Abstract

This study is based on the analysis of 10 years of data for radar reflectivity factor Ze as derived from the TRMM Precipitation Radar (PR) measurements. The vertical structure of active convective clouds at the PR pixel scale has been extracted by defining two types of convective cells. The first one is cumulonimbus tower (CbT), which contains Ze ≥ 20 dBZ at 12-km altitude and is at least 9 km deep. The other is intense convective cloud (ICC), which belongs to the top 5% of the population of the Ze distribution at a prescribed reference height. Here two reference heights (3 and 8 km) have been chosen. Regional differences in the vertical structure of convective cells have been explored by considering 16 locations distributed across the tropics and two locations in the subtropics. The choice of oceanic locations is based on the sea surface temperature; that of the land locations is based on propensity for intense convection. One of the main findings of the study is the close similarity in the average vertical profiles of CbTs and ICCs in the mid- and lower troposphere across the ocean basins whereas differences over land areas are larger and depend on the selected reference height. The foothills of the western Himalaya, southeastern South America, and the Indo-Gangetic Plain contain the most intense CbTs; equatorial Africa, the foothills of the western Himalaya, and equatorial South America contain the most intense ICCs. Close similarity among the oceanic profiles suggests that the development of vigorous convective cells over warm oceans is similar and that understanding gained in one region is extendable to other areas.

1. Introduction

The importance of cumulonimbus (Cb) clouds in the energy balance of the tropical atmosphere has long been recognized (e.g., Riehl and Malkus 1958). Even in a mesoscale convective system (MCS), which is an organized form of Cb convection (e.g., Cotton and Anthes 1989; Houze 1993), the vertical transport of water and energy between the atmospheric boundary layer and the upper troposphere is accomplished by Cb clouds. Our understanding of convective clouds is not commensurate with their role in weather and climate systems because of a lack of observations on several key aspects (e.g., vertical velocity, hydrometeor concentration, and the distribution of the latter between liquid and ice phases) in different parts of the globe. Studies on the vertical structure of convective clouds are relatively few and are mainly based on measurements made with instrumented aircraft (e.g., Heymsfield et al. 2010, hereinafter H10), with weather radars (e.g., Zipser and Lutz 1994, hereinafter ZL94), and, in recent years, with the Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite (Kummerow et al. 1998) and a cloud-profiling radar on board CloudSat (Stephens et al. 2002). In situ measurements using instrumented aircraft are likely to be biased toward less intense or smaller clouds because of aircraft safety considerations (H10). Hence, radar remote sensing is the main observational approach available for investigating intense convective clouds. Radar provides the equivalent radar reflectivity factor (hereinafter Ze or reflectivity), which is a proxy for hydrometeor concentration (Houze 1993). The vertical distribution of Ze is often used as a proxy for the intensity of convection because higher Ze in the mid- and upper troposphere corresponds to higher vertical velocities (Xu and Zipser 2012, hereinafter XZ12).

A majority of the studies that are based on the analysis of radar data are on cloud systems, for example, MCSs (e.g., Houze 1993) and precipitation features (e.g., Nesbitt et al. 2000), and the number of studies that address Cb clouds at the scale of a radar beam is very limited (e.g., ZL94; Yuan and Qie 2008; Liu et al. 2012; Saikranthi et al. 2014; Bhat and Kumar 2015, hereinafter BK15). ZL94 used radar data collected at different locations (covering coastal, land, and oceanic areas), and constructed the vertical profile of radar reflectivity (VPRR). ZL94 showed that Ze rapidly decreases with height above the freezing level, with the rate of decrease being larger in oceanic systems than in tropical continental and midlatitude systems. In the convective portion of MCSs, Ze increases toward the surface below the freezing level over oceanic areas, whereas the opposite behavior is observed in continental systems (ZL94). By analyzing data measured over the South China Sea area, Yuan and Qie (2008) show that high Ze in the mixed-phase region promotes lightning activity. XZ12 show that the vertical structure of deep convective clouds over continents and oceans and in monsoonal systems depends on the updrafts in the mixed-phase region and on microphysics rather than on the cloud depth or ice depth. BK15 used PR data to study the vertical structure of Cb clouds embedded in South Asian summer monsoon systems. BK15 adopted the VPRR method of ZL94 and defined convective cells with reference to 3-, 8-, and 12-km heights. BK15 show that some of the conclusions on the regional differences in the vertical structure of Ze depend on the reference height selected.

Measurements made in tropical convective systems with a nadir-looking Doppler radar fitted on a high-flying aircraft show updraft widths of 5–6 and 8–10 km over oceanic and land areas, respectively (H10; Guimond et al. 2010). Here updraft width is defined as a stretch along the aircraft path that has vertical velocities of more than 5 m s−1 at 10-km altitude. In almost all of the cases, peak updrafts were observed above the 10-km level and land-based and sea-breeze convection had higher Ze than did oceanic and tropical-cyclone convection (H10).

The spatial coverage of ground radars and aircraft campaigns is very limited. It is not clear from past studies how the vertical profiles of hydrometeors in active Cb clouds compare in different parts of the tropics. This study is aimed at filling this gap in knowledge, and the main objective is to document the vertical structure of Ze in intense convective clouds in the tropics. We consider both land and oceanic areas. Section 2 describes the data and methods used in the study, sections 3 and 4 contain results and discussion, respectively, and section 5 concludes the paper.

2. Data and methods

The primary data used in the study are TRMM PR 2A25 (version 6) attenuation-corrected Ze (Iguchi et al. 2000, 2009; Masunaga et al. 2002). The sensitivity of the PR is ~17 dBZ, and its pixel size is 4.3 km × 4.3 km (~5 km × 5 km after the TRMM orbit boost in August of 2001) in the horizontal plane, with samples collected every 0.25 km along the beam (vertical) direction (Kummerow et al. 1998). There are 80 vertical levels, and the height corresponds to the distance measured along the radar beam from the point of intersection between the beam and Earth’s ellipsoid and not to the local vertical height. The difference in the maximum heights at nadir and extreme angles is ~0.85 km (BK15). Hence, corrections to the pixel height have been applied (see BK15), and the remapped data are used in the analysis. Other data include TRMM Microwave Imager (TMI) sea surface temperature (SST; Wentz et al. 2000) (the data were downloaded from ftp://ftp.remss.com/tmi/) and TRMM precipitation product 3B42 (Huffman et al. 2007), for a 10-yr period (2001–10).

The PR data products have several advantages, including that the same instrument provides Ze around the globe, that there is a relatively long time series, and that the quality of the data is high. The 2A25 data volume is very large, and carrying out the analysis at every horizontal grid point is a difficult task. Hence we have selected 18 locations on the basis of the following considerations. The choice of oceanic locations is based on SST because of its strong influence on deep convection (e.g., Gadgil et al. 1984; Graham and Barnett 1987; Waliser and Graham 1993). The chosen oceanic areas have climatologically the warmest SSTs in the respective seasons and basins (Fig. 1; Table 1). The “Maritime Continent” (MC), one of the rainiest regions on the planet (Ramage 1968), is included. Land locations are chosen on the basis of either seasonal rainfall amount or known propensity for intense convection. Six land areas have been selected: equatorial Africa (AF), equatorial South America (LAM), the western Himalayan foothills (WHF), the Indo-Gangetic Plain (IGP), northern Australia (AUS), and southeastern South America (SESA). Of the 18 areas, 16 are in the tropics (i.e., within 30° latitude from the equator). The remaining two (WHF and SESA) lie at the edge of the tropics (Fig. 1; Table 1) and are included in the analysis since they are known to contain the most intense convection on Earth (Zipser et al. 2006, hereinafter Z06; Houze et al. 2007; Romatschke et al. 2010).

Fig. 1.

Seasonal-average TMI SST and TRMM 3B42 precipitation for the 10-yr period from 2001 to 2010: (a) SST for June–September, (b) SST for January–March, (c) June–September daily average precipitation, and (d) January–March daily average precipitation. Color bars on the right show SST (°C) or precipitation (mm day−1). Areas selected for detailed analysis are shown by rectangular boxes. See Table 1 for their description.

Fig. 1.

Seasonal-average TMI SST and TRMM 3B42 precipitation for the 10-yr period from 2001 to 2010: (a) SST for June–September, (b) SST for January–March, (c) June–September daily average precipitation, and (d) January–March daily average precipitation. Color bars on the right show SST (°C) or precipitation (mm day−1). Areas selected for detailed analysis are shown by rectangular boxes. See Table 1 for their description.

Table 1.

Areas selected for comparison, reflectivity thresholds for ICC3 and ICC8 cells, and the number of convective cells. For the area/region names, lowercase letters s and w refer to boreal summer and boreal winter, respectively.

Areas selected for comparison, reflectivity thresholds for ICC3 and ICC8 cells, and the number of convective cells. For the area/region names, lowercase letters s and w refer to boreal summer and boreal winter, respectively.
Areas selected for comparison, reflectivity thresholds for ICC3 and ICC8 cells, and the number of convective cells. For the area/region names, lowercase letters s and w refer to boreal summer and boreal winter, respectively.

The method for the identification of convective cells is explained in detail in BK15 and is discussed briefly here. Figure 2 shows a vertical cross section through a cloud system captured by the PR. In Fig. 2, tops of three convective clouds extend beyond 17 km, and their lateral sizes are more than 10 km (i.e., much larger than that of a PR pixel), which could indicate areas of coherent updrafts or downdrafts (H10). These are examples of convective cells referred to in this study. For identifying convective cells embedded in a 3D reflectivity field, a Ze threshold needs to be specified, often at some predetermined height. For example, Dixon and Wiener (1993) use thresholds in the 40–50-dBZ range for identifying individual convective cells in the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm. ZL94 specify 40 and 35 dBZ at 4.4- and 3.9-km heights, respectively, for defining convective clouds over the midlatitudes and tropics, respectively. H10 specified a Ze threshold of 20 dBZ at 12-km height for defining hot towers.

Fig. 2.

Section through a cloud system observed by the PR over the Indian region on 24 Jul 2008. The inset shows a horizontal section taken at 3.5-km altitude (the abscissa and ordinate are longitude and latitude, respectively). The vertical section is taken along the lines that cut through active convective clouds and is plotted against latitude. The color bar on the right shows Ze (dBZ).

Fig. 2.

Section through a cloud system observed by the PR over the Indian region on 24 Jul 2008. The inset shows a horizontal section taken at 3.5-km altitude (the abscissa and ordinate are longitude and latitude, respectively). The vertical section is taken along the lines that cut through active convective clouds and is plotted against latitude. The color bar on the right shows Ze (dBZ).

In this study, the threshold for defining an intense convective cell (ICC) is different at each location (and reference height) and is chosen such that no more than 5% of the pixels at the reference height have their Ze above this value. We consider two heights for ICCs (3 and 8 km), and the ICCs that correspond to these two heights are denoted by ICC3 and ICC8, respectively. Table 1 shows Ze thresholds in different areas. It is observed that ICC8 and ICC3 thresholds are in the 28–35- and 40–43-dBZ ranges, respectively. Similar to H10, a cumulonimbus tower (CbT) is defined by referring to a fixed Ze threshold value of 20 dBZ at 12-km altitude, and its base is located below the 3-km altitude (BK15). ICCs and CbTs have been constructed following the VPRR method (ZL94), and the procedure is the same as that employed in BK15. For each TRMM pass over a location, the highest Ze (i.e., Zemax) at the reference height lying within the boundaries of the location is searched in the remapped 2A25 data. If Zemax equals or exceeds the threshold, a cloud cell is constructed by picking up the local maximum Ze at each level from the radar volume containing the beam having Zemax and its immediate eight neighboring beams. After excluding the beams associated with the cell(s) already constructed, the procedure is repeated until the pixels at the reference level that satisfy the threshold criteria are exhausted. The lateral dimensions of CbT and ICC are the same as that of a PR pixel.

The physical interpretation of CbT and ICC, assuming that they belong to the Cb cloud type, is as follows. In a Cb cloud, hydrometeors are lifted to the upper troposphere during its growth phase, and, once the updrafts start weakening, larger hydrometeors begin their downward journey and grow along their trajectory. The Ze reaches a peak value in the upper troposphere in the early mature phase of a Cb cloud (Williams et al. 1989). For example, cell B in Fig. 2 has high Ze values in the upper troposphere, whereas cell C has relatively weak echo in the upper troposphere but high Ze in the lower troposphere and is perhaps in a later stage of the life cycle relative to that of cell B. ICC8 cells are representative of clouds such as cell B. As hydrometeors descend below the freezing level, Ze increases rapidly because of cloud microphysical processes and the change from ice to liquid phase (Houze 1993; Fabry and Zawadzki 1995). We expect peak Ze to occur at 3-km altitude in the middle and later stages of the mature phase, and ICC3 should correspond to such clouds. With a Ze threshold of 20 dBZ, CbTs may contain both growing and mature Cb clouds. The ICC8 reference level lies in the upper mixed-phase region in the tropical atmosphere (e.g., Stith et al. 2002) where convective and stratiform clouds constitute the cloudy pixels (Li and Schumacher 2011). Precipitating convective and stratiform clouds contribute to the population of the cloudy pixels at 3 km. Thus, CbT, ICC8, and ICC3 capture Cb clouds in different stages of their development, and the microphysical processes associated with them are also different.

3. Results

Figure 3 shows the average vertical profiles of CbT, ICC8, and ICC3 over different locations. The differences among the pure oceanic CbTs are small (<2 dBZ), and the weakest CbTs (Fig. 3a) are present over the Indian Ocean western box during the boreal winter [January–March (JFM)]. Among the land areas, the decreasing order in the intensity of cells (in the layer between 5 and 10 km) is WHF, SESA, IGP, AUS, AF, and LAM. CbTs over the Maritime Continent and northern Bay of Bengal (BOB) are almost identical above 6 km, and their profiles are placed between continental and other oceanic profiles. The BOB box is adjacent to the landmass, and continental influence is felt here (i.e., it is not purely oceanic). In a similar way, oceanic influence cannot be ignored over the Maritime Continent. To highlight the most active CbTs, the averages of those that contain the top 25% Ze values at 12-km height among the CbTs (denoted by CbT25) are shown in Fig. 3b. The largest differences observed in the vertical profiles of Ze are seen in the CbT25 profiles. The most intense CbT25s form over WHF and SESA, and the 40-dBZ echo extends above 12 km. This observation is consistent with the findings reported in Z06, Houze et al. (2007), and Romatschke and Houze (2010). The differences among the oceanic CbT25 profiles are relatively small (maximum difference is ~3 dBZ), and profiles over BOB and the Maritime Continent area are closer to the oceanic ones.

Fig. 3.

Average profiles of convective cells: (a) CbT, (b) CbT25, (c) ICC8, and (d) ICC3. To reduce confusion, mainly extreme cases have been labeled and “TOC” refers to the remaining cases. Land and oceanic profiles are shown in thick solid and dashed lines, respectively. A level is shown for an area if the number of data points in averaging is not less than 10% of the maximum number of profiles for the area or 50, whichever is higher.

Fig. 3.

Average profiles of convective cells: (a) CbT, (b) CbT25, (c) ICC8, and (d) ICC3. To reduce confusion, mainly extreme cases have been labeled and “TOC” refers to the remaining cases. Land and oceanic profiles are shown in thick solid and dashed lines, respectively. A level is shown for an area if the number of data points in averaging is not less than 10% of the maximum number of profiles for the area or 50, whichever is higher.

The differences among the land locations are fewer for ICC8 (Fig. 3c) relative to those seen in CbT25. One important observation from Fig. 3c is that WHF and AF contain the most intense ICC8 clouds at subfreezing temperatures. SESA has the weakest ICC8 clouds among the land areas above 3 km, and the weakest ICC8 cells among all locations are seen over the Atlantic Ocean [June–September (JJAS)] followed by the Indian Ocean [east and summer (IOEs); west and winter (IOWw)]. ICC8 clouds over IGP and AUS are almost identical. Oceanic profiles of ICC8 cluster together. In the case of ICC3 (Fig. 3d), land cells are stronger than their oceanic counterparts; the interarea differences are relatively small below 5 km, however, and land and ocean profiles separate above 7 km. Location AF contains the strongest ICC3, followed by WHF and LAM; the weakest ICC3 is observed over the central Pacific Ocean during the boreal summer.

The 40-dBZ Ze threshold is often used as a signature of convective precipitation (Awaka et al. 1997; Steiner et al. 1995), and the maximum height of 40-dBZ Ze is also used as a measure of the intensity of convection (Z06; XZ12). Figure 4 shows the distribution of the maximum heights of 40-dBZ Ze for CbTs and ICCs. For CbTs (Fig. 4a), the mode in the distribution of 40-dBZ top heights occurs between 4 and 6 km over oceans and the Maritime Continent, and the fraction of clouds with Ze ≥ 40 dBZ above 10-km altitude is extremely low over these locations. The height of penetration of 40-dBZ Ze is the maximum in CbTs over WHF and SESA. The modal height of 40-dBZ tops in ICC8s is elevated (~7 km) in clouds over WHF, AF, and LAM relative to other areas (Fig. 4b). For all ICC3s, the modal height of 40-dBZ tops lies between 4 and 5 km (Fig. 4c), and two locations—namely, central Pacific in summer (CPs) and IOWw—show a secondary mode at 3 km. In general, a narrow distribution implies close similarities among convective clouds whereas a wider distribution suggests larger cloud-to-cloud differences.

Fig. 4.

Frequency distribution of the top height of 40-dBZ reflectivity for (a) CbT, (b) ICC8, and (c) ICC3. Oceanic areas are shown by dashed lines, and solid lines show land areas. To reduce confusion, mainly extreme cases have been labeled and “rest” refers to the remaining cases.

Fig. 4.

Frequency distribution of the top height of 40-dBZ reflectivity for (a) CbT, (b) ICC8, and (c) ICC3. Oceanic areas are shown by dashed lines, and solid lines show land areas. To reduce confusion, mainly extreme cases have been labeled and “rest” refers to the remaining cases.

Table 1 shows that Ze thresholds of ICC3 are greater than or equal to 40 dBZ. Such high values of Ze at 3-km altitude are normally associated with deep convective clouds. The ICC8 Ze threshold is minimum for the Atlantic Ocean in summer (ATs; 28 dBZ), and maximum for AF (35 dBZ). The ratio of the number of CbTs to the number of ICC3s differs among the regions; the maximum value of the ratio is 2.4 and occurs over AF, and the minimum value (0.08) occurs over the IOWw area. (A value of unity for the ratio means that the fraction of cloudy pixels at 3 km that qualify as CbTs is 5%.) Note that the average SST over IOWw is 29.3°C, which is comparable to SSTs at other locations (Table 1). The ratio of the number of CbTs to the number of ICC8s is more than unity, with its maximum and minimum values of 13.4 and 1.6 found over CPw and IOEw, respectively. Thus, more than 65% of the cloudy pixels at 8 km qualify as CbTs over the CPw area, whereas the corresponding value is just 8% over IOEw. North Pacific in summer (NPs) and CPw have comparable SSTs (Table 1), but the ratio of the number of CbTs to the number of ICC8s over these areas is very different. The above observations suggest that, in addition to SST, local factors (such as vertical wind shear and dryness of the midtroposphere) could matter when finer details of intense convective clouds are considered, which is not obvious from the average profiles (Fig. 3).

4. Discussion

This study quantified the vertical structure of intense convective clouds, primarily in the tropics. The average vertical profiles of intense convective clouds (Fig. 3), as depicted by CbT, ICC8, and ICC3, show similarities as well as differences. Close similarity among the oceanic cells, especially in the mid- and lower troposphere, suggests that the development of vigorous convective cells over warm oceans is similar and that understanding gained in one region is extendable to other areas. This result is encouraging from a cloud-modeling perspective. For land areas, the regional differences are large. The strongest CbTs form over WHF and SESA, which are located in the subtropics. It is observed from Fig. 1 that these two regions are not the areas of highest rainfall over land. So, convection is not frequent, but, when it occurs, it tends to be very intense. Medina et al. (2010) argue that very intense convection develops over WHF as a result of surface flux feedbacks and region-specific features. Instability builds up but does not get easily released over WHF because of a stable layer that caps the moist boundary layer. The potentially unstable flow is orographically lifted to saturation over small hills, and intense convection develops (Medina et al. 2010). This suggests that, to understand convection over land areas, advection of moisture, the local surface characteristics, and environmental conditions aloft should be taken into account.

The maximum in the average profile of CbTs (Figs. 3a,b) is observed between 3 and 5 km; Ze decreases toward the surface or remains nearly constant below the peak (depending on whether the location is continental or oceanic) while it decreases with height above. The change in slope above 12 km could be due to Ze values approaching the PR detection limit. In the case of ICC8 and ICC3, Ze decreases toward the surface in the lowest 3 km of the atmosphere at all locations. The decrease of Ze in the continental clouds below 3 km is related to the humidity structure of the lower troposphere and cloud microphysics (Liu and Zipser 2013). In the case of ICC3, Ze decreases rapidly between 5 and 7 km (i.e., in the mixed-phase region) and absolute slopes of the profiles are small above 9 km. The average absolute slopes of the Ze profiles between 5- and 7-km altitudes have been calculated and are shown in Fig. 5a. The slopes of ICC3 (4–6 dBZ km−1) profiles are nearly 2 times that of CbTs and ICC8s, and land cells have smaller slopes relative to oceanic ones. CbT25 cells have the smallest slopes (~1–3 dBZ km−1), and those of ICC8s are marginally larger. Oceanic CbT25s have comparable slopes that are larger than those of land clouds. The slopes shown in Fig. 5a are related to the precipitation process and to the phase change of water. Above the freezing level (which is typically around 5 km in the tropical atmosphere during summer), conversion from liquid to ice phase decreases Ze by 6.5 dBZ (Fabry and Zawadzki 1995) because Ze is calculated assuming the complex refractive index of liquid water, which is nearly 4.5 times that of the ice. Ice phase also accelerates the growth of hydrometeors as the cloud air mass ascends in the mixed-phase region; larger hydrometeors descend and grow further along their downward trajectory and thus Ze increases, since it is proportional to the sixth power of hydrometeor size (Houze 1993). The net result is that Ze decreases rapidly with height in the mixed-phase region. This mechanism is favored if the updraft velocities in the mixed-phase region are low, as in, for example, oceanic cloud systems (Lucas et al. 1994). In the case of intense convective clouds, a different mechanism may be operating, as explained below.

Fig. 5.

(a) Average slope of Ze (absolute value) in the atmospheric layer between 5- and 7-km altitudes for the profiles shown in Fig. 3. The numbers along the x axis refer to the number in the first column in Table 1. (b) Change in cloud ice water content between 7- and 5-km altitudes (g m−3).

Fig. 5.

(a) Average slope of Ze (absolute value) in the atmospheric layer between 5- and 7-km altitudes for the profiles shown in Fig. 3. The numbers along the x axis refer to the number in the first column in Table 1. (b) Change in cloud ice water content between 7- and 5-km altitudes (g m−3).

The convective cells studied in this work may be treated as very intense since either they extend deep into the upper troposphere (CbT) or are in the top-5% Ze bracket at their respective levels. In intense convective clouds, updraft velocities tend to be high, leaving less time for hydrometeors to grow in size during the growth phase, and more hydrometeors are carried to the upper troposphere (H10). Once the updrafts weaken in the upper troposphere, larger hydrometeors start their downward journey and grow along their path through ice-phase cloud microphysical processes up to the melting level. In this scenario, the slope of the profile in the mixed-phase region depends more on the growth of hydrometeors than that due to the phase-change effect (by the time hydrometeors arrive here from the upper troposphere, the cloud would have glaciated). Then, a convective cloud with a larger (absolute) slope suggests a higher growth rate of hydrometeors. From this point of view, hydrometeor growth rate along the downward trajectory is highest in ICC3s, slowest in CbT25s, and more efficient in oceanic clouds than in continental clouds (Fig. 5a). A careful analysis reveals that this need not always be true, however, because the slope shown in Fig. 5a is calculated taking Ze expressed in reflectivity decibel units (i.e., the original Ze has been subjected to a logarithmic transformation). A consequence of the log transformation is that small values get magnified and larger values get compressed. Therefore, to understand which cloud cells have a greater precipitation growth rate, the relationship between Ze and cloud ice water content M (the amount of glaciated water per unit volume of cloud air) needs to be considered. These two quantities are empirically related by an expression of the form (here Ze has for units: mm6 m−3; Smith 1984). Values of c and d depend on the type of precipitation (Black 1990). For glaciated convective clouds inside a hurricane (mostly containing graupel), Black’s (1990) study gives c = 915 and d = 1.51 (for which M has for units: g m−3). Assuming these values for the present convective cells (the main purpose is to get at least an approximate picture of changes in the hydrometeor concentration in clouds over different regions), the increase in M between the 7- and 5-km levels (ΔM) has been calculated and is shown in Fig. 5b. It is observed that the growth rate of hydrometeors is higher in ICC8 than in ICC3. The lowest growth rate is observed in the oceanic CbTs, whereas over land it is in ICC3. The highest growth of hydrometeors (~0.5 g m−3) occurs in CbT25s over IGP.

The reasons for smaller slopes of the average profiles above 12 km are not clear. The effect of the sensitivity of the PR being ~17 dBZ will be felt at these levels, and sampling could be an issue. For example, Fig. 6 shows the number of ICC3 profiles as a function of height. It is observed that almost all ICC3 clouds extend up to 5 km and that their number decreases rapidly with height above 6 km. Figure 6 should be interpreted with care. Over the tropical oceans where fallout of precipitation could be rapid above the freezing level, hydrometeor concentration may reduce to a level at which the echo strength is less than the PR detection limit. The actual cloud-top height as well as the number of convective clouds could be much higher at upper levels than that indicated by Fig. 6 (Sindhu and Bhat 2013). If the PR sensitivity is not a factor, then smaller slopes above 12 km mean low growth of hydrometeor concentration/size at these heights.

Fig. 6.

Number of ICC3 profiles at different altitudes normalized by their number at 3 km.

Fig. 6.

Number of ICC3 profiles at different altitudes normalized by their number at 3 km.

5. Conclusions

There are four main conclusions of this study:

  1. Intense convective clouds over the ocean have similar average vertical structure across basins. Convective clouds over land exhibit larger regional variability, and intense convective clouds over the northern Bay of Bengal and the Maritime Continent are closer to oceanic cells.

  2. Reflectivity decreases toward the surface below 4 km for ICC8 and ICC3 over both land and ocean, whereas this happens for land areas only in the case of CbTs.

  3. The slope of the radar reflectivity factor is larger for the oceanic profiles between 7 and 5 km; the increase in the amount of hydrometeors in this layer is not less in continental clouds relative to oceanic ones, however.

  4. The most intense CbTs form over the foothills of the western Himalaya and southeastern South America, and the most intense ICC3s develop over equatorial Africa.

Acknowledgments

This work is supported by grants from the Department of Science and Technology, New Delhi, Indian, and the Ministry of Earth Sciences, New Delhi. TRMM 2A25 (http://mirador.gsfc.nasa.gov/cgi-bin/mirador/presentNavigation.pl?tree=project&dataset=2A25%20%28Version%20007%29:%20Radar%20Rainfall%20Rate%20and%20Profile%20%28PR%29&project=TRMM&dataGroup=Orbital&version=007) and 3B42 (http://mirador.gsfc.nasa.gov/cgi-bin/mirador/presentNavigation.pl?tree=project&dataset=3B42:%203-Hour%200.25%20x%200.25%20degree%20merged%20TRMM%20and%20other%20satellite%20estimates&project=TRMM&dataGroup=Gridded&version=007) data are taken from NASA’s Earth–Sun System Division website. The authors thank the anonymous reviewers for their constructive suggestions. TMI data were produced by Remote Sensing Systems, Inc., and were sponsored by the NASA Earth Sciences Division.

REFERENCES

REFERENCES
Awaka
,
J.
,
T.
Iguchi
,
H.
Kumagai
, and
K.
Okamoto
,
1997
: Rain type classification algorithm for TRMM Precipitation Radar. Proc. 1997 Int. Geoscience and Remote Sensing Symp., Singapore, Institute of Electrical and Electronics Engineers, 1633–1635, doi:.
Bhat
,
G. S.
, and
S.
Kumar
,
2015
:
Vertical structure of cumulonimbus towers and intense convective clouds over the South Asian region during the summer monsoon season
.
J. Geophys. Res. Atmos.
,
120
,
1710
1722
, doi:.
Black
,
R. A.
,
1990
:
Radar reflectivity–ice water content relationships for use above the melting level in hurricanes
.
J. Appl. Meteor.
,
29
,
955
961
, doi:.
Cotton
,
W. R.
, and
R. A.
Anthes
,
1989
: Storm and Cloud Dynamics. Academic Press, 882 pp.
Dixon
,
M.
, and
G.
Wiener
,
1993
:
TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology
.
J. Atmos. Oceanic Technol.
,
10
,
785
797
, doi:.
Fabry
,
F.
, and
I.
Zawadzki
,
1995
:
Long-term radar observations of the melting layer of precipitation and their interpretation
.
J. Atmos. Sci.
,
52
,
838
851
, doi:.
Gadgil
,
S.
,
P. V.
Joseph
, and
N. V.
Joshi
,
1984
:
Ocean–atmosphere coupling over monsoon regions
.
Nature
,
312
,
141
143
, doi:.
Graham
,
N. E.
, and
T. T.
Barnett
,
1987
:
Sea surface temperature, surface wind divergence, and convection over tropical oceans
.
Science
,
238
,
657
659
, doi:.
Guimond
,
S. R.
,
G. M.
Heymsfield
, and
F. J.
Turk
,
2010
:
Multiscale observations of Hurricane Dennis (2005): The effect of hot towers on rapid intensification
.
J. Atmos. Sci.
,
67
,
633
654
, doi:.
Heymsfield
,
G. M.
,
L.
Tian
,
A. J.
Heymsfield
,
L.
Li
, and
S.
Guimond
,
2010
:
Characteristics of deep tropical and subtropical convection from nadir-viewing high-altitude airborne Doppler radar
.
J. Atmos. Sci.
,
67
,
285
308
, doi:.
Houze
,
R. A.
,
1993
: Cloud Dynamics. Academic Press, 496 pp.
Houze
,
R. A.
,
D. C.
Wilton
, and
F. B.
Smull
,
2007
:
Monsoon convection in the Himalayan region as seen by the TRMM Precipitation Radar
.
Quart. J. Roy. Meteor. Soc.
,
133
,
1389
1411
, doi:.
Huffman
,
G. J.
, and Coauthors
,
2007
:
The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multilayear, combined-sensor precipitation estimates at fine scales
.
J. Hydrometeor.
,
8
,
38
55
, doi:.
Iguchi
,
T.
,
T.
Kozu
,
R.
Meneghini
,
J.
Awaka
, and
K.
Okamoto
,
2000
:
Rain-profiling algorithm for the TRMM Precipitation Radar
.
J. Appl. Meteor.
,
39
,
2038
2052
, doi:.
Iguchi
,
T.
,
T.
Kozu
,
J.
Kwiatkowski
,
R.
Meneghini
,
J.
Awaka
, and
K.
Okamoto
,
2009
:
Uncertainties in the rain profiling algorithm for the TRMM Precipitation Radar
.
J. Meteor. Soc. Japan
,
87A
,
1
30
, doi:.
Kummerow
,
C.
,
W.
Barnes
,
T.
Kozu
,
J.
Shiue
, and
J.
Simpson
,
1998
:
The Tropical Rainfall Measuring Mission (TRMM) sensor package
.
J. Atmos. Oceanic Technol.
,
15
,
809
817
, doi:.
Li
,
W.
, and
C.
Schumacher
,
2011
:
Thick anvils as viewed by the TRMM Precipitation Radar
.
J. Climate
,
24
,
1718
1735
, doi:.
Liu
,
C.
, and
E. J.
Zipser
,
2013
:
Why does radar reflectivity tend to increase downward toward the ocean surface, but decrease downward toward the land surface?
J. Geophys. Res.
,
118
,
135
148
, doi:.
Liu
,
C.
,
D. J.
Cecil
,
E. J.
Zipser
,
K.
Kronfeld
, and
R.
Robertson
,
2012
:
Relationships between lightning flash rates and radar reflectivity vertical structures in thunderstorms over the tropics and subtropics
.
J. Geophys. Res.
,
117
, D06212, doi:.
Lucas
,
C.
,
E. D.
Zipser
, and
M. A.
LeMone
,
1994
:
Vertical velocity in oceanic convection off tropical Australia
.
J. Atmos. Sci.
,
51
,
3183
3193
, doi:.
Masunaga
,
H.
,
T.
Iguchi
,
R.
Oki
, and
M.
Kachi
,
2002
:
Comparison of rainfall products derived from TRMM Microwave Imager and Precipitation Radar
.
J. Appl. Meteor.
,
41
,
849
862
, doi:.
Medina
,
S.
,
R. A.
Houze
Jr.
,
A.
Kumar
, and
D.
Niyogi
,
2010
:
Summer monsoon convection in the Himalayan region: Terrain and land cover effects
.
Quart. J. Roy. Meteor. Soc.
,
136
,
593
616
, doi:.
Nesbitt
,
S. W.
,
E. J.
Zipser
, and
D. J.
Cecil
,
2000
:
A census of precipitation features in the tropics using TRMM radar, ice scattering, and lightning observations
.
J. Climate
,
13
,
4087
4106
, doi:.
Ramage
,
C. S.
,
1968
:
Role of a tropical “Maritime Continent” in the atmospheric circulation
.
Mon. Wea. Rev.
,
96
,
365
370
, doi:.
Riehl
,
H.
, and
J. S.
Malkus
,
1958
:
On the heat balance in the equatorial trough zone
.
Geophysica
,
6
,
503
538
.
Romatschke
,
U.
, and
R. A.
Houze
Jr.
,
2010
:
Extreme summer convection in South America
.
J. Climate
,
23
,
3761
3791
, doi:.
Romatschke
,
U.
,
S.
Medina
, and
R. A.
Houze
Jr.
,
2010
:
Regional, seasonal, and diurnal variations of extreme convection in the South Asian region
.
J. Climate
,
23
,
419
439
, doi:.
Saikranthi
,
K.
,
T. N.
Rao
,
B.
Radhakrishna
, and
S. V. B.
Rao
,
2014
:
Morphology of the vertical structure of precipitation over India and adjoining oceans based on long-term measurements of TRMM PR
.
J. Geophys. Res. Atmos.
,
119
,
8433
8449
, doi:.
Sindhu
,
K. D.
, and
G. S.
Bhat
,
2013
:
Comparison of CloudSat and TRMM radar reflectivities
.
J. Earth Syst. Sci.
,
122
,
947
956
, doi:.
Smith
,
P. L.
,
1984
:
Equivalent radar reflectivity factors for snow and ice particles
.
J. Appl. Meteor. Climatol.
,
23
,
1258
1260
, doi:.
Steiner
,
M.
,
R. A.
Houze
, and
S. E.
Yuter
,
1995
:
Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data
.
J. Appl. Meteor.
,
34
,
1978
2007
, doi:.
Stephens
,
G. L.
, and Coauthors
,
2002
:
The CloudSat mission and the A-TRAIN: A new dimension to space-based observations of clouds and precipitation
.
Bull. Amer. Meteor. Soc.
,
83
,
1771
1790
, doi:.
Stith
,
J. L.
,
J. E.
Dye
,
A.
Bansemer
,
A. J.
Heymsfield
,
C. A.
Grainger
,
W. A.
Petersen
, and
R.
Cifelli
,
2002
:
Microphysical observations of tropical clouds
.
J. Appl. Meteor.
,
41
,
97
117
, doi:.
Waliser
,
D. U.
, and
N. E.
Graham
,
1993
:
Convective cloud systems and warm-pool sea surface temperatures’ coupled interactions and self-regulation
.
J. Geophys. Res.
,
98
,
12 881
12 893
, doi:.
Wentz
,
F. J.
,
C.
Gentemann
,
D.
Smith
, and
D.
Chelton
,
2000
:
Satellite measurements of sea surface temperature through clouds
.
Science
,
288
,
847
850
, doi:.
Williams
,
E. R.
,
M. E.
Weber
, and
R. E.
Orville
,
1989
:
The relationship between lighting type and convective state of thunderstorms
.
J. Geophys. Res.
,
94
,
13 213
13 220
, doi:.
Xu
,
W.
, and
E. J.
Zipser
,
2012
:
Properties of deep convection in tropical continental, monsoon, and oceanic rainfall regimes
.
Geophys. Res. Lett.
,
39
, L07802, doi:.
Yuan
,
T.
, and
X.
Qie
,
2008
:
Study on lightning activity and precipitation characteristics before and after the onset of the South China Sea summer monsoon
.
J. Geophys. Res.
,
113
, D14101, doi:.
Zipser
,
E. J.
, and
K. R.
Lutz
,
1994
:
The vertical profile of radar reflectivity of convective cells: A strong indicator of storm intensity and lightning probability?
Mon. Wea. Rev.
,
122
,
1751
1759
, doi:.
Zipser
,
E. J.
,
D. J.
Cecil
,
C.
Liu
,
S. W.
Nesbitt
, and
D. P.
Yorty
,
2006
:
Where are the most intense thunderstorms on Earth?
Bull. Amer. Meteor. Soc.
,
87
,
1057
1071
, doi:.