JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, D16205, doi:10.1029/2009JD013355, 2010
Differences in cyclonic raindrop size distribution from southwest to northeast monsoon season and from that of noncyclonic rain B. Radhakrishna1 and T. Narayana Rao1 Received 7 October 2009; revised 4 February 2010; accepted 15 March 2010; published 21 August 2010.
[1] The raindrop size distributions (RSDs) measured with an impact‐type disdrometer have
been utilized to study the differences in cyclonic RSD (1) from southwest monsoon (SWM) to northeast monsoon (NEM), (2) from that of noncyclonic rain, and (3) from cyclonic rain elsewhere. The stratified (based on rainfall rate R) cyclonic RSD exhibits significant seasonal variation, with more large drops and fewer small drops in SWM than in NEM. The big drops are almost absent in cyclonic RSD, whereas the small and medium‐sized drops are larger in number than they are in noncyclonic rain. The average cloud effective radius in cyclones is nearly equal in SWM and NEM, suggesting that the nature of the cyclonic cloud may be similar (oceanic) in both seasons. The cyclonic RSD in the Bay of Bengal is consistent qualitatively with that observed elsewhere, but there exist some differences in rainfall bulk parameters. Implications of the observed seasonal and cyclonic to noncyclonic differences in RSD on quantitative rainfall estimation and cloud‐modeling studies are also discussed. Citation: Radhakrishna, B., and T. Narayana Rao (2010), Differences in cyclonic raindrop size distribution from southwest to northeast monsoon season and from that of noncyclonic rain, J. Geophys. Res., 115, D16205, doi:10.1029/2009JD013355.
1. Introduction [2] Raindrop size distribution (RSD) is known to vary from storm to storm, within the storm, and from one season to the other [Atlas et al., 1999; Tokay et al., 2002; Rao et al., 2001, 2009; Bringi et al., 2003; Rosenfeld and Ulbrich, 2003; Kozu et al., 2006; Ulbrich and Atlas, 2007; Radhakrishna et al., 2009, hereafter RR09]. Nevertheless, the RSD is expected to be similar in environments that are similar. In other words, if the data are stratified on the basis of either rainfall rate (R) or reflectivity factor (Z), the distribution should look similar [Sauvageot and Lacaux, 1995; Kostinski and Jameson, 1997; Nzeukou et al., 2004]. However, recent RSD observations (stratified based on R) at Gadanki (13.5°N, 79.2°E), a tropical station in southeast India, have shown significant seasonal differences with larger mass‐weighted mean diameter (Dm) values in the southwest monsoon season (SWM; June– September) than in the northeast monsoon season (NEM; October–December) [Kozu et al., 2006; Rao et al., 2009]. Similar seasonal differences are also found over Cuddalore (11.8°N, 79.8°E), a station on the east coast in southern peninsular India. Nevertheless, the seasonal differences in RSD are pronounced at Gadanki [RR09]. The differences in RSD are thought to be due to, in the first order, the nature of precipitating systems (continental in SWM and oceanic in NEM). Then, one would expect no or less seasonal differ1 Department of Space, National Atmospheric Research Laboratory, Gadanki, India.
Copyright 2010 by the American Geophysical Union. 0148‐0227/10/2009JD013355
ences in RSD observed in cyclones, because they originate over the ocean. But does the cyclonic RSD really show insignificant seasonal differences? How different are cyclonic RSD from those observed in noncyclonic rain (rain originated by other mechanisms)? In the present article, for the first time, an attempt is made to address the above issues through observations. [3] There exist few RSD observations in cyclones with both airborne in situ probes and a ground‐based disdrometer in the literature [Ulbrich and Lee, 2002; Tokay et al., 2008, and references therein]. The aircraft observations, initially with foil impactors and later with optical probes, showed that the RSD follows an exponential distribution [Merceret, 1974; Scott, 1974; Jorgensen and Willis, 1982]. These studies observed few big drops and more small to medium‐sized drops in tropical cyclones. The ground‐based disdrometers, which have been used extensively in recent years, confirmed the presence of more small to medium‐sized drops in tropical cyclones [Ulbrich and Lee, 2002; Maeso et al., 2005; Tokay et al., 2008]. Nevertheless, the above observations were made in cyclones or hurricanes in the Atlantic and Pacific oceans. On the other hand, in spite of being an important region (receiving the highest mean precipitation of the entire Asian monsoon [Zuidema, 2003]), cyclonic RSD observations in the Bay of Bengal (BOB) are yet to be documented. Do the cyclonic RSD in BOB resemble those in other tropical regions? If they are different, what are their characteristics? The Joss‐Waldvogel disdrometer (JWD) measurements [Joss and Waldvogel, 1969] collected during the passage of 11 cyclones (five in SWM and six in NEM) are utilized to compare the cyclonic DSD parameters with those observed in other tropical regions.
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Figure 1. Cyclone tracks during 1907–2007 in (a) southwest monsoon (SWM) and (b) northeast monsoon (NEM). The cyclonic track data are prepared by the India Meteorological Department (IMD).
[4] The general rainfall features over Gadanki, in relation to cyclones, in different monsoon seasons are briefly described in section 2. It also describes the JWD data and its validation during the passage of cyclones. The synoptic setting, variation of surface meteorological parameters, and RSD observations during the passage of two cyclones (one from each season) are discussed in section 3. The statistical comparison of RSD in cyclonic rain from noncyclonic rain and also between seasons is made in this section. The cyclonic RSD observations over BOB are compared with those made elsewhere in section 4. The differences in RSD, if any, have implications on reflectivity‐rainfall (Z‐R) relations and also on parameterization of RSD in tropical cyclones. These issues are discussed in section 5. The results are summarized in section 6.
[6] The JWD detects raindrops in the range of 0.3–5.3 mm and distributes them in 20 channels [Joss and Waldvogel, 1969]. It generates RSD profiles with better than 95% accuracy with 1‐min temporal resolution. These high‐resolution RSD data are used to estimate R, Z, and Dm by employing the following standard formulae: R¼
20 3:6 1 X ni D3i 3 6 10 Ft i¼1
ð1Þ
20 1X ni D6i Ft i¼1 V ðDi Þ
ð2Þ
Z¼
N ðD i Þ ¼
2. Data and Geographical Importance of the Location [5] Gadanki and its surrounding regions receive significant rainfall in both SWM and NEM seasons (53% and 33% of annual rainfall, respectively) and, therefore, form an ideal test bed for studying seasonal variability. Considerable rainfall occurs in this region during the passage of cyclones, particularly in NEM. In NEM, the majority of cyclones originate in the central or southeastern BOB and move toward the southeast coast, whereas others turn around (recurvature) and move toward the north‐northeast (Figure 1). The cyclone tracks shown in Figure 1 are based on a 100‐yr climatological cyclone e‐Atlas prepared by the India Meteorological Department (IMD). In SWM, on the other hand, most of the cyclones or depressions originate in the head of the BOB and move northwest to form a quasi‐permanent monsoon trough. Only on occasion, do they form in the south (in the central or southeast BOB) and move northwest, close to the Gadanki location. Therefore, a few low‐pressure systems in SWM, which are not intensified to the magnitude of cyclone but produced a good amount of rainfall at Gadanki, are also included in the present study to obtain robust statistics. We follow the IMD definitions for low pressure, depression, cyclone, and super‐cyclone. To be precise, we considered that the rain (which occurred for at least 6–8 hours in a day) is associated with a cyclone if the surface winds exceed or are equal to 17 knots within the closed isobar.
20 P
Dm ¼
i¼1 20 P i¼1
ni F:t:vðDi ÞDDi N ðDi ÞD4i DDi
ð3Þ
ð4Þ
N ðDi ÞD3i DDi
where ni is the number of drops measured in drop size class i, Di is the average diameter of the drops in class i, F is the area of sensitive surface of the disdrometer (50 cm2), t is the time interval for one measurement (60 s), V(Di) is the fall velocity of a drop with the diameter Di (cm) estimated using the relation V(Di) = 9.65 − 10.3 exp(−6 Di), and DDi is the diameter interval of the drop size class i. [7] The optical rain gauge (ORG‐815) provides accurate rain measurement with an error of less than 5% in rain accumulation. It has a wide dynamic range and therefore can detect rain with 1‐min integrated R in the range of 0.1– 500 mm hr−1. The tipping bucket rain gauge is a standard rain gauge with a resolution of 0.5 mm and an accuracy of 1 mm. The JWD and ORG‐815 are very closely spaced (separated by only 1 m); however, the JWD and tipping bucket rain gauge are separated by ∼120 m. [8] Given the sensitivity of the JWD to strong wind that generally accompanies the rainfall in cyclones, it is better to ascertain the quality of the data by comparing the accumu-
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Table 1. Significant Cyclonic Rainfall Over Gadanki During SWM and NEM and Comparison of Accumulated Rainfall
Storm No.
Date
Disdrometer Accumulated Rain (mm)
ORG‐815 Accumulated Rain (mm)
1 2 3 4 5
22–23 Aug 2000 14–15 Sep 2006 21–22 Jun 2007 1–3 Aug 2007 17–18 Sep 2007
17.23 45.07 23.08 169.31 8.41
Southwest Monsoon 14.01 38.26 15.18 146.88 7.84
6 7 8 9 10 11
29–30 Nov 2000 26–29 Oct 2006 11–12 Dec 2006 27–29 Oct 2007 18–20 Dec 2007 26–28 Nov 2008
34.03 33.32 43.16 103.39 156.94 108.5
Northeast Monsoon 35.3 34.25 44.62 – 144.43 –
AWS Accumulated Rain (mm)
r
SD
– 39 – 161.5 6.5
0.94 0.98 0.95 0.99 0.99
0.15 0.9 0.14 1.21 0.16
– 31 40.5 98 153 105.5
0.98 0.93 0.98 – 0.98 –
0.44 1.0 0.86 – 1.02 –
lated rainfall with an independent measurement (Table 1). It also contains the days during which the rainfall associated with cyclones is nearly continuous for a long period (8 hours or more in each spell). The disdrometer‐derived accumulated rainfall is nearly equal to the optical rain gauge and tipping bucket rain gauge accumulations for most of the cases (except for 2 days: 22–23 August 2000 and 21–22 June 2007, when the rain accumulations were not very high), assuring the quality of RSD measurements. The time series of R derived by the disdrometer and ORG‐815 are correlated to ascertain data quality. The correlation is found to be good in all cases with a correlation coefficient, r, (significant at 99%) >0.9. [9] A total of 46,223 1‐minute RSD spectra, including 14,213 and 32,010 minutes of observations in cyclonic and noncyclonic periods, respectively, are used in the present study. For a sensible comparison of RSD in different seasons, Rao et al. [2009] grouped the RSD data into 11 rain regimes or classes, based on R. The rain rate interval of each class is chosen in such a way that each class contains a sufficient number of data points, and also the mean R in that class is more or less equal in both seasons. Figures 2a and 2c show the number of 1‐minute cyclonic and noncyclonic RSD measurements in each class for SWM and NEM seasons. Except for the last two or three classes, the number of data points is large in all classes, and the difference in seasonal mean R (in terms of percentage) in each class is small ( 20 mm h−1.
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[14] The RSD in the above two cases (corresponding to classes 4 and 6) are compared in Figure 5 to show the seasonal differences in cyclonic RSD. The average R for SWM and NEM and the number of 1‐min RSD used to obtain average R (in parentheses) are also shown in Figure 5. In both seasons, R is nearly same, and a sufficient number of samples in each season offers meaningful comparison. It is clearly apparent from Figure 5 that the small drop concentration is more prevalent in NEM than in SWM in both the classes. Small drops are more prevalent in NEM than in SWM by a factor of 3–5. The difference is statistically significant in most of the classes as it is larger than the statistical error (standard error = standard deviation/√j; j is the number of data points). An opposite feature is seen at the large drop end, i.e., more drops in SWM than in NEM. Nevertheless, the seasonal variations are pronounced at small R. Similar seasonal differences are observed in the total rain (i.e., data are not stratified as cyclonic and noncyclonic) [Rao et al., 2009]. [15] To better understand the seasonal differences in cyclonic RSD and also from cyclonic RSD to noncyclonic RSD in each season, a parameter, d(D, R), is defined [see RR09]. For instance, d(D, R) for cyclones in any season is estimated from the ratio of cyclonic N(D, R) (= N(D) corresponding to the rain rate R) in that season to the cyclonic N(D, R) in both seasons together. These definitions are also included in Figure 6. For this analysis, the data collected during the passage of all cyclones mentioned in Table 1 are considered. The cyclonic RSD is distinctly different in SWM from that in NEM, with relatively more medium and big drops and fewer small ( 20 mm h−1), the number of small drops in cyclones is large, but the big drops are almost absent (d(D, R) values are small) in both seasons. Also, the medium‐sized drops (1–2 mm) are slightly greater in number in cyclones than in noncyclonic rain in both the seasons (d(D, R) 50–60%). Lack of big drops in cyclones is also observed in Atlantic and Pacific cyclones [Tokay et al., 2008]. [17] Figure 8 shows a comparison of Dm values in different rain rate classes in cyclonic and noncyclonic rain in SWM and NEM. It is very clear from Figure 8 that the Dm values in cyclonic and noncyclonic rain are larger in SWM than in NEM. Although the seasonal difference in Dm values are not the same at all rain rates, they are distinctly larger in SWM by 0.1–0.3 mm. The seasonal difference (in both cyclonic and noncyclonic rain) is, of course, large at low rain rates, consistent with the large differences in d(D, R) values in Figures 6 and 7. Also, the seasonal difference in Dm is relatively more in noncyclonic rain than in cyclonic rain, particularly from class 6, i.e., R > 2 mm h−1. On the other hand, comparison of Dm in
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Figure 3. (a) IR image of Kalpana‐I satellite at 0830 LT on 22 June 2007 during the passage of a cyclone in SWM. (b) The track of the cyclone. Surface pressure, temperature, and relative humidity are shown (c)–(e), respectively. (f) and (g) Temporal variation of cyclonic RSD (N(D) in logarithmic units) and corresponding rain parameters (R, Dm, and Z). cyclonic and noncyclonic rain during SWM clearly shows smaller values in cyclones than in noncyclonic rain at all rain rates. Although the difference in Dm is small at low rain rates, it is large for R > 8 mm h−1. This is consistent with Figure 7a and also with earlier results [Tokay et al., 2008]. The lack of big drops and presence of more small and medium‐sized
drops in cyclones reduces Dm value, in particular at large rainfall rates. But in NEM, the Dm values in cyclones are either equal to or larger than those in noncyclonic rain for R < 8 mm h−1. The reason for this feature is not immediately obvious. However, for R > 8 mm h−1, the cyclonic Dm is smaller than noncyclonic Dm.
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Figure 4. Same as Figure 3, but for the cyclone in NEM (26–28 November 2008). [18] From Figures 6–8, it is clear that the cyclonic RSD is distinctly different from noncyclonic RSD, and these figures also show significant seasonal variation. The seasonal difference in RSD is also observed in earlier studies (not in cyclones), but for the total RSD data [Rao et al., 2009]. What makes the cyclonic RSD different in the two monsoon seasons and also from noncyclonic RSD? Is it the nature of clouds [Rosenfeld and Ulbrich, 2003] or the evolution during their descent? To examine the differences in the nature of clouds, the cloud effective radius (CER) for ice, water. and
mixed‐phase hydrometeors are estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) level 3 data. A similar exercise, but with a visible and infrared sensor (VIRS) onboard Tropical Rainfall Measuring Mission (TRMM) measurements, has been done by Rosenfeld and Ulbrich [2003] to understand the differences in RSD in different types of clouds (continental, maritime, and intermediate). The MODIS data (CER) over the Gadanki region (1° × 1°) during the cyclone period (when the disdrometer measured cyclone‐induced rain) and on rainy days are aver-
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Figure 5. Comparison of mean cyclonic RSD corresponding to classes 4 (bottom) and 6 (top) between SWM and NEM. aged and are used for comparison (Table 2). The number of CER values available for obtaining mean CER is limited, i.e., 32, 32, and 19 (28, 33, and 25) samples of CER for ice, mixed, and water phases in SWM (NEM). The mean CER for ice, water, and mixed phases in SWM and NEM show not much seasonal variation in cyclones. Only CER for water is slightly larger (by 1.4 mm) in SWM than in NEM, but the difference is not statistically significant. On the other hand, significant seasonal variation in CER was observed when we considered
Figure 6. (a) and (b) The contribution of cyclonic raindrops in SWM and NEM, respectively, to the total number of cyclonic raindrops as a function of D and R.
Figure 7. (a) and (b) The contribution of cyclonic raindrops in SWM and NEM to the total number of raindrops (cyclonic and noncyclonic together) in SWM and NEM, respectively. all rain events (Table 2) and also noncyclonic rain events (not shown in the table, but their CER values for ice, water, and mixed phases of hydrometeors are nearly equal to those of all rain events in both seasons). The CER values are larger in NEM than in SWM by ∼4 mm and also nearly equal to the values of cyclones. This suggests that the clouds in NEM and also in cyclones are primarily oceanic in nature. It also hints at the seasonal differences in microphysical processes during the drop descent or ascent as the primary cause for the seasonal difference in the observed cyclonic RSD at the surface. [19] Evaporation, drop sorting, and collision coalescence and breakup are the important microphysical and dynamic processes that can occur during the drop descent to ground. The average surface temperature and relative humidity in
Figure 8. Variation of Dm as a function of class (R) in cyclonic and noncyclonic rain in NEM and SWM seasons.
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Figure 9. The mean values of Dm, R, and LWC (numbers given adjacent to symbols) in cyclonic and noncyclonic rain in SWM and NEM at 40 dBZ. cyclones during SWM are, respectively, 24.7° and 84.9%, whereas in NEM they are 22.6° and 94.7%. Higher temperature and lower relative humidity means that the evaporation effect may be relatively more in SWM. Rao et al. [2006] have also shown that evaporation is significant in SWM using the vertical distribution of RSD derived from wind profilers. Furthermore, the convective activity is more active in SWM. The cumulative distribution of rain with R > 10 mm h−1 shows larger population in SWM than in NEM at R > 20 mm h−1 [Rao et al., 2009]. The vertical drafts are also strong in SWM [Uma and Rao, 2009], with speeds often exceeding 10–15 m s−1 in the middle and upper troposphere. In the presence of such large vertical updrafts, small drops are either suspended aloft or transported to higher altitudes and then advected to trailing transition and stratiform regions. However, big drops descend because of their weight. Also, the medium‐sized drops grow when they collide with suspended small drops. Therefore, the medium‐sized drops grow at the expense of small drops. Also, strong vertical updrafts take super‐cooled water to higher heights and thereby enhance the growth of drops by a riming process [Rosenfeld and Ulbrich, 2003]. Strong convection in SWM means the effects of drop sorting and formation of big drops primarily from riming are greater in SWM. All of the above processes reduce or redistribute small drops and also increase the number of big drops during their descent to the ground in SWM. [20] On the other hand, the mean surface temperature for noncyclonic rain in SWM and NEM are 28 and 23.7°C, respectively. The surface humidity values are also larger during the cyclonic period than the noncyclonic period. Relatively high surface temperature and dry weather (in comparison with those observed in cyclones) indicate that more evaporation may be occurring in noncyclonic rain. This may be one of the reasons for the presence of few small to medium‐sized drops in noncyclonic rain.
4. Comparison of Cyclonic RSD Between BOB and Other Regions [21] The cyclonic RSD in BOB is consistent qualitatively with the RSD in cyclones elsewhere [Atlantic and Pacific; Tokay et al., 2008, and references therein]. The cyclonic RSD
over all these locations show more small to medium‐sized drops and few large drops. To facilitate comparison with the cyclonic RSD over the Atlantic and Pacific oceans, the data between 39 and 41 dBZ are grouped, following Tokay et al. [2008]. The average rainfall bulk parameters (R, Dm, and liquid water content (LWC)) are estimated separately for cyclonic and noncyclonic rain data in SWM and NEM seasons (Figure 9). Larger R and LWC and smaller Dm are observed in NEM than in SWM for both cyclonic and noncyclonic data. Between the cyclonic and noncyclonic rain parameters, R and LWC are found to be large in cyclonic data, at fixed Z (∼40 dBZ). On the other hand, Dm is smaller in cyclonic RSD than in noncyclonic RSD at the same Z value. This is possible given the absence of big drops and predominance of small and medium‐sized drops in cyclones and also in NEM. Contrasting the bulk parameters in BOB cyclones with that in the Atlantic and Pacific reveals, in general, that cyclones in BOB produce smaller R and LWC and larger Dm than elsewhere [Tokay et al., 2008]. The cyclonic rainfall parameters in the Atlantic are somewhat equal to that in BOB cyclones in NEM. In SWM, as discussed above, the microphysical and dynamic processes associated with convection and evaporation changes the RSD (increases Dm) during their descent to the ground. Therefore, the bulk parameters show different characteristics from those observed in cyclones elsewhere.
5. Seasonal Differences in RSD: Implications [22] It is now clear from the above discussion that there exists significant seasonal difference in cyclonic RSD. Also, the cyclonic RSD is found to be somewhat different from that obtained in noncyclonic rain, particularly at large rainfall rates. These differences have important implications on radar rainfall estimation and also on cloud‐modeling studies that require DSD parameterization. These issues are discussed in this section. [23] The reflectivity‐rainfall rate (Z‐R) relations are derived for cyclonic and noncyclonic rain corresponding to SWM and NEM. Regression analysis (linear least squares fit) has been carried out on log Z (on the x‐axis) minus log R (on the y‐axis) plots to obtain the coefficients of the Z‐R relation of the form Z = ARb. The prefactor and exponents of the relation for cyclonic and noncyclonic rain in SWM and NEM are shown in Table 3. The seasonal variation is also seen in prefactor and exponent values in both cyclonic and noncyclonic rain. The prefactor (exponent) in SWM is larger (smaller) than that observed in NEM by a factor of 2 (by Table 3. Prefactor and Exponents of Z‐R Relation for Cyclonic and Noncyclonic Rain in SWM and NEM and Also for Different Types of Rain (Convection, Transition, and Stratiform)a Transition
Stratiform
Date
A
Seasonal b
Convection A
b
A
b
A
b
SWMCYC NEMCYC SWMNCYC NEMNCYC
275.25 142.04 317.45 161.05
1.39 1.55 1.434 1.553
36.18 20.36 30.67 10.4
1.901 2.026 1.997 2.264
55.39 42.88 61.2 30.64
2.18 2.269 2.169 2.39
311.26 185.69 402.91 283.59
1.483 1.762 1.582 1.94
a NEMCYC, northeast monsoon cyclonic; NEMNCYC, northeast monsoon noncyclonic; SWMCYC, southwest monsoon cyclonic; SWMNCYC, southwest monsoon noncyclonic.
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feature is more prominent at low rain rates, the seasonal difference in L is also significant at these rain rates. On the other hand, the seasonal variation in m is not clear and also small, even if present. The variation of m with R is also small in comparison with the variation of L. The m values for cyclonic and noncyclonic rain are in the range of 5–7 up to a rain rate of 4 mm h−1 (with a decreasing trend with increasing R), increases to 7–8 at 16 mm h−1, and then decreases again to 6–7 at 64 mm h−1. The range of m values observed in cyclones is in good agreement with those reported elsewhere [Tokay et al. 2008].
6. Conclusions
Figure 10. The variation of g parameters (shape and slope) as a function of R in cyclonic and noncyclonic rain in SWM and NEM. >0.1). More small drops at low rain rates in NEM decrease the slope and also the intercept on the log R axis, resulting in large b and small A values. On the other hand, the difference in RSD during the cyclonic and noncyclonic period is not much at low rain rates in both seasons. As a result, the difference in the coefficients (both A and b) is also small. For instance, the differences in A and b between cyclonic and noncyclonic period are 20 and ∼0.04 and 40 and 0.003 in NEM and SWM, respectively. [24] Ulbrich and Atlas [2007] argued that most of the earlier studies have not apportioned the RSD according to the rain type as convection, transition, and stratiform. Therefore, the corresponding Z‐R relations may not represent a particular rain type as the dataset is a nonlinear combination of various rain types. In the present study, the data are stratified into convection, stratiform, and transition rain types following Rao et al. [2001]. This classification scheme depends on the variation of Dm with R. The rain is termed as convection, stratiform, or transition, respectively, depending on whether Dm /R is 0.5, or in between those thresholds. The coefficients derived for different types of rain are also shown in Table 3. It is clearly evident from Table 3 that the coefficients are different in different types of rain systems. In particular, the coefficients of convection and transition differ from stratiform rain by a large magnitude. The difference in the magnitude of coefficients between seasons is small compared to that between the rain types. [25] The other important application of the RSD is its utilization in cloud‐modeling studies. To facilitate this, the gamma parameters are estimated using the central moments (second, third, and fourth), following Smith [2003]. The seasonal variation of the shape (m) and slope (L) parameters for cyclonic and noncyclonic rain as a function of class (or R) is shown in Figure 10. The slope parameter for cyclonic and noncyclonic rain and in both seasons shows a similar pattern of variation with R, i.e., a monotonous decrease with increase in R. Nevertheless, like RSD, L also shows significant seasonal variation with large values in NEM. The L values are found to be in the range of 14–4/5 mm−1 in NEM and 12– 4 mm−1 in SWM. The presence of relatively more small drops and fewer big drops in NEM increases the slope. As this
[26] The RSD measurements made using JWD during the passage of 11 cyclones near to Gadanki were utilized to study the differences in the cyclonic RSD as a function of season, from that of noncyclonic rain, and also from cyclonic rain elsewhere. The important conclusions are summarized below: [27] 1. The cyclonic RSD exhibits significant seasonal variability with relatively more large drops and fewer small drops in SWM than in NEM at all R. In particular, small drops in NEM contribute profoundly (60–80%) to the total number of small drops in weak rain. The observed seasonal RSD variation is not only confined to cyclones but also seen in the total rain data [Rao et al., 2009]. Accordingly, the cyclonic Dm values show significant seasonal variation. [28] 2. Comparison of RSD in cyclones with that of in noncyclonic rain shows lack of big drops in cyclones at all rain rates in both monsoon seasons. The number of small drops is large in cyclones at high rainfall rates and also the number of medium‐sized drops is, in general, slightly larger in cyclones. The Dm values in cyclones are nearly equal to those observed in noncyclonic rain at small rain rates, but are considerably smaller in cyclones at R > 8 mm h−1. [29] 3. Comparing the cyclonic RSD with that observed elsewhere reveals that they are consistent qualitatively with the lack of big drops and the presence of small to medium‐ sized drops in cyclones. But for the same Z, the R and LWC are smaller, whereas Dm is larger in cyclones in SWM over BOB than in the Atlantic [Tokay et al., 2008]. [30] 4. The observed differences in RSD (seasonal as well as from cyclonic to noncyclonic rain) have important implications for quantitative rainfall estimation with weather radar and also on cloud‐modeling studies. The Z‐R relations are found to be different in different seasons. The Z‐R relation for SWM is somewhat similar to that for the National Weather Service default relation (Z = 300R1.4). The coefficients of the Z‐R relation for rain‐stratified data differ significantly from convection/transition to stratiform rain, in fact larger than the seasonal differences. The slope parameter of the g distribution varies significantly with rain rate and also exhibits seasonal variation with larger values in NEM. Variation of the shape parameter with R is relatively small.
References Atlas, D., C. W. Ulbrich, F. D. Marks Jr., E. Amitai, and C. R. Williams (1999), Systematic variation of drop size and radar‐rainfall relations, J. Geophys. Res., 104(D6), 6155–6169, doi:10.1029/1998JD200098. Bringi, V. N., V. Chandrasekar, J. H. Hubbert, E. Gorgucci, W. L. Randeu, and M. Schoenhuber (2003), Raindrop size distribution in different
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climatic regimes from disdrometer and dual‐polarized radar analysis, J. Atmos. Sci., 60, 354–365. Jorgensen, D. P., and P. T. Willis (1982), A Z‐R relationship for hurricanes, J. Appl. Meteor., 21, 356–366. Joss, J, and A. Waldvogel (1969), Raindrop size distribution and sampling size errors, J. Atmos. Sci., 26, 566–569. Kostinski, A. B., and A. R. Jameson (1997), Fluctuation properties of precipitation. Part I: On deviations of single‐size drop counts from the Poisson distribution, J. Atmos. Sci., 54, 2174–2186. Kozu, T., K. K. Reddy, S. Mori, M. Thurai, J. T. Ong, and D. N. Rao (2006), Seasonal and diurnal variations of raindrop size distribution in Asian monsoon region, J. Meteor. Soc. Japan, 84A, 195–209. Maeso, J., V. N. Bringi, S. Cruz‐Pol, and V. Chandrasekar (2005), DSD characterization and computations of expected reflectivity using data from a two‐dimensional video disdrometer deployed in a tropical environment, Proc. Int. Geosci. and Remote Sensing Symp., IEEE, Seoul, South Korea, pp. 5073–5076. Merceret, F. J. (1974), On the size distribution of raindrops in hurricane Ginger, Mon. Weather Rev., 102, 714–716. Nzeukou, A., H. Sauvageot, A. D. Ochou, and C. M. F. Kebe (2004), Raindrop size distribution and radar parameters at Cape Verde, J. Appl. Meteor., 43, 90–105. Radhakrishna, B., T. N. Rao, D. N. Rao, N. P. Rao, K. Nakamura, and A. K. Sharma (2009), Spatial and seasonal variability of raindrop size distributions in southeast India, J. Geophys. Res., 114, D04203, doi:10.1029/2008JD011226. Rao, T. N., D. N. Rao, K. Mohan, and S. Raghavan (2001), Classification of tropical precipitating systems and associated Z‐R relationships, J. Geophys. Res., 106(D16), 17,699–17,711, doi:10.1029/2000JD900836. Rao, T. N., N. V. P. Kirankumar, B. Radhakrishna, and D. N. Rao (2006), On the variability of the shape‐slope parameter relations of the gamma raindrop size distribution model, Geophys. Res. Lett., 33, L22809, doi:10.1029/2006GL028440.
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Rao, T. N., B. Radhakrishna, K. Nakamura, and N. P. Rao (2009), Differences in raindrop size distribution from southwest monsoon to northeast monsoon at Gadanki, Quart. J. R. Meteor. Soc., 135, 1630–1637. Rosenfeld, D., and C. W. Ulbrich (2003), Cloud microphysical properties, processes, and rainfall estimation opportunities, Meteor. Monogr., 30(52), 237–258. Sauvageot, H., and J. Lacaux (1995), The shape of averaged drop size distributions, J. Atmos. Sci., 52, 1070–1083. Scott, W. D. (1974), In situ measurements of rainwater in tropical storm Felice and hurricane Debby, J. Meteor. Soc. Japan, 52, 440–447. Smith, P. L. (2003), Raindrop size distributions: Exponential or gamma. Does the difference matter?, J. Appl. Meteor., 42, 1031–1034. Tokay, A., A. Kruger, W. F. Krajewski, P. A. Kucera, and A. J. P. Filho (2002), Measurements of drop size distribution in the southwestern Amazon basin, J. Geophys. Res., 107(D20), 8052, doi:10.1029/2001JD000355. Tokay, A., P. G. Bashor, E. Habib, and T. Kasparis (2008), Raindrop size distribution measurements in tropical cyclones, Mon. Weather Rev., 136, 1669–1685. Ulbrich, C. W., and D. Atlas (2007), Microphysics of raindrop size spectra: Tropical continental and maritime storms, J. Appl. Meteor. Climatol., 46, 1777–1791. Ulbrich, C. W., and L. G. Lee (2002), Rainfall characteristics associated with the remnants of tropical storm Helene in upstate South Carolina, Weather Forecasting, 17, 1257–1267. Uma, K. N., and T. N. Rao (2009), Characteristics of vertical velocity cores in different convective systems observed over Gadanki, India, Mon. Weather Rev., 137, 953–977. Zuidema, P. (2003), Convective clouds over Bay of Bengal, Mon. Weather Rev., 131, 780–798. B. Radhakrishna and T. Narayana Rao, Department of Space, National Atmospheric Research Laboratory, SVU Campus, PO Box 123, Gadanki 517 502, India. (
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