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Variability in the Characteristics of Precipitation Systems in the Tropical Pacific. Part I:
Spatial Structure
HIROHIKO MASUNAGA, TRISTAN S. L’ECUYER, AND CHRISTIAN D. KUMMEROW
Colorado State University, Fort Collins, Colorado(Manuscript received 22 January 2004, in final form 7 September 2004)
ABSTRACTRegional and temporal variability in the vertical and horizontal characteristics of tropical precipitating
clouds are investigated using the Precipitation Radar (PR) and the Visible and Infrared Scanner (VIRS) on
board the Tropical Rainfall Measuring Mission (TRMM) satellite. The present study focuses on the three
oceanic regions (west, central, and east Pacific) together with two continental regions for comparison andthe two separate time periods (February 1998 and February 2000) under different phases of the El Niño–
Southern Oscillation (ENSO) in order to examine regional and ENSO-related variations. The heightspectrums of storms are investigated in terms of radar echo-top height and infrared brightness temperature.
The variability in the spectrum clearly correlates with the large-scale circulation and its ENSO-relatedchange. On the basis of the height spectrum, storm systems are classified into the four categories of shallow,
cumulus congestus, deep stratiform, and deep convective. The deep stratiform and deep convective categories, both of which have very cold cloud tops, are differentiated by radar echo-top heights so that deep
convective systems are accompanied with an appreciable amount of large frozen particles aloft. While
shallow events are dominant in the probability of occurrence over relatively cold oceans, deep convective
systems take their place for warmer sea surface temperatures (SSTs). The turnover occurs at the SST
threshold of 28°–29°C for all the oceanic regions and years investigated except the west Pacific in 2000, forwhich deep convective systems prevail over the entire range of SST. Rain correlation-scale length (RCSL)
and cloud correlation-scale length (CCSL) are introduced as statistical indicators of the horizontal scale ofstorms. While the RCSL is 8–18 km for shallow- and cumulus congestus–type clouds without significant
regional and temporal variations, the RCSL and CCSL associated with deep stratiform and deep convective
systems consistently exceed 100 km and exhibit a systematic variability. The RCSL and CCSL in the central
and east Pacific, particularly, increase significantly in the El Niño year.

1. Introduction
Tropical rainfall systems not only drive the atmospheric circulation dynamically through the release oflatent heat, but can also influence the earth’s radiationbudget through their associated cloud fields. It is therefore crucial to clarify the roles of tropical rain systems
in terms of both the dynamical and radiative energy
budget for an understanding of the mechanisms governing climate variability. Interactions between dynamical and radiative processes are key factors thatconnect climate variabilities on different spatial andtemporal scales such as the response of the global climate to El Niño–Southern Oscillation (ENSO) events.
The size and height spectra of storms have been extensively studied over decades in order to characterize
a wide variety of rain systems, ranging from isolated

Corresponding author address: Dr. Hirohiko Masunaga, Department of Atmospheric Science, Colorado State University,
Fort Collins, CO 80523.
E-mail: masunaga@atmos.colostate.edu© 2005 American Meteorological Society

JCLI3304

convection to mesoscale convective system (MCS) in
size and from shallow cumulus to deep convection inheight. From early studies (López 1976, 1977; Houze
and Cheng 1977) to recent ones (e.g., Rickenbach andRutledge 1998; Johnson et al. 1999), ground radar has
been a preferred tool for this purpose. Satellite remotesensing is another useful means especially when a wide
area of coverage beyond the scale of a ground-based
radar is of interest, as is the case in the present study.
To this end, geostationary satellites have been utilized
sincethe 1980s to acquire data of homogeneous quality
on a synoptic to global scale (Williams and Houze 1987;
Machado et al. 1992; Machado and Rossow 1993;
Mapes and Houze 1993). Use of a geostationary satellite, however, limits the observational target to clouds,
which are only an indirect proxy for precipitation. This
could be an obstacle if one wishes to examine dynamical and radiative impacts of storms separately.
Microwave sensors such as the Special Sensor Microwave Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) satellites provide a more
direct means for measuring precipitation from space.

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Mohr and Zipser (1996) utilized the SSM/I 85-GHz
brightness temperature to study the size spectra of
MCSs in various geographical regions across the globe.
The 85-GHz ice-scattering signals exhibit a more direct
link to rainfall intensity than the cloud signature in infrared radiances, although correlation between ice particles aloft and surface rainfall is not always optimal.
The Tropical Rainfall Measuring Mission (TRMM)
satellite was launched in 1997 carrying the Precipitation
Radar (PR), the first spaceborne radar measuring rainfall, together with the TRMM Microwave Imager(TMI) and the Visible and Infrared Scanner (VIRS), as
well as the Lightning Imaging Sensor (LIS) and the
Clouds and Earth’s Radiant Energy System (CERES)
instrument. The TRMM satellite and PR, in particular,
have significantly improved our capability of measuring
rainfall from space and have spawned numerous newstudies into the characteristics of precipitating cloudsystems. Nesbitt et al. (2000) extended the Mohr andZipser (1996) technique by adding PR information sothat precipitation features without ice-scattering signatures could be discriminated from those with ice. Inoueand Aonashi (2000) examined the performance of theinfrared technique to detect rainfall using VIRS radiances with the help of PR information. Del Genio andKovari (2002) employed TMI-identified rainfall to obtain the storm size statistics and discussed the dependence of the rainfall characteristics upon environmental
factors. The PR has also facilitated the direct measurement of vertical profiles of precipitation on a global
scale. Short and Nakamura (2000) investigated theglobal distribution of PR echo-top height and founddistinct shallow and deep modes in the histogram ofecho-top heights. Takayabu (2002) confirmed structural differences in rain profile spectra obtained from
PR observations and found that their frequency depended on oceanic/continental and convective/stratiform categories.
Most previous studies mentioned above focused oneither precipitation (using a microwave radiometer
and/or radar) or cloud temperature (using an infrared
imager). The present study, in contrast, aims to understand statistical trends in size and height of both precipitation and associated clouds through a simultaneous
analysis of PR and VIRS observations. The VIRS provides infrared brightness temperature as a proxy ofcloud-top temperature if clouds are optically thick. Infrared brightness temperature does not necessarily
trace the physical temperature of clouds for thin cirrus.
In practice, thin cirrus clouds do not have a large impact
on the present analysis because only raining clouds
identified by collocated PR pixels are taken into account. Cirrus could occasionally overlap with shallow
convection beneath. This case is identified by the illmatched combination of a cold infrared brightness temperature and a low radar echo-top height. The contamination of high clouds is therefore easily avoidable (section 3).

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While VIRS infrared radiances outline the cloud top,
PR echo-top height represents the maximum altitude
for precipitation-size particles. The detectable particle
size is limited by the sensitivity of PR or 17 dBZ(TRMM PR Team 2000). Uncertainties in the microphysical properties of frozen hydrometeors such as size,
shape, and density lead to a somewhat mixed ability of
the PR to detect the actual precipitation height (Masunaga et al. 2002). The combination of VIRS brightness
temperature and PR echo-top height is therefore an
indicator of the microphysical properties of ice particles
aloft, that is, snow, graupel, and hail, as well as thevertical extent of a storm. This paper focuses only on
echo-top height without looking further into the detailed structure of rainfall. A well-defined quantity suchas echo-top height is sufficient for a robust storm categorization. A separate effort by the authors is underway to retrieve the vertical structure of precipitationfrom TRMM data.
Combined use of PR and VIRS also provides ameans to study the horizontal scale of precipitating
cloud systems. The conventional approach to evaluate
storm area from satellite data is to count contiguous
pixels that have infrared/microwave brightness temperatures lower than a certain threshold. Such methods,
however, often encounter limitations resulting from orbit geometry as well as storm morphology. First, thecontiguous pixel method identifies separate cells as individual small systems even if they belong to a single
larger system. Del Genio and Kovari (2002) found,through visual inspection, that the vast majority ofstorms defined in this way using TMI pixels were classified as isolated storms instead of belonging to a larger
system evident in infrared imagery. Its quantitative impact, however, is unclear when the same method is applied to the finer PR resolution. Arbitrariness in theinfrared/microwave threshold can also create an ambiguity in the estimated storm size. Finally, the truncationof storms due to the finite swath width of low earthorbiting (LEO) satellites can result in an underestimation of storm areas. This effect could be significant for
the narrow PR swath (215 km). Nesbitt et al. (2000)
estimated that 17% of total rain systems are truncatedby the edge of the PR swath, and this rate increases to
as much as 82% when MCSs are isolated.
Here, the “correlation-scale length” (CSL) of storms
is introduced to evaluate their horizontal scales without
the limitations that constrain the contiguous pixel
method. The rain (cloud) CSL, defined as the expectation of finding precipitation (a cloud) as a function of
distance from a given raining point, reflects the size ofa spatially correlated or organized system regardless ofcontiguity of raining/cloudy areas in it. The CSL has theadvantage of not requiring an infrared threshold temperature (except to define storm categories) and ismuch less susceptible to the effect of finite swath width.
The present paper is dedicated to 1) investigating theheight spectra of storm systems in terms of VIRS

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brightness temperature and PR echo-top height and defining storm categories based on them, 2) estimating
the raining/cloud CSL for each storm category, and 3)
examining regional and ENSO-related variations in thevertical and horizontal scales of storms over tropical
Pacific oceans. After describing satellite data used inthis study in section 2, storms are categorized in termsof infrared brightness temperature and radar echo-top
height in section 3. The methodology to derive CSL is
introduced and applied to satellite data in section 4.
Section 5 is devoted to discussions and comparison toprior studies. The findings are summarized in the final
section (section 6).

2. Data
The TRMM satellite, launched in November 1997(see Kummerow et al. 1998 for detailed descriptions for
the TRMM sensors), has a sun-asynchronous orbit
with a 35° inclination and an altitude of 350 km (402km after August 2001). The TRMM PR is a single-frequency (13.8 GHz) radar having horizontal and verticalresolutions of 4.3 km and 0.25 km, respectively, at nadir. The PR sensitivity or minimum detectable echo is
17 dBZ after the system noise is subtracted (TRMM PR
Team 2000). Scan angles of PR range between ⫾17°,
resulting in a swath width of 215 km. The VIRS has fivechannels at the wavelengths of 0.63, 1.6, 3.7, 10.8, and12.0 ␮m. The nadir instantaneous field of view (IFOV)
is 2.1 km, and the swath width is 720 km for VIRS. The
spatial resolutions and swath widths have changedslightly since the TRMM orbit was raised in August
2001. The present study uses data acquired prior to theorbit boost.
The PR 2A23 dataset, one of the TRMM standard
products available in the archive, contains “storm
height” or the height of PR echo top measured from theellipsoid representing the earth’s surface. The 2A23
storm height is therefore designated as the “echo-top
height” of PR in this paper in order not to confuse thisterm with the VIRS cloud-top temperature, whichmore closely represents the actual “height” of a given
storm. The echo-top height is adopted as is for oceanic
rainfall while local elevation is subtracted from it forcontinental rain. In addition to the echo-top height, thestratiform and convective classification is provided by
2A23. The rain type classification by 2A23 is divided
into “stratiform (convective) certain,” “probably stratiform (convective),” and “maybe stratiform (convective)” with further subcategories, all of which together
are simply designated as stratiform (convective) in thisstudy. VIRS channel-4 (10.8 ␮m) radiance in the 1B01
dataset is converted to brightness temperature, which isdesignated as “infrared Tb” or simply “Tb” in this paper.
VIRS pixels are collocated with PR over the inner
portion of the VIRS swath that overlaps the narrower825

PR swath. Adjacent 5 ⫻ 5 (9 ⫻ 9) pixels for PR (VIRS)
around the assigned pixel are stored as well in order totake into account the horizontal variability of storms.
All orbits during the two separate time periods, February 1998 and February 2000, are analyzed in the present
paper. Since tropical climate experienced an El Niño
event from 1997 to the middle of 1998, these twomonths are expected to show a sharp contrast in climate
signals between the different phases of ENSO.
This study focuses on three oceanic areas and twocontinental regions between 10°S and 10°N defined as
the west Pacific (105°–150°E), the central Pacific (180°–
150°W), and the east Pacific (120°–90°W), as well as theSouth American and African continents. Although thecurrent study places particular emphasis on the Pacificregions, the two tropical continents are included to contrast the properties of oceanic precipitation systems
with those of continental systems. Land and coastal areas are excluded from the analysis for the three Pacificregions. Remote Sensing Systems (RSS) distributes adaily sea surface temperature (SST) dataset derived
from TMI (available online at http://www.ssmi.com/
tmi/tmi_browse.html). This TRMM-based dataset isadopted to examine the correlation between storm
characteristics and SST in section 3 since it minimizes
errors due to mismatches in space and time. SST over aheavily raining area where the data are deficient is interpolated from surrounding nonraining areas.

3. Identification of storm categories
As noted above, the height spectra of precipitating
cloud systems have traditionally been investigatedbased on either radar or infrared measurements. In thissection, these measurements are combined to obtain a
two-dimensional histogram of PR echo-top height and
VIRS brightness temperature. These histograms arepresented for the five regions of interest to this study in
Fig. 1. Figure 1 shows two noticeable features: a peak in
the lower-left corner of many panels and a verticalridge at 5 km in PR echo-top height. The first peakcorresponds to systems with echo-top heights of 2 km
and infrared Tb of 280 K, indicating the existence of a
distinct group of rainfall trapped in the boundary layer.
This group, defined as shallow convection, is the mostabundant component of oceanic storms although it
makes only a minor contribution to total rainfall. Shortand Nakamura (2000) estimated the rain contributionof these systems to be approximately 20% over tropicaloceans. Ascribing a physical interpretation to the vertical ridge at an echo-top height of 5 km is more difficult. It is likely an apparent feature rather than a physical entity. As mentioned in section 1, PR echo-top
height is sensitive to ice particle characteristics because
the PR sensitivity to precipitation ice is marginal andthe shape and size of ice particles are highly variable.
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FIG. 1. Two-dimensional histograms of radar echo-top height and infrared brightness temperature along with an illustration showing
the storm categories superposed on this diagram. The west Pacific, central Pacific, east Pacific, South America, and Africa are shown,respectively, from left to right for (top) Feb 2000 and (bottom) Feb 1998. The color bar is labeled in the unit of % km⫺1 K⫺1.The bin
size used for mapping the histograms is 0.48 km in PR echo-top height and 4.8 K in infrared Tb, with linear interpolation used forsmoothing.

above the melting level, which is 4–5 km in the Tropics,
unless the frozen hydrometeors aloft are large enoughto be detectable by PR. We infer, therefore, that theobserved ridge in the histograms is merely an artifact of
the level at which hydrometeors transform from ice toliquid in many storm systems.
It is nevertheless important to note that the range ofthe freezing-level (FL) ridge in infrared Tb varies between regions and in time. For example, the ridge iswell pronounced for 220 K ⬍ Tb ⬍ 260 K in the west
Pacific in 2000, while the central and east Pacific exhibit
only a vague remnant of the FL ridge with infrared Tb
warmer than 260 K. In most other cases, including thecentral and east Pacific in 1998, the FL ridge is split into
a pair of peaks at 250–260 and 220–230 K. This split
may be associated with the presence of cumulus congestus in addition to deep convection. Johnson et al.
(1999) found that cumulus congestus clouds, which
overshoot the freezing level by 1 to 2 km, consist of aseparate component from both shallow cumulus andcumulonimbus in the tropical rainfall spectra. A cloud
top at 1–2 km above the freezing height is consistent

with the peak at 250–260 K. Machado et al. (1998) discussed the infrared threshold of clouds associated withdeep convection and estimated it to be 245 K.
When El Niño is absent (February 2000), the shallow-convection peak is well pronounced in the central
and east Pacific, but it is less clear in the west Pacificand completely missing in the continental areas. This
trend, consistent with the findings of Short and Nakamura (2000), is understood in terms of the climatological pattern of tropical rainfall. Convection is deepest
over the west Pacific warm pool among the three Pacific regions while rainfall is still active along the intertropical convergence zone (ITCZ) but gradually weakens toward the east Pacific. In contrast, the continental
regions show a much lower frequency of occurrence of
these shallow rain systems. Closer examination, however, reveals that South America shows a weak signature of shallow convection, while Africa does not. Thissuggests that South American storms form an intermediate type lying somewhere between oceanic rain andhighly continental rain, consistent with the “green
ocean” (description offered by Williams et al. 2002).

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Recent studies have revealed that wet-season convection over the Amazon has two distinct modes called thewesterly and easterly regimes (Halverson et al. 2002;
Petersen et al. 2002). As a result of local geography,rain systems tend to be more oceanic during the westerly regime and more continental during the easterly
regime. These two regimes together would produce a
mixture of oceanic and continental rain spectra inmonthly statistics seen in Fig. 1.
During El Niño (February 1998), more frequent deep
convection takes the place of shallow rain in the central
and east Pacific, while the inverse trend is observed in
the west Pacific. This pattern is consistent with the eastward shift of heavy precipitation associated with
ENSO. Nevertheless, the population of deep convection is still not negligible in the west Pacific in contrast
to the central and east Pacific in the normal year. Thismay be related to the consistently warm SSTs across theTropics during El Niño as mentioned later in this section. The lack of the noticeable shallow-convection
peak, particularly for Africa, makes continental rainfall
look more homogeneous over the two periods compared to oceanic regions. This implies that rainfall isdominated more by local conditions over continents
than by large-scale circulation. South America, however, exhibits a weak but clear ENSO-related variation,that is, an increase of shallow convection at the expenseof deep convection in an El Niño year. This is plausible
since ENSO likely has some connection with the synoptic-scale environment that dominates the easterly
and westerly regimes in tropical South America. Interestingly, the South American trend is opposite to thecentral and east Pacific but more like the west Pacific.
The notable change of oceanic storm spectra in response to ENSO may be related to SST. Figures 2 and3 show the height spectra of storms as presented in Fig.
1, but partitioned by SST, into the three ranges of cool(⬍26°C), moderate (26°–29°C), and warm (⬎29°C)
SSTs. The histogram is weighted by the frequency ofoccurrence of SST and is completely blanked for the
SST range that never occurred in each region. This SST
variability partly accounts for the regional variation in
the height spectrum of storms when superposed over
the whole range of SST (Fig. 1). For example, the shallow-convection peak is clearly separated from the FL
ridge by SST in the west Pacific in 1998, so that thewestern Pacific spectrum looks more similar to that of
the central and east Pacific during El Niño when warm
SSTs are isolated. Indeed, the storm spectra are quite
coherent across the regions including the west Pacific in
2000 for the warmest range of SST, commonly exhibiting the pronounced FL ridge accompanied with a groupof even deeper systems. On the other hand, the storm
spectra are less coherent for the lower SST range of
26°–29°C. For these moderate SSTs, the west Pacific in
2000 lacks the shallow-convection peak in contrast to
the other Pacific regions in the same year, where shallow convection is overwhelming in number. This zonal

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gradient in rain spectrum reflects the pattern of theWalker circulation: deep convection is enhanced (suppressed) under continuous large-scale ascent (subsidence). The zonal gradient is somewhat reversed in1998 for the same SST range, as expected from a change
in large-scale circulation associated with ENSO.
Based on Figs. 1, 2, and 3, the observed precipitation
events are readily separated into the four storm categories summarized in Table 1. It is noted that the terminology is not precisely parallel to the conventional definitions of shallow convection, cumulus congestus, andthe stratiform and convective rains of MCSs. (Thestorm categories are set in italics throughout the paper
to avoid confusion.) In Table 1, the threshold for echotop height at 4 km (6 km) was chosen as slightly below(above) the FL ridge. The Tb threshold of 260 K ischosen since clouds warmer than this temperature arelikely to contain a substantial amount of liquid cloudwater including supercooled droplets. A colder threshold of 245 K was chosen to partition the FL ridge following Machado et al. (1998). It was found that changing this value by 5° did not to have any qualitative
impact on the results discussed below. There are two
regimes that do not belong to any of the four categories;
echo tops higher than 6 km with Tb warmer than 245 K
and echo tops lower than 4 km with Tb colder than 260
K, both of which make only negligible contributions to
the total population of storms. The former case is physically implausible since radar echo-top heights are unlikely to exceed cloud-top heights. The latter case corresponds to shallow rain overlapped with separate highor middle-level clouds.
A major component of the first category is the shallow-convection peak, and hence this category is designated by Shallow. The warm-rain process dominates
rainfall production in this category because cloud top is
not high enough to be capable of producing an appreciable number of frozen particles. The Cumulus congestus category contains rainfall that develops beyond
the freezing height by a kilometer or two, but convection is not deep enough to reach the tropopause. The
third category, denoted as Deep stratiform, consists ofevents that have very cold cloud tops while their echotop heights remain at the freezing level. Deep convective events, which constitute the final category, havecloud tops as cold as Deep stratiform events but haveecho-top heights developing far beyond the freezing
level. The Deep stratiform and Deep convective categories, separated by a difference in their capability to create large ice hydrometeors, were named based on thecommon understanding of the MCS structure (e.g., Fig.
9.34 of Houze 1993); large ice particles formed in theconvective region, which would create detectable radar
echoes much above the freezing level, are advected
through the upper troposphere to the stratiform region
where they precipitate in the absence of sustained vertical updrafts. As a consequence, rainfall near or within
the deep convective cores would be categorized as

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FIG. 2. Same as in Fig. 1, but partitioned by SST. Histograms of cool SSTs (⬍26°C), moderate SSTs (26°–29°C), and warm SSTs(⬎29°C) are shown, respectively, from top to bottom: for the (left) west, (middle) central, and (right) east Pacific. The time period isFeb 2000. The color bar is labeled in the unit of % km⫺1 K⫺1.

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FIG. 3. Same as in Fig. 2, but for Feb 1998.

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JOURNAL OF CLIMATETABLE 1. Definition of storm categories.

Storm category

Radar echo-top height

Infrared Tb

Shallow
Cumulus congestus
Deep stratiform
Deep convective⬍4 km
4–6 km
4–6 km⬎6 km⬎260 K⬎245 K⬍245 K⬍245 K

Deep convective systems, and Deep stratiform eventstake their place farther away from the convective cores.
It is important to note that Deep convective events may
contain a considerable amount of “stratiform” rain in
the vicinity of deep convective cores where large frozen
particles can be as abundant as in the convective regions. The statistical relation between the storm categories and the traditional stratiform/convective classes is
examined in section 5.
Figures 4 and 5 show rain probability or raining area
fraction as a function of SST for each storm category
identified above. The histogram of SST is given as well
to demonstrate the relative frequency of SST occur-

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rence. The histograms imply that 1) in a normal year
(Fig. 4), the west Pacific exhibits a peak as high as
29°–30°C, while the maximum SST probability decreases to around 25°C for the central and east Pacific,
and 2) SST is consistently warmer than 25°–26°C overthe entire tropical Pacific during the 1997–98 El Niño
event (Fig. 5). The absence of warm or cold SSTs insome cases accounts for blank panels in Figs. 2 and 3;
for example, SST ⬍ 26°C in 1998.
The dependence of rain probability on SST varies
with regions and years. There is nevertheless a generaltrend that Shallow events have the highest probabilityof occurrence for relatively cold SSTs, while Deep convective events take their place for warmer SSTs. The
turnover occurs at 28° or 29°C for all the regions andyears except the west Pacific in 2000, where deep systems dominate for the entire range of SST. Once SST
exceeds this threshold, the probability of occurrence ofDeep convective systems shows a rapid increase with
SST, while Shallow and Cumulus congestus events generally exhibit only a weak dependence on SST above
the threshold. The value of 28°–29°C is therefore a re-

FIG. 4. (top) Rain probability, or raining area fraction, as a function of SST for each storm category over the (left) west, (middle)
central, and (right) east Pacific in Feb 2000. (bottom) The histogram of SST for all raining and nonraining pixels. The SST range
corresponding to a less than 1% chance of occurrence is shaded.

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FIG. 5. Same as in Fig. 4, but for Feb 1998.

gime-independent SST threshold beyond which deep
systems generally prevail, with the exception of thewestern Pacific warm pool in a normal year where deep
systems dominate for all SST. This accounts for the coherent spectral pattern for warm SSTs that we haveseen in Figs. 2 and 3.
Figures 4 and 5 indicate that rain probability tends todecrease near the high end of SST range except in thewest Pacific in 2000. It is, however, not clear that thistrend is real because such high SSTs are so rare that its
statistical significance is not necessarily assured (theSST range that corresponds to a less than 1% chance ofoccurrence is shaded in the figures). A more extensive
study using a larger dataset is necessary to discuss thephysical mechanism behind the interaction between
precipitation and environmental factors such as SST.

4. Correlation-scale length of storms
a. Theory and case study
The categorization described in section 3 is based
solely on the vertical extent of storms. It is also important to develop a framework for investigating theirhorizontal properties to allow us to draw a threedimensional picture of various tropical storms. The
CSL is introduced for this purpose, and its mathematical basis is provided in this section. Together with theclassification system developed in section 3, the CSL
will be used to analyze the character of precipitation
systems inferred from satellite data later in this section.
First, we introduce the fractional coverage of raining
scenes, at a distance d from a reference point xref,
fr共di, xref兲 ⫽nr共di兲
,ntot共di兲

共1兲

where nr is the number of raining PR pixels and ntot is
the total number of PR pixels within a distance bin
defined around d ⫽ di. Similarly, the fractional coverage of cloudy scenes is given as
nc共di兲
,共2兲
fc共di, xref兲 ⫽ntot共di兲which is (conceptually) the number ratio of cloudy
VIRS pixels nc to the total VIRS pixels. Cloudiness is,however, difficult to define uniquely from infrared radiances, and hence we introduce the average brightness
temperature within a distance bin around d ⫽ di,

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Tb共di, xref兲 ⫽兺T 共d 兲 ,
b

i

共3兲

ntot共di兲as a proxy of fc based on the relationship between Tb
and fc described as follows. The summation of Tb in (3)may be interpreted as a combination of the cloud-top
temperature, Tc, and ambient field (sea surface and/or
extended low clouds) temperature, Ta, that is,兺T 共d 兲 ⬇ T n 共d 兲 ⫹ T 关n 共d 兲 ⫺ n 共d 兲兴.
b

i

c c

i

a

tot

i

c

i

共4兲

The equality in (4) is exact if clouds consist of opticallythick high clouds with a homogeneous top height andambient low clouds (or no low clouds). Combining (3)and (4), one gets
Tb共di, xref兲 ⫽ 共Tc ⫺ Ta兲fc共di兲 ⫹ Ta,

共5兲

which claims that Tb(di, xref) is linearly related to fc(di).

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Figure 6 shows a snapshot of an MCS observed by
TRMM, demonstrating examples of fr (d, xref) andTb(d, xref) for four reference points that belong to different storm categories. In Fig. 6, the satellite swath
split into three slips is illustrated to show infrared Tb,
PR echo-top height, and the storm categories. An MCS
is outlined by very cold Tb (blue and purple regions in
the bottom slip), including deep convective cores punctuated by PR echo-top heights as high as 10 km (purple
in the middle slip) and stratiform regions having relatively lower echo-top heights. The storm categories
shown in the top slip clearly separate the MCS traced
by Deep stratiform (blue) or Deep convective (purple)
from the sparsely distributed components of Cumulus
congestus (green) and Shallow (red).
Figure 6 also shows fr (d, xref) and Tb(d, xref) computed for each of the four reference points, around

FIG. 6. A snapshot of an observed MCS by TRMM, showing (bottom slip) infrared Tb, (middle slip) radar echo-top height, and (topslip) storm categories, together with fr(d) and Tb(d) computed around each of four sample points belonging to different storm
categories. The positions of the sample points on the satellite swath are designated by arrows. A pair of numbers provided for eachsample points (i.e., 292 K, 1.8 km) indicates infrared Tb and radar echo-top height, respectively, at that point.

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which (1) and (3) were performed over nearby satellite
pixels. For example, fr around the Deep stratiform event
remains as high as 60% (and Tb as cold as 230 K) evenfor d ⬎ 100 km, indicating that this event is surrounded
by a very extensive high cloud with precipitation over
60% of it in areal coverage.1 One can see that the Deep
convective and Deep stratiform events exhibit a turnover at d ⫽ 100 km in fr and Tb, detecting the adjacentraining cells. As such, fr and Tb are sensitive to neighboring cells and therefore reflect the horizontal extent
of an entire organized system regardless of the contiguity of raining areas. A distinctive cluster of Cumulus
congestus can be seen right next to the MCS, showing a
rapid decay in fr and a nearly constant Tb. This implies
that Cumulus congestus systems are constituted of isolated rainfall associated with extended clouds, most ofwhich are nonprecipitating. A similar trend is foundeven more clearly for the Shallow event, which is so
isolated and sparse that fr reaches zero at 100 km awayfrom the reference point.
Before proceeding, some thought experiments areapplied to provide insight into the physical meaning of
the CSL. Figures 7a–d schematically demonstrate therelations between fr and storm size including the caseswith storms truncated by the swath edges. In every case,Di (i ⫽ a–d) is given as a reference dimension charac-

1
In this context, “fr (or Tb) around a Deep convective event”
means “fr (or Tb) of any kind of rainfall (or clouds) surroundinga Deep convective event” and should not be considered as “fr (Tb)
deduced exclusively from the raining (cloudy) pixels classified
into Deep convective systems.” The former definition is crucial for
addressing problems such as a variation in the horizontal extent of
cirrus anvils associated with a certain type of storms, which is amajor interest of this study.

teristic of each graph of fr(d). The simplest case, a1,
illustrates an isolated convective cell with a reference
point nearly at its center, where Da1 provides a reasonable approximation of the cell radius. For another reference point closer to the edge of the same convective
cell (case a2), fr is expected to decrease with increasing
d more rapidly near d ⫽ 0 but have a longer “tail” at alarge d since the cell is measured across from one edge
to the other, and thus Da2 is closer to the diameter than
to the radius. In case b, representative of an extensive
and more or less circular system, Db is still close to theactual storm size despite the fact that the storm is solarge that it is truncated by the satellite swath. A heavily elongated storm such as the “MCS linear” systemdescribed by Rickenbach and Rutledge (1998) can bemeasured along either its short or long axis, Dc1 or Dc2
in cases c1 and c2, depending on the storm orientation.
If a significant fraction of the storm lies outside theswath as shown in case d, Dd could be smaller than theactual size of a storm. A parallel discussion applies to
Tb by replacing fr with fc.
Although Di in an individual case is not always asatisfying representative of storm size, it is possible toextract a more robust and objective estimation by examining the statistics of a large number of observations.
The expectation of finding rain, 具 fr典, at a distance dfrom a given raining point is therefore introduced as

具 fr典共d兲 ⫽

1
Ar

冕

fr共d, xref兲dxref,

共6兲

Ar

where fr (d, xref) is integrated over reference points
across the raining area of interest, Ar. In the actual
analysis of finite datasets, (6) is rewritten as

FIG. 7. Schematic illustrations of fr(di, xref) for several examples, where di is the distance of the ith circular bin (dashed circles) froma given reference point (the center of the innermost bin), xref. In each case, an example of an observed rainfall system and the overlaid
satellite swath (shaded) are illustrated together with the expected form of fr(di, xref), where each plot represents the raining-areacoverage within a circular bin, and Di denotes a reference dimension. See text for more details.

834

JOURNAL OF CLIMATEN

1 ref
fr共di, xref兲,Nref

具 fr典共di兲⫽兺

共7兲

where Nref is the total number of reference points. Similarly, the expectation of Tb(d, xref), 具Tb典, is defined as

具Tb典共d兲 ⫽

1
Ar

冕

N

Tb共d, xref兲dxref ⬇

Ar

1 ref
Tb共di, xref兲.
Nref

兺

共8兲
In the averaging process, fr with different reference
points for the same storm system such as in cases a1 anda2 would result in a smoothly decaying function for 具 fr典
with a decaying rate indicative of the storm size, likely
somewhere between its radius and diameter. Similarly,
the extreme cases as shown in c1 and c2 of Fig. 7 tend tostatistically cancel each other out, leaving a moderatevalue between Dc1 and Dc2, if the storm orientation
with respect to the flying direction of the satellite
should be randomly distributed. Furthermore, the underestimation of storm size as seen in case d does notconsiderably affect 具 fr典 as long as the number of observations is sufficient to dilute statistically rare events
such as case d. As a result, 具 fr典 is far less sensitive to thetruncation effect compared to the contiguous pixel
method, which would underestimate the size of storms
for 17% of total rain systems and 82% of MCSs in theuse of TRMM PR (Nesbitt et al. 2000).
Computing (1) and (7) across a given geographicalregion and time period, one obtains 具 fr典 and its standard
deviation, ␴r, as functions of di. The resultant 具 fr典 is
expected to decrease monotonically with increasing dbecause storm size spectra are known to be decreasing
functions in general (López 1976, 1977; Machado et al.
1992; Machado and Rossow 1993; Del Genio and Kovari 2002). An exponential function is adopted to fit thedata for extracting a characteristic scale length from 具 fr典
as a function of d. The RCSL, ␭r, is thus defined byminimizing

兺冋
i

册

ln具 fr典共di兲 ⫺ lnFr0 ⫹ di Ⲑ␭r 2
␴r共di兲

共9兲

to find the least square fit to the data using an exponential form,

具 fr典共d兲 ⬃ Fr0 exp共⫺dⲐ␭r兲.

共10兲

The fitting function for the cloud CSL (CCSL), ␭c, issimilarly defined from (5) as

具Tb典共d兲 ⬃ 共Tc ⫺ Ta兲 exp共⫺dⲐ␭c兲 ⫹ Ta,

共11兲

where Tc and ␭c are determined under a given Ta. The
ambient field temperature Ta is determined from infrared observations as described in the next subsection.

VOLUME 18

b. Results
The RCSL and CCSL introduced above are now applied to satellite data and sorted by the storm categories
defined in section 3. The procedure of the analysis canbe seen in Fig. 8, which illustrates 具 fr典 and 具Tb典 alongwith fitted exponential curves as a function of d. While
only western Pacific storms in February 2000 are
shown, the results for all other cases are qualitatively
similar. Figure 8 should not be confused with the probability distribution constructed by the contiguous pixel
method, where a storm size is defined individually foreach separate system, as often found in the literature.
The definitions of 具 fr典 and 具Tb典 are based on the spatial
structure of the rain/cloud fraction without explicitly
identifying “size” for individual storms (see the previous subsection for details).
For rain systems around Shallow events, 具 fr典 shows arapid decrease for d ⬍ 10 km until it reaches a constantvalue at 具 fr典 ⫽ 10% on a larger scale. As demonstrated
in Fig. 6, this implies that unorganized convective cells
whose horizontal scales do not exceed 10 km are typically observed around Shallow events. The flat component in 具 fr典 (d ⬎30 km) has been excluded from theexponential fit because it only reflects the ambient fieldof sparsely distributed cumulus clouds that do not consist of a single organized system. Infrared brightness
temperature is found to be independent of d for the
Shallow category over all scales of interest. This feature, together with the trend in 具 fr典, indicates that Shallow events are embedded in an extended ambient field,
90% of which consists of nonprecipitating clouds and/or
clear sky. For the Shallow category, therefore, 具Tb典 isfitted by a straight horizontal line instead of an exponential curve. This horizontal line defines Ta in (4),which reads Ta ⫽ 275 K in this particular case. The
ambient field temperature defined in this way for eachgeographical region is adopted to determine the CCSL
around Deep stratiform and Deep convective systems
according to (11). Such low, extensive clouds as thosethat contribute to Ta are expected to cool the planet byreflecting more solar radiation to space than can becompensated for by latent heat released in their formation.
In contrast to Shallow events, both 具 fr典 and 具Tb典 exhibit an extensive structure over the whole range of daround Deep convective systems, indicating that they
are generally organized on a scale of hundreds of kilometers. A plateau is observed in the inner portion of
具Tb典 while 具 fr典 drops more quickly as d increases, indicative of a raining core associated with a more extended high cloud such as anvil cirrus. Precipitating
cores in those storms release a significant amount oflatent heat, likely overwhelming the infrared heating
and the shortwave cooling by the associated clouds.
The general properties of Cumulus congestus events(not shown) resemble those of Shallow events, while
Deep stratiform systems are more similar to Deep con-

15 MARCH 2005

835

MASUNAGA ET AL.

FIG. 8. (top) Here 具fr典 and (bottom) 具Tb典 are shown as functions of distance for the (left) Shallow and (right) Deep
convective categories. All cases exhibit qualitatively similar patterns to this particular case of the west Pacific in Feb
2000. Open circles, error bars, and solid curves show the average, standard deviation, and fitted exponentialfunction, respectively.

vective ones. The CCSL is not well defined around theShallow and Cumulus congestus categories because themajor cloud component consists of extensive nonprecipitating clouds that are not associated with the storm.
The CCSL is therefore evaluated only for the clouds
surrounding Deep stratiform and Deep convective
events. The RCSL is estimated only from the inner
portion of data (d ⬍ 10 km) around Shallow and Cumulus congestus events, whereas it is derived from theentire range of d when calculated around Deep stratiform and Deep convective systems. Note that the RCSL
and CCSL can exceed the largest d used for the exponential fit when 具 fr典 and 具Tb典 decay very slowly with d.
Table 2 summarizes estimated values of RCSL and
CCSL for February 1998 and 2000. The RCSL ranges
from 8 to 13 km for Shallow events and from 13 to 18
km for Cumulus congestus systems over the five regions
studied. No notable regional and ENSO-related vari-

TABLE 2. RCSL and CCSL. The CCSL is not defined for Shallow and Cumulus congestus categories. The results correspond to themonths of Feb 2000 and Feb 1998 (in parentheses).

RCSL (km)Shallow
Cumulus congestus
Deep stratiform
Deep convective
CCSL (km)Deep stratiform
Deep convective

West PacificCentral Pacific

East PacificSouth America

Africa9.25(7.82)
13.0(14.0)
153
(156)
126
(136)

9.33
(9.16)
15.9(16.6)
137
(221)
124
(170)

9.50
(12.4)
14.7(17.8)
111
(189)
103
(151)

12.6
(11.8)
16.1(15.8)
165
(142)
120
(122)

12.3
(12.9)
14.9(14.6)
144
(120)
119
(101)

324
(365)
233
(270)

220
(498)
188
(317)

180
(382)
134
(250)

278
(309)
173
(231)

293
(270)
215
(178)

836

JOURNAL OF CLIMATE

ability is found in RCSL for these storms. On the other
hand, Deep stratiform and Deep convective events areaccompanied with RCSL and CCSL consistently larger
than 100 km. The RCSL and CCSL around Deep stratiform and Deep convective systems over ocean exhibit asignificant increase during the 1998 El Niño event compared to the year 2000, and the increase is most conspicuous for the central and east Pacific. Furthermore,while western Pacific convection is generally not as
deep in El Niño years, the RCSL and CCSL increaseslightly during the 1998 El Niño event in the west Pacific.
Figure 9 illustrates the relationship between CCSLand RCSL for Deep stratiform and Deep convective
events for both February in 1998 and 2000. As expected, the CCSL is always larger than the RCSL because anvil cirrus detrained from deep convection can
extend horizontally far beyond the raining area. Twoother noticeable but less expected features are also
found in Fig. 9. First, all plots are more aligned thanscattered in Fig. 9, independent of storm categories andgeographical regions. Second, both the RCSL and
CCSL are always larger around Deep stratiform events
than Deep convective events. The excess of Deep stratiform CCSL over Deep convective CCSL is most outstanding for the central and east Pacific in 1998, where
the former is more than 1.5 times larger than the latter.
The temporal change in the stratiform/convective ratio
through an MCS life cycle discussed in the next section
helps explain some of these behaviors.

5. Discussions
This section is dedicated to discussing our results inthe context of those presented in related prior studies.

VOLUME 18

We start with an attempt to relate the present storm
categorization with common classifications of tropicalstorms.

a. Comparison to convective/stratiform rain types
Although the present storm categorization is by nomeans intended to replace the conventional stratiform/
convective classification, these two different categorizations are briefly compared in this subsection. The
stratiform (convective) area fraction, as defined by the
number of PR stratiform (convective) pixels divided by
the total raining pixels, is summarized in Table 3 foreach storm category as well as the mean, integrated
over all the categories. With no qualitative difference
between the two years, the Shallow and Deep stratiform
categories exhibit the highest stratiform fractions. In
contrast, Cumulus congestus and Deep convective categories contain larger fractions of convective rain area
than its mean value.2 Deep convective events are madeup of twice as much convective rainfall as Deep stratiform events, as expected from the nomenclature. At thesame time, however, Deep convective events include asignificant fraction of stratiform rain. As pointed out insection 3, Deep convective systems contain the stratiform regions of MCSs in the neighborhood of deep
convective cores where large ice particles created in theconvective cores directly flow in.
The difference in the stratiform/convective ratio
could account for the difference in RCSL and CCSL

2
The high stratiform ratio for Shallow events should be interpreted with care because Schumacher and Houze (2003) pointed
out that shallow, isolated rain is classified as stratiform in the
2A23 dataset in spite of its convective nature.

FIG. 9. RCSL vs CCSL for Deep stratiform and Deep convective events. Lines and symbols are designated in
the figure.

15 MARCH 2005

MASUNAGA ET AL.

TABLE 3. Stratiform/convective percentage relative to total
rainfall for each storm category with the mean ratio integrated
over all the categories. The residual percentage unlisted here isclassified as “others” in the TRMM operational rain classification(2A23) dataset.
Stratiform

Shallow
Cumulus congestus
Deep stratiform
Deep convective
Mean

Convective

2000

1998

2000

1998

83%
74%
83%
60%
74%

85%
73%
86%
70%
77%

14%
21%
11%
22%
17%

12%
21%
9%
17%
14%

837

grated over the entire life cycle of MCS, Deep stratiform systems would therefore be accompanied with alarger CSL than Deep convective events.
Takayabu (2002) demonstrated that rain intensity
monotonically increases as rain tops become higher,
with the exception of stratiform rain with the rain-top
height exceeding 8–9 km. She also found that convective rain has a higher rain rate than stratiform rain fora given rain-top height. The high echo-top heights together with the high convective ratio, therefore, allow
Deep convective events to produce the most intense
rainfall over the Tropics.

b. Alternate classifications
between the Deep convective and Deep stratiform categories in the context of the MCS life cycle. Individual
convective cells grow and merge in the developing stageof the life cycle of an MCS (Fig. 10a), but it is not until
the mature stage that an extensive stratiform region
develops (Fig. 10b). Convective elements weaken during the decaying stage of the life cycle, leaving behindstratiform precipitation (Fig. 10c; see also chapter 9.1.3
of Houze 1993). The developing stage would therefore
produce Deep convective profiles with a relatively small
CSL, and the extensive stratiform component would
contribute to a large CSL around Deep stratiform
events through the mature and dissipating stages. Inte-

Rickenbach and Rutledge (1998) defined four rainfall classes based on the horizontal scale and morphology of radar-echo distributions: sub-MCS nonlinear,
sub-MCS linear, MCS nonlinear, and MCS linear. They
investigated rainfall/number distributions of convective
feature height separately for each of the four classes.
They found that MCS linear events are associated withdeep convection, while sub-MCS nonlinear events exhibit a distinct frequency peak at 3–4 km in verticalprofiles. A similar relationship between the vertical extent and horizontal organization was found in section 4.
The RCSL around Shallow and Cumulus congestus
events indicates that they represent no more than

FIG. 10. Schematic diagram illustrating the life cycle of an MCS. Deep convective systems, sensitive to convective cores, are observedat the (a), (b) earlier stages of an MCS life cycle, while Deep stratiform events, more sensitive to stratiform rain, take the place at the(b), (c) later stages. Accordingly, the RCSL and CCSL are expected to be smaller for Deep convective systems than for Deep stratiform
systems when integrated over the life cycle.

838

JOURNAL OF CLIMATE

small-scale isolated convection, while the CSL associated with Deep stratiform and the Deep convective represents organized systems as large as an MCS scale.
Size distributions of cloud clusters have also beenstudied using infrared brightness temperatures acquired by geostationary satellites. The mean size ofcloud clusters ranges from 50 to 300 km in terms of theequivalent radius (or radius of a circle having the samearea) as estimated from contiguous cloud areas colder
than a certain threshold (e.g., Machado et al. 1992;
Chen et al. 1996). The CCSL obtained in the present
study is at the high end of the largest clusters found inpast work. This is reasonable since the CSL is moresensitive to the size of a whole system rather than thatof an individual element identified as a contiguous area.
More recently, the contiguous pixel method was applied directly to rainfall based on microwave scattering
signals at 85 GHz (Mohr and Zipser 1996; Nesbitt et al.
2000) and the TMI operational rainfall product (DelGenio and Kovari 2002). Their results indicate that thevast majority of storms have equivalent radii smaller
than 100 km, while our result shows that the RCSL
splits into two distinct groups of 10–20 and 100–200 km
depending on the storm category. There are two reasons for the discrepancy between the past studies andpresent work. The first is the same as that already described for cloud clusters. The difference in size between an organized system and an individual element
in it is even greater for rainfall since each raining cellis less extensive than a cloud element. The second isdue to the truncation by the finite satellite swath. Allspaceborne microwave sensors currently fly on LEO,
limiting the swath width and hence the size of storms
measured by the contiguous pixel method. The CCSLand RCSL, in contrast, are less sensitive to the satellite
swath width, as is demonstrated in section 4.

c. Dependence on SST
Del Genio and Kovari (2002) examined the influenceof large-scale vertical motion and SST upon storm radius and other storm characteristics based on the datafor 1–5 February 1998. They found a significant positive
dependence of storm radius on SST when the sample
was limited to MCS and a weak positive correlation
when all storms were included. This fact is consistent
with our results given in section 3, which indicate that
Deep convective systems exhibit a rapid increase with
SST in the probability of occurrence once SSTs exceed
28°–29°C and that Shallow events are less sensitive to
SST. Furthermore, Fig. 9 of Del Genio and Kovari(2002) indicates that storms occur independently oflarge-scale vertical motion if SST is warmer than
⬃28°C, but storms are otherwise capable of developing
only when large-scale vertical motion is positive ornearly zero. This supports the 28°–29°C threshold identified from our results (section 3).

VOLUME 18

6. Summary and conclusions
The characteristics of precipitating cloud systems
over the tropical Pacific have been examined in termsof their vertical and horizontal extent using infrared
brightness temperature and radar echo-top height. The
present study employed TRMM VIRS and PR data
during February 1998 and February 2000 for three regions over tropical oceans (west, central, and east Pacific) and two tropical continents (South America andAfrica) for comparison. The two time periods adopted
correspond to different phases of ENSO: February
1998 experienced the 1997/98 El Niño, while climatic
patterns had returned to normal by February 2000 after
the La Niña year of 1999.
The height spectrum of tropical convective systems
was investigated in terms of infrared Tb and radar echotop heights. The storm spectrum exhibits a variability
consistent with the climatological pattern in tropicalrainfall: deep systems are common over the western
Pacific warm pool, while shallow convection takes theirplace in the central and east Pacific in the absence of El
Niño. In contrast, rainfall systems frequently develop
beyond the freezing level everywhere across the tropical Pacific during the El Niño event in 1998. Thesesystematic variations in the storm spectrum are accounted for by the climatological pattern of the Walker
circulation and its ENSO-related change. African rainsystems lack the shallow-convection peak and show less
ENSO-related variations than the oceanic regions. Precipitation systems over tropical South America have aweak similarity to the oceanic rainfall variability under
the influence of a synoptic environmental change.
Four storm categories were defined based on VIRS
infrared Tb and PR echo-top height, namely, Shallow,
Cumulus congestus, Deep stratiform, and Deep convective. The Shallow category consists of shallow convection that has a cloud top much warmer than the freezing
temperature and an echo-top height below the freezing
level. This category contains a distinct group of storms
over ocean and accounts for the vast majority of storms
in the central and east Pacific in February of a normal
year. Cloud-top heights and radar echo-top height canexceed the freezing level but are not high enough toreach the tropopause in the Cumulus congestus category. Cumulus congestus events constitute a weak but
nevertheless distinct peak in the height spectrum ofstorms in some cases. Both the Deep stratiform andDeep convective categories represent rain systems withvery cold cloud tops. These two categories are differentiated in the way that radar echo-top heights remain
near the freezing level for Deep stratiform systems,
while Deep convective events are capable of developing
far above the freezing height. A PR echo-top heightmuch higher than the freezing level implies the presence of large ice hydrometeors, which are produced
only in deep convection. Deep convective events are
therefore likely to be found in deep convective cores or15 MARCH 2005

839

MASUNAGA ET AL.

TABLE 4. The percentage of occurrence of each storm category, normalized in each region and year. The uncategorized regimes(blanks in Fig. 1, top; see also section 3) account for the residual percentage (10%–15%) in each column.
West Pacific

Shallow
Cumulus congestus
Deep stratiform
Deep convective

Central Pacific

East PacificSouth America

Africa

2000

1998

2000

1998

2000

1998

2000

1998

2000

1998

12%
15%
32%
27%

24%
13%
20%
31%

50%
17%
8%
15%

13%
10%
31%
32%

54%
20%
7%
8%

17%
12%
27%
29%

8%
18%
33%
28%

12%
17%
27%
30%

2%
14%
25%
49%

4%
16%
28%
42%

their neighborhood, while the Deep stratiform category
traces the stratiform region far away from the convective region. The probability of occurrence of each storm
category in each region and year is summarized inTable 4.
Shallow events generally have the highest probabilityof occurrence for relatively cold SSTs, while Deep convective events show a rapid increase with SST to overwhelm the other storm categories once SSTs are higher
than 28° or 29°C. The value of 28°–29°C is a regimeindependent SST threshold beyond which deep systems
prevail with the exception of the west Pacific in 2000,
where Deep convective systems dominate over the entire range of SST.
Rain correlation-scale length (RCSL) and cloud correlation-scale length (CCSL) were introduced to assess
the horizontal extent of storms. The RCSL (CCSL) is
defined based on the expectation of finding rain(clouds) as a function of distance around a given raining
point. Compared to conventional approaches where araining or cloudy area is estimated from contiguous satellite pixels, the RCSL and CCSL represent the size ofan entire organized system independent of the contiguity. Moreover, the CSLs are less severely limited by thefinite swath width of LEO satellite such as TRMM. The
expectation of finding rain declines quickly as distanceincreases around Shallow and Cumulus congestus
events, while Deep stratiform and Deep convective systems were found organized over a scale of hundreds ofkilometers. Clouds associated with Shallow and Cumulus congestus storm systems constitute a larger nonprecipitating cloud field instead of a single organized storm
system, and hence the CCSL is not defined for thosecategories. In contrast, clouds are more extensively organized than rainfall around Deep stratiform and Deep
convective systems, indicating that those systems consistof MCSs with extensive cirrus anvils. The qualitative
characteristics of storms are summarized in Table 5 foreach storm category.

The character of clouds and rainfall associated with
Shallow and Cumulus congestus events does not depend strongly on the time periods or geographical regions analyzed, showing small values of RCSL and
CCSL in the range of 8–18 km. On the other hand, theDeep stratiform and Deep convective categories exhibit
significant regional and ENSO-related variations in
RCSL and CCSL for oceanic storms. The CCSL shows
the most dramatic increase in the central and east Pacific during an El Niño year, reaching up to 500 km. The
CCSL/RCSL associated with Deep stratiform systems
exhibits a coherent variability with that around Deep
convective events, but the CSLs around Deep stratiform
systems are consistently larger than those around Deep
convective ones.
The variability of CCSL leads to an important, controversial issue: how much radiative impact do anvil
clouds associated with deep convection have on theearth’s energy budget (Del Genio and Kovari 2002, andreferences therein)? Two hypotheses concerning thisissue have been proposed in the context of radiative
feedback working between SST and anvil cirrus. The“thermostat” hypothesis (Ramanathan and Collins
1991) states that anvil clouds extend more as the ocean
warms up, as opposed to the “adaptive iris” hypothesis(Lindzen et al. 2001) where the warming of SST reduces the cloud fraction. Interestingly, both hypotheses
are said to oppose global warming, the first through theincreased reflection of incoming shortwave radiationand the second through increased infrared emission to
space. Anvil clouds are capable of both heating andcooling the earth depending on the trade-off between
the albedo and greenhouse effects. This double-edged
nature allowed the conflicting thermostat and iris hypotheses to lead to the same conclusion: the cloud feedback works negatively to suppress an increase in SST.
Impacts of the variability in the storm characteristics
upon the earth’s radiation budget will need to be examined in future work. Studies of that kind are one of

TABLE 5. Summary of storm characteristics for each category. The predominant rain type is defined depending on which of thestratiform or convective rain types dominates relative to their means integrated across all the storm categories (see Table 3).
Storm category

Infrared Tb

Radar echo-top height

Predominant rain type

RCSL

CCSL

Shallow
Cumulus congestus
Deep stratiform
Deep convective

Warm
Moderate
Cold
Cold

Low
Freezinglevel
Freezinglevel
High

Stratiform
Convective
Stratiform
Convective

Small
Small
Very large
Large

N/A
N/A
Very large
Large

840

JOURNAL OF CLIMATE

the important subjects to which the methodology proposed by this study could be applied.
Acknowledgments. This research is supported by
NOAA’s Office of Global Programs Grant
NA17RJ1228#15 and by NASA’s Precipitation Program Grant NAG5-13694. The authors thank W. Bergand A. Rapp for helpful comments and discussions. The
TRMM datasets (1B01 and 2A23) were provided by the
National Aeronautics and Space Administration(NASA) Goddard Space Flight Center (GSFC). The
SST dataset retrieved from TMI was obtained from the
Remote Sensing Systems (RSS; at the web Site http://
www.ssmi.com/tmi/tmi_browse.html), sponsored by
NASA’s Earth Science Information Partnerships(ESIP)—a federation of information sites for Earth Science—and by NASA’s TRMM Science Team.
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