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An Ensemble Study of Wet Season Convection in Southwest Amazonia: Kinematics and
Implications for Diabatic Heating
ROBERT CIFELLI
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
LAWRENCE CAREY*
Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina
WALTER A. PETERSEN
ESSC/NSSTC, University of Alabama in Huntsville, Huntsville, Alabama
STEVEN A. RUTLEDGE
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
(Manuscript received 3 November 2003, in final form 30 April 2004)
ABSTRACT
Dual-Doppler radar data from the Tropical Rainfall Measuring Mission Large Scale Biosphere–Atmosphere
Experiment in Amazonia (TRMM-LBA) field campaign are used to determine characteristic kinematic and
reflectivity vertical structures associated with precipitation features observed during the wet season in the southwest region of Amazonia. Case studies of precipitating systems during TRMM-LBA as well as overarching
satellite studies have shown large differences in convective intensity associated with changes that develop in
low-level easterly flow [east regime (ER)] and westerly flow [west regime (WR)]. This study attempts to examine
the vertical kinematic and heating structure of convection across the spectrum of precipitation features that
occurred in each regime.
Results show that convection in the ER is characterized by more intense updrafts and larger radar reflectivities
above the melting level, in agreement with results from lightning detection networks. These regime differences
are consistent with contrasts in composite thermal buoyancy between the regimes: above the boundary layer,
the environment in the ER is characterized by a greater virtual temperature excess for near-surface rising parcels.
Both regimes showed a peak in intensity during the late afternoon hours, as evidenced by radar reflectivity and
kinematic characteristics, consistent with previous studies of rainfall and lightning in the Rondônia (TRMMLBA) region. After sunset, however, convective intensity in the WR decreases much more abruptly compared
to the ER. In the stratiform–weak convective region, the ER showed both reflectivity and kinematic characteristics
of classic stratiform structure after sunset through the early morning hours, consistent with the life cycle of
mesoscale conjective systems (MCSs). Apparent heating (Q1 ) profiles were constructed for each regime assuming
the vertical advection of dry static energy was the dominant forcing term. The resulting profiles show a peak
centered near 8 km in the convective regions of both regimes, although the ER has a broader maximum compared
to the WR. The breadth of the ER diabatic heating peak is consistent with the more dominant role of ice processes
in ER convection.
1. Introduction
The majority of rainfall on earth occurs within the
tropical regions defined roughly by the latitude belts of
* Current affiliation: Department of Atmospheric Sciences, Texas
A&M University, College Station, Texas.
Corresponding author address: Dr. Robert Cifelli, Department of
Atmospheric Science, Colorado State University, Fort Collins, CO
80523-1371.
E-mail: rob@atmos.colostate.edu
q 2004 American Meteorological Society
308N and 308S. This rain is accompanied by diabatic
processes and associated mass fluxes, which influence
the surrounding environment through compensating vertical air motions (Yanai et al. 1973). The distribution of
this diabatic heating within tropical precipitating cloud
systems plays an important role in driving quasi-stationary large-scale circulation features such as the Walker and Hadley cells as well as nonstationary features
such as the Madden–Jullian oscillation (Hartmann et al.
1984; DeMaria 1985; Lau and Peng 1987).
A number of previous studies have shown that the
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CIFELLI ET AL.
vertical distribution of diabatic heating, reflecting to
large degree the convective and stratiform vertical motions within precipitation systems, can show considerable temporal and spatial variability (Houze 1989; Lin
and Johnson 1996; Cifelli and Rutledge 1998; and others). Because general circulation models (GCMs) and
numerical weather forecast models cannot explicitly resolve the diabatic processes resulting from cloud and
mesoscale precipitating systems [e.g., mesoscale convective systems (MCSs)], these processes must be parameterized. The Tropical Rainfall Measuring Mission
(TRMM) was conceived in part to obtain a more accurate depiction of the four-dimensional distribution of
diabatic (especially latent) heating across the global
Tropics in order to improve modeling of the tropical
general circulation (Simpson et al. 1988).
As part of TRMM, a number of field programs were
conducted at various tropical locations in order to provide observational validation for numerical models and
satellite retrieval algorithms. One of those field programs, the TRMM Large Scale Biosphere–Atmosphere
Experiment in Amazonia (TRMM-LBA), was carried
out over a tropical continental location in the southwest
region of Amazonia. TRMM-LBA included a number
of ground-based and airborne platforms to study the
characteristics of convection during the Amazon wet
season (Silvia Dias et al. 2002).
Several studies (Halverson et al. 2002; Petersen et al.
2002; Rickenbach et al. 2002) have documented the
occurrence of two primary meteorological regimes in
this region of the Amazon based on the direction of the
low-level zonal wind field which, in turn, is forced by
changes in the large-scale flow patterns over South
America and the position of the South Atlantic convergence zone. During periods of easterly flow (i.e., east
regime, hereafter ER), convection typically occurs in
environments of higher convective available potential
energy (CAPE) and larger vertical wind shear, and is
more vertically developed compared to convection occurring in periods of westerly flow (i.e., west regime,
hereafter WR). Moreover, ER convection is highly electrified compared to the WR (Petersen et al. 2002; Williams et al. 2002), suggesting important differences in
their vertical air motion/vertical hydrometeor profiles,
and thus respective latent heating profiles. Petersen et
al. (2002) extended the study of the Amazon wet season
convection over a 3-yr period using TRMM precipitation radar (PR), microwave imager (TMI), and lightning
imaging sensor (LIS) data and found persistent changes
in precipitation system vertical structure associated with
bimodal changes in the aforementioned low-level wind
field.
Selected case studies from TRMM-LBA have been
conducted to quantify the kinematic and microphysical
differences of east and west convection (e.g., Cifelli et
al. 2002; Williams and Rutledge 2003, manuscript submitted to Mon. Wea. Rev.); however, these studies were
limited in scope (contrasting one event in the ER with
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one event in the WR) and did not address the issue of
whether systematic differences were found across the
entire dataset sampled during TRMM-LBA.
The goal of this study is to examine the spectrum of
convection sampled in this region during TRMM-LBA
and quantify differences in kinematic and inferred diabatic heating structure across the ensemble of precipitation features that were sampled during the field program. Dual-Doppler radar syntheses are used to derive
kinematic characteristics of systems that were sampled
during January and February 1999 near Rondônia, Brazil. In a forthcoming paper, dual-polarization data from
the National Center for Atmospheric Research (NCAR)
S-band dual-polarized (S-POL) radar are utilized to describe the microphysical aspects of TRMM-LBA convection. Together, these studies will provide the most
complete picture of the kinematic and microphysical
characteristics of wet season precipitation features in
southwest Amazonia to date.
2. Data and methodology
Data for this study are derived primarily from the SPOL and Tropical Ocean Global Atmosphere (TOGA)
radars used during TRMM-LBA. A description of the
radar locations and salient characteristics are shown in
Cifelli et al. (2002). Briefly, the National Aeronautics
and Space Administration (NASA) TOGA radar operates at C band and collects reflectivity, radial velocity,
and spectral width information. The NCAR S-POL radar
operates at S band and has dual-polarization capability.
In addition to the parameters listed above for TOGA,
S-POL also collected additional polarimetric variables
including: differential reflectivity (ZDR ); linear depolarization ratio (LDR); total differential phase (CDP ); and
zero lag correlation between copolar horizontal and vertical polarized electromagnetic waves (rHV ). Both the
TOGA and S-POL platforms collected data to a distance
of 150 km of each radar site. The relative locations of
the radars are shown in Fig. 1.
a. Kinematic synthesis procedure
Figure 2 provides an overview of the analysis procedure that was used to derive 3D kinematic fields using
the TOGA and S-POL radar data. Most of the procedures
outlined in Fig. 2 are identical to those utilized in Cifelli
et al. (2002), with two exceptions. For this study, an
automated algorithm was utilized to unfold radial velocity
measurements outside the radar Nyquist velocity range
(13 m s 21 for TOGA and 21 m s 21 for S-POL) following
a procedure described in Miller et al. (1986). Specifically,
radial velocities were ‘‘locally’’ unfolded during the interpolation to Cartesian space using the NCAR REORDER software package (Mohr et al. 1986). A ‘‘global’’
unfolding procedure was then employed using the NCAR
Custom Editing and Display of Reduced Information in
Cartesian Space (CEDRIC) software program (Mohr and
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FIG. 1. Location of selected TRMM-LBA instrumentation used in
this study. RGN refers to rain gauge network.
Miller 1983). Comparisons of over 30 radar volumes
unfolded and synthesized using the preceding procedure
with the same volumes unfolded via manual techniques
showed that, in some cases, echo regions associated with
large horizontal radial velocity gradients were not unfolded properly using the automated technique. However,
over the domain of the dual-Doppler network (Fig. 1)
the difference in the synthesis results (as expressed in
grid average vertical air motion) using manual and automated unfolding techniques were within 20% at all
heights in the grid domain (not shown), indicating that,
in a statistical sense, the automated unfolding algorithm
could identify the salient kinematic characteristics of precipitation features sampled in the TRMM-LBA region.
Because of the large number of radar volumes to be
processed, the automated technique was adopted for this
study. The other procedural difference between this study
and Cifelli et al. (2002) is that the grid domain used for
dual-Doppler synthesis in this study is larger (40 000 km 2
vs ; 16 000 km 2 ) and has a coarser horizontal resolution
(2 km versus 1 km)—see Fig. 1. The larger domain was
utilized in order to maximize statistics of convection and
to more closely match the domain used in the microphysical analysis, which will be presented in a subsequent
paper. As in Cifelli et al. (2002), the vertical resolution
of the grid domain is 0.5 km.
During TRMM-LBA, the ground radars had a number
of data collection responsibilities [see Silva Dias et al.
(2002) for an overview of TRMM-LBA goals] and dualDoppler scanning could not be performed 24 h day 21
over the course of the field program. Therefore, the first
major task for this study was to identify which radar
volumes were suitable for dual-Doppler processing and
to apply objective quality control (QC) procedures for
the resulting syntheses.
For a pair of TOGA and S-POL radar files to be
combined in dual-Doppler synthesis, it was required that
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the radar scan volume be a plan position indicator (PPI)
scan with the maximum elevation angle of at least 108
(corresponding to a height of 15 km at a range of 85
km) and that the start times of the two radar volumes
be within 3 min of each other. Once all the candidate
radar volumes were processed and the syntheses were
complete, a program was utilized to compute 3D histograms [e.g., Contour Frequency by Altitude Diagrams
(CFADs); Yuter and Houze 1995] of radar reflectivity
and vertical air motion for each synthesis volume. The
CFADs were used as a check to ensure that the synthesis
results were reasonable. Specifically, the CFAD had to
show a continuous distribution in height and magnitude
for both radar reflectivity and vertical air motion for the
synthesis volume to be tagged for further processing.
After the ‘‘bad’’ synthesis volumes were eliminated, a
total of 2250 ‘‘good’’ synthesis volumes remained for
subsequent analysis, representing some 260 h of radar
observations.
The resulting dual-Doppler synthesis volumes were
then sorted according to their respective regimes (ER
versus WR) based on definitions of Rickenbach et al.
(2002) and Cifelli et al. (2002). Note that data from one
day on either side of the transition from west to east
regime and vice versa (i.e., transition periods) are not
used in the analysis in order to emphasize the representative statistics from each regime. Figures 3a,b show
the number of synthesis volumes (each representing an
approximate 8-min period) constructed for each day of
TRMM-LBA and the number of synthesis volumes in
each regime as a function of the diurnal cycle, respectively. Dual-Doppler radar sampling began on 18 January and continued for a portion of each day throughout
the remainder of the field program, which ended on 28
February (Fig. 3a). With the exception of 2 days in
January (one of which was a transition day and was not
used for analysis), there were at least 20 reliable dualDoppler syntheses conducted from each day of the field
program after 17 January. Figure 3b shows that all hours
of the day are represented in the dual-Doppler analysis;
however, the number of synthesis volumes increases in
the afternoon hours, especially for the ER. The increase
was due primarily to the pronounced diurnal cycle of
convection favoring afternoon and evening precipitation
systems. In fact, the trends shown in Fig. 3b closely
match trends in conditional rain rate calculated for each
regime (Rickenbach et al. 2002).
The last step in the analysis was to compile statistics
of kinematic features (zonal, meridional, and vertical
wind, horizontal divergence, and vertical mass flux) and
radar reflectivity. At this point, a final quality control
check was implemented to ensure that each grid column
in the remaining synthesis volumes likely topped the
radar echo and obtained a representative sample of upper-level divergence (LeMone and Jorgensen 1991;
Mapes and Houze 1995; Cifelli et al. 1996). Specifically,
it was required that the top of each grid column have
a reflectivity less than 10 dBZ for any of the data in the
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FIG. 2. Flowchart illustrating the procedure to derive the principal statistics used in this study.
column to be included in the statistical analysis. The
resulting number of grid points available for statistical
analysis are shown in Fig. 4.
b. Errors in retrieved vertical air motion
Aside from radial velocity unfolding issues, errors in
the resulting vertical air motion field arise due to errors
in beam geometry, uncertainty in the radial velocity
estimates, and the integration procedure itself (e.g., Ray
et al. 1980; Nelson and Brown 1987; Matejka et al.
1997). As noted in Matejka et al. (1997), a particularly
large source of error is the boundary conditions that are
applied in the integration procedure. In this study, we
attempted to quantify systematic bias in the vertical air
motion results by examining the sensitivity to the upper
boundary condition. Following Cifelli et al. (2002), we
set the upper boundary condition (i.e., location where
vertical air motion is assumed to be zero) in each grid
column to be one-half vertical grid space above the
highest measured divergence in that grid column (i.e.,
0.25 km above highest divergence value). We also employed a downward integration procedure as opposed
to variational adjustment (O’Brien 1970), since over the
entire domain, a reliable bottom boundary condition
could not be established with any degree of confidence.
Following Nelson and Brown (1987), we examined the
cumulative errors in the downward vertical air motion
estimate by artificially extending the grid domain downward to the ground level and assuming horizontal divergence is constant between the lowest grid level (0.5
km) and the ground surface. As noted in Nelson and
Brown (1987), the resulting difference between the integrated value of vertical air motion derived at the
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FIG. 3. (a) Time series of number of radar synthesis volumes utilized. Here, E, T, and W refer to east, transition, and west precipitation
regimes. (b) Time series of the number of synthesis files as a function
of the diurnal cycle. Solid and dashed lines refer to the east and west
precipitation regimes, respectively.
ground surface and zero likely reflects a number of possible error sources, including poor upper boundary condition selection and inappropriate assumptions about the
structure of horizontal divergence between the ground
and the lowest grid level.
Figure 5a shows a histogram of retrieved vertical air
motion at the ground level. Although there are occasional large deviations from zero, the accumulated bias,
on average, is very small. We also examined the sensitivity of the kinematic results to the upper boundary
FIG. 5. (a) Histogram of ground-level vertical air motion for all
syntheses. (b) Profiles of vertical air motion for all syntheses utilizing
a top boundary condition of w 5 0 at 0.25 km above the highest
measured divergence (solid line), 1.0 km above the highest measured
divergence (dashed line), and 2.0 above the highest measured divergence (dashed–dotted line).
FIG. 4. Number of available data points used for statistical analysis.
Solid and dashed lines refer to the east and west precipitation regimes,
respectively.
condition by changing the location of assumed zero vertical air motion systematically upward from the location
of highest measured divergence in each grid column.
The results are shown in Fig. 5b and indicate that raising
the location of the upper boundary increases the magnitude of vertical air motion near the top of the profile,
but has little impact on the overall profile structure.
These sensitivity tests suggest that although there are
undoubtedly errors in the magnitude of vertical air motion estimates, the structure of the profiles are robust up
to at least 12 km and provide confidence in the interpretation of relative differences between precipitation
regimes. As will be shown in a forthcoming paper, the
kinematic results are consistent with the microphysical
results.
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c. Calculation of thermal buoyancy and Q1
Upper-air soundings from the Abracos Hill site (located near the TOGA radar site—see Fig. 1) were used
to calculate profiles of virtual temperature excess between the environment and a rising parcel (thermal
buoyancy) for each regime. To ensure that the resulting
profiles were representative of undisturbed conditions,
the same set of QC’d soundings used in Halverson et
al. (2002) were utilized for this study (91 in the ER and
40 in the WR). A 50-mb mixed layer depth was specified
for all soundings and pseudoadiabatic ascent (no contribution from ice processes) was assumed. The virtual
temperature difference was calculated by solving the
expression for moist entropy (Emanuel 1994).
Following the methodology of Cifelli and Rutledge
(1998), apparent heat source (Q1 ; Yanai et al. 1973)
profiles were generated by assuming that the vertical
advection of dry static energy (s) is the dominant term
in the following equation:
]s
]s
]s
Q1 [
1 v · =s 1 v ù v ,
]t
]p
]p
(1)
where s represents dry static energy (C p T 1 gz), v is
the vertical pressure velocity in isobaric coordinates and
the overbars represent horizontal averages. Because the
dual-Doppler data were interpolated to a Cartesian grid,
the Q1 profiles were calculated in Cartesian coordinates
assuming that v is well approximated by the hydrostatic
assumption as
v ù 2wrg,
(2)
where w is the vertical air motion diagnosed in Cartesian
coordinates, r is the air density, and g is the acceleration
of gravity. The estimated Q1 in (1) is equal to the sum
of phase changes in liquid and ice water (latent heating)
and radiative processes as well as to eddy flux terms in
the dry static energy equation. Following Johnson
(1984), the Q1 profiles were partitioned into convective
and stratiform components and normalized by the distribution of rainfall within each regime. That is
Q1
f
(1 2 f )
[ Q91 5
Q1m 1
Q1c ,
P0
Pm
Pc
(3)
where Q91 is the normalized budget, f is the fraction of
precipitation associated with mesoscale (stratiform) features, and P m , P c , and P 0 are the stratiform, convective,
and area-averaged rain rates, respectively.
The rain-rate values were derived from S-POL polarimetric data using an optimization methodology described in Cifelli et al. (2002) and Carey et al. (2000).
The synthesis radar data were partitioned into convective and stratiform components using a reflectivity texture algorithm similar to Steiner et al. (1995) and Rickenbach and Rutledge (1998). Moreover, similar to Cifelli et al. (2002), we define the partitioned ‘‘stratiform’’
region as weak convective–stratiform (WCSF) to reflect
the fact that the partitioning algorithm has difficulty in
FIG. 6. Pressure–height profiles of thermal buoyancy for the east
regime (solid) and west regime (dashed). The 08C level is indicated
by the horizontal line. The number of soundings used in each profile
composite is shown in the plot.
properly discriminating between these precipitation features.
3. Results
In this section, statistics of radar reflectivity, horizontal divergence, and vertical air motion are shown for
both the ER and WR and compared with environmental
thermodynamic characteristics. The statistics are then
composited separately into different parts of the diurnal
cycle in order to contrast the regime profiles with respect
to the solar cycle. Finally, the kinematic and thermodynamic data are combined in order to construct normalized profiles of Q1 for each regime (i.e., ER and
WR). The structure of the Q1 and kinematic profiles are
contrasted with similar radar-generated profiles from
other tropical regions.
a. Thermal buoyancy forcing
Regime composite profiles of the thermal buoyancy
are shown in Fig. 6. As noted in section 2, only sounding
data during relatively undisturbed conditions were used
to construct the thermal buoyancy profiles. While it
should be kept in mind that entrainment and water loading effects are neglected in the calculation of virtual
temperature excess, comparison of the ER and WR profiles provides insight into the differences in convection
that could be expected in the two regimes. Figure 6
shows that the ER profile has a larger temperature excess
and, perhaps more important, a larger gradient of virtual
temperature excess extending from above the boundary
layer to near 600 mb. A two-sided Student’s t test was
performed on the mean profile data shown in Fig. 6.
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With the exception of pressure levels where the ER and
WR profiles cross, the t statistic exceeds the 99% confidence level. The differences in regime structure are
similar to differences reported by Cifelli and Rutledge
(1998) for break (continental) versus monsoon (oceanic)
convection near Darwin, Australia.
The differences in the overall magnitude of thermal
buoyancy are not entirely unexpected given the fact that
Halverson et al. (2002) found that the ER had larger
convective available potential energy (CAPE) and convective inhibition (CIN) compared to the WR1. However, Fig. 6 suggests that the CAPE is distributed differently in the two regimes. The contrast in profile shape
is similar to the results from Lucas et al. (1994) with
the ER more characteristic of a continental profile (wider
region of positive buoyancy) while the WR profile has
more similarities to an oceanic profile of thermal buoyancy (narrower and slightly deeper region of positive
buoyancy). Since CIN is approximately a factor of 2
larger, when combined with the larger positive gradient
of thermal buoyancy in the ER profile, the results suggest that convective development would be more explosive in the ER compared to the WR. This is certainly
consistent with the field observations made by the authors. The larger temperature excess would be expected
to allow for stronger updrafts, the more pronounced role
of mixed phase microphysics, and more lightning in the
ER. As discussed later, these results are in agreement
with the corresponding profiles of radar reflectivity, kinematic structure, and lightning flash-rate differences
between the ER and WR, with markedly larger flash
rates in the ER (Petersen et al. 2002).
b. Composite radar reflectivity structure
Cumulative frequency distributions (CFDs) and composite mean profiles2 of radar reflectivity for the ER and
WR are shown in Fig. 7. In the convective region (Fig.
7c). the ER profile is approximately 2–4 dB greater than
the corresponding WR reflectivity profile at all heights.
Note that the profiles diverge above about 7 km, indicating the occurrence of larger and or higher concentration of precipitation ice above the melting level in
the ER. These results are consistent with Cifelli et al.
(2002) which showed similar differences when contrasting one MCS in the ER with one MCS in the WR.
The differences in mean structure can be understood by
examining the convective CFDs (Figs. 7a,b), which
show that the major difference in convective radar reflectivity structure occurs in the tails of the respective
1
The mean CAPE (CIN) in the ER is about 1517 J kg 21 (228 J
kg 21 ) compared to about 1022 J kg 21 (213 J kg 21 ) in the WR.
2
For all profiles shown in this paper, a minimum of 1000 points
was required for a mean value to be determined at any given height
level. The ER and WR profiles in Figs. 7–12 were compared using
a Student t test at all levels. Except at heights where the curves cross,
the t statistic results indicate that the profile differences are significant
at the 99% confidence level.
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distributions. For example, the 99.9% occurrence of a
40-dBZ convective echo extends to a height of 8 (6)
km in the ER (WR). The higher radar reflectivities above
the melting level in ER convection are in agreement
with lightning results reported by Petersen et al. (2002)
and suggest the presence of more robust mixed phase
processes in the ER. Drop size characteristics and the
implications for subsequent rainfall production in the
two regimes will be contrasted in a subsequent paper.
In the WCSF region, the profiles and distributions of
radar reflectivity are similar (Figs. 7d,e,f). Both regimes
show a weak radar bright band near 4.5 km with reflectivity falling off below that height, presumably due
to drop breakup and evaporation. Above the melting
level (approximately 5 km), the ER profile is several
dB higher and decreases less sharply than the corresponding WR.
c. Composite horizontal divergence structure
CFDs and mean profiles of horizontal divergence are
shown in Fig. 8. Note that the mean profiles (Figs. 8c,f,i)
show ‘‘fractional’’ divergence. To derive fractional divergence, the mean divergence at each height level has
been normalized by the number of grid points at the
same height level that actually contribute to divergence.
The normalization was done in order to compensate for
the fact that in the upper troposphere, mean horizontal
divergence becomes relatively large due to the fact that
there is typically large amounts of divergence originating from relatively small echo regions (i.e., the top portions of convective echo features) and it is difficult to
quantify differences in the profiles. At levels below
about 9 km, the fractional divergence profiles are nearly
indistinguishable from mean (nonnormalized) divergence. However, in the upper troposphere, the peaks in
the fractional divergence profiles deviate from mean divergence and emphasize the regions where divergence
is maximized relative to its frequency of occurrence in
the grid domain.
In the convective region, both regimes exhibit similarshaped profiles, with anticipated low-level convergence
and upper-level divergence (Fig. 8c). Consistent with
the reflectivity statistics, the ER has a higher level of
nondivergence, larger overall magnitude and shows upper-level divergence maximized above 10 km compared
to the WR where upper-level divergence is maximized
below that level. The CFDs reveal that it is the relatively
infrequent occurrence of strong lower (upper) level convergence (divergence) that is primarily responsible for
shifting the ER mean profile relative to the WR.
In the WCSF region, there are some important differences between the regime profiles (Fig. 8f). The ER
profile shows a rather typical stratiform kinematic structure with midlevel convergence situated between regions of divergence. The WR WCSF profile has a similar upper- and lower-level shape as the corresponding
ER divergence profile but higher-order structure in the
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FIG. 7. Cumulative frequency distributions of radar reflectivity (S-POL) for the (top) east regime
and (middle) west regime. The 50% and 99.0% contours are highlighted. (bottom) Mean radar
reflectivity for the east (solid) and west (dashed) regimes. From left to right in all rows are
convective, WCSF, and total (convective 1 WCSF). The cumulative frequency distributions were
constructed using a 4-dB bin size and 15 total bins.
3–7-km height range. The source of the high-order structure in the WR is not clear, possibly being related to
either a separate population of midlevel clouds in the
WR as was noted in the west Pacific (Johnson et al.
1999) or perhaps as entrainment–detrainment feature of
WR deep convection, similar to observations of Amazon
coastal squall lines (Garstang et al. 1994). Note that this
spike in WCSF midlevel divergence is also manifested
in the total (convective plus WCSF) WR profile (Fig.
8i).
The profiles of total divergence shown in Fig. 8 from
LBA can be contrasted to similar profiles generated from
aircraft radar data collected during TOGA Coupled
Ocean–Atmosphere Response Experiment (COARE)
MCSs in the west Pacific (Mapes and Houze 1995, their
Fig. 113). The oceanic profiles from the west Pacific
suggest a significantly deeper region of low-level con3
The Mapes and Houze study included a composite of the total of
143 divergence profiles representing convective, intermediary, and
stratiform MCS features.
vergence with the level of nondivergence near 9.7 km
compared to 7 km (4.5 km) for the LBA ER (WR)
regime. Interestingly, the LBA ER total divergence profile is in very good agreement (in terms of magnitude
and height of nondivergence) with a similar composite
profile of precipitation features sampled in the east Pacific warm pool region ahead of easterly wave troughs
(Petersen et al. 2003). The Petersen et al. results were
based on a composite of nine divergence profiles generated from shipborne radar data. As discussed in Petersen et al., the northerly phase was generally characterized by vertically well developed and electrified
precipitation features which contrasted sharply with the
structure following the passage of the easterly wave.
Although caution should be exercised in comparing kinematic profiles generated using different sampling
techniques and analysis methodology, these results lend
further support to previous studies showing that the kinematic features of precipitation vary not only temporally within a given location but also from one geographic region to another.
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FIG. 8. Same as in Fig. 7 except for horizontal divergence. The 1%, 50%, and 99% contours
are highlighted. The cumulative frequency distributions were generated using a 0.5 3 10 23 s 21
interval and a total of 21 bins. Note that the mean profiles in the bottom row display fractional
divergence as described in the text.
d. Composite vertical air motion structure
CFDs and profiles of vertical air motion are shown
in Fig. 9. As with the previously described parameters,
the median (50% contour) of the distributions shown in
the CFDs for each regime category are similar. However,
note that the convective ER CFD is considerably wider
compared to the corresponding WR CFD (Figs. 9a,b).
The differences in the distribution of the strong but infrequent updrafts have a large impact on the relative
differences in the mean profiles (Fig. 9c). This is especially true above the melting level where the convective ER profile is approximately 30%–50% larger
than the corresponding WR profile between heights of
5–10 km. As mentioned previously, these results are in
agreement with the profiles of thermal buoyancy in Fig.
6, which show a larger magnitude of virtual temperature
excess in the ER compared to the WR extending from
the lower troposphere to near 13 km. Both the ER and
WR convective vertical air motion profiles (Fig. 9c)
show peak updrafts in the upper troposphere (;10 to
12 km). Similar results were found for monsoon (oceanic) MCSs in the Darwin, Australia, region (Cifelli and
Rutledge 1998). Note, however, that the LBA vertical
air motion structure is quite different compared to monsoon break (continental) MCSs near Darwin which
showed a prominent peak in the lower troposphere associated with collision–coalescence processes along the
leading edge of squall-line systems. As noted in Cifelli
and Rutledge (1998), the difference may reflect the fact
that most Darwin-area precipitation systems were sampled in their late stages of evolution after the trailing
upper-level convective cores in the squall-line systems
had decayed.
In the WCSF region, the major difference in the regime vertical air motion profiles is the absence of lowlevel descending motion in the WR compared to the ER
profile (Fig. 9f). These differences correspond to the
lack of midlevel convergence in the WR WCSF category (Fig. 8f). Also note that the ER WCSF updraft
profile is slightly weaker compared to the WR at upper
levels but contains stronger downdrafts at lower levels
in the rain region. The combined effect of stronger convective drafts and weaker WCSF drafts in the upper
troposphere of the ER regime tend to cancel each other
15 DECEMBER 2004
CIFELLI ET AL.
4701
FIG. 9. Same as in Fig. 7 except for vertical air motion. The 1%, 50%, and 99% contours are
highlighted. The cumulative frequency distributions were generated using a 1.0 m s 21 bin size
and a total of 31 bins.
so that the overall magnitude of the ER and WR total
profiles are similar (Fig. 9i).
e. Diurnal variation of reflectivity and kinematic
structure
Previous studies on the diurnal cycle of convection
in southwestern Amazonia have shown pronounced differences in convective intensity in terms of lightning
flash rate and rainfall characteristics (Petersen et al.
2002; Rickenbach et al. 2002). In order to better understand the diurnal variability of kinematic structure
and its impact on rainfall and lightning activity in each
regime, the dual-Doppler radar data were subdivided
into four categories spanning the diurnal cycle: 0610–
1200, 1210–1800, 1810–0000, and 0010–0600 LT. The
data were then partitioned into different precipitation
categories and statistics generated as previously described.
Profiles of composite radar reflectivity, fractional divergence, and vertical air motion for the convective and
WCSF categories are shown in Figs. 10, 11, and 12,
respectively. In the ER (Figs. 10a, 11a, 12a), the con-
vective region shows a systematic increase in intensity
from the morning hours after sunrise (0610–1200 LT)
through the afternoon (1200–1800 LT), in agreement
with the cycle of solar insolation and surface equivalent
potential temperature (Wallace 1975; Betts et al. 2002).
The ER peak in the afternoon hours is also consistent
with the peak in corresponding rainfall intensity (Rickenbach et al. 2002). After sunset (1800–0000 LT), the
intensity of ER convection decreases in the lower troposphere, as revealed by a slight decrease in peak radar
reflectivity (Fig. 10a), low-level convergence (Fig. 11a),
and low-level updraft intensity (Fig. 12a), but remains
relatively unchanged in the upper troposphere. Indeed,
the period after sunset is characterized by an approximate factor of 3 increase in the mean convective updraft
magnitude in the vicinity of the melting level during
ER events (not shown). This time period is also marked
by a peak in lightning flash rate (Petersen et al. 2002),
suggesting that the strong updrafts allow for vigorous
mixed phase processes during this time period. Note
that the post-sunset time period is characterized by a
significant shift in the ER convective divergence structure (Fig. 11a). In particular, the level of nondivergence
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VOLUME 17
FIG. 10. Mean radar reflectivity profiles for different periods of the diurnal cycle. Colors correspond
to times (LT). East (west) profiles are shown in the left (right) columns with convection (WCSF)
categories in the top (bottom) rows.
and associated depth of low-level convergence shifts
upward from approximately 4.5 km in the afternoon
hours to near 6 km after sunset. The shift is likely a
consequence of the fact that during the daytime hours,
the radars sampled a wide range of cloud types (shallow
and deep). In contrast, the radars sampled a larger fraction of organized convection (i.e., MCSs) in the evening
to early morning hours with an accompanying deep layer of convergence.
During the same 1800–0000 LT period, the ER WCSF
region shows the development of a radar bright band
(Fig. 10b) associated with pronounced midlevel convergence peaking near the 6–7-km height level (Fig.
11b) and an associated updraft–downdraft couplet (Fig.
12b). In the late night hours (0010–0600 LT), the radar
bright-band signature increases slightly (Fig. 10b) as
peak convergence sharpens and shifts downward (Fig.
11b). The changes in the ER WCSF structure is accompanied by a marked reduction in ER convective intensity
in the same late night time period, in agreement with
Rickenbach et al. (2002) who showed a peak in nonconvective area coverage after local midnight.
A recent study by Rickenbach (2004), using TOGA
radar and geostationary satellite IR data, showed that
nocturnal squall lines propagated through the radar domain in the early morning hours (;0100–0400 LT) during both the ER and WR. As demonstrated by Rickenbach (2004), these nocturnal squall lines did not form
within the TRMM-LBA sampling area but originated
along the northeast-coast of Brazil several days earlier
and propagated across the Amazon basin, similar to previous studies of Amazonian convection (Kousky 1980;
Molion 1987; Garstang et al. 1994). These propagating
squall lines were manifested in the TOGA radar data as
a small increase in the diurnal convective area fraction
and echo top heights in the early morning hours.4
The results presented herein (Figs. 10–12) do not
show a similar increase in ER vertical structure characteristics (i.e., radar reflectivity, vertical air motion, or
horizontal divergence) in the early morning hours, even
when the 6-h early morning time period (0010–0600
4
Rickenbach (2004) did not subdivide the nocturnal events into
ER and WR.
15 DECEMBER 2004
CIFELLI ET AL.
4703
FIG. 11. Same as in Fig. 10 except for fractional divergence.
LT) is further subdivided into two periods: 0010–0300
and 0310–0600 LT (not shown). Comparison of the nocturnal squall-line time periods sampled by TOGA-only
in Rickenbach (2004) with the dual-Doppler retrievals
of this study revealed that coordinated dual-Doppler
sampling was conducted on each of the nocturnal squallline events discussed in Rickenbach (2004). However,
dual-Doppler sampling was not always conducted for
the entire time period when a nocturnal squall line was
within the radar domain. Apparently, these missing time
blocks of coordinated dual-Doppler sampling were large
enough so that the small signature of the nocturnal
events could not be captured in the diurnal profiles (Figs.
10–12). The absence of the propagating nocturnal
squall-line signature, however, does not change the
overall diurnal signal of in situ ER convection. The ER
profiles in Figs. 10–12 are consistent with life cycle
transitions of organized MCSs from primarily convective in the afternoon and evening hours to persistent
stratiform features in the late night–early morning time
period (McAnelly and Cotton 1989).
In the WR, the diurnal evolution of vertical structure
is more complicated. Similar to the ER, there is an increase in convective intensity during the daytime hours
(1210–1800 LT) with a deepening and strengthening of
upper-level reflectivity (Fig. 10c), divergence (Fig. 11c),
and ascending motion (Fig. 12c). Also, similar to the
ER convective profiles the intensity of WR convection
decreases during the 1810–0000 LT time period; however, the decrease in the WR is much more dramatic in
terms of both kinematic and radar reflectivity structure.
For example, during this time period the gradient of
reflectivity in convective regions above the melting level
in the WR decreases much more abruptly compared to
the ER profile (cf. similar profiles in Figs. 10a and 10c).
This suggests that the WR convection has reduced
mixed-phase processes in the evening hours, which is
in agreement with previous kinematic and microphysical
observations of TRMM-LBA case studies (Cifelli et al.
2002) and studies of lightning frequency (Petersen et
al. 2002). Also, in contrast to the ER, the level of nondivergence in the WR convective profile does not shift
appreciably upward while the magnitude of low-level
convergence decreases after sunset (Fig. 11c). Hence,
the intensity of WR convection (as inferred from the
vertical air motion profile; Fig. 12c) decreases markedly
after sunset.
Unlike the ER, the early morning (0010–0600 LT)
WR convective profiles indicate a significant increase
in intensity, especially at upper levels. The early morn-
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VOLUME 17
FIG. 12. Same as in Fig. 10 except for vertical air motion.
ing increase is due almost entirely to a nocturnal event
that occurred on 27 February, which comprises about
20% of the WR early morning time period sample. Similar to the ER nocturnal events described above, the 27
February event was a squall line that propagated from
the northeast coast of Brazil (Rickenbach 2004). Without the inclusion of the 27 February event, the early
morning WR convective profiles change little in the
lower troposphere but are significantly weaker than the
corresponding ER profiles above about 8 km (not
shown).
Large differences in kinematic structure between ER
and WR also occur after sunset in the WCSF category
(Figs. 11b,d and 12b,d). Unlike the classic stratiform
midlevel convergence structure in the ER (Fig. 11b),
the WR WCSF profile in the 1810–0000 LT time period
(Fig. 11d) indicates weak midlevel divergence with resulting weak ascent through the troposphere (Fig. 12d).
Moreover, despite the fact that the ER and WR have
similar WCSF reflectivity profiles during the early
morning 0010–0600 LT period (Figs. 10b and 10d), the
kinematic profiles show important differences. Specifically, midlevel convergence in the WR is much less
pronounced compared to the ER in the early morning
hours (Figs. 11b and d). The absence of midlevel con-
vergence in the WR WCSF early morning profile is even
more apparent if the 27 February data are not included.
This time period is also marked by larger rain rates in
the nonconvective regions of the WR (Rickenbach et
al. 2002). Because of the close correspondence between
divergence and diabatic heating (e.g., Mapes and Houze
1995), these observations suggest that the ER and WR
regimes have different WCSF diabatic heating profiles
in the early morning hours despite similar hydrometeor
(reflectivity) profiles. This result has important implications for TRMM algorithms that combine passive microwave observations and a cloud model database of
hydrometeor profiles to infer latent heating and surface
rainfall (Kummerow et al. 1996; Olson et al. 1999; Yang
and Smith 1999; Bauer et al. 2000).
The difference in WCSF regime structure continues
after sunrise in the 0610–1200 LT profiles where the
WR shows a pronounced radar bright band associated
with deeper midlevel convergence (Figs. 10d and 11d).
Interestingly, this is the only time period when the WR
shows a vertical air motion profile resembling classic
stratiform structure, with weak ascent in the upper troposphere above descending motion in the lower troposphere (Houze 1989).
15 DECEMBER 2004
CIFELLI ET AL.
4705
FIG. 13. Height profiles of normalized Q1
for the (a) convective region, (b) WCSF region, and (c) total (convective 1 WCSF region). The East (west) profiles are indicated
by the solid (dashed) lines. (a)–(c) Average
rain rates are indicated.
f. Normalized Q1 profiles
Normalized profiles of Q1 determined from Eqs. (1)
and (3) are shown in Fig. 13. We emphasize that the
profiles shown in Fig. 13 are an approximation to Q1
since the horizontal advection and storage terms in Eq.
(1) are ignored. However, previous studies have shown
that the vertical advection of dry static energy is often
the principal term in determining Q1 (Johnson and
Young 1983) so that Q1 as determined herein can provide at least a crude measure of diabatic heating structure in the ER and WR. Moreover, while recognizing
the potential pitfalls in making comparisons with results
from other studies using different sampling platforms
and techniques, we attempt several comparisons since,
as pointed out in section 1, large-scale models are quite
sensitive to variability in diabatic heating structure (e.g.,
Hartmann et al. 1984). This is especially true in convective regions of precipitation features since diabatic
heating estimates are rarely based on direct observation
of the accompanying kinematic structure (Houze 1989).
Herein, we restrict our comparison to other studies with
direct measurements of vertical air motions.
In the convective region (Fig. 13a), the normalized
Q1 profiles are similar with a heating peak of about 158C
day 21 (cm day 21 ) 21 near 7 km. Note that the peak is
considerably broader in the ER profile, extending from
6–9 km. The breadth emphasizes the importance of ice
processes in determining the shape of the ER heating
profile compared to the WR. The location of peak heating in these LBA profiles is similar to results for monsoon (oceanic) MCSs sampled near Darwin, Australia,
using wind profiler data (Cifelli and Rutledge 1998),
though the magnitude of the LBA profiles is about a
factor of 2 larger than the Darwin profiles. The difference is due mostly to the larger average rain rates in
the Darwin study. The Darwin study also showed an
additional heating peak in the lower troposphere of the
monsoon break (continental MCSs) profile, which is not
apparent in this study. The low-level peak in the break
monsoon MCSs profile was due to vertical air motions
along the leading edge of squall lines near 4 km that
passed over the wind profiler. Although many LBA
events had linear squall-line characteristics (especially
in the ER; Halverson et al. 2002), the sample used in
this study includes many other types of precipitation
features with varying degrees of organization.
The vertical location of the LBA convective heating
peak is also considerably higher than Q1 estimates from
a West African squall line using dual-Doppler data and
a thermodynamic retrieval technique (Chong and Hauser
1990). Somewhat better agreement is found with results
using vertical air motions from wind profiler observations on the west Pacific Island of Pohnpei (Balsley et
al. 1988) in combination with a one-dimensional en-
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training jet model (Houze 1989). Presumably, the higher
Q1 peak in the LBA and Pohnepei studies suggests the
importance of ice processes at the expense of low-level
condensation in determining the shape of the diabatic
heating profile.
In the WCSF region (Fig. 13b), the differences in the
LBA ER and WR profiles are relatively large in the
lower troposphere with the ER showing cooling below
7 km compared to warming in the corresponding WR
profile. Also, as expected from the differences in vertical
air motion structure, the ER WCSF Q1 profile is weaker
in the upper troposphere compared to the WR profile.
The combined effect of the differences in WCSF structure produces weaker heating in the ER total compared
to the corresponding WR total (Fig. 13c). The ER WCSF
profile in Fig. 13b is similar to the Q1 profile diagnosed
for Darwin area MCSs (both monsoon and break),
though the crossover from cooling to heating is about
1 km lower in the LBA profiles. The LBA stratiform
Q1 crossover level is, however, several kilometers above
similar levels diagnosed in the West African squall line
(Chong and Hauser 1990) as well as in a one-dimensional entraining jet model (Houze 1989) using vertical
air motion data from an eastern Atlantic oceanic MCS
(Houze and Rappaport 1984).
4. Conclusions
This study uses dual-Doppler radar data from TRMMLBA to contrast kinematic and reflectivity characteristics of precipitation features occurring in the east and
west regimes. A composite of upper-air sounding data
in each regime suggested that the two regimes have
different profiles of thermal buoyancy with the ER profile being somewhat wider (larger virtual temperature
excess) over a slightly shallower layer of the troposphere compared to the WR profile.
Consistent with thermodynamic characteristics of the
environment convection in the ER was shown to have
larger magnitudes of vertical air motion, upper-level divergence, and radar reflectivity compared to similar features in the WR. In the WCSF region, the major difference between the two regimes occurred in the lower
troposphere. The effect of the WCSF region on the total
profiles was to produce larger vertical air motion and
normalized heating at low levels in the WR compared
to the ER.
Because of sampling issues related to the nocturnal
propagating squall lines noted in section 3e, caution
must be applied in detailed interpretation of the ER and
WR diurnal profiles, especially in the early morning
hours. However, some generalizations can be made with
regard to the salient in situ features. The sequence of
ER profiles is consistent with a transition from deep and
intense convection in the late afternoon to weaker, more
stratiformlike features in the early morning hours. The
WR profiles suggest a similar trend during the postsunrise morning and afternoon hours but the WR con-
VOLUME 17
vective intensity decreases much more rapidly during
the evening hours after sunset compared to the ER, indicating important differences in mixed-phase microphysics during the evening hours. Also, the WCSF divergence profiles in the evening and early morning
hours of the WR do not show the classic midlevel convergence signature that is present in the ER, despite
having similar brightband profiles.
Heating (Q1 ) profiles were generated for each regime.
Both the ER and WR convective regions had peaks near
8 km, though the ER peak was considerably broader
than the WR, emphasizing the more important role of
ice microphysics in the ER compared to the WR. As
anticipated from the vertical air motion profiles, the major difference in the total Q1 occurred at low levels
where the ER had low-level cooling compared to warming in the WR.
Acknowledgments. This work was supported by
NASA TRMM Grant NAG5-4754. The TRMM-LBA
field program was carried out under the oversight of R.
Kakar. Partial support for the S-POL radar was provided
by the National Science Foundation. Otto Thiele of the
NASA TRMM Office was instrumental in helping establish TRMM-LBA and ensuring that it was a highly
successful field program. We thank Jay Miller of NCAR
for assistance in the Doppler radar processing techniques used for this study. Brian Mapes (NOAA CDC),
Richard Johnson (CSU), Tom Rickenbach (NASA
GSFC), and Steve Nesbitt (CSU) provided helpful feedback on the interpretation of the radar results. Three
anonymous reviewers also provided helpful suggestions
that improved the manuscript.
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