possible for small drops in the Pacific Ocean TCs to evaporate 429

between these pressure levels. The above explanation provides possible reasons for the 430

occurrence of more large drops in Pacific Ocean TCs and more small drops in Indian Ocean 431

TCs. 432

433

5. Summary 434

For the first time, RSD characteristics of Indian Ocean and Pacific Ocean TCs are 435

compared using Parsivel disdrometers installed at observational sites in India and Taiwan. The 436

contribution of mid-size and large drops is higher in Pacific Ocean TCs than in Indian Ocean 437

TCs. The probability distribution of rain integral parameters for Indian Ocean and Pacific 438

Ocean TCs showed distinct distributions. RSDs classified into different rainfall rate classes as 439

well as precipitation types (stratiform and convective) showed a greater concentration of mid-440

20

size and large drops in Pacific Ocean TCs. In different rainfall rate classes and precipitation 441

types, higher (lower) mass-weighted mean diameter (normalized intercept parameter) values 442

are observed for Pacific Ocean TCs. The derived empirical relations (Dm-R, log10Nw-R, μ-Λ Dm-443

σm, log10Nw-Dm and Z-R) are found to differ between the Pacific Ocean and Indian Ocean TCs, 444

confirming that must adopt TC-specific empirical relations in remote sensing and radar rainfall 445

estimation algorithms. Relatively higher convective activity and water vapor in the Pacific TCs 446

resulted in a greater number of large drops in Pacific TCs than Indian TCs through different 447

microphysical processes. 448

449

450

451

Acknowledgments 452

All authors thank IMD and JMA for providing TCs track information. This work is 453

supported by the Ministry of Science and Technology (MOST), Taiwan, under grant numbers 454

MOST 104-2923-M-008-003-MY5, MOST 106-2625-M-008-013, MOST 106-2811-M-008-455

084, MOST 107-2111-M-008-038, and MOST 108-2625-M-008-011, and partially by 456

“Earthquake-Disaster & Risk Evaluation and Management Center, E-DREaM” from The 457

Featured Areas Research Center Program within the framework of the Higher Education Sprout 458

Project by the Ministry of Education (MOE), Taiwan. The first author, Jayalakshmi Janapati 459

acknowledges MOST in carrying out this work under grant numbers MOST 104-2811-M-008-460

064, MOST 106-2811-M-008-084, MOST 107-2811-M-008-2551, and MOST 108-2811-M-461

008-558. The second author, Balaji Kumar Seela, acknowledges Academia Sinica, Taiwan, for 462

providing the graduate fellowship under Taiwan international Graduate Program (TIGP) and 463

MOST for providing the graduate fellowship under grant numbers MOST 106-2625-M-008-464

013 and MOST 107-2625-M-008-002. The second author also acknowledges MOST for 465

21

providing the post-doctoral fellowship under grant numbers MOST 107-2111-M-008-038, 466

MOST 108-2625-M-008-011 and MOST 108-2811-M-008-595, and the Central Weather 467

Bureau (CWB), Taiwan under the grant number CWB 1072019C. 468

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12, 415-12, 433. 652

Wen, L., K. Zhao, G. Chen, M. Wang, B. Zhou, H. Huang, D. Hu, W.-C. Lee, and H. Hu, 2018: 653

Drop size distribution characteristics of seven typhoons in China. J. Geophys. Res. 654

Atmos., 123, 6529–6548. 655

Williams, C. R., V. N. Bringi, L. D. Carey, V. Chandrasekar, P. N. Gatlin, Z. S. Haddad, R. 656

Meneghini, S. Joseph Munchak, S. W. Nesbitt, W. A. Petersen, S. Tanelli, A. Tokay, 657

A. Wilson, and D. B. Wolff, 2014: Describing the shape of raindrop size distributions 658

using uncorrelated raindrop mass spectrum parameters. J. Appl. Meteor. Climatol., 53, 659

1282–1296. 660

29

Wu, Q., Z. Ruan, D. Chen, and T. Lian, 2015: Diurnal variations of tropical cyclone 661

precipitation in the inner and outer rainbands. J. Geophys. Res. Atmos., 120, 1–11. 662

Yuter, S. E., D. E. Kingsmill, L. B. Nance, and M. Löffler-Mang, 2006: Observations of 663

precipitation size and fall speed characteristics within coexisting rain and wet snow. J. 664

Appl. Meteor. Climatol., 45, 1450–1464. 665

Zhang, G., J. Vivekanandan, and E. Brandes, 2001: A method for estimating rain rate and drop 666

size distribution from polarimetric radar measurements. IEEE T. Geosci. Remote., 39, 667

830–841. 668

Zhang, G., J. Sun, and E. A. Brandes, 2006: Improving parameterization of rain microphysics 669

with disdrometer and radar observations. J. Atmos. Sci., 63, 1273–1290. 670

Zhang, G., J. Vivekanandan, E. A. Brandes, R. Meneghini, and T. Kozu, 2003: The shape–671

slope relation in observed gamma raindrop size distributions: Statistical error or useful 672

information? J. Atmos. Oceanic Technol., 20, 1106–1119. 673

674

675

676

677

30

List of Tables: 678

Table 1: Duration, rainfall accumulation, and rainfall rate statistics (maximum, mean, and 679

standard deviation (std)) of Indian Ocean and Pacific Ocean tropical cyclones (TCs). 680

681

Table 2. Rainfall rate statistics of Indian Ocean and Pacific Ocean tropical cyclones (TCs) in 682

six rainfall rate classes (C1-C6). 683

684

Table 3: Mean and standard deviation (std) values of Dm (mass-weighted mean diameter, mm), 685

log10Nw (where Nw is the normalized intercept parameter in mm−1 m−3), μ (shaper 686

parameter, dimensionless), and Λ (slope parameter, mm−1) for Indian Ocean and Pacific 687

Ocean tropical cyclones (TCs) in six rainfall rate classes (C1-C6). 688

689

Table 4: The μ-Λ relations of current study tropical cyclones (TCs) and other parts of the world. 690

691

Table 5 Mean and standard deviation (std) values of Dm (mass-weighted mean diameter, mm), 692

log10Nw (normalized intercept parameter Nw, mm−1 m−3), μ (shape parameter), and Λ 693

(slope parameter, mm-1) for stratiform and convective precipitations of Pacific Ocean 694

and Indian Ocean tropical cyclones (TCs). 695

696

697

698

699

700

701

702

31

List of figures: 703

Figure 1. Tracks of Indian Ocean and Pacific Ocean tropical cyclones and the locations of 704

Parsivel disdrometer (red color stars) over India and Taiwan. 705

706

Figure 2. Quality control procedure applied to raw raindrop size distributions of Pacific Ocean 707

and Indian Ocean tropical cyclones measured by Parsivel disdrometers over (a) Taiwan 708

and (b) India, respectively. The black solid line is the empirical fall speed-diameter 709

relations from Atlas et al. (1973), and the black dotted lines are the threshold curves (± 710

60% of Atlas et al. (1973) curve), used for filtering the data. 711

712

Figure 3. Mean raindrop concentration of Indian Ocean and Pacific Ocean tropical cyclones 713

(TCs). Numbers of 1-min raindrop size distributions samples of TCs are shown in 714

legend parenthesis. The error bars represent the standard error of each raindrop diameter 715

bin sample. The mean values of Dm (mass-weighted mean diameter, mm), R (rainfall 716

rate, mm h-1), and log10Nw (the normalized intercept parameter, Nw in mm−1 m−3) for 717

Indian Ocean and Pacific Ocean TCs rainfall are depicted in the figure. 718

719

Figure 4. The probability distribution functions (PDF) of (a) log10R, where R is rainfall rate 720

(mm h−1) (b) log10W, where W is liquid water content (g m−3), (c) mass-weighted mean 721

diameter, Dm (mm), (d) log10Nw, where Nw is the normalized intercept parameter (mm−1 722

m−3), for Indian Ocean and Pacific Ocean tropical cyclones (TCs). 723

724

Figure 5. Mean raindrop concentration (N(D), m−3 mm−1) of Indian Ocean and Pacific Ocean 725

tropical cyclones (TCs) in six rainfall rate (R) classes (C1:0.1–1, C2:1–2, C3:2–5, 726

C4:5–10, C5:10–20, and C6: >20 mm h−1). 727

32

728

Figure 6. Box and whisker plot of mass-weighted mean diameter, Dm (mm) and log10Nw, where 729

Nw is the normalized intercept parameter (mm−1 m−3) for Indian Ocean (blue) and 730

Pacific Ocean (red) tropical cyclones in six rainfall rate (R) classes (C1:0.1–1, C2:1–2, 731

C3:2–5, C4:5–10, C5:10–20, and C6: >20 mm h−1). The center line of the box indicates 732

the median, and the bottom and top lines of the box indicate the 25th and 75th percentiles, 733

respectively. The bottom and top of the dashed vertical lines indicate the 5th and 95th 734

percentiles, respectively. 735

736

Figure 7. Scatter plots of Dm (mass-weighted mean diameter, mm) and log10Nw (where Nw is 737

the normalized intercept parameter in mm−1 m−3) with rainfall rate for Indian Ocean 738

and Pacific Ocean tropical cyclones (TCs). 739

740

Figure 8. Scatterplots of μ versus Λ for (a) Pacific Ocean and (b) Indian Ocean tropical 741

cyclones (TCs). The gray solid circles and stars in (a) and (b), respectively, are data 742

points with rainfall rates > 5 mm h−1. The red and blue lines in (a) and (b) represent the 743

least squares fit applied (expression of µ in terms of Λ) to filter data of Pacific Ocean 744

and Indian Ocean TCs, respectively. The green line corresponds to the μ-Λ relation of 745

Zhang et al. (2003). 746

747

Figure 9. Scatter plot of mass-weighted mean diameter (Dm) and standard deviation of mass 748

spectrum (σm) for Indian Ocean and Pacific Ocean tropical cyclones (TCs). 749

750

Figure 10. Variation of raindrop concentration (N(D), m−3 mm−1) with drop diameter for 751

precipitation types for Indian Ocean and Pacific Ocean tropical cyclones (TCs). 752

33

753

Figure 11. Variation of log10Nw (where Nw is the normalized intercept parameter in mm−1 m−3) 754

with Dm (mass-weighted mean diameter in mm) in stratiform and convective regimes 755

of Indian Ocean and Pacific Ocean tropical cyclones (TCs). The horizontal gray dashed 756

line is the Marshall-Palmer value of log10Nw (3.9) for exponential shape. The green dash 757

dotted line is the stratiform and convective separation line of Bring et al. (2003). 758

759

Figure 12. Radar reflectivity-rainfall rate (Z-R) relations for Indian Ocean and Pacific Ocean 760

tropical cyclones (TCs) and their precipitation types (CON: convective, STF: 761

stratiform). 762

763

Figure 13. Box and whisker plot of (a) convective available potential energy (CAPE, J Kg−1) 764

and (b) vertical integral of water vapor (kg m−2) for the disdrometer observational 765

periods of Indian Ocean and Pacific Ocean tropical cyclones (TCs). The center line of 766

the box indicates the median, and the bottom and top lines of the box indicate the 25th 767

and 75th percentiles, respectively. The bottom and top of the dashed vertical lines 768

indicate the 5th and 95th percentiles, respectively. 769

770

Figure 14. Box and whisker plot of cloud top temperature (oC) for the disdrometer 771

observational periods of Indian Ocean and Pacific Ocean tropical cyclones (TCs). 772

773

Figure 15. Vertical profiles of (a) temperature (oC) and (b) relative humidity (%) for the 774

disdrometer observational periods of Indian Ocean and Pacific Ocean tropical cyclones 775

(TCs). 776

777

778

34

Table 1: Duration, rainfall accumulation, and rainfall rate statistics (maximum, mean, and standard deviation (std)) of Indian Ocean and Pacific 779

Ocean tropical cyclones (TCs). 780

Oceanic

Region

TC name TC life time TC observation period by Parsivel Rainfall

accumulation

(mm)

Rainfall rate (mm h–1)

Beginning Ending Maximum Mean std

Indian Ocean Jal 04-08

November 2010

06:30 LST,

07 November 2010

07:00 LST,

08 November 2010

56.6 55.63 2.86 5.46

Depression 04-8

December 2010

10:30 LST,

06 December 2010

10:30 LST,

08 December 2010

42.34 17.09 1.10 1.55

Nilam 28 October- 01

November 2012

05:30 LST,

31 October 2012

23:00 LST.

31 October 2012

44.36 60.28 4.92 8.18

Depression 13-17

November 2013

17:00 LST,

16 November 2013

10:30 LST,

17 November 2013

12.56 22.64 1.53 2.51

Madi 06-13

December 2013

01:00 LST

13 December 2013

12:00 LST

13 December 2013

1.07 4.14 0.51 0.84

Matmo 16-25 01:00 LST 24:00 LST 125.59 126.25 7.81 15.28

35

Pacific

Ocean

July 2014 22 July 2014 23 July 2014

Fung-wong 17 -23

September 2014

21:00 LST

19 September 2014

21:30 LST

22 September 2014

89.76 75.14 3.37 4.45

Linfa 01-10

July 2015

01:00 LST

06 July 2015

11:00 LST

09 July 2015

51.04 37.39 2.56 3.83

Dujuan 19-30

September 2015

19:00 LST

28 September 2015

23:00 LST

29 September 2015

110.02 164.29 6.38 13.08

Nepartak 02-10

July 2016

01:00 LST

08 July 2016

18:00 LST

09 July 2016

164.87 123.17 7.76 14.88

Aere 04-10

October 2016

02:00

05 October 2016

11:00 LST

10 October 2016

129.20 58.77 3.20 5.48

781

782

783

784

785

36

Table 2. Rainfall rate statistics of Indian Ocean and Pacific Ocean tropical cyclones (TCs) in 786

six rainfall rate classes (C1-C6). 787

Rainfall

rate class

Rainfall

rate

threshold

(mm h–1)

Pacific Ocean TCs Indian Ocean TCs

No. of

samples

Mean

(mm h–1)

Standard

deviation

(mm h–1)

No. of

samples

Mean

(mm h-1)

Standard

deviation

(mm h–1)

C1 0.1< R<1 3510 0.43 0.25 2000 0.44 0.25

C2 1<R<2 1302 1.45 0.28 680 1.42 0.28

C3 2<R<5 1612 3.29 0.85 696 3.07 0.81

C4 5< R<10 1019 7.06 1.38 216 6.89 1.38

C5 10<R<20 547 13.79 2.76 128 13.5 2.8

C6 R >20 402 41.14 23.04 56 30.06 8.99

All

classes

8392 4.77 10.28 3776 2.35 4.55

788

789

37

Table 3: Mean and standard deviation (std) values of Dm (mass-weighted mean diameter, mm), log10Nw (where Nw is the normalized intercept 790

parameter in mm−1 m−3), μ (shaper parameter, dimensionless), and Λ (slope parameter, mm−1) for Indian Ocean and Pacific Ocean tropical 791

cyclones (TCs) in six rainfall rate classes (C1-C6). 792

Rainfall

rate

Class

Pacific Ocean TCs Indian Ocean TCs

Dm

(mm)

log10Nw

(Nw in mm–1

m–3)

μ (-) Λ (mm–1) Dm

(mm)

log10Nw

(Nw in mm–1

m–3)

µ (-) Λ (mm–1)

Mean Std Mean Std Mean Std Mean Std Mean Std Mean Std Mean Std Mean Std

C1 1.07 0.28 3.23 0.5 10.95 9.83 15.9 12.81 0.83 0.21 3.76 0.53 18.46 13.39 29.6 19.96

C2 1.3 0.26 3.39 0.41 5.79 5.81 8.37 6.7 0.96 0.26 4.06 0.55 13.44 9.5 21.04 14.43

C3 1.44 0.26 3.52 0.38 4.16 4.13 6.12 4.26 1.02 0.26 4.24 0.51 12.22 7.31 17.96 10.16

C4 1.61 0.29 3.62 0.35 3.04 2.77 4.66 2.63 1.16 0.2 4.29 0.35 9.99 6.42 12.99 7.19

C5 1.74 0.28 3.75 0.31 2.67 2.19 4.05 1.83 1.33 0.19 4.28 0.26 7.36 4.03 8.91 4.01

C6 2.02 0.27 3.88 0.24 2.34 2.07 3.26 1.33 1.58 0.16 4.26 0.17 5.79 1.73 6.25 1.27

All classes 1.33 0.39 3.42 0.47 6.93 7.93 10.11 10.3 0.94 0.27 3.96 0.56 15.36 11.69 23.91 17.75

793

38

Table 4: The μ-Λ relations of current study tropical cyclones (TCs) and other parts of the world. 794

Author Location/Oceanic Basin Number of TCs Disdrometer type μ-Λ relations

Present study Southern India, Indian

Ocean TCs

five TCs Parsivel Λ=0.0129μ2 + 0.836μ+2.226

μ= -0.0124Λ2 + 1.13Λ-1.824

Present study South Taiwan, Pacific

Ocean TCs

six TCs Parsivel Λ=0.021μ2 + 0.654μ+2.088

μ=-0.0227Λ2 +1.317Λ-2.232

Chen et al. (2012) Fujian Typhoon Morakot Parsivel disdrometer Λ=0.0253μ2 + 0.633μ+1.524

Chang et al. (2009) NCU, north Taiwan Typhoons Two-dimensional Video

disdrometer

Λ=0.0136μ2 + 0.6984μ+1.5131

Chu and Su (2008) NCU, North Taiwan four types of weather

systems (typhoon, cold

front, stationary front,

convective cloud)

Two-dimensional Video

disdrometer

Λ=0.017μ2 + 1.303μ+1.833 (for

low order moment)

Λ = 0.007μ2 + 1.362μ+1.569 (for

low order moment)

Wen et al. (2018) China Seven typhoons Two-dimensional Video

disdrometer

μ= -0.019Λ2 + 1.09 Λ-3.119

39

795

Table 5 Mean and standard deviation (std) values of Dm (mass-weighted mean diameter, mm), log10Nw (normalized intercept parameter Nw, mm−1 796

m−3), μ (shape parameter), and Λ (slope parameter, mm–1) for stratiform and convective precipitations of Pacific Ocean and Indian Ocean 797

tropical cyclones (TCs). 798

Rain type Pacific Ocean TCs Indian Ocean TCs

Dm (mm) log10Nw (Nw in

mm–1 m–3)

μ (-) Λ (mm–1) Dm (mm) log10Nw (Nw in

mm–1 m–3)

μ (-) Λ (mm–1)

Mean std Mean std Mean std Mean Std Mean Std Mean Std Mean Std Mean Std

Stratiform 1.32 0.32 3.35 0.41 2.19 1.56 3.73 1.4 0.92 0.26 4.02 0.55 11.22 6.23 15.28 8.71

Convective 1.65 0.38 3.69 0.37 2.66 2.31 4.02 1.99 1.21 0.27 4.26 0.31 7.86 5.11 9.73 5.52

799

800

801

802

803

804

40

805

Figure 1. Tracks of Indian Ocean and Pacific Ocean tropical cyclones and the locations of 806

Parsivel disdrometer (red color stars) over India and Taiwan. 807

808

809

810

811

812

813

814

815

816

817

818

819

820

821

41

822

Figure 2. Quality control procedure applied to raw raindrop size distributions of Pacific Ocean 823

and Indian Ocean tropical cyclones measured by Parsivel disdrometers over (a) Taiwan 824

and (b) India, respectively. The black solid line is the empirical fall speed-diameter 825

relations from Atlas et al. (1973), and the black dotted lines are the threshold curves (± 826

60% of Atlas et al. (1973) curve), used for filtering the data. 827

828

42

829

Figure 3. Mean raindrop concentration of Indian Ocean and Pacific Ocean tropical cyclones 830

(TCs). Numbers of 1-min raindrop size distributions samples of TCs are shown in 831

legend parenthesis. The error bars represent the standard error of each raindrop diameter 832

bin sample. The mean values of Dm (mass-weighted mean diameter, mm), R (rainfall 833

rate, mm h-1), and log10Nw (the normalized intercept parameter, Nw in mm−1 m−3) for 834

Indian Ocean and Pacific Ocean TCs rainfall are depicted in the figure. 835

836

43

837

Figure 4. The probability distribution functions (PDF) of (a) log10R, where R is rainfall rate 838

(mm h−1) (b) log10W, where W is liquid water content (g m−3), (c) mass-weighted mean 839

diameter, Dm (mm), (d) log10Nw, where Nw is the normalized intercept parameter (mm−1 840

m−3), for Indian Ocean and Pacific Ocean tropical cyclones (TCs). 841

842

843

844

845

846

847

848

849

44

850

Figure 5. Mean raindrop concentration (N(D), m−3 mm−1) of Indian Ocean and Pacific Ocean 851

tropical cyclones (TCs) in six rainfall rate (R) classes (C1:0.1–1, C2:1–2, C3:2–5, 852

C4:5–10, C5:10–20, and C6: >20 mm h−1). 853

854

855

856

45

857

Figure 6. Box and whisker plot of mass-weighted mean diameter, Dm (mm) and log10Nw, where 858

Nw is the normalized intercept parameter (mm−1 m−3) for Indian Ocean (blue) and 859

Pacific Ocean (red) tropical cyclones in six rainfall rate (R) classes (C1:0.1–1, C2:1–2, 860

C3:2–5, C4:5–10, C5:10–20, and C6: >20 mm h−1). The center line of the box indicates 861

the median, and the bottom and top lines of the box indicate the 25th and 75th percentiles, 862

respectively. The bottom and top of the dashed vertical lines indicate the 5th and 95th 863

percentiles, respectively. 864

865

866

867

868

869

46

870

Figure 7. Scatter plots of Dm (mass-weighted mean diameter, mm) and log10Nw (where Nw is 871

the normalized intercept parameter in mm−1 m−3) with rainfall rate for Indian Ocean 872

and Pacific Ocean tropical cyclones (TCs). 873

874

875

876

877

878

879

880

881

882

47

883

Figure 8. Scatterplots of μ versus Λ for (a) Pacific Ocean and (b) Indian Ocean tropical 884

cyclones (TCs). The gray solid circles and stars in (a) and (b), respectively, are data 885

points with rainfall rates > 5 mm h−1. The red and blue lines in (a) and (b) represent the 886

least squares fit applied (expression of µ in terms of Λ) to filter data of Pacific Ocean 887

and Indian Ocean TCs, respectively. The green line corresponds to the μ-Λ relation of 888

Zhang et al. (2003). 889

890

891

892

893

894

895

896

897

898

899

900

48

901

Figure 9. Scatter plot of mass-weighted mean diameter (Dm) and standard deviation of mass 902

spectrum (σm) for Indian Ocean and Pacific Ocean tropical cyclones (TCs). 903

904

905

906

907

908

909

910

911

912

913

914

915

916

917

49

918

Figure 10. Variation of raindrop concentration (N(D), m−3 mm−1) with drop diameter for 919

precipitation types for Indian Ocean and Pacific Ocean tropical cyclones (TCs). 920

921

922

923

924

925

926

927

928

929

930

931

50

932

Figure 11. Variation of log10Nw (where Nw is the normalized intercept parameter in mm−1 m−3) 933

with Dm (mass-weighted mean diameter in mm) in stratiform and convective regimes 934

of Indian Ocean and Pacific Ocean tropical cyclones (TCs). The horizontal gray dashed 935

line is the Marshall-Palmer value of log10Nw (3.9) for exponential shape. The green dash 936

dotted line is the stratiform and convective separation line of Bring et al. (2003). 937

938

939

940

941

942

943

944

51

945

Figure 12. Radar reflectivity-rainfall rate (Z-R) relations for Indian Ocean and Pacific Ocean 946

tropical cyclones (TCs) and their precipitation types (CON: convective, STF: 947

stratiform). 948

949

950

951

952

953

954

955

956

957

958

52

959

Figure 13. Box and whisker plot of (a) convective available potential energy (CAPE, J Kg−1) 960

and (b) vertical integral of water vapor (kg m−2) for the disdrometer observational 961

periods of Indian Ocean and Pacific Ocean tropical cyclones (TCs). The center line of 962

the box indicates the median, and the bottom and top lines of the box indicate the 25th 963

and 75th percentiles, respectively. The bottom and top of the dashed vertical lines 964

indicate the 5th and 95th percentiles, respectively. 965

966

967

968

969

970

971

972

973

974

975

976

977

978

53

979

Figure 14. Box and whisker plot of cloud top temperature (oC) for the disdrometer 980

observational periods of Indian Ocean and Pacific Ocean tropical cyclones (TCs). 981

982

983

984

985

986

987

988

989

990

991

992

993

994

995

996

54

997

Figure 15. Vertical profiles of (a) temperature (oC) and (b) relative humidity (%) for the 998

disdrometer observational periods of Indian Ocean and Pacific Ocean tropical cyclones 999

(TCs). 1000

1001