Journal
Decemberof the2018
Meteorological Society of Japan,
T. KADOYA
Vol. 96, No.
and 6,
H.pp.
MASUNAGA
535−548, 2018
DOI:10.2151/jmsj.2018-054

535

New Observational Metrics of Convective Self-Aggregation:
Methodology and a Case Study
Toshiki KADOYA
Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan

and
Hirohiko MASUNAGA
Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan(Manuscript received 5 October 2017, in final form 1 August 2018)

Abstract
A new observational measure, the Morphological Index of Convective Aggregation (MICA), is developed toobjectively detect the signs of convective self-aggregation on the basis of a simple morphological diagnosis ofconvective clouds in satellite imagery. The proposed index is applied to infrared imagery from the Meteosat-7
satellite and is assessed with sounding-array measurements in the tropics from Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011)/Dynamics of the Madden Julian Oscillation (MJO)(DYNAMO)/Atmospheric Radiation Measurement (ARM) MJO Investigation Experiment (AMIE). The precipitation events during the observational period are first classified by MICA into “aggregation events” and “nonaggregation events”. The large-scale thermodynamics implied from the sounding-array data are then examined, with afocus on the difference between the two classes. The composite time series show that drying proceeds over 6 – 12h as precipitation intensifies in the aggregation events. Such drying is unclear in the nonaggregation events. The
moisture budget balance is maintained in very different manners between the two adjacent sounding arrays forthe aggregation events, in contrast to the nonaggregation events that lack such apparent asymmetry. These results
imply the potential utility of the proposed metrics for future studies in search of convective self-aggregation in
the real atmosphere.
Keywords convective self-aggregation; tropical meteorology; satellite remote sensing; ground-based observations

1.

Introduction

It has been found in radiative-convective equilibrium (RCE) simulations that when certain conditions
are met, a random ensemble of convective clouds
become localized or aggregate into a single cluster, aphenomenon that is known now as convective selfCorresponding author: Toshiki Kadoya, Graduate Schoolof Environmental Studies, Nagoya University, Furo-cho,
Chikusa-ku, Nagoya 464-8601, Japan
E-mail: kadoya@satellite.isee.nagoya-u.ac.jp
J-stage Advance Published Date: 24 August 2018

aggregation (Held et al. 1993; Tompkins 2001; Bretherton et al. 2005). Self-aggregation proceeds with adramatic change in the thermodynamic fields consisting of an expanding dry area with few cold clouds anda moist precipitating area being increasingly confined
into a smaller domain. These processes, characteristicof convective self-aggregation, are explained in termsof various physical feedbacks. Bretherton et al. (2005)
found that convective-water-vapor-radiation feedbacks
are important for the progression of convective selfaggregation. Cloud radiative effects are another key
factor for self-aggregation owing to the shallow©The Author(s) 2018. This is an open access article published by the Meteorological Society of Japan undera Creative Commons Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/license/by/4.0).

536

Journal of the Meteorological Society of Japan

circulation caused by radiation cooling at the top ofclouds, as demonstrated with a cloud-resolving model(Muller and Held 2012) and a general circulation
model (Coppin and Bony 2015). Wing and Emanuel(2014) and Wing et al. (2017) quantitatively evaluatedthe physical mechanism of self-aggregation and foundthat the feedbacks among shortwave radiation, longwave radiation, and surface fluxes play an importantrole, and the energy convergence due to horizontaltransport strongly acts as positive feedback during
the intermediate stages of the aggregation process. In
order to investigate the validity of idealized simulations of self-aggregation, Holloway (2017) performed
a set of sensitivity experiments with a realistic configuration to compare with idealized simulations. They
confirmed that the aggregation mechanisms, as identified in the idealized simulations, are overall useful for
analyzing the real atmosphere as well.
The signs of convective self-aggregation have been
searched for also in observations. Tobin et al. (2012,2013) studied the dependence of tropospheric relative
humidity and outgoing longwave radiation on the
degree of convective aggregation in satellite observations and found that those features are consistent withthe results from existing numerical simulations, as
reviewed above. Stein et al. (2017), investigating theobserved relationship between convective aggregationand the vertical structure of clouds, discovered systematic behaviors in support of some known aspectsof convective self-aggregation.
However, the observational evidence for convective
self-aggregation is limited, and it remains unclear towhat extent the known aspects of convective self-aggregation, as identified by idealized simulations, arerelevant to the real atmosphere (Holloway et al. 2017).
In this study, we develop a new observational measure
to detect convective self-aggregation in satellite infrared imagery and apply it to a case study with sounding-array observations in the tropics for assessing thepotential utility of the proposed method.
The data and analysis framework used to conduct
the study is described in Section 2. In Section 3, a
new analysis method is proposed to detect the possible signs of convective aggregation from satellite
snapshots. The results are presented in Section 4 anddiscussed further in Section 5. A summary is given in
Section 6.
2.

Data sources
The datasets are outlined in this section.

Vol. 96, No. 6

2.1 Geostationary meteorological satellite
measurements
In order to track the temporal variability of convective clouds, the brightness temperature dataset from theMeteosat-7 Channel 8 (10.5 – 12.5 µm) measure­ments
is analyzed. Meteosat-7 is a European meteorologicalsatellite that was launched to fly over 0° in longitudein 1997 but was moved to 57.5°E in 2006. From July
2006 until March 2017, Meteosat-7 has been operated
to acquire data capturing the entire Indian Ocean(25 – 115°E, 45°S – 45°N). The current target area is
72 – 81°E and 8°S – 7°N, a domain just covering the insitu observational area described below, with a 30 min
temporal resolution and 0.045° × 0.045° spatial resolution. In this study, convective clouds are identified
by a constant threshold applied to infrared brightness
temperature. The threshold value is chosen, according
to the previous studies (e.g., Maddox 1980; Roca andRamanathan 2000), to be 240 K.
2.2 Sounding observations
The atmospheric thermodynamic field is analyzed
using the radiosonde observation network from the
Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011)/
Dynamics of the Madden Julian Oscillation (MJO)(DYNAMO)/Atmospheric Radiation Measurement(ARM) MJO Investigation Experiment (AMIE)(Yoneyama et al. 2013). This field campaign was carried out in the Indian Ocean region near the equator
during the period from late 2011 to early 2012. The
observational stations were deployed to form twosounding networks straddling the equator, that is,
the northern sounding array (NSA) and the southernsounding array (SSA), comprised of six sounding
stations (see Fig. 1, top). In this study, we used theColorado State University (CSU) array-averaged(i.e., averaging from the soundings consisting ofeach array) analysis product version 3a (Johnson andCiesielski 2013) and the area-averaged precipitationof TRMM 3B42v7 product, with averaging applied toall quarter-degree grid boxes within each array. Representativeness issues potentially arising from sporadic
station samples will be briefly discussed in Section
5. The column radiative heating stored in the CSU
DYNAMO product is originally taken from the Clouds
and the Earth’s Radiant Energy System (CERES)
data product. In this analysis, precipitation, watervapor mixing ratio, radiative heating, and moisturebudget parameters (the vertically integrated moisturetendency, horizontal advection, vertical advection,and budget-derived rainfall) are used from the CSU

December 2018

T. KADOYA and H. MASUNAGA

537

Fig. 1. Top. Snapshot of brightness temperature from Meteosat-7 infrared data at 0000 UTC 2 Oct 2011. The
rectangular area indicates the analysis domain of this study with the DYNAMO/CINDY2011/AMIE sounding
arrays also shown. The right figure is an enlarged view of the analysis region. The hatched area shows the convective area and the nonhatched area shows the clear-sky area. Bottom. Four example snapshots: (a) 0200 UTC
20 Oct 2011, MICA = 0.00238, SCAI = 13.6; (b) 0200 UTC 13 Oct 2011, MICA = 0.0555, SCAI = 7.72; (c)0200 UTC 6 Oct 2011, MICA = 0.215, SCAI = 0.872; (d) 0100 UTC 8 Oct 2011, MICA = 0.212, SCAI = 0.251.

DYNAMO product. The analysis period is threemonths, from October 1, 2011, to December 31, 2011,
and the observational time interval is 3 h.
3. Analysis framework
Tobin et al. (2012, 2013) devised the Simple Con-

vective Aggregation Index (SCAI) as an indicator of
aggregation, which is a parameter that combines thenumber of cloud clusters (N ) and the mean distance
between the clusters (D0):538

Journal of the Meteorological Society of JapanN D0
SCAI =
×1000,
N max L
D0 =

n

n

∏ d,
i=1

i

n = N ( N −1) / 2.
Here, Nmax is the maximum possible number or half
the total number of pixels occupying the study area
and L is the length scale of this area. SCAI shows
the degree of disaggregation; that is, SCAI is smaller
where cloud clusters are more aggregated. However,as Tobin et al. (2012) pointed out, SCAI by design
requires multiple cloud clusters to exist in the observed region, and SCAI is ill-defined in cases where
the number of cloud clusters is 0 or 1 since N = 0 and
D 0 = 0, respectively. In addition, SCAI is practically ameasure of the number of cloud clusters, as confirmed
later, and does not explicitly reflect the areal extent ofclear sky, which is one of the remarkable features ofself-aggregation. The predominance of clear sky in an
aggregated state is not directly considered for otherproposed metrics of the degree of convective aggregation either, including the organization index (Tompkins
and Semie 2017) and the convective organization
potential (COP) (White et al. 2018). In this study, we
devised a new index of convective self-aggregation,with these issues taken into account.
3.1 Morphological Index of Convective Aggregation(MICA)In this study, the following equation is proposed as
an index for the degree of self-aggregation:
MICA =∑ i Sc , i [ Aobs − Acls ]
×
.
Acls
Aobs

Here, Aobs is the total observed area, Acls is the area
enclosed by the smallest possible rectangle that can be
drawn around all cloud clusters in the domain, and Sc, iis the area of the i th cloud cluster or the contiguous
area with infrared brightness temperatures below
240 K. This nondimensional index, hereafter called
the Morphological Index of Convective Aggregation(MICA), is intended to measure the degree of convective aggregation, with a higher value representing ahigher degree of aggregation. As reviewed in Section
1, convective self-aggregation proceeds as convective
clouds aggregate into a limited number of large cloudclusters in a small confined area, while the clear-sky
area expands further. Therefore, in MICA, the degreeof aggregation is quantitatively evaluated by howVol. 96, No. 6

densely cloud clusters are aggregated into a confinedarea (å i Sc, i /Acls) and, at the same time, by how mucharea the clear sky outside the aggregated convective
area occupies relative to the whole observed domain([Aobs - Acls] /Aobs). Both of these elements increase as
the aggregation proceeds, and indeed MICA exhibits alarge value indicative of convective aggregation only
when these two parameters show high values simultaneously. Otherwise, MICA stays minimal, with theresult of either of these factors being small.
In order to visually demonstrate the performance ofMICA, MICA is applied to selected convective eventsin Fig. 1 (top). The observational domain (Aobs ) is arectangle of 9° × 15° or roughly 1,000 km × 1,700
km in size, which is comparable to or larger than thecomputational domain typical of RCE simulations
conducted in existing convective aggregation studies.
In this particular example, Acls is represented by ahatched square, which is confined to the lower halfof the observed domain (considered as an aggregatedstate). Figure 1 (bottom) shows the results of MICA
and SCAI applied to a few cases with different cloud
distributions. When convective clouds are sporadic
across the whole area (Fig. 1a), MICA becomes small(a, MICA = 0.00238) supposedly owing to the large
Acls expected from the widely spread cloud clusters.
In this case, SCAI is as high as 13.6, which implies adisaggregated state as well. As aggregation progresses(Figs. 1b, c), MICA turns out to be a large value(b, MICA = 0.0555, SCAI = 7.72; c, MICA = 0.215,
SCAI = 0.872). These examples demonstrate that
MICA can characterize the clumping state of cloudclusters as SCAI does. However, MICA does notwork as intended in cases where there is one cloudcluster covering the whole area, or in other extreme
cases where a few small cloud clusters happen to befound in one corner of the observed domain that isotherwise clear (as in Fig. 1d). In the former case,
MICA becomes 0 because a clear-sky area is requiredby MICA to always accompany aggregation. If theobserved region is set to be sufficiently large, however, such situations can be practically eliminated. In
the latter case, the index value increases for a false
reason (d, MICA = 0.212, SCAI = 0.251), making it
impossible to distinguish from the bona fide aggregation. Although this case is rather rare, it is difficult to
exclude mechanically from MICA only. MICA will
not work properly either when an “outlier” exists inthe farthest corner of the domain from the main groupof clumped clusters, in which case [Aobs - Acls] /Aobs
would be nearly 0 for a potentially aggregated state.
For these reasons, it is necessary to omit the cases

December 2018

T. KADOYA and H. MASUNAGA

where the cloud amount in the observational region
is extremely close to 0 or 1, and it is desirable as wellto use independent quantities such as precipitation or
cloud coverage together with MICA as a reference
to avoid erroneous estimates. Unlike SCAI, MICA
does not evaluate the randomness of convective distribution, but MICA has a unique advantage of fully
exploiting the morphological properties of satellite
infrared imagery including nonprecipitation regions.
3.2 Compositing analysis
In order to investigate the temporal variability of
MICA and meteorological parameters associatedwith the possible occurrence of convective selfaggregation, a composite analysis is performed in
the manner described as follows. Figure 2 shows thecomplete time series of the array-averaged rainfall

539

from the TRMM 3B42v7 product, MICA, SCAI, andthe number of convective clusters for a three-month
period. MICA is plotted upside down so it compares
directly with SCAI, with the downward direction indicating a higher degree of aggregation. While SCAI isclearly influenced by three MJO episodes (Yoneyama
et al. 2013), MICA exhibits no sign of intraseasonal
modulation because the clear-sky term, considered
as a signal of aggregation in MICA, stays very small
within MJO convective envelopes. Nevertheless,
MICA and SCAI overall agree in that convective
aggregation tends to proceed in early October,early December, and late December, all during theinactive phases of the MJO. On the other hand, only
SCAI finds spells of aggregation in early and middle
November. This discrepancy is practically insignificant because these periods do not contain major

Fig. 2. Time series of (a) DYNAMO/CINDY2011/AMIE averaged rainfall over the total NSA + SSA domain (black),(b) aggregation index MICA (black, labeled on the left) and SCAI (gray, labeled on the right) with MICA plotted upside down, and (c) the number of convective clusters for the same region. The light-blue line and orange
line indicate DYNAMO NSA-averaged precipitation and DYNAMO SSA-averaged precipitation, respectively.
The arrows in (a) show the aggregation (red) and nonaggregation (blue) events, classified according to themethod described in Section 3.

540

Journal of the Meteorological Society of Japan

Vol. 96, No. 6

Fig. 3. Scatter diagram of the relationship between the aggregation index (left: MICA; right: SCAI) and column
water vapor.

precipitation events in any case (Fig. 2a). Figures 2band 2c show that SCAI closely traces the number ofcloud clusters as expected from the way it is defined(Tobin et al. 2012). Figure 3 shows the relationshipbetween MICA/SCAI and the array-averaged column
water-vapor mixing ratio during the whole observation period. This provides evidence that MICA and
SCAI each suggest a negative correlation between
moisture and the degree of aggregation (recall that
SCAI decreases as aggregation advances) as known
from previous studies, although the spread is so largethat the systematic correlation is marginal.
The ± 24 h time sequence is sampled around eachprecipitation peak (time = 0) on the basis of MICA
and then classified as either “aggregation events” or“nonaggregation events”. It is noted that this nomenclature remains tentative until MICA is proven firmly
in future investigations to be a reliable measure ofself-aggregation as known from the literature. The
extracted time series are composited into a statisticaltime series representing each of the aggregation andnonaggregation events. Here, the aggregation eventsare defined at the precipitation peak when MICA ≥0.1 and precipitation ≥ 5 mm day−1 (red arrows in Fig.
2a), and the nonaggregation events are identified with
MICA < 0.1 and precipitation ≥ 5 mm day−1 (bluearrows in Fig. 2a). The precipitation peaks are firstsought individually in the NSA- and SSA-averaged
rainfall, and if multiple peaks from either array arepresent within consecutive ± 24 h, only the highest
among them is selected. Precipitation peaks lower
than 5 mm day−1 are not used for the composite analysis. We repeated the analysis by changing the threshold definitions within a plausible range and confirmedthat it does not qualitatively affect the subsequent discussions (not shown). By this method, 9 aggregation
events and 17 nonaggregation events are obtained.

Figure 4 shows snapshots of all the extracted aggregation events at the hour of peak precipitation. These
snapshots show that major, if not whole, portions of
aggregated clouds and the surrounding clear-sky areas
are overall captured by the sounding arrays, giving
credibility to the thermodynamic budget analysis presented later at least around the time of peak activity. It
is not ensured, however, whether the same argument
holds over an extended period of time because convective systems often move into and out of the study
domain as they develop and dissipate on their own.
Cloud clusters traversing the domain could introduce
misleading signals in the statistics, although the key
physical signatures intrinsic to the convective dynamics would not be entirely lost by including propagating
convective systems together in the composite (Masu­
naga 2015).
It may be noteworthy that the aggregation events
identified in this work prefer relatively quiescentperiods not heavily disturbed by frequent intrusionsof cloud clusters. While systematic zonal propagationof disturbances into the study domain is evident
during three MJO passages as noted earlier (Fig. 5a),such extensive zonal propagation is less clear outside
the MJO convective envelopes. No clear signal of
meridional propagation is found at any time (Fig. 5b).
With the exception of the November 9th event, which
probably belongs to a propagating system, most of theconvective events outside the active MJO phase areunlikely to be totally aliased by external disturbancesthat accidentally traverse the study domain. Given thatthe aggregation events detected in the study are mostly
outside the active phases of MJO (Fig. 2a), it would
not be very likely that convective aggregation as discussed here is heavily affected by advection from theneighboring regions. With that being said, the possible
effects of propagating systems on the interpretation

December 2018

T. KADOYA and H. MASUNAGA

541

Fig. 4. Snapshots of infrared brightness temperature for all nine observed aggregation events at the time of peak
precipitation.

of MICA are not fully sorted out by the present observations alone and have yet to be examined further
elsewhere.
4.

Results

Figure 6a shows the composite time series of MICA
for the aggregation events (red line) and for the nonaggregation events (blue line). For the aggregation
events, a conspicuous increase in MICA is observed
toward the hours of peak precipitation, with the amplitude ranging over an order of magnitude from 0.01 to
above 0.1. For the nonaggregated precipitation event,on the other hand, MICA stays small by definition.
This confirms that the aggregation events are isolated

from the nonaggregation events as effectively as
intended. It is noted that the variation of MICA for theaggregation events is confined within ± 12 h aroundthe precipitation peak time (at 0 h). This time scale
may be set either by a local development of convec­
tive systems or by propagating systems entering andleaving the study domain, so it is unclear for the
moment whether it physically represents the aggregation processes. If this time scale is actually relevantto the aggregation in nature, it contrasts with theprevious studies based on numerical simulations,where convective self-aggregation makes progress
more slowly over a few weeks to several tens of days
(Holloway et al. 2017).

542

Journal of the Meteorological Society of Japan

Vol. 96, No. 6

Fig. 5. (a) Time–longitude (averaged over 8°S – 7°N) and (b) time–latitude (averaged over 72 – 81°E) sections ofinfrared brightness temperature. The area bound between two white lines or 72 – 81°E in (a) and 8°S – 7°N in (b)indicates the observed region to define MICA.

Fig. 6. Composite time series of (a) the aggregation index MICA, (b) DYNAMO/CINDY2011/AMIE averaged
rainfall over the total NSA + SSA domain, (c) DYNAMO NSA-averaged precipitation, and (d) DYNAMO
SSA-averaged precipitation for the aggregation (red line) and nonaggregation (blue line) events. The time axis
spans 24 h before and after the time of precipitation peak at t = 0. Note that the range of precipitation varies
among panels (b) – (d). All aggregation and nonaggregation events for (a) and (b), while aggregation events are
sampled only from the cases where convection resides in SSA for (c) and (d).

December 2018

T. KADOYA and H. MASUNAGAIn Fig. 2a, precipitation averaged within NSA (lightblue line) and SSA (orange line) is shown together
with the mean of these two (black line). For the aggregation events (red arrows in Fig. 2a), precipitation
is localized in either NSA or SSA most of the time,
whereas in the nonaggregation events (blue arrows
in Fig. 2a), precipitation occurs simultaneously inboth NSA and SSA in most cases. This is as expectedfrom the definition of MICA, designed to embody thehorizontal concentration of precipitation characteristicof self-aggregation. The domain-mean or NSA-andSSA-combined precipitation (Fig. 6b) shows that the
nonaggregation events produce significantly heavier
rainfall than the aggregation events. This contrast
virtually reflects the MJO variability because theaggregation events prefer the dry phases of MJO.
Ideally, it is desirable for a fair comparison to sample
the aggregation and nonaggregation events that fall ina similar precipitation range, but this is not feasible
for the limited samples of this work. Figures 6c and543

6d show the composite time series of precipitationin NSA and SSA during aggregation (red) and nonaggregation (blue). Because precipitation is mostly
localized in SSA (see Figs. 2a, 4), the composite
analysis hereafter (Figs. 6c, d, 9) excludes the caseof December 8 where precipitation dominates NSA
for clarity of presentation. As a result, precipitation
is virtually absent in NSA for the aggregation events,
making a sharp contrast to the nonaggregation events
showing a distinct peak at t = 0. The precipitation
evolution for the nonaggregation events, thoughqualitatively similar, differs in magnitude by 50 % at
the peak between NSA and SSA. In SSA, the rainfall
evolves over time in a similar manner, although theamplitude of variability is greater in the aggregation
events than in the nonaggregation events.
Figure 7 shows the composite time series of thewater-vapor mixing ratio anomaly defined against thetemporal average during ± 24 h. Figures 7a and 7cshow NSA and SSA for the aggregation events, andFig. 7. Composite time series of water-vapor mixing ratio anomaly (g kg−1) with respect to the average overall composite times (± 24 h) from DYNAMO NSA (a, b) and DYNAMO SSA (c, d) for the aggregation or
nonaggregation events. For the aggregation events, only the cases where precipitation is localized in SSA are
included in the analysis. Panels (a) and (c) use SSA only aggregation events. Panels (b) and (d) use all nonaggregation events.

544

Journal of the Meteorological Society of Japan

Figs. 7b and 7d present the two sounding arrays forthe nonaggregation events. Nonaggregation precipitation systems (Figs. 7b, d) accompany a moist anomaly
during 12 h before the precipitation. In the aggregation events, on the contrary, a dry anomaly persists inthe midtroposphere for 6 h before and after the precipitation peak in both arrays (Figs. 7a, c). Drying before
the precipitation peak is at odds with the known characteristics prior to developing precipitation in general(e.g., Sherwood 1999), but it does not contradict the
expansion of the drying area accompanying convective self-aggregation. In order to investigate the potential factors responsible for the differences between theaggregation and nonaggregation events, we conduct amoisture budget analysis in the next section.
5.

Discussion

5.1 Time variation of the water vapor and radiation
fieldsThe midtropospheric moisture variability was found
to exhibit contrasting behaviors between the aggregation and nonaggregation events. The possible factors
controlling the observed water-vapor fluctuations are
explored in terms of the moisture budget analysis. The
vertically integrated large-scale moisture budget iswritten as follows:
∂L q
∂Lv q
= − ν ⋅∇Lv q − ω v − Q2
∂p
∂t
∂L q
= − ν ⋅∇Lv q − ω v + Lv ( E − P ),
∂p
where
⋅ ≡

1 psfc(⋅) dp,g ∫ prq is the water-vapor mixing ratio, Lv is the specificlatent heat of vaporization, ν is the horizontal flow,ω is the vertical pressure velocity, Q 2 is the apparent
moisture sink (Yanai et al. 1973), P is precipitation, E
is the surface evaporation rate, g is the gravitational
acceleration, psfc is the surface pressure, and pr is the
top of the atmosphere chosen to be 50 hPa. Each termof the budget formula is calculated using the arrayaveraged radiosonde observations except for the Q2
term. Since it is difficult to directly observe Q 2, it iscalculated as the residual once the other terms are
given.
Figure 8 shows the results of the column-integrated
water-vapor budget analysis. As mentioned above,
the aggregation composite consists only of the cases

Vol. 96, No. 6

where the precipitation peak resides in SSA. In
aggregation events, subtle drying is observed prior
to the peak precipitation in NSA, whereas in SSA,
notable drying spells appeared multiple times during
the evolution. These drying events are driven mainly
by lower- and midtropospheric variability, though notentirely in a coherent manner across altitudes (Fig. 7),
which does not contradict the expansion of dry areas
as known for the self-aggregation. The nonaggregation events experience notable moistening before t =0, followed by a neutral or marginally drying spell(Figs. 8b, d). The horizontal advection of water vapor
has little contribution to the moisture budget in allcases. This is in line with a previous study analyzing
DYNAMO datasets, showing that the horizontal advection plays only a minor role in the moisture budget(Ruppert and Johnson 2015). Inoue and Back (2015)
also confirmed the smallness of horizontal moistureadvection for a short-time variability of one to twodays, although horizontal advection can be important
on intraseasonal time scales. However, care must be
taken regarding the uncertainties in the horizontal advection estimates. The horizontal moisture advection
could be erroneous when the horizontal moisture flux
is poorly sampled by the limited number of stations
consisting of the DYNAMO sounding arrays (Hannah
et al. 2016).
On the other hand, the difference is evident in thevertical moisture advection between the aggregationand nonaggregation events. In the aggregation events(Figs. 8a, c), the vertical moisture advection remains
small in NSA, whereas it grows significantly in SSA,
confirming a vigorous development of convective
clouds only in SSA (recall that we have selected thecases with convection residing in SSA only). Meanwhile, in the nonaggregation events (Figs. 8b, d),
the vertical moisture advection has considerable
magnitude in both NSA and SSA. Moistening owing
to vertical advection is largely counteracted by Q 2 ,leaving behind a small moisture tendency.
The vertical moisture advection term and Q 2 roughly counteract each other for the nonaggregation events.
For the aggregation events in SSA, however, Q 2 does
not precisely coincide with the vertical advection,
resulting in a moisture budget imbalance that accounts
for the negative moisture tendency mentioned earlier.
It is suggested that efficient precipitation for the aggregation events temporarily exceeds moistening byvertical advection and causes drying. As such, drying
is driven by moist convection itself for those events,
whereas in the RCE experiments, the primary sourceof drying is the subsidence in the clear-sky region.

December 2018

T. KADOYA and H. MASUNAGA

545<dq/dt>-<wdq/dp>-<udq/dp>-<Q2>

Fig. 8. Composite time series of vertically integrated moisture budget parameters (W m−2) from DYNAMO NSA(a, b) and DYNAMO SSA (c, d) for the aggregation or nonaggregation event. Budget terms are calculated asshown in Section 5, with the Eulerian tendency (black), horizontal advection (yellow), vertical advection (red),and apparent sources (green). Panels (a) and (c) use SSA only aggregation events. Panels (b) and (d) use all
nonaggregation events.

To examine the budget imbalance in light of thesatellite-observed precipitation, Figs. 9a and 9b show
the time series of TRMM 3B42v7 precipitation andQ 2-derived precipitation in SSA for the aggregation
events and nonaggregation events, respectively.
The evaporation flux required for evaluating the Q 2derived precipitation is taken from the Woods Hole
Oceanographic Institution product as stored in the
CSU DYNAMO dataset. The Q 2-derived precipitation
captures a broad enhancement of rainfall but fails
to reproduce a sharp peak as implied by the satellite
observations. A plausible source of this discrepancy
arises from uncertainties in the horizontal moistureadvection as mentioned above. If the difference inprecipitation in Fig. 9a is due to errors in the horizontal advection of water vapor, it is suggested that theexcessive precipitation in Q 2 before -12 h should be
attributed to drying by horizontal advection, while anunderestimated precipitation around the precipitation

peak might imply moistening by horizontal advection,
respectively. Although it is difficult to conclude thatwith confidence from the present data, the evolutionof the aggregated events may be associated with moisture import to or moisture export from the precipitating atmospheric column, which is worthy of a future
investigation in light of the similar dynamic processes
known for convective self-aggregation. Figures 9c
and 9d show the composite time series of radiative
heating rates for each sounding array. The striking
enhancement of radiative heating (or suppressed cooling) around t = 0 for aggregation events is explained
presumably by the greenhouse effect of high clouds
developing as precipitation intensifies. It may bealiased by the diurnal cycle of shortwave heating tosome degree, but not entirely because the aggregation
events currently sampled are not synchronized inlocal time and would not provide a clear t = 0 peakas observed by the diurnal solar cycle alone. The non-

546

Journal of the Meteorological Society of Japan

Vol. 96, No. 6

TRMM rain
Q2-derived rain

NSA
SSA
Fig. 9. Composite time series of (a, b) DYNAMO SSA-averaged rainfall (mm day−1) from TRMM 3B42v7
product (solid line) and Q2-budget-derived value (dash-dotted line). (c, d) Column net radiation (W m−2) fromDYNAMO/CINDY2011/AMIE observation region (breakdown in NSA and SSA) for the aggregation or nonaggregation events. Panels (a) and (c) use SSA only aggregation events. Panels (b) and (d) use all nonaggregation
events.

aggregation events experience a similar variability ofradiative heating, whereas the amplitude of variability
is relatively modest.
5.2 Do these observations show signs of convective
self-aggregation?
The aspects of the aggregation events extracted
from the present analysis resemble the known characteristics from past numerical studies, including the
growth of dry areas in the vicinity of intensifying
convection and the resulting enhancement of radiative
cooling. On the other hand, dissimilarities are also
evident, such as the absence of notable subsidence inthe current observations, since the vertical moistureadvection does not imply significant drying in the
nonprecipitating domain (Fig. 8a). Convective events
analyzed in this work are rather limited in number,making it impossible to subsample the data by, forinstance, precipitation regimes as done by Tobin et al.

(2012) for a robust interpretation of the results. It
is discussed in this section whether the signs of theself-aggregation, as suggested by MICA, are relevant
to what we know from the literature, particularly inthe contexts of the MJO and of the aggregation timescales.
A vast majority of the aggregation events identified
in the current observations occur in the absence of
MJO convection, whereas precipitation peaks during
active MJO phases are mostly classified as nonaggregation events. A possible explanation for this is thatconvective aggregation favors a relatively quiescentenvironment presumably because an intrusion of synoptic-scale disturbances would destroy the large-scale
thermodynamic balance required to keep the aggregation in progress (e.g., Wing and Emanuel 2014). In areal atmosphere being constantly disturbed by external
dynamic forcing, convective aggregation is unlikely
to proceed over an extended period of time as in ide-December 2018

alized simulations (e.g., Bretherton et al. 2005). The
current finding is at odds with the existing hypothesisthat the convective envelope of the MJO may be amanifestation of self-aggregation (Arnold and Randall
2015; see also Holloway et al. 2017). This hypothesis
is not necessarily supported by a broad consensus (e.g.,
Mapes 2016) and has yet to be tested further.
The observed aggregation events evolve over atime scale of about 24 h, which is not surprising giventhat tropical cloud clusters often have a lifetime of
one to two days (Nakazawa 1988). This time scale,
however, is substantially shorter than typically seenin RCE simulations of convective self-aggregation(Holloway et al. 2017). The time scale difference ispartly due to the fixed spatial scale imposed by the
DYNAMO sounding arrays, which may introduce acertain temporal scale of artificial origin. This also
explains why the observed aggregation events are
disaggregated as quickly while the aggregated state in
RCE simulations lasts much longer. Another plausible
reason for this discrepancy arises because incessantly
disturbed environments hinder the sustainable development of aggregation as mentioned above. Idealized
simulations carried out for studying convective aggregation are typically initialized with a homogeneous
RCE state, which may inevitably require tens of days
until the whole domain is “disturbed” as normallyobserved in the real atmosphere. The third possibility
is that numerical models might not properly simulate
physical elements crucial for the rapid evolution of
convective aggregation, including, for example, thecongestus-mode dynamics fed by cirrus radiative
effects (Masunaga and Bony 2018). These issues arelargely speculative at this point and are left for future
investigations to seek further evidence.
6.

547

T. KADOYA and H. MASUNAGA

Conclusion

In this study, we detected and analyzed convective
self-aggregation in real atmosphere on the basis ofsatellite and field observations. An objectively definedindex (MICA) based on satellite infrared imagery,in the same spirit as SCAI (Tobin et al. 2012), wasproposed to quantify the degree of convective selfaggregation. The time series of MICA is obtained
from Meteosat-7 observations and is related to thethermodynamic fields derived from the DYNAMO/CINDY2011/AMIE sounding-array measurements.
Precipitation events captured during the DYNAMO
campaign are categorized with MICA into aggregationand nonaggregation events and are used to contrast
the composite time series associated with convective
self-aggregation against those without. The temporalvariability of the thermodynamic field accompanying
self-aggregation was found to exhibit several features
reminiscent of the existing knowledge of convective
self-aggregation, although some discrepancies are evident as well. MICA and the number of cloud clusters,a proxy of SCAI, vary over time in a coherent manner.
The current observations imply that remarkable aggregation, as suggested by MICA, proceeds within aperiod of 24 h, substantially shorter than a typical timescale targeted in previous studies on self-aggregation.
Otherwise, the characteristics known for self-aggregation, such as drying of a large-scale field, localizationof the precipitation area, and enhanced radiative
cooling, are present in the observations. A large-scalemoisture budget analysis suggests an overall balance between the vertical moisture advection andthe apparent moisture sink, while there remains thepossibility that the horizontal advection plays a rolein transporting moisture between the precipitating
domain and adjacent dry area. Although the aspects ofthe current results suggest possible signs of convective self-aggregation in nature, the present work only
opens a long pathway in search for more firm observational evidence to be confirmed in the future. The
present study period is limited to the three months ofintensive observation of DYNAMO, so the representativeness of the present findings needs to be assessedsomewhere else. In the future, it will be interesting tocompare the performance of MICA in greater detailin light of other related indices such as SCAI (Tobin
et al. 2012), the organization index (Tompkins andSemie 2017), and the COP (White et al. 2018) and therelevant thermodynamic fields on a global, long-termbasis.
Acknowledgments
The Meteosat-7 infrared data during the DYNAMO/CINDY2011/AMIE campaign are archived andprovided by the DYNAMO Data Archive Center at
National Center for Atmospheric Research (NCAR)
Earth Observing Laboratory (EOL) (https://www.
eol.ucar.edu/field_projects/dynamo). The DYNAMO
sounding array data are provided by Colorado
State University Mesoscale Dynamics Group and areavailable online from http://johnson.atmos.colostate.
edu/dynamo/products/array_averages/. This work is
supported by Japan Society for the Promotion of
Science (JSPS) Grants-in-Aid for Scientific Research(KAKENHI) Fund for the Promotion of Joint International Research Development (15KK0157).

548

Journal of the Meteorological Society of Japan

References
Arnold, N. P., and D. A. Randall, 2015: Global-scale convective aggregation: Implications for the Madden–
Julian Oscillation. J. Adv. Model. Earth Syst., 7, 1499–
1518.
Bretherton, C. S., P. N. Blossey, and M. Khairoutdinov,
2005: An energy-balance analysis of deep convective
self-aggregation above uniform SST. J. Atmos. Sci.,
62, 4273–4292.
Coppin, D., and S. Bony, 2015: Physical mechanisms controlling the initiation of convective self-aggregation
in a General Circulation Model. J. Adv. Model. Earth
Syst., 7, 2060–2078.
Hannah, W. M., B. E. Mapes, and G. S. Elsaesser, 2016:
A Lagrangian view of moisture dynamics during
DYNAMO. J. Atmos. Sci., 73, 1967–1985.
Held, I. M., R. S. Hemler, and V. Ramaswamy, 1993:
Radiative-convective equilibrium with explicit twodimensional moist convection. J. Atmos. Sci., 50,
3909–3927.
Holloway, C. E., 2017: Convective aggregation in realistic
convective-scale simulations. J. Adv. Model. Earth
Syst., 9, 1450–1472.
Holloway, C. E., A. A. Wing, S. Bony, C. Muller, H. Masunaga, T. S. L’Ecuyer, D. D. Turner, and P. Zuidema,
2017: Observing convective aggregation. Surv. Geophys., 38, 1199–1236.
Inoue, K., and L. Back, 2015: Column-integrated moist static
energy budget analysis on various time scales during
TOGA COARE. J. Atmos. Sci., 72, 1856–1871.
Johnson, R. H., and P. E. Ciesielski, 2013: Structure andproperties of Madden-Julian oscillations deduced fromDYNAMO sounding arrays. J. Atmos. Sci., 70, 3157–
3179.
Maddox, R. A., 1980: Mesoscale convective complexes.
Bull. Amer. Meteor. Soc., 61, 1374–1387.
Mapes, B. E., 2016: Gregarious convection and radiative
feedbacks in idealized worlds. J. Adv. Model. Earth
Syst., 8, 1029–1033.
Masunaga, H., 2015: Assessment of a satellite-based atmospheric budget analysis method using CINDY2011/
DYNAMO/AMIE and TOGA COARE sounding arraydata. J. Meteor. Soc. Japan, 93A, 21–40.
Masunaga, H., and S. Bony, 2018: Radiative invigorationof tropical convection by preceding cirrus clouds. J.
Atmos. Sci., 75, 1327–1342.
Muller, C. J., and I. M. Held, 2012: Detailed investigation of
the self-aggregation of convection in cloud-resolving
simulations. J. Atmos. Sci., 69, 2551–2565.
Nakazawa, T., 1988: Tropical super clusters within intraseasonal variations over the western Pacific. J. Meteor.

Vol. 96, No. 6

Soc. Japan, 66, 823–839.
Roca, R., and V. Ramanathan, 2000: Scale dependence ofmonsoonal convective systems over the Indian Ocean.
J. Climate, 13, 1286–1298.
Ruppert, J. H., Jr., and R. H. Johnson, 2015: Diurnally
modulated cumulus moistening in the preonset stageof the Madden-Julian Oscillation during DYNAMO. J.
Atmos. Sci., 72, 1622–1647.
Sherwood, S. C., 1999: Convective precursors and predictability in the tropical western Pacific. Mon. Wea. Rev.,
127, 2977–2991.
Stein, T. H. M., C. E. Holloway, I. Tobin, and S. Bony,
2017: Observed relationships between cloud verticalstructure and convective aggregation over tropicalocean. J. Climate, 30, 2187–2207.
Tobin, I., S. Bony, and R. Roca, 2012: Observational evidence for relationships between the degree of aggrega­tion of deep convection, water vapor, surface fluxes,
and radiation. J. Climate, 25, 6885–6904.
Tobin, I., S. Bony, C. E. Holloway, J.-Y. Grandpeix, G.
Sèze, D. Coppin, S. J. Woolnough, and R. Roca, 2013:
Does convective aggregation need to be represented
in cumulus parameterizations? J. Adv. Model. Earth
Syst., 5, 692–703.
Tompkins, A. M., 2001: Organization of tropical convection
in low vertical wind shears: The role of water vapor.
J. Atmos. Sci., 58, 529–545.
Tompkins, A. M., and A. G. Semie, 2017: Organization oftropical convection in low vertical wind shears: Roleof updraft entrainment. J. Adv. Model. Earth Syst., 9,1046–1068.
White, B. A., A. M. Buchanan, C. E. Birch, P. Stier, and K.
J. Pearson, 2018: Quantifying the effects of horizontal
grid length and parameterized convection on the
degree of convective organization using a metric of
the potential for convective interaction. J. Atmos. Sci.,
75, 425–450.
Wing, A. A., and K. A. Emanuel, 2014: Physical mechanisms controlling self-aggregation of convection in
idealized numerical modeling simulations. J. Adv.
Model. Earth Syst., 6, 59–74.
Wing, A. A., K. Emanuel, C. E. Holloway, and C. Muller,
2017: Convective self-aggregation in numerical simulations: A review. Surv. Geophys., 38, 1173–1197.
Yanai, M., S. Esbensen, and J.-H. Chu, 1973: Determinationof bulk properties of tropical cloud clusters fromlarge-scale heat and moisture budgets. J. Atmos. Sci.,
30, 611–627.
Yoneyama, K., C. Zhang, and C. N. Long, 2013: Tracking
pulses of the Madden-Julian oscillation. Bull. Amer.
Meteor. Soc., 94, 1871–1891.