Journal of the Meteorological Society of Japan, Vol. 87A, pp. 295–315, 2009
DOI:10.2151/jmsj.87A.295

295

A 9-season TRMM Observation of the Austral Summer MJO
and Low-frequency Equatorial Waves
Hirohiko MASUNAGA
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan(Manuscript received 18 June 2008, in final form 28 October 2008)

Abstract
A long-term observation from the Tropical Rainfall Measuring Mission (TRMM) is analyzed toinvestigate the Madden-Julian Oscillation (MJO), Kelvin wave, and equatorial Rossby (ER) wave in australsummer seasons. TRMM Precipitation Radar (PR) and Visible/Infrared Scanner (VIRS) measurements arejointly used to clarify convective progression associated with individual modes of the tropical oscillations.
Variability in the dynamic and thermodynamic environment involving sea surface temperature (SST),
column relative humidity (CRH), and moisture convergence is also examined. A sea surface warming isfound to precede the peak MJO convection by ~10 days, while a prior SST increase is not as evident for the
Kelvin and ER waves. Moisture convergence and CRH exhibit a horseshoe-like pattern in the composite
MJO map, constituted of a pair of off-equatorial maxima and a weak equatorial peak. The Kelvin wave
has a moist anomaly on the equator leading the convective peak as theoretically expected, while themoist anomaly also extends poleward without being accompanied by moisture convergence. Moisture
convergence leads CRH by a day or two for the Kelvin and ER waves. Moisture convergence is, in contrast,
virtually concurrent with CRH for the MJO. The correlation between CRH and deep stratiform coverage
is diverse among the three modes of the tropical oscillations. Shallow cumulus and cumulus congestus
lead the MJO convective peak by ~1 day, followed by lingering non-precipitating high clouds. ER wave
convection is led by moisture convergence but lagged by CRH. The convective progression appears not toproceed in a monotonic way for the ER wave.
A possible mechanism to explain MJO propagation is discussed as suggested by a synthesis of thepresent findings. The Kelvin wave guides the eastward migration of MJO convection onset over the Indian
Ocean. The role of the Kelvin wave in MJO propagation diminishes as the MJO enters the west Pacific withthe convective area shifting away from the equator. Instead, the Kelvin wave convective heating induces
poleward moisture transport and moistens the off-equatorial mid troposphere. The resultant moist anomaly
is hypothesized to help trigger the MJO convective burst upon the arrival of ER wave disturbances. Suchcooperative processes involving the Kelvin and ER waves could act as a driving engine of some, if not all,MJO episodes.

1. Introduction
A family of tropical clouds contains a rich variety of
convective systems ranging over a broad spectrum
from shallow cumulus to cumulonimbus. The regional
Corresponding author: Hirohiko Masunaga, Hydro­
spheric Atmospheric Research Center, Nagoya Uni­
versity, Furocho, Chikusa-ku, Nagoya 464-8601, Japan
E-mail: masunaga@hyarc.nagoya-u.ac.jp©2009, Meteorological Society of Japan

gradient of tropical cloud population often shows apattern related to large-scale circulations, where deep
(shallow) systems prevail under an ascending(descending) branch of the circulation. Besides suchclimatological regionality, various types of clouds areoften observed to be organized together in individual
meteorological disturbances. A classical example ismesoscale convective systems (MCSs), where deep
convective cores are typically accompanied byshallower cumulus on the leading edge as well as
trailing stratiform precipitation. A similar pattern of296

Journal of the Meteorological Society of Japan

convective progression has been also documented inassociation with convectively coupled equatorial
waves. Notable examples are the quasi 2-day wave
(Takayabu et al. 1996), Kelvin wave (Straub andKiladis 2003a), and intraseasonal oscillations (Lin andJohnson 1996; Kiladis et al. 2005; Morita et al. 2006).
Mapes et al. (2006) analyzed in situ measurement dataand cloud resolving model outputs to test the hy­­potheses explaining the coherent development of con­vection observed across a wide range of spatial andtemporal scales. Haertel et al. (2008) investigated theheating profiles and the corresponding variation of
convective clouds in terms of their thermodynamicroles in large-scale waves. A long-term, wide-area
observations have yet to be analyzed for obtaining
robust climatological evidence beyond the reach of afield campaign, in particular for studying synopticand planetary-scale disturbances. The present paper
explores convective variability associated with largescale tropical oscillations on the basis primarily ofsatellite data analysis.
Satellite measurements have been often utilized for
detecting different modes of convectively-coupledequatorial waves. Many studies were carried out with
infrared imagery (Takayabu 1994; Wheeler andKiladis 1999; Wheeler et al. 2000; Roundy and Frank
2004; Masunaga 2007) while some used direct
precipitation measurements (Cho et al. 2004; Masu­
naga ­et al. 2006). Little attempt, however, has been
made to distinguishably identify the individual con­vective families such as shallow cumulus and cumulo­
nimbus associated with the observed equatorial waves.
Shallow clouds are difficult to isolate in infrared
satellite images, because cloud tops are so warm thatthey are virtually indiscernible from the backgroundthermal radiation. Optically thick clouds with cold
tops are distinctly observable, whereas infrared
radiation has no sensitivity to the in-cloud structure
shielded by the cloud top. A breakthrough was broughtby the Tropical Rainfall Measuring Mission (TRMM)
Precipitation Radar (PR). The TRMM PR, capable ofprofiling the vertical precip­itation structure, has been
proved to be useful for illustrating the global
climatology of shallow precip­itation as well as moredeveloped systems with higher radar-echo tops (Shortand Nakamura 2000). The TRMM PR, however, isless sensitive to frozen hydro­meteors than to rain
drops of the same size, so that a considerable amountof ice particles aloft could be left undetected(Masunaga et al. 2002). Exploiting radar and infraredmeasurements complementarily, Masu­naga et al.

Vol. 87A(2005) proposed a simple scheme to classify tropicalprecipitating clouds. The four “storm cate­gories” ofshallow, cumulus congestus, deep stratiform, and deep
convective systems were identified in terms of PR
echo-top height and TRMM Visible/Infrared Scanner(VIRS) infrared brightness temperature (IR Tb)(Masunaga et al. 2005; Masunaga and Kummerow2006). The present analysis follows their storm cate­gorization scheme.
The study target is three modes of the tropical lowfrequency oscillations, i.e., the Madden-Julian Oscil­lation or MJO (Madden and Julian 1972), Kelvin wave,
and n = 1 equatorial Rossby (ER) wave coupled withmoist convection1. The current analysis is focused onthese particular modes for two major purposes. First,
TRMM PR measurements are impractical for studying
high-frequency waves such as the mixed Rossbygravity wave and inertio-gravity waves. The PR swath
sweeps only ~1/4 of the tropical band on daily basis,so that the high-frequency modes are difficult tosample. The second purpose is to investigate thepotential roles of the Kelvin and ER waves in the MJO
driving mechanism as suggested by Masunaga et al.
(2006). Masunaga et al. (2006), analyzing 10 MJO
events recorded in early observations from theTRMM, found that the MJO often accompanies agroup of Kelvin wave packets and intruding ER wave
disturbances. They discussed the hypothesis thatinteractions between the Kelvin and ER waves might
explain aspects of the propagation mechanism of the
MJO convective envelope. The proposed mechanism
was further explored by Masunaga (2007), analyzing a
25-yr record of observed outgoing longwave radiation(OLR) and reanalysis datasets. The present study tests
and elaborates the hypothesis by exploiting a decadal
dataset of the TRMM-observed precipitating clouds
and thermodynamic environment. The study period isnine seasons of austral summer starting from late
1998. Austral summer is chosen because it is known tobe the season when the MJO is most outstanding (e.g.,
Madden 1986). We prefer to focus on the single season
rather than including the entire period of time, in order
not to let meaningful signals washed out by averaging
the opposite phases of seasonality together.
The datasets used for this work are described in
Section 2. Convective variability and the thermo­
dynamic environment is examined by a lagged com­posite analysis in Section 3. Relationships among
1 These three modes are hereafter called ‘the tropicaloscillations’ wherever mentioned collectively.

March 2009297

H. MASUNAGA

different types of convective clouds and the envi­
ronmental parameters are explored in terms of thespatial correlation among the lagged composite
diagrams (Section 4). The results are discussed andconcluded in Section 5.

Table 1. Deﬁnition of storm categories.

2. Data
The TRMM version-6 standard products are
adopted for detecting precipitating clouds. PR echotop height is estimated from 2A25 radar reflectivity
profiles. In earlier work (Masunaga et al. 2005;
Masunaga and Kummerow 2006), PR echo-top height
was taken from the PR storm height provided by the
TRMM 2A23 product. The 2A23 storm height, how­ever, depends on the minimum detectable echo of thePR, which decreased by ~1 dBZ when the TRMM
orbit was changed in August 2001 from 350 km to 402
km in altitude. In the present study, PR echo-top
height is directly computed as the altitude of thehighest PR range bin that contains detectable echo.
Similarly to the 2A23 storm height, three or moreconsecutive bins are required to have detectable radar
echoes when the echo-top height is determined. The
minimum detectable reflectivity is defined here as 19
dBZ, which is applied uniformly throughout theperiods before and after the satellite orbital change.
VIRS infrared brightness temperature is obtained
from the channel-4 (10.8 μm) radiance in the TRMM
1B01 product. The PR and VIRS footprints arematched up on a quarter-degree global grid, where
only the nearest neighbors to each grid point are
selected. In the matchup procedure, multiple VIRS
footprints are averaged together with a twodimensional Gauss­ian weight corresponding to the PR
beam pattern, in attempt to mitigate the discrepancy inspatial res­olution between the sensors. VIRS
measurements falling outside the narrower PR swath
are discarded. The TRMM data of every 15–16 orbits
are jointly mapped in a daily dataset.
Shallow, cumulus congestus, deep stratiform, anddeep convective systems are separately identified fromPR echo-top height and VIRS infrared brightness
temperature (Tb) (Table 1). The definitions are equiv­
alent to those proposed by Masunaga et al. (2005)
except for the shallow category. The constraint on
infrared radiance (Tb > 260 K) originally imposed toshallow cumulus has been removed so that low-levelclouds overlapped with high clouds are allowed to join
the shallow category. This revision leads to a distinct
increase of the shallow cloud coverage in convectively
active regions, where shallow cumulus is frequently

overcast by detrained cirrus clouds. An additional
category of non-precipitating high clouds is also
considered in this work. Non-precipitating high clouds
are defined to have cloud tops as cold as the deep
stratiform and deep convective categories (Tb < 245 K)
but are required not to have detectable PR echo.
Sea surface temperature (SST) and precipitablewater (PW) are obtained from the TRMM Microwave
Imager (TMI) datasets provided by Remote Sensing
Systems. Column relative humidity (CRH), or PW
normalized by saturation water vapor path, is used as
the measure of tropospheric moisture. Bretherton etal. (2004) found that CRH is more directly related toprecipitation than PW across all tropical oceans. PW
retrieved by the TMI is used to compute CRH. The
TMI-estimated PW is also used to compute moisture
convergence, assuming that a substantial portion of
tropospheric water vapor lies within the planetary
boundary layer (PBL):∇ · (ρq v v)dz ≈ ∇ · v PBL

ρqv dz= ∇ · (v PBL PW),

(1)

where p, qv , v, and z are the dry-air density, vapor
mixing ratio, horizontal velocity vector, and verticalcoordinate, respectively. The horizontal velocity with­­in the PBL, vPBL , is replaced with v at 1000 hPa, takenfrom the National Center for Environmental Prediction(NCEP)/National Center for Atmospheric Research(NCAR) Reanalysis daily dataset. Altering the 1000hPa level to 925 hPa for the definition of vPBL wasfound to introduce no notable change to the geo­
graphical pattern of moisture convergence. Air tem­
perature is also adopted from the NCEP/NCAR
Reanalysis data for calculating the saturation water
vapor to define CRH.
The contribution of free-tropospheric humidity tomoisture convergence is not precisely taken into
account in the approximate formula above (Eq. 1).

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Vol. 87A

Fig. 1. Time-longitude diagrams of 5-month (from November to March) sequences for 9 consecutive years as
indicated at the top of each panel. Color shade illustrates deep stratiform coverage, smoothed by threepasses of the 1-2-1 filter applied temporally and spatially. MJO-filtered deep stratiform coverage isindicated by white contour drawn at + 0.5% in anomaly. The Kelvin and ER waves are contoured in black
at + 0.65%, where the eastward-propagating (westward-propagating) component represents the Kelvin(ER) wave.

This simplification has been examined for validity incomparison with another estimate of moisture con­vergence, calculated strictly from the vertical profilesof humidity and horizontal wind in the NCEP/NCAR
reanalysis dataset. Qualitative patterns in composite
and correlation diagrams presented later were foundvery insensitive to the way how moisture convergenceis calculated (not shown). On the other hand, the“exact” moisture convergence is considerably weakerin magnitude than the approximate moisture con­vergence given by Eq. (1). Although the approximate
formula might possibly exaggerate the magnitude ofmoisture convergence to some extent, the negative bias
in amplitude is presumably because tropical moisturevariability tends to be underestimated in the NCEP/
NCAR reanalysis data (Trenberth and Guillemot1998). We therefore prefer to employ observationallydetermined humidity for defining moisture con­vergence at the expense of potential higher-ordererrors introduced by the simplification.

3. Composite horizontal structure
3.1 Filtering and compositing methods
Wheeler and Kiladis (1999) devised a technique to299

H. MASUNAGA

ER wave

Kelvin wave

MJO

March 2009

-4 -3

-2 -1

0

1

2

3

4

Deep Stratiform coverage [%]

-4 -3

-2 -1

0

1

2

3

4

Columnar Relative Humidity [%]

-4 -3

-2 -1

0

1

2

3

Shallow coverage [%]4

Fig. 2. Lagged composite diagrams for the MJO (top 3 rows), the Kelvin wave (middle 3 rows), and the ER
wave (bottom 3 rows). Lag is labelled on the top of each panel. Left column: Deep stratiform coverage(shaded) and SST (contoured at 1 K intervals with negative anomalies dashed). Middle column: CRH(shaded) and moisture convergence (contoured at 2 × 10 −5 kg m−2 s−1 intervals with negative anomalies
dashed). Right column: Shallow cumulus coverage (shaded) and non-precipitating high cloud coverage(contoured at 2% intervals with negative anomalies dashed).

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Journal of the Meteorological Society of Japan

isolate different modes of convectively­coupled equa­
torial wave by applying space-time bandpass filters tooutgoing longwave radiation (OLR) data. Masunaga etal. (2006) slightly modified their method to distill thetropical oscillations from TRMM observations. The
Masunaga et al. (2006) scheme, adopted in this study
with minor revisions, is outlined as follows. The area
coverage of deep stratiform systems (hereafter denotedby deep stratiform coverage) is first calculated andaveraged over each 1.25° × 1.25° grid box. Deep
stratiform coverage is used as an index of convective
activity throughout this paper because mature con­vective systems often accompany extensive stratiform
precipitation. The daily global maps of deep stratiform
coverage are then integrated along meridian over
20°S–20°N. Narrowing the meridional coverage wasfound not to introduce any qualitative change to theoverall pattern in the time-longitude space. Although
TRMM PR overpasses do not entirely cover the globe
for a single day, missing data are all complemented
when grid boxes are accumulated meridionally.
Bandpass filters are then applied to the timelongitude section of deep stratiform coverage. The
MJO filter is defined as 1 ≤ k ≤ 7, 0.0125 cpd ≤ w ≤0.05 cpd (20–80 days in period), where k and w arezonal wavenumber and frequency, respectively. The
Kelvin-wave filter is bounded by 1 ≤ k ≤ 10, 0.05 cpd≤ w ≤ 0.25 cpd (4–20 days in period), and a pair oftheoretical dispersion relations with the equivalent
depths of 5 m and 100 m. The n = 1 ER-wave filter isdefined between −10 ≤ k < 0 and two dispersion
relations with the same pair of equivalent depths.
The 10-year sequence of TRMM observation from1998 to 2007 is divided into three separate periods: I )January 1, 1998 to August 6, 2001 (1314 days), II )August 23, 2001 to September 4, 2002 (378 days), andIII ) October 18, 2002 to December 31, 2007 (1901days). The gaps between consecutive periods are dueto the TRMM orbital change in August 2001 and atemporary shutoff of the VIRS instrument from
September to October 2002. Filtering analysis does
not allow a gap to exist in the data, and is performed
with the three periods of time separately.
Figure 1 shows the filtered MJO, Kelvin wave, andER wave as well as the original field of deep stratiform
coverage for 5-month sequences from November toMarch. Note that a first few months of the 2002–03
season is not properly filtered because these months
are so close to the beginning of the period II thatsignals are lost by the data tapering. The overall
pattern is as expected from similar filtering analyses

Vol. 87A

in the literature (e.g., Wheeler and Weickmann 2001).
Major MJO episodes extending from the Indian Ocean(~60°E) to the east of the dateline are observed
typically once or twice every season. Some earliest
MJO events for the season start in November (e.g., the2000-01 season) and the last events can be as late asnear the end of March (e.g., 2003–04). In the present
paper, austral summer is defined as the five months
from November to March so that all major MJO eventsof the season are included in the analysis. The Kelvinand ER waves are observed ubiquitously except over
the African and South American continents (~30°E
and ~100°W). Convectively coupled equatorial waves
are known to weaken generally over a landmass (e.g.,
Masunaga 2007). The composite and correlation
analyses performed below are applied only to oceanic
scenes.
Convective variability and the associated envi­
ronmental field are composited in a way similar to thescheme developed by Masunaga et al. (2006) and
Masunaga (2007). The base point for the composite
analysis is zonally movable along the ridge and troughof the bandpass-filtered deep storm coverage (Fig. 1).
The ridge is defined as the local maximum of afiltered wave that exceeds twice the standard deviation(2σ ) in amplitude. Similarly, the trough is identified
as the local minimum lower than −2σ . The ridge andtrough are used for compositing the wet (or con­vectively active) and dry (or inactive) phases of thetropical oscillations, respectively. The backgroundfield, present regardless of phases of the tropicaloscillations, is then eliminated by subtracting the dry
phase composite from the wet phase composite. The
total numbers of the base points used in the current
analysis are listed in Table 2.
The parameters to be composited are the anomaliesof SST, CRH, moisture convergence, and the area
coverages of shallow, cumulus congestus, deep
stratiform, and deep convective systems. The TRMMretrieved parameters, particularly those from PR
measurements, do not fill a daily longitude-latitude
map as mentioned earlier. In addition, the TMIretrieved SST and PW are not available within heavily

Table 2. Statistics of composite base points for the wetphase and for the dry phase (numbers in parenthesis).

H. MASUNAGA

”
“y
“x

”l

ag
s

0

”l

ag
s

“x

”

y

“y

Developing phase

Lag [day]

Decaying phase

March 20090Lag [day]Developing phasex

Decaying phaseFig. 3. Schematic illustration of a lagged
correlation diagram, where the correlation
coefficient between the variables x and yis shown for different lags with respect tothe convective peak. The variable y (x)lags the other in the domain above (below)
the diagonal line. Negative and positive
lags correspond to the developing and de­­caying phases, respectively, of the tropicaloscillation.

precipitating scenes. While appreciable areas could be
devoid of observation in instantaneous snapshots,
portions without data compensate each other when
projected together on the composite plane.
The com­posite analysis is performed for different
daily lags. Zero longitude at lag 0 corresponds bydefinition to where and when the tropical oscillation
peaks in convective activity, and is hereafter called theconvective peak.
3.2 Lagged composite diagram
Figure 2 shows lagged composite diagrams cal­culated over the whole tropical oceans (20°S–20°N).
The left column illustrates the composite SST andconvective variability. While a convective area slowly
migrates to the east in association with the MJO (top 3
panels of the left column), sea surface warms up inprior to the convective peak and cools down afterward.
An increase in SST leading MJO convection has been
well recognized by past observational studies(Nakazawa 1995; Zhang 1996; Hendon and Glick
1997; Kemball-Cook and Weare 2001; Benedict andRandall 2007, among others). The amplitude of deep

301

stratiform coverage and SST is greater in the southernhemisphere than in the northern hemisphere, since thecurrent analysis is focused on austral summer events.
The MJO-composited humidity distribution (CRH)
exhibits a horseshoe-like structure extending acrossthe equator (top 3 panels in the middle column).
Composite moisture convergence has a remarkableanomaly around 10°S or even further south, roughly
collocated with the CRH maximum. A weaker but
distinct anomaly is observed at the other end of thehorseshoe in the northern hemisphere. The meridional
asymmetry in amplitude is again due to the season
studied in this study. The third peak, fainter than thefirst two, of moisture convergence anomaly lies on theequator to the east of the MJO convection peak (i.e., ata positive longitude).
Shallow cumulus composited with the MJO (top 3
panels in the right column) exhibits a horseshoe
pattern similar to CRH and moisture convergence,
although shallow cumulus suffers from small-scale
fluctuations. The small-scale noise implies that shal­
low cumulus is not as tightly coupled with the MJO
dynamics as deep stratiform systems. The frequencyof shallow cumulus occurrence is known to be lesssensitive to climatological large-scale circulation thandeep convective and stratiform systems (Masunaga
and Kummerow 2006). The area coverage of nonprecipitating high clouds closely follows the composite
spatial distribution of deep stratiform coverage. A
majority of the detected non-precipitating high clouds
are likely cirrus anvils accompanying MJO con­vection. Shallow clouds lie to the east of non-precip­
itating high clouds near the equator, implying thatshallow cumulus leads cirrus clouds.
The SST variation associated with the Kelvin wave
is far less clear (middle 3 rows of Fig. 2) than seen
above with the MJO. Deep stratiform coverage isroughly symmetric about the equator, consistent withthe theoretical expectation for the Kelvin wave
(Matsuno 1966). CRH and moisture convergence areconsiderably weaker in magnitude for the Kelvinwave than for the MJO. The day-0 composite dia­gram nevertheless shows evidence for a moistening(drying) to the east (west) of the Kelvin wave con­vective peak, as originally theorized by Wang (1988)
and Wang and Rui (1990). A moisture devel­opment
has been observed to occur within a few days before
Kelvin wave convection peaks (Straub and Kiladis
2003a). A close examination reveals that moisture
convergence is centered slightly to the east of CRH.
This implies that moisture convergence leads CRH for

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Vol. 87A

Fig. 4. Correlation coefficients between SST (abscissa) and deep stratiform coverage (ordinate) estimatedfrom the lagged composite diagram for the MJO (right), Kelvin wave (middle), and ER wave (left). Numbers
labelled on each axis refer to lags in day with respect to the convective peak (see Fig. 3). Solid (dashed)
contour, drawn at intervals of 0.1, shows positive (negative) correlation. Areas of positive correlation withstatistical significance levels of 99% or higher are shaded. Diamonds indicate the location of the maximum
correlation along every horizontal slice of the diagram. The horizontal slice at y = 0 (or day 0 in deep
stratiform coverage) is delineated in the bottom row. The study region is indicated at the top of each panel.

the Kelvin wave as will become clearer in the lagged
correlation analysis (Section 4). The composite CRH
structure has off-equatorial wings extending to thenorth and south from the equatorial peak, somewhat
reminiscent of the horseshoe pattern identified for the
MJO. No corre­sponding pattern is recognizable inmoisture con­vergence. This topic will be revisited laterin Section 5.1. Shallow and nonprecipitating highclouds resemble deep stratiform systems in composite
structure except for the small-scale fluctuations
present in shallow cumulus coverage. A subtle
eastward displacement of shallow clouds relative to
non-precipitating high clouds is again barely present,
although the dis­placement is much less apparent thanseen for the MJO.

The n = 1 ER wave is characterized by a pair ofgyres, one in each hemisphere, symmetric about theequator (Matsuno 1966). This basic structure isvisually confirmed in the composite ER wave (bottom
3 rows of Fig. 2), although the meridional asymmetry
in amplitude is clear here again. The sign of apreceding sea surface warming is absent or barely
recognized as is the case with the Kelvin wave. On theother hand, composite maps at days 0 and +7 eachshow a slight eastward shift of negative SST anomaly
relative to deep stratiform coverage, indicative of asurface cooling slightly lagging the convective peak.
The ER wave is comparable to the MJO, and muchgreater than the Kelvin wave, in the amplitudes ofcomposite CRH and moisture convergence. The pairedMarch 2009

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303

Fig. 5. Same as Fig. 4 but for the correlation between moisture convergence and CRH.

Rossby gyres are tilted meridionally. The meridional
tilt is not explained by the shallow water theory withno background flow, but is likely to exist in thepresence of mean flow vertical shear (Xie and Wang
1996). Moisture convergence is similar to CRH in
pattern but is shifted westward in phase, indicative ofthat moisture convergence leads CRH. The composite
patterns of shallow and non-precipitating high clouds
are overall analogous with those of deep stratiform
coverage.

4. Lagged correlation analysis
The results from the lagged composite analysis
imply close relationships between the different pa­­
rameters examined, sometimes with a distinct lag, inassociation with the tropical oscillations. Such rela­tionships are further investigated in a quantitative
manner based on a lagged correlation analysis. Results
are demonstrated following a brief description on theanalysis method.

4.1 Method
The spatial correlation between given two param­
eters, denoted by x and y, is calculated from thecomposite anomaly fields as shown by Fig. 2. The
composite domain zonally bound between ±45° istaken into account for computing correlation co­­efficients. This calculation is repeated over different
combinations of lags with respect to the convective
peak for a given pair of parameters. One obtains n × ncorrelation coefficients in total, where n is the num­
ber of lags considered. The correlation coefficients arethen mapped on a twodimensional plane as sche­matically illustrated by Fig. 3. The diagonal line parti­
tions the lagged correlation diagram into the regions
where x lags y and where y lags x. Day 0 on theabscissa and ordinate represents the convective peakof the tropical oscillation as defined in Fig. 2. Negativeand positive lags correspond to the con­vectively
developing and decaying phases, respec­tively.
The nature of convective development and decay

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Vol. 87A

Fig. 6. Same as Fig. 4 but for the correlation between CRH and deep stratiform coverage.

associated with the tropical oscillations is influenced
by regionality. The convective onset of the MJO
occurs in the Indian Ocean, while MJO convection
matures typically over the west Pacific warm pool.
The study area is therefore broken down into two
major oceanic regions in order to separate the regional
properties of convective variability. The current lagged
correlation analysis is focused on the Indian Ocean(45°E–105°E and 20°S–20°N) and the west Pacific
Ocean (120°E–180°E and 20°S–20°N) with islands andcontinents excluded. These definitions apply to thelocation of compositing base points: regions outside
the designated area are therefore included when thebase point is in the vicinity of the regional boundaries.
4.2 Resultsa. Sea surface temperature
Figure 4 shows the correlation between SST anddeep stratiform coverage. A lag of deep stratiform
coverage behind SST by about 10 days is consistentthroughout the MJO life cycle in both the Indian andwest Pacific Oceans. There is, however, a difference
between the two oceans in the timing when thecorrelation is maximized. The correlation is highest
when deep stratiform coverage is in its peak activity(i.e., y = 0) in the Indian Ocean. This confirms that a
prior surface warming has a close connection to thedevelopment of MJO convection. The magnitude ofcorrelation gradually decreases from the developing
phase to the decaying phase along the “ridge” of thecorrelation pattern. In contrast, the correlation stays
high along the ridge extending beyond 5 days or more
after the convective peak in the west Pacific. It follows
that, once the MJO enters the west Pacific, a sea
surface cooling during the convectively active phase(Fig. 2) is linked to the subsequent decay of MJO
convection as much as the preceding warming is to theconvective development.
A positive correlation with SST is present but
relatively weak for the Kelvin and ER waves, as weMarch 2009

H. MASUNAGA

305

Fig. 7. Same as Fig. 4 but for the correlation between shallow and deep stratiform coverages.

have seen in the composite diagram (Fig. 2). SST
varies roughly in quadrature with MJO and Kelvinwave convections (see bottom panels of Fig. 4),
whereas the oscillatory period is quite different
between the MJO and Kelvin wave. The SST variationis nearly 180° out of phase, aside from a 2-day lag,with ER wave convection. A surface cooling anomaly
takes the place of the prior warming as early as a few
days before the ER wave convective peak.
b. Moisture convergence and column relative humidity
The correlation between moisture convergence and
CRH is drawn in Fig. 5. CRH lags moisture conver­gence by ~1 day for the Kelvin wave. The CRH lag
can be as much as 2 days for the ER wave particularly
when it travels over the west Pacific. In contrast, nosign of a moisture convergence leading CRH is found
for the MJO. CRH even slightly precedes moisture
convergence for the MJO over the west Pacific. Past
studies (Hendon and Salby 1994; Jones and Weare

1996; Maloney and Hartmann 1998; Kiladis et al.
2005) implied the presence of moisture convergence(or boundary-layer convergence) leading MJO con­vection. This is seemingly contrary to the present
result with regard to the MJO. The existing analyses,
however, focus exclusively on the equatorial conver­gence peak as we found to the east of (i.e., in prior to)
MJO convection in Fig. 2. The contribution of theequatorial convergence peak is washed out in thecurrent correlation analysis because the off-equatorial
moisture convergence largely dominates the equatorial
component in amplitude. Given the faintness of theequatorial convergence anomaly, the role of fric­tional moisture convergence associated with the MJO
in a way postulated in the literature may need to bereconsidered. This issue will be discussed in Section
5.3.
The correlation diagram between CRH and deep
stratiform coverage is depicted by Fig. 6. An one-day
lag behind CRH is observed throughout the MJO

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Fig. 8. Same as Fig. 4 but for the correlation between cumulus congestus and deep stratiform coverages.

cycle, although the correlation curve is approximately
symmetric in time about lag 0. The lag is virtuallyabsent in the Kelvin wave. Somewhat puzzling is thatdeep stratiform coverage leads CRH by a day or the
ER wave. The ER wave shows a heavily skewed
correlation curve.
c. Convective variability
The progression of convective clouds associatedwith the tropical oscillations is next explored. The
lagged correlation diagram between shallow and deep
stratiform coverages (Fig. 7) indicates that the areawith positive correlations extends slightly wider
toward the upper-diagonal domain from the y = x line
than to the lower-diagonal. This temporal asymmetry
is present over the MJO cycle except for early days ofthe developing phase (lag < -10) in the Indian Ocean,i.e., a stage before the preceding SST warming. The
Kelvin and ER waves also show a small lead ofshallow cumulus, although not as consistently as the

MJO. Straub and Kiladis (2002) found that it takes
only a half day for organized convection to develop
from shallow cumulus during a passage of the Kelvinwave. The dataset analyzed in the current work does
not have a sufficient temporal resolution to clearly
detect any phenomena changing at sub-daily timescales.
Cumulus congestus resembles shallow cumulus in
the correlation diagram (Fig. 8) for the MJO. No leador lag of cumulus congestus is identified for the Kelvinwave. Not readily expected is the fact that cumulus
congestus lags deep stratiform systems associated withthe west Pacific ER wave. Deep convective systems
are precisely in phase with deep stratiform systems for
all the tropical oscillations, except that a subtle delayof deep stratiform systems is barely recognizable for
the Indian Ocean MJO (Fig. 9). This result does notnecessarily deny a few-day lag between stratiform andconvective rains during the MJO mature phase (Lin etal. 2004). The analysis by Morita et al. (2006) showedMarch 2009

H. MASUNAGA

307

Fig. 9. Same as Fig. 4 but for the correlation between deep convective and deep stratiform coverages.

that convective and stratiform rains composited with
the MJO vary almost in phase (their Fig. 5) while
stratiform rain has a long tail of weakly precipitatingarea extended to the west (their Fig. 6). The signal oftrailing stratiform precipitation is so weak compared
to intensive rainfall at the convective peak that it isperhaps not well detectable by correlation analysis asconducted here.
Finally, Fig. 10 illustrates the lagged correlation of
non-precipitating high clouds with deep stratiform
coverage. Non-precipitating high clouds associatedwith the MJO shows a lag not longer than one day,suggestive of lingering high clouds detrained fromdeep convective systems. The lag is physically limited
to ~1 day, given the lifetime of detrainment cirrus of
30 ± 16 h (Luo and Rossow 2004). The Kelvin and ER
waves show no sign of lingering high clouds.

5. Concluding discussion
In the present work the variability of convection andrelevant environmental parameters associated with thetropical oscillations has been analyzed primarily from
TRMM satellite observations. A synthesis of thecurrent findings is presented in this section for
individual modes of the tropical oscillations studied.
5.1 Kelvin wave
A visual inspection of the composite and correlation
diagrams (Figs. 2 and 9) suggests that dominant modesof the Kelvin wave have a zonal wavenumber of 6–8
and a period of 5–6 days. This combination of k and ω
implies the equivalent depths of 10–25 m, as expected
for the convectively coupled equatorial waves(Takayabu 1994; Wheeler and Kiladis 1999). The SST
variation leading convection by 90° out of phase ispresent but faint. A preceding SST increase is
explained by strong insolation under the clear-sky
condition, while the subsequent surface cooling can bebrought by the enhanced evaporation, developing
ocean mixing layer, and/or the cloud shielding of308

Journal of the Meteorological Society of Japan

Vol. 87A

Fig. 10. Same as Fig. 4 but for the correlation between non-precipitating high cloud and deep stratiform
coverages.

incoming shortwave flux (e.g., Flatau et al. 1997). This
regulatory cycle is likely at work on intraseasonal timescales, but is perhaps much less efficient for a period
typical of the Kelvin wave. The development of Kelvinwave convection therefore proceeds without the helpof an external thermodynamic driver similar to thedischarge-recharge mechanism proposed to explain
the MJO periodicity (Bladé and Hartmann 1993).
The Kelvin wave dynamics per se does not favor thecooperation between convective heating and largescale warming (Emanuel et al. 1994). The frictional
moisture convergence, however, allows this coop­eration to occur, so that the generation of eddy
available potential energy prevents the Kelvin wave
from damping (Wang and Rui 1990; Hendon and
Salby 1994; Salby et al. 1994). The composite diagram
indeed shows a moist anomaly centered on the equatorto the east of the Kelvin wave convective peak (Fig. 2),
led by moisture convergence by a day (Fig. 5). It is lessobvious what gives rise to the off-equatorial wings ofmoist anomaly (Fig. 2). Figure 11a shows boundarylayer (1000 hPa) wind and moisture convergencecomposited with respect to the Kelvin wave. The
boundary wind field is overall as predicted by theoriesof the frictionally-modified Kelvin wave (cf., Salby etal. 1994; Moskowitz and Bretherton 2000), where
anomalous wind is predominantly zonal but slightly
tilted. There is no evident feature responsible for theoff-equatorial moist anomaly in Fig. 11a. A possible
mechanism accounting for the off-equatorial moist
anomaly is a local enhancement of evaporation. Thereis however no supporting evidence for this possibility
because neither SST anomaly (Fig. 2) nor near-surface
wind anomaly (Fig. 11a) do not geographically matchthe observed CRH wings. Another potential mech­anism is midtropospheric moisture convergence if thefree troposphere has a relevant dynamics. This isactually feasible, as Fig. 11b reveals a mid-tropospheric(600 hPa) flow heading away from the equator near 0°
in relative longitude. This poleward flow, likely a part

March 2009309

H. MASUNAGA

a)b)Fig. 11. Day-0 composite diagram for the Kelvin wave. a) 1000 hPa horizontal wind and moisture conver­gence contoured at every 1 × 10 −5 kg m−2 s−1 with negative anomalies dashed. b) 600 hPa horizontal wind
and CRH contoured at every 0.5% with negative anomalies dashed. The wind field is drawn only where thesignificance level is 95% or higher.
a) Kelvin wave schematic
Deep Stratiform

Deep Convectiveb) ER wave schematicN

Cumulus Congestus
Shallow

E

W
Mid-troposphericpoleward flow

S

Moisture
Convergence

Moist anomaly

Fig. 12. Schematic of (a) Kelvin wave and (b)ER wave. Solid and dashed con­tours
represent moisture convergence andpositive humidity anomaly, respectively.
Black arrows denote convergent bound­ary-layer wind.

of the Rossby wave response to convective heating(Gill 1980), appears to converge into the off-equatorial
humidity peak at 0° longitude and 20°S. The midtropospheric convergence presumably helps feed theoff-equatorial moist anomaly with equatorial humid
air. It is noted that while the Kelvin wave-composited
wind structure is generally zonal in the lower
troposphere, a vortical distortion has been often foundin the upper-tropospheric wind field flanking the
Kelvin wave convective peak (Nakazawa 1988;
Takayabu and Murakami 1991; Masunaga 2007).
The off-equatorial moist anomaly is potentiallyascribable to the extratropical origin. The gyrestructure in Fig. 11b somewhat resembles a portion of
the Rossby wave train in the subtropical jet initiating
the Kelvin wave (Straub and Kiladis 2003a). Strauband Kiladis (2003a) presented observational evidence

suggesting that the subtropical jet in the southernhemisphere dynamically forces the Kelvin wave. Notethat their analysis is focused on austral winter on thecontrary to the current paper. Figure 2 of Straub andKiladis (2003a) suggests that a single pair of gyres
also emerge in the northern (or summer) hemispherebut only after Kelvin wave convection reaches thepeak activity. As such, the summer hemispheric gyres
accompanying the Kelvin wave, relevant to thesouthern hemispheric moist anomaly in the present
analysis, is most likely viewed as the Rossby wave
response as interpreted earlier. The northern counter­part of the off-equatorial moist anomaly, not discussed
in this work, could be under the strong influence of
extratropical disturbances.
For the Indian Ocean Kelvin wave (top-middle panelof Fig. 4), the correlation between moisture con­vergence and CRH abruptly rises along the ridge as
the developing phase gives way to the decaying phase.
In other words, the frictional moisture feedback works
more efficiently for the waves emanating to the east of
the convective peak than those coming in from thewest. This may suggest that the Kelvin wave tends togrow in amplitude while passing over the Indian
Ocean.
In summary, the progression of Kelvin wave
convection and associated dynamic and thermo­
dynamic environment are schematically illustrated inFig. 12a. No discernible phase lag has been observed
between different types of clouds except for a slight(< ~1 day) lead of shallow cumulus. The deepening ofclouds toward the convective peak may have a role inreleasing the boundary-layer moisture into the freetroposphere (Straub and Kiladis 2002). The resultant
increase in the mid-tropospheric humidity is spec­
ulated here to be transported poleward and moisten
off-equatorial air. The fact that the Kelvin wave

310

Journal of the Meteorological Society of Japana) Kelvin wave and westward moving cloud clustersE

W

initialmaturedissipating

Timeb) ER wave and westward moving cloud clusters

E

trai

lea

din

ling

ge

edg

e

dge

W

Time
Fig. 13. Speculated configurations of cloudclusters embedded in (a) Kelvin wave and(b) ER wave. The Kelvin wave con­taining
westward moving cloud clusters inside, as
discovered by Nakazawa (1988), would
experience the initial, mature, anddissipating stages of cloud clusters in thisorder when observed at a fixed location.
Same cloud clusters within the ER wave
envelope would result in the reverse life
cycle when composited by a fixed point
observation.

accompanies off-equatorial moist anomalies will be a
key ingredient of the proposed MJO mechanism dis­cussed in Section 5.3 below.
5.2 Equatorial Rossby wave
An intriguing feature regarding the ER wave is thata moist anomaly develops a day after deep stratiform
coverage does (Fig. 6). Given that it takes 2 days for amoist anomaly to catch up with the preceding moisture
convergence (Fig. 5), ER wave convection reaches its

Vol. 87A

peak activity in between the moisture convergence andhumidity maxima. Another result seemingly puzzlingis that the deepening of convective clouds does notprogress monotonically; shallow cumulus barely leads
deep stratiform systems but cumulus congestus
develops afterward (Fig. 8). A schematic of the ER
wave structure is shown by Fig. 12b.
These behaviors peculiar to the ER wave might beunderstood in terms of the hierarchical structure ofconvectively coupled tropical disturbances. Synopticscale convective variability is known to involve finerscale convective organization called cloud clusters(Nakazawa 1988). Nakazawa (1988) illustrated thatcloud clusters migrate to the west while the envelopeof cloud clusters or a super cloud cluster, likely
coupled with the Kelvin wave, propagates eastward. It
follows that an individual cloud cluster is initiated on
the leading edge of the super cloud cluster andmarches westward as it matures until it eventually diesout near the trailing edge. It is postulated here that the
ER wave also has an embedded structure of cloudclusters moving to the west. The cloud clusters thus
migrate in the same direction as the ER wave but at afaster speed. On the contrary to the Kelvin wave, cloudclusters should be initiated on the trailing edge of the
ER wave and decay on the leading edge. An observerrecording a temporal sequence of cloud clusters at a
fixed point would report the reverse life cycle of cloudclusters during a passage of the ER wave (Fig. 13). It isvery speculative but tempting to ascribe the observed
lags of cumulus congestus and CRH to the composite
convective progression traced backward in time. It isbeyond the capacity of the current work to test thisspeculation and is left for future studies.
5.3 Madden-Julian Oscillation
It has been recognized in the literature that
equatorial waves tend to be subject to a large-scale
amplitude modulation within the MJO convective
envelope (Dunkerton and Crum 1995; Straub andKiladis 2003b; Masunaga et al. 2006). The MJO
modulation of Kelvin wave packets is visually
identifiable from time to time in Fig. 1, where notable
examples are found in Jan–Feb 1999, Nov–Dec 2000,
Jan–Feb 2001, and Jan–Feb 2004 among others. It isalso seen in Fig. 1 that a ridge of the ER wave
intersecting the MJO envelope occasionally accom­
panies convective burst. Some MJO events are notaccompanied by Kelvin and ER wave packets within
the convective envelope. Masunaga et al. (2006)
discussed the hypothesis that the driver of MJO

H. MASUNAGA

Normalized wave power

March 2009

Fig. 14. The lag-longitude diagram of the
Kelvin wave (top) and ER wave (bottom)
composited with respect to the MJO con­vective peak. The compositing method is
as described in Section 3. The power ofKelvin/ER wave-filtered deep stratiform
coverage, normalized by the variance ofeach mode, is contoured at intervals of
0.3. Contours are dashed for negative
anomalies. Positive anomalies are shaded.
The MJO composited with respect toitself is drawn for reference by thick con­tour at twice the variance.

propagation in some cases might be a group of Kelvinwave packets dragging convective spots slowly to theeast. In the hypothesis, the ER wave was speculated tocontribute to the grouping of the wave packets byinitiating and terminating Kelvin wave convection.
Masunaga et al. (2006), however, did not demonstrate
how the Kelvin wave could interact with the ER wave.
In this closing section, an attempt is made to explore apossible mechanism of the wave interaction as inferredfrom the present findings.
Figure 14 shows the lag-longitude diagram of the
Kelvin and ER waves composited with respect to the
MJO convective peak. It is evident that the Kelvinwave is strongly modulated by the MJO. The Kelvinwave packet is slightly offset to the west (east) of the
MJO envelope for negative (positive) lags, propagating

311

eastward as a whole somewhat faster than the MJO. It
follows that the Kelvin wave approaches from the west
before the MJO convective peak, while emanating to
the east after the convective peak. In contrast, the
MJO-composited ER wave shows little systematic
pattern. The amplitude of the ER wave is therefore notaffected much by changing MJO phases, as one may
expect from Fig. 1. Nevertheless, the composite ER
wave diagram shows a patch of weak positive anomaly
loosely collocated with the area of the approaching
Kelvin wave. Although it is not a primary driver of the
MJO, the ER wave may act as a stochastic trigger
fuelling MJO convection, activated when it intersects
with the Kelvin wave.
It is noted that such systematic relationship between
the MJO and the Kelvin and ER waves is not as clearlyobserved in OLR as in precipitation data. It is typicallythe case that OLR does not exhibit visible evidence ofKelvin and ER wave modulation while the modulation
is clear in TRMM data for the same MJO event. OLR
data, of course, captures the overall pattern of wave
propagation, but noises from non-precipitating com­ponents such as cirrus clouds perhaps make it difficult
to precisely trace the amplitude evolution and dissi­
pation of convectively coupled waves. The composite
analysis same as Fig. 14 but with OLR shows aqualitatively consistent result but is much noisier(Masunaga 2007).
Figure 15 is a schematic picture of the australsummer MJO, based on the results from the current
analysis combined with general knowledge and the
proposed hypothesis. A cycle of the MJO starts withthe convective onset over the Indian Ocean. We haveseen that a prior sea surface warming emerges as a
distinct correlation maximum in the Indian Ocean(Fig. 4). A Kelvin wave packet emanating in responseto the convective onset is accompanied by convective
clouds moving eastward. Kelvin wave convection,
however, ceases to be a critical component of the MJO
once the MJO convective envelope leaves the Indian
Ocean. While the convective onset in the Indian
Ocean is initiated near the equator, the geographicalcenter of deep stratiform coverage shifts away fromthe equator as MJO convection moves into the west
Pacific (Fig. 16). The spatial maximum of deep
stratiform coverage associated with the west Pacific
MJO is located at 10°S, which is no longer explainedby the Kelvin wave dynamics.
Figure 16 illustrates that the southeastward shift ofthe convective area occurs in parallel with thedevelopment of the horseshoe pattern of humidity

312

Journal of the Meteorological Society of Japan

Vol. 87A

Austral summer MJO schematic
Non-precip high cloudN

Kelvin wave
E

W

Preceding SST increase

Mid-troposphericpoleward flow

Indian Ocean (onset)

Moist
anomalyS

Moisture
Convergence

ER wave

West Pacific (convective burst)

ER

longitude

KelvinMJO

time
Fig. 15. Same as Fig. 12 but for the MJO. The lower half shows the projection onto the time-longitude plane,
constructed in combination with the hypothesis proposed by Masunaga et al. (2006).

anomaly. The equatorial tip of the horseshoe is likely
ascribed to the frictional moisture convergence
brought by the Kelvin wave. The both ends of thehorseshoe requires another mechanism that transports
moisture away from the equator. We discussed earlier
that the mid-tropospheric convergence as a dynamical
response to the Kelvin wave convective heating
enables poleward moisture transport. The resulting
off-equatorial spot of a high mid-tropospheric humid­
ity would generate a condition favorable for organized
convective systems to develop. It is conjectured that an
MJO convective burst breaks out when an ER wave
disturbance comes in the moist spot preconditioned by
the Kelvin wave. As such, the spatial maximum in the
MJO-composited moisture convergence (Fig. 2) is
considered to originate from the ER wave dynamics.
A moist anomaly is set up partly by the Kelvin wave
mid-tropospheric flow before the arrival of the ER
wave convergence. This perhaps explains why humid­
ity does not lag or even leads moisture convergence for
the MJO as we have seen in Fig. 5. The precedingmoisture convergence lying around the equator is so
subtle that it is unlikely important for the MJO.
Meanwhile, the frictional moisture feedback is prob­
ably crucial for the propagation of the Kelvin wave

itself.
A schematic of the time-longitude section of the
MJO is delineated in the lower half of Fig. 15. The
MJO convective burst brought upon the arrival of the
ER wave may be slightly pushed back to the east by
the continuing propagation of the ER wave, or could
stay for a while decoupled from the wave dynamics(Masunaga et al. 2006). The convective envelope
resumes its eastward migration when another ridge ofthe Kelvin wave comes in. This whole process could
occur at multiple times in a single MJO event. Masu­
naga et al. (2006) argued that the MJO convective
envelope consists of a group of Kelvin wave packets
and sporadic convective bursts triggered by intruding
ER waves. In this theory, occasional interruptions of
the Kelvin wave account for the slowness of MJO
propagation. It is noted again that the hypothesis
delineated here presumably explains aspects of major
MJO events in austral summer but does not accountfor every case.
The convective progression associated with the MJO
may be interpreted in part by superposing those with
the Kelvin and ER waves. The MJO-composited shal­
low cumulus coverage (Fig. 2) resembles the horseshoe
pattern, showing a prominent equatorial tip of theMarch 2009313

H. MASUNAGA

0 2 4 6 8 10
CRH anomaly [%]Fig. 16. Lagged composite diagram for the Indian Ocean MJO (left) and west Pacific MJO (right). Deep
stratiform coverage is contoured at intervals of 0.1% with negative anomalies dashed. CRH is shaded withnegative anomalies omitted.

Kelvin wave origin. The convective progression starts
with shallow cumulus ahead by ~1 day for the Kelvinwave, and so does it for the MJO (Fig. 7). On the other
hand, composite cumulus congestus (not shown) ex­­hibits an off-equatorial pattern more reminiscent of
the ER wave (Fig. 16). Figure 8 shows that cumulus
congestus lags deep stratiform systems for the ER
wave while the former leads the latter for the MJO. It
follows that cumulus congestus is observed on theeastern edge in both cases (Figs. 12 and 15), given that
the ER wave propagates westward and the MJO
migrates to the east.
Figure 10 implies that detrained cirrus lingers forabout a day. Extensive cirrus clouds could have an
important effect on the local thermodynamic cycle
oscillating on an intraseasonal time scale. Radiative
effects of high clouds may enhance the upper-tro­
pospheric cooling (Hu and Randall 1994), promote thegreenhouse effect (Stephens et al. 2004), or simply
induce an energy transfer from the ocean to the atmo­
sphere (Sobel and Gildor 2003). A locally regulated
thermodynamic cycle would be a key that controls the
MJO period (Bladé and Hartmann 1993), since theinteraction between the Kelvin and ER waves dis­cussed above could explain the propagation of the
MJO, but not its periodicity.

Acknowledgments
The author appreciates insightful comments of ananonymous reviewer, which helped improve the paper.
The TRMM 1B01 and 2A25 products were provided
by Japan Aerospace Exploration Agency (JAXA)
Earth Observations Research Center (EORC). The
TMI datasets of SST and PW are produced by Remote
Sensing Systems (www.remss.com) and sponsored by
the National Aeronautics and Space Administration(NASA) Earth Science REASoN DISCOVER Project.
NCEP/NCAR Reanalysis data were provided by the
National Oceanic and Atmospheric Administration(NOAA) Earth System Research Laboratory (ESRL)
Physical Science Division (PSD), Boulder, Colorado(www.cdc.noaa.gov). This work is supported by the
Ministry of Education, Culture, Sports, Science, andTechnology (MEXT) Grant-in-Aid for Young Scien­tists Nos. 19840023 and 20740269.

314

Journal of the Meteorological Society of Japan

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