Radar Background - IPRC
6.158
Radar Meteorology in the Tropics
Courtney Schumacher
Department of Atmospheric Sciences, Texas A&M University, USA
Key words: radar, tropics, precipitation, convection, stratiform rain, mesoscale convective systems
Contents
1. Introduction
2. Radar Background
3. Field Campaigns
3.1 Hot towers and mesoscale convective systems (MCSs)
3.2 Barbados Meteorological Experiment 1968
3.3 VIMHEX 1969 and 1972
3.4 GATE 1974
3.5 COPT 1981
3.6 Winter and Summer MONEX 1978-1979
3.7 EMEX/AMEX/STEP 1987
3.8 ITEX/DUNDEE/MCTEX/TWP-ICE 1988-2006
3.9 TOGA COARE 1992-1993
3.10 TEPPS 1997
3.11 SCSMEX 1998
3.12 TRMM-LBA 1999
3.13 KWAJEX 1999
3.14 EPIC 2001
3.15 RICO 2005
4. Tropical Cyclones
5. Satellite Radars
5.1 TRMM Precipitation Radar 1997-present
5.2 CloudSat 2006-present
6. Conclusion
Bibliography
Summary
Radar technology has provided an unprecedented view of storm structure in the tropics. Beginning in the late 1960s, radar deployments during field campaigns have highlighted all aspects of tropical convection, including its distribution and vertical structure, how it organizes, and how it interacts with the large-scale circulation via transports of moisture, heat, and momentum. In addition, radar observations from operational aircraft have provided exceptional details on tropical cyclone structure and evolution. Finally, spaceborne radars have allowed a tropics-wide view of precipitation and clouds.
1. Introduction
Tropical storm systems provide on the order of 1-2 m of rain across the tropics each year. In some locations, annual rainfall exceeds 3 m. This rain is essential to the livelihoods of people that live in the tropics and is also a main driver of the global circulation. Radar provides a good measure of rainfall over large areas (typically a 150 km radius from the radar itself). In addition, radar is a unique tool in that it can observe the internal structure of storms.
Most insight on tropical weather using radars has come from field campaigns. Large field experiments provide the infrastructure necessary for the deployment of radars, whether they are on the ground, on ships, or on aircraft. A few longer-term ground radar datasets exist, but since most of the tropics are ocean there are limited locations to host a long-term ground site. Within the past decade, a precipitation radar and cloud radar have been launched in space, thus providing consistent radar coverage of tropical cloud systems over both land and ocean.
Section 2 provides a brief introduction on radar technology and capabilities. Section 3 highlights radar insights about tropical convection from important field campaigns over the past 40 years, while Section 4 focuses on insights about tropical cyclones using airborne radar. Section 5 discusses satellite-based radar observations of the tropics.
2. Radar Background
Radar is an acronym that stands for “Radio Detection and Ranging”. The technology grew from the idea of using pulses of energy for target detection during World War II. After the war, research into the uses of radar for weather studies (i.e., the detection of a volume of precipitation-size particles instead of the detection of individual ships and planes) blossomed.
A radar consists of a transmitter that generates an electromagnetic signal and an antenna to send out the signal and receive echo back from a target. The total received radar power, P, can be expressed as:
P = Z – 20 log r – C
where P is measured in decibels referenced to 1 milliwatt (dBm); Z is the reflectivity factor referenced to 1 mm6 m-3 (dBZ) and is determined by the number, size, and composition of the target; r is the range of the target from the radar in km; and C is a radar constant measured in decibels (dB) that is dependent on the hardware of the system such as wavelength and beamwidth.
When measuring rain or cloud, a radar receives power from many targets in a sample volume. Assuming all of the scatterers are water, the effective radar reflectivity factor, Ze, can be expressed as:
[pic]
where D is the diameter of the hydrometeor and N is the drop concentration. Note the D6 dependence, which places a heavier weight on the size rather than the number of hydrometeors.
Precipitation ranges in size from 0.5-6 mm (or even larger for hail); electromagnetic wavelengths of 2-10 cm (or frequencies of 3-15 GHz) are used to detect these size particles. Thus, radars that operate at these wavelengths are often called precipitation or weather radars. The most commonly used weather radar wavelengths are 10 cm (S-band), 5 cm (C-band), and 3 cm (X-band). See Table 2.1 for a full list of radar bands, wavelengths, and frequencies. More recently, efforts have been made exploring the meteorological uses of mm-wavelength radars (or frequencies of 35-94 GHz). Radars that operate at these wavelengths are called cloud radars because they are more sensitive than precipitation radars and can see cloud-size particles. However, they attenuate in moderate and heavy rain. The most commonly used cloud radar wavelengths are 8.6 mm (Ka-band) and 3.3 mm (W-band). Most of the following discussion will focus on our understanding of tropical meteorology via cm-wavelength radars, but mm-wavelength technology offers a nice complement to precipitation radars when studying tropical cloud systems.
Beyond sensing the power returned from a volume of precipitation or cloud particles, many radars also have the ability to determine the speed and direction of the particles (i.e., Doppler capabilities). Doppler radars measure the speed of a target toward or away from the radar (also called the radial velocity) using a shift of the return frequency. When two Doppler radars view the same region of a storm, dual-Doppler analysis can be done to better constrain horizontal and vertical air motions.
Some radars can send and receive electromagnetic radiation with different orientations (i.e., polarimetric capabilities). This ability allows radars to receive information about the polarization of the scatterers, which can then be used to tell something about a particle’s shape, size, and composition. For example, as rain drops grow they become more oblate such that the horizontal reflectivity (ZHH) will be larger than the vertical reflectivity (ZVV). Small rain drops (< 1 mm in diameter) are spherical and will return equal ZHH and ZVV. Hail, which by definition is > 5 mm in diameter and often tumbles when it falls, will also return nearly equal ZHH and ZVV but with a larger magnitude of ZHH because of its large size. Common polarimetric variables are differential reflectivity (ZDR), which is the ratio of ZHH and ZVV, and differential propagation phase (KDP), which is determined based on a phase shift of the radar wave and provides an indication of the liquid water content along the path of the radar beam. Polarization diversity allows improved hydrometeor classification and rain estimation.
Radars typically scan in azimuth and in elevation. A scan in azimuth with a fixed elevation is called a plan position indicator (PPI) and provides a horizontal view of a storm. A scan in elevation with a fixed azimuth is called a range-height indicator (RHI) and provides a vertical view of a storm. Multiple PPIs at different elevations allow the radar to scan the full volume of a storm. A cross-section of a storm at a constant height can be interpolated from the full volume data and is called a constant altitude PPI (CAPPI). Figure 2.1 shows an example radar scan strategy with 16 tilts ranging from 0.5( to 33( in elevation. The curvature of the earth causes the beams to curve upward relative to the surface. The refractivity of the atmosphere can also affect the slant range of a radar beam. Because the lowest tilt becomes progressively higher in height away from the radar, a data gap occurs near the surface that can be quite pronounced (e.g., the center of a 0.5( beam will be located 2.5 km above the surface 150 km from the radar assuming a standard atmosphere). Thus, a radar can only observe “near-surface” reflectivity.
Reflectivity near the surface is useful in order to calculate surface rainfall. The most common method to convert reflectivity (Z) to rain rate (R) is to use a Z-R relation. Z-R relations proliferate the literature but Table 2.2 lists some representative Z-R relations measured during tropical field campaigns. Implicit in this list is the fact that multiple rain rates can be associated with the same reflectivity because of variations in the drop-size distribution. For example 40 dBZ is equal to 23.2 mm h-1 using the GATE Z-R relation, while it equal to 10.9 mm h-1 using the EPIC Z-R relation. Variations in the drop-size distribution (and thus Z-R relation) can be temporal and geographical so it is hard to know if an instantaneous rain rate calculated with a fixed Z-R relation is necessarily correct, but overall accumulations should be reasonable if an appropriate climatological Z-R relation is used. In addition, one must take into account a host of other potential issues that may affect rain rate estimation from near-surface reflectivity (e.g., evaporation, horizontal advection, vertical air motions, attenuation, partial beam filling, and bright band contamination). Despite these issues, radar is an excellent tool with which to measure rain over large spatial domains, as well as to investigate vertical storm structure.
3. Field Campaigns
3.1 Hot towers and mesoscale convective systems (MCSs)
Riehl and Malkus (1958) introduced the idea that undilute cumulonimbus towers (or “hot towers”) are necessary to export the net energy gain at the surface in equatorial regions toward higher latitudes. Riehl and Simpson (1979) revisited Riehl and Malkus (1958) and used radar observations to support the concept that the vertical energy transport in cumulonimbus clouds is important in maintaining the tropospheric energy balance. In particular, they used radar echo top information to indicate the deep vertical extent of the hot towers and used radar observations to help quantify the number of hot towers needed to maintain tropospheric energy balance by overcoming the mid-tropospheric minimum of moist static energy (i.e., 1500-5000 in the equatorial trough zone). In the past 50 years, a large body of research has been focused on the nature of tropical convection and how models capture it. Toward this goal, radar has played an important role in physical process studies and model parameterization and validation.
The tropics are also home to larger, more organized convective systems. These mesoscale convective systems (MCSs) contain both deep convective cells and a more horizontally uniform precipitating anvil cloud (also called the stratiform rain region). Tropical squall lines, which are fast moving MCSs with an arc-shaped leading convective edge, were initially inferred from conventional meteorological observations by Hamilton and Archbold (1945). It wasn’t until field campaigns in the late 1960s and early 1970s that tropical squall lines and other types of MCSs were documented using radar. These radar observations laid the foundation for understanding the importance of convective organization in tropical cloud systems. In particular, the mesoscale portion of these storms was shown to have its own kinematic structure and to be significant in terms of rain production. In addition, organized convective systems were shown to be important in the source and transport of heat, moisture, and momentum, which in turn affect the large-scale atmospheric circulation.
The rest of this section will discuss insights gleaned from the use of radar data in tropical field campaign settings. The locations of some of the campaigns to be discussed are shown in Fig. 3.1. Field campaigns associated with tropical cyclone research will be covered in the following section.
3.2 Barbados Meteorological Experiment 1968
Using aircraft radar observations made during the 1968 Barbados Meteorological Experiment, Lopez (1973) analyzed the lifetimes of individual convective cells within about a dozen tropical cloud clusters that occurred in July and August. The ultimate goal of this analysis was to use the radar-observed cloud properties in conjunction with a numerical cloud model to investigate the effects cumulus clouds have on their synoptic environment. The scope on the S-band radar onboard the US Navy’s Weather Reconnaissance Squadron Four aircraft was photographed every other scan. From the picture sequences, Lopez (1973) found that the average echo lifetime was 14.5 min, with the distribution skewed toward shorter-lived echoes.
Lopez (1976) revisited the 1968 Barbados Meteorological Experiment radar observations and extended the radar analysis to include echoes besides just isolated convective cells (see Figure 3.2 for an example composite radar map from 22 July). He also examined echo area and height statistics. Lopez (1976) showed that, like echo duration, echo area is positively skewed; and while small (< 100 km2) echoes are most common, large (> 1000 km2) echoes contribute more to the total rain area and thus accumulation. Echo heights were observed by an APS-45 (3 cm) radar operating in RHI mode. The echo heights were also positively skewed, with an average echo height of 4.9 km. Lopez (1976) generalized his findings to state that the distribution of echo area and height is lognormal and showed the importance of cell merging in determining the observed convective cloud distributions. Lopez (1977) further showed that these lognormal distributions seem to be universal based on radar analysis from other tropical and mid-latitude locations. Lopez (1976) also postulated that the large population of small, short-lived convective clouds are likely important in moistening the environment for the relatively infrequent but larger and longer-lived deep convective cells. These scale interactions remain an area of active research in tropical meteorology.
3.3 VIMHEX 1969 and 1972
The following year, the first Venezuelan International Meteorological and Hydrological Experiment (VIMHEX) took place in northern Venezuela between June and September 1969. Betts (1973) composited data from a 10-cm radar and a single rawinsonde station located at Anaco (9.4ºN, 64.5ºW) to determine the flow and thermodynamic variables in and around the observed storms. The radar observations were based on Cruz’s (1973) radar analysis of 232 storms from about 70,000 scope photographs taken during VIMHEX. One of Cruz’s criteria was that the echo area of the storm had to be > 400 km2, larger than a single cumulonimbus (which typically has a radius < 5 km), but on the small side for an MCS (which has at least 100 km of contiguous precipitation in one horizontal direction according to the definition in Houze [1993]). To ensure a similar synoptic environment, Betts (1973) only composited storms that occurred in low-level easterly flow and in which the storms moved faster than the low-level flow. The 90 composited storms were dominated by updrafts in the growth phase and downdrafts in the decay phase; the net vertical mass flux was positive throughout the troposphere with a slight decrease from 800 to 600 mb (Figure 3.3).
Knowledge of mass transport is essential in understanding how convection is coupled to the large-scale circulation. Betts (1973) confirmed the importance of downdrafts to the total convective mass transport. His radar-oriented methodology was complementary to the numerical model approaches of Yanai et al. (1973) and others that diagnose convective cloud properties from their observed effects on the large-scale environment. Houze and Leary (1976), using radar and rawinsonde data from the west Pacific, showed that convective mass fluxes calculated via radar observations and synoptic-scale rawinsonde data are in good agreement, although discrepancies may occur at low levels because of the inability of radar to sense shallow convective clouds that don’t precipitate. The use of radar observations during the early 1970s was beginning to play an important role in determining the physical basis for the parameterization of convection in large-scale atmospheric models.
During the second VIMHEX in 1972, Betts et al. (1976) used 10-cm radar and rawinsonde data from Carrizal (9.4ºN, 66.9ºW) to create composites of four squall lines that occurred between June and September. Betts et al. wanted to test whether observations of squall lines supported theoretical calculations of propagation speed and mass, heat, and momentum transports to the synoptic-scale environment. Betts et al. (1976) found that the squall lines propagated at speeds ranging from 10.7-16.4 ms-1, in reasonable agreement with the steady-state analytic solutions of Moncrieff and Miller (1976). In addition, the squall lines affected both the thermodynamic and dynamic synoptic-scale fields. Warm, moist boundary layer air was transported into higher levels and was replaced by cooler, drier mid-level air, and easterly momentum increased at lower levels while westerly momentum increased at upper levels. However, the interactions between the squall lines and the large-scale environment remained unresolved.
3.4 GATE 1974
The Global Atmospheric Research Program’s Atlantic Tropical Experiment (GATE) took place June-September 1974 just off the coast of West Africa in the intertropical convergence zone (ITCZ) of the East Atlantic (5-12ºN, 20-27ºW). The goal of the experiment was to more fully describe tropical convection and how tropical convective systems relate to the large-scale atmosphere via vertical transports of heat, moisture, and momentum (Houze and Betts 1981). GATE, the largest tropical field campaign ever assembled, combined satellite, radar, aircraft, and sounding data in support of these objectives. In particular, four ships carried digital C-band radars with which to document the three-dimensional precipitation structure of tropical East Atlantic cloud systems (Arkell and Hudlow 1977).
The radar network in GATE provided data for both case studies and statistical characterization of convection. For example, Houze and Cheng (1977) corroborated Lopez’s (1976) Barbados findings concerning the lognormal distribution of echo characteristics with the GATE radar observations (Figure 3.4) and Szoke et al. (1986) analyzed the vertical profile of reflectivity in convective cells, which tended to have modest strength (i.e., a mean of 45 dBZ at the surface and a 20-dBZ echo top at 8.2 km). Houze and Cheng (1977) further analyzed the occurrence of relatively uniform regions of precipitation, which at the time they called anvil but is now more commonly referred to as stratiform rain. Figure 3.5 shows an example cross section of a mature MCS observed during GATE by Leary and Houze (1979b). A decaying convective cell is evident in the right half of the figure, ~55 km from the R/V Researcher radar, while the rest of the system has a more layered appearance with an enhanced layer of reflectivity just below 4 km in height and –65 to +35 km from the radar. This layer is called the bright band and is characteristic of stratiform rain regions. Houze and Cheng (1977) found that 40% of the total GATE rainfall was accounted for by stratiform rain.
At the time of GATE, the existence of stratiform rain in the tropics had only been recently deduced from aircraft observations during the 1967 Line Island experiment (Zipser 1969). Radar observations during GATE played an essential role in describing the structure and overall importance of stratiform rain regions in tropical convective systems. Houze (1997) provides an excellent discussion about how our understanding of stratiform rain in the tropics has evolved since GATE.
Perhaps the best-known case study from GATE is the 4-5 September 1974 squall line. Houze (1977) made extensive use of ship radar data to describe the structure and dynamics of the system. He showed that the squall line had a leading line of discrete convective elements that formed ahead of the squall line, weakened toward the rear of line, and then blended into the trailing stratiform region as they dissipated. The convective elements had echo tops up to 16-17 km, thus penetrating the tropopause at their most intense. Convective-scale downdrafts spread out at low levels and enhanced lift at the leading edge of the convective elements. There was also a mesoscale downdraft in the stratiform rain region. Because 40% of the squall line’s total rainfall came from the stratiform rain region, Houze (1977) postulated the presence of a mesoscale updraft. Houze’s (1977) results generally corroborated Zipser’s (1969) conceptual model of a squall line and the convective and mesoscale motions were consistent with the Venezuelan squall lines analyzed by Betts et al. (1976).
Leary and Houze (1979a) more generally described the life cycle of a non-squall (i.e., slower moving and less organized) MCS based on ship radar observations of a group of convective systems that occurred on 5 September 1974. Figure 3.6 shows Leary and Houze’s schematic of the life cycle of an MCS as viewed by radar. The formative stage (AA’) has a line of isolated cumulonimbus oriented perpendicular to the low-level wind. In the intensifying stage (BB’), individual cells merge and a non-precipitating anvil cloud extends downwind. The mature stage (CC’) indicates convective cells along a leading edge and a large area of stratiform precipitation behind the line. The stratiform rain region is composed of dying convective cells and has a bright band just below the 0(C level. Convective cells cease to form in the dissipating stage (DD’), but the stratiform rain area persists for many hours. It should be noted that this life cycle also applies to tropical squall lines.
Both Houze (1977) and Leary and Houze (1979a) suggested three mechanisms for the stratiform rain formation and maintenance in Figure 3.6c: 1) hydrometeors diverge or are advected away from the top of deep convective cells, 2) older cells die and become stratiform, or 3) by growth in the mesoscale updraft. Based on the GATE storms and later field campaign studies, it appears that one or all of these mechanisms can contribute to stratiform rain production depending on the nature of the system and its environment.
Using vertical distributions of radar reflectivity along with measurements from soundings, aircraft, and surface drop-size distributions, Leary and Houze (1979b) inferred the stratiform rain region’s microphysical structure. A side note is that the GATE radar data was archived both digitally and photographically. The following analysis uses the digital form of the radar data. The vertical profile of reflectivity in the stratiform rain region (Figure 3.7) tends to have values < 25 dBZ aloft in the ice region, rapidly increasing to ~40 dBZ just below the 0(C level, and decreasing to 30-35 dBZ in the lowest few kilometers. This near-surface reflectivity range equates to surface rain rates generally < 5 mm h-1, while rain rates in the convective region are often > 10 mm h-1. The nimbostratus cloud associated with the stratiform rain region typically has a base between 3.5-4.5 km and a top at 12 km or above (Figure 3.7). Vapor deposition occurs in the upper levels of the cloud, while aggregation and possibly riming occurs above the melting layer. In the melting layer (which is just below the 0(C level), there is a region of enhanced reflectivity (i.e., the bright band) and 1-7 K h-1 of cooling. Evaporation causes cooling rates from 0.2-6 K h-1 below the melting layer. Both the melting of precipitation particles at cloud base and the evaporation of rain beneath cloud base were postulated by Leary and Houze (1979b) to contribute to initiating and maintaining the mesoscale downdraft beneath cloud base.
The bright band itself warrants more discussion since it is an important radar feature. Bright bands had been previously observed in the tropics in monsoon precipitation over India (Murty et al. 1965, Figure 3.8) and in hurricane rainbands (Atlas et al. 1963). The increase in reflectivity below the 0(C line occurs when relatively large snow particles begin to melt and are coated by a film of water that the radar sees as enhanced reflectivity (Austin and Bemis 1950). The aggregation of sticky snowflakes can further increase the size of the particles being sensed by the radar, causing even more reflectivity enhancement. Once the particles melt, they become smaller and rapidly evacuate the melting layer, causing a relative minimum in reflectivity beneath the bright band.
Returning to the mesoscale kinematics of a tropical squall line, Gamache and Houze (1982) determined the horizontal and vertical air motions of the 12 September 1974 GATE squall line using surface and upper-air wind observations composited with respect to ship radar data in a moving coordinate system. Importantly, they separated the echo into convective and stratiform regions, which is difficult to do with satellite or other ground sensors. Figure 3.9 shows strong low-level convergence in the convective region corresponding to deep convective updrafts, while the stratiform rain region had mid-level convergence corresponding to a mesoscale updraft within the anvil cloud and a mesoscale downdraft below. These vertical velocity profiles confirmed and quantified the mesoscale up and downdrafts of the qualitative squall line model. Houze and Rappaport (1984) analyzed a different GATE squall line that had both a leading and trailing anvil, moved more slowly, and had different relative horizontal flow through system and found similar mesoscale kinematic results.
Gamache and Houze (1983) used the winds from Gamache and Houze (1982) along with observed humidity fields and radar-derived surface rainfall to calculate the water budget terms for the 12 September 1974 GATE squall line (Figure 3.10). The observed terms include the condensation in the updraft and the evaporation in the downdraft of the convective and stratiform rain regions (Cu, Ecd, Cmu, Emd), the surface rainfall in each region (Rc, Rm), and the detrainment to the environment (Ece, Eme). The transport of condensate from the convective to the stratiform rain region (CA) is determined as a residual. The water budget framework extends the idealized work of Leary and Houze (1980) since Leary and Houze lacked observations of multiple terms. Gamache and Houze (1983) found that the average rain rate in the stratiform region was 2.6 mm h-1 (or 29 dBZ) and roughly half of the rain came from the stratiform region. In addition, the mesoscale updraft accounts for 25-40% of the condensate in the stratiform cloud, while the rest of the condensate is from the horizontal transfer from the convective region. Even though CA was determined as a residual with large potential uncertainties, the attempt to quantify the water budget of tropical convective systems is important in understanding the resultant diabatic heating and the role of this heating on the large-scale circulation. Research is ongoing to better quantify the latent, radiative, and sensible heating components of tropical cloud systems.
Gamache and Houze (1985) did a final set of calculations on the 12 September 1974 GATE squall line. Using an objective analysis technique, they calculated the vorticity of the system and found peak values around 650 mb in the stratiform rain region. A mid-level mesoscale vortex was also noted by Houze (1977) in the 4 September 1974 GATE squall line. This feature is now called a mesoscale convective vortex (MCV) and has been postulated to be important for tropical cyclone genesis.
3.5 COPT 1981
While GATE measured storms over the East Atlantic just off the West African coast, the Convection Profonde Tropicale 1981 (COPT 81) experiment gathered a large radar data set over West Africa near Korhogo, Ivory Coast (9.4ºN, 5.6ºW) in May and June 1981. The objectives of COPT 81 were to study convective initiation and evolution, precipitation mechanisms and efficiency in deep convection, and electrical processes (Sommeria and Testud 1984). Toward these objectives two C-band Doppler radars with a 35 km baseline were sited in the Korhogo region.
The two Doppler radars allowed dual-Doppler analysis of the thermodynamic (Roux et al. 1984) and kinematic (Chong et al. 1987) structure of a squall line that occurred on 22 June 1981. Dual-Doppler analysis provides a three-dimensional view that is difficult to match with conventional sounding or radar observations. The line was fast-moving (19 m s-1 to the west southwest) and exhibited many similarities to the squall lines observed during VIMHEX and GATE (Figure 3.11).
Roux et al. (1984) calculated pressure and temperature fields using dual-Doppler observations of the 22 June squall line that agreed well with the surface network. The inferred thermodynamic structure indicated a low-level frontward flow resulting mostly from a density current of cold air from the stratiform rain region’s mesoscale downdraft (Figure 3.12). The frontward flow helped initiate and maintain the frontal updraft in the convective region through nonhydrostatic pressure perturbations and temperature differences with the low-level inflow. Mixing at higher altitudes and cloud water loading caused weaker buoyancy in the frontal updraft. Once the water content precipitated, weak convective updrafts appeared. The conceptual model in Figure 3.12 is in general agreement with Lemone’s (1983) aircraft composite of a GATE squall line, although differences in the role of the convective downdrafts are probably related to the life cycle stage (i.e., the COPT 81 storm was in a mature to dissipating stage while the GATE storm was sampled in the intensifying to mature stage).
Chong et al.’s (1987) analysis of the 22 June squall line’s dual-Doppler wind fields showed that there was front-to-rear flow at all levels of the leading edge and rear-to-front flow below 3 km in the stratiform rain region (Figure 3.13), which was deeper flow than had been previously reported. A maximum updraft of 13 m s-1 was observed at 2.5 km height, while maximum downdrafts were 4 m s-1. A secondary updraft maxima of 6-10 m s-1 occurred rearward of the main convective updraft from 4-13 km in altitude. This updraft could have benefited from increased buoyancy from less water loading and from the latent heat of freezing. Vertical velocities in the stratiform region obtained from single-Doppler Velocity Azimuth Display (VAD) analysis (Figure 3.14) confirmed the mesoscale up and downdrafts in Gamache and Houze (1982), although Chong et al. (1987) found stronger values (0.4 vs. 0.15 m s-1 for the mesoscale updraft and 0.25 vs. 0.06 m s-1 for the mesoscale downdraft), likely because of the different measurement techniques.
Chong and Hauser (1989) analyzed the water budget of the 22 June COPT 81 squall line using Doppler radar observations (as opposed to soundings as in Gamache and Houze [1983]). While the radar analysis provides more information on internal structure, it is limited to the precipitating region of the storm that the radar can see. As in the GATE squall lines, 35-45% of the total rainfall was stratiform. The convective region provided three-quarters of the storm condensate, a third of which was transported to the stratiform region. The mesoscale updraft and horizontal transfer of hydrometeors from the convective region contributed equally to the total condensate in the stratiform region. A third of condensate in stratiform region was lost in the evaporative downdraft, causing a stronger mesoscale downdraft and a deep layer of cold, subsaturated air in the convective region. Only 9% of the storm condensate was lost to the environment compared to 21% in Gamache and Houze (1983). Precipitation efficiency, an important quantity that is difficult to measure, was estimated to be ~50% in the convective and stratiform rain regions. Very few studies have subsequently tried to quantify the water budget of a tropical convective system because of the difficulty in obtaining sufficient observations.
More recent field work took place over the GATE and COPT-81 regions in August and September 2006. The African Monsoon Multidisciplinary Analyses (AMMA) was an international project to improve knowledge and understanding of the West African monsoon. The Massachusetts Institute of Technology (MIT) C-band radar was located at Niamey, Niger in support of these objectives. In addition, the U.S. National Aeronautics and Space Administration (NASA) formulated a complementary mission over the East Atlantic (NAMMA) to examine the formation and evolution of tropical cyclones that eventually may affect the east coast of the U.S. TOGA, a C-band Doppler radar, was located at the Cape Verde Islands and NPOL, an S-band polarimetric radar was located in Senegal on the African coast. A dual-frequency airborne radar was also flown on the DC-8. Research is still underway on these datasets.
3.6 Winter and Summer MONEX 1978-1979
The winter Monsoon Experiment (MONEX) took place in December 1978 to March 1979 to study the global and regional aspects of the monsoon circulation over East Asia and the maritime continent in Northern Hemisphere winter and to provide a better description of winter monsoon clouds and precipitation (Johnson and Houze 1987). The MIT C-band radar was located on the north coast of Borneo (3.2ºN, 113ºE) during the experiment. Houze et al. (1981) used the MIT radar to observe that the deep convection that forms over land in the afternoon and evening was not strongly affected by changes in the synoptic flow. However, new convection would form offshore around midnight in association with a land breeze interacting with the monsoon northeasterly flow (Figure 3.15). The offshore convection regularly transitioned into a mature MCS in the early morning and began dissipating by midday with the onset of the sea breeze, which concentrated low-level convergence over northern Borneo. This diurnal cycle of offshore convection was one of the more important results from winter MONEX and led to more studies by Mori, Mapes, and Zuidema in other regions of the tropics.
Churchill and Houze (1984) examined radar observations of a group of winter MONEX MCSs that occurred over the South China Sea on 10 December 1978. The MCSs adhered to the conceptual model of deep convective cells developing into a system containing both convective cells and a large stratiform rain region. Figure 3.16 quantifies this development in terms of rain area and rain accumulation for one of the MCSs. Note that the contribution of stratiform rain to the storm total precipitation area is greater than its contribution to total rain accumulation because stratiform rain rates are generally much weaker than convective rain rates. Churchill and Houze (1984) also quantified the MCS ice particle structures inferred by Leary and Houze (1979b) by using aircraft microphysical observations. The dominant particle type in the convective rain regions was graupel, indicating riming as the primary growth mode and consistent with the 4-17 m s-1 updraft speeds measured by the aircraft. There were lower ice concentrations and weaker vertical motions in the stratiform rain regions and the dominant growth modes appeared to be vapor deposition and aggregation.
Summer MONEX took place from May to August 1979 to study the global and regional aspects of the monsoon circulation over India and surrounding regions during Northern Hemisphere summer (Fein and Kuettner 1980). One well-studied monsoon depression that occurred over the Bay of Bengal 3-8 July 1979 further confirmed the precipitation structure and ice microphysical properties of equatorial MCSs. Houze and Churchill (1987) used radar and cloud microphysical probes onboard the NOAA WP3D (or P-3) research aircraft to show that the summer MONEX precipitation systems were either organized as leading convective lines with a trailing stratiform rain region or as convection embedded in the stratiform rain region. The latter may appear messy on a radar screen, but these types of systems are common over the tropical oceans. Houze and Churchill also showed that updrafts above 0(C in the stratiform rain region appeared to be strong enough for ice particle growth, but were not strong enough to prevent the sedimentation of ice particles with fall speeds of 1-2 m s-1.
Based on radar studies of GATE and MONEX storms, Houze (1982) determined the vertical distribution of diabatic heating in the mature stage of an idealized oceanic MCS. The total diabatic heating is the sum of the latent, sensible, and radiative heating components in the convective and stratiform rain regions and the non-precipitating anvil cloud. Houze (1982) found that the latent and sensible heating in the upper levels of the stratiform rain region, in addition to the upper level radiative heating in the anvil cloud, elevated the total diabatic heating associated with tropical MCSs in comparison to a convective-only profile (Figure 3.17). Houze’s (1982) diabatic heating profiles estimated from storm-centered measurements agreed well with large-scale sounding budget heating profiles calculated at times when mature convective systems were present in the sounding network (Johnson 1984). Houze (1989) revisited his 1982 study with observations of the vertical distribution of vertical air motions from more field campaigns. The vertical air motions came from sounding and aircraft wind data, as well as from Doppler radar observations, and can be used as a proxy for the heating associated with an MCS through condensation and evaporation (although processes such as melting and radiation won’t be captured). Houze (1989) reaffirmed his 1982 study concerning the importance of the upper level heating (and low-level cooling) in the stratiform rain region in elevating the total heating profile and showed that stratiform air motion profiles were more consistent from case to case than convective air motion profiles. The large-scale significance of this elevated heating profile has been demonstrated in general circulation models forced by the convective-only versus the MCS heating profiles (e.g., Hartmann et al. 1984). The MCS heating profile provides a more realistic tropical circulation response. This work also suggests that the parameterization of tropical convection needs to take into account the mesoscale transports of mass, heat, and moisture.
3.7 EMEX/AMEX/STEP 1987
In 1987, three coincident field campaigns occurred in the vicinity of northern Australia during the monsoon season. The Equatorial Monsoon Experiment (EMEX) was an aircraft program over the tropical ocean around northern Australia to study oceanic MCSs in monsoon flow (Webster and Houze 1991). EMEX had the additional goal to better understand the relationship between convection and the large-scale dynamics and climate and acted as a pre-cursor for Tropical Oceans Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE). The Australian Monsoon Experiment (AMEX) used surface radar and enhanced soundings over northern Australia to address many of the broader goals of EMEX (Holland et al. 1986, Gunn et al. 1989). The Stratosphere-Troposphere Exchange Tropical Project (STEP Tropical) was an aircraft program to investigate exchange and dehydration in the tropical troposphere/stratosphere region (Russell et al. 1993) and did not emphasize radar analysis.
Mapes and Houze (1992) analyzed the aircraft radar data from EMEX and showed that the majority of MCSs were sampled in a mature to late lifecycle stage and had similar characteristics to MCSs observed in other parts of the tropics. For example, stratiform precipitation usually evolved in place (as in the non-squall GATE systems) although it also occurred from overhanging anvils produced from upper level shear. Mapes and Houze (1993) analyzed the divergence profiles in nine MCSs and found an intermediary category in addition to the convective and stratiform divergence profiles discussed by Gamache and Houze (1982). Each rain type had characteristic vertical structure (Figure 3.18): the convective profile had low-level convergence peaking at 2-4 km with divergence above 6 km, the intermediary profile had upper level convergence peaking near 10 km, and the stratiform profile had mid-level convergence from 4-8 km. Mapes and Houze (1993) showed that the elevated convergence in the stratiform rain region causes low-level upward displacements near the MCS, which favors additional convection (described as “gregarious convection” in a later paper by Mapes).
While previous studies had used rain gauge and satellite observations to analyze the diurnal cycle of convection in the tropics, Keenan et al. (1989a) used 10-minute base scans from the Gove C-band radar (12.3ºS, 136.8ºE) to study the diurnal cycle of convective and stratiform rainfall (defined as echo > 44 dBZ and echo = 15-30 dBZ, respectively) over Northern Australia during AMEX. Over land they found that there was an afternoon maximum in convective echo and a three-hour delay in the stratiform echo maximum (Figure 3.19). Weaker convective and stratiform echo area maxima occurred in late morning over water.
Keenan and Carbone (1992) analyzed the AMEX TOGA C-band radar at Darwin to determine differences in convective characteristics during the active and break periods of the Australian monsoon. Break period systems, representing characteristics of land convection, have extreme vertical development, reflectivities 10-20 dBZ higher above the melting layer, and a lack of stratiform rain areas (except for squall lines). Active monsoon systems, representing characteristics of ocean convection, appear to have significant warm rain coalescence and weak updrafts that can’t support large drops. Keenan and Carbone (1992) found that storms during the active monsoon can form over a large range of CAPE values and low-level shear, while break squall lines translate at high speed (~11 m s-1) in the direction of the 700 mb easterly jet, similar to the propagation of the COPT-81 squall lines. Momentum transport and cold pool dynamics were postulated to play an important role in break period squall line propagation.
3.8 ITEX/DUNDEE/MCTEX/TWP-ICE 1988-2006
After EMEX and AMEX there were a series of field campaigns in the Northern Australian region to further study aspects of deep convective systems during the Australian monsoon. The first of these was the Island Thunderstorm Experiment (ITEX) in November and December 1988 to study the diurnal cycle of convection over the Tiwi Islands (11.5ºS, 131ºE) in a pre-monsoon environment (Keenan et al. 1989b). Using the TOGA C-band radar in Berrimah (20 km south-southeast of Darwin and approximately 100 km south of the Tiwi Islands), Keenan et al. (1990) found that the sea breeze and island escarpment create preferred genesis regions with storm commonly developing between 1230 and 1530 LST. The afternoon convection occurred in a low-shear/moderate CAPE/high-moisture regime. The Richardson number was suggestive of multicellular development and convective organization generally occurred through mergers. Cloud resolving modelers tend to like Tiwi Island convection because the forcing is clear and well constrained.
The Down Under Doppler and Electricity Experiment (DUNDEE) occurred over two monsoon seasons from 1988 to 1990 at Darwin to study the electrical properties of monsoon convection (Rutledge et al. 1992). Using the MIT C-band radar located approximately 30 km east of Darwin, Williams et al. (1992) found that the ground flash production per kg of precipitation is 10 times greater in continental hot towers than in monsoon convection. Reflectivity must be > 30-40 dBZ between 0 and –20(C before a strong electric field occurs, which is often observed in deep convective cells over land (Figure 3.20). Monsoon convection is closer to the condition of moist neutrality and has less ice-phase condensate in the mixed-phase region, thus producing less lightning.
Zipser and Lutz (1994) used TOGA C-band measurements during DUNDEE to construct vertical profiles of maximum reflectivity in convective cells to be used as proxies of storm intensity and lightning probability. Over land (both tropical and midlatitude), maximum reflectivity is generally observed above the surface and decreases gradually with height above the 0(C level (Figure 3.21). Over ocean, maximum reflectivity occurs near the surface and a much sharper decrease is observed above the 0(C level. Zipser and Lutz hypothesize that the sharp decrease and associated lack of lightning in oceanic convection is due to weaker vertical velocities (i.e., < 6-7 m s-1).
Although not directly associated with ITEX or DUNDEE, Steiner et al. (1995) used February 1988 data from the TOGA C-band Doppler radar located at Berrimah to design an algorithm to automatically separate radar data into convective and stratiform regions. The algorithm examines the peakedness of the low-level horizontal reflectivity field to determine the location of convective cores. An intensity-dependent area surrounding each core is also assigned as convective. The rest of the reflectivity field is assigned as stratiform. Figure 3.22 shows that the reflectivity distributions of each rain type overlap somewhat, but that convective rain peaks in frequency around 40 dBZ, while stratiform rain peaks in frequency around 20 dBZ. The Steiner et al. (1995) algorithm is the standard method of convective-stratiform rain classification in radar meteorology.
The Maritime Continent Thunderstorm Experiment (MCTEX) took place over the Tiwi Islands November-December 1995 to investigate the life cycle of island-initiated mesoscale convective systems in the Maritime Continent (Keenan et al. 2000). Carey and Rutledge (2000) described the first polarimetric study of tropical convection using C-Pol (a C-band polarimetric radar operated by the Australian Bureau of Meteorology) measurements during MCTEX. They linked ice phase precipitation and lightning production in the 28 November 1995 Hector over the Tiwi Islands. Convection started on a sea breeze and was dominated by warm rain processes. Gust front forcing helped the convection deepen into more intense multi-cellular convection dominated by mixed-phase precipitation processes. The mature phase was well correlated with cloud-to-ground lightning and C-Pol showed continuous lofting of supercooled drops to –10 and –20(C (Figure 3.23). Carey and Rutledge (2000) postulated that the freezing and riming of these drops (as seen in the graupel mass) contributed to storm electrification via the non-inductive charging mechanism. May et al. (1999) also addressed better rain estimation using polarimetric measurements from C-Pol during MCTEX.
The Tropical Warm Pool International Cloud Experiment (TWP-ICE) was the most recent Australian radar campaign and took place January-February 2006 in the vicinity of Darwin (May et al. 2008). While data from this campaign are still being analyzed, Frederick and Schumacher (2008) recently published results quantifying anvil (i.e., thick non-precipitating cloud associated with deep convection) with C-Pol. They found that anvil typically lasted 4-10 hours after the initial convective rain area peak and covered almost 10% of the radar grid during the campaign. The average anvil thickness ranged from 6.7 km to 2.8 km depending on the composition of the anvil (i.e., if its base was below or above the 0(C level). These anvil properties can be related to the radiative importance of clouds associated with deep convection. TWP-ICE also had a vertically pointing mm-wavelength radar to complement the precipitation and thick cloud observations made with C-Pol.
3.9 TOGA COARE 1992-1993
The Tropical Oceans Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE), the next most comprehensive tropical field campaign after GATE, took place in the west Pacific warm pool in 1992-1993 to study air-sea interactions and MCSs (Webster and Lucas 1992). Short et al. (1997) provided an overview of the precipitation properties from the TOGA and MIT C-band radars, which were both located on ships. Stratiform rain accounted for 71% of the total rain area and 26% of the total rainfall (with convective rain accounting for the other portions). However, stratiform rain accounted for more rain (40%) in active situations, in agreement with the GATE case studies of large MCSs, and less rain (15%) in inactive situations. The average daily rainfall during TOGA COARE was 4.8 mm, with a range between 0.4 and 30 mm. The convective rain maximum was around midnight, somewhat similar to the convective ocean diurnal cycle observed by Keenan et al. (1989a) during AMEX; however, the convective rain maximum during TOGA COARE was midday in quiescent periods, which is in better agreement with the AMEX diurnal cycle over land. Short et al. (1997) also showed that reflectivity profiles increase toward the surface for convective rain, but are stable in stratiform rain (similar to the GATE stratiform rain profiles described by Leary and Houze [1979b]).
DeMott and Rutledge (1998) used the MIT C-band radar onboard the R/V Vickers to more fully describe echo top and 30-dBZ heights during TOGA COARE (Figure 3.24). Cruise 1 was convectively inactive with relatively shallow echo tops, cruise 2 was in a convectively active phase of the intraseasonal oscillation (ISO) and had the tallest echo tops, and cruise 3 experienced moderate convection with intermediate echo top heights. However, the 30-dBZ heights varied much less between cruises although more rain fell from cells with high 30-dBZ heights during cruises 1 and 3. Echo top and rain rate correlations were ~0.55, while 30-dBZ height and rain rate correlations were ~0.8 with less scatter. It is interesting that even though the echo tops were the lowest during cruise 1, the higher 30-dBZ heights indicated more ice water above the 0(C level.
Rickenbach and Rutledge (1998) also used the MIT C-band radar observations onboard the R/V Vickers to describe the horizontal scale and morphology of TOGA COARE convective systems. Four-fifths of the total rainfall was from MCSs with some linear feature and these storms were most common prior to a low-level westerly wind maximum. Isolated cells produced 12% of the total rainfall and occurred in very weak and very strong low-level winds. Even though the rain contributions were quite different, MCSs and fields of isolated cells occurred equally. Rickenbach and Rutledge also found that a significant portion of the convective rainfall was associated with cells 4-10 km in height, suggesting that cumulus congestus is an important convective cloud type over the tropical ocean.
Johnson et al. (1999) built upon the radar and cloud observations made during TOGA COARE to show that cumulus congestus are, in fact, just as important as shallow cumulus and cumulonimbus in the tropics (i.e., that a trimodal convective cloud population exists). The TOGA COARE radar echo tops showed three peaks associated with these cloud types at the heights of known stable layers, i.e., the trade layer ~2 km, the 0(C level ~5 km, and the tropopause ~16 km (Figure 3.25). Johnson et al. (1999) also showed that the trimodal convective cloud types vary significantly at intraseasonal time scales, spawning extensive research on the role that the trimodal cloud population plays in the evolution of the Madden-Julian Oscillation (MJO) and in the large-scale circulation in general (e.g., Mapes et al. 2006).
Lemone et al. (1998) examined the role of shear on the organization of deep convection based on multiple MCSs observed by the two NOAA P-3s and the NCAR Electra during TOGA COARE. Lemone et al. showed that the orientation of the primary convective band is determined by low-level shear if the shear is strong enough (Figure 3.26, top and bottom right). If there is weak low-level shear, lines form parallel to the mid-level shear (Figure 3.26, bottom left). In the absence of low- or mid-level shear, convection will be unorganized (Figure 3.26, top left). These results are useful for parameterization of momentum transport.
Jorgenson et al. (1997) presented a case study of a TOGA COARE squall line using aircraft Doppler radars to perform multi-Doppler scanning. This scanning allowed explicit derivation of velocity at echo top rather than assuming it is zero (a traditional assumption in dual-Doppler analyses). The aircraft observed the squall line as it transitioned from a linear feature to a three-dimensional bowed line (Figure 3.27). Many features of the squall line were consistent with previously documented tropical squall line features; however, this system had line-end vortices, multiple maxima in updraft strength, precipitation bands extending rearward transverse to the principal line, and rapid recovery of the boundary layer in the wake of the squall line.
Mapes and Houze (1995) used airborne Doppler radar measurements to calculate horizontal divergence in ten MCSs during TOGA COARE. The aircraft flew “purl” patterns (i.e., small circles along the flight path) in order to be able to calculate divergence with a single Doppler radar. The divergence profiles were similar to those found by Mapes and Houze during EMEX, but with further detail highlighting features such as reverberations in the divergence profile near the melting layer. Mapes and Houze (1995) explain that their measurements represent diabatic divergence, i.e., the horizontal wind divergence that prevents temperature from changing in the presence of diabatic heating, and showed that the diabatic divergence associated with MCSs is spectrally simple and can be decomposed into just two modes and their associated gravity waves – the fast convective mode and slower stratiform mode. They also showed that large-scale surface winds are dependent on the shape of the diabatic divergence profile.
Houze et al. (2000) used ship- and aircraft-based radars to study the convective structure of very large MCSs (or “super convective systems”) in relation to the near-equatorial atmospheric Kelvin-Rossby wave that dominated the dynamic environment of TOGA COARE (Figure 3.28). The study focused on Doppler radial velocities of systems observed in the westerly onset and strong westerly regions of the Kelvin-Rossby wave. Mesoscale midlevel inflow developed in the widespread stratiform rain region and subsided. The direction of the midlevel inflow was dictated by the large-scale flow. Momentum transport caused a negative feedback to the Kelvin-Rossby wave in the westerly onset region, but caused a positive feedback in the strong westerly region. Houze et al. (2000) also showed that the convective-scale momentum feedbacks are not systematic in a given part of the wave and may be opposite in sign to the mesoscale feedbacks. They also pointed out these large MCSs overturn in the along-shear direction as opposed to smaller MCSs that typically overturn transverse to the large-scale shear, thus allowing the momentum transport in large MCSs to have a greater impact on the large-scale flow. Houze et al. (2000) and the Mapes and Houze (1995) study discussed in the previous paragraph used radar observations to better understand the relationship between storms systems and the large-scale circulation.
A smaller scale set of studies during TOGA COARE contributed to our understanding of the microphysics of storms observed by radar. Tokay and Short (1996) used RD-69 surface disdrometer measurements to show that stratiform rain has more large drops and fewer small to medium drops compared to convective rain at the same rainfall rate; therefore, the reflectivity values will be different (Figure 3.29). More generally, they found that the exponent (b) will be lower and the intercept (N0) will be higher for tropical stratiform rain. However, Yuter and Houze (1997) showed that if DSDs from a particle-image probe onboard the NOAA P-3 were classified according to the convective-stratiform rain regions observed by the aircraft’s lower-fuselage radar, convective and stratiform rain regions do not show unique small- and large-drop spectra (Figure 3.30). Yuter and Houze (1997) pointed out that stratiform rain has a large distribution of drop spectra, with small drops resulting from the melting of small ice and large drops resulting from fallstreaks. Therefore, they argue that it is not appropriate to apply separate Z-R relations to convective and stratiform reflectivity observations. The radar community remains somewhat split on the usefulness of applying separate Z-R relations for different rain or storm types.
3.10 TEPPS 1997
The Pan American Climate Studies (PACS) Tropical Eastern Pacific Process Study (TEPPS) was a ship-based field campaign to understand why passive microwave and infrared satellite rain retrievals disagree so strongly in the East Pacific (Yuter and Houze 2000). TEPPS took place August 1997 at 7.8(N, 125(W. It was the maiden voyage of the NOAA R/V Ronald H. Brown and a special mast was installed on the ship in order to host a scanning C-band Doppler radar. Results from the cruise show that precipitating systems of shorter duration and/or smaller scale did not always have cloud tops colder than 235 K, which would lead the infrared sensor to erroneously retrieve lower rain amounts in the East Pacific.
The TEPPS cruise also provided the framework for studies concerning the role of large-scale waves on convective organization. Straub and Kiladis (2002) showed that a large-scale Kelvin wave passed over the ship from 18 to 19 August 1997. Using the ship radar data, they illustrated that convective activity increased from shallow cumulus to deep cumulonimbus within a 24 h period (Figure 3.31). More organized convection, including a stratiform component that eventually became dominant over the radar domain, occurred over the next 24 h. These observations suggested that the Kelvin wave envelope is more convectively active on its eastern side with more widespread stratiform rain on its western side. In addition, Serra and Houze (2002) analyzed the importance of easterly waves to convective organization during TEPPS. Variability from 3-6 days was the prominent time scale in meridional wind and humidity data and radar activity was maximum when the southerlies were strongest. A Hoevmoeller diagram of the radar data (Figure 3.32) shows that convective events tended to travel from east to west at a speed of 8.3 m s-1, except for the Kelvin wave case studied by Straub and Kiladis (2002).
3.11 SCSMEX 1998
The South China Sea Monsoon Experiment (SCSMEX) occurred in the South China Sea in May and June 1998 to study the onset and evolution of the Southeast Asia monsoon (Lau et al. 2000). Wang (2004) performed a dual-Doppler analysis of an MCS that occurred on 15 May 1998 during the early onset of the monsoon using the C-Pol and TOGA radars (the former was located on Dongsha Island [20.7(N, 116.7(E], while the latter was onboard the R/V Shiyan). It was atypical of tropical MCSs because of significant interaction with a subtropical frontal passage. Wang and Carey (2005) performed dual-Doppler and dual-polarimetric analysis of another MCS that occurred on 24 May 1998 at the end of the monsoon onset. It was also atypical of tropical MCSs in that it had very high reflectivities in the convective region and very little stratiform rain production, most likely due to dry air aloft. The height and magnitude of the differential reflectivity was also fairly low compared to other studies of tropical MCSs, indicating that supercooled drops and enhanced ice mass associated with frozen drops were only present in the lowest portions of the mixed-phase zone. These radar case studies suggest that convection that occurs during the onset of the South China Sea monsoon has characteristics somewhat different from other tropical oceanic systems.
Johnson et al. (2005) used C-Pol to study the full onset period from 15-25 May 1998. They employed the base scan reflectivity to determine the dominant organizational modes in the vein of Lemone et al.’s (1998) TOGA COARE study. Low- and mid-level vertical shear appeared to control the MCS structures, with both shear-parallel and shear-perpendicular modes being observed. In addition to the modes identified by Lemone et al. (1998), Johnson et al. (2005) observed two new shear-parallel modes likely resulting from the midlatitude influences (i.e., strong westerlies and midtropospheric dryness) in the region. Johnson et al. also found that the stratiform rain fraction was generally small (26%) during the onset period because of weak instability from low SSTs and a relatively dry upper troposphere.
3.12 TRMM-LBA 1999
The Tropical Rainfall Measuring Mission Large-Scale Biosphere–Atmosphere Experiment in Amazonia (TRMM-LBA) occurred in the southwestern Brazilian Amazon (13-9(S, 64-60(W) in January and February 1999. Its purpose was to observe convective processes over a tropical continent and relate them to TRMM satellite retrievals (Silva Dias et al. 2002). Various studies found distinct differences between convective systems that form in easterly and westerly low-level wind regimes as measured in the southwest Amazon using the NASA TOGA C-band radar (Halverson et al. 2002, Laurent et al. 2002, Rickenbach et al. 2002). The local low-level wind changes are related to large-scale circulation changes over South America. During the easterly regime, convective systems were more intense and produced more lightning. During the westerly regime, convective cells were weaker and relatively more stratiform rain was produced.
Cifelli et al. (2002) performed analysis on two MCSs with dual-Doppler and dual-polarimetric radar observations from the TOGA and NCAR S-pol radars. The 26 January squall line formed in low-level easterly flow with an intense leading line, while the 25 February MCS formed in low-level westerly flow with embedded convective cells in a large stratiform precipitation area. In agreement with the above studies, the easterly regime MCS had stronger peak reflectivities and vertical air motions and a more active mixed phase (4-8 km) with more precipitation ice and hail (Figure 3.33). The easterly regime case also exhibited a distinct life cycle in its kinematic and microphysical properties compared to the westerly regime MCS, which did not show much of a life cycle (e.g., see the mass transport time series in Figure 3.34).
The TRMM-LBA radar observations also highlighted diurnal variations in convection over the southwestern Amazon. Machado et al. (2002) showed a peak in radar echo coverage ~15 local time (LT) with a secondary peak of weaker reflectivity ~3 LT. Rickenbach et al. (2002) showed a similar diurnal variability for conditional rain rates, with a stronger afternoon peak seen in the easterly regime. While the afternoon peak occurs near the time of maximum insolation, Rickenbach (2004) demonstrated that the early morning peak results from organized but weaker nocturnal systems. Some of the nocturnal systems propagate in from the east while others are drizzle decks that form locally.
3.13 KWAJEX 1999
The Kwajalein Experiment (KWAJEX) occurred in the Marshall Islands (7-10(N, 166-169(E) from 24 July to 15 September 1999 to study convective processes over the open tropical ocean and how they relate to TRMM satellite retrievals (Yuter et al. 2005). The KWAJEX radar data set is also part of a longer radar data set at Kwajalein for TRMM satellite validation (Schumacher and Houze 2000, Houze et al. 2004, Wolff et al. 2005). During KWAJEX, Sobel et al. (2004) used rain rates calculated from the Kwajalein S-band radar to show that the largest rain events were associated with large-scale envelopes of convection. The major modes were Kelvin and mixed Rossby-gravity waves and tropical depression-type disturbances.
Cetrone and Houze (2006) showed that the Kwajalein convection had many similarities to the convection observed over the East Atlantic during GATE by revisiting the statistics presented in Houze and Cheng (1977). In particular, Cetrone and Houze (2006) found that the KWAJEX echo population had similar (lognormal) distributions of area, height, and lifetimes and the relatively frequent occurrence of echo mergers and splits and shear-parallel and shear-normal lines. However, KWAJEX echo had higher tops and shorter lifetimes and slightly different propagation characteristics likely due to large-scale environmental differences. In addition, KWAJEX echo exhibited a pronounced bimodal distribution in orientation at right angles to each other. Regardless, radar-observed convective characteristics appear fairly similar between tropical ocean basins, albeit with different parameters of the distribution.
3.14 EPIC 2001
The East Pacific Investigation of Climate (EPIC) field campaign took place in September and October 2001 and was organized to document the physical processes important to climate models in the tropical East Pacific. EPIC had two components: one to study convective systems in the warm SST region north of the equator along a north-south transect at 95(W (Raymond et al. 2004) and the other to study the stratocumulus clouds in the cooler SST regions south of the equator (Bretherton et al. 2004). The R/V Ron H. Brown carried its C-band scanning Doppler radar as well as a vertically pointing K-band radar for cloud studies. The stratus component of EPIC was complemented by two DYCOMS-II flights in July 2001 equipped with a W-band cloud radar (Stevens et al. 2003).
Petersen et al. (2003) used the Brown’s C-band Doppler radar observations to study the convective structure in the northeast Pacific ITCZ as a function of 3-5 day easterly wave phase. Their work expanded upon more limited radar studies of easterly waves during GATE and TEPPS (e.g., Thompson et al. 1979 and Serra and Houze 2002). Composite reflectivity fields show more intense convection before the trough and more widespread weaker reflectivity associated with stratiform rain after the trough (Figure 3.35). This transition in rain type and organization is consistent with budget studies of easterly wave properties.
Comstock et al. (2005) used the Brown’s C-band scanning radar as well as the vertically pointing K-band radar to study drizzle properties in the southeast Pacific stratocumulus region. Comstock et al. showed that drizzle occurs when there is increased mesoscale variability in cloud and boundary layer properties and is associated with open-cell satellite cloud patterns (Stevens et al. 2005). Figure 3.36 shows that the drizzle cells observed by the C-band radar can have areas up to 100 km2 of 5 dBZ reflectivity with average reflectivity of up to 15 dBZ (or ~1 mm h-1). The drizzle cells last about 2 hours instead of raining themselves out in a half hour, which suggests that updrafts are providing (possibly recycled) moisture.
Mapes and Lin (2005) presented a new analysis method to calculate wind divergence from large single-Doppler radar data sets using cylindrical gridding and a velocity-azimuth display (VAD) analysis method. One of the main goals was to provide heating profile information for a large array of weather situations. Mapes and Lin used radar observations from a variety of field campaigns (TOGA COARE, TEPPS, SCSMEX, TRMM-LBA, JASMINE, KWAJEX, and EPIC) but highlighted EPIC divergence structures. Figure 3.37 shows a diverse set of divergence profiles based on two days of EPIC radar observations from 23-24 September 2001. While deep convective systems are evident at end of the day on the 23rd and the middle of the day on the 24th, reliable divergence profiles are also evident for less convectively active hours. Figure 3.38 shows lagged divergence per mm h-1 versus surface rain for all of the field campaigns listed above. Most of the field campaigns indicate low-level convergence and/or mid-level divergence at a lag of -10 hours. The low-level convergence strengthens and becomes deeper, eventually becoming elevated with divergence occurring at low levels at a lag of +5 hours. This signature represents the transition from shallow convection to deeper convection to stratiform rain in tropical convective systems.
3.15 RICO 2005
The Rain in Shallow Cumulus over the Ocean (RICO) campaign occurred November 2004-January 2005 near the Caribbean islands of Antigua and Barbuda to study the initiation and effects of precipitation in shallow cumulus (Rauber et al. 2007). Operations were centered around the NCAR SPolKa dual-wavelength and dual-polarization radar with three aircraft and a research ship deployed within the SPolKa domain. X- and W-band radars were present on some of the mobile platforms. Research using the RICO radar data is still ongoing, but initial results can be found in Rauber et al. (2007). For example, echo coverage (composed mostly of shallow convection with tops lower than 700 mb) tended to be less than 2% of the observed area and area-averaged rain rates were less than 3 mm day-1; however, half of the rain observed during the field campaign was associated with two periods of deeper convection. Organization occasionally occurs from outflow boundaries of deeper cells (Figure 3.39).
4. Tropical Cyclones
Another chapter focuses on tropical cyclones (TCs), so this section will emphasize the contribution of radar to our understanding of mature TC structure. Radar images of TCs extend back to the late 1940s after the use of surplus radars from WWII for meteorological purposes began in earnest. Maynard (1945) and Wexler (1947) first described the structure of hurricanes using radar, while Senn and Hiser (1959) and Atlas et al. (1963), among others, highlighted the spiral bands. Figure 4.1 shows an example photograph from Senn and Hiser (1959) of an airborne radar scope of Hurricane Daisy (1958). In 1962, the United States government began Project Stormfury with the goal of weakening TCs by seeding them with silver iodide and disrupting their inner structure. The last modification flight was in 1971. While the modification hypothesis proved incorrect, a large number of radar observations (e.g., see Black et al. 1972) were made that have been used to understand the track and intensification of TCs.
In the mid-1970s, NOAA purchased two WP-3D (P-3) aircraft to study TCs and other vital meteorological and oceanographic phenomena, e.g., the P-3s were also deployed at EMEX and COARE (Aberson et al. 2006). The most advanced meteorological equipment on each aircraft was two digital radars: a C-band lower fuselage radar that measures the horizontal distribution of reflectivity at all azimuth angles and an X-band tail radar that measures reflectivity perpendicular to the aircraft track or at angles fore and aft within 25( of the aircraft heading.
Jorgensen (1984) used P-3 radar observations from four mature hurricanes (Anita 1977, David 1979, Frederic 1979, and Allen 1980) to describe common convective-scale and mesoscale hurricane features. Figure 4.2 shows an example radar cross section from Anita along with coincident wind measurements. In each TC, the eyewall convection tended to slope radially outward with height, suggesting sloped updrafts following lines of constant angular momentum that slope with height because of the baroclinicity in the vortex. Maximum reflectivity in the eyewall occurred several kilometers outward from the radius of maximum winds at low levels, but coincided at middle and upper levels. Maximum updrafts always occurred radially inward from the reflectivity maximum. In addition, a region of stratiform rain was adjacent to the deep convection in the eyewall and rainbands contained both convective and stratiform regions. In general, substantial amounts of stratiform rain were present in each storm outside of the eyewall.
The winds in Jorgenson (1984) were limited to measurements made along the aircraft flight track. Marks and Houze (1984) were the first to perform dual-Doppler analysis using the P-3 X-band tail radar data to provide a three-dimensional description of Hurricane Debby’s (1982) winds. In particular, they described three mesoscale disturbances in the wind field of the developing inner core, highlighting asymmetries in Debby’s structure.
Marks and Houze (1987) extended Marks and Houze’s (1984) work by studying an already mature hurricane, Alicia (1983), with the P-3 tail radar data and analyzing the vertical as well as horizontal winds. Marks and Houze (1987) showed that the inner-core structure compiled from flight-level data of many storms is present in an individual hurricane. They also described circulation features that are difficult to measure in situ (especially upper-level outflow from the eyewall and lower-level convective downdrafts) and detailed precipitation growth processes in the inner-core region. The upper panel in Figure 4.3 is a schematic illustrating the circulation in Alicia’s inner core in relation to regions of enhanced reflectivity in the eyewall and bright band. The primary circulation (dashed lines) has a core of maximum tangential wind that slopes outward with height with peak winds between 1.5 and 2.5 km. The secondary circulation (hatched arrows) exhibits a layer of lower tropospheric inflow with a maximum between 2 and 4.5 km and intense outflow from the eyewall at upper levels between 10 and 14 km. The radial flow also slopes out from the center in agreement with theoretical studies. Convective updrafts enhance the secondary circulation, especially above 6 km, while convective downdrafts (solid arrows) are coincident with the strongest reflectivity and interrupt the secondary circulation. Mesoscale up- and downdrafts (broad arrows), similar to previous studies of tropical squall lines, were also evident. The Doppler wind analysis also allowed the construction of hydrometeor trajectories in the inner core of Alica (Figure 4.3, lower panel). Large hydrometeors forming in the upward branch of the secondary circulation fell to the surface within 10 min and formed the reflectivity maximum in the eyewall. Small hydrometeors were swept up to the upper level outflow layer and took 1-2 h to fall to the surface. During that time, the small hydrometeors seeded the stratiform rain region and circulated around the storm up to one and a half times.
Black et al. (1996) examined vertical motions from seven Atlantic TCs using P-3 Doppler velocities from vertically pointing radar rays and bulk estimates of particle fall speeds, similar to the methodology in Marks and Houze (1987). The majority of vertical motions ranged from -2 to 2 m s-1. These values are generally weaker than most continental convection, likely due in part to water loading. In the eyewall, ~5% of the updrafts were greater than 5 m s-1 with the largest values in the upper troposphere. When considering only up and downdrafts with absolute values > 3 m s-1, updrafts occurred at least two times as often as downdrafts (Figure 4.4). Updrafts were also wider, although most updrafts were < 3 km wide and only 5% were wider than 6 km. These vertical velocity statistics were consistent with flight-level data in previous studies, but painted a more complete picture by using radar Doppler measurements.
Reasor et al. (2000) employed dual-Doppler P-3 observations to study the evolution of Hurricane Olivia’s (1994) inner core. Reasor et al. related the weakening of the primary circulation to axisymmetric vortex spindown assisted by vertical shear that caused an azimuthal wavenumber 1 pattern in convection. The asymmetric vorticity dynamics were reflected in wavenumber 2 features at low levels (Figure 4.5). Reasor et al. (2000) also presented the first evidence of vortex Rossby waves outside of the eyewall. This study was an excellent observational complement to previous theoretical and numerical work concerning the role of vortex evolution in TC intensity changes.
A TC can also weaken when an outer convective ring forms around a preexisting eyewall and then contracts, strangling the original eyewall. Willoughby et al. (1982) used P-3 reflectivity observations to help describe the evolution of concentric eyewalls and Figure 4.6 shows a concentric eyewall observed by a P-3 radar in Hurricane David (1979). A TC will strengthen again if the remaining convective ring intensifies. Concentric eyewalls most frequently occur in intense, symmetric TCs.
Dodge et al. (1999) used P-3 Doppler observations to document the kinematics of concentric eyewalls. They particularly focused on Gilbert (1988), which was the first TC with a pressure < 900 mb to be observed by Doppler radar. In Gilbert’s inner eyewall, vertical profiles of tangential wind and reflectivity were more erect than in weaker storms and winds > 50 m s-1 extended ~3 km deeper than previously reported for other hurricanes (Figure 4.7). While the inner eyewall had weak inflow through most of the depth of the storm, the outer eyewall had shallow inflow and deep outflow. Weak stratiform rain existed between the two eyewalls (Figure 4.8) with downward motion generally observed beneath the melting level. In addition, the outer eyewall consisted almost half of stratiform rain.
While many studies of TCs focus on the eyewall region, most TCs have spiral bands of convection that can span hundreds of kilometers and potentially play a major role in the modulation of hurricane intensity (e.g., by preventing subcloud air from reaching the eyewall). The two NOAA P-3s were used in a set of experiments in 1981 to study rainband thermodynamics, kinematics, and structure. Barnes et al. (1983) highlights results found during flights through Hurricane Floyd. Floyd’s rainbands represented a partial mesoscale barrier to inflow, decreasing radial velocities by 6 m s-1 in the subcloud layer (Figure 4.9). Much lower (e was also found in the lowest kilometer on the exit side of the rainband. The upwind side of the band was mainly convective and became more stratiform toward the downwind side.
The P-3 radars were not Doppler capable during Floyd and many questions remained concerning the effect rainbands have on internal vortex structure and intensity. A more recent field campaign, the Hurricane Rainband and Intensity Change Experiment (RAINEX), took place in 2005 (a record-breaking TC season in the Atlantic) to better describe the dynamical interactions between the eyewall and rainbands (Houze et al. 2006). It was the first field experiment to use three dual-Doppler airborne systems, including the high resolution ELDORA, to observe hurricanes. Other recent field campaigns, namely the Convection and Moisture Experiment (CAMEX) 3 (1998), CAMEX 4 (2001), and the Tropical Cloud Systems and Processes (TCSP) mission (2005) used airborne radar systems to study tropical cyclogenesis in the Atlantic and East Pacific basins (Kakar et al. 2006, Halverson et al. 2007). The datasets from these field campaigns are still in the process of being analyzed. As a final note, Frank Marks’s (2003) chapter, “State of the science: Radar view of tropical cyclones”, in Radar and atmospheric science: A collection of essays in honor of David Atlas is a useful resource for further reading.
5. Satellite Radars
5.1 TRMM Precipitation Radar 1997-present
The TRMM satellite, a joint mission between NASA and NASDA (now JAXA, Japan’s space agency), was launched November 1997 and has been in orbit over 10 years. The Precipitation Radar (PR) was the first quantitative weather radar placed in space and is one of the main precipitation instruments on the TRMM satellite (Kummerow et al. 1998, Kozu et al. 2001). The PR scans 17( to either side of nadir and has a vertical resolution of 250 m at nadir. Before (after) the boost on 7 August 2001, the TRMM satellite had an operating altitude of 350 km (400 km) giving the PR a swath width of 215 km (240 km) and a horizontal footprint of 4.3 km (5 km) at nadir. The PR operates at Ku band (2.17-cm wavelength) and is thus subject to strong attenuation. Owing to power constraints, the PR has a sensitivity of ~17 dBZ (18 dBZ) pre (post) boost. This sensitivity threshold equates to a rain rate ~0.5 mm h-1. The TRMM 2A25 algorithm provides attenuation-corrected reflectivities and calculates rain rates using the PR observations (Iguchi et al. 2000). The TRMM 2A23 algorithm separates PR-observed rain into convective and stratiform components using the horizontal method of Steiner et al. (1995) and vertical structure information, including the presence of a bright band (Awaka et al. 1997). At the time of writing, a search on “TRMM and radar” in the Web of Science brings up almost 500 papers, speaking to the huge success of the TRMM PR program. The following section will only be able to cover a handful of these studies and will focus on physical processes that the PR has highlighted.
Numerous field campaigns starting with GATE have shown that significant amounts of rain in tropical convective systems come from stratiform rain. However, the radars and convective-stratiform separation methods vary from study to study. The launch of the TRMM PR has allowed the analysis of radar observations across the tropics using the same radar and radar retrievals. Schumacher and Houze (2003a) took advantage of this capability to study the tropics-wide distribution of stratiform rain. They found that stratiform rain accounts for 40% of the total rain and 73% of the total rain area observable by the PR (i.e., reflectivities > 17 dBZ), very similar to the values in previous ground-based radar studies. However, higher stratiform rain fractions are seen over ocean versus land, with a maximum of ~60% in the central east Pacific and a minimum of ~20% over the Congo (Figure 5.1). Schumacher and Houze (2003a) postulated that this occurs because ocean environments provide a warm, moist boundary layer with only a weak diurnal variation and/or more uniform buoyancy throughout the troposphere, which help sustain a more continual source of convective cells needed to promote stratiform rain production. Higher stratiform rain fractions occur over land during monsoons and during seasons with larger occurrences of very large mesoscale convective systems.
Using the TRMM PR, Lin et al. (2004) showed that stratiform precipitation contributes more to intraseasonal rainfall variation associated with the MJO (60%) than it does to seasonal-mean rainfall (40%). Stratiform rain heats the upper troposphere and cools the lower troposphere, making the MJO heating profile top heavy, in agreement with TOGA COARE sounding budget studies. However, models consistently do not recreate the elevated heating seen in the observations.
Tao et al. (2001) discusses how to use TRMM satellite observations to estimate the four-dimensional latent heating across the tropics. They show heating estimates from three methods – the Goddard profiling (GPROF), the hydrometeor heating (HH), and the Goddard Space Flight Center convective-stratiform heating (CSH) algorithms – but only the last uses radar information. The CSH algorithm is similar in concept to Houze (1982) in that model profiles are applied to convective and stratiform rain amounts observed by the TRMM PR. The model profile look-up table is composed of an assortment of cloud-resolving model runs. The tropics-wide heating shows low-level cooling over the Pacific and Indian Oceans and low-level heating over the continents. A single, broad maximum heating level is present in all geographical regions, with the highest maximum heating levels of ~8 km over the ocean. Maximum estimated heating rates are on the order of 4-6 K day-1. Shige et al. (2004) introduced the spectral latent heating (SLH) algorithm as a refinement of the CSH algorithm by utilizing information on echo-top height and the precipitation rate at the melting level. Thus, the SLH algorithm can differentiate between shallow and deep convection, as well as including heating profiles from echo that does not reach the surface.
Schumacher et al. (2004) used a technique similar to the CSH algorithm to estimate the tropics-wide latent heating based on TRMM PR observations of shallow convective, deep convective, and stratiform rain. The idealized latent heating profiles for each rain type are shown in Figure 5.2 along with examples of total latent heating profiles for 300 mm mo-1 rain accumulation and sample percentages of each rain type. The geographical variability of latent heating vertical structure observed by the TRMM PR is shown in Figure 5.3 and were used to force an idealized general circulation model (GCM) to obtain the quasi-steady-state atmospheric response to the estimated heating. The resultant circulation anomalies varied in height and vertical extent as opposed to when the model was forced with a vertically uniform heating field. These variations were especially pronounced during the 1998 El Niño, when the trans-Pacific gradient in stratiform rain fraction strengthened. These results stress the importance of accurately representing stratiform rain processes in GCMs.
Short and Nakamura (2000) examined shallow precipitation (i.e., echo tops < 3 km) observed by the TRMM PR across the tropics. While there wasn’t a significant peak in shallow rain over land, they found a strong shallow peak over the tropical ocean. Short and Nakamura (2000) demonstrated that shallow rain accounts for 20% of the total precipitation over ocean and is the dominant mode over the stratocumulus and trade wind regions (Figure 5.4). However, significant counts of shallow precipitation are also found in the ITCZ and South Pacific convergence zone, although shallow rain only accounts for ~5% of the total rain in these regions. Short and Nakamura (2000) also found it puzzling that the TRMM PR V5 2A23 algorithm classified more than half of the shallow precipitation as stratiform, even in regions where deep convection was rare. Schumacher and Houze (2003b) examined the rain types in V5 of 2A23 and showed that much of the shallow rain is isolated from other echo and as such should be considered convective.
Petersen and Rutledge (2001) used observations from the TRMM PR and Lightning Imaging Sensor (LIS) to study the regional variability of tropical precipitation vertical structure, with the largest variability being found above the 0(C level. Figure 5.5 shows that continental locations such as Florida and the Congo tend to have a higher frequency of reflectivity > 30 dBZ at upper levels, while ocean locations have the lowest frequency. Regions affected by the monsoon fall somewhere in the middle. Similar conclusions were drawn about precipitation ice water content between 7-9 km and lightning flash density, with larger values of these variables being associated with the largest rain rates stressing the importance of mixed-phase processes in precipitation production over land. However, ocean systems tend to have relatively more reflectivity > 30 dBZ below the 0(C level stressing the importance of warm rain processes over the ocean. Petersen et al. (2002) used a similar set of analysis tools to focus on the TMRM-LBA easterly and westerly wind regimes over the southwestern Amazon. They found that the results of TRMM-LBA (i.e., that convective systems take on more continental or oceanic characteristics depending on the 850-700 mb winds) were true for multiple years and over a large region of the Amazon and central and southern South America.
Nesbitt et al. (2000) melded information from the TRMM PR and TRMM Microwave Imager (TMI) to identify precipitation features (i.e., storm systems > 75 km2 in size) over select land and ocean regions of the tropics. The precipitation features (or PFs) include systems without ice scattering (i.e., PR-observed rain but no 85.5-GHz polarization corrected temperatures [PCT] < 250 K), systems with ice scattering (i.e., PR-observed rain with at least one data bin with PCT < 250 K but not meeting the criteria of an MCS), and MCSs (i.e., an area > ~2000 km2 with PCT < 250 K and ~185 km2 with PCT < 225 K within the rain area of the system). It is a useful technique to be able to compare statistics of individual storm systems. They showed that features over land were more intense than similar features over the ocean in terms of the maximum height of the 30-dBZ echo and 6-km reflectivities. In addition, significantly more of the rain over the ocean came from systems with weak ice-scattering signatures or from rain outside of the 250-K PCT isotherm. This algorithm and database has been used extensively in studying convection in the tropics.
One such study is Toracinta et al. (2002), which analyzed PFs over the east and west Pacific, South America, and Africa. They focused on the small fraction of features that produce the majority of rain. For a given 20-dBZ echo-top height, the 30- and 40-dBZ echo heights were several kilometers higher in the continental features (Figure 5.6). The weaker reflectivity profiles in the oceanic features are consistent with studies that have shown weaker vertical velocities in ocean systems and thus less ability to supply supercooled water to the mixed phase region of the cloud. In addition, one-third of ocean features do not have reflectivities > 40 dBZ. While lightning is much more common over land, when normalized by radar echo-top heights, continental features remain much more likely to produce lightning suggesting more efficient electrification processes.
Nesbitt and Zipser (2003) used the PF database to study the diurnal cycle of tropical precipitation. The inclusion of the TRMM PR in the database allows more direct observations of the timing of surface precipitation, since previous studies have looked at the diurnal cycle using passive sensors, which only indirectly measure surface rain. Nesbitt and Zipser (2003) found that the diurnal cycle of rainfall over ocean is small, but that the observed early morning maximum occurs because of an increase in the number of MCSs rather than an increase in system coverage or conditional rain rate (Figure 5.7). The diurnal cycle of rainfall over land is much larger, with the afternoon maximum occurring because of an increase in convective intensities and the number of non-MCS features (Figure 5.8). The diurnal cycle of rainfall associated with MCSs over land varies significantly because of their sensitivity to environmental conditions besides solar insolation.
Liu and Zipser (2005) used the PF database to determine the distribution of overshooting convective tops into the tropical upper troposphere/lower stratosphere. They employed five reference heights (i.e., 14 km, the NCEP reanalysis tropopause, the level where (=380 K, and two measures of the level of neutral buoyancy) and recorded when PFs contained 20-dBZ echo above the reference height. One percent of deep convection reached 14 km and 0.1% of deep convection reached the 380 K level. By all measures, overshooting PFs were most common over land, especially over central Africa (Figure 5.9).
Overshooting PFs are naturally associated with strong updrafts. Zipser et al. (2006) extended the overshooting convection results to examine the PF database for the most intense thunderstorms in the tropics. Using a suite of metrics, including the maximum height of the 40-dBZ echo contour (Figure 5.10), convection over land was shown to be much stronger than convection over ocean. However, convection did not tend to be as intense over regions with high annual rainfall and the most intense systems showed more distinct diurnal and seasonal variations than less intense systems. The most intense thunderstorm observed in the database occurred on 30 December 1997 over Argentina. Reflectivity values of 40 dBZ were observed at 19.5 km.
As mentioned in the beginning of the section, there has been an extraordinary amount of science published using the TRMM PR and it would be impossible to cover it all in detail. For example, PR observations have also been used to study vertical variations in rainfall profiles (Liu and Fu 2001, Takayabu 2002, Hirose and Nakamura 2004) and orographic precipitation processes (Barros et al. 2000, Houze et al. 2007) as well as to perform a host of surface precipitation and satellite validation exercises. The reader is urged to explore TRMM PR results for topics that were not covered above.
5.2 CloudSat 2006-present
More recent advances have been made using mm-wavelength radars to study cloud properties not observable by cm-wavelength radars. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program has been involved in ground-based cloud radar measurements in the tropical West Pacific since 1999 with a focus on understanding cirrus properties (e.g., Comstock et al. 2002). On 28 April 2006 NASA launched CloudSat, which carries the Cloud Profiling Radar (CPR), a 94 GHz nadir-pointing active microwave sensor with a 2.5 km footprint (Mace et al. 2007). The vertical resolution of the CPR is 480 m, but the backscattered signal is oversampled to provide 240 m range gate spacing. The minimum detectable signal is approximately -32 dBZ, which is sensitive enough to see most clouds except for some fraction of thin cirrus, mid-level liquid water clouds, and non-drizzling stratocumulus.
Haynes and Stephens (2007) focused on CloudSat observations over the tropical oceans during June-August 2006. Clouds with tops at 2 and 12 km predominate, with a weaker maximum of clouds at midlevels between 5 and 8 km. The midlevel maximum becomes relatively more important when only precipitating clouds are considered, which supports Johnson et al.’s (1999) argument of the importance of a trimodal population of tropical oceanic convection. Clouds tend to be deeper over the Indian and West Pacific and are 1-2 km deeper if the clouds are precipitating. CloudSat observed clouds to cover almost 30% of the tropical oceans, with 18% the observed clouds to be raining (Figure 3.11). More in depth studies of tropical clouds with CloudSat are underway and should provide interesting insight to complement previous studies done with precipitation radars.
6. Conclusion
While radar observations have contributed tremendously to our understanding of tropical convection, how it organizes, and the manner in which it interacts with the large-scale tropical circulation, more tropical field campaigns and longer satellite radar datasets will help improve this understanding. New technologies will also play a role in our improved scientific understanding. For example, polarimetric and multi-Doppler observations continue to become more feasible and discussion is underway to place a Doppler radar in space. Radar will also continue to be an important tool in quantifying how much rain falls in the tropics.
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Biographical Sketch
Courtney Schumacher received her Ph.D. in atmospheric sciences from the University of Washington, Seattle, Washington, USA in spring 2003 and she joined the Department of Atmospheric Sciences at Texas A&M University, College Station, Texas, USA as an Assistant Professor in fall 2003. She has taken part in multiple tropical field campaigns and has been an active member of the TRMM science team since the launch of the satellite in 1997. Dr. Schumacher is also a member of the American Meteorological Society and the American Geophysical Union.
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Figure 2.1 An example volume scan strategy.
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Figure 3.1 Adapted from Webster and Houze (1991).
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Figure 3.2 Adapted from Lopez (1976).
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Figure 3.3 Adapted from Betts (1973).
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Figure 3.4 Adapted from Houze and Cheng (1977).
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Figure 3.5 Adapted from Leary and Houze (1979b).
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Figure 3.6 Adapted from Leary and Houze (1979a).
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Figure 3.7 Adapted from Leary and Houze (1979b).
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Figure 3.8 Adapted form Murty et al. (1965).
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Figure 3.9 Adapted from Gamache and Houze (1982).
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Figure 3.10 Adapted from Gamache and Houze (1983).
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Figure 3.11 Adapted from Chong et al. (1987).
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Figure 3.12 Adapted from Roux et al. (1984).
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Figure 3.13 Adapted from Chong et al. (1987).
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Figure 3.14 Adapted from Chong et al. (1987).
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Figure 3.15 Adapted from Houze et al. (1981).
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Figure 3.16 Adapted from Churchill and Houze (1984).
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Figure 3.17 Adapted from Houze (1982).
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Figure 3.18 Adapted from Mapes and Houze (1993).
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Figure 3.19 Adapted from Keenan et al. (1989).
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Figure 3.20 Adapted from Williams et al. (1982).
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Figure 3.21 Adapted from Zipser and Lutz (1994).
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Figure 3.22 Adapted from Steiner et al. (1995).
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Figure 3.23 Adapted from Carey and Rutledge (2000).
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Figure 3.24 Adapted from DeMott and Rutledge (1998).
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Figure 3.25 Adapted from Johnson et al. (1999).
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Figure 3.26 Adapted from Lemone et al. (1998).
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Figure 3.27 Adapted from Jorgenson et al. (1997).
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Figure 3.28 Adapted from Houze et al. (2000).
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Figure 3.29 Adapted from Tokay and Short (1996).
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Figure 3.30 Adapted from Yuter and Houze (1997).
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Figure 3.31 Adapted from Straub and Kiladis (2002).
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Figure 3.32 Adapted from Serra and Houze (2002).
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Figure 3.33 Adapted from Cifelli et al. (2002).
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Figure 3.34 Adapted from Cifelli et al. (2002).
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Figure 3.35 Adapted from Petersen et al. (2003).
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Figure 3.36 Adapted from Comstock et al. (2005).
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Figure 3.37 Adapted from Mapes and Lin (2005).
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Figure 3.38 Adapted from Mapes and Lin (2005).
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Figure 3.39 Adapted from Rauber et al. (2007).
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Figure 4.1 Adapted from Senn and Hiser (1959).
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Figure 4.2 Adapted from Jorgensen (1984).
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Figure 4.3 Adapted from Marks and Houze (1987).
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Figure 4.4 Adapted from Black et al. (1996).
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Figure 4.5 Adapted from Reasor et al. (2000).
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Figure 4.6 Adapted from Willoughby et al. (1982).
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Figure 4.7 Adapted from Dodge et al. (1999).
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Figure 4.8 Adapted from Dodge et al. (1999).
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Figure 4.9 Adapted from Barnes et al. (1983).
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Figure 5.1 Adapted from Schumacher and Houze (2003a).
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Figure 5.2 Adapted from Schumacher et al. (2004).
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Figure 5.3 Adapted from Schumacher et al. (2004).
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Figure 5.4 Adapted from Short and Nakamura (2000).
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Figure 5.5 Adapted from Petersen and Rutledge (2001).
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Figure 5.6 Adapted from Toracinta et al. (2002).
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Figure 5.7 Adapted from Nesbitt et al. (2003).
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Figure 5.8 Adapted from Nesbitt et al. (2003).
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Figure 5.9 Adapted from Liu and Zipser (2005).
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Figure 5.10 Adapted from Zipser et al. (2006).
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Figure 5.11 Adapted from Haynes and Stephens (2007).
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