Journal
Decemberof the2015
Meteorological Society of Japan, Vol.H.
93A,
MASUNAGA
pp. 21−40, 2015
DOI:10.2151/jmsj.2015-032

21

Assessment of a Satellite-Based Atmospheric Budget Analysis Method Using
CINDY2011/DYNAMO/AMIE and TOGA COARE Sounding Array Data
Hirohiko MASUNAGA
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan(Manuscript received 8 February 2015, in final form 16 April 2015)

Abstract
A satellite-based method of moisture and thermal budget analysis is examined in comparison with sounding
array observations from Cooperative Indian Ocean experiment on Intraseasonal variability in the Year of 2011
(CINDY2011)/Dynamics of the Madden-Julian Oscillation (MJO) (DYNAMO)/Atmospheric Radiation Measurements (ARM) MJO Investigation Experiment (AMIE) and from the Tropical Ocean Global Atmosphere Coupled
Ocean Atmosphere Response Experiment (TOGA COARE). Overall, the satellite analysis is found to quantitatively
reproduce the statistical behaviors of large-scale mean vertical motion, moisture convergence, and moist static
energy (MSE) convergence as observed from the sounding arrays. However, individual convective events generally
do not delineate a systematic evolutionary track but are heavily spread around the ensemble mean of moisture
and MSE convergences in composite space.
Next, the convective events are broken down into “developing”, “off-centered”, and “passing-by” classes using
geostationary infrared measurements in an attempt to sort irrelevant samples that are not representative of convective dynamics. All the three composite classes show qualitatively similar evolutions except for the amplitude ofvariability, with genuine developing events being greatest in amplitude and passing-by disturbances being weakest.
The spread among individual events is substantially reduced when the convective events immune to strong synoptic-scale influences are isolated and the contribution of horizontal advection is excluded from MSE convergence.
Keywords

1.

convection; satellite remote sensing; tropical dynamics

Introduction

A moisture and thermal budget analysis of thetropical atmosphere is often applied to the diagnosisof large-scale dynamics involving moist convection. Since early studies (e.g., Yanai 1961; Reed andRecker 1971; Yanai et al. 1973), in-situ observations
from sounding arrays have provided excellent opportunities, while satellites had limited capabilities toprofile the entire vertical structure of the atmosphere.
Corresponding author and present afffilation: Hirohiko
Masunaga, Institute for Space-Earth Environmental
Research, Nagoya University, Furo-cho Chikusa-ku,
Nagoya 464-8601, Japan
E-mail: masunaga@hyarc.nagoya-u.ac.jp©2015, Meteorological Society of JapanA breakthrough was brought about by new satellite
instruments launched into the orbit in the past two
decades. The Atmospheric Infrared Sounder (AIRS)
and Advanced Microwave Sounding Unit (AMSU)
suite (hereafter AIRS/AMSU) aboard the Aqua satellite has made it possible to observe vertical temperature profiles at a 1-km resolution (Aumann et al.
2003), and the Tropical Rainfall Measuring Mission(TRMM) and CloudSat radars have enabled us to
study the internal structure of clouds and precipitation
to a great detail.
Taking advantage of such recent technologicaladvances in remote sensing from space, Masunaga(2013) devised a budget analysis scheme that isdesigned for an exclusive use of satellite data. Masunaga and L’Ecuyer (2014) further advanced this meth-22

Journal of the Meteorological Society of Japanodology to derive large-scale mean vertical velocity.
The aim of this study is to shed new light on the longstanding problems in tropical meteorology regarding
the interplay of convective clouds and large-scale
dynamics from the thermodynamic budget perspectives. Numerous studies have been conducted withfocus on gross moist stability (GMS) (Neelin andHeld 1987; Raymond et al. 2009). The analysis
method of Masunaga and L’Ecuyer (2014) provides
an observational tool to diagnose tropical dynamics
in light of GMS in a way, unlike reanalysis datasets,fully independent of parameterized physics.
The strategy of Masunaga (2013) is unique in a
few aspects compared with conventional approaches
employed for thermodynamic budget analysis. First,
it is applied to a large number of satellite snapshots projected onto a composite time axis definedwith respect to the timing of convective occurrence(Masunaga 2012a). This statistical approach circumvents the practical limitation that individual meteorological events are unable to monitor continuously
over time from low Earth orbiting (LEO) satellites. Second, a new strategy has been explored tocompute large-scale mean horizontal convergence(and hence vertical motion) without direct knowledgeof wind estimates since no present spaceborne sensor
measures the vertical structure of winds (see Section
2 for a summary of technical details). This non-traditional method has yet to be validated against independent sources of observations.
In this study, sounding array data from two field
campaigns are utilized as the reference for an assessment of the satellite analysis. The primary reference
dataset analyzed here was acquired during the threefield campaigns conducted closely in collaboration as follows: Cooperative Indian Ocean experiment on Intraseasonal variability in the Year of2011 (CINDY2011), Dynamics of the Madden-Julian Oscillation (MJO) (DYNAMO), and Atmospheric Radiation Measurements (ARM) MJO
Investigation Experiment (AMIE) (Yoneyama
et al. 2013). A secondary reference is the data fromthe Tropical Ocean Global Atmosphere Coupled
Ocean Atmosphere Response Experiment (TOGA
COARE) (Webster and Lukas 1992). It is not
attempted to restrict satellite snapshots to the regions
and periods of the field campaigns, because the satellite-based composite time series by design requirea far greater volume of samples to be statisticallyrobust. Therefore, the comparison will not be precise
on an event-by-event basis but relies on the assumption that the fundamental characteristics of tropicalVol. 93A

convection and its thermodynamic environment arenonetheless shared among all the measurements
analyzed (see Section 5 for discussion on this issue).
The data and composite analysis method are
first described in Section 2, and the results from thecomposite analysis are presented in Section 3. The
composite analysis is examined with a morphologicaldiagnosis based on geostationary satellite imagery
in Section 4 and discussed further in Section 5. The
present findings are summarized in Section 6.
2.

Data and method

The datasets and analysis method are outlined inthis section.
2.1 LEO satellite observations
The vertical profiles of humidity and temperatureobserved from the AIRS/AMSU serve as a primary
input into the moisture and thermal budget analysis.
The budget equation is completed with additionalparameters such as precipitation and surface wind
retrieved from other A-Train instruments and Quick
Scatterometer (QuikSCAT). The Tropical Rainfall
Measuring Mission (TRMM) Precipitation Radar(PR) is employed to determine the timing of precipitation occurrence. The target area is global tropical oceans between 15°S and 15°N and the analysis
period spans from December 1, 2002 to November
30, 2009.
The overall analysis procedure is briefly outlined
below. Satellite soundings collected over the entire
analysis period are sorted by the observational timedifferences relative to the moment when the TRMM
detects precipitation. The accumulated snapshotsof temperature and moisture are averaged into acomposite time series as a statistical representationof the atmospheric variability over time associatedwith the development of moist convection (Masunaga 2012a). The composite time series is constructedfrom ~ 30,000 satellite snapshots within an hourly
bin at the time of peak convection and more than ~300,000 for hours away from active convection. The
sample size varies over time because infrared soundings become more difficult to sample as cloud cover
increases.
The composite moisture and dry static energy(DSE) are vertically integrated over the free troposphere (FT), defined here as the entire troposphericlayer above cloud base (CB), and the sub-cloud layer(SC) so that the FT convergence of moisture, q, andDSE, s, is deduced as a residual term in the budget
equations:December 2015−〈∇·qv〉FT = ∂ 〈q〉+〈∇·qv〉SC − E + P ,∂t

H. MASUNAGA(1)

the boundary condition at CB imposed by QuikSCAT
wind,ω CB = 〈∇·qv〉SC .

and−〈∇·sv〉FT = ∂ 〈s〉+〈∇·sv〉SC − S − LP −〈QR 〉, (2)∂twhere 〈〉FT and 〈〉SC designate the vertical integral
over FT and SC, respectively, and the notation follows
the convention for meteorological parameters. The
overbar indicates horizontal averaging over a circularlarge‑scale domain. The large-scale domain diameteris chosen in this study to be 600 km unless otherwise noted so it is roughly comparable in size to the
CINDY2011/DYNAMO/AMIE (hereafter CINDY/DYNAMO for brevity) and TOGA COARE sounding
arrays.
Under the assumption that SC convergence isdetermined with QuikSCAT wind, the RHS of equations (1) and (2) can be evaluated solely by satellite
measurements currently available (Masunaga 2013).
The evolution of moisture and DSE convergences on
the LHS thus may be derived as constrained by the setof satellite observations projected onto the composite
time axis.
The vertically integrated convergence of moisture
and DSE may be interpreted mathematically as lowerand upper-troposphere weighted convergence profiles,
so Eqs. (1) and (2) together could give a clue for thevertical structure of horizontal divergence and henceof vertical velocity, ω. Masunaga and L’Ecuyer(2014) extended this idea to a satellite-based scheme
to estimate a full vertical profile of large-scale mean
vertical motion, facilitated by a vertical mode decomposition of ω into the first baroclinic, second baroclinic, and shallow modes in addition to a time-independent, background ω profile. Two of the three equations to solve the three ω modes are as follows:∂ω∑ q ∂ pi FT = −〈∇·qv〉FT +〈v·∇q〉FT ,(3)∂ω∑ s ∂ pi FT = −〈∇·sv〉FT +〈v·∇s〉FT ,
i

(4)

i

where the subscript i represents each vertical mode.
The first term on the RHS of Eqs. (3) and (4) (convergence) are as given by Eqs. (1) and (2). Although relatively minor in the current applications (see Section
5), the second term or horizontal advection, can also
be determined by an iterative procedure so as tosatisfy the given observations. The third equation is

23

(5)

Further technical details may be found in the references mentioned above.
2.2 Sounding arrays
Two adjacent sounding arrays, or the northernsounding array (NSA) and southern sounding array(SSA), were established over the central Indian Ocean
for CINDY/DYNAMO (Yoneyama et al. 2013).
NSA and SSA each comprise four land or ship sitesthat are approximately 5° to 8° apart along each sideof the array, facing each other nearly on the equator
where two of the six sites are shared. Array-averaged
estimates of meteorological parameters are provided
from the Colorado State University (CSU) DYNAMO
product version 2a (Johnson and Ciesielski 2013). The
analysis period spans three months from October 1 to
December 31, 2011. The TOGA COARE intensive
flux array (IFA) is comparable in size and in latitude
to the CINDY/DYNAMO arrays but is located in thewestern tropical Pacific to the east of the maritime
continent. The CSU TOGA COARE IFA-averaged
product version 2 is analyzed (Ciesielski et al. 2003)
for four months from November 1, 1992 to February
28, 1993.
In this study, moisture and MSE convergences arecalculated with vertical and horizontal advections
from the CINDY/DYNAMO and TOGA COARE
datasets using the identity relation as follows:∂q−〈∇·qv〉 = q ∂ω −〈v·∇q〉 = − ω−〈v·∇q〉,∂p∂p(6)−〈∇·hv〉 = h ∂ω −〈v·∇h〉 = − ω ∂ h −〈v·∇h〉,
∂p
∂p
(7)
where h indicates MSE and angle brackets designate
the vertical integral over the entire troposphere from
50 hPa to surface.
The time series of moisture and MSE convergences are composited with respect to precipitation
occurrences so they are analyzed in a format comparable as closely as possible to the satellite-based estimates. For the CINDY/DYNAMO arrays, the basepoints that define time zero in composite time series
are given by local maxima in the temporal sequenceof surface precipitation. The 3-hourly TRMM 3B42

24

Journal of the Meteorological Society of Japan

Vol. 93A(a) CINDY/DYNAMO/AMIE NSA-averaged rainfall

Rainfall [mm/d]

120
100
80
60

I

40
20
0

10/08

10/22

11/05

11/19

12/03

12/17

12/31

12/17

12/31

(b) CINDY/DYNAMO/AMIE SSA-averaged rainfall

Rainfall [mm/d]

120
100
80
60
40

I

I

I

20
0

10/08

10/22

11/05

11/19

12/03

(c) TOGA COARE IFA-averaged Tb
300

IR Tb [K]

280
260
240
220
200

11/14

11/28

12/12

12/26

01/09

01/23

02/06

02/20

Fig. 1. (a) Raw (black) and smoothed (cyan) time series of CINDY/DYNAMO NSA-averaged precipitation. The
precipitation peaks selected for the compositing base points are marked with magenta squares. Isolated events
analyzed later (see Section 5) are indicated by “I.” (b) As (a) but for CINDY/DYNAMO SSA‑averaged precipitation. (c) As (a) but for TOGA COARE IFA-averaged infrared brightness temperature from GMS. No smoothed
time series is plotted for (c).

version 7 product (Huffman et al. 2007) averaged
over each array is smoothed over time by ±6-hourrunning mean to filter high-frequency noises. Thebase points are selected at times when the peaks inthe smoothed precipitation exceed one standard deviation, 11.2 mm d−1, above the average, 8.54 mm d−1,
as obtained from the entire time sequence of the NSA
and SSA precipitation. The precipitation maxima too
close to either end of the period have been excluded
so as to assure that a ±72-h long sequence is sampledaround every base point. Figures 1a, b shows the timeseries of precipitation and its peaks chosen as thebase points, which are 15 in total for NSA and 17 for
SSA. A majority of the selected base points appear
to have occurred in association with the three MJO
events observed in late October, late November, andmiddle December (Yoneyama et al. 2013) with a few
exceptions. An analogous compositing approach has
been applied to in-situ polarimetric radar analysis

from CINDY/DYNAMO (Zuluaga and Houze 2013;
Powell and Houze 2013).
The base points for TOGA COARE are definedsimilarly but using minima in Geostationary Meteorological Satellite (GMS) infrared brightness temperature (Tb) averaged over IFA because any adequate
precipitation dataset is unavailable for the TOGACOARE period. The array-averaged infrared Tb isalready smooth by nature and hence a running-meanfilter is not applied. Totally 35 base points, determinedwhen the infrared minima are colder than one standard deviation (20.2 K) below the mean (269 K), areidentified for TOGA COARE.
3.

Composite time series

3.1 Large-scale mean vertical motion
The evolution of large-scale mean vertical motion(ω) from the satellite, CINDY/DYNAMO, and TOGACOARE composites is illustrated in Fig. 2. Each timeDecember 201525

H. MASUNAGA
background(a) Satellite Composite

CB
Sfc

CB
Sfc

(b) CINDY/DYNAMO/AMIENSA Composite(c) CINDY/DYNAMO/AMIESSA Composite(d) TOGA COAREIFA Composite

ω [hPa/h]ω [hPa/h]

Fig. 2. (a) Satellite-based composite time series of large-scale mean vertical motion (ω) anomaly [hPa h–1]. The
time axis spans 72 h before and after the time of precipitation occurrence at t = 0. The background ω is plotted on
the right. The two lowest levels correspond to surface (Sfc) and cloud base (CB), and thus vary in pressure withtime. (b) As (a) but for CINDY/DYNAMO NSA-averaged ω, for which the temporal mean over the entire period
defines the background. All pressure levels are fixed over time. (c) As (b) but for CINDY/DYNAMO SSA-averaged ω. (d) As (b) but for TOGA COARE IFA-averaged ω.

sequence is shown in terms of the anomaly against
the background profile plotted in the right column. A
rapid development of large-scale updraft throughout
the troposphere several hours prior to the peak precipitation (time zero) is a consistent property shared by
all the four composites. Furthermore, an upper-tropospheric layer of updraft lingering after t = 0 as ωdiminishes more quickly in the lower troposphere iscommonly observed, suggestive of a buildup of thesecond baroclinic (or stratiform) mode. It is noted thatthe sounding array data are not required by design toexhibit baroclinic mode signatures, which in contrast
are built in the satellite composite algorithm. Therefore, the comparison offers a verification of the mode
decomposition technique used for the satellite anal-

ysis.
One can see several features specific to certain
composites. A layer of updraft confined to below 500
hPa emerges and thickens as time progresses to thefull depth of the troposphere in the satellite composite(Fig. 2a), while such a gradual deepening is notevident in the sounding array measurements. The
CINDY/DYNAMO NSA composite (Fig. 2b) shows ahint of periodicity with a cycle of 2 to 3 days, consistent with the striking influence of 2-day disturbanceson NSA as documented by Johnson and Ciesielski(2013). The variability of ω is somewhat weaker inamplitude for TOGA COARE than the other composites (Fig. 2d), presumably because infrared minima
are less efficient than precipitation maxima to sharply

26

Journal of the Meteorological Society of Japandefine the composite temporal sequences. It may beconcluded that the satellite composite reasonably
captures the fundamental characteristics in the evolution of ω anomaly, given that the differences between
the satellite and sounding array observations are no
greater than the diversity among the three sounding
arrays.
However, the satellite composite contrasts against
the CINDY/DYNAMO and TOGA COARE composites in the background ω profile. Although they
all agree in that a weak top-heavy structure dominates the background vertical motion, the satellite
composite exhibits a shallow secondary peak below
800 hPa that is absent in the sounding array composites. The disagreement may be partly ascribed to theprocedural differences in calculating the backgroundvertical motion. The background profile is simply
the temporal mean for the sounding array composites
while determined so as to be as close as possible to
the radiatively driven subsidence under the energy
budget constraint for the satellite composite [seeMasunaga and L’Ecuyer (2014) for technical details].
The present satellite-based algorithm should be carefully assessed for validity to mitigate potential methodological problems, although the secondary peak
may not necessarily be fictitious because a shallow
large-scale circulation is present widely over the tropical oceans as well as deep Hadley and Walker cells(Trenberth et al. 2000; Zhang et al. 2008).
3.2 Moisture convergence and MSE convergence
Next, vertically integrated moisture and MSE
convergences are examined. The composite timeseries from CINDY/DYNAMO and TOGA COARE
are compared with the satellite estimates in Fig. 3.
The satellite estimate of moisture convergence (Fig.
3a) follows an evolutionary track closely resembling
the sounding array measurements, where moisture
convergence increases toward the peak at time zeroand then turns to a decrease. The sounding arrays, inparticular, CINDY/DYNAMO NSA, exhibit a somewhat larger moisture convergence than the satellite
during hours away from time zero. This might originate from preceding or subsequent disturbances,e.g., two-day disturbances as we have seen in the
NSA-composite vertical motion (Fig. 2b), that havenot entirely been averaged. On the other hand, thediscrepancy near time zero, may be partly becauseof a minor but non-negligible difference in the sizeof the analysis domain. A ±200-km perturbation
to the large-scale domain diameter is indicated bygray shade as a measure of the dependence upon theVol. 93A

domain size, indicating a striking enhancement ofsensitivity around the time of peak convection.
MSE is weakly diverging until a sharp onset of
MSE divergence that occurs as the convection intensifies around time zero (Fig. 3b). The satellite andsounding array composites are in remarkable agreement throughout the evolution of MSE convergence.
The timing and magnitude of variability are surprisingly consistent, given the fact that all the composites
are constructed with various sets of samples acquired
from different regions and periods of time and even indifferent analysis strategies.
It is the ensemble mean over different events thatis plotted in Fig. 3 for the sounding array composites. However, the ensemble mean may not always be
representative of individual convective events. Figure
4 shows moisture convergence for all individual
events projected onto composite space as well as theirensemble mean. The spread over individual samples is
so large that it would be difficult to clearly identify asystematic evolution without averaging over a numberof samples. Individual events relatively well converge
around the ensemble average for CINDY/DYNAMOSSA, while for NSA, some outliers deviate far off
the mean from time to time. The TOGA COARE IFA
composite is even noisier and the amplitude of moisture convergence enhancement is least prominent, as
one might expect from the relatively muted variability
in IFA-averaged vertical motion (Fig. 2c).
The event-by-event spread may seem much larger
for MSE convergence (Fig. 5) than that for moisture
convergence. However, it should be noted that theordinate in Fig. 5 measures a much narrower range
than in Fig. 4 since moisture convergence and dry
static energy (DSE) convergence largely offset eachother in magnitude when combined into MSE convergence. Possible origins of the spread will be discussed
in Section 5.
4.

Developing and passing-by disturbances

The composite time series from satellite measurements shown above and in the previous papers (Masunaga 2012a, b, 2013, 2014; Masunaga and L’Ecuyer2014) were constructed in the Eulerian framework,where it is unable to determine whether the detected
convective system stays within the analysis domain
during the entire evolution or swiftly moves in andout of the domain without completing a life cycle.
In the latter case, the composite evolution may notdirectly result from the dynamic processes involving
the buildup and dissipation of convective systems.
Hence, it is unclear that the dynamic evolution of

December 201527

H. MASUNAGA

Moisture Conv. [W/m2](a) Moisture Convergence
1000
900
800
700
600
500
400
300
200
100
0
-80

Satellite
NSA
SSA
TOGA

-60

-40

-20

0

20

40

60

80

40

60

80

Composite time [h]MSE Conv. [W/m2](b) MSE Convergence
0
-50
-100
-150
-200
-250
-300
-350
-80

Satellite
NSA
SSA
TOGA
-60

-40

-20

0

20

Composite time [h]Fig. 3. (a) Composite time series of vertically integrated moisture convergence [W m−2] from the CINDY/DYNAMO NSA (blue solid), CINDY/DYNAMO SSA (magenta dashed), TOGA COARE IFA (green dotted),and satellite estimates (black curve with gray shade). The width of the gray shade shows a range of large-scaledomain diameters being varied from 400 km to 800 km, while the control analysis (600 km) is indicated by blackline. Only the ensemble mean is plotted for the sounding array composites. (b) As (a) but for MSE convergence.

convective systems is captured to its entirety from thecurrent compositing technique. This problem would
create a serious ambiguity in the physical interpretation of the analysis results.
In this section, it is attempted to address thisconcern by means of a morphological diagnosis ofgeostationary satellite imagery over the central Indian
Ocean during CINDY/DYNAMO. These observations are useful, as demonstrated below, for separately
assessing the composite evolution of evolving convective systems and those without systematic evolution.
On the other hand, the satellite-based composite
is comprised of sporadic snapshots sampled out ofcontext regarding whether the parent convective
system is developing or non-developing. In this section,

the individual convective events captured during
CINDY/DYNAMO are examined to sort how many
events are dynamically evolving or merely passing
through the array. Implications obtained in thismanner are expected to be applicable to the satellite
composite as well as the sounding array, given that thesatellite-based and sounding-array composites sharefundamental characteristics as shown in Figs. 2–5. As
such, the strategy here is to exploit the statistics fromsounding-array measurements to study the nature of
representative convective systems that constitute thecomposite time series from satellite observations. The
TOGA COARE data are not utilized for this section to
reduce the analysis burden.

28

Journal of the Meteorological Society of Japan

Vol. 93A

2

Moisture Conv. [W/m ](a) CINDY/DYNAMO/AMIE NSA Composite
1200
1000
800
600
400
200
0
-200
-400
-80

-60

-40

-20

0

20

40

60

80

Composite time [h]2Moisture Conv. [W/m ](b) CINDY/DYNAMO/AMIE SSA Composite
1200
1000
800
600
400
200
0
-200
-400
-80

-60

-40

-20
0
20
Composite time [h]

40

60

80

60

80

2

Moisture Conv. [W/m ](c) TOGA COARE IFA Composite
1200
1000
800
600
400
200
0
-200
-400
-80

-60

-40

-20

0

20

40

Composite time [h]Fig. 4. (a) Vertically integrated moisture convergence [W m−2] in composite space for CINDY/DYNAMO NSA.
The time axis spans 72 h before and after the time of precipitation occurrence at t = 0. Individual samples are
drawn in gray and their ensemble mean in blue. (b) As (a) but for CINDY/DYNAMO SSA. (c) As (a) but for
TOGA COARE IFA.

December 201529

H. MASUNAGA

2MSE Conv. [W/m ](a) CINDY/DYNAMO/AMIE NSA Composite
100
50
0
-50
-100
-150
-200
-250
-300
-80

-60

-40

-20

0

20

40

60

80

60

80

60

80

Composite time [h]2

MSE Conv. [W/m ](b) CINDY/DYNAMO/AMIE SSA Composite
100
50
0
-50
-100
-150
-200
-250
-300
-80

-60

-40

-20
0
20
Composite time [h]402MSE Conv. [W/m ](c) TOGA COARE IFA Composite
100
50
0
-50
-100
-150
-200
-250
-300
-80

-60

-40

-20

0

20

40

Composite time [h]Fig. 5.

As Fig. 4 but for vertically integrated MSE convergence.

4.1 Classification of convective disturbances
Half-hourly maps from Meteosat-7 infrared Tb are
analyzed for categorizing observed disturbances into

three “composite classes” of developing, off-centered,and passing-by categories. The developing classcomprises “bona-fide” cases where convective

30

Journal of the Meteorological Society of Japan(a) Developing

Vol. 93A(b) Off-centered(c) Passing by

Tb

Tb

Array
Outer/Inner field
Tb

Field averaged Tb

Array averaged Tb

t

t

t

Fig. 6. Schematic to illustrate the classification method into (a) developing, (b) off-centered, and (c) passing-by
classes. The top row depicts disturbances marching through the sounding array with its temporal order indicatedby an arrow. The bottom graphs are the time series of infrared brightness temperature spatially averaged over thesounding array (solid) and the encompassing field (dotted).

systems largely evolve within a sounding array (NSAor SSA). In the off-centered class, the life cycle ofa convective system is partially captured, whereasa main portion of the system resides outside thedomain. The disturbance is classified as a passing-by
system when the temporal modulation likely resultsfrom the advective effects as the disturbance entersand leaves the array, and thus may not well reflect theconvective life cycle.
The classification is made on the basis of time-series segments of infrared Tb sampled around eachcompositing base point. Next, the infrared Tb isspatially averaged over the sounding array and overa broader field. Here the “field,” or later termed more
specifically as “inner/outer field,” refers to a reference
domain encompassing the sounding array, introduced
for reasons that will become clear below. As illustrated in Fig. 6, passages of disturbances would berecorded in infrared Tb in distinguishable ways for thedifferent composite classes. Infrared Tb would varywith time in a coherent manner between the array andfield averages for developing disturbances, althoughcold cloud emissions are heavily smoothed in thefield average (Fig. 6a). This pattern would be slightly
modified when the disturbance track is off‑centeredfrom the array, resulting in a warmer array-averaged
Tb than the field average during the times when cold
clouds stay outside the array (Fig. 6b). In cases where
the disturbance only passes by without any appreciable development, the field averaged Tb would bepractically invariant over time while the array-averaged Tb should be modulated as the disturbancemoves in and out of the array (Fig. 6c). As such, aninspection of the array‑ and field-averaged Tbs could
offer a qualitative guidance to differentiate developing, off-centered, and passing-by disturbances.
This idea is tested with Meteosat data during the
CINDY/DYNAMO (Figs. 7, 8). Two field boxes withdifferent sizes (hereafter inner and outer fields) areexamined in order to ensure that the classification
is not affected by the choice of field size. The inner
field is a rectangular area bound at 2°S, 8°N, 70°E,
and 83°E for NSA and at 9°S, 1°N, 70°E, and 83°E
for SSA. The outer field is a broader region containing
the inner field inside and is bound at 4°S, 10°N, 65°E,
and 88°E, for NSA and at 11°S, 3°N, 64°E, and 88°E

December 201531

H. MASUNAGACINDY/DYNAMO/AMIE NSA
300

IR Brightness Temperature [K]

280

300
16/Oct 3h

280300
22/Oct 0h

300
24/Oct 3h

280

280

300
26/Oct 6h

280

260

260

260

260

260

240

240

240

240

240

220

“Passing by”

220“Developing”

220“Developing”

220“Off-centered”

220

28/Oct 9h“Passing by”

200
200
200
200
200
-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24
300
300
300
300
300
280

29/Oct 3h

28030/Oct 3h“Developing”

20/Nov 18h

280

280

24/Nov 3h“Off-centered”

280

260

260

260

260

240

240

240

240

240

220

220

220

220

220

“Passing by”“Off-centered”

260

24/Nov 15h“Developing”

200
200
200
200
200
-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24
300
300
300
300
300
280

27/Nov 3h

280

28/Nov 0h

16/Dec 21h

280

280

17/Dec 3h

280

21/Dec 15h

260

260

260 “Developing”

240

240

240

240

240

220

220

220

260

“Developing”

260“Off-centered”“Passing by”

220“Passing by”

220

200
200
200
200
200
-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24

Relative time [h]Relative time [h]Relative time [h]Relative time [h]Relative time [h]Fig. 7. Temporal sequences of Meteosat infrared Tb around every base point defined for CINDY/DYNAMONSA. The abscissa shows hours relative to the base point as time-stamped on the top-right corner of each panel.
Infrared Tb is spatially averaged over the sounding array (solid) and the inner and outer fields (dotted and dashed,respectively). The sector bound between the array averaged Tb and the outer-field averaged Tb is shaded where
the latter exceeds the former for visual guidance. The composite class is identified as indicated, and is highlighted
in red for the representative cases selected for Fig. 9.

for SSA. Figures 7 and 8 imply that overall the innerand outer fields appear to provide consistent references for the classification as illustrated in the schematics (Fig. 6).
However, actual observations shown by Figs. 7
and 8, are often not clearly classifiable in light of theidealized schematic illustrated in Fig. 6. The classification is subjectively made by visual inspection since
it is difficult to implement an automated procedurein an objective fashion. To briefly assess the performance of the classification scheme, geographicalmaps of infrared Tb for selected events are presented
in Fig. 9. The 22 October event observed in the NSA(second from left in the top row of Fig. 7) is selected
as a typical developing case resembling the schematic of Fig. 6a. A sequence of Tb images for thisevent are shown in Fig. 9a. Two major disturbances
are present at 12 h UTC on October 21, one to thewest and another to the east of the array, but each ofthem break apart as time proceeds. In fact, pieces ofsmaller clouds inside the array gradually organize

themselves into a new disturbance toward 0 h UTC
on October 22, which subsequently begins to resolve
as emanating remnant high clouds away from thearray. An example for the off-centered class is theNovember 9 event captured in the SSA (leftmost of
the second row in Fig. 8). The field-averaged Tb isinitially colder than the array average, suggestingthat a convective disturbance stays off the array inthe beginning as evidenced by Fig. 9b. An entire life
cycle of the disturbance is clearly traceable from thesequential images, although the array captures only aportion of the evolution.
The October 18 SSA event (middle of the top row
in Fig. 8) is chosen as a passing-by case since Tbhardly changes when averaged over the outer array.
However, this particular case does not provide clear
evidence for a disturbance traveling across the area
as speculated by Fig. 6c as far as one can tell from
Fig. 9c, where patches of clouds irregularly change inshape and size over time. It is often the case in reality,
as mentioned above, that a convective disturbance32

Journal of the Meteorological Society of Japan

Vol. 93A

CINDY/DYNAMO/AMIE SSA
300
280

300
6/Oct 3h280

300
15/Oct 3h

300
18/Oct 15h

280

300
27/Oct 3h

280

280

260

260

260

260

260

240

240

240

240

240

220

220

220

“Developing”

220“Developing”“Passing by”“Developing”

220

28/Oct 0h“Developing”

IR Brightness Temperature [K]

200
200
200
200
200
-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24
300
300
300
300
300
280

9/Nov 0h28024/Nov 3h

25/Nov 3h

280

28/Nov 3h

280“Developing”

280

260

260

260

260

240

240

240

240

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220

220

220

220

220

“Off-centered”“Off-centered”“Off-centered”

260

28/Nov 12h“Developing”

200
200
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200
200
-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24
300
300
300
300
300
280

16/Dec 3h

280

16/Dec 9h

16/Dec 15h

280

16/Dec 21h

280

280

260

260

260

260

260

240

240

240

240

240

220

“Passing by”

220“Passing by”

220“Passing by”

220“Passing by”

220

18/Dec 3h“Passing by”

200
200
200
200
200
-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24
300
300
280

20/Dec 18h

280

260

260

240

240

220

“Off-centered”

220

21/Dec 0h

Relative time [h]Relative time [h]Relative time [h]“Off-centered”

200
200-24-18-12 -6 0 6 12 18 24-24-18-12 -6 0 6 12 18 24

Relative time [h]Relative time [h]Fig. 8.

As Fig. 7 but for CINDY/DYNAMO SSA.

does not unambiguously fall in any one of the threecomposite classes. Observed disturbances more likely
have hybrid properties that potentially ascribable tomultiple classes at a time. Therefore, it is important
to be aware that the identified classes claimed in Figs.
7 and 8 are not an unequivocal definition but a subjective reference for the primary (if not the sole) natureof individual disturbances.
4.2

Composite time series for different compositeclasses
All events observed in NSA and SSA (Figs. 4a, b)
are grouped separately into the composite classes.
Qualitative features of moisture convergence in all
the classes (Fig. 10) are as expected from Fig. 4.
However, the amplitude of the variability differs
across the three classes; being most outstandingfor developing disturbances, while barely visible in
the passing-by class. This is also the case for MSE
convergence plotted in Fig. 11, where the onset of

MSE divergence following the peak precipitation is
sharpest in the developing class. It follows that theprominent characteristics in composite space, i.e., anenhancement of moisture convergence during convection and the subsequent outbreak of MSE divergence,
may be principally attributed to the dynamic evolution of a convective system that completes its life
cycle within the analysis domain. Off-centered andpassing-by disturbances each occur as frequently
as developing systems but are considered to be lessresponsible for the major features captured in Figs.
4 and 5. This result is supportive of the implicit
conjecture underlying our analysis that major properties in composite space physically represent theconvective development rather than mere advective
effects. This conclusion arguably applies not only to
the CINDY/DYNAMO measurements but also to thesatellite observations as far as the underlying statistics are shared in common, which is a presumption
corroborated to the extent suggested by the resem-

December 2015

H. MASUNAGA

33

Fig. 9. Plan view of Meteosat infrared Tb for representative events selected for each composite class of (a) developing, (b) off‑centered, and passing by. Five panels in each row are a sequence of 6-hourly snapshots centeredaround the time of peak precipitation (or the base point). A black quadrangle in each panel illustrates the sounding
array and white boxes encompassing the array show the inner and outer fields.

blance between the satellite-based and sounding-arraycomposites as confirmed earlier in Section 3.
5.

Discussion

The composite time series from satellite observations was found to be in good agreement with thestatistical properties of convective disturbances fromthe sounding-array measurements during CINDY/DYNAMO and TOGA COARE. Physical implications of the observed characteristics were discussed
by Masunaga and L’Ecuyer (2014) as follows. The
buildup of moisture convergence is brought by a
low-level convergence associated with the first baroclinic mode, which develops following a modest,
gradual increase of the positive second baroclinic (orcongestus) mode. The positive second mode imports
MSE against a preexisting weak MSE divergence andhelps destabilize the atmosphere. Then, the second
mode is switched in sign to a negative mode (or the
stratiform mode), resulting in an abrupt onset of
MSE divergence that leads to a restabilization of theatmosphere. The vertical profile of horizontal diver-gence calculated from a reanalysis dataset during the
CINDY/DYNAMO by Zuluaga and Houze (2013)shows a composite temporal evolution closely parallelwith the present result. A CINDY/DYNAMO budget
analysis itself has revealed from convective heating
estimates that transitions occur from shallow to deep
convective and stratiform clouds (Johnson et al.
2015). A theoretical framework along the same line
as above has been explored over the past decade (e.g.,Peters and Bretherton 2006; Khouider and Majda
2006). However, there is a remaining concern that thesystematic patterns observed in the ensemble mean
are in general not visually recognizable in individual
events. Potential origins for this include contaminating influences from other disturbances nearby. It is
implicitly assumed so far that the large-scale environment is not affected by multiple convective systems
at any given time. This assumption is obviously not
guaranteed to be valid particularly for the events
sampled from a synoptic-scale environment favorable
for intensive convection such as the active MJO envelope.

34

Journal of the Meteorological Society of Japan

Vol. 93A

Moisture Conv. [W/m 2](a) Developing
1200
1000
800
600
400
200
0
-200
-400
-80

-60

-40

-20

0

20

40

60

80

40

60

80

40

60

80

Composite time [h]Moisture Conv. [W/m 2](b) Off-centered
1200
1000
800
600
400
200
0
-200
-400
-80

-60

-40

-20

0

20

Composite time [h]Moisture Conv. [W/m 2](c) Passing by
1200
1000
800
600
400
200
0
-200
-400
-80

-60

-40

-20

0

20

Composite time [h]Fig. 10. Vertically integrated moisture convergence [W m−2] in composite space for the different composite classes:(a) Developing, (b) Off-centered, and (c) Passing by. Individual samples are drawn in gray and their ensemble
mean in blue.

To minimize the contaminating effects from co-existing disturbances, convective events not under
the apparent influence of the MJO during CINDY/DYNAMO are subjectively selected from Meteosat

IR imagery to define “isolated” disturbances. Four
isolated convective events (indicated by “I” in Fig.
1) are identified around October 16 (NSA), October
6 (SSA), October 15 (SSA), and November 9 (SSA).

December 201535

H. MASUNAGA

2MSE Conv. [W/m ](a) Developing
100
50
0
-50
-100
-150
-200
-250
-300
-80

-60

-40

-20

0

20

40

60

80

40

60

80

40

60

80

Composite time [h]2

MSE Conv. [W/m ](b) Off-centered
100
50
0
-50
-100
-150
-200
-250
-300
-80

-60

-40

-20
0
20
Composite time [h]2

MSE Conv. [W/m ](c) Passing by
100
50
0
-50
-100
-150
-200
-250
-300
-80

-60

-40

-20

0

20

Composite time [h]Fig. 11.

As Fig. 10 but for vertically integrated MSE convergence.

All the four events turn to follow a coherent track ofmoisture convergence even without averaging (Fig.
12a) but are heavily spread in MSE convergenceparticularly for positive hours (Fig. 12b). However,MSE convergence better converges into an evolutionary path reminiscent of the ensemble mean from

Fig. 5 when horizontal advection is excluded from
the MSE convergence estimate (Fig. 12c). Verticallyintegrated convergence may be broken down into
horizontal and vertical advections as shown in Eq.
(7). The systematic evolution in terms of the verticalmode decomposition as described earlier is accounted

36

Journal of the Meteorological Society of Japan

Vol. 93A

2

Moisture Conv. [W/m ](a) Moisture Convergence for Isolated Events
1200
1000
800
600
400
200
0
-200
-400
-80

16/Oct 3h NSA
6/Oct 3h SSA
15/Oct 3h SSA
9/Nov 0h SSA

-60

-40

-20

0

20

40

60

80

60

80

Composite time [h]2

MSE Conv. [W/m ](b) MSE Convergence for Isolated Events
200
150
100
50
0
-50
-100
-150
-200
-250
-300
-80

-60

-40

-20
0
20
Composite time [h]402MSE Conv. [W/m ](c) MSE Convergence w/o horizontal advection for Isolated Events
200
150
100
50
0
-50
-100
-150
-200
-250
-300
-80

-60

-40

-20

0

20

40

60

80

Composite time [h]Fig. 12. Composite time series for isolated convective events as indicated in the top panel. (a) Moisture convergence. (b) MSE convergence. (c) MSE convergence with the contribution of horizontal advection excluded.

for by vertical advection, largely intrinsic to the local
dynamics. On the other hand, the horizontal advection of MSE depends on horizontal moisture gradientin the large-scale environment and could irregularly
modulate the thermodynamic variability that other-wise would be controlled by internal convective
dynamics. While horizontal advection could play
crucial roles in the driving mechanism of the MJO(e.g., Benedict and Randall 2007; Maloney 2009;
Sobel et al. 2014), the relative magnitude of hori-

December 201537

H. MASUNAGA

Moisture Conv. [W/m 2](a) Moisture Convergence Breakdown
800
700
600
500
400
300
200
100
0
-100
-200
-80

-60

-40

-20
0
20
Composite time [h]

40

60

80

40

60

80

MSE Conv. [W/m2](b) MSE Convergence Breakdown
20
0
-20
-40
-60
-80
-100
-120
-140
-160
-80

NSA ver. adv.
NSA hor. adv.
SSA ver. adv.
SSA hor. adv.
-60

-40

-20

0

20

Composite time [h]Fig. 13. CINDY/DYNAMO composites of the vertical integral of vertical advection (solid) and horizontal advection (dashed), which together account for vertically integrated convergence. (a) Moisture advections. (b) MSE
advections.

zontal and vertical advections is dependent on thecycle of variability and the horizontal component isleast important at daily time scales (Inoue and Back2015). Figure 13 shows the breakdown of moisture/MSE convergence into horizontal and verticaladvections calculated from the ensemble mean of
all CINDY/DYNAMO events. Horizontal moistureadvection stays weakly negative fluctuating between
0 and −100 W m−2, nearly leaving vertical moistureadvection to entirely account for moisture convergence (Fig. 13a). A rapid development of vertical
moisture advection (or moisture convergence) islargely canceled by precipitation during the time ofpeak convection (Masunaga 2013; Chikira 2014). In
the MSE budget, horizontal advection has a comparable magnitude to vertical advection during hours

away from t~0. On the other hand, vertical advection is solely responsible for the abrupt onset of MSE
divergence after t = 0. These results are supportive of
the present interpretation of the composite evolution
from the perspective of large-scale vertical modes.
The satellite-based composite presented in thisstudy is constructed with observations over all tropical oceans, although it would be desirable to limit
satellite measurements to strictly over the sounding
arrays for fair comparison with the field campaign
data. However, such a local subset of the satellite
composite would suffer from an intolerable level ofsampling noise because the compositing techniquerequires a huge volume of observations to retain
robust statistics. The present comparison relies on theunderlying assumption that the regional climatology

38

Journal of the Meteorological Society of Japan

Background vertical velocity

All Tropics
Indian Ocean
West Pacific
East PacificCB
Sfc
Fig. 14. Regional breakdown of backgroundvertical velocity derived for the satellite-based
composite: all tropical oceans (black solid),
Indian Ocean (red dashed), west Pacific (green
dot‑dashed), and east Pacific (blue dotted).

specific to the CINDY/DYNAMO or TOGA COARE
site is not critically different from the global tropicalclimatology. This is a speculation that has yet to beverified.
To this end, the regional climatology in largescale dynamics is examined with background verticalvelocity derived for the satellite-based composite.
Figure 14 depicts the regional breakdown of background ω into the Indian Ocean (50°E–90°E), west
Pacific (140°E–180°), and east Pacific (50°W–
10°W), bound meridionally between 15°S and 15°N
with islands excluded. All the profiles share common
features including a weak top-heavy ascent and ashallow updraft, while the amplitude varies from one
region to another. The Indian Ocean profile closely
resembles the tropical mean over the entire thickness of the troposphere, implying that the present
satellite-based composite is safely comparable with
the CINDY/DYNAMO soundings. The west Pacificexhibits a stronger upper-tropospheric peak in the
background ω profile than the tropical mean. Thismight be partially responsible for the difference
between the satellite and TOGA COARE compos-

Vol. 93A

ites. In contrast, the east Pacific is characterized witha modest upper-tropospheric ascent and a robust
shallow updraft compared to other regions as one
might expect from the climatologically shallow circulation typical of this region (e.g., Zhang et al. 2004).
6.

Summary

A satellite-based method previously developed by
the author for moisture and thermal budget analysis isassessed in comparison with sounding array measurements from CINDY/DYNAMO and TOGA COARE.
The result shows that the composite time series oflarge-scale mean vertical motion from a suite of satellite observations closely resembles the sounding arrayestimates, whereas the satellite composite exhibitsa shallow updraft in the background that is absent in
the in-situ soundings. Moisture convergence and MSE
convergence are qualitatively consistent between thesatellite and sounding array composites, although asignificant spread resides around the ensemble mean
among individual convective events.
Convective disturbances are categorized into developing, off-centered, and passing-by classes using
geostationary infrared measurements in attempt toseparate irrelevant samples where convective development mainly takes place outside the array or disturbances pass over the array without experiencing
systematic development. All the three cases are foundqualitatively similar, except that developing eventsare greatest in the amplitude of variability while thevariability becomes less coherent for off-centered andpassing-by disturbances. It follows that the fundamental large-scale thermodynamics associated withconvective development is reasonably captured in
the satellite composite to the extent not to be heavily
distorted by stochastic influences from the environment. In fact, moisture convergence plotted for individual events converges into a coherent evolution
resembling the ensemble mean when the convective
events immune to apparent synoptic-scale influences
are isolated. This is also the case for MSE convergence once the contribution of horizontal advection
is excluded. Vertical advection is largely responsible
for the notable evolutionary properties characterizing
moisture and MSE convergences, although horizontal
advection has a comparable contribution to MSE
convergence during spells without vigorous convection.
Acknowledgments
The author especially thanks Dick Johnson andPaul Ciesielski for providing the CINDY/DYNAMO

December 2015

H. MASUNAGA

and TOGA COARE sounding array datasets and fortheir comments on portions of this work. Meteosat
infrared data during the DYNAMO campaign arearchived and provided by the DYNAMO Data
Archive Center at National Center for AtmosphericResearch (NCAR) Earth Observing Laboratory(EOL). The AIRS/AMSU product was obtained from
Goddard Earth Sciences (GES) Data and Information
Services Center (DISC), the GPROF 2010 precipitation product from Colorado State University, theTRMM PR (2A25) dataset from the Japan Aerospace
Exploration Agency (JAXA), the AMSR-E CWVand SST datasets from Remote Sensing Systems, theCloudSat 2B-FLXHR-LIDAR and 2B‑CLDCLASS
products from CloudSat Data Processing Center, and
QuikSCAT SeaWinds ocean wind vector data from
Physical Oceanography Distributed Active Archive
Center (PO. DAAC) at NASA Jet Propulsion Laboratory. The author is grateful to Brian Mapes for
inspiring discussions on the satellite composite analysis, which have led to the second half of the present
paper, and to Kunio Yoneyama for his open invitation
to the CINDY2011 science community. This work is
supported by the Japan Society for the Promotion of
Science (JSPS) Grant-in-Aid for Scientific Research(B) (26287113) and for Challenging Exploratory
Research (26610150).
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