Microphysical characteristics of MJO convection over the Indian

PUBLICATIONS
Journal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/2013JD020799
Special Section:
The 2011-12 Indian Ocean
Field Campaign: AtmosphericOceanic Processes and
MJO Initiation
Microphysical characteristics of MJO convection
over the Indian Ocean during DYNAMO
Angela K. Rowe1 and Robert A. Houze Jr.1
1
Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA
Abstract The microphysical characteristics of precipitating convection occurring in various stages of the
Key Points:
• Peak MJO rainfall coincided with peak
wet aggregates in stratiform
• Infrequent graupel concentrated near
melting level
• December MCSs shallower with
weaker brightband
Correspondence to:
A. K. Rowe,
[email protected]
Citation:
Rowe, A. K., and R. A. Houze Jr. (2014),
Microphysical characteristics of MJO
convection over the Indian Ocean
during DYNAMO, J. Geophys. Res. Atmos.,
119, 2543–2554, doi:10.1002/
2013JD020799.
Received 26 AUG 2013
Accepted 3 FEB 2014
Accepted article online 7 FEB 2014
Published online 14 MAR 2014
Madden-Julian Oscillation (MJO) over the Indian Ocean are determined from data obtained from the National
Center for Atmospheric Research dual-polarimetric Doppler S-band radar, S-PolKa, deployed as part of the
Dynamics of the MJO (DYNAMO) ﬁeld experiment. Active MJO events with increased rainfall occurred in
October, November, and December 2011. During each of these active MJO phases, in addition to enhanced
rainfall, convection became deeper and ice-phase microphysics played a greater role. S-PolKa consistently
showed nonoriented small ice particles dominating the radar echoes at altitudes of 9–10 km, dry aggregates
concentrated between 7 and 9 km, and wet aggregates and graupel near the melting level (~5 km). Graupel
occurred mainly in actively convective towers, while the wet aggregates occurred almost exclusively in the
stratiform regions of mesoscale convective systems (MCSs). During each of the three multiweek MJO active
phases, the maximum rainfall occurred in short bursts lasting a few days. Each multiday rainy period began
with deepening convective elements and a concurrent increase in occurrence of dry aggregates, which
maximized just prior to organization into MCSs. The peak rainfall occurrence coincided with the maximum
coverage of the radar domain by MCSs, reﬂecting large stratiform regions that exhibited the most frequent
occurrence of wet aggregates. During the December active MJO phase, however, the MCSs were shallower
and had a slightly lower tendency for wet aggregates in the stratiform regions and, therefore, generally
weaker brightbands.
1. Introduction
The Madden-Julian Oscillation (MJO) [Madden and Julian, 1971, 1972] dominates intraseasonal (30–90 day)
variability in the tropics, yet forecasting skill for the MJO is limited. Zhang’s [2005] review of the state of
understanding of the MJO suggests that this lack of skill is due in large part to the poor representation of
convection over the Indian Ocean where convective coupling with the large-scale environment occurs.
Maloney and Hartmann [1998] and Haertel et al. [2008] demonstrated that MJO simulations are sensitive to
the relative proportions of different categories of convection. Observations [Lin et al., 2004; Kiladis et al., 2005]
and reanalysis data [Jiang et al., 2011; Ling and Zhang, 2011; Zuluaga and Houze, 2013] indicate a westward tilt
in the vertical heating structure resulting from a transition of the cloud population toward having more deep
convection and stratiform precipitation. Therefore, as global models include increasingly realistic representations
of cumulus and cumulonimbus convection, the need for more detailed knowledge of the convection becomes
acute. The microphysics and dynamics of the convection need to be determined from observations to assess
whether model representations are realistic.
The Dynamics of the Madden-Julian Oscillation (DYNAMO) ﬁeld campaign, as a component of the Cooperative
Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (CINDY2011), was conducted in the Indian
Ocean region to observe the structure and evolution of the cloud population, describe the interaction with the
large-scale environment, and evaluate the role of air-sea processes during the MJO phases. To address this
complex problem, measurements included atmospheric proﬁles from a sounding array, air-sea ﬂuxes, upper
ocean observations from ships and moorings, satellite and aircraft data, and data from a radar network. The
objective of the radar network was to characterize the spectrum of MJO convection and how the ensemble
evolves from one stage to the next. Inclusion of the National Center for Atmospheric Research (NCAR) S-PolKa
radar was essential to this effort due to its Doppler and dual-polarization capabilities, allowing for inferences of
the most likely microphysical mechanisms producing the observed precipitation. This study uses this S-band
dual-polarimetric data from DYNAMO to describe the general microphysical characteristics of the precipitating
cloud population in different phases of the MJO. More speciﬁcally, we show (1) how various hydrometeor
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10.1002/2013JD020799
species are vertically distributed in deep convective systems during active MJO phases and how these vertical
proﬁles compare to those for convection during suppressed phases, (2) whether hydrometeor characteristics
were the same for each active MJO phase observed during the ﬁeld campaign, and (3) what microphysical
differences existed between the convective and stratiform portions of the observed cloud systems.
These objectives allow for improved understanding of the physical properties of convection during DYNAMO,
and of the feedback on the surrounding environment, and aid in the evaluation of convective parameterizations
and microphysical schemes used in models for MJO prediction.
2. Data
2.1. S-PolKa Scanning Strategy
The S-PolKa radar was located in the Maldives at the Addu Atoll (0.6°S, 73.1°E) from 1 October 2011 to 15
January 2012. The scanning strategy was a 15 min cycle including 360° azimuthal surveillance scans at eight
constant elevation angles from 0.5° to 11.0° followed by two sectors of range-height indicator (RHI) scans
at 1° azimuth intervals. Each RHI scanned up to 45° in elevation over two azimuthal sectors: one to the NE at
4°–83° to collect data over the ocean with reduced clutter and a narrower sector to the SE (114°–140° azimuth)
to include information over the vertically pointing Department of Energy (DOE) Ka-band Atmospheric Radiation
Measurement (ARM) zenith radar (KAZR) located 9 km from S-PolKa as part of the DOE ARM MJO Investigation
Experiment (AMIE). This study focuses on data from the RHIs due to the ﬁner vertical resolution and reduced
clutter and blockage in those azimuthal ranges.
2.2. S-PolKa Quality Control
Considerable efforts have been taken by the NCAR S-PolKa group to provide a reliable data set. Advanced
techniques, described by Dixon and Hubbert [2012], were developed to separate noise and signal components,
providing less noisy data in low signal-to-noise regions and for nonmeteorological echoes. S-band atmospheric
attenuation was estimated using the technique presented in Doviak and Zrnić [1993]. The self-consistency
calibration method of Vivekanandan et al. [2003] was applied to three time periods during DYNAMO to verify the
S-band reﬂectivity calibration; results indicated well-calibrated measurements with biases falling within the
expected accuracy range of this technique. A bias in differential reﬂectivity (ZDR) of 0.285 was determined and
corrected using vertically pointing radar data from the ﬁeld.
Instantaneous rain rates were computed using clutter-ﬁltered, bias-removed values of Z, ZDR, and speciﬁc
differential phase (KDP) at each gate. KDP was derived by applying a 21-gate, along-beam ﬁnite impulse
response ﬁlter to ΦDP with a slope interval selected based on reﬂectivity values [Hubbert and Bringi, 1995]. A
hybrid rain rate product was created using the “best” rate determined by the Z-R, Z-ZDR, Z-KDP, and ZDR-KDP
relationships. This process is similar to that described in Chandrasekar et al. [1990] and further by Ryzhkov et al.
[2005] and uses coefﬁcients from previous tropical experiments, including Mirai Indian Ocean cruise for the
Study of the MJO-convection Onset (MISMO) in the Indian Ocean region [Yoneyama et al., 2008]. There is
ongoing work within the DYNAMO community to recompute these coefﬁcients for the DYNAMO data set, but
for the purposes of this study, where only relative changes in precipitation is discussed, the hybrid rain rates
described here are sufﬁcient. Additional details about this method and further quality control of the data are
described at http://www.eol.ucar.edu/projects/dynamo/spol/.
2.3. Particle Identiﬁcation
The most probable, dominant hydrometeor type is inferred from S-PolKa data using a fuzzy logic algorithm
described in detail by Vivekanandan et al. [1999]. In this method, membership functions determine the
degree to which a radar variable contributes to each particle type by applying a weight from 0 to 1. The
dual-polarimetric radar variables used in this algorithm include ZH, ZDR, providing information about
oblateness of the hydrometeors, linear depolarization ratio (LDR) and the cross-correlation coefﬁcient (ρHV ),
both helping to distinguish between pure rain and mixtures of hydrometeors, and KDP, which is a measure
of drop size and water content. For a comprehensive description of dual-polarimetric variables, see Bringi
and Chandrasekar [2001].
The weighted sums of each variable, along with temperature measured from local soundings, determine the
dominant species at that location. The ﬁnal particle identiﬁcation categories include the following:
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Table 1. Approximate Ranges of Values for Particle Categories With Weight of 1
Drizzle/light rain
Moderate rain
Heavy rain
Graupel/rimed aggregates
Graupel/rain
Wet aggregates
Dry aggregates
Nonoriented small ice
Horizontally oriented small ice
ZH (dBZ)
ZDR (dB)
5–35
35–45
45–55
30–50
30–50
7–45
15–33
0–15
0–15
0–1.8
0.01–3
0.3–4.3
0.1–0.7
0.7–0.8
0.5–3
0–1.1
0–0.7
1–6
LDR (dB)
33–
31–
31–
25–
25–
25–
26–
31–
31–
27
24
24
20
20
17
23
23
23
KDP (° km
1
0.03–0.3
0.01–3
0.09–15
0.08–1.6
0.1–1.7
0.1–1
0–0.17
0–0.1
0.08–0.6
)
ρHV
0.98–0.99
0.97–0.99
0.97–0.99
0.85–0.95
0.85–0.95
0.75–0.98
0.97–0.98
0.97–0.98
0.97–0.98
T (°C)
1–40
1–40
1–40
50–30
30–25
50–8
50– 1
50– 1
50–1
1. Drizzle/light rain (< 10 mm h 1), moderate rain (< 40 mm h 1), and heavy rain (> 40 mm h 1) characterized by increasing ZH, ZDR, and KDP as drops become more oblate and/or water content increases with
increasing rain rate;
2. A mixture of graupel and rain indicated by reduced values of ZDR and ρHV with increasing LDR due to the
mixed-phase properties of graupel melting to rain and/or graupel mixed with rain and the more spherical
nature of graupel particles;
3. A mixture of graupel and rimed aggregates with membership functions that overlap with graupel/rain
except with further reductions in ZDR and KDP as this category is characterized by more spherical particles;
4. Wet aggregates that exhibit a large range in the dual-polarimetric variables depending on the degree of
aggregation and melting, with the most pronounced signature of this category located near the melting
level characterized by enhanced ZH, ZDR, and LDR and reductions in ρHV; and
5. Small ice crystals which are subdivided into nonoriented and horizontally oriented ice, with the latter
being characterized by higher ZDR values.
This algorithm was tuned for the DYNAMO data, particularly for the purpose of eliminating unrealistic
graupel identiﬁed below the melting level. Spurious graupel pixels near the surface were mostly
eliminated by reducing the lower bound of ZDR for rain and lowering the weight of noisier KDP. A
questionable thin layer of a graupel-rain mixture just below the melting level was removed by extending
the temperature thresholds for wet snow to warmer values, consistent with the brightband signature
being observed at temperatures warmer than +5°C. Table 1 provides approximate values associated with
weights of 1 for each variable and helps to gain a sense of the range of values associated with each of the
hydrometeor categories.
This study focuses primarily on the ice categories, and relative percentages of these particle types
are presented to describe their vertical distribution as a function of all hydrometeors observed in the 3-D
S-PolKa echo. As a result of this normalization, the focus will not be on the exact counts of each particle
species, as the particle identiﬁcation (PID) algorithm only provides the probable dominant type, but will
provide a sense of the general microphysical characteristics of precipitating echo observed by S-PolKa
during DYNAMO.
2.4. Convective/Stratiform Partitioning
In order to partition the echo into convective and stratiform components, the S-PolKa polar data from the
RHIs were ﬁrst interpolated to a 0.5 km horizontal and vertical Cartesian grid. The Steiner et al. [1995]
partitioning method, as adapted by Yuter and Houze [1997], was then applied to the interpolated reﬂectivity
ﬁeld at the 2.5 km vertical level after ﬁne-tuning the parameters for this particular region. When tuning the
input parameters, it is important to consider how to treat the “gray” area of decaying, convective cells as the
inﬂuence of this partitioning on the broader picture is different for latent heating and microphysical
considerations. For the purposes of this study, focusing on microphysical processes, the algorithm was tuned
so radar echo characteristics such as reﬂectivity fallstreaks were considered convective elements, albeit in
their decaying stage as they transition to stratiform echo. This was accomplished by increasing the minimum
reﬂectivity difference (the a parameter described in the partitioning references from Steiner et al. and Yuter
and Houze) and through an increase in the general convective reﬂectivity threshold due to the occasional
presence of 50+ dBZ reﬂectivity brightbands in the stratiform regions.
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Figure 1. Time series of (a) daily total (black) and stratiform (blue) rainfall within 150 km radius of S-PolKa and mean daily
echo-top heights (red) with one standard deviation plotted as error bars, (b) percent of S-PolKa horizontal grid covered with
points associated with mesoscale features, (c) percent of total daily hydrometeor species identiﬁed as wet (WA, black) and
dry (DA, red) aggregates, and (d) percent of total daily hydrometeor species identiﬁed as nonoriented small ice (NOI, black)
and graupel (GR, red). While the S-PolKa radar operated through 14 January, as indicated in the rainfall time series, additional parameters for this study were cut off at 31 December due to the inactivity during January.
2.5. Feature Identiﬁcation
To distinguish organized precipitating systems from more isolated convection during DYNAMO, a feature
identiﬁcation method was applied to the gridded reﬂectivity data at a vertical height of 1 km. Precipitation
features were identiﬁed as contiguous areas (including corner pixels) of reﬂectivity values greater than
15 dBZ; the same threshold used when applying this method to S-PolKa data from the North American
Monsoon Experiment [Lang et al., 2007; Rowe et al., 2011, 2012]. An ellipse ﬁtting technique, described in
Nesbitt et al. [2006], was then applied to each feature to determine the major axis length. Any feature with a
major axis length greater than 100 km was considered to be a mesoscale convective system (MCS), consistent
with the description of organized precipitation systems from Houze [1993, chapter 9].
3. Results
3.1. Time Series
The time series of S-PolKa daily accumulated rainfall normalized by grid area (Figure 1a) highlights three
multiweek active MJO events characterized by increased rainfall during the latter halves of 15–31 October,
15–30 November, and 15–28 December. These extended periods of enhanced precipitation, constituting an
active MJO phase, featured several rainy episodes of varying duration: 2-day periods during October and
longer 4-to 6-day periods in November and December. Overlaid on this ﬁgure are daily mean echo top
heights (determined by the maximum height of 0-dBZ reﬂectivity at each grid point) with standard deviation
plotted as error bars. Each active MJO phase began with deepening convection, and while echo-top heights
were greatest during the periods of enhanced rainfall, there was a large spread in the vertical extent of echo.
These observations are consistent with satellite data that show a broad range of cloud sizes [Lau and Wu,
2010] and the presence of convection of all depths [Del Genio et al., 2012] during all phases of the MJO.
However, as shown in this time series, the population tends toward a greater occurrence of deep convection
during the active MJO phases.
Peaks in total and stratiform rainfall coincided with the occurrence of MCSs (Figure 1b), consistent with a
Tropical Rainfall Measuring Mission-based study of the MJO precipitating cloud population by Barnes and
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Figure 2. Probability distribution functions of (a and b) echo-top heights (deﬁned as the maximum height of 0-dBZ echo
for each grid point) and (c and d) maximum heights of 30-dBZ reﬂectivity for features identiﬁed as sub-MCS (features with
major axes < 100 km) and MCS (features with major axes < 100 km) in Figures 2a and 2c and for elements of MCSs classiﬁed
as convective and stratiform in Figures 2b and 2d. Distributions are further subdivided by month.
Houze [2013] who found that broad stratiform regions dominate variability in areal coverage, and reach a
maximum, during active phases. When echo was detected by the radar, the occurrence of wet, melting
aggregates, relative to all particles identiﬁed in the 3-D echo on that day, generally peaks with MCS coverage
and stratiform rain, highlighting the importance of stratiform echo in organized systems to the precipitation
during active phases of the MJO.
The occurrence of dry aggregates in precipitating echo (Figure 1c) was minimized during inactive periods,
with peaks occurring generally just prior to those for the wet, melting aggregates. This sequence reﬂects a
transition from convective to stratiform echo during the rainy periods, as described in Zuluaga and Houze
[2013]. In that study, which also used S-PolKa data, they showed that statistical composites of echo structure
during the rain episodes mirrored the lifecycle of individual cloud systems: a transition from deep convection
during the earlier part of the 2-day episode to wider echo and eventually broad stratiform during the later
stages of those rainy periods.
Time series of the relative occurrence of nonoriented small ice crystals and graupel particles, presented in
Figure 1d, are noisier than the more selective periodic enhancement of the dry and wet aggregate categories
during the active phases. Recall that these are relative frequencies, representing the nature of the precipitating
features and not the absolute amounts of observed convection. With this in mind, the nonoriented small-ice
category dominated the inferred hydrometeors when echo was observed by S-PolKa during DYNAMO. While
there were localized peaks in this ice species during periods of MCS occurrence and wet aggregates, there were
also peaks present during the suppressed MJO phases at the beginning of the months when rainfall was
reduced. Similarly, the PID-inferred graupel occurred both during the suppressed and active phases; however,
relative percentages of graupel occurrence in precipitating 3-D echo were more than an order of magnitude
lower than the other ice categories with less than 1% of daily hydrometeor types classiﬁed as graupel
throughout the project. The infrequency of graupel in convection during DYNAMO is consistent with other
studies in moist, oceanic tropical environments that showed weaker vertical velocities compared to convection
over land [e.g., Szoke et al., 1986; Lucas et al., 1994; May and Ballinger, 2007], yet the relative consistency of this
radar-inferred low presence of graupel throughout the observation period, regardless of phase, is a unique
ﬁnding worth further investigation. Where did the graupel occur within the storms? Did the vertical distribution
of graupel vary between the inactive and active phases? And more generally, were the vertical characteristics of
precipitating features similar for each active phase? To address these questions, the reﬂectivity and particle
information were categorized according to their horizontal sizes and vertical extents.
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Figure 3. Difference contoured frequency by altitude diagrams (CFADs) of reﬂectivity for (a–c) MCS convection and (d–f) MCS
stratiform echoes for each month. Frequencies are normalized by the maximum frequency for each respective month, then
subtracted from the total normalized frequency for all months combined. Warm (cool) colors denote frequencies greater
(lesser) than the frequency for all months.
3.2. Radar Echo Structures
To compare vertical characteristics of reﬂectivity, we divided the echoes horizontally into MCS (> 100 km major
axis) and sub-MCS (< 100 km major axis) features and further subdivided the samples by month. First, statistics of
the echo-top heights were compiled and are shown in Figure 1a, which indicate a general deepening of echo
during active phases of the MJO. Probability distribution functions of echo-top heights, subdivided by month and
feature type, show that deeper echo was associated with MCSs for all months (Figure 2a). There appeared to be
slightly shallower sub-MCS echo during December, and while echo-top heights peaked at 10 km for MCSs during
all months, the distribution was also shifted to slightly lower heights for December. After separating the MCSs into
convective and stratiform components (Figure 2b), it becomes clear that the slightly lower heights observed
during December were mostly associated with the convective elements of MCSs.
Distributions of maximum 30-dBZ heights (Figures 2c and 2d) show similar trends to those for echo-top
heights, with a greater vertical extent of 30-dBZ echo for MCSs compared to sub-MCSs and slightly lower
heights for MCSs during December compared to MCSs in other months, due primarily to convective echo.
Regardless of the greater heights for MCSs during the earlier months, the 30 dBZ echo rarely exceeded a few
kilometers above the melting level (located around 5 km during DYNAMO). The presence of 30 dBZ and
greater reﬂectivities above the freezing level has been associated with lightning production [Zipser, 1994;
Petersen et al., 1996; Lang and Rutledge, 2011]. This result is consistent with the fact that scientists at S-PolKa
seldom saw lightning and also consistent with the real-time World Wide Lightning Location Network data
archived on the DYNAMO data catalog (http://catalog1.eol.ucar.edu/dynamo/), which showed lightning to be
exceptionally rare in the region represented by S-PolKa. Another characteristic of these distributions is the
double peak in 30-dBZ heights in stratiform echo: one occurring near the melting level, reﬂecting the
presence of a reﬂectivity brightband; and another, below 2 km, suggesting decaying convective elements
within the regions identiﬁed as stratiform as described for tropical MCSs by Houze [1997].
To further investigate potential differences in echo characteristics of MCSs during DYNAMO, contoured
frequency by altitude diagrams (CFADs) [Yuter and Houze, 1995] of reﬂectivity (convective and stratiform)
were created for each month. These were normalized by the maximum frequency in each month then
subtracted from the normalized reﬂectivity CFAD for all months combined to look at the relative differences
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Figure 4. Vertical proﬁles of relative percentages of wet aggregates (WA, black), dry aggregates (DA, blue), nonoriented
small ice (NOI, green), and graupel (GR, red). Percent on the x axis indicates the relative amount compared to all hydrometeor species for (a) sub-MCS and (b) mesoscale features. Percentages in parentheses indicate the total percentage for
each category over all heights.
between each month (Figure 3). The shallower convection in December suggested by the probability
distribution functions is also highlighted in this ﬁgure, which shows greater reﬂectivities in the upper levels
during November (Figure 3b) with more bottom-heavy convection present in December (Figure 3c); CFADs
for sub-MCS convection (not shown) display similar results. All three stratiform CFADs show the presence of a
reﬂectivity brightband near the melting level, but this brightband appeared to be weakest during December
(Figure 3f). Are these differences between active phases reﬂected in comparisons of ice hydrometeors?
Vertical proﬁles of hydrometeor occurrence were created to address this question.
3.3. Vertical Proﬁles
Echo-top height distributions showed deeper convection associated with organized features compared to
sub-MCS convection. To investigate the vertical distributions of hydrometeors in precipitating echo, vertical
proﬁles of ice hydrometeor species, inferred from the dual-polarimetric variables, are shown in Figure 4 for
both sub-MCS and MCS features. Both feature types were dominated by nonoriented small ice in the upper
levels, consistent with the greater occurrence, compared to the other ice species, shown in Figure 1d.
This subdivision by feature type shows, though, that MCSs generally contained more of these nonoriented
ice particles. Dry aggregates peaked at lower heights than the nonoriented small ice, with slightly lower
percentages than the small ice crystals, but occurring more frequently than the wet aggregates, which
peaked near the melting level. While these melting aggregates peaked near 5 km for both MCSs and sub-MCS
features, consistent with the presence of a reﬂectivity brightband at this level (Figures 2 and 3), both dry
aggregates and nonoriented ice in MCSs peaked several kilometers above those for sub-MCS features,
reﬂecting the deeper echo present in organized systems.
Similar to the wet aggregates, graupel peaked at nearly the same height (just above 5 km) regardless of
organization but an order of magnitude less frequently than the other ice categories. In this mean sense,
when graupel was present, it generally occurred only within a few kilometers of the melting level, consistent
with maximum heights of 30-dBZ echo being limited to below 7 km overall and minimal lightning occurrence
observed in this and other data sets [e.g., Carey and Rutledge, 2000; May and Ballinger, 2007; Lang and
Rutledge, 2011]. Another small peak is noted in the graupel proﬁle near the surface. Despite the efforts to
reduce this spurious echo, as was described in the methods section, some pixels still remain. The relative
frequency of these values, however, are less than 0.05% and do not inﬂuence the general trends that are
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Figure 5. Proﬁles as in Figure 4 except divided into (a–c) MCS convection and (d–f) MCS stratiform, with further subdivisions by month.
described in this paper. Ongoing efforts are underway to remove these remaining graupel points near the
ground for the next version of this data set.
Figures 2 and 3 suggested slightly different echo characteristics for mesoscale systems occurring in
December compared to those observed during the earlier months. Vertical proﬁles of ice particles for MCSs
partitioned into convective/stratiform and subdivided by month (Figure 5) generally appear similar, with
nonoriented ice peaking around 9–10 km, above the dry aggregates, and wet aggregates and graupel
occurring most frequently near the melting level at 5 km. These distributions were similar for both convective
and stratiform echoes, but the relative frequencies of the ice species differed. For example, while nonoriented
small ice crystals had the highest percentages for both convective and stratiform echoes, both this category
and wet aggregates occurred more frequently in stratiform precipitating echo.
Graupel occurrence, although an order of magnitude less frequent than the other ice species, was slightly
greater in convection, owing to increased upward motion compared to stratiform regions. The presence of
graupel near the melting level in stratiform echo could be attributed, in part, to embedded decaying
convective cells (Figure 6). In this RHI, from 23 November 2011, the remnants of embedded convective echo
contain PID-identiﬁed graupel in a pocket above the melting level near 90 km range within a greater horizontal
extent of wet aggregates near 5 km in the widespread stratiform echo. This graupel is also identiﬁed using
another fuzzy logic-based hydrometeor identiﬁcation algorithm, described by Dolan and Rutledge [2009] and
Dolan et al. [2013], in a region characterized by Z > 40 dBZ, ZDR ~ 0 dB, low LDR, and ρHV near 1.
There were instances, however, when graupel was identiﬁed by the PID algorithm within a thin layer just
above the melting level, such as in the RHI presented in Figure 7. This example, 12 h later on 23 November
2011, highlights the vertical structure of stratiform echo during this active phase after the embedded
convective cells had decayed and contributed to this widespread precipitation. A brightband signature is
clearly evident as localized maxima in reﬂectivity (50 dBZ) near 5 km. This increase in Z reﬂects the wetting
that occurs on the outside of aggregates or other large ice particles. As melting progresses, the variety of
shapes and sizes of hydrometeors increases, leading to a drop in ρHV and an increase in LDR. ZDR peaks slightly
below Z as most of the wet aggregates begin to collapse into smaller rain drops, thus reducing Z, but the few
remaining large aggregates maximize ZDR [e.g., Zrnić et al., 1993; Andrić et al., 2013]. In this region, the PID
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10.1002/2013JD020799
Figure 6. RHI through stratiform echo at 0050 UTC on 23 November 2011 at an azimuth of 48°. Variables from left to right, top to
bottom include reﬂectivity (dBZ), differential reﬂectivity (dB), NCAR’s particle identiﬁcation (CD: cloud droplets, DZ: drizzle, LR: light
rain, MR: moderate rain, HR: heavy rain, HA: hail, RH: rain-hail, GH: graupel/hail mixtures or rimed aggregates, GR: graupel/rain
mixture, DS: dry aggregates, WS: wet aggregates, OI: horizontally oriented small ice crystals, and II: small ice crystals with no
preferred orientation), linear depolarization ratio (dB), cross-correlation coefﬁcient, and the other hydrometeor identiﬁcation
algorithm described in the text (DZ: drizzle, RN: rain, IC: ice crystals, AG: aggregates, WS: wet snow, VI: vertically oriented small ice
crystals, LG: low-density graupel, HG: high-density graupel, HA: hail, and BD: big drops).
identiﬁes wet aggregates (the WS category in orange), but the thin layer of green above this layer suggests a
degree of riming. Where this graupel-hail category is deﬁned, reﬂectivity values increase to > 40 dBZ, ZDR
values are near 0 dB, LDR values are small, and ρHV is near unity.
As a sanity check, the other hydrometeor identiﬁcation algorithm previously mentioned was also applied to
this RHI. While the categories differ slightly from those of the NCAR PID, this algorithm also identiﬁes graupel
within this region just above the brightband. Veriﬁcation of these hydrometeor identiﬁcation algorithms is
Figure 7. As in Figure 6 except for 1235 UTC on 23 November 2011 at an azimuth of 131°.
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©2014. American Geophysical Union. All Rights Reserved.
2551
Journal of Geophysical Research: Atmospheres
10.1002/2013JD020799
difﬁcult and rare, but aircraft
observations have provided insight into
particle types in stratiform echo. For
example, in situ observations in a
stratiform region of an Oklahoma MCS
revealed the presence of graupel that
was reproduced using a onedimensional cloud model [Zrnić et al.,
1993]. In that case, it was suggested
that the graupel was generated locally
in regions of higher cloud liquid
content in localized areas of stronger
updrafts. That study attributed the lack
Figure 8. Cumulative distribution functions of (a) column water and (b) of previous observations of this feature
column ice mass for MCS convection and stratiform echo divided into
to its transient nature. More recently,
month. Masses are normalized by the total number of points in the colBouniol et al. [2010], in their study of
umn included in the summation.
West African MCS anvils, presented
2D-C images that contained complexshaped rimed crystals and aggregates of rimed crystals in stratiform regions, similar to results from Heymsﬁeld
et al. [2002] in Brazil.
More relevant to tropical oceanic studies, Leary and Houze [1979] presented a schematic of MCSs during
GARP Atlantic Tropical Experiment suggesting small graupel or heavily rimed aggregates just above the
melting level in the stratiform region where the reﬂectivities increase with decreasing height (see their
Figure 8). Furthermore, videosonde data from the MISMO project in the Indian Ocean showed ice crystals,
graupel, and aggregates near and above the freezing level in regions of stratiform precipitation [Suzuki et al.,
2006]. The NOAA P-3 ﬂew through MCSs during DYNAMO (see Guy and Jorgensen [2014] for details about
these ﬂights), but only one of these ﬂights was within the domain of S-PolKa and it was during early
December, thus not representing a case like that presented in Figures 6 and 7. Despite this lack of in situ
aircraft data in these questionable graupel regions, the ﬂight logs in several instances noted the presence of
graupel, particularly hearing it strike the plane. In addition, Precipitation Image Probe data from a ﬂight on
24 November 2011 showed both aggregates and graupel as the P-3 descended through the 5–4.5 km layer
(N. Guy, personal communication, 2014); these data, however, are still in its preliminary stages of analysis.
Whether or not this layer is actually graupel or large aggregates with some degree of riming, the occurrence
of this feature is still relatively infrequent compared to graupel present in decaying convection, and even
further minimal compared to the other hydrometeor species, and does not affect the overall trends described
in this study (i.e., location of peak and similar frequencies between months and periods).
As previously mentioned, wet aggregates were the dominant hydrometeor type near the melting level, with
higher relative percentages in stratiform echo (Figure 5); however, consistent with the stronger reﬂectivity
brightbands observed during October and November (Figure 3), the frequency of wet aggregates was
greater during those months compared to December. The other ice species were similar between months for
stratiform echo, but dry aggregates and nonoriented ice were slightly less frequent in convection during
December (Figure 5c). This difference is likely a reﬂection of the deeper echo that occurred during those
earlier active phases.
3.4. Column Liquid Water and Ice
The aforementioned similarities are also revealed in comparisons of column-integrated liquid water and ice
mass (Figure 8), which were computed using the difference reﬂectivity method of Golestani et al. [1989],
tuned for DYNAMO, using Z-M relationships described by Carey and Rutledge [2000]. Each column mass total
was normalized by the total number of points in the vertical that were included in the summation, providing
a relative means to compare masses for each month. Convective elements of MCSs contained more ice and
water mass than stratiform regions, with generally similar cumulative frequencies for each month. For larger
amounts of liquid water mass, MCS convection contained slightly greater amounts during December, while
ice mass tended to be overall lower compared to the earlier months. This agrees with the tendency for
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©2014. American Geophysical Union. All Rights Reserved.
2552
Journal of Geophysical Research: Atmospheres
10.1002/2013JD020799
December convection to be more bottom-heavy, shallower, and less frequently containing wet aggregates.
Overall, though, these differences were relatively small, especially when comparing with the differences in
masses between the convective and stratiform components of organized systems and those between MCSs
and sub-MCSs.
4. Conclusions
Data from NCAR’s S-PolKa radar, deployed during DYNAMO, was used to describe the microphysical
characteristics of hydrometeors in the precipitating clouds occurring in various stages of the MJO. Active
phases of the MJO over the Indian Ocean occurred during October, November, and December, and each was
characterized by elevated rainfall amounts, deeper convection, and an increased importance of ice-phase
microphysics. The S-PolKa radar observed an increase in the relative occurrence of wet aggregates,
coinciding with the presence of mesoscale convective systems and enhanced stratiform rainfall during the
active MJO phases. In the vertical, these wet aggregates peaked in occurrence near the melting level, with dry
aggregates concentrated between 7 and 8 km, and nonoriented ice crystals dominating the radar echoes at
altitudes above 9 km. Time series of dry and wet aggregates showed that while both were enhanced during
rainy periods, a slight lag existed between these two categories, with dry aggregates leading wet, thereby
reﬂecting a transition from convective to stratiform echo. Graupel occurred generally near the 0°C level,
maintaining a relatively infrequent presence throughout all MJO phases of DYNAMO.
Division of the data into MCS- and sub-MCS-scale features, with further subdivision by month, highlighted
deeper convection associated with MCSs and a shift in echo-top distributions to lower heights during
December. Similarly, maximum heights of 30-dBZ echo were greater for organized systems with slightly
lower heights observed overall during December; however, 30-dBZ echo rarely extended more than 2 km
above that level, consistent with the observed general lack of lightning production in the region represented
by the S-PolKa data. This assessment was in agreement with the limit of graupel to within a few kilometers of
the melting level in convection observed by S-PolKa.
Reﬂectivity brightbands appeared less robust during December, consistent with a reduction in the amount of
wet snow aggregates observed in MCSs during that later active phase. Therefore, despite a general trend for
vertical distribution of hydrometeor types in convection during DYNAMO to be relatively independent of
whether the convection was organized into MCSs, differences in stratiform echo characteristics between the
active phases were notable and need to be investigated further. It is possible that this change in the character
of the convection was related to a change in the large-scale wind proﬁle, which exhibited stronger low-level
westerlies in December. The next step will be to examine the radar data within the context of the MCS
environments in more detail for each of the MJO active phases in DYNAMO.
Acknowledgments
This research was supported by
Department of Energy Atmospheric
Sciences Research grant DE-SC0008452
and National Science Foundation grant
AGS-1059611. We appreciate the data
collection and quality control efforts of
the NCAR S-PolKa team and of Stacy
Brodzik for locally managing the radar
data and software. We would also like to
acknowledge Beth Tully for her work
with the graphics and the helpful
comments from Aurelie Moulin and
three anonymous reviewers.
ROWE AND HOUZE
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