From the Strüngmann Forum Report, Clouds in the Perturbed Climate System:
Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation
Edited by Jost Heintzenberg and Robert J. Charlson. 2009.
© MIT Press ISBN 978-0-262-01287-4

5Clouds and Precipitation
Extreme Rainfall and Rainfrom Shallow Clouds
Yukari N. Takayabu1 and Hirohiko Masunaga2
1Center for Climate System Research, University of Tokyo, Kashiwa, Chiba, Japan
2Hydrospheric Atmospheric Research Center, Nagoya University,

Forocho Chikusa-ku, Nagoya, Japan

Abstract
This chapter reviews present knowledge on extreme precipitation and moderate rainfall
from low-level clouds. Primary focus is on the statistics of precipitation characteristics
rather than on a detailed description of individual case studies. First, observed variability of precipitation from low-level clouds and the existing techniques to separate different microphysical stages from remote-sensing measurements are reviewed. Over thetropical areas of Pacific and Atlantic oceans, the global distribution of shallow rainfall
exhibits a “butterfly” pattern. This feature encompasses heavily precipitating regions
such as the intertropical, south Pacific, and south Atlantic convergence zones (ITCZ,
SPCZ, and SACZ, respectively); the northern hemispheric counterpart of SPCZ and
SACZ emerges only when shallow rain is isolated.
The nature of extreme precipitation varies temporally. On a timescale of about aday, extreme precipitation is associated with synoptic-scale disturbances, including a
notable example known as tropical plumes or moist conveyer belt, which could giverise to extreme daily precipitation in downstream arid regions. On an hourly timescale,extreme precipitation is caused by mesoscale moisture convergence, which is so intensethat it maintains a continuous overturning of saturated air. Satellite observations imply
that the global distribution of extreme precipitation shows a systematic difference fromthe total rainfall map in terms of, for example, the contrast between land and ocean. The
distribution of low-level, precipitation-related latent heating associated with warm rain
coincides with the butterfly pattern. Its cohabitation and separation with the deep heating suggests that warm rain plays a role in providing a thick layer of moist static energy
source to the convection, and that it is also related to the tropical plumes which cause
extreme precipitation in the semiarid west coasts of continents.

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Y. N. Takayabu and H. Masunaga

Precipitation from Low-level Clouds
Low-level clouds are typically non-precipitating, or just lightly precipitating,and are thus unlikely to cause hazardous weather events. Nevertheless, precipitation from low-level clouds is important meteorologically and climatologically for the following reasons: Precipitation from stratocumulus has a direct
impact on the dynamic maintenance of its parent cloud (Nicholls 1984). In
terms of the so-called second indirect effect of aerosols, an increase in aerosol
concentration is hypothesized to suppress cloud droplet growth, leading to alonger cloud lifetime as the cloud drizzles out less efficiently (Albrecht 1989).
Precipitation from stratocumulus clouds can thus be a controlling factor of the
Earth’s radiation budget, although they do not necessarily make a crucial contribution to global water cycle. Tropical and subtropical shallow cumulus yieldsignificant precipitation through warm rain processes. Short and Nakamura(2000) estimated that shallow rainfall accounts for more than 20% of the total
rainfall over tropical oceans.
Precipitation from low-level clouds comes generally in the form of warm
rain, except in cases where a very low surface temperature does not allow
cloud droplets to exist in the liquid phase. The microphysical processes involved in warm rain are relatively well understood (e.g., Mason 1952). Cloud
droplets begin to grow in size, initially through vapor condensation to liquidwater in supersaturated air. Condensational droplet growth is followed by thedrizzling stage, onset by collision and coalescence with distinctly large droplets (20–100 μm) in a turbulent cloud layer. Drizzle turns to warm rain as thedroplet coalescence proceeds further and precipitation rate increases.
A number of field campaigns with airborne probes have provided opportunities to study the microphysical properties of clouds in detail. In-situ measurements need to be complemented, however, by wide-area observations, such as
satellite remote sensing, to obtain robust climatological evidence of large-scalevariability. Yet how can we diagnose a microphysical condition from miles
away, relying only on information carried by radiation emitted or scatteredby cloud particles? To address this concern, let us look at some of the current
remote-sensing methodologies used to detect cloud microphysical characteristics and discuss the global climatology of drizzle and shallow rain.
Regional and Global Observations of Drizzle and Warm Rain
Cloud Optical Thickness versus Effective Droplet Radius
Cloud droplets are distributed around 5–10 μm in radius, so that the geometrical optics approximation is valid for cloud droplets at visible wavelengths.
Cloud optical thickness, τc, measured remotely in the visible (0.65 μm wavelength) spectrum is thus a practical proxy of the second moment of the cloud
droplet size distribution integrated over the whole cloud layer:

Extreme Rainfall and Rain from Shallow Clouds
Ĳ c | 2ʌ ³ dz ³ dr n(r )r 2 ,

109
(5.1)

where z, r, and n(r) denote the cloud layer depth, cloud droplet radius, anddroplet size distribution, respectively. To translate τc into a parameter of meteorological relevance, such as liquid water path (LWP):
4
LWP { ʌȡw ³ dz ³ dr n(r )r 3 ,(5.2)
3
where ρw =1g cm–3 is the liquid water density, conversion from observed τc to
LWP requires knowledge on the relation between the second and third moments of n(r). Combining Equations 5.1 and 5.2:
2
LWP { ȡw Ĳ c re ,
(5.3)
3
where

³ dr n(r )r ,2
³ dr n(r )r
3

re {

(5.4)

and re is the effective droplet radius, representing the area-weighted average ofcloud droplet radius. Nakajima and King (1990) established a methodology toretrieve cloud optical thickness and effective droplet radius from visible andnear-infrared (typically at 3.7 μm) radiances.
The joint use of τc and re allows different microphysical stages to be separated. LWP increases with re and τc while cloud droplet number concentration
remains nearly constant during the condensational droplet growth. By contrast,
the subsequent stage promoted by droplet coalescence is not accompanied by
an appreciable increase in LWP. Cloud optical thickness reduces with increasing re when LWP is unchanging or is only weakly changing, as inferred from
Equation 5.3. The optical thickness versus effective radius diagram constructed from airborne remote sensing (Nakajima and Nakajima 1995) shows a clear
contrast between drizzling and non-drizzling clouds as they vary from one area
to another. Figure 5.1 shows a pair of sample plots representing both extreme
cases. Figure 5.1a depicts clouds that stay at the condensational growth stage,
where τc and re are positively correlated with each other. The correlation turns
to negative in Figure 5.1b, implying that droplet collision and coalescence areat work. A very similar evolutionary track in the τc-re plane is obtained by abin-microphysical model simulation (Suzuki et al. 2006; see also Nakajima
and Schulz, this volume). The effective droplet radius does not reach 15 μm
for non-drizzling clouds (Figure 5.1a), whereas re can be as large as 20 μm for
drizzling clouds (Figure 5.1b). A number of studies, including airborne probe
measurements and bin-microphysical model calculations, concluded independently that the transition from the condensational to collisional growth, or the
initiation of drizzle, occurs when effective droplet radius reaches about 15 μm(e.g., Gerber 1996; Pinsky and Khain 2002).

Y. N. Takayabu and H. Masunaga
(a)

(b)

Effective radius (μm)

25
20

B-1

15
10
5

0 10 20 30 40
Cloud optical thickness

25
Effective radius (μm)

110

C-2

20
15
10
5

0 10 20 30 40 50 60
Cloud optical thickness

Figure 5.1 Scatterplot of τc and re for two observed scenes representing different
microphysical states (adapted from Nakajima and Nakajima 1995).

Effective Droplet Radius versus Cloud-top TemperatureWithin ascending moist air, cloud droplets grow in size so that droplet size increases generally with height inside a cloud layer. The droplet size at cloud top,
however, is not uniquely determined for a given cloud thickness. For example,
a high concentration of cloud condensation nuclei (CCN) could suppress drizzling for a cloud that otherwise develops deep enough to precipitate. Rosenfeld
and Lensky (1998) demonstrated that the cloud-top temperature (Tc) versus an
re diagram delineates closely the microphysical evolution that occurs in deepening clouds; it relies on the assumption that the local statistics of Tc and re arerepresentative of various stages of a developing cloud. Case studies performedby Rosenfeld and Lensky (1998) were successful in identifying the distinct
variation of cloud microphysics from one area to another in satellite imagery.
Deepening clouds are occasionally not accompanied with significant droplet
growth below the level at which droplets freeze (Figure 5.2a, b). In contrast,
there are regions where clouds are allowed to reach immediately the drizzling
threshold of re = 15 μm (Figures 5.2c, d). Rosenfeld and Lensky (1998) hypothesized that this contrast results from the difference in aerosol environment
between continental and maritime air. The aerosol influence on clouds, as inferred by Rosenfeld and Lensky (1998) and their subsequent papers, remains
controversial (see Ayers and Levin, this volume).
Cloud-top Droplet Radius versus Layer-averaged Droplet Radius
Droplet size increases with height toward the cloud top in non-precipitating
clouds; however, the vertical gradient in droplet size should be counteracted
by drizzle and rain drops falling toward the cloud base soon after the onset of
precipitation. This change in vertical gradient is potentially useful to identify
observationally the cloud microphysical stages. The vertical gradient of re is,however, not directly observable from the satellite-based schemes introduced
above, given that infrared and near-infrared radiation do not penetrate deeply
into a cloud layer. In contrast, a liquid cloud can be thoroughly scanned byExtreme Rainfall and Rain from Shallow Clouds
(a)

(b)

–30

–30
–20
T (°C)

T (°C)

4

–10
0

1

4

–10

2

0

1

10

10
20

5

5

–20

111

0

5

10 15 20 25
reff (μm)

20

30

(c)

0

5

10 15 20 25
reff (μm)

30

10 15 20 25
reff (μm)

30

(d)
–30

–30

5

–20
4

–10
0

T (°C)

T (°C)

–20

2

10
20

5

–10

4

0

3

10
1

0

5

10 15 20 25
reff (μm)

30

20

2

0

5

Figure 5.2 Local correlation between Tc and re for selected scenes. Different lines
are 10th, 25th, 50th, 75th, and 90th percentiles of the re for each 1°C interval. Five microphysical stages: (1) condensational growth zone, (2) collisional growth zone, (3)rainout zone, (4) mixed-phase zone, and (5) glaciated zone. Panels (a) and (b) are constructed from continental scenes, and (c) and (d) are from maritime scenes. Adaptedfrom Rosenfeld and Lensky (1998).

microwave radiation, to which cloud water is far less absorptive. Masunaga
et al. (2002) proposed a technique to analyze the vertical inhomogeneity indroplet size by the combined use of a visible/infrared imager and a microwave
radiometer. In their algorithm, a cloud layer-averaged re (or reave) is derived
from Equation 5.3 with microwave-retrieved LWP and visible-retrieved τc. At
the same time, cloud-top re (or retop) is estimated using the Nakajima and King
(1990) method. The partial beam-filling effect in microwave radiometry is corrected using the cloud fraction within a microwave field of view, which isdetermined from collocated visible radiance. Areas with cloud fractions lower
than one-third are excluded from the analysis so that the retrieval noise that
results from the uncertainties in surface microwave emission is kept at a minimum. Error analysis and further details in the methodology may be found in
Masunaga et al. (2002). Target clouds are limited to those with top temperatures warmer than 273 K.
The global distributions of reave and retop are shown in Figure 5.3. The twodifferent versions of effective droplet radius share a common overall pattern,
except that the variability of reave is significantly larger in amplitude than retop.
The effective droplet radii are greatest in the tropical convergence zones,
where reave exceeds significantly the drizzling threshold or 15 μm, indicativeof the frequent occurrence of warm rain. An intriguing feature is a northernmirror image of the SPCZ, which extends from the equatorial west Pacific112

Y. N. Takayabu and H. Masunaga
Re(MV)
35°N
25°N
15°N
5°N
5°S
15°S
25°S
35°S
0°

60°E

120°E

180°

120°W

60°W

0°

120°W

60°W

0°

Re(NV)
35°N
25°N
15°N
5°N
5°S
15°S
25°S
35°S
0°

60°E

0

120°E

4

8

12

180°

16

20

25

30

50

70

Effective droplet radius (μm) January–March 2000

Figure 5.3 The monthly mean global distribution of reave (top) and retop (bottom) for
January to March, 2000. Adapted from Masunaga et al. (2002).

to the northeast near Hawaii. A similar “butterfly” pattern is observed also
in the Atlantic Ocean. A pair of similar butterfly wings is observed in shallow rain climatology (discussed later), whereas the southern wing overwhelms
the northern counterpart in magnitude when precipitation from deep clouds
is included.
There are areas where effective droplet radius does not reach the drizzling
threshold. Particularly notable are subtropical oceans near the western coasts
of major continents and the East China Sea, where reave is even smaller than
retop. The droplet growth in low-level clouds may be suppressed for a numberof reasons including high lower-tropospheric stability (Klein and Hartmann1993) and high aerosol concentration (Twomey 1977; Albrecht 1989). Fromtheir analysis of reave and retop , Matsui et al. (2004) confirmed that the extent
to which the drizzling and warm rain processes proceed is systematically correlated with both lower-tropospheric stability and aerosol concentration. The
impacts of static stability and aerosols on low clouds are discussed individuallyin greater detail elsewhere in this volume (e.g., Bretherton and Hartmann;
Feingold and Siebert).
Spaceborne Cloud-profiling Radar
CloudSat, which was launched in 2006, contains W-band (94 GHz) cloudprofiling radar (CPR) and has literally brought a new dimension to our ability
to observe clouds from space. As a part of the A-Train constellation, CloudSat
is particularly useful when combined with passive sensors on other satellites

Extreme Rainfall and Rain from Shallow Clouds

113

flying in formation. Stephens and Haynes (2007) have attempted to estimate
the droplet coalescence rate based on radar reflectivity observed by CPR together with τc and re retrieved by MODIS. Suzuki and Stephens (2008) deviseda method to separate observationally the condensational growth stage and coalescence stage by a combined use of CPR reflectivity and reave evaluated from
the MODIS and AMSR-E. More studies proposing new ideas that exploit the
CloudSat and A-Train capabilities are expected in the future.
Global Climatology of Shallow Rain
Shallow cloud tops do not sharply contrast the background surface in satellite
infrared imagery. Microwave radiometry is not sensitive either to the depthof a cloud or precipitation layer for a given LWP. It is therefore difficult, inprinciple, to isolate shallow rain through conventional satellite remote sensing. A breakthrough was brought about by the Tropical Rainfall Measuring
Mission (TRMM) precipitation radar (PR), which is able to profile directly
the vertical structure of precipitation. Short and Nakamura (2000) analyzed
globally PR echo-top height (or storm height) and found that shallow cumulus constitute a distinct peak in the storm height histogram over tropical andsubtropical oceans. In this section, we present the global climatology of shallow rain constructed from the 9-year TRMM PR observations: 1998–2006. We
analyze the monthly PR near-surface rainfall projected on a half-degree global
grid stored in the TRMM 3A25 dataset. Shallow rain is defined as those cases
where echo-top heights are substantially lower than the freezing level. According to the TRMM product convention, shallow rain is divided into the two
subcategories (isolated and non-isolated), depending on whether or not there ismore developed precipitation nearby. As such, non-isolated shallow clouds arepresumably a part of organized precipitation systems, such as mesoscale convective systems and tropical cyclones. PR sensitivity (> 17–18 dBZ) to drizzle
and light rain (< 1 mm hr–1) is marginal, so that shallow precipitation estimatedfrom PR measurements is attributed mainly to warm rain from (relatively developed) shallow convective clouds. We note that there is so little precipitationfrom stratocumulus and thus it is not observable in the results below.
In Figure 5.4a, b, monthly rainfall is shown from isolated shallow clouds
for February and August, respectively. Isolated shallow clouds precipitate mostheavily over subtropical oceans as well as in the ITCZ. The overall global distribution is reminiscent of that for cloud droplet radius (Figure 5.3). The “butterfly” pattern mentioned earlier is again observed in the Pacific and Atlantic
oceans. Non-isolated shallow rain (Figures 5.4c, d) contrasts clearly in spatial
distribution with the isolated shallow rain. Whereas non-isolated shallow cumulus is, in general, tightly concentrated near the cores of the tropical convergence zones, isolated shallow clouds prefer the fringes of the convergencezones or are outside of the zones, as pointed out by Schumacher and Houze(2003). Non-isolated shallow cumulus can be greater in rain rate than isolated

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Y. N. Takayabu and H. Masunaga

(a)

(b)

Isolated shallow rain (mm) for Feb.

30°N
10°N
10°S
30°S

Isolated shallow rain (mm) for Aug.

30°N
10°N
10°S
30°S
0

60°E
2

(c)

120°E

180°

120°W

60°W

4
6
8 10 12 14 16 18
Non-isolated shallow rain (mm) for Feb.

0

0

20

60°E
2

(d)

120°E

180°

120°W

60°W

4
6
8 10 12 14 16 18
Non-isolated shallow rain (mm) for Aug.

0

20

30°N
10°N
10°S
30°S

30°N
10°N
10°S
30°S
0

(e)

60°E
2

120°E

180°

120°W

60°W

4
6
8 10 12 14 16 18
Shallow contribution to total rain for Feb.

30°N
10°N
10°S
30°S
0

0

0

20

60°E
2

(f)

120°E

180°

120°W

60°W

0

4
6
8 10 12 14 16 18 20
Shallow contribution to total rain for Aug.

30°N
10°N
10°S
30°S
60°E

120°E

180°

120°W

60°W

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0

0

60°E

120°E

180°

120°W

60°W

0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Figure 5.4 The climatology of monthly rainfall from shallow isolated clouds for February (a) and August (b), constructed from the TRMM PR 3A25 dataset. Shallow nonisolated rain is shown in (c) and (d), with the same parameters; (e) and (f) show the ratioof shallow rain (with both isolated and non-isolated) to the total precipitation.

shallow clouds, but the shallow cumulus contribution to total precipitation is
only secondary in the tropical convergence zones (Figure 5.4e, f). In contrast,
shallow rain explains more than 90% of the total precipitation in several limited regions, including the subtropical oceans, particularly in the winter hemisphere, and at the eastern edge of the SPCZ and SACZ. All of these regions
are located under a subsiding branch of the Hadley and Walker circulations,accompanied by a relatively warm sea surface. The strong inversion in suchareas hampers deep convection from developing and traps moisture supplied
from the underlying ocean, which allows shallow clouds to predominate.
Figure 5.4 shows that there is very little warm rainfall from shallow cumulus over tropical continents compared to tropical oceans, except for the Amazon basin in the wet season (Figure 5.4c). The factors that suppress continental
shallow precipitation include (but may not be limited to) the abundance ofaerosols acting as CCN and relatively limited moisture supply over land.
Summary
The observed characteristics of large-scale variability in precipitation fromlow-level clouds have been described. Such observations require a remotesensing technique to separate drizzling and raining clouds from non-precipitating clouds. Three existing methods were reviewed that utilize (a) cloudoptical thickness and effective droplet radius, (b) cloud-top temperature andeffective droplet radius, and (c) layer-top and layer-averaged effective droplet
radii. Results from these analysis methods are consistent with our knowledgeof the low-cloud properties that we have gained from in-situ observations inExtreme Rainfall and Rain from Shallow Clouds

115

past decades. Satellite observations have shown systematic variability in cloud
microphysical status over a wide range of spatial scales beyond the reach ofindividual field campaigns. Satellite data analysis reveals a large-scale gradientin low-level cloud properties from the deep tropics, where shallow rain occurs
frequently, toward the subtropical western coasts of major continents, typicalof maritime stratocumulus with little precipitation.
Layer-averaged effective droplet radius is as large as 50 μm or even larger,
indicating a frequent occurrence of warm rain in broad areas across the tropical and subtropical oceans. Such areas include not only the ITCZ, SPCZ, and
SACZ but also the northern hemisphere counterparts of SPCZ and SACZ. The
predominance of shallow rainfall over the northern hemispheric subtropicaloceans in February and the southern Indian Ocean in August is confirmed by
TRMM PR measurements. Rich moisture supply from warm sea surface andthe prevalence of strong trade inversion are presumably major factors thatmake these regions favorable to shallow rainfall. We do not, however, fully understand why such conditions appear in a quasi-symmetric “butterfly” pattern.
In contrast, the tropical convergence zones, which geographically constrain
deep convective rainfall, are highly asymmetric about the equator.
As mentioned earlier, precipitation from low-level clouds is not at all hazardous. Shallow rainfall may, however, contribute thermodynamically to extreme precipitation events. Below, we will discuss the possibility that lower
tropospheric moistening by shallow clouds could play a role in the development of deeper and more organized convective systems.

Extreme Rainfall
In view of the impact of climate change on human society, changes in extreme
rainfall are as important as changes in total rainfall, especially in terms ofdisaster prevention. It has often been pointed out that while global atmosphericmoisture increases with a warming climate (as diagnosed from the Clausius–Clapeyron equation) ~6.5% per Kelvin, projections from climate models indicate that the change in global mean precipitation would be 3.4% per Kelvin.
This discrepancy is understood in the context of energy balance, as global mean
precipitation is constrained by the atmospheric radiative cooling rate rather
than the atmospheric moisture content (e.g., Hartmann and Larson 2002). It isalso suggested that while the mean precipitation does not increase at the rateof the atmospheric moisture content, frequency of extreme precipitation would
increase more significantly.
Karl et al. (1995) analyzed long-term trends in the daily precipitation station data over the United States, the former Soviet Union (FSU), and China.
They found a significant increase in the annual proportion of extreme precipitation category (>50.8 mm d–1) over the U.S. for the period of 1911–1994, although the proportions of moderate (12.7–25.4 mm d–1) and light precipitation

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Y. N. Takayabu and H. Masunaga

categories (2.54–12.7 mm d–1) decreased. A summertime increase in extreme
precipitation exceeded 2% of the total summer precipitation; this corresponds
to an average of adding one extreme event every two years. They also notedthat this increase did not result from an increase in intensity but rather to an increase in the number of days. Over the FSU or China, little systematic change
was found in precipitation variability.
In this section, we provide an overview of the future projection of extreme
rainfall in climate models. To address current understanding of extreme rainfall, we discuss individual phenomena related to extreme rainfall and emphasize that it cannot be defined without considering temporal or spatial scales.
Extreme Rainfall and Future Change from a Climate Model Perspective
What is “extreme rainfall”? One can easily imagine how the same level ofprecipitation, 50 mm d–1, can be viewed differently in the wet monsoon region versus in the semiarid subtropics. Certainly, it cannot be defined with one
global threshold of rainfall intensity. Thus to address the issue of representing extreme rainfall in the climate models, local values of extreme precipitation (e.g., 20-yr return values of annual extremes of 24-hr precipitation) areoften utilized.
Using six climate model experiments, future changes in dynamic and thermodynamic components of mean and extreme precipitation were examined
separately by Emori and Brown (2005). For extreme precipitation, the multiyear mean of the fourth largest value was utilized at each grid point. Emori
and Brown showed that the global mean change of average precipitation forthe 2081–2100 A1B scenario, run from 1981–2000, was 6%, while that for
extreme precipitation was as large as 13%. Most of the extreme precipitation
change resided in the thermodynamic component, which showed an overall
increase even for the areas of modest change or decreasing mean precipitation.
They postulated that with a moister atmosphere, the same amounts of massconvergence or divergence results in larger moisture convergence and divergence, and that this brought about larger increases in extreme precipitation.
Kharin et al. (2007) examined extreme precipitation with a 20-yr return
period in an ensemble of global coupled climate models as part of the IPCC
diagnostic exercise for the Fourth Assessment Report. For future projections of
extreme precipitation, multi-model median 20-yr return values of daily precipitation increase with SRES A1B experiment was 6–7% per Kelvin, which wasclose to the projected Clausius–Clapeyron rate, while the corresponding mean
precipitation change was 3.4 % per Kelvin. This result was also consistent withthe study by Emori and Brown (2005). Kharin et al. noted that the simulatedpresent-day precipitation extremes were plausible in the extratropics, but inthe tropics, large intermodel discrepancies existed. Uncertainties in the tropics were very large, not only in the models but also in the available reanalysis

Extreme Rainfall and Rain from Shallow Clouds

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data for comparison, and this abated the confidence in the projected changes inextreme precipitation.
State-of-the-art climate model results suggest strongly that some physical processes associated with extreme precipitation are not well represented
in the models, especially over the tropics. Although most model studies on
extreme rainfall are based on daily precipitation, we must be mindful that
“extremes” vary largely, depending on temporal scales of phenomena as wellas the temporal resolution of data. Next we review the observations to examine what phenomena contribute to the extreme precipitation in different
temporal resolutions.
Extreme Rainfall from Observational Aspects
Figure 5.5 shows the depth–duration relationships of the world record rainfalls
and Japanese record rainfalls, based on precipitation data collected by Jennings(1950) and plotted by Ninomiya and Akiyama (1978, pers. comm.). Dependingon the temporal scale, changes in record points indicate that various types ofheavy precipitation systems have contributed to rainfall accumulation records
on different temporal scales. On a scale of annual to ten days, record precipitation is found in Cherrapunji (northeast India). On a scale of a week to one day,accumulation is greatest in the tropics. From one day to 30 minutes, record
levels are found in the subtropics, whereas for less than one hour, record precipitation occur in the midlatitudes and tropics.
50,00020,00010,000J

5,000J

Depth (mm)

2,000
J

1,000
J

500

J
J

200

Cherrapunji

J

100

TropicalJ

50

Sub-tropicalJ

Middle latit.
J

20

1

5 10

minutes30 1

2

6 12 1hours

2

5 10 20 1

days

Japan

2

6 12 24monthsDuration

Figure 5.5 Depth–duration relationships of the world record rainfalls and Japaneserecord rainfalls. Adapted from Ninomiya and Akiyama (1976).

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Y. N. Takayabu and H. Masunaga

Trenberth (1999) stressed the importance of understanding the change inprecipitation that is associated with different types of storms, and recommended that hourly precipitation datasets be collected. Below, we present examples
of how various types of storms contribute to extreme precipitation in different
regions and on different temporal scales.
Tropical–Extratropical Interactions
Several studies suggest that extreme precipitation occurs over the arid or semiarid regions, along the west coasts of continents, in association with tropical–
extratropical interactions. Higgins et al. (2000) utilized 3-day accumulationsof precipitation data along the West Coast of the U.S. and found that extreme
precipitation events occur during neutral winters just prior to the onset of El
Niño. This is a period when intraseasonal activity is large in the tropical Pacific. Jones (2000) examined the daily precipitation in California and showedthat frequency of extreme events are more common when tropical convective
activity associated with the Madden-Julian Oscillation is high.
Extreme daily precipitation during the winter in the semiarid southwestern U.S., as analyzed by Higgins et al. (2000) and Jones (2000), is related tothe enhancements of tropical plumes (i.e., bands of clouds that extend fromthe deep tropics into subtropical and midlatitudes and last only a few days)(McGuirk et al. 1988; Knippertz and Martin 2007). Figure 5.6 provides anexample of the eastern Pacific tropical plume captured by the infrared image
from GOES West. Knippertz and Martin (2007) showed that the tropical plumeor the “moist conveyer belt” enhances the water vapor fluxes above the planetary boundary layer at around 700 hPa, and causes potential instability inconcert with the overriding dry air.

Figure 5.6 An example of the tropical plume over the eastern Pacific, captured in
the infrared image of GOES West at 00z January 14, 2007. Obtained from http://www.
satmos.meteo.fr/cgi-bin/qkl_sat/quicklook.pl

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Tropical plumes are also observed in association with extreme precipitationin arid northwest Africa. Knippertz and Martin (2005) examined three cases ofextreme rain over western Africa. For two cases, precipitation exceeded half ofthe local climatological annual precipitation within just a few days. The othercase exceeded the corresponding monthly climatological value. Associatedwith the deepening of the extratropical trough into the tropics, a tropical plume
was observed, which transported moisture in the middle (~600 hPa) level, to
produce a potentially unstable stratification in which convection could begin.
Extreme Rain in Japan Associated with the Baiu Front
Japan is located in a region that is subjected to exceptionally heavy rainfall
for its latitude. Figure 5.5 shows the rainfall accumulation records for Japanon different timescales. As can be seen, the observed levels of rainfall fromseveral hours to one-day accumulation in Japan are comparable to corresponding world records. This is because extreme rain, which is associated with mesoscale precipitation systems, occurs frequently over Japan, because it is located in a moist subtropical region affected both by subtropical active frontal
and depression systems and tropical moist air masses. We note that a similar
environmental condition is present in the southeast U.S.
Ninomiya and Akiyama (1978) examined the distributions of rainfall extremes in Japan on different temporal scales and found that the meridional
gradient in extreme 10-min accumulated rainfall distribution was small. It was
similar to the gradient of the total precipitable water content. They showed thatthe 10-min rainfall record value corresponded to the maximum precipitablewater (~50 mm/10 min) in the atmosphere, or one overturning of the saturated
atmosphere. From this point of view, hourly accumulated rainfall represents
how many times the moist atmosphere is overturned, or how effective the mesosystems can gather the moisture into the convection. Assuming the typicalspatial scale of the extreme rainfall with 50 mm/10 min to be 104 km2, an areaof 6 × 104 km2 needs to be contracted into 104 km2 in one hour to realize theextreme hourly precipitation of 300 mm hr –1. This corresponds to the convergence of about 2 × 10–4 sec–1, which is the typical value of the mesoscale
systems. Thus, it is estimated that a continuous overturning for one hour (~300
mm hr–1) is possible with the existence of mesoscale systems. Moreover, distribution of hourly extremes showed a larger meridional gradient compared to the
10-min extremes. This is most likely attributable to the oceanic southerly flow,which prepares a more effective moisture convergence in mesoscale systems,
but is not yet limited to particular geographical locations.
Daily extremes, however, were found to be relatively locked into geographic locations. In contrast to hourly rainfall records, the daily extreme
value is far from a continuous overturning of the moist atmosphere: There isno ~7200 mm day–1 precipitation, and since large-scale disturbances cannot

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Y. N. Takayabu and H. Masunaga

gather moisture as effectively, daily extremes consist of intermittent recovery
of mesoscale systems.
Intense Thunderstorms Observed from TRMM
Short-term extreme precipitation is considered to be associated with intense
thunderstorms. Employing various kinds of indices obtained from the precipitation features (PFs) database, Zipser et al. (2006) used satellite observations
from TRMM to describe the global distribution of extremely intense thunderstorms. Figure 5.7 shows the distributions of extreme (uppermost 0.01% frequency) PFs with different indices. It is indicated that the most extreme PFs
are observed over Equatorial Africa, southeastern U.S., and southeast SouthAmerica. During the months of March–May and June–August, extreme PFs
are found in the southeastern area of Africa, northern and eastern areas of India

Minimum 37 GHz PCT < 178.0 (K)Minimum 85 GHz PCT < 75.1 (K)Maximum height of 40 dBZ > 14.2 (km)
DJF
MAM
JJA
SON

Flash rate > 126.7 (#/min)Figure 5.7 Seasonal cycle of the extreme categories with a threshold of 0.01 % occurrences for different parameters obtained from the precipitation feature database. The
top two panels are based on minimum precipitation from TRMM microwave observations with the frequency of 37 GHz and 87 GHz, respectively. The third panel is basedon the TRMM precipitation radar reflectivity with a threshold of maximum height of 40
dBZ > 14.2 km. The bottom panel is based on the TRMM LIS observations with flash
rate > 126.7 min–1. Adapted from Zipser et al. (2006).

Extreme Rainfall and Rain from Shallow Clouds

121

and Indochina, and eastern China, whereas from December–February PFs arefound in the northern part of Australia. Extremely intense thunderstorms are
distributed very differently from total rainfall amount for which regions (e.g.,
Amazon and the Maritime Continent) would be dominant. These distributionsof extreme events correspond instead to the distributions of intense mesoscaleconvective complexes.
Takayabu (2006) calculated rainyield per flash (RPF) values with the
TRMM precipitation radar and lightning imaging sensor (LIS) data. RPF is thetotal rainfall normalized with lightning flash numbers; thus, it represents therainfall characteristics irrespective of the rainfall amount. Figure 5.8 presents
the 8-year mean distribution of RPF and the average rainfall rate. Regions ofmost intense thunderstorms are indicated with dark shades of small RPF values. Above all, a strong contrast of RPF between land and ocean is seen, consistent with the observations that most thunderstorms are observed over land.
This feature is in contrast with the average rainfall rate, which exhibits rather
continuous distribution of tropical rain bands over ocean and over land. The
distribution of extremely small RPF values corresponds well to that of extreme
PF events in Figure 5.7 over Equatorial Africa, southeastern U.S., southeasternSouth America, along the periphery of the Tibetan Plateau, and the northernpart of Australia. It is also readily observed that regions of largest total annual
TRMM RPF Rain / FlRate (*e7 kg fl–1) Dmean 1998–2005M
ANN98–05
30°N
20°N
10°N
0°
10°S
20°S
30°S
30°W

0

30°E 60°E 90°E 120°E 150°E 180 150°W 120°W 90°W 60°W 30°W
0

3

5

7

10 15 20 30 50 70 100 150 300 500 1000

30°N
20°N
10°N
0°
10°S
20°S
30°S
30°W

0

30°E 60°E 90°E 120°E 150°E 180 150°W 120°W 90°W 60°W 30°W
0

0.01

0.03

0.05

0.1

0.2

0.3

0.4

0.5

Figure 5.8 Global distributions of 8-yr mean rain yield per flash (a) and unconditionalmean rain rate averaged throughout the period (b). Units for the color scales are 107 kg
fl–1 for (a) and mm hr–1 for (b). Rain yield per flash averages are obtained by dividing
the total precipitation amount by the total flash number for the averaging period.

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Y. N. Takayabu and H. Masunaga

precipitation, such as Amazonia and the Maritime Continent, are secondary interms of intensity of the thunderstorms with small RPF values. This is consistent with Zipser et al.’s (2006) conclusions. The overall correspondence of raincharacteristics, represented by RPF and PFs, indicate that satellite observations
effectively detect extreme rain associated with intense mesoscale convective
systems, the distribution of which differs from accumulation extremes which
occur over a few days.
Discussion
Thus far, extreme rainfall addressed in climate models has been based primarily on daily precipitation. However, recent satellite observations are able
to detect effectively the extreme rainfall associated directly with the mesoscale convective systems. As suggested by prior studies (e.g., Ninomiya
and Akiyama 1978; Trenberth 1999; Knippertz and Martin 2005), extreme
hourly precipitation corresponds to intense mesoscale systems, whereas extreme daily precipitation is controlled by larger synoptic-scale systems. We
must recognize that factors controlling the intensity of synoptic-scale systems
and mesoscale systems are entirely different. There has been a discussion withclimate model simulations that increasing rate of the extreme rainfall is proportional to the increase of atmospheric moisture. However, while the mesoscale systems’ convergence can attain the amount of continuous atmospheric
overturning, synoptic-scale convergence cannot maintain continuous overturning. Therefore, when daily extreme precipitation is considered, we must treat
both the atmospheric moisture content as well as adequate representations ofsynoptic-scale systems as essential. Although the assumption may be correctthat atmospheric moisture content affects extreme rainfall more directly than
the total rainfall amount, quantitative conclusions with daily rainfall extremes
require reexamination.
Finally, we wish to discuss the relationship between shallow convection anddeep and intense convection. Figure 5.9 shows the distribution of convective
heating in terms of Q1–QR, defined by Equation 5.5, at 7.5 km and 2.0 km,
respectively, for the December–February season averaged for nine years of
TRMM observation. This was calculated with the spectral latent heating (SLH)
algorithm introduced by Shige et al. (2004). Q1 is called as the apparent heating (Yanai et al. 1973),
Q1 {

Ds
Dt

ws
ws
 v x s  Ȧ
wt
wp

QR  L(c  e )   x scvc 

w scȦcwp(5.5)

where s = CpT + gz, overbars represent large-scale values and primes represent
eddy values. L is the latent heat of vaporization, QR is the atmospheric radiative heating, c is the condensation rate, and e is the evaporation rate, where
Cp is the heat capacity for a constant pressure, T is the temperature, and gz is
the geopotential. Note that these two altitudes are typical for cold and warm

Extreme Rainfall and Rain from Shallow Clouds

123

UncondMean Q1R 97–06 DJF Lev: 7.5 km, 2.0 km & OISSTv2
30°N
20°N
10°N
0°
10°S
20°S
30°S
60°W 30°W

030°E 60°E 90°E 120°E 150°E 180 150°W 120°W 90°W 60°W 30°W

30°N
20°N
10°N
0°
10°S
20°S
30°S
60°W 30°W

030°E 60°E 90°E 120°E 150°E 180 150°W 120°W 90°W 60°W 30°W
0

0.5

1.1

1.7

2.3

2.9

3.5

4.1

4.7

Figure 5.9 Global distributions unconditional mean Q1–QR (apparent heat source
minus the radiative heating) averaged for December–February seasons of 1997–2005.
Upper panel (a) shows those at the level of 7.5 km and the lower panel (b) is for the 2.0
km. Unit in the gray scale is K d–1.

rainfall, corresponding to two peaks appearing in the average heating profile.
From the SLH tables, it is also known that the 2 km heating peak corresponds
to the 4–5 km rain top height (not shown).
The distribution of the taller (7.5 km) heating looks similar to the familiar
total rainfall distribution. The shallower (2.0 km) heating distribution corresponds to that shown in Figure 5.4 for shallow clouds, and indicates some differences from the distribution of taller heating. There is a more distinct land–sea contrast, and shallow heating is observed almost exclusively over ocean
and not much over land, except for over land in the Maritime Continent region.
Many studies suggest the importance of preconditioning with the shallow convection to moisten the lower troposphere, prior to the mature convection ofoceanic large-scale organized convective systems. Over the Indian Ocean, thewestern Pacific warm pool, ITCZ, and SPCZ, it is apparent that tall and shallow
convective heating coexist. In these regions, shallow convection is considered
to prepare the moist lower troposphere for tall convection to start.
There are, however, particular regions where shallow heating is ubiquitous
but not the taller heating. These regions are found where the sea surface temperature is relatively high and at the periphery of the subtropical high pressure area latitudes. One region is located in the northern wing of the “butterfly shape” of shallow clouds, extending from the western Pacific warm pool
toward the Hawaii Islands (discussed above). A corresponding northern wing

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Y. N. Takayabu and H. Masunaga

without taller heating is also found from the western to central Atlantic. Since
the 2.0 km heating peak corresponds to warm rain with 4–5 km rain top heightin SLH tables, the lower troposphere is moistened with this shallow convection
over these regions, whereas tall convection is suppressed by the subsidence
under the influence of subtropical highs. It is interesting to notice that the two
tropical plume regions mentioned earlier, over the eastern Pacific and over the
Atlantic, extend from the eastern ends of these northern wings to the arid andsemiarid continents. It is reported that the axis of moisture transport by tropicalplumes is 600–700 hPa and that it originates from moderate convection, which
seems to correspond to the warm rain observed here. Tropical plumes bridge
lower to middle tropospheric moisture from the warm rain region over the arid
and semiarid continents.
In conclusion, these results suggest that a realistic reproduction of synoptic
systems as well as a proper representation of shallow convection and its interaction with synoptic-scale systems are indispensable if we wish to reproduce
extreme daily precipitation adequately.

References
Albrecht, B. A. 1989. Aerosols, cloud microphysics, and fractional cloudiness. Science
245:1227–1230.
Emori, S., and S. J. Brown. 2005. Dynamic and thermodynamic changes in mean andextreme precipitation under changed climate. Geophys. Res. Lett. 32:L17706.
Gerber, H. 1996. Microphysics of marine stratocumulus clouds with two drizzle modes.
J. Atmos. Sci. 53:1649–1662.
Hartmann, D. L., and K. Larson. 2002. An important constraint on tropical cloud–climate feedback. Geophys. Res. Lett. 29:1951.
Higgins, R. W., J.-K. Schemm, W. Shi, and A. Leetmaa. 2000. Extreme precipitation
events in the western United States related to tropical forcing. J. Climate
13:793–820.
Jones, C. 2000. Occurrence of extreme precipitation events in California andrelationships with the Madden-Julian Oscillation. J. Climate 13:3576–3587.
Karl, T. R., R. W. Knight, and N. Plummer. 1995. Trends in high-frequency climate
variability in the twentieth century. Nature 377:217–220.
Kharin, V. V., F. W. Zwiers, X. Zhang, and G. C. Hegerl. 2007. Changes in temperatureand precipitation extremes in the IPCC ensemble of global coupled model
simulations. J. Climate 20:1419–1444.
Klein, S. A., and D. L. Hartmann. 1993. The seasonal cycle of low stratiform clouds.
J. Climate 6:1587–1606.
Knippertz, P., and J. E. Martin. 2005. Tropical plumes and extreme precipitation in
subtropical and tropical West Africa. Q. J. Roy. Meteor. Soc. 131:2337–2365.
Knippertz, P., and J. E. Martin. 2007. A Pacific moisture conveyor belt and its relationshipto a significant precipitation event in the semiarid southwestern United States.
Wea. Forecast 22:125–144.
Mason, B. J. 1952. Production of rain and drizzle by coalescence in stratiform clouds.
Q. J. Roy. Meteor. Soc. 78:377–386.

Extreme Rainfall and Rain from Shallow Clouds

125

Masunaga, H., T. Y. Nakajima, T. Nakajima et al. 2002. Physical properties of maritime
low clouds as retrieved by combined use of tropical rainfall measuring mission
microwave imager and visible/infrared scanner: Algorithm. J. Geophys. Res.
107(D10):4083.
Matsui, T., H. Masunaga, and R. A. Pielke, Sr. 2004. Impact of aerosols and atmospheric
thermodynamics on cloud properties within the climate system. Geophys. Res. Lett.
31: L06109.
McGuirk, J. P., A. H. Thompson, and J. R. Schaefer. 1988. An eastern Pacific tropicalplume. Mon. Wea. Rev. 116:2505–2521.
Nakajima, T., and M. D. King. 1990. Determination of the optical thickness and effective
particle radius of clouds from reflected solar radiation measurements. Part I: Theory.
J. Atmos. Sci. 47:1878–1893.
Nakajima, T. Y., and T. Nakajima. 1995. Wide-area determination of cloud microphysicalproperties from NOAA AVHRR measurements for FIRE and ASTEC regions.
J. Atmos. Sci. 52:4043–4059.
Nicholls, S. 1984. The dynamics of stratocumulus: aircraft observations and comparisonswith a mixed-layer model. Q. J. Roy. Metetor. Soc. 110:783–820.
Ninomiya, K., and T. Akiyama. 1978. Characteristics of torrential rain in Japan,
observed from its intensity and the moisture budgets. Research report No.A-53-4
for the Special Research on Natural Disasters. Tokyo: Mext.
Pinsky, M. B., and A. P. Khain. 2002. Effects of in-cloud nucleation and turbulenceon droplet spectrum formation in cumulus clouds. Q. J. Roy. Meteor. Soc.
128:501–534.
Rosenfeld, D., and I. M. Lensky. 1998. Satellite-based insights into precipitation
formation processes in continental and maritime convective clouds. Bull. Amer.
Meteor. Soc. 79:2457–2476.
Schumacher, C., and R. A. Houze, Jr. 2003. The TRMM precipitation radar’s view ofshallow, isolated rain. J. Appl. Meteor. 42:1519–1524.
Shige, S., Y. N. Takayabu, W.-K. Tao, and D. E. Johnson. 2004. Spectral retrieval oflatent heating profiles from TRMM PR data. Part 1: Development of a model-basedalgorithm. J. Appl. Meteor. 43:1095–1113.
Short, D. A., and K. Nakamura. 2000. TRMM radar observations of shallow precipitation
over the tropical oceans. J. Climate 13:4107–4124.
Stephens, G. L., and J. M. Haynes. 2007. Near global observations of the warm rain
coalescence process. Geophys. Res. Lett. 34:L20805.
Suzuki, K., T. Nakajima, T. Y. Nakajima, and A. Khain. 2006. Correlation pattern
between effective radius and optical thickness of water clouds simulated by aspectral bin microphysics cloud model. SOLA 2:116–119.
Suzuki, K., and G. L. Stephens. 2008. Global identification of warm cloud
microphysical processes with combined use of A-train observations. Geophys. Res.
Lett. 35:L08805
Takayabu, Y. N. 2006. Rain-yield per flash calculated from TRMM PR and LIS dataand its relationship to the contribution of tall convective rain. Geophys. Res. Lett.
33:L18705.
Trenberth, K. E. 1999. Conceptual framework for changes of extremes of thehydrological cycle with climate change. Climate Change 42:327–339.
Twomey, S. 1977. The influence of pollution on the shortwave albedo of clouds.
J. Atmos. Sci. 34:1149–1152.

Yanai, M., S. Esbensen, and J.-H. Chu. 1973. Determination of bulk properties oftropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci.
30:611–627.
Zipser, E. J., D. J. Cecil, C. Liu, S. W. Nesbitt, and D. P. Yorty. 2006. Where are the mostintense thunderstorms on earth? Bull. Amer. Meteor. Soc. 87:1057–1071.