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The DOI for this manuscript is DOI:10.2151/jmsj.2020-015 J-STAGE Advance published date: February 1st 2020 The final manuscript after publication will replace the preliminary version at the above DOI once it is available. 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: [email protected]
1
1 Abstract
2 We made an effort to inspect the raindrop size distribution (RSD) characteristics of
3 Indian Ocean and Pacific Ocean tropical cyclones (TCs) using ground-based disdrometer
4 measurements from observational sites in India and Taiwan. Five TCs (2010–2013) from the
5 Indian Ocean and six TCs (2014–2016) from the Pacific Ocean were measured using particle
6 size and velocity disdrometers installed in south India and south Taiwan, respectively.
7 Significant differences between the RSDs of Indian Ocean and Pacific Ocean TCs are noticed.
8 For example, a higher number of small drops is observed in Indian Ocean TCs, whereas Pacific
9 Ocean TCs have more mid-size and large drops. RSDs of Pacific Ocean TCs have higher mass-
10 weighted mean diameter and lower normalized intercept parameter than Indian Ocean TCs.
11 RSD values quantified based on rainfall rate and precipitation types also showed similar
12 characteristics between Indian Ocean and Pacific Ocean TCs. The radar reflectivity and rainfall
13 rate (Z-R) relations and shape and slope (μ-Λ) relations of both oceanic (Indian and Pacific)
14 TCs are found to be distinctly different. Possible causes for the dissimilarities in RSD features
15 between Indian Ocean and Pacific Ocean TCs are due to relative differences in water vapor
16 availability and convective activity between TCs in these two oceanic basins.
17
18 Keywords: tropical cyclones (TCs), Raindrop size distribution (RSD), rainfall rate
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25
2
26 1. Introduction
27 Tropical cyclones (TCs) are a severe natural hazard that cause significant property
28 damage and loss of life when making landfall, in part due to torrential rainfall. The study of
29 raindrop size distribution (RSD) in TCs can be useful for better understanding cloud
30 microphysics and improving the cloud models (Tokay et al. 2008; Zhang et al. 2006), and
31 assessing rainfall-caused erosivity (Janapati et al. 2019). There have been reports on RSD
32 characteristics of TCs around the globe. Over the Atlantic Ocean, Merceret (1974) found no
33 distinct differences in RSD characteristics between the rainbands and eyewall region of
34 Hurricane Ginger. Additionally, Jorgensen and Willis (1982) did not observed much variation
35 in radar reflectivity and rainfall rate (Z-R) relations between the eyewall and outer rainband
36 regions at 3 km above the surface and below. Using airborne radar and disdrometer
37 measurements, Marks et al. (1993) observed significant differences in the eyewall and outer
38 rainband Z-R relations (eyewall: Z = 253R1.3; outer rainband Z = 341R1.25; total Z = 311R1.27).
39 A clear demarcation in RSD characteristics from before and during the passage of Hurricane
40 Helene (2000) was observed by Ulbrich and Lee (2002), who found that Z-R relations (Z =
41 118R1.48) of TCs differ from those of tropical Z-R (Z = 250R1.2) and default Z-R relations (Z =
42 300R1.4). An analysis of seven Atlantic TCs by Tokay et al. (2008) revealed the presence of
43 more small and mid-size drops and fewer large drops, with a maximum diameter seldom
44 exceeding 4 mm. Chang et al. (2009) explored drop shape and RSD characteristics of typhoon
45 rainfall during landfall over north Taiwan and found a maritime convective type RSD for
46 typhoon systems. They mentioned that typhoon convective systems influenced by Taiwan’s
47 terrain had RSD features of intermediate to maritime and continental clusters. Radhakrishna
48 and Narayana Rao (2010) explored seasonal variations of cyclonic and non-cyclonic RSD
49 characteristics over southern India and perceived large numbers of small and medium drops
50 with an almost absence of large drops in cyclonic precipitation. With the aid of the Particle
3
51 Size and Velocity (Parsivel) disdrometer, Chen et al. (2012) analyzed the RSD characteristics
52 of Typhoon Morakot (2009) and noted substantial differences between precipitation
53 characteristics of the eyewall and outer rainbands. Wind profiler and disdrometer observations
54 from Kim et al. (2013) showed strong and weak bright bands in the rainband and eyewall
55 regions of Typhoon Kompasu, respectively. Further, they noticed a higher mass-weighted
56 mean diameter (Dm) in the outer rainband than in the eyewall region. Differences between
57 cyclonic and northeast monsoon thunderstorm rainfall RSDs was detailed by Kumar and Reddy
58 (2013). Over east India, Bhattacharya et al. (2013) noticed stratiform features before and after
59 Tropical Cyclone Aila in the Bay of Bengal. Kumari et al. (2014) illustrated RSD differences
60 between two TCs that passed over southern India. Over Korea, Suh et al. (2016) analyzed the
61 RSD characteristics of nine rainfall groups and noticed smaller Dm and normalized intercept
62 parameter (Nw) values in typhoon rainfall than in other rainfall categories. Higher
63 concentrations of small drops in TC eyewalls and large drops in outer rainband regions was
64 observed over Darwin, Australia, by Deo and Walsh (2016). Wang et al. (2016) demonstrated
65 the microphysical characteristics in the rainbands of Typhoon Matmo (2014) over eastern
66 China using ground-based radar and disdrometer measurements. Kim and Lee (2017) perceived
67 different microphysical characteristics between stratiform and mixed stratiform-convective
68 regimes of the rainbands of Typhoon Bolaven (2012) over South Korea. Janapati et al. (2017)
69 detected clear differences in RSD characteristics in precipitation of TCs from the Bay of
70 Bengal, before and after landfall. Recently, Wen et al. (2018) investigated the RSD
71 characteristics of seven typhoons observed over China, and noticed higher raindrop
72 concentrations and lower rain drop diameters for typhoon convective precipitation than the
73 maritime convective clusters of Bringi et al. (2003).
74
4
75 Thus far in the literature, TC RSD have been limited to case studies or to particular
76 oceanic regions. Additionally, there have been no comparison studies of RSD characteristics
77 between one oceanic region and another. Hence, this study reports on RSD differences between
78 Indian Ocean and Pacific Ocean TCs using Parsivel disdrometer data from stations in southern
79 India and Taiwan. The remainder of this paper is ordered as follows: Section 2 outlines the data
80 and methodology, Section 3 provides results and discussion, and Section 4 gives a summary.
81
82 2. Data and methodology
83 2.1 Tropical cyclones
84 A total of five Indian Ocean TCs (2010–2013) and six Pacific Ocean TCs (2014–2016)
85 were measured using Parsivel disdrometers at Yogi Vemana University in Kadapa, India
86 (14.4742°N, 78.7098°E, 138 m above sea level) and at Shu-Te University in Kaohsiung,
87 Taiwan (120.3746oE, 22.7621oN, 9 m above sea level). The tracks of these TCs and locations
88 of the disdrometers (indicated by red stars) are shown in Fig. 1. Track information for the
89 Indian Ocean TCs was obtained from the India Meteorological Department (IMD) best track
90 archive (http://www.rsmcnewdelhi.imd.gov.in). Track information for the Pacific Ocean TCs
91 was obtained from the Japan Meteorological Agency (JMA) best track database
92 (https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/besttrack.html). Table 1 lists
93 the Indian Ocean and Pacific Ocean TCs used in this study, with their names, life span,
94 disdrometer measurement periods, total rain accumulations, and rainfall rate statistics
95 (maximum, mean, and standard deviation). Rainfall amounts for a location are considered to
96 be attributed to TCs if that location is within 500 km of the TC center (Deo and Walsh 2016;
97 Jiang and Zipser 2010; Prat and Nelson 2013; Wu et al. 2015). Hence, in this study, the RSD
98 measurements of selected TCs (as listed in Table 1) from Parsivel disdrometers in south Taiwan
5
99 and south India are considered for further analysis if the distance between the disdrometer site
100 and the TC center is 500 km or less (Deo and Walsh 2016).
101
102 2.2 Parsivel disdrometer
103 The Parsivel (Löffler-Mang and Joss 2000; Yuter et al. 2006) is a laser-based
104 disdrometer that can simultaneously measure precipitating particle size (liquid particles: 0.2 to
105 5 mm and solid particles: 0.2 to 25 mm) and fall speed (0.2 to 20 ms−1) and records them in
106 32×32 drop size and fall velocity classes. This instrument has an optical sensor that generates
107 a 650 nm 3 mW laser beam 180 mm long, 30 mm wide, and 1 mm high. A decrease in the laser
108 signal occurs when precipitating particles pass through the light sheet. The signal deviation
109 amplitude is a measure of particle size and the signal deviation duration allows estimation of
110 particle fall velocity. Detailed explanation of the Parsivel disdrometer along with the
111 assumptions used to determine hydrometeor size and velocity can be found in Löffler-Mang
112 and Joss (2000), Battaglia et al. (2010), Jaffrain and Berne (2011), Friedrich et al. (2013b),
113 Tokay et al. (2014) and references within.
114
115 Parsivel experiences some instrumental errors in strong wind, marginal effect, and
116 splashing effect conditions. Particles falling through the edges of the sample area appear as
117 small particles moving faster than the empirical relationship between fall velocity and particle
118 diameter, a phenomenon known as the marginal effect. Raindrops that hit the surface of the
119 Parsivel itself break apart and bounce back into the sampling area, a phenomenon known as
120 the splashing effect. To minimize these measurement errors, quality control procedures are
121 applied to disdrometer data. In this study, the first two size bins of disdrometer data are
122 discarded because of their low signal-to-noise ratio. Further, 1-min samples with fewer than 10
123 drops or rainfall rates less than 0.1 mm h−1 are considered noise and are thus discarded (Tokay
6
124 et al. 2013). Additionally, raindrops with diameters greater than 6 mm and fall speeds 60%
125 above or below the Atlas et al. (1973) empirical fall velocity-diameter relation (Jaffrain and
126 Berne 2011) are discarded (Fig. 2). During the passage of TCs at both observational sites, we
127 did not observe any solid precipitation with either disdrometer.
128
−3 129 The quality-controlled raw spectra are used to estimate raindrop concentration N(Di) (m
130 mm−1) using the following equation (Friedrich et al. 2013b; Tokay et al. 2014) .
32 푛푖푗 131 푁(퐷푖) = ∑푗=1 − − − − − (1) 퐴푒푓푓 ∆푡 푉(퐷푖) ∆퐷푖
132 To compute N(Di) for each diameter class (i = 1 to 32), number concentrations were summed
133 over all velocity classes (j = 1 to 32) (Friedrich et al. 2013b). Here, nij is the number of drops
134 reckoned in size bin i (i = 1 to 32), velocity bin j (j = 1 to 32), Δt (s) is the sampling time (60
th −1 135 seconds), ΔDi (mm) is the width of the i class diameter, V(Di) (m s ) =
th 136 9.65−10.3exp(−0.6*Di) is the fall velocity of the i size bin drops (Atlas et al. 1973), and Aeff
퐷 137 (m2) is the effective sampling area expressed by 퐴 = 10−6 × 퐿 (퐵 − 푖) (Battaglia et al. 푒푓푓 2
th 138 2010; Löffler-Mang and Joss 2000; Tokay et al. 2014), in which Di (mm) is the i bin size drop
139 diameter, L is the length of the Parsivel beam (180 mm), and B is the width of the Parsivel
140 beam (30 mm).
141 The rainfall rate R (mm h−1), sixth moment (Rayleigh) reflectivity/radar reflectivity factor Z
6 −3 −3 −3 142 (mm m ), liquid water content W (g m ), and total number concentration (Nt, m ) are derived
143 (Friedrich et al. 2013a; Friedrich et al. 2013b) by the following expressions.
32 −1 −4 3 144 푅 (푚푚 ℎ ) = 6π × 10 ∑ 푁(퐷푖) 퐷푖 푉(퐷푖) 훥퐷푖 − − − − − (2) 푖=1
32 6 145 푍 (푑퐵푍) = 10푙표𝑔10 ∑ 푁(퐷푖) 퐷푖 ∆퐷푖 − − − − − (3) 푖=1
146
7
32 휋 147 푊 (𝑔 푚−3) = × 10−3휌 ∑ 푁(퐷 ) 퐷3 ∆퐷 − − − − − (4) 6 푤 푖 푖 푖 푖=1
148
−3 149 where, ρw (1 g cm ) is the density of water.
32 −3 150 푁푡 (푚 ) = ∑ 푁(퐷푖)∆퐷푖 − − − − − (5) 푖=1
151
152 The nth order moment (in mmn m−3) of the drop size distribution can be expressed as
퐷푚푎푥 푛 153 푀푛 = ∫ 퐷 푁(퐷)푑퐷 − − − − − −(6) 퐷푚푖푛
154
155 where n = 3 for the third moment, 4 for the fourth moment, and 6 for the sixth moment of the
156 size distribution.
157 The mass-weighted mean diameter (Dm, mm), shape parameter (µ, dimensionless), and slope
158 parameter (Λ, mm−1) are obtained (Bringi et al. 2003; Tokay and Short 1996; Ulbrich 1983)
159 from the third, fourth, and sixth moments of the size distribution as
푀4 160 퐷푚 = − − − − − −(7) 푀3
161 The slope parameter Λ (mm−1) is given (Tokay and Short 1996) by
( µ + 4)푀 162 Ʌ = 3 − − − − − −(8) 푀4
163 where µ is the shape parameter (dimensionless) and is given (Tokay and Short 1996) by
(11퐺 − 8) + √퐺(퐺 + 8) 164 µ = − − − − − −(9) 2(1 − 퐺)
165 where G is
3 푀4 166 퐺 = 2 − − − − − (10) 푀3 푀6
8
−1 −3 167 The normalized intercept parameter Nw (mm m ) is defined by Bringi et al. (2003) as
44 103푊 168 푁푤 = ( 4 ) − − − − − −(11) 휋휌푤 퐷푚
169 where W (g m−3) represents the liquid water content for the corresponding size distribution.
170
171 The mass spectrum standard deviation σm (mm) can be expressed in terms of Dm and N(D)
172 (Thurai et al. 2014; Ulbrich 1983; Williams et al. 2014) as
1 32 2 3 ⁄2 ∑푖=1 (퐷푖 − 퐷푚) 푁(퐷푖) 퐷푖 ΔDi 173 휎푚 = [ 32 3 ] − − − − − −(12) ∑푖=1 푁(퐷푖) 퐷푖 푑퐷
174
175 2.3 MODIS and ERA-Interim data
176 Along with the disdrometer measurements, Moderate Resolution Imaging
177 Spectroradiometer (MODIS) and European Centre for Medium-Range Weather Forecasts
178 (ECMWF) Interim Re-Analysis (ERA-Interim) datasets over the observational sites in Taiwan
179 (22.75o–22.875oN, 120.25o–120.375oE) and India (14.375o–14.5oN, 78.625o–78.75oE) were
180 used for the TCs dates listed in Table 1. Convective available potential energy (CAPE, J kg−1:
181 available every three hours) and vertical integral water vapor (W, kg m−2; available every six
182 hours) from ECMWF ERA-Interim (Dee et al. 2011) with a 0.125o × 0.125o grid resolution are
183 used. Cloud top temperatures (CTT, oC) from the MODIS level 3 data product (Platnick 2015)
184 are used. The level 3 daily data product (MOD08_D3) of MODIS consists of 1° × 1° grid
185 average values of atmospheric parameters related to aerosol particle properties, water vapor,
186 and cloud optical and physical properties. Details about the MODIS cloud product algorithms
187 are provided in Platnick et al. (2003) and King et al. (2003). CTT data, which are available at
188 a 1° × 1° grid resolution, are interpolated to a 0.125° × 0.125° grid resolution.
189
190
9
191 3. Results
192 The disdrometers at the observational sites in Taiwan and India recorded a 8392 RSD
193 samples (1-min) for six Pacific Ocean TCs and 3776 RSD samples (1-min) for five Indian
194 Ocean TCs. The variations of mean raindrop concentration (N(D), m−3 mm−1) with drop
195 diameter (D, mm) for Indian Ocean and Pacific Ocean TCs are illustrated in Fig. 3. Throughout
196 this paper, raindrops with diameters of 1–3 mm are considered as mid-size drops, and drops
197 below and above this range are considered, small and large drops, respectively (Janapati et al.
198 2017; Kumar and Reddy 2013; Kumari et al. 2014; Tokay et al. 2008). Figure 3 clearly
199 demonstrates that the raindrop concentration of mid-size and large drops is higher for Pacific
200 Ocean TCs than Indian Ocean TCs. The Pacific Ocean TCs have higher mean rainfall rates (R,
−1 201 mm h ), mass-weighted mean diameters (Dm, mm), and lower normalized intercept parameters
−1 −3 202 (Nw, mm m ) than Indian Ocean TCs (Fig. 3). A higher concentration of small drops and
203 lower concentration of mid-size and large drops in Indian Ocean TCs results in lower Dm values
204 than Pacific Ocean TCs.
205
206 Further, to recognize dissimilarities in rain parameters (log10R, log10W, Dm, and
207 log10Nw) of Indian Ocean and Pacific Ocean TCs, the probability distribution functions (PDF)
208 of these parameters are computed and illustrated in Fig. 4. The Pacific Ocean TCs have larger
−1 209 values than Indian Ocean TCs for log10 R > 0.5, where R is in mm h , and the Indian Ocean
210 TCs show peak distributions at lower rainfall intensities (log10R = 0) (Fig. 4a). The liquid water
211 content (log10W) PDF shows relatively greater frequency in Indian Ocean TCs for log10W <
212 −0.6 (here W is in g m−3) than in Pacific Ocean TCs (Fig. 4b). A clear variation in PDF
213 distributions of Dm for Indian Ocean and Pacific Ocean TCs can be seen (Fig. 4c). The Indian
214 Ocean and Pacific Ocean TCs have peak PDF distributions of Dm at around 0.8 mm and 1.4
215 mm, respectively. Like Dm, the normalized the intercept parameter (log10Nw) also showed
10
216 distinct differences in PDF distributions between Indian Ocean and Pacific Ocean TCs (Fig.
217 4d). The PDF of log10Nw shows a higher percentage at lower log10Nw values in the Pacific
218 Ocean TCs, and a higher percentage at higher log10Nw values in the Indian Ocean TCs (Fig.
219 4d). Additionally, to show the dissimilarities between Indian Ocean and Pacific Ocean TCs
220 rain parameters, a Student’s t-test is executed for log10R, log10W, Dm, and log10Nw, and the
221 results reject the null hypothesis H0(log10RPacific = log10RIndian; log10WPacific = log10WIndian;
222 Dm_Pacific = Dm_Indian; log10Nw_Pacific = log10Nw_Indian) at significance levels of 0.05 and 0.01.
223
224 3.1 RSD in different rainfall rate classes
225 The mean raindrop concentrations of Indian Ocean and Pacific Ocean TCs are classified
226 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)
227 and are shown in Fig. 5. The rain statistics of these six rainfall rate classes for Indian Ocean
228 and Pacific Ocean TCs are provided in Table 2. For C1 and C2, there are higher concentrations
229 of mid-size and large drops in Pacific Ocean TCs than in Indian Ocean TCs (Fig. 5a and b).
230 Additionally, raindrops with diameters of > 1.4 mm and 1.6 mm in C3 and C4, respectively,
231 have greater concentrations in Pacific Ocean TCs (Fig. 5c and d). In C5 and C6 (Fig. 5e and f),
232 raindrops larger than 2 mm in diameter are more common in Pacific Ocean TCs than Indian
233 Ocean TCs. From Fig. 5, it can be seen that even after separating the Pacific Ocean and Indian
234 Ocean TCs raindrop spectra into different rainfall rate classes, mid-size and large drops are
235 more common in Pacific Ocean TCs than Indian Ocean TCs. For both oceanic TCs, it can be
236 seen that the concentration of large drops and spectral width increase with increased rainfall
237 rate.
238
239 Variations of Dm and log10Nw values in the six rainfall rate classes of both oceanic TCs
240 are shown in Fig. 6 with a box and whisker plot. It is obvious from Fig. 6a that Pacific Ocean
11
241 TCs have higher Dm values than Indian Ocean TCs. In contrast, Dm values increase with
242 increased rainfall rate classes for the TCs in both oceans. In contrast to Dm, the log10Nw values
243 are higher for Indian Ocean TCs than Pacific Ocean TCs. Higher mid-size and large drop
244 concentrations in Pacific Ocean TCs result in higher Dm values for Pacific Ocean TCs. The
245 mean and standard deviation values of Dm, log10Nw, μ, and Λ for Pacific Ocean and Indian
246 Ocean TCs are provided in Table 3. For both oceanic TCs, increased rainfall rates correlate
247 with increasing Dm values due to the increase in the mid-size and large drop concentration with
248 increased rainfall rate.
249
250 3.2 Dm-R and Nw-R relations
251 The normalized intercept parameter (log10Nw) and mass-weighted mean diameter (Dm)
252 infer the RSD features, and these parameters varied with rainfall intensity for different
253 precipitating cloud systems (Chen et al. 2013; Marzano et al. 2010; Thurai et al. 2010). Fig. 7
254 shows scatter plots of Dm and log10Nw with rainfall rates for both oceanic TCs. An apparent
255 distinction in the distribution of Dm with rainfall rate can be seen between Indian Ocean and
256 Pacific Ocean TCs. The Dm values in Pacific Ocean TCs (Fig. 7a) are distributed between 0.4
257 and 3 mm, with few points around 3–3.5 mm, whereas the Dm values for Indian Ocean TCs are
258 scattered from 0.4 to 2 mm, with few points between 2–2.5 mm. Despite this, as rainfall rate
259 increases, the distribution of Dm narrows for both oceanic TCs, which is consistent with
260 previous studies (Chang et al. 2009; Kumar and Reddy 2013; Wen et al. 2018). Lower variation
261 of Dm with increased rainfall rate could be due to the RSD reaching equilibrium at higher
262 rainfall rates, in which raindrop breakup and coalescence reach a near balance (Hu and
263 Srivastava 1995), and further rainfall rates increases under RSD equilibrium conditions are due
264 to an increase in number concentration (Bringi and Chandrasekar 2001). The power law fitting
265 equations derived for Dm-R and log10Nw-R are also shown in Fig. 7. The Dm-R relations
12
266 obviously show that the Pacific Ocean TCs have higher coefficient and exponent values than
267 Indian Ocean TCs. This implies that for a given rainfall rate, Pacific Ocean TCs have higher
268 Dm values than Indian Ocean TCs, which can be seen in Fig. 6. In contrast, the coefficient and
269 exponent values of log10Nw-R relations are higher for Indian Ocean TCs than Pacific Ocean
270 TCs, indicating the higher concentration of small drops in Indian Ocean TCs. Over east China,
271 Chen et al. (2016) estimated the Dm-R and Nw-R relations for a continental squall line at four
272 different locations, with coefficient and exponential values of Dm-R relations of 1.642–1.725
273 and 0.1–0.13, respectively. The coefficient values of Dm-R relations reported by Chen et al.
274 (2016) are higher than the coefficient values of both oceanic TCs in this present study. In
275 contrast, the coefficient and exponent values of log10Nw-R relations of the continental squall
276 line ranged were 2.843–2.855 and 0.053–0.073, respectively, and their coefficient values were
277 lower than the coefficient values of both oceanic TCs in this study. This clearly demonstrates
278 that both oceanic TCs have higher concentrations of small drops than the continental squall
279 line observed over east China.
280
281 3.3 Shape and slope (μ-Λ) relations
282 The μ-Λ relations provide useful information for understanding RSD characteristics and
283 retrieving RSD parameters from polarimetric radar observations through the constrained-
284 gamma method (Brandes et al. 2004; Cao et al. 2008; Zhang et al. 2001; Zhang et al. 2006;
285 Zhang et al. 2003). These relations were found to vary by region and rain type (Seela et al.
286 2018; Tang et al. 2014; Zhang et al. 2003), which necessitates investigation of each region’s
287 representative μ-Λ relations. Fig. 8 shows scatterplots for the μ and Λ values of Indian Ocean
288 and Pacific Ocean TCs. To estimate the μ-Λ relations of Indian Ocean and Pacific Ocean TCs,
289 we adopted criteria similar those of Cao et al. (2008). That is, if the sum of the count of particles
290 from drop channels 3–21 is less than 1000 or the rainfall rate is less than 5 mm h−1, those
13
291 datasets would not be used to derive μ-Λ relations. Because μ and Λ values greater than 20 and
292 20 mm−1, respectively, are ascribed to measurement error rather than storm physics (Zhang et
293 al. 2003), μ-Λ relations are estimated for μ < 20 and Λ < 20 mm−1, and are shown in Fig. 8. For
294 rainfall in Indian Ocean TCs, 50.13% of the data points are associated with μ > 20 and Λ >20
295 mm−1, whereas for Pacific Ocean TCs, 13.32 % of the data points are associated with μ > 20
296 and Λ >20 mm−1. The red and blue solid lines in Fig. 8a and Fig. 8b represent the polynomial
297 least squares fit for the data points of Pacific Ocean and Indian Ocean TCs, respectively. The
298 computed μ-Λ relations of both oceanic TCs are depicted in Fig. 8, and these relations vary
299 greatly between one another. The green solid line in Fig. 8 represents the equation from Zhang
300 et al. (2003). Current μ-Λ relations, as well as previously reported μ-Λ relations of TCs in
301 different parts of the world (Chang et al. 2009; Chen et al. 2012; Chu and Su 2008; Wen et al.
302 2018), are provided in Table 4.
303
304 3.4 The mass-mean diameter and standard deviation of mass spectrum ( Dm-σm) relations
305 Fig. 9 shows scatterplots of mass-weighted mean diameter and the standard deviation
306 of mass spectrum for Indian Ocean and Pacific Ocean TCs. The data points shown in Fig. 9a
307 and b are those that satisfy the quality control procedure of Williams et al. (2014), i.e., each 1-
308 min raindrops spectra is considered if 1) there are at least 50 raindrops in at least three different
309 diameter bins, 2) the reflectivity factor is greater than 10 dBZ, and 3) the rainfall rate is greater
−1 310 than 0.1 mm h , and σm values corresponding to Dm < 0.5 mm are discarded. After applying
311 this quality control procedure, 3611 and 8297 min of raindrop spectra are observed in Indian
312 Ocean and Pacific Ocean TCs, respectively. If fitting is performed for data with Dm > 0.5 mm,
1.35 1.71 313 the fitted equation becomes σm = 0.36 Dm and σm = 0.29 Dm for the Pacific Ocean and Indian
314 Ocean TCs, respectively. Using large data sets (24,872 min) of two-dimensional video
315 disdrometer (2DVD) measurements over Huntsville, Alabama, Williams et al. (2014)
14
1.36 1.65 316 computed the σm-Dm relation as σm = 0.30Dm . A similar relation (σm = 0.266Dm ) was
317 evaluated by Thurai et al. (2014) for 10 months of 2DVD observations over Alabama. Thurai
0.94 318 et al. (2017) estimated σm-Dm relations (σm = 0.48Dm ) for two precipitation events measured
319 in Colorado and Huntsville, Alabama, using 2DVD. The differences in the Dm-σm relations of
320 Thurai et al. (2017) and our results could be due to Thurai et al. (2017) using a droplet
321 spectrometer and the truncation effect of the Parsivel disdrometer used in this study.
322
323 3.5 RSD in stratiform and Convective precipitation
324 It has been well documented that RSD features significantly change between convective
325 and stratiform precipitation types (Tokay and Short 1996; Ulbrich and Atlas 2007). To classify
326 precipitation into stratiform and convective types, different researchers adopted various
327 classification criteria (Bringi et al. 2003; Das et al. 2017; Krishna et al. 2016; Steiner et al.
328 1995; Tokay and Short 1996). Among previous precipitation classifications studies, Bringi et
329 al. (2003) documented the RSD characteristics of different precipitation types over a wide
330 range of climatic regimes and observed profound variations between stratiform and convective
331 regimes in maritime and continental clusters. The RSDs of Indian Ocean and Pacific Ocean
332 TCs are categorized into stratiform and convective types by adopting the threshold criteria (i.e.,
333 standard deviation of rainfall rate) of Bringi et al. (2003). In this study, 10 consecutive 1-min
334 RSD samples were considered to be stratiform type if the mean value of R > 0.5 mm h−1 and
−1 335 the standard deviation of R (σR) < 1.5 mm h , and convective type if the mean value of R > 5
−1 −1 336 mm h and the standard deviation of R (σR) >1.5 mm h . Samples not meeting these criteria
337 were discarded. With these classification criteria, 66.67% of data points in the Pacific Ocean
338 TCs are stratiform type and 33.33% are convective type. For the Indian Ocean TCs, 82.35 %
339 of the data points are stratiform type and 17.65% are convective type.
340
15
341 The raindrop concentrations of convective and stratiform regimes of Indian Ocean and
342 Pacific Ocean TCs are shown in Fig. 10. For both oceanic TCs, a relatively higher drop
343 concentration can be seen for convective regimes than stratiform regimes (Fig. 10a and b). For
344 both oceanic TCs, the stratiform regimes have nearly exponential distributions, whereas the
345 convective regimes have broad distributions, which might be due to the collisional breakup of
346 large drops in convective rain (Hu and Srivastava 1995). To compare the raindrop
347 concentration of Indian Ocean and Pacific Ocean TCs with respect to precipitation type, Fig.
348 10a and b are re-plotted into Fig. 10c and d. Even within the different precipitation types we
349 see a strong distinction in RSDs, with a higher concentration of mid-size and large drops in
350 Pacific Ocean TCs. It is also worth noting that larger numbers of small drops can be seen in
351 Indian Ocean TCs.
352
353 The distribution of the mean Dm and log10Nw values in the stratiform and convective
354 regimes of Indian Ocean and Pacific Ocean TCs are illustrated in Fig. 11. The gray rectangular
355 boxes in Fig. 11 are the maritime and continental clusters defined by Bringi et al. (2003). For
356 both oceanic TCs, convective regimes have higher mean Dm and log10Nw values than stratiform
357 regimes. In contrast, Pacific Ocean TCs have higher Dm and lower log10Nw values than Indian
358 Ocean TCs in both convective and stratiform precipitations. The Indian Ocean TCs have
359 smaller drop diameter values than the maritime convection of Bringi et al. (2003). The mean
360 and standard deviation values of Dm, log10Nw, μ, and Λ in the stratiform and convective regimes
361 of the Pacific Ocean and Indian Ocean TCs are listed in Table 5. If we compare our Dm and
362 log10Nw distributions with those of the maritime and continental clusters of Bringi et al. (2003),
363 only Pacific Ocean convective precipitation is near the maritime-like clusters, with the rest
364 having lower Dm values than the convective clusters of Bringi et al. (2003). Additionally,
365 Bringi et al. (2003) proposed two different microphysical processes that lead to large Dm and
16
366 log10Nw variations in stratiform rain. Larger Dm and smaller log10Nw values occur due to the
367 melting of large snowflakes and smaller Dm and larger log10Nw values are due to the melting of
368 tiny graupel or smaller rimed ice particles. As shown in Fig. 11, Indian Ocean TCs stratiform
369 precipitations have lower Dm and larger log10Nw values than Pacific Ocean TCs, implying that
370 Indian Ocean TCs stratiform precipitation is associated with melting of tiny graupel or smaller
371 rimed ice particles, whereas Pacific Ocean TCs feature melting of large snowflakes.
372
373 3.6 Radar reflectivity and rainfall rate (Z-R) relations
374 Z-R relations play a vital role in quantitative precipitation estimation from radar
375 measurements, and these relations were found to vary by geographical location and storm type
376 and strongly depend on RSD characteristics (Rosenfeld and Ulbrich 2003; Seela et al. 2017).
377 The uncertainties in estimating rainfall rate from weather radars can be minimized using
378 indigenous Z-R relations rather than default or tropical Z-R relations (Ulbrich and Lee 2002).
379 In Z = ARb relations, the presence of large or small drops can be inferred from the coefficient
380 A and the exponent b representing microphysical processes. If b is greater than unity, then
381 collision-coalescence (size or mixed controlled) is the characteristic feature. If b equals unity,
382 then the collision, coalescence, and breakup processes (number controlled) are associated with
383 homogeneous rainfall (Atlas and Williams 2003; Atlas et al. 1999; Steiner et al. 2004). The Z-
384 R relations for Indian Ocean and Pacific Ocean TCs are deduced by applying linear regression
385 to logarithmic values of rainfall rate (R, mm h−1) and radar reflectivity (Z, mm6 m−3), and are
386 provided in Fig. 12. A clear demarcation in the coefficient and exponent values of Z-R relations
387 can be seen between the Indian Ocean and Pacific Ocean TCs. For the observational site in
388 India (Kadapa), Jayalakshmi and Reddy (2014) estimated Z-R relations for seasonal rainfall
389 (southwest monsoon (SW): Z = 300.5R1.375 and northeast (NE) monsoon: Z = 163.324R1.35) as
390 well as for precipitation type (SW stratiform: Z = 334.13R1.424, NE stratiform: Z = 245.35R1.283,
17
391 SW convective: Z = 265.59R1.341, NE convective: Z = 122.41R1.430). The coefficient A in the
392 Z-R relations of Indian Ocean TCs (A = 148.44) is smaller than the seasonal rainfall
393 coefficients, which indicates that the raindrop sizes are relatively smaller in TCs than in
394 seasonal rainfall. Over northern Taiwan, Seela et al. (2018) computed the Z-R relations for
395 summer (stratiform: Z = 276.13R1.41, convective: Z = 237.88R1.41, total: Z = 266.42R1.38) and
396 winter (stratiform: Z = 127.67R1.54, convective: Z = 142.94R1.52, total: Z = 129.76R1.55) rainfall
397 using Joss-Waldvogel disdrometer measurements. For the same observational site over
398 northern Taiwan, Chang et al. (2009) evaluated the Z-R relations (Z = 206.83R1.45) of typhoon
399 rainfall using 2DVD measurements. The current Z-R relations of Pacific Ocean TCs (stratiform
400 Z = 368.28R1.49, convective: Z = 328.73R1.42, total: Z = 346.03R1.42) are different from those of
401 seasonal and typhoon rainfall, and these variations could be due to either the use of different
402 instruments to estimate Z-R relations or Taiwan’s complex orography. The estimated Z-R
403 relations of Indian Ocean and Pacific Ocean TCs will enhance the quantitative precipitation
404 estimation of TCs rainfall for these two oceanic basins.
405
406 4. Discussion
407 To determine the possible rationale for RSD variations between Indian Ocean and
408 Pacific Ocean TCs, the CAPE(J kg−1), water vapor (Kg m−2), vertical profiles of temperature
409 (oC), and relative humidity values from ERA-Interim, and CTT from MODIS are considered
410 for disdrometers’ measurement periods (as listed in Table 1). Figure 13 shows a box and
411 whisker plot of CAPE and water vapor values for the disdrometer observational periods of
412 Indian Ocean and Pacific Ocean TCs. It is apparent that Pacific Ocean TCs have relatively
413 higher water vapor and strong convective activity (higher CAPE) than Indian Ocean TCs.
414 Relatively higher water vapor with vigorous updrafts and downdrafts leads to the growth of
415 cloud particles (both liquid and solid) to a sufficiently larger size (by aggregation, riming, and
18
416 collision-coalescence processes) in Pacific Ocean TCs than in Indian Ocean TCs. The MODIS-
417 obtained CTT values are slightly higher for Pacific Ocean TCs than Indian Ocean TCs (Fig.
418 14). Melting of large (small) particles in the Pacific (Indian) TCs results in relatively higher
419 (lower) Dm and lower (higher) log10Nw values in the Pacific (Indian) TCs (Fig. 11). A Student’s
420 t-test is applied to the CAPE, water vapor, and CTT values of the Indian Ocean and Pacific
421 Ocean TCs. The test results showed a significant difference at the 0.01 level in CAPE and
422 column water vapor between Indian Ocean TCs and Pacific Ocean TCs, whereas, there was
423 not a significant difference in CTT. Further mean vertical profiles of temperature and relative
424 humidity for Pacific Ocean and Indian Ocean TCs are shown in Fig. 15. The temperature
425 profiles of Pacific Ocean TCs show relatively higher values than Indian Ocean TCs (Fig. 15a)
426 at all pressure levels. However, the relative humidity profiles showed higher values below 925
427 hPa and above 775 hPa (Fig. 15b) for the Pacific Ocean TCs. Higher temperature and lower
428 relative humidity values can be seen for Pacific Ocean TCs between 775 and 925 hPa (~2.2–
429 0.76 km), which shows that it is possible for small drops in the Pacific Ocean TCs to evaporate
430 between these pressure levels. The above explanation provides possible reasons for the
431 occurrence of more large drops in Pacific Ocean TCs and more small drops in Indian Ocean
432 TCs.
433
434 5. Summary
435 For the first time, RSD characteristics of Indian Ocean and Pacific Ocean TCs are
436 compared using Parsivel disdrometers installed at observational sites in India and Taiwan. The
437 contribution of mid-size and large drops is higher in Pacific Ocean TCs than in Indian Ocean
438 TCs. The probability distribution of rain integral parameters for Indian Ocean and Pacific
439 Ocean TCs showed distinct distributions. RSDs classified into different rainfall rate classes as
440 well as precipitation types (stratiform and convective) showed a greater concentration of mid-
19
441 size and large drops in Pacific Ocean TCs. In different rainfall rate classes and precipitation
442 types, higher (lower) mass-weighted mean diameter (normalized intercept parameter) values
443 are observed for Pacific Ocean TCs. The derived empirical relations (Dm-R, log10Nw-R, μ-Λ Dm-
444 σm, log10Nw-Dm and Z-R) are found to differ between the Pacific Ocean and Indian Ocean TCs,
445 confirming that must adopt TC-specific empirical relations in remote sensing and radar rainfall
446 estimation algorithms. Relatively higher convective activity and water vapor in the Pacific TCs
447 resulted in a greater number of large drops in Pacific TCs than Indian TCs through different
448 microphysical processes.
449 450
451
452 Acknowledgments
453 All authors thank IMD and JMA for providing TCs track information. This work is
454 supported by the Ministry of Science and Technology (MOST), Taiwan, under grant numbers
455 MOST 104-2923-M-008-003-MY5, MOST 106-2625-M-008-013, MOST 106-2811-M-008-
456 084, MOST 107-2111-M-008-038, and MOST 108-2625-M-008-011, and partially by
457 “Earthquake-Disaster & Risk Evaluation and Management Center, E-DREaM” from The
458 Featured Areas Research Center Program within the framework of the Higher Education Sprout
459 Project by the Ministry of Education (MOE), Taiwan. The first author, Jayalakshmi Janapati
460 acknowledges MOST in carrying out this work under grant numbers MOST 104-2811-M-008-
461 064, MOST 106-2811-M-008-084, MOST 107-2811-M-008-2551, and MOST 108-2811-M-
462 008-558. The second author, Balaji Kumar Seela, acknowledges Academia Sinica, Taiwan, for
463 providing the graduate fellowship under Taiwan international Graduate Program (TIGP) and
464 MOST for providing the graduate fellowship under grant numbers MOST 106-2625-M-008-
465 013 and MOST 107-2625-M-008-002. The second author also acknowledges MOST for
20
466 providing the post-doctoral fellowship under grant numbers MOST 107-2111-M-008-038,
467 MOST 108-2625-M-008-011 and MOST 108-2811-M-008-595, and the Central Weather
468 Bureau (CWB), Taiwan under the grant number CWB 1072019C.
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670 with disdrometer and radar observations. J. Atmos. Sci., 63, 1273–1290.
671 Zhang, G., J. Vivekanandan, E. A. Brandes, R. Meneghini, and T. Kozu, 2003: The shape–
672 slope relation in observed gamma raindrop size distributions: Statistical error or useful
673 information? J. Atmos. Oceanic Technol., 20, 1106–1119.
674
675
676
677
29
678 List of Tables:
679 Table 1: Duration, rainfall accumulation, and rainfall rate statistics (maximum, mean, and
680 standard deviation (std)) of Indian Ocean and Pacific Ocean tropical cyclones (TCs).
681
682 Table 2. Rainfall rate statistics of Indian Ocean and Pacific Ocean tropical cyclones (TCs) in
683 six rainfall rate classes (C1-C6).
684
685 Table 3: Mean and standard deviation (std) values of Dm (mass-weighted mean diameter, mm),
−1 −3 686 log10Nw (where Nw is the normalized intercept parameter in mm m ), μ (shaper
687 parameter, dimensionless), and Λ (slope parameter, mm−1) for Indian Ocean and Pacific
688 Ocean tropical cyclones (TCs) in six rainfall rate classes (C1-C6).
689
690 Table 4: The μ-Λ relations of current study tropical cyclones (TCs) and other parts of the world.
691
692 Table 5 Mean and standard deviation (std) values of Dm (mass-weighted mean diameter, mm),
−1 −3 693 log10Nw (normalized intercept parameter Nw, mm m ), μ (shape parameter), and Λ
694 (slope parameter, mm-1) for stratiform and convective precipitations of Pacific Ocean
695 and Indian Ocean tropical cyclones (TCs).
696
697
698
699
700
701
702
30
703 List of figures:
704 Figure 1. Tracks of Indian Ocean and Pacific Ocean tropical cyclones and the locations of
705 Parsivel disdrometer (red color stars) over India and Taiwan.
706
707 Figure 2. Quality control procedure applied to raw raindrop size distributions of Pacific Ocean
708 and Indian Ocean tropical cyclones measured by Parsivel disdrometers over (a) Taiwan
709 and (b) India, respectively. The black solid line is the empirical fall speed-diameter
710 relations from Atlas et al. (1973), and the black dotted lines are the threshold curves (±
711 60% of Atlas et al. (1973) curve), used for filtering the data.
712
713 Figure 3. Mean raindrop concentration of Indian Ocean and Pacific Ocean tropical cyclones
714 (TCs). Numbers of 1-min raindrop size distributions samples of TCs are shown in
715 legend parenthesis. The error bars represent the standard error of each raindrop diameter
716 bin sample. The mean values of Dm (mass-weighted mean diameter, mm), R (rainfall
-1 −1 −3 717 rate, mm h ), and log10Nw (the normalized intercept parameter, Nw in mm m ) for
718 Indian Ocean and Pacific Ocean TCs rainfall are depicted in the figure.
719
720 Figure 4. The probability distribution functions (PDF) of (a) log10R, where R is rainfall rate
−1 −3 721 (mm h ) (b) log10W, where W is liquid water content (g m ), (c) mass-weighted mean
−1 722 diameter, Dm (mm), (d) log10Nw, where Nw is the normalized intercept parameter (mm
723 m−3), for Indian Ocean and Pacific Ocean tropical cyclones (TCs).
724
725 Figure 5. Mean raindrop concentration (N(D), m−3 mm−1) of Indian Ocean and Pacific Ocean
726 tropical cyclones (TCs) in six rainfall rate (R) classes (C1:0.1–1, C2:1–2, C3:2–5,
727 C4:5–10, C5:10–20, and C6: >20 mm h−1).
31
728
729 Figure 6. Box and whisker plot of mass-weighted mean diameter, Dm (mm) and log10Nw, where
−1 −3 730 Nw is the normalized intercept parameter (mm m ) for Indian Ocean (blue) and
731 Pacific Ocean (red) tropical cyclones in six rainfall rate (R) classes (C1:0.1–1, C2:1–2,
732 C3:2–5, C4:5–10, C5:10–20, and C6: >20 mm h−1). The center line of the box indicates
733 the median, and the bottom and top lines of the box indicate the 25th and 75th percentiles,
734 respectively. The bottom and top of the dashed vertical lines indicate the 5th and 95th
735 percentiles, respectively.
736
737 Figure 7. Scatter plots of Dm (mass-weighted mean diameter, mm) and log10Nw (where Nw is
738 the normalized intercept parameter in mm−1 m−3) with rainfall rate for Indian Ocean
739 and Pacific Ocean tropical cyclones (TCs).
740
741 Figure 8. Scatterplots of μ versus Λ for (a) Pacific Ocean and (b) Indian Ocean tropical
742 cyclones (TCs). The gray solid circles and stars in (a) and (b), respectively, are data
743 points with rainfall rates > 5 mm h−1. The red and blue lines in (a) and (b) represent the
744 least squares fit applied (expression of µ in terms of Λ) to filter data of Pacific Ocean
745 and Indian Ocean TCs, respectively. The green line corresponds to the μ-Λ relation of
746 Zhang et al. (2003).
747
748 Figure 9. Scatter plot of mass-weighted mean diameter (Dm) and standard deviation of mass
749 spectrum (σm) for Indian Ocean and Pacific Ocean tropical cyclones (TCs).
750
751 Figure 10. Variation of raindrop concentration (N(D), m−3 mm−1) with drop diameter for
752 precipitation types for Indian Ocean and Pacific Ocean tropical cyclones (TCs).
32
753
−1 −3 754 Figure 11. Variation of log10Nw (where Nw is the normalized intercept parameter in mm m )
755 with Dm (mass-weighted mean diameter in mm) in stratiform and convective regimes
756 of Indian Ocean and Pacific Ocean tropical cyclones (TCs). The horizontal gray dashed
757 line is the Marshall-Palmer value of log10Nw (3.9) for exponential shape. The green dash
758 dotted line is the stratiform and convective separation line of Bring et al. (2003).
759
760 Figure 12. Radar reflectivity-rainfall rate (Z-R) relations for Indian Ocean and Pacific Ocean
761 tropical cyclones (TCs) and their precipitation types (CON: convective, STF:
762 stratiform).
763
764 Figure 13. Box and whisker plot of (a) convective available potential energy (CAPE, J Kg−1)
765 and (b) vertical integral of water vapor (kg m−2) for the disdrometer observational
766 periods of Indian Ocean and Pacific Ocean tropical cyclones (TCs). The center line of
767 the box indicates the median, and the bottom and top lines of the box indicate the 25th
768 and 75th percentiles, respectively. The bottom and top of the dashed vertical lines
769 indicate the 5th and 95th percentiles, respectively.
770
771 Figure 14. Box and whisker plot of cloud top temperature (oC) for the disdrometer
772 observational periods of Indian Ocean and Pacific Ocean tropical cyclones (TCs).
773
774 Figure 15. Vertical profiles of (a) temperature (oC) and (b) relative humidity (%) for the
775 disdrometer observational periods of Indian Ocean and Pacific Ocean tropical cyclones
776 (TCs).
777 778
33
779 Table 1: Duration, rainfall accumulation, and rainfall rate statistics (maximum, mean, and standard deviation (std)) of Indian Ocean and Pacific
780 Ocean tropical cyclones (TCs).
Oceanic TC name TC life time TC observation period by Parsivel Rainfall Rainfall rate (mm h–1)
Region Beginning Ending accumulation Maximum Mean std
(mm)
Indian Ocean Jal 04-08 06:30 LST, 07:00 LST, 56.6 55.63 2.86 5.46
November 2010 07 November 2010 08 November 2010
Depression 04-8 10:30 LST, 10:30 LST, 42.34 17.09 1.10 1.55
December 2010 06 December 2010 08 December 2010
Nilam 28 October- 01 05:30 LST, 23:00 LST. 44.36 60.28 4.92 8.18
November 2012 31 October 2012 31 October 2012
Depression 13-17 17:00 LST, 10:30 LST, 12.56 22.64 1.53 2.51
November 2013 16 November 2013 17 November 2013
Madi 06-13 01:00 LST 12:00 LST 1.07 4.14 0.51 0.84
December 2013 13 December 2013 13 December 2013
Matmo 16-25 01:00 LST 24:00 LST 125.59 126.25 7.81 15.28
34
Pacific July 2014 22 July 2014 23 July 2014
Ocean Fung-wong 17 -23 21:00 LST 21:30 LST 89.76 75.14 3.37 4.45
September 2014 19 September 2014 22 September 2014
Linfa 01-10 01:00 LST 11:00 LST 51.04 37.39 2.56 3.83
July 2015 06 July 2015 09 July 2015
Dujuan 19-30 19:00 LST 23:00 LST 110.02 164.29 6.38 13.08
September 2015 28 September 2015 29 September 2015
Nepartak 02-10 01:00 LST 18:00 LST 164.87 123.17 7.76 14.88
July 2016 08 July 2016 09 July 2016
Aere 04-10 02:00 11:00 LST 129.20 58.77 3.20 5.48
October 2016 05 October 2016 10 October 2016
781
782
783
784
785
35
786 Table 2. Rainfall rate statistics of Indian Ocean and Pacific Ocean tropical cyclones (TCs) in
787 six rainfall rate classes (C1-C6).
Rainfall Rainfall Pacific Ocean TCs Indian Ocean TCs
rate class rate No. of Mean Standard No. of Mean Standard
threshold samples (mm h–1) deviation samples (mm h-1) deviation
(mm h–1) (mm h–1) (mm h–1)
C1 0.1< R<1 3510 0.43 0.25 2000 0.44 0.25
C2 1 C3 2 C4 5< R<10 1019 7.06 1.38 216 6.89 1.38 C5 10 C6 R >20 402 41.14 23.04 56 30.06 8.99 All 8392 4.77 10.28 3776 2.35 4.55 classes 788 789 36 790 Table 3: Mean and standard deviation (std) values of Dm (mass-weighted mean diameter, mm), log10Nw (where Nw is the normalized intercept 791 parameter in mm−1 m−3), μ (shaper parameter, dimensionless), and Λ (slope parameter, mm−1) for Indian Ocean and Pacific Ocean tropical 792 cyclones (TCs) in six rainfall rate classes (C1-C6). Rainfall Pacific Ocean TCs Indian Ocean TCs –1 –1 rate Dm log10Nw μ (-) Λ (mm ) Dm log10Nw µ (-) Λ (mm ) –1 –1 Class (mm) (Nw in mm (mm) (Nw in mm m–3) m–3) 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 37 794 Table 4: The μ-Λ relations of current study tropical cyclones (TCs) and other parts of the world. Author Location/Oceanic Basin Number of TCs Disdrometer type μ-Λ relations Present study Southern India, Indian five TCs Parsivel Λ=0.0129μ2 + 0.836μ+2.226 Ocean TCs μ= -0.0124Λ2 + 1.13Λ-1.824 Present study South Taiwan, Pacific six TCs Parsivel Λ=0.021μ2 + 0.654μ+2.088 Ocean TCs 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 Λ=0.0136μ2 + 0.6984μ+1.5131 disdrometer Chu and Su (2008) NCU, North Taiwan four types of weather Two-dimensional Video Λ=0.017μ2 + 1.303μ+1.833 (for systems (typhoon, cold disdrometer low order moment) front, stationary front, Λ = 0.007μ2 + 1.362μ+1.569 (for convective cloud) low order moment) Wen et al. (2018) China Seven typhoons Two-dimensional Video μ= -0.019Λ2 + 1.09 Λ-3.119 disdrometer 38 795 −1 796 Table 5 Mean and standard deviation (std) values of Dm (mass-weighted mean diameter, mm), log10Nw (normalized intercept parameter Nw, mm 797 m−3), μ (shape parameter), and Λ (slope parameter, mm–1) for stratiform and convective precipitations of Pacific Ocean and Indian Ocean 798 tropical cyclones (TCs). Rain type Pacific Ocean TCs Indian Ocean TCs –1 –1 Dm (mm) log10Nw (Nw in μ (-) Λ (mm ) Dm (mm) log10Nw (Nw in μ (-) Λ (mm ) mm–1 m–3) mm–1 m–3) 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 39 805 806 Figure 1. Tracks of Indian Ocean and Pacific Ocean tropical cyclones and the locations of 807 Parsivel disdrometer (red color stars) over India and Taiwan. 808 809 810 811 812 813 814 815 816 817 818 819 820 821 40 822 823 Figure 2. Quality control procedure applied to raw raindrop size distributions of Pacific Ocean 824 and Indian Ocean tropical cyclones measured by Parsivel disdrometers over (a) Taiwan 825 and (b) India, respectively. The black solid line is the empirical fall speed-diameter 826 relations from Atlas et al. (1973), and the black dotted lines are the threshold curves (± 827 60% of Atlas et al. (1973) curve), used for filtering the data. 828 41 829 830 Figure 3. Mean raindrop concentration of Indian Ocean and Pacific Ocean tropical cyclones 831 (TCs). Numbers of 1-min raindrop size distributions samples of TCs are shown in 832 legend parenthesis. The error bars represent the standard error of each raindrop diameter 833 bin sample. The mean values of Dm (mass-weighted mean diameter, mm), R (rainfall -1 −1 −3 834 rate, mm h ), and log10Nw (the normalized intercept parameter, Nw in mm m ) for 835 Indian Ocean and Pacific Ocean TCs rainfall are depicted in the figure. 836 42 837 838 Figure 4. The probability distribution functions (PDF) of (a) log10R, where R is rainfall rate −1 −3 839 (mm h ) (b) log10W, where W is liquid water content (g m ), (c) mass-weighted mean −1 840 diameter, Dm (mm), (d) log10Nw, where Nw is the normalized intercept parameter (mm 841 m−3), for Indian Ocean and Pacific Ocean tropical cyclones (TCs). 842 843 844 845 846 847 848 849 43 850 851 Figure 5. Mean raindrop concentration (N(D), m−3 mm−1) of Indian Ocean and Pacific Ocean 852 tropical cyclones (TCs) in six rainfall rate (R) classes (C1:0.1–1, C2:1–2, C3:2–5, 853 C4:5–10, C5:10–20, and C6: >20 mm h−1). 854 855 856 44 857 858 Figure 6. Box and whisker plot of mass-weighted mean diameter, Dm (mm) and log10Nw, where −1 −3 859 Nw is the normalized intercept parameter (mm m ) for Indian Ocean (blue) and 860 Pacific Ocean (red) tropical cyclones in six rainfall rate (R) classes (C1:0.1–1, C2:1–2, 861 C3:2–5, C4:5–10, C5:10–20, and C6: >20 mm h−1). The center line of the box indicates 862 the median, and the bottom and top lines of the box indicate the 25th and 75th percentiles, 863 respectively. The bottom and top of the dashed vertical lines indicate the 5th and 95th 864 percentiles, respectively. 865 866 867 868 869 45 870 871 Figure 7. Scatter plots of Dm (mass-weighted mean diameter, mm) and log10Nw (where Nw is 872 the normalized intercept parameter in mm−1 m−3) with rainfall rate for Indian Ocean 873 and Pacific Ocean tropical cyclones (TCs). 874 875 876 877 878 879 880 881 882 46 883 884 Figure 8. Scatterplots of μ versus Λ for (a) Pacific Ocean and (b) Indian Ocean tropical 885 cyclones (TCs). The gray solid circles and stars in (a) and (b), respectively, are data 886 points with rainfall rates > 5 mm h−1. The red and blue lines in (a) and (b) represent the 887 least squares fit applied (expression of µ in terms of Λ) to filter data of Pacific Ocean 888 and Indian Ocean TCs, respectively. The green line corresponds to the μ-Λ relation of 889 Zhang et al. (2003). 890 891 892 893 894 895 896 897 898 899 900 47 901 902 Figure 9. Scatter plot of mass-weighted mean diameter (Dm) and standard deviation of mass 903 spectrum (σm) for Indian Ocean and Pacific Ocean tropical cyclones (TCs). 904 905 906 907 908 909 910 911 912 913 914 915 916 917 48 918 919 Figure 10. Variation of raindrop concentration (N(D), m−3 mm−1) with drop diameter for 920 precipitation types for Indian Ocean and Pacific Ocean tropical cyclones (TCs). 921 922 923 924 925 926 927 928 929 930 931 49 932 −1 −3 933 Figure 11. Variation of log10Nw (where Nw is the normalized intercept parameter in mm m ) 934 with Dm (mass-weighted mean diameter in mm) in stratiform and convective regimes 935 of Indian Ocean and Pacific Ocean tropical cyclones (TCs). The horizontal gray dashed 936 line is the Marshall-Palmer value of log10Nw (3.9) for exponential shape. The green dash 937 dotted line is the stratiform and convective separation line of Bring et al. (2003). 938 939 940 941 942 943 944 50 945 946 Figure 12. Radar reflectivity-rainfall rate (Z-R) relations for Indian Ocean and Pacific Ocean 947 tropical cyclones (TCs) and their precipitation types (CON: convective, STF: 948 stratiform). 949 950 951 952 953 954 955 956 957 958 51 959 960 Figure 13. Box and whisker plot of (a) convective available potential energy (CAPE, J Kg−1) 961 and (b) vertical integral of water vapor (kg m−2) for the disdrometer observational 962 periods of Indian Ocean and Pacific Ocean tropical cyclones (TCs). The center line of 963 the box indicates the median, and the bottom and top lines of the box indicate the 25th 964 and 75th percentiles, respectively. The bottom and top of the dashed vertical lines 965 indicate the 5th and 95th percentiles, respectively. 966 967 968 969 970 971 972 973 974 975 976 977 978 52 979 980 Figure 14. Box and whisker plot of cloud top temperature (oC) for the disdrometer 981 observational periods of Indian Ocean and Pacific Ocean tropical cyclones (TCs). 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 53 997 998 Figure 15. Vertical profiles of (a) temperature (oC) and (b) relative humidity (%) for the 999 disdrometer observational periods of Indian Ocean and Pacific Ocean tropical cyclones 1000 (TCs). 1001 54