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A Satellite Study of Tropical Moist Convection and Environmental Variability:
A Moisture and Thermal Budget Analysis
HIROHIKO MASUNAGA
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan(Manuscript received 5 October 2012, in final form 21 February 2013)
ABSTRACTThe thermodynamic variability associated with moist convection over tropical oceans is analyzed by making
use of a variety of satellite sensors including radars, an infrared and microwave sounder unit, and a microwave
radiometer and scatterometer aboard different platforms. Satellite measurements of atmospheric parameters
including air temperature, water vapor, cumulus cloud cover, and surface wind are composited with respect tothe temporal lead or lag from Tropical Rainfall Measuring Mission (TRMM)-detected convection to obtainstatistically continuous time series on hourly to daily time scales. The Atmospheric Infrared Sounder (AIRS)observed temperature and humidity profiles, representing cloud-cleared sounding, are combined with semitheoretical estimates of in-cloud temperature and humidity to construct the large-scale mean field. Those
measurements are ingested to the moisture and thermal budget equations integrated vertically over each layer
separated by cloud base. This strategy makes it possible to evaluate the free-tropospheric (FT) convergence ofmoisture and dry static energy and their vertical flux at cloud base from satellite observations alone. The main
findings include the following: 1) vertical moisture transport at cloud base is the dominant source of FT
moistening prior to isolated cumulus development while overwhelmed by horizontal moisture convergencefor highly organized systems; 2) FT diabatic heating is largely offset on an instantaneous basis; and 3) FTmoistening by convective eddies amounts to a half of the total cloud-base moisture flux in the backgroundstate, while large-scale mean updrafts modulate the variability of cloud-base flux when highly organizedsystems develop. The known correlation between congestus clouds and FT moisture before deep convection
may be accounted for by large-scale mean moisture updraft rather than congestus eddy moistening.

1. Introduction
Despite the long established understanding that moist
convection is a key ingredient in the dynamics of tropical atmosphere (Riehl and Malkus 1958), a number ofoutstanding issues have challenged our efforts to fully
understand the physics governing tropical convective
clouds and their environment. Convective processes andlarge-scale environment are technically (and often
conceptually) difficult to explicitly deal with at the sametime because of the vast temporal and spatial gaps lying
in between. In practice, the ‘‘unresolved’’ effects of
convective processes can be diagnosed with large-scalemean parameters under a certain closure constraintsought by means of a moisture and thermal budget

Corresponding author address: Hirohiko Masunaga, Hydrospheric Atmospheric Research Center, Nagoya University,
Furocho, Chikusa-ku, Nagoya 464-8601, Japan.
E-mail: masunaga@hyarc.nagoya-u.ac.jp
DOI: 10.1175/JAS-D-12-0273.1
Ó 2013 American Meteorological Society

analysis (Yanai et al. 1973; Arakawa and Schubert1974). Pioneering observational work in this field typically relies on a relatively limited number of opportunities provided by intensive field campaigns to obtaina full spectrum of meteorological parameters (e.g., Reed
and Recker 1971; Yanai et al. 1973; Nitta and Esbensen
1974; Johnson 1976; Thompson et al. 1979; Luo andYanai 1984; Lin and Johnson 1996). The present work
shares scientific interests with these earlier studies but
explores a different approach based exclusively on satellite observations instead of ground observational
networks.
Recent progress in satellite sensor technology, exemplified by hyperspectral sounders and cloud andprecipitation radars among others, has significantly expanded the capability of atmospheric observations fromspace. Satellite observations provide full global coverage over a long period of time, far beyond the reach ofindividual field campaigns. There are, however, a few
major challenges to the utility of satellite remote sensing

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in light of the present research goals. First, low Earthorbiting (LEO) satellites, flying over a given geographical location twice or three times at best per day,are so limited in sampling frequency that it is difficult tomonitor hourly to subdaily scale variability critical ofmoist convective processes. This limitation is unavoidable when the analysis involves the LEO sensors that do
not currently have any substitute instrument in thegeostationary orbit (microwave and active sensors, forinstance). Some past work has addressed this problem ina statistical space by projecting many LEO observations
onto a composite time axis constructed against an independent infrared or rainfall dataset (Kondo et al.
2006; Zelinka and Hartmann 2009; Mapes et al. 2009) oranother LEO satellite (Masunaga 2012a,b). Our strategy here is a direct extension of the method proposedby Masunaga (2012a). Second, thermal emissions fromhydrometeors degrade or totally disable infrared soundings of temperature and humidity in cloudy skies. The lackof measurements within clouds can be a serious obstacle,
particularly when the effects of convective clouds on
atmospheric thermodynamics are of central interest.
This paper explores a semitheoretical approach wherea hypothetical adiabatic air parcel is combined with theambient air soundings to obtain in-cloud estimates.
Third, no satellite instrument in operation remotely
measures wind except near the sea surface, where scatterometer measurements are available. The divergenceterms in the thermal and moisture budget equations
would remain undetermined without the knowledge ofwind observations. The lack of wind observations fromspace can be compensated for by prescribed assumptions provided by offline cloud model simulations as
implemented in existing satellite algorithms for convective heating (Tao et al. 2006, and references therein).
This study, in contrast, pursues a somewhat different
goal without the help of any external knowledge of dynamic environment. Instead of deriving a full verticalstructure of the heating source and moisture sink, a
simplified tropospheric model is used here that consistsof two layers separated by cloud base, and the divergenceterms in each layer are deduced from observable quantities under the heat and water budget constraints.
A relevant research topic of interest is the potential roles of congestus clouds in free-tropospheric (FT)moistening. Growing evidence appears to suggest the
FT moisture control on convection as implied by observations (Numaguti et al. 1995; Yoneyama and Fujitani
1995; Brown and Zhang 1997; Sherwood 1999; Holloway
and Neelin 2009), theoretical work (Raymond 2000),
and cloud-resolving model simulations (Derbyshire et al.
2004; Takemi et al. 2004). Being a key member inthe family of tropical convection along with shallow

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cumulus and deep convection (Johnson et al. 1999),
congestus clouds have gained increasing attention as
a possible driver of the FT moistening that creates anenvironment favorable for convection to intensify. The
transition from congestus to deep convection appears toaccompany a deepening of the moist FT layer during
a cycle of equatorial waves and the Madden–Julian oscillation (MJO) (Takayabu et al. 1996; Kemball-Cookand Weare 2001; Straub and Kiladis 2003; Mapes et al.
2006). It is less evident whether the congestus moistening is a universal effect that leads to convective deepening even without the aid of wave dynamics. Few
studies, with some exceptions (Kuang and Bretherton
2006; Waite and Khouider 2010; Hohenegger and Stevens
2013), have been targeted specifically on the physicalprocesses controlling congestus moistening and the resultant deepening of convection. Waite and Khouider(2010) found that congestus moistening offers a plausible mechanism for preconditioning deep convective
development, while Hohenegger and Stevens (2013)
argued otherwise based on a variety of evidence.
The main objectives of this study are 1) to develop ananalysis method to derive the short-term variability oflarge-scale mean temperature and humidity from multisatellite observations, 2) to carry out a moisture andthermal budget analysis involving the development ofdifferent types of convective systems, and 3) to discuss
the observational implications for relevant issues including FT congestus moistening. Data and analysis
methods are described in section 2, and the observed
thermodynamic variability is shown in section 3. The
strategy and results of the budget analysis are presented
in section 4, followed by section 5, where the current
findings are discussed. The conclusions are summarized
in section 6.

2. Data and method
a. Satellite instruments and data products
Satellite instruments involved in the current analysis
are summarized in Fig. 1. A precipitating cloud is defined to be present where flagged as ‘‘rain certain’’ in the
Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) level 2 products (TRMM PR
Team 2005). Satellite sensors of central importance forthis work besides the TRMM PR are the Atmospheric
Infrared Sounder and Advanced Microwave Sounder
Unit (AIRS/AMSU, hereafter AIRS collectively) andthe Advanced Microwave Scanning Radiometer for
Earth Observing System (AMSR-E), both carried by the
Aqua satellite. Cloud-cleared air temperature and vapor
mixing ratio are obtained from the AIRS level 2 Standard

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FIG. 1. Analysis flowchart. Dashed box indicates the algorithm to estimate the large-scale mean
thermodynamic field (section 3a), where individual humidity and air temperature estimates are shown by
shaded boxes. See text for details.

Physical Retrieval (AIRX2RET) version 5 dataset(Susskind et al. 2003, 2011). AMSR-E column water
vapor (CWV) and sea surface temperature (SST) areprovided by the Remote Sensing Systems (RSS) (Wentz
and Meissner 2000). AMSR-E precipitation is retrievedby the Goddard Profiling (GPROF) 2010 algorithm(Kummerow et al. 2001) and is employed for composite
surface precipitation in the budget analysis describedlater. The CloudSat Cloud Profiling Radar (CPR) CloudClassification (2B-CLDCLASS) (Wang and Sassen 2001)
and Fluxes and Heating Rates (2B-FLXHR) (L’Ecuyer
and Stephens 2003; L’Ecuyer et al. 2008) products are
employed for cumulus cloud cover and radiative heating
rate, respectively. Near-surface wind vector is obtained
from the Quick Scatterometer (QuikSCAT) SeaWindslevel 3 daily gridded data (Perry 2001). Aqua andCloudSat are part of the Afternoon Train (A-Train)
constellation and have overpasses around 0130 and 1330
local time (LT), while the QuikSCAT satellite had been
flying in a morning (evening) sun-synchronous orbit
fixed around 0600 (1800) LT until it ceased to operatein November 2009. Local time of TRMM observations
varies from orbit to orbit.
The footprint size varies from sensor to sensor: 4.3 km
at nadir for the PR, 13.5 km at nadir for the AIRS,
74 km 3 43 km (6.4 GHz) to 6 km 3 4 km (89 GHz) for
the AMSR-E, 1.7 km 3 1.3 km for the CloudSat radar,and 37 km 3 25 km for the QuikSCAT scatterometer.
The diversity in spatial resolution among different satellite instruments would lead to a systematic inconsistency
in instantaneous measurements on a footprint scale. Thisissue is greatly alleviated in the current analysis since all
the parameters are averaged over a common large-scaledomain, as noted later.

Pressure levels are chosen to match the AIRS data
product, except for the bottom two levels that are thesurface and cloud base. Cloud-base pressure pCB is defined at the lifting condensation level (LCL) computedfrom the observed surface air temperature and humidity. Surface pressure ps and cloud-base pressure eachvary with space and time. While the AIRS productcontains air temperature just above the surface, thelowermost vapor mixing ratio in the AIRS dataset is thelayer average between 1000 and 925 hPa, and it could beconsiderably smaller than the surface value. The AIRS
lowermost vapor estimate is thus extrapolated down to
the surface, assuming a prescribed lower-tropospheric
thermodynamic structure typical of a cloud-topped
boundary layer over tropical oceans [see appendix of
Masunaga (2012a) for details]. Pressure levels farther
above are fixed at 925, 850, 700, 600, 500, 400, 300, 250,
200, 150, and 100 hPa. Surface pressure is interpolated to
AIRS footprints internally in the AIRS operational algorithm from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast.
Observations over global tropical oceans (158S–158N)
are analyzed. Continents and islands are all excluded
because passive sensor measurements tend to be degraded or unavailable over land. The analysis periodspans 7 yr from 1 December 2002 to 30 November 2009,
except for CloudSat, where the period is 3 yr and 5
months from 1 July 2006 to the same ending date.

b. Composite analysis method
The temporal variability associated with convective
development is obtained by the composite analysis
method devised by Masunaga (2012a), with minor updates as outlined below. The basic idea is to combine two

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LEO satellites flying in different orbits, the Aqua andTRMM, for example, and composite observations withrespect to the overpass time difference. Air temperature
and water vapor measurements from the Aqua AIRS
are recorded every time when the TRMM PR reports an
occurrence of convection nearby sometime before orafter. Each AIRS measurement is sorted by the observational time difference and geographical distance fromthe TRMM-detected convection. The time difference
drifts randomly from one overpass to another since the
TRMM satellite, flying in a sun-asynchronous orbit, intersects the sun-synchronous Aqua orbit at an irregulartime interval. AIRS measurements collected over a period of time would thus eventually include numerous
samples having a series of different lags or leads fromthe TRMM and would yield a statistically continuous
time sequence of air temperature and water vapor priorand subsequent to convection when averaged together
in each time bin. Each snapshot in composite spaceconstitutes a vertical–horizontal cross section, where thehorizontal dimension is composited from a series of
distance bins circled around the convection. The TRMM
PR is insensitive to small cloud droplets but measures
precipitation with rain rates higher than ;0.7 mm h21(Kummerow et al. 1998). The base point, or the zero on
the time and distance axes in composite space, therefore
represents the occurrence of precipitating clouds such as
cumuli congestus, cumulonimbi, and nimbostratus.
AMSR-E, CloudSat, and QuikSCAT data are also
composited similarly against the TRMM-detected convection in order to obtain a whole spectrum of environmental variables. QuikSCAT wind vector is projected
into two-dimensional local polar coordinates when
composited, where the radial axis is defined along thedirection from the TRMM-detected convection withoutward positive. The radial component is stored for
calculating the subcloud-layer divergence. QuikSCAT
scalar wind is averaged separately in composite spacefor use by evaporation and sensible heat flux estimates.
The composite time series of all parameters are smoothed
over time by 62-h running mean to filter out highfrequency noise irrelevant to the present analysis.
Observations sampled for the composite analysis arebroken down by the population density of TRMM
convection, hereafter the TRMM precipitation coverage, defined by the number of raining PR pixels acrossthe surrounding 18 3 18 domain. The TRMM precipitation coverage serves as an index of the degree of
convective organization as demonstrated by Masunaga(2012b). Figure 2 shows the vertical structure of CloudSat
cumulus cloud cover (defined below) composited separately for three ranges of the TRMM precipitation
coverage of 0%–25%, 25%–50%, and 50%–100%. The

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left column is the composite time series while the rightcolumn is the vertical–horizontal cross sections at time
zero. Cumulus cloud cover is the area fraction occupied
by stratocumulus (Sc), cumulus congestus (Cu), nimbostratus (Ns), or deep convective clouds as identified in
the CloudSat CLDCLASS product. These cloud types
are selected as the key players of moist convection withthe root on the planetary boundary layer.
When the TRMM precipitation coverage is smaller
than 25%, composite clouds are mostly shallow cumuli
having tops around 2 km and hardly grow verticallybeyond the middle troposphere even at time zero (Fig.
2a). Cumulus cloud cover stays as small as 0.3, varying
little over distance (Fig. 2d). This category thus appears
to consist of isolated cumulus clouds not standing out
much above the background shallow cumuli. The opposite extreme may be seen for TRMM precipitation
coverages exceeding 50% (Figs. 2c,f). In this case cumulus cloud towers penetrate the entire troposphereand develop into a horizontally extensive structure overa few hundred kilometers at the time of peak convection(t 5 0). Highly organized clouds such as mesoscaleconvective systems (MCSs) are a plausible candidate forthis category. The middle row (Figs. 2b,e) is an intermediate class in between, where a systematic verticaland horizontal development is clearly visible, but a majority in this class does not reach as high as the tropopause. The three composite categories may be thus
termed as isolated cumulus clouds (TRMM precipitation
coverage ,25%), moderately developed clouds (25%–50%), and highly organized systems (.50%). This terminology is used for labeling each composite category in
the remainder of this paper.
The three composite categories may seemingly correspond to shallow cumulus, cumulus congestus, andcumulonimbus (Johnson et al. 1999) as far as the vertical
cloud extent is concerned. In reality, the degree of horizontal organization also systematically varies among
different categories. In light of a cloud classification
employed by recent satellite studies (Romatschke andHouze 2010; Rasmussen and Houze 2011), moderately
developed clouds may contain more ‘‘wide convective
cores’’ than isolated cumuli and highly organized systems would likely include a majority of ‘‘broad stratiform regions.’’ A one-to-one comparison in terms ofcloud morphology is, however, difficult since each composite category, unlike existing snapshot-based categorizations, consists of a full time series where the spatial
structure of a cloud system changes over its life cycle. The
current terminology therefore only designates the mostrepresentative convective system of each category, while
different types of cloud systems may coexist during thecourse of evolution in the composite statistics.

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FIG. 2. The composite time series of cumulus cloud cover at distance zero (R 5 0) for (a) isolated cumulus clouds, (b) moderately
developed clouds, and (c) highly organized systems in the z–t plane. (d)–(f) As in (a)–(c), but for the vertical–horizontal (z–R) cross section
at time zero.

3. Thermodynamic variability
This section presents the strategy to evaluate largescale mean temperature and water vapor fields anddemonstrates its application to the composite analysis.

by the dashed box. Large-scale mean air temperatureand vapor mixing ratio, T( p) and q( p), respectively, aredefined asT( p) 5a. Synthesis of large-scale mean fields
The utility of infrared sounding is limited whereclouds are present. Although the operational AIRS
product does not attempt to retrieve temperature andwater vapor inside clouds, it uses a stochastic algorithm
to obtain cloud-cleared soundings in partially cloudy
columns in attempt to minimize the clear-sky bias
(Susskind et al. 2003, 2011). The previous approach(Masunaga 2012a), where the AIRS 18 3 18 gridded
product was adopted as it is, is updated in this paper tocombine the AIRS measurements with a separate estimate of in-cloud vapor and temperature to construct the‘‘all sky’’ thermodynamic field averaged over a largescale domain of O(100 km). The analysis procedure is
described below.
Figure 1 outlines the analysis flowchart, where thealgorithm module illustrated in this section is indicatedq( p) 5

1
pR2
1
pR2

ðR
0
ðR

^ p, r)2pr drT(q^( p, r)2pr dr ,(1)0where r denotes the distance axis in composite space,
R is the radius of a circular large-scale domain, andp designates the pressure coordinate. All-sky temperature and vapor mixing ratio, denoted by T^ and q^, respectively, are a weighted average of in-cloud properties,
Tc and qc, and the ambient air properties outside cloud
cells, Ta and qa, as^ p, r) 5 s ( p, r)T ( p, r) 1 [1 2 s ( p, r)]T ( p, r)
T(
c
c
c
a

(2)

and
q^( p, r) 5 sc ( p, r)qc ( p, r) 1 [1 2 sc ( p, r)]qa ( p, r) ,

(3)

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where sc is the cumulus cloud cover (see section 2b andFig. 2). The vertical dimension in the CloudSat measurements of sc is converted from the altitude to pressure coordinates using the AIRS geopotential (describedlater in this section). Note that all composite variables
are a function of time, but the argument t is omitted fromthe equations for brevity.
The ambient air properties (Ta and qa) are as provided
by the AIRS data product. On the other hand, in-cloud
temperature and water vapor are difficult to evaluate directly from satellite measurements. The current strategy is
to combine a semitheoretical estimate of temperatureand vapor mixing ratio for a saturated air parcel, T* andq*, with the ambient air properties to characterize thein-cloud thermodynamic state asTc ( p, r) 5 gc ( p, r)T*(p, r) 1 [1 2 gc ( p, r)]Ta ( p, r)

(4)

and
qc ( p, r) 5 gc ( p, r)q*( p, r) 1 [1 2 gc ( p, r)]qa ( p, r) .

(5)

A hypothetical undiluted air parcel, initially given theobserved temperature and vapor mixing ratio of Ta(ps, r)
and qa(ps, r),1 is lifted dry-adiabatically from the surface
to cloud base, beyond which T*(p, r) and q*(p, r) arecalculated following the moist adiabat until the level ofneutral buoyancy is reached. The dilution fraction gc in(4) and (5) takes into account the entrainment of dry
ambient air into the cloud in an idealized manner as[1 2 gc ( p, r)]qa ( p, r)
5 zc (r)c ( p, r) ,g c ( p, r)q*( p, r)or equivalently,
gc ( p, r) 5

qa ( p, r),qa ( p, r) 1 zc (r)c ( p, r)q*( p, r)

(6)

where
8
1 ›sc
>
<2
s
c ( p, r) 5
c ›z
>
:
0

for

›sc
,0
›z

(7)

otherwiseis an approximate measure of entrainment rate and zc is
a proportionality constant independent of height (butallowed to vary horizontally). In-cloud vapor mixing
ratio is considered in (6) and (7) to be diluted with theambient air at the rate of how rapidly cumulus cloud
cover decays over height. The entrainment is assumed tobe negligible when cumulus cloud cover does not decrease upward. The parameter zc, having the dimensionof length, by definition represents the vertical scale overwhich a diluted portion of the cloud air evaporates out as
it ascends. It would be safe to assume that zc is verticallyconstant to the extent that such mixing processes may beconsidered to rely on general aerodynamic properties
insensitive to altitude, although this simplification is yet
to be verified elsewhere.
The above set of equations constitutes a closed system
to yield q and T once zc is known. The computational
procedure begins with an arbitrary guess of zc, withwhich a tentative estimate of q^ is obtained from (3) and(5)–(7). The initial trial of zc is then adjusted to the additional constraint ofðpsdp
CWV(r) 5(8)q^( p, r) ,g0
where CWV is given by AMSR-E observations and g is
the gravitational acceleration. The equality in (8) isjustified by the fact that microwave radiometry, unlike
infrared sounding, has sensitivity to in-cloud water
vapor as well as to the ambient moisture field. The
solutions of zc and q^ are updated iteratively until (3) and(5)–(8) are all satisfied. Once zc is finalized, T and q are
evaluated from (1).
Geopotential, required later to evaluate dry static
energy (DSE), is derived from AIRS temperature andwater vapor (for virtual temperature) through the hypsometric equation. The hypsometric equation is not applied to Tc and qc inside clouds where the hydrostatic
assumption may be invalid. Instead, the in-cloud and ambient pressures are assumed to be locally equalized at any
height above cloud base as is often done in a parcel model,
in which case a geopotential surface is horizontal acrosscloud boundaries. The composite AIRS geopotentialu( p, r) thus may be interpolated into clouds as it is andaveraged straightforwardly into the large-scale mean u( p).
All parameters are binned every 1 h over time andevery 25 km over the radius in composite space. This
radial bin size is chosen so that each bin contains at least
several AIRS 13.5-km footprints, but at the same time is
not so large as to smear out the radial gradient of composite parameters (see Fig. 2 for a rapid change of sc
over radius). To calculate large-scale means, the integral
over radius in (1) is replaced by summation as[][

Note that T^ 5 Ta and q^ 5 qa below cloud base, where sc vanishes by definition.
1

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1
R2

ðR
0

[    ]2r dr 5

1 N
2
),
å [    ]i2(1/2) (ri2 2 ri21
R2 i51(9)

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FIG. 3. The time–pressure cross section of large-scale mean vapor mixing ratio (q) anomaly (g kg21) in composite space for (a) isolated
cumulus clouds, (b) moderately developed clouds, and (c) highly organized systems (see section 2b for definition). The bottom two layers
are partitioned by cloud base or LCL where labeled by CB on the pressure axis. The anomaly is color shaded against the background fields
given on the right side computed by temporally averaging the first and last 12-h time series (272 to 260 h and 160 to 172 h) together.

where r0 5 0, rN 5 R, and [    ]i2(1/2) designates theaverage within a 25-km ring bound by ri21 and ri. The
large-scale domain size is chosen to be R 5 100 km
throughout this paper. Increasing R would reduce theamplitude of variability in the composite parameters but
not qualitatively alter the results.
The large-scale mean thermodynamic field is often
replaced by the ambient sounding under the assumptionof sc  1. Cumulus cloud cover may be negligibly small
when averaged over a time much longer than the lifetime of individual cloud systems, but sc is by no means
small even in a 100-km scale mean at instances whenvigorous convective systems develop as one can see in
Figs. 2e and 2f. At the same time, sc never reaches unity
and the ambient sounding is always available in composite space, although instantaneous AIRS observations
are totally contaminated by clouds from time to time.
Both the in-cloud and ambient terms in (2) and (3) are
therefore retained throughout this paper.

b. Composite vapor mixing ratio and dry static energy
The composite large-scale mean vapor mixing ratio q
is plotted for different convective systems in Fig. 3.
Color shaded are the anomalies of q against the background field, which is defined as the temporal mean of
the first and last 12-h sequences combined together andshown in panels on the right. A moistening prior toconvection (t 5 0) in the lower free troposphere is visible for all types of convective systems, with a larger
magnitude for more-developed systems. The moistening is limited to below the middle troposphere for isolated cumulus clouds, while the moist layer thickens
farther upward into the upper troposphere when convection is organized and grows deep. A well-known
vertical tilt is evident in vapor mixing ratio anomaly forhighly organized systems, that is, upper-troposphericmoisture does not reach the maximum until when thelower troposphere begins to dry just after convection

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FIG. 4. As in Fig. 3, but for large-scale mean DSE s (kJ kg21).

peaks (Sherwood and Wahrlich 1999; Mapes et al.
2006; Zelinka and Hartmann 2009; Masunaga 2012a).
In particular, the very bottom layer exhibits a sudden
drying immediately after time zero for highly organizedsystems.
Large-scale mean DSE per unit mass iss( p) 5 cp T( p) 1 u( p) ,(10)

where cp is the specific heat of dry air at constant pressure. As seen in Fig. 4, a negative DSE anomaly in thelower to middle troposphere emerges as convection
develops in parallel with a positive anomaly growing
in the upper troposphere. Such bipolar anomalies, only
barely present for isolated cumuli, are robust for highlyorganized systems. A minor third pole of negative anomaly is also visible near the tropopause around t 5 0. These
characteristics are mainly attributed to the cp T term. A
similar dipole temperature anomaly has been observedin the past studies cited above with respect to the water
vapor variability.

4. Budget analysis
The methodology and results of the water and heatbudget analysis are presented in this section. Uncertainty estimates for the budget parameters are given in
the appendix.

a. Tropospheric moisture and DSE convergences
The moisture and thermal budget equations are›q›qv
1 $  qv 1
5 e 2 c,
›t
›p
›s
›sv
1 $  sv 1
5 L(c 2 e) 1 QR ,
›t
›p

(11)

where is v the horizontal velocity vector, v is the verticalp velocity, e is the reevaporation rate per unit mass, c isthe condensation rate per unit mass, L is the specificlatent heat of liquid water, and QR is radiative heating
rate per unit mass. Vertically integrating and horizontally averaging (11), one finds

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hqi 1 h$  qvi 5 E 2 Ps ,›t›hsi 1 h$  svi 5 S 1 LPs 1 hQR i ,
›t

and
(12)

where E is the surface evaporation flux, Ps is the surface
precipitation rate, S is the surface sensible heat flux, andangle brackets denote the vertical integral:
ðp
s
dp
,
(13)

hi[
gpT
where pT is fixed at 100 hPa as a rough measure of thetropopause pressure. Here ps and pT are implicitly assumed to be independent of time, so the pressure integral and temporal derivative are interchangeable, thatis, h›/›t i 5 ›/›thi. The surface turbulent fluxes areevaluated with the bulk formulas* 2 q( ps )]jv10 j,
E 5 ra CE [qssS 5 ra Cu cp [uss 2 u( ps )]jv10 j ,(14)

where ra, q*,
ss u, uss, and v10 are dry air density, saturation
vapor mixing ratio for SST, potential temperature, sea
surface potential temperature (which is numericallyequivalent to SST), and wind vector at 10 m above thesea surface, respectively. The bulk transfer coefficients
CE and Cu, adopted from Large et al. (1994), areqﬃﬃﬃﬃﬃﬃﬃ
CE 5 3:46 3 1022 CD ,qﬃﬃﬃﬃﬃﬃﬃ
Cu 5 3:27 3 1022 CD ,where the drag coefficient CD is given by
jv10 j 21
1 1:42 3 1024
(m s21 )
jv10 j25.
1 7:64 3 10(m s21 )CD 5 2:7 3 1023



Observations yield the time series of E and S through(14) with observed v10 as well as hqi, hsi, hQR i, and Ps incomposite space, where large-scale means are as givenby (9) and pressure integrals are carried out numericallyover discrete pressure levels. On the other hand, nosatellite sensor currently available is capable to measurewind, and hence the convergence field, far above thesurface. A solution to this problem is to employ thevertically integrated budget equations (12) for the diagnosis of moisture and DSE convergences, that is,›
2h$  qvi 5 hqi 2 E 1 Ps
›t

(15)

›
2h$  svi 5 hsi 2 S 2 LPs 2 hQR i .
›t

(16)

Figure 5 shows the moisture budget breakdown.
Evaporation E and precipitation Ps are roughly balanced
against each other throughout when composited withrespect to isolated cumulus clouds (Fig. 5a). Both E andPs stay close to the background level of 0.2 mm h21,
except for a slight enhancement in Ps near t 5 0. Moisture convergence is comparable to E near its maximum
but virtually absent during the rest of the period andonly makes a modest contribution to the moisture budget. It follows that precipitation from isolated cumulus
clouds does little more than consume the local moisturesupply by evaporation from beneath. Precipitation in
the background state itself is likely produced by isolated
congestus clouds or precipitating shallow cumuli, which
are ubiquitous across tropical oceans (Johnson et al.
1999; Short and Nakamura 2000).
In contrast, moisture convergence greatly strengthens
as convection develops for more organized convective
systems (Figs. 5b,c). Given that evaporation varies little
beyond the background level in all cases, water vapor
fed into intensifying rainfall must be accumulated dynamically through horizontal convergence. In particular, the moisture budget during highly organized systems
virtually consists of a sole balance between precipitationand moisture convergence. As such, the nature of moisture budget associated with convective development
qualitatively changes depending on the degree of convective organization.
The DSE budget is illustrated in Fig. 6. Isolated
cumuli accompany little systematic variation in the
DSE budget balance, where DSE divergence and column radiative cooling have a nearly equal amplitude of;0.1 kW m22 and together offset the net latent heating.
Sensible heat flux stays negligibly small. In cases of moredeveloped convection, DSE divergence and latent heating
overwhelm QR and S and almost precisely counteract
each other during active convection.
Typical values of the tropospheric budget parameters
are comparable to in situ observations from past field
campaigns. Some examples are given in the appendix.

b. FT moisture and DSE balance
The budget equations are further exploited to analyze
the FT moisture and DSE budget. To facilitate this, thetroposphere is divided into two layers separated by cloudbase. The layers below and above cloud base define thesubcloud layer (SC) and free troposphere, respectively,
through the rest of the paper. The divergence terms arebroken down into SC and FT components,

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VOLUME 70

FIG. 5. The composite time series of large-scale mean moisture convergence 2h$  qvi (solid blue line),precipitation with sign reversed 2Ps (dashed green line), and surface evaporation flux E (dotted red line)(mm h21) for (a) isolated cumulus clouds, (b) moderately developed clouds, and (c) highly organized systems.

h$  qvi 5 h$  qviFT 1 h$  qviSC ,h$  svi 5 h$  sviFT 1 h$  sviSC ,$  [q( ps )v10 ]
(17)

where
h    iFT [h    iSC [

ðp

CB



dp
,
g



dp
.
g

pT

ðp

s

pCB

The subcloud layer is assumed to be well mixed overheight dynamically and thermodynamically at any moment, so that q, s, and v10 are each vertically homogeneous below cloud base. The SC divergence terms arethen explicitly expressed in terms of observed q( ps),s( ps), and v10 as
h$  qviSC 5 $  [q( ps )v10 ]h$  sviSC 5 $  [s( ps )v10 ]

DpSC
,
g

DpSC
,
g

(18)

where DpSC [ ps 2 pCB. The SC divergence turns out toconsist of known quantities only and is differentiated fornumerical integration as
DpSC 
g i2(1/2)

)

DpSC
[rq( ps )u10 ]i 2 [rq( ps )u10 ]i2152,2g
ri2 2 ri21
i2(1/2)Dp $  [s( ps )v10 ] SC 
g i2(1/2)
)
(

DpSC
[rs( ps )u10 ]i 2 [rs( ps )u10 ]i2152.
2g
ri2 2 ri21
i2(1/2)(Large-scale mean SC divergence (18) is rewritten withthe help of (9) intoDp
$  [q( ps )v10 ] SC
g


DpSC
2 N
,
5 2 å f[rq( ps )u10 ]i 2 [rq( ps )u10 ]i21g
g i2(1/2)
R i51$  [s( ps )v10 ]
5

DpSC
g



DpSC
2 N
f[rs(
p
)u
]
2[rs(
p
)u
]
g
,
å
s 10 is 10 i21
g i2(1/2)R2 i51(19)

AUGUST 2013

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MASUNAGA

FIG. 6. As in Fig. 5, but for DSE convergence 2h$  svi (solid blue line), net latent heating LPs (dashedgreen line), sensible heat flux S (dotted red line), and column radiative heating hQR i (dot–dashed brownline).

where u10 is the radial component of v10. The summation would collapse to the boundary (i 5 N) term in
the divergence theorem, but all terms must be retainedin our case. Those terms are not precisely, thoughmostly, cancelled out because DpSC changes with r
depending on q( ps) and T( ps). Equations (12), (17),and (18) lead toDp›
2h$  qviFT 5 hqi 1 $  [q( ps )v10 ] SC 2 E 1 Ps
›t
g

(20)

1 hQR iSC ,Dp›
2h$  sviFT 5 hsi 1 $  [s( ps )v10 ] SC 2 S
›t
g
(21)

which offer an observational diagnosis of FT
convergence.
The large-scale SC budget equations, obtained by integrating (11) over the subcloud layer and applying
domain average, are(22)

where PCB is the rate of precipitation entering the subcloud layer from above through cloud base. The tendency
terms integrated over a well-mixed subcloud layer are

›q
›t

›s
›t

and

2 LPs 2 hQR i ,



›q
qv
1 h$  qviSC 2  5 E 1 PCB 2 Ps ,
›t SC
g CB


›s
sv
1 h$  sviSC 2  5 S 1 L(Ps 2 PCB )
›t SC
g CB

5

›q( ps ) DpSC
,
›t
g

5

›s( ps ) DpSC
.
›t
g

SC

SC

(23)

The term PCB 2 Ps in (22) accounts for the moistening
and cooling due to reevaporation from raindrops within
the subcloud layer. Since the SC reevaporation is very
difficult to observationally constrain, the effects ofreevaporation are characterized by a prescribed coefficient aSC, defined as the SC reevaporation rate normalized by surface rain rate:

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VOLUME 70

FIG. 7. As in Fig. 5, but for FT moisture convergence 2h$  qviFT (solid blue line), vertical moisture flux
at cloud base 2qv/gjCB (dotted red line), and FT-integrated water vapor tendency h›q/›tiFT (shaded inyellow where positive and in light blue where negative).

PCB 2 Ps [ aSC Ps .

(24)

Throughout this section aSC is fixed at zero, that is,
all rainwater falling into the subcloud layer is assumed
to precipitate out to the surface without being evaporated at all. This assumption is not fully valid, and thepotential impacts of a nonzero aSC will be discussed in
section 5d.
Equations to evaluate the vertical transport (defined
positive upward) are derived from (18), (22), (23), and(24) asDp›q( ps ) DpSC
qv
2 $  [q( ps )v10 ] SC
2  52›t
g CB
gg
1 E 1 aSC Ps(25)

for moisture andDp›s( ps ) DpSCsv
2 $  [s( ps )v10 ] SC
2  5 2
g CB
›t
g
g
1 S 2 aSC LPs 1 hQR iSC
for DSE.

(26)

FT moisture convergence and vertical moisturetransport at cloud base are now ready to be individually
evaluated with (20) and (25). These two quantities are
the origins of FT moistening during convection as seenin the FT-integrated moisture budget,

›q
›t

FT

5 2h$  qviFT 2qv
2 PCB .
g CB

(27)

The result for isolated cumuli (Fig. 7a) shows that thefree troposphere is constantly exposed to a vertical
moisture flux of ;0.5 mm h21 through cloud base (red),
while horizontal moisture flow remains weakly diverging(blue). Horizontal moisture convergence changes its sign
to positive momentarily for moderately developed clouds
but is still consistently weak (Fig. 7b). The primary sourceof the FT moistening associated with these clouds ishence the moist SC air beneath without much help fromlateral convergence. When highly organized systems
occur (Fig. 7c), FT moisture convergence largely exceeds vertical moisture transport during 66 h aroundthe convective peak. FT convergence sharply rises toward its peak slightly before time zero, while verticaltransport reaches a modest peak at 26 h and then drops
until it hits the minimum at 16 h.

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FIG. 8. As in Fig. 7, but for FT DSE convergence 2h$  sviFT (solid blue line, labeled on left), vertical
DSE flux at cloud base 2sv/gjCB (dotted red line, labeled on left), FT diabatic heating LPs 1 hQR iFT(dashed black line, labeled on left), and FT-integrated DSE tendency h›s/›tiFT (shaded, labeled onright).

The tendency of FT-integrated water vapor h›q/›tiFT(shaded in Fig. 7) is positive until convection peaks andthen suddenly jumps down to negative, although persistently weak in magnitude compared to the dominantmoisture sources discussed above. It is implied from (27)that an FT moisture accumulation due to lateral convergence and vertical transport should to a large degree
precipitate out instantly, whereas a measurable amountof moisture residual (deficit) is left behind before (after)
the time of peak convection. The moisture storage isabout 60.1 mm h21 over an extended period of time,barely standing out above moisture budget uncertainties(see the appendix). The FT moistening before convection and drying after are visually evident in the vertical
vapor structure, as we have seen in Fig. 3.
Figure 8 shows the FT DSE budget, where horizontal
convergence and vertical transport at cloud base look as
if they were mirrored to each other. The two curves havea comparable amplitude, but a close examination reveals that DSE is laterally diverged out of the free troposphere somewhat more than input from the subcloudlayer during the convectively active phase. The DSE
tendency is considerably small in magnitude relative toother budget parameters (note that the tendency plot isvertically stretched by 5 times and labeled on the right).
Net latent heating and radiative cooling, plotted together in the dashed curve, therefore virtually offset the
deficit in the total (horizontal plus vertical) DSE convergence, as inferred from the FT DSE budget balance,

›s
›t


sv
5 2h$  sviFT 2  1 LPCB 1 hQR iFT .
g CB
FT
(28)

No attempt has been made so far to break down thevertical moisture or DSE transport into large-scale andeddy components. The effects of large-scale mean updraft and convective eddies are separately examinednext.

c. Vertical eddy transport at cloud baseVertical eddy transport at cloud base can be diagnosed by the remainder after subtracting the large-scaleterm (vertical flux due to large-scale mean updraft or
downdraft) from the net vertical transport derived in theprevious section,

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q v
q0 v0 qv2
5
2
1 CB CB ,


g CB
g CB
g


0
0
s v
s v 
sv
5 2  1 CB CB .
2
g CB
g CB
g

tendency is not necessarily negligible compared to theother terms.
(29)

The last term is explicitly computed from individual
large-scale mean parameters. Large-scale mean verticalvelocity at cloud base is computed from the incompressible
continuity equation,vCB 5 v( ps ) 2ðpCB$  v dp,

(30)

ps

where v( ps ) is practically negligible and will be omitted
hereafter. Recalling the assumption that the subcloudlayer is well mixed, (30) is simplified similarly to (18) and(19) intovCB 5 ($  v10 )DpSC
52 N
å [(ru10 )i 2 (ru10 )i21 ](DpSC )i2(1/2) .
R2 i51

VOLUME 70(31)

Large-scale mean vapor mixing ratio and DSE at cloudbase are chosen to be ‘‘upwind’’ depending on the signof vCB : qCB and sCB are replaced by the SC values, q(ps )and s( ps ), in case of updraft (vCB , 0), while otherwise
observations from the layer immediately above cloudbase are adopted. Vertical eddy transport is evaluatedwith (25), (26), and (31) substituted for the right-handside of (29).
As shown in Fig. 9, the large-scale and eddy components of vertical moisture transport are comparable inmagnitude to each other in the background state. Eddyflux does not much deviate from the background leveleven for highly organized systems, while large-scalemean updraft is significantly modulated during convection. Large-scale mean updraft hits a minimum of nearzero at 16 h for highly organized systems, presumably
signifying a mesoscale downdraft (Zipser 1969, 1977)
just as strong as entirely cancels large-scale mean updraft. The tendency of SC vapor mixing ratio stays
minimal throughout, except for a momentary negative(SC ventilation by convection), barely recognizable aroundthe peak of organized convection. Figure 10 illustrates
the vertical DSE transport terms, where large-scalemean updraft (labeled on right) is larger by an order ofmagnitude or two than the eddy contribution (labeledon left), regardless of the degree of convective organization. The SC DSE tendency is overall small, except forseveral hours after time zero, during which the DSE

5. Discussion
In this section, the present results are discussed in lightof the implications for related research topics.

a. Subcloud-layer budget
1) ORIGINS OF SC COOLING DURING CONVECTION
Figure 4 shows that the lower troposphere, including
the subcloud layer, experiences a temporary cool anomaly
during convection. The plausible sources of the cool
anomaly include rain reevaporation and large-scale ascent, although they each have a difficulty in thoroughly
explaining the SC cooling as observed. The SC cool
anomaly strengthens toward t 5 0 and then gradually
weakens (Fig. 4), indicating a cooling before time zeroand a heating after. This is, however, contrary to what
one might expect from rain reevaporation. Lowertropospheric evaporative cooling is characteristic ofstratiform precipitation, which is known to be mostdominant at later stages in the life cycle of organized
convective systems. Figure 4c shows that the lowertropospheric cool anomaly for highly organized systems
continues to grow even during the peak rainfall (slightly
earlier than time zero, as observed in Fig. 5c), which islikely brought primarily by deep convective precipitation. On the other hand, the observed DSE profile does
not appear to support the adiabatic cooling hypothesis
either. Updraft velocity is generally greatest in the
midtroposphere, and the static stability is weakest near
the surface, as seen from the background DSE profile inFig. 4. Neither of these facts favors the presence of acooling peak near the bottom of the atmosphere, as
observed in Fig. 4.
Evaporative and adiabatic coolings are thus unlikely
capable to individually account for the SC cooling, although both in tandem might be able to better explain it.
Unfortunately, it is difficult in any case to isolate theorigins of SC cooling from the thermal budget perspective because the DSE storage term is minimalthroughout the evolution (Fig. 10). The SC DSE tendency
in Fig. 4 shows an approximately 1 kJ kg21 decrease over
12 h, resulting in a cooling as small as 1022 kW m22 for an
SC thickness of 50 hPa. The SC budget terms stay wellbalanced nearly all the time, suggesting a mechanismthat instantly removes an excessive SC moisture andDSE through convective updraft and associated downdraft (Raymond 1995; Emanuel 1995).
In contrast to its minimum consequences for thethermal budget, SC cooling (along with moistening)

AUGUST 2013

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MASUNAGA

FIG. 9. As in Fig. 7, but for vertical moisture transport at cloud base due to large-scale motions
2qCB vCB /g (solid blue line) and due to convective-scale eddies 2q0 v0 /gjCB (dotted red line) and the SC
moisture tendency h›q/›tiSC (shaded).

leads to a significant modulation of cloud-base height,
which could be a key factor governing the convective
onset and intensity. This is examined next.

2) MODULATION OF SC THICKNESS
Cloud-base pressure experiences a systematic temporal variation in the course of convective development.
Figure 11a shows that cloud base decreases in height asconvection intensifies and then increases back afterconvection ceases, with a greater magnitude for organized convective systems than for isolated cumuli.
Cloud-base pressure rises from 955 to 975 hPa toward
the convective peak for highly organized systems.
Fluctuations in cloud-base height, by definition of LCL,
result from temperature and water vapor perturbations
near the surface. A slight but consistent SC moistening
for hours prior to convection (Fig. 11c) explains theinitial, gradual decrease of cloud-base height. An SC
temperature drop immediately before the convective
peak (Fig. 11d) leads to an increase of SC relative humidity and, as a result, further lowers the cloud base,while a subsequent temperature recovery appears to
rapidly raise cloud-base height back toward the initiallevel. In contrast to pCB, surface pressure stays virtuallyinvariable during the whole period of time analyzed(Fig. 11b), letting cloud base be solely responsible forthe variability of SC thickness.
A thinning of the subcloud layer would reduce convective inhibition and potentially facilitate the onset of
convection. On the other hand, Masunaga (2012a) foundthat the SC cooling results in a remarkable decreaseof convective available potential energy (CAPE), andthe troposphere does not as a whole stay in a quasiequilibrium state. These behaviors appear to favor the‘‘activation control’’ hypothesis for the large-scale variability of deep convection (Mapes 1997).

3) SCALE ANALYSIS
A scale analysis is carried out here for the SC budget
in order to provide an intuitive interpretation of theeddy moisture and DSE transports at cloud base. The
large-scale mean moisture convergence may be approximated in the integral form [see note following(19)] ash$  qviSC 5

1
A

ðp

dp
pCB g
s

þ
qu dl ,

(32)

where A designates the large-scale domain area, the line
integral over dl applies to the domain border, and u is

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VOLUME 70

FIG. 10. As in Fig. 9, but for vertical DSE transport at cloud base due to large-scale motions 2sCB vCB /g(solid blue line, labeled on right) and due to convective-scale eddies 2s0 v0 /gjCB (dotted red line, labeledon left) and SC DSE tendency h›s/›tiSC (shaded, labeled on left).

the normal component of horizontal wind on the border(outward positive). The mass conservation over thelarge-scale domain (30) is rewritten similarly as
vCB 5

1
A

ðp

s

þ
dp u dl .

pCB

þ
ðp
s dp
q v
1
u dl 5 CB CB , (33)
qCB
A
g
pCB g

where SC vapor mixing ratio, being constant acrossheight, has been replaced by the value at its upper end,
qCB , for later convenience. A parallel argument applies
to the DSE budget as well, ands v
h$  sviSC ’ CB CB .
g

(35)


s0 v0 2
’ S 2 aSC LPs 1 hQR iSC .
g CB

(36)

and

Since it can be safely assumed for a scale analysis that SC
vapor mixing ratio is nearly uniform throughout thelarge-scale domain of interest, q can be moved out of theintegral in (32) as
h$  qviSC ’q0 v0 2
’ E 1 aSC Ps
g CB

(34)

Inserting (24), (29), (33), and (34) into the SC budget
equation (22), one obtains

We have assumed that the moisture and DSE tendencies are practically negligible, as we have seen earlier.
Equations (35) and (36) state that vertical eddy transport at cloud base adjusts itself to balance with surface
turbulent fluxes and reevaporation (and radiative cooling) within the subcloud layer, while SC horizontal
convergence is largely compensated by large-scale mean
updraft through cloud base [(33) and (34)]. In otherwords, large-scale dynamics hardly involves a net change
in the SC budget, leaving surface forcing and diabatic
sources alone to be counteracted by vertical eddy flux atcloud base. The vertical eddy flux of SC moisture is expected from (35) to simply equate with evaporationfrom below when aSC is negligibly small. One can seethat moisture eddy transport (Fig. 9) is indeed in close
accordance with surface evaporation flux in Fig. 5.

AUGUST 2013

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2459

FIG. 11. The composite time series of large-scale mean (a) cloud-base pressure pCB (hPa), (b) surfacepressure ps (hPa), (c) SC vapor mixing ratio (kg kg21), and (d) SC air temperature (K) for isolated
cumulus clouds (dotted), moderately developed clouds (dashed), and highly organized systems (solid).

Similarly, (36) implies that DSE eddy transport at cloudbase would compensate for sensible heat flux and SCintegrated radiative cooling, which, however, make little
contribution to the DSE budget in the first place. As
a result, vertical DSE eddy flux stays considerably small
compared to large-scale mean updraft, as seen earlier in
Fig. 10. It is noted that although vertical eddy flux atcloud base is minimal across tropical oceans, DSE eddy
flux would play a critical role for afternoon storms overland where surface sensible heat flux is a dominantfactor in the thermal budget.

b. FT budget
1) CONGESTUS MOISTENING
An FT moistening prior to convection is clearly observed in the present analysis in line with many existingstudies reviewed in section 1. It was found that eddy
moisture flux by background congestus clouds, composed of nearly equal shares of eddy and large-scale
contributions, balances approximately with surface
evaporation, as shown by (35). Evaporation, in turn, islargely offset by precipitation from isolated cumuli
themselves (Fig. 5a). This equilibrium between eddy
moisture flux and congestus precipitation accounts forthe moisture balance in the background state being awayfrom the times of organized convection. After a longspell with no systematic trend in the background state,a small but consistent FT moistening begins (shading in
Fig. 7c) and convective clouds start to deepen (Fig. 2c)around 30 h before highly organized systems reach thepeak. Beyond this point, large-scale mean updraft
strengthens while eddy moisture flux remains hardly
changed (Fig. 9c), and then horizontal moisture convergence takes the lead of FT moistening within a half
day before the peak of highly organized systems (Fig.
7c). FT moistening within a few days before peak convection is therefore explained principally by large-scalemean updraft, with the effect of convective eddies being
secondary.
Hohenegger and Stevens (2013) showed that congestus clouds moisten the free troposphere too slowly to
explain the fast transition to deep convection as observed, while large-scale ascent provides a more efficientmechanism of FT moistening. Their conclusion, although obtained from a quite different approach fromthe present analysis, corroborates the current findings.
The known relationship of FT moistening with congestus clouds (see section 1 for references) is probably
an indirect correlation via large-scale vertical motion
associated with deep convective development, so thatthe causal link between the congestus eddy moistening

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and subsequent deep convection is weak on daily andhourly time scales. This point was not addressed by
Kuang and Bretherton (2006) and Waite and Khouider(2010), who did not impose a large-scale vertical motionin their model simulations.
It should be noted, however, that observed large-scalemean updraft includes not only external dynamics suchas synoptic-scale waves but the local effect of convective
updraft unless convective updraft is largely cancelledout by environmental subsidence. It is very difficult totrack down in observations to what extent the qCB vCB
term in (29) is driven by an external large-scale forcing
or a local congestus-induced effect. It would be safe toconclude that congestus eddy moistening is unlikely
a chief mechanism for preconditioning deep convective
development, while congestus clouds could help moisten
the free troposphere to some degree through their own
updraft that survives large-scale averaging. Furthermore, the possibility is not ruled out that congestus
moistening could be more important on a much longer
time scale (e.g., MJO) than currently considered.

2) OTHER IMPLICATIONSFT moisture is weakly diverging in the absence ofvigorous convection growing beyond isolated cumuli(Fig. 7a) and turns to converging only when convection
is developed into a more or less organized system (Figs.
7b,c). DSE diverges out of the free troposphere all thetime and, contrary to FT moisture, becomes even morediverging during organized convection (Fig. 8). The
weak FT divergence for isolated cumuli is explained bya modest updraft from the subcloud layer associatedwith shallow, sporadic convection that diverges out immediately above cloud base. Once convection becomesdeep and organized, the lower free troposphere should
be now converging in phase with the subcloud layer andaccompany a significant upper-tropospheric divergencethat exports updraft air from beneath. Such an overturning circulation with a lower inflow and a upper
outflow would give rise to a moisture convergence anda DSE divergence as observed, given that water vapor
mixing ratio decreases with height while DSE increases(see background fields in Figs. 3 and 4).
The FT DSE divergence and vertical DSE transport
through cloud base are together largely cancelled out by
domain-averaged diabatic heating on an instantaneousbasis. This is as expected since an FT temperature perturbation would quickly dissipate out by gravity waves
in the tropics. Nevertheless, a small but finite DSE imbalance is left behind, giving rise to a well-defined bipolar pattern containing a lower-tropospheric negative
anomaly and an upper-tropospheric positive anomaly(Fig. 4). This feature implies that the troposphere is

VOLUME 70

temporarily stabilized to some extent in the course of
the deep convective development (Masunaga 2012a).
Possible mechanisms relating convection and stability
over a time scale of present interest include the ‘‘diurnal
dancing’’ hypothesis proposed by Chen and Houze(1997). Raymond and Sessions (2007) discussed a potential mechanism where such a stable perturbation favors the convective intensification in the context oftropical cyclogenesis.
The causes for the FT warming and cooling anomalies
are difficult to identify, just as was the case for the SC
budget discussed in section 5a(1). An FT DSE perturbation could be due partly to local diabatic heating
and/or large-scale adiabatic cooling. The full verticalstructure of atmospheric heating rate is not available inthe current two-layer model approach and should bestudied elsewhere so as to track down the origins ofthermal budget imbalances in the free troposphere.

c. Regional variability
The composite plots presented so far represent the
global tropical ocean as a whole, although the moistureand thermal budget characteristics may vary from region
to region. This section is devoted to a brief assessment of
the regional variability with focus on four major oceanic
basins: Indian (508–908E), western Pacific (1408E–1808),
eastern Pacific (1308–908W), and Atlantic (508–108W)
Oceans. All of the regions are meridionally bound between 158N and 158S, with landmasses excluded.
Figure 12a shows composite FT moisture convergenceand vertical moisture flux at cloud base for highly organized systems (cf. Fig. 7c). The isolated cumulus andmoderately developed cloud categories are omitted
because well-developed convective systems are moreclearly linked to the regionality than shallower clouds(e.g., Masunaga and Kummerow 2006). The regional
breakdown makes the composite curves somewhat noisy
as a result of the reduction of sample size compared to
the global statistics. Besides the statistical fluctuations,no apparent regional difference is observed in the
moisture budget characteristics. The qualitative behavior is similar across all regions also in the thermal budget
properties (Fig. 12b), except that the eastern Pacificappears to have a slightly larger amplitude than otherregions in both FT DSE convergence and vertical DSE
flux at cloud base.
The DSE budget directly reflects the dynamic environment as implied by Fig. 12c. SC wind convergence,
2h$  viSC , stays strongest, marginally but consistently
over time, in the eastern Pacific, presumably related to awell-defined band of the intertropical convergence zone
(ITCZ) unique to this region. Precipitation, meanwhile,
is far less regionally sensitive (Fig. 12d), assuring that theAUGUST 2013

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2461

FIG. 12. Regional breakdown for the composite time series of large-scale mean (a) FT moisture convergence (solid) and vertical moisture transport at cloud base (dotted) (mm h21), (b) FT DSE convergence (solid) and vertical DSE transport at cloud base (dotted) (kW m22), (c) SC divergence (1025 s21),and (d) precipitation with sign reversed (solid, labeled on left) and evaporation (dotted, labeled on right)(mm h21). Four oceanic basins (Indian, western Pacific, eastern Pacific, and Atlantic Oceans) are separated by color as indicated at the top. Only the highly organized systems category is shown.

fundamental properties of cloud systems within thehighly organized systems category are common to all theregions studied. It is inferred that rainfall of a similarintensity accompanies a slightly stronger SC convergence in the eastern Pacific compared to other tropicaloceans. Such regionality is obscure in the moisturebudget because an excess of SC wind convergence in theeastern Pacific is offset by a lower evaporation rate there(Fig. 12d). The sensible heat flux is too weak in the firstplace to counteract the dynamic forcing in the DSE
balance.

d. SC reevaporation
Reevaporation from rain falling within the subcloudlayer has been assumed to be absent but should be taken
into account in general. The impact of a finite SC
reevaporation on the moisture and thermal budget is
examined in this section.
Risi et al. (2008) showed in their Fig. 4 that reevaporation within the subcloud layer accounts for 1% of the
SC moisture sources in the tropics. Bacmeister et al.
(2006) demonstrated in a variety of model experimentsthat the fractional reevaporation, or the vertically integrated reevaporation normalized by surface rain rate,
ranges from much below 1 to as high as 2 over tropicaloceans. A large portion of this reevaporation, however,occurs above cloud base. Bacmeister et al. (2006) also
showed that reevaporation moistening is roughly homogeneous over height (except for a sharp minimum
near the boundary layer top) from the surface to
600 hPa, beyond which it smoothly decreases upward
until it vanishes around 200 hPa. Given the SC thicknessof ;(25–50) hPa (Fig. 11), it is crudely estimated that SC
reevaporation moistening constitutes 5%–10% of thetotal reevaporation throughout the troposphere. The
estimates above together imply that the SC reevaporation efficiency aSC defined by (24) varies from near zero
to about 0.2.
Additional runs with re-evaporation rate perturbed(aSC 5 0.05, 0.1, and 0.2) are performed for highly organized systems (Fig. 13). A change to SC reevaporation
modifies the vertical transport at cloud base (eddy andtotal) as expected from (25), (26), (35), and (36), whilehorizontal convergence and large-scale mean updraft

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FIG. 13. The composite time series with various SC reevaporation rates for large-scale mean (a) FTmoisture convergence (solid) and vertical moisture transport at cloud base (dotted) (mm h21), (b) FT
DSE convergence (solid) and vertical DSE transport at cloud base (dotted) (kW m22), (c) large-scalemean moisture updraft (solid) and vertical eddy moisture transport (dotted) at cloud base (mm h21), and(d) large-scale DSE updraft (solid) and vertical eddy DSE transport (dotted) at cloud base (kW m22).
Different values of aSC (0, 0.05, 0.1, and 0.2) are separated by color as indicated at the top. Parameters
independent of SC reevaporation are drawn in black. Only the highly organized systems category is shown.

(black lines in Fig. 13) remain unaffected. Vertical
moisture transport at cloud base somewhat enhances as
aSC increases during a half day around when convection
develops (Fig. 13a). The dependence of vertical moisture transport on aSC arises from its eddy component, as
depicted in Fig. 13c. Eddy moisture transport increases
in magnitude rapidly with aSC, because an additional SC
moistening from reevaporation must be balanced out by
a larger eddy moisture export through cloud base under
the observational constraint (35). The enhanced eddy
moistening, however, remains temporally confined to
the vicinity of time zero, so that the FT moisture source
several hours in advance of deep convection is still owing to large-scale mean updraft. It is not until large-scalemean moisture updraft turns to weaken (after 26 h) that
eddy moisture flux could dominate large-scale mean
vertical motion. In addition, lateral moisture convergence overwhelms the entire cloud-base moisture flux
during 66 h (Fig. 13a) regardless of SC reevaporation
rate perturbed within a reasonable range.
In the thermal budget, an increase of SC reevaporation lowers eddy DSE transport at cloud base so as tocompensate for the evaporative cooling. As a result,a positive (but very small) eddy DSE flux in the noreevaporation case changes its sign to negative wheneven a slightest SC reevaporation (aSC 5 0.05) exists.
Such changes in eddy DSE flux, however, are still too
weak to introduce an appreciable impact on the total
vertical transport of DSE (Fig. 13b).

6. Conclusions
A composite analysis scheme proposed by Masunaga(2012a) is extended in this paper to evaluate large-scale(;100 km) mean thermodynamic fields before and afterconvective development over tropical oceans. Cloudcleared infrared soundings are augmented by semitheoretical in-cloud estimates derived with the aid ofcloud radar and microwave radiometer measurements.
The resulting composite anomalies of vapor mixing ratio
and DSE show a systematic variability closely parallelwith previous studies. These large-scale means are ingested to a heat and water budget analysis, where thetroposphere is represented by a two-layer model constituted of the free troposphere and well-mixed subcloudlayer. This simplified approach enables the diagnosis of

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the FT convergence and vertical transport at cloud base ofmoisture and DSE with satellite observations alone.
The main findings and implications are summarized asfollows.
1) Rainfall from isolated cumuli is overall balanced
against local evaporation, while it is large-scale mean
moisture convergence that feeds precipitation fromorganized convective systems.
2) Vertical moisture transport at cloud base is thedominant source of FT moistening as long as isolated
cumuli prevail without deep convection nearby. In
contrast, the vertical moisture flux is overwhelmedby horizontal moisture convergence during the times
when highly organized systems develop. FT diabatic
heating is, to a large extent, immediately cancelled
out, as expected.
3) FT moistening by convective eddies amounts to halfof the total moisture transport through cloud base in
the background state, while large-scale mean updraft
is mainly responsible for the modulation of cloudbase moisture flux during a few days preceding
the development of highly organized systems. Eddy
moisture flux at cloud base could be significantly
enhanced if reevaporation moistening is efficient in
the subcloud layer, although this effect is temporally
limited to near the time of peak convection. The
congestus ‘‘preconditioning’’ effect on a daily or subdaily time scale is thus primarily attributed to largescale mean moisture updraft rather than brought by
convective eddy moistening.
4) The SC cooling and moistening preceding convection
lower cloud base, leading to a thinning of the subcloudlayer by 20 hPa when highly organized systems develops. The decrease of cloud-base height would
reduce the convective inhibition and presumably
facilitate the onset of convection.
Acknowledgments. The author is grateful to Scott
Powell and two anonymous reviewers for their helpfulcomments and suggestions. The TRMM PR (2A25)
dataset was provided by the Japan Aerospace Exploration Agency (JAXA), the GPROF 2010 precipitation
product by Colorado State University (rain.atmos.
colostate.edu/RAINMAP10), the AMSR-E CWV and
SST product by Remote Sensing Systems (www.remss.
com), the CloudSat 2B-CLDCLASS and 2B-FLXHR
datasets by the CloudSat Data Processing Center (www.
cloudsat.cira.colostate.edu), QuikSCAT SeaWinds ocean
wind vector data by the Physical Oceanography
Distributed Active Archive Center (PO.DAAC) at theNASA Jet Propulsion Laboratory (podaac.jpl.nasa.
gov), and the AIRS/AMSU product by Goddard Earth

Sciences (GES) Data and Information Services Center(DISC) (disc.sci.gsfc.nasa.gov). This work is supportedby the Ministry of Education, Culture, Sports, Science,
and Technology (MEXT) Grant-in-Aid for Young Scientists No. 23740351.

APPENDIX
Error Analysis
The water and thermal budget parameters derived inthis work are subject to random error and systematic
bias originating from different sources of uncertainty.
The error sources include the instrumental noise andretrieval errors inherent in individual satellite observations
as well as additional uncertainties resulting from our own
assumptions made. While it is impractical to build a complete error propagation model to thoroughly examine theanalysis procedure, a crude error analysis simply combining known uncertainties intrinsic to original satellite data
products is feasible and useful. A quick quality assessment
along this line is carried out in this appendix.
The QuikSCAT wind has a root-mean-square (rms)error less than 1 m s21 and a bias of 0.1 m s21 at most,
except for very rare occasions with extremely high winds(Ebuchi et al. 2002; Bourassa et al. 2003; Fangohr and
Kent 2012). These values are adopted for the error estimates of evaporation and SC convergence. SST uncertainties from AMSR-E observations are quite small
over tropical oceans (an rms error of ,1 K with virtuallyzero bias) (Gentemann et al. 2010) and practically would
not affect the evaporation estimates. The relative rms
error in AMSR-E oceanic precipitation is about 200% at
a rain rate of 0.2 mm h21 (Lin and Hou 2008), and therelative precipitation bias varies from 25% to 217%,
depending on the ground stations compared (Wolff andFisher 2009). Precipitation uncertainties are estimatedhere to have a random error twice as large as the
background level and a negative bias of 15%.
L’Ecuyer et al. (2008) assessed the CloudSat radiativeflux product and found the rms errors (biases) of 4.4(0.03), 26.7 (5.5), 11.5 (13.2), and 15.2 (16.1) W m22 foroutgoing longwave, outgoing shortwave, downwellingsurface longwave, and downwelling surface shortwave
fluxes, respectively, against the Clouds and the Earth’s
Radiant Energy System (CERES) Fast Longwave andShortwave Radiative Fluxes (FLASHFlux) data.A1 These
numbers amount to the rms error of 33.1 W m22 and the

A1

Note that surface flux estimates from the CERES FLASHFlux product are analyzed from satellite measurements and are notstrictly the ‘‘ground truth.’’

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TABLE A1. Error statistics for the budget analysis. Numbers in parentheses are the relative percentages normalized by the
background level.

Standard error
Bias
Background

E (mm h21)

Ps (mm h21)25

24

2.7 3 10(0.014%)
3.1 3 1023
(1.5%)
0.20

h$  qviSC (mm h21)

2.9 3 10(0.13%)
23.5 3 1022
(215%)
0.23

bias of 234.8 W m22 for hQR i. AIRS air temperature at
the lowermost level has a relative rms error of 1.5 K anda bias of 20.5 K (Susskind et al. 2011). Susskind et al.
(2011) evaluated the relative rms error of AIRS water
vapor to be about 10% in the lowermost layer, but did
not mention the water vapor bias in that layer. Since
their estimate of the total precipitable water bias is as
small as 20.06 cm, we assume here that the near-surface
water vapor bias is also practically negligible. Thesetemperature and water vapor uncertainties are used forevaluating SC moisture and DSE convergence errors
together with the QuikSCAT wind uncertainty.
Random errors associated with instantaneous satellite
retrievals would be largely cancelled out when averaged
into composite time series. For the current analysis, thesample size is O(106) for the QuikSCAT and AMSR-E
parameters, O(104–105) for the AIRS variables, andO(104) for the CloudSat data, with the numerical factor
varying over time in composite space. The standard error, that is, individual rms errors divided by the squareroot of the sample size, is computed for the error statistics shown next as a measure of the robustness ofcomposite means.
Table A1 summarizes the estimated uncertainties forselected budget parameters. Other variables are secondary estimates derived with the budget equations andare not listed. It is noted that the large-scale mean
moisture and DSE transports at cloud base, that is,
qCB vCB /g and sCB vCB /g, are virtually equivalent to the
SC convergence [see (33) and (34)] and have been
omitted from the table to avoid redundancy. Standard
errors listed in Table A1 are computed with the minimum sample size (i.e., the worst case) in the whole
composite time series, which typically occurs near time
zero for highly organized systems. Also presented inparentheses are the relative values normalized by the
background level of each parameter. The backgroundvalues, shown in the bottom row of Table A1, are definedas the first and last 12-h time series averaged together
from the isolated cumulus composite. The standard error
is found to be consistently small.
Systematic biases, in contrast, do not reduce as the
sample size increases and can be much larger. Precipitation25

9.9 3 10(0.062%)
2.5 3 1023
(1.5%)
0.16

hQR i (kW m22)
24

2.1 3 10(0.22%)
23.5 3 1022
(36%)
20.097h$  sviSC (kW m22)
1.0 3 1024
(0.014%)
1.1 3 1022
(1.5%)
0.76

rate has a negative bias of 0.035 mm h21 (or 15% of the
background as noted above), and the column-integrated
radiative heating suffers from a cooling bias of 0.035 kW m22(the relative bias of 36%). The radiative cooling bias isseemingly large but is not critical for the thermal budget
analysis, where the magnitude of hQRi itself stays constantly minor. Care must be taken when a quantitative
assessment is attempted on a subtle budget imbalance,
although any of those bias estimates are not so severe as
to question the major conclusions discussed in this paper.
The relative bias of other budget parameters is as small
as 1%–2%.
Other uncertainty sources that potentially affect thecurrent budget analysis include diurnal sampling bias.
The composite parameters are evaluated from sunsynchronous satellite measurements and can be biased
to a certain phase of diurnal cycle. This problem may beeven more complicated by the fact that A-Train and
QuikSCAT overpasses are 4.5 h apart in local time. The
diurnal variation associated with tropical disturbances isgenerally modest over ocean, with the relative magnitude of 14% for rainfall (Imaoka and Spencer 2000) and
30% for surface divergence (Deser 1994). The actual
diurnal bias in the present analysis would be muchsmaller than these values since the opposite phases ofdiurnal variation would be largely averaged out between
ascending and descending satellite paths, leaving behind
only a small residual ascribed to higher-order diurnal
harmonics.
Finally, in situ measurements documented in pastwork are briefly compared with the current analysis for
an independent verification. Thompson et al. (1979)
applied tropical Atlantic field experiment data to a tropospheric budget analysis and found that Ps 5 12.5 mm day21(0.52 mm h21), E 5 3.8 mm day21 (0.16 mm h21), andmoisture convergence is 9.5 mm day21 (0.40 mm h21) on
average, while Ps 5 22.0 mm day21 (0.92 mm h21), E 5
4.1 mm day21 (0.17 mm h21), and moisture convergenceis 19.4 mm day21 (0.81 mm h21) during a passage oftrough. These values not only fit within the range between the background state and convective peak given
by Fig. 5, but also support the aforementioned findingthat precipitation and moisture convergence vary

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MASUNAGA

concurrently and roughly cancel each other whileevaporation stays small. As for the thermal budget,
LPs 5 0.357 kW m22, S 5 0.012 kW m22, and DSE
convergence is 20.284 kW m22 on average, and LPs 5
0.629 kW m22, S 5 0.018 kW m22, and DSE convergence is 20.597 kW m22 during a trough (Thompson
et al. 1979). Similarly to the moisture budget, these estimates reasonably agree with the present outcomes inFig. 6.
The vertical eddy transport of moist static energy(MSE) at cloud base,
h0 v0 s0 v0 1 Lq0 v0 
5
2
2
 ,
g CB
g
CB

(A1)

is estimated from the present results to be 0.15–
0.18 kW m22 for all three composite categories when
averaged over time from 224 h to 124 h. These numbers
are in reasonable agreement with the estimate by Yanai
et al. (1973), who showed in their Fig. 11 that the lower
tropospheric vertical eddy flux of MSE reads about
370 cal cm22 day21 or 0.18 kW m22. Equation (A1) may
be approximated using (35) and (36) by
h0 v0 ’ LE 1 S 1 hQR iSC .
2g CB
It is suggested that the vertical eddy flux of MSE at cloudbase is mainly determined by surface latent heat flux LE,
with the other two terms in the right-hand side together
being an order of magnitude smaller over ocean.
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