JULY 2017

SEKARANOM AND MASUNAGA

1867

Comparison of TRMM-Derived Rainfall Products for General and Extreme
Rains over the Maritime Continent
ANDUNG BAYU SEKARANOMa
Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan

HIROHIKO MASUNAGA
Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan(Manuscript received 9 August 2016, in final form 17 April 2017)
ABSTRACT
Properties of the rain estimation differences between Tropical Rainfall Measuring Mission (TRMM)
Precipitation Radar (PR) 2A25, TRMM Microwave Imager (TMI) 2A12, and TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 are investigated with a focus on distinguishing between nonextreme andextreme rains over the Maritime Continent from 1998 to 2014. Statistical analyses of collocated TMI 1B11
85-GHz polarization-corrected brightness temperatures, PR 2A23 storm-top heights, and PR 2A25 verticalrain profiles are conducted to identify possible sources of the differences. The results indicate that a large
estimation difference exists between PR and TMI for the general rain rate (extreme and nonextreme events).
The PR–TMI rain-rate differences are larger over land and coast than over ocean. When extreme rain isisolated, a higher frequency of occurrence is identified by PR over ocean, followed by TMI and TMPA. Over
land, TMI yields higher rain frequencies than PR with an intermediate range of rain rates (between 15 and25 mm h21), but it gives way to PR for the highest extremes. The turnover at the highest rain rates arises
because the heaviest rain depicted by PR does not necessarily accompany the strongest ice-scattering signals,
which TMI relies on for estimating precipitation over land and coast.

1. Introduction
The Maritime Continent (MC), as introduced byRamage (1968), defines an archipelagic area over thetropics and is primarily characterized as one of thehighest precipitation areas on Earth. The MC covers awide area that surrounds Southeast Asian countries
between the Indian Ocean and the Pacific Ocean, that is,peninsular Malaysia, Singapore, Indonesia, Brunei,
Timor, and New Guinea (Qian 2008). Considerable
amounts of precipitation occur over the MC because of a
combination of complicated environmental factors. One

Denotes content that is immediately available upon publication
as open access.
aCurrent affiliation: Faculty of Geography, Universitas GadjahMada, Yogyakarta, Indonesia.

Corresponding author: Andung Bayu Sekaranom, andungbayu@
geo.ugm.ac.idof the most influential components is the existence ofwarm surface water in the western Pacific Ocean and theeastern Indian Ocean, which are labeled as ‘‘warm
pools’’ (Ramage 1968). High evaporation rates overthese warm pools provide a large moisture supply
around the MC, which subsequently becomes a sourceof precipitation over the area. However, despite thelarge moisture supply, precipitation over the MC would
be less without an enhancement effect from the Southeast Asian monsoon, which is known as one of the most
predominant monsoons in the world (Kripalani andKulkarni 1997). This monsoon transports moisture fromthe surrounding ocean directly toward the center of theMC. From December to February, the westerly monsoonal wind dominates and carries abundant moisturefrom the Indian Ocean, whereas in June through August, the moisture is primarily transported from the
Pacific Ocean (Aldrian and Dwi Susanto 2003; Changet al. 2005). Large amounts of precipitation frequently
occur when large volumes of moisture carried by themonsoon reach close to the islands, transported farther
to the land particularly during late afternoon and

DOI: 10.1175/JAMC-D-16-0272.1
Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).

1868

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY

condensed because of a lifting process as a result of
the island’s mountainous landform (Mori et al. 2004;
Ichikawa and Yasunari 2006). Although there are significant variations caused by the local topography, theresulting processes generally yield high precipitation
concentrations in the central part of the land (Qian2008).
The environmental characteristics specific to the MC
are responsible not only for the high annual precipitation, which is approximately 1500–3000 mm yr21
over land (As-Syakur et al. 2013), but also for the highfrequency of extreme precipitation that can trigger
hazards over the MC. An analysis of 11-yr records ofglobal flood frequency in 1998–2008 conducted by
Adhikari et al. (2010) shows that Indonesia (whichconstitutes the largest part of the MC) is listed as one of
the top 10 countries for the number of flood events.
Several studies have aimed to characterize the environmental factors accompanying the extreme events,
and these studies indicated that there are various intraannual and interannual factors involved. For example,
an analysis of extreme events over peninsular Malaysia
and northern Borneo conducted by Salahuddin andCurtis (2011) from 1998 to 2007 revealed that the combination of the South China Sea circulation and theMadden–Julian oscillation (MJO) is a factor that generates the extreme events. The MJO influence was also
identified by Tangang et al. (2008) in peninsular Malaysia during flood events in December 2006 to January
2007, where an active phase of the Indian Ocean dipole
was also viewed as another factor responsible for theextremes. However, because of a lack of observations,the general mechanism for the extreme precipitation
dynamics over the MC is still not fully understood.
Substantial amounts of precipitation data over the
global tropics have been collected through the TropicalRainfall Measuring Mission (TRMM). The long-term
data series (from the end of 1997 to 2014) from the
TRMM have enabled the detection of numerous rare
events related to precipitation, particularly extreme
rainfall. Information regarding various key aspects related to extreme events might be retrieved from rainfall
estimates derived from TRMM, particularly from
TRMM Precipitation Radar (PR) as an active sensor
and TRMM Microwave Imager (TMI) as a passive microwave sensor. PR is the first spaceborne precipitation
radar with a 13.8-GHz frequency band with verticalprofiling ability in measuring precipitation (Iguchi et al.
2000). Conversely, TMI estimates precipitation basedon microwave emissions and ice-scattering signals fromprecipitating clouds and provides vertical hydrometeor
profiles (Kummerow and Giglio 1994; Kummerow
et al. 1996).

VOLUME 56

Because of the different nature of the active andpassive sensors in the TRMM, different rain-rate estimations exist among the TRMM-derived rainfall products. Various studies have been conducted to identify
the differences in TRMM-derived rainfall products on a
global scale by directly comparing the precipitation estimates (Berg et al. 2006; Kumar et al. 2009; Gopalan
et al. 2010) or by identifying the factors underlying thedifferences, for example, based on precipitation water
path and precipitation water column (Masunaga et al.
2002). Several studies have highlighted the fundamental
causes for the differences. One of the currently known
sources of biases comes from the TMI ice-scattering
signals. For example, an investigation by Rajendran andNakazawa (2005) revealed that higher ice-scattering
signals are acquired by TMI at mature-to-decaying
stages of convection, where the storm-top height ismaximum with abundant ice particles. However, thesetypes of events may have low near-surface rain becauseof a time lag between the processes occurring at thecloud top and the surface (Furuzawa and Nakamura
2005). A comparison study with ground radar data
conducted by Zagrodnik and Jiang (2013) showed that
TMI has a significant overestimation with respect to ice
particles. Very high rain rates are often estimated by
TMI at low brightness temperatures, particularly below
220 K, while PR shows a less significant relationshipbetween rain-rate and ice-scattering signals.
In addition to the global comparison, a large numberof regional comparison and validation studies have been
conducted to analyze the differences, for example, by
examining with other satellite products, ground radar,and rain gauge data (Fu and Liu 2003; Prakash et al.
2012). The above studies indicate significant improvements for the most recent TRMM algorithms, and theglobal rain estimations by PR and TMI show a better
agreement, although the regional-scale differences remain large. It has also been suggested that the different
estimations emerge because of regional variations ofclimate characteristics affecting precipitation-related
processes (Berg et al. 2006).
The MC’s regional characteristics are often described
as an area with intense deep convections (Qian 2008).
The deep convections are known to have tall cloud
structures, with low temperatures at the top and large
precipitation amounts at the bottom. Theoretically, thestronger and taller the deep convective structures, themore rain it will produce. However, recently, Hamada
et al. (2015) showed that the heaviest rain near thesurface is often produced by a system with a relatively
low storm height. Using the TRMM PR reflectivity data,
they showed that the heaviest rain events occur at alower PR echo height below the uppermost limit of the

JULY 2017

SEKARANOM AND MASUNAGA

1869

FIG. 1. Map of the study area with classified surface types based on the TMI 2A12 surface flag. Dark shaded colors
represent higher mean rain rates from the PR 2A25 near-surface rain averaged from 1998 to 2014.

highest echo height. A more favorable environmental
condition at the lower troposphere, where humidity ishigher, is suggested as the main reason for the growth ofrain drops through collision and coalescence processes.
An important implication of their study is that the relation between the cloud microphysics of ice particles
aloft and surface rainfall depends systematically on theenvironmental conditions. This may introduce a potential bias in rainfall estimates between the PR and TMI
products, particularly over land.
Although a number of studies have used TRMM data
to measure precipitation over the study area, particularly
related to the variation of diurnal cycles (Mori et al. 2004;
Ichikawa and Yasunari 2006), very few studies have addressed issues related to data comparison and validation
with a focus on the MC (As-Syakur et al. 2013), particularly in light of extreme rain events. To this end, thispaper presents a comparative analysis of the TRMMderived precipitation products over the MC, with a focus
on extreme rainfall. An outline of the data and methods
used in this research are presented in section 2. The statistical comparison results are presented in section 3.
These results are discussed further in section 4. A comparison with a focus on the extreme cases is provided in
section 5, and the discussion and conclusions related tothe main properties of rain-rate estimation differences
between each product are presented in section 6.

2. Data and methods
a. Dataset
There are several precipitation estimates from theTRMM, which are primarily derived from PR and TMI.
An estimate from combined passive microwave and IR
data obtained from the TRMM and other satellites,
defined as TRMM Multisatellite Precipitation Analysis(TMPA), is also available (Huffman et al. 2007). This

product is based on observations with a finer temporal
resolution and global spatial coverage, and thus it enhances the individual measurement capability limited bysporadic overpasses and a finite swath width of hundredsof kilometers. This work focuses on the rain-rate estimation differences among three TRMM-derived precipitation data, namely, PR 2A25 (NASA 2011a), TMI
2A12 (NASA 2011b), and TMPA 3B42 (NASA 2011c).
The three precipitation datasets from the TRMM temporal data records of 1998–2014 are examined. Slightchanges in the original spatial resolution and swath
widths due to an orbit boost in 2001 are considered to be
negligible for the present purposes. Only the most recent version of the TRMM products (version 7) is usedin this study. The study area is bounded between 158N
and 158S and between 908 and 1508E, as shown in Fig. 1.
PR 2A25, a TRMM level 2 product, produces instantaneous three-dimensional rainfall structures at
4.3-km (5 km) horizontal resolution and 250-m verticallevel within the total swath width of 215 km (247 km)
during preboost (postboost) (Iguchi et al. 2000). The
vertical profiling ability from PR is augmented with a
combination of the Hitschfeld–Bordan method and thesurface reference technique (SRT) for estimating the drop
size distribution based on radar reflectivity and pathintegrated attenuation (PIA) (Iguchi and Meneghini 1994;
Seto and Iguchi 2007). The Hitschfeld–Bordan method
produces the best estimate at light rain rates, but it fails forhigh rain rates. Furthermore, this method often generates
higher PIA as an effect of the cumulative error from its
vertical profiling. In contrast, SRT can produce better
results for heavy rain, but it has no vertical information.
The combination is achieved by adjusting the PIA fromthe Hitschfeld–Bordan method with the SRT to obtain
optimal estimates.
TMI 2A12, which is also a level 2 product, provides
estimations of surface rainfall based on passive

1870

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY

microwave radiometry. The 2A12 product measures instantaneous rain rates based on a given set of brightness
temperature inputs from the emission bands (10, 19, 21, and
37 GHz) and 85-GHz ice-scattering bands (Kummerow
et al. 1998). The TMI finest horizontal resolution is approximately 5 km 3 12 km, which is larger than PR. The
TMI swath is nearly 3 times as wide as the PR swath (approximately 878 km after orbit boost in August 2001).
There are different streams in the TMI algorithm for estimating surface rainfall depending on the surface types ofocean, land, and coast. Over ocean, the algorithm utilizes
all the nine channels, including the emission and icescattering channels to retrieve rain rates. In contrast to
the ocean algorithm, the land and coast algorithms are only
able to estimate rain rates based on the 85-GHz icescattering channel to avoid the large uncertainty in the
microwave surface emissivity (Kummerow et al. 2001).
Therefore, it is challenging to accurately estimate rain rates
over land and coast, which is a major weakness of TMI.
TMPA 3B42 is one of the level 3 TRMM products,
which produces adjusted merged passive microwave–
infrared (IR) precipitation at the global scale with
0.258 3 0.258 spatial resolution and 3-hourly temporal
resolution (Huffman et al. 2007). The global coverage(up to 508S–508N) and a fine temporal resolution areachieved by combining input from several data providers, namely, 1) level 1 TRMM TMI brightness temperature (2A12), 2) level 2 PR–TMI combined rainfall
profile (2B31), 3) NSIDC level 2 AMSR-E precipitation
estimate, 4) NOAA/NCDC M-CLASS SSMIS brightness temperature, 5) NOAA/NCDC CLASS SSMIS
antenna temperature, 6) NESDIS Microwave Surface
and Precipitation Products System (MSPPS) level 2
AMSU-B precipitation estimate, 7) NESDIS MSPPS
operational level 2 Microwave Humidity Sounder(MHS) precipitation estimate, 8) NOAA/NCDC level 3
Gridded Satellite (GridSat-B1) IR brightness temperature, and 9) NOAA/NWS/CPC merged 4-km geostationary satellite IR brightness temperature (Huffman
and Bolvin 2013). The 3B42 final product is later obtained by further calibrating the data with GPCC level 3
precipitation gauge analysis on a monthly basis to generate more accurate estimates than the near-real-time
product, and it is released approximately two months
later.
The minimum rain rate differs among the products.
The minimum PR echo detectability is approximately
17 dBZ (Iguchi et al. 2000), close to 0.5 mm h21. The
latest version of the TMI algorithm (version 7) removes
the screening process over ocean and provides rainprobability parameters, with a minimum detectability of
0.4 mm h21 (Zagrodnik and Jiang 2013). Although nospecific detectability threshold is given for TMPA, it is

VOLUME 56

considered to be a less reliable method for determining
light rain fields. Low IR cloud temperatures, but nosurface rain, can be mistaken as rain fields by TMPA,
particularly because of the existence of ice particles
from cirrus clouds or decaying precipitation systems(Rosenfeld 2007).

b. Data preprocessing
Because of differences in area coverage and spatial andtemporal resolutions among the three products, a directcomparison among these data is impossible in the originalformat of each dataset; therefore, a data preprocessing
routine is required. In this research, we consider the data
only in which a PR swath exists, neglecting the TMI andTMPA data falling outside the PR field of view because
the PR swath is narrowest. The omission of any measurements outside the PR swath leads to a bias in evaluating the range of sensors contributing to the TMPA
because its design does not usually allow non-TRMM
satellites in the zone that the PR covers. As such, thepresent strategy is somewhat of limited utility for evaluating the TMPA rainfall. A more thorough assessment of
TMPA would require the whole TMPA fields to be analyzed at the expense of the loss of sampling consistency
with PR and TMI overpasses. Such an extensive analysis
is outside the scope of the present study.
Since the spatial resolution is approximately 5 km 3
5 km for PR, 5 km 3 12 km for TMI, and 0.258 3 0.258 for
TMPA (approximately 28 km 3 28 km in the studyarea), a spatial averaging technique is implemented. We
average a set of pixels from PR and TMI data locatedwithin each TMPA grid to match the TMPA since it has
the coarsest resolution. The different temporal resolutions between instantaneous rain rates from PR and
TMI and 3-h rain rates from TMPA could also produce a
temporal mismatch in the analysis. To mitigate themismatch, the time records of PR and TMI overpasses
closest to the 3-hourly time stamp of TMPA are selected
and stacked together into a single time bin. Thus, thecomparison between the three data is calculated foreach 3-h time bin, which contains a single overpass foreach of the three products. The maximum temporal
difference allowed between TMPA and the two products in each calculated bin falls within a range from 21.5
to 1.5 h. This time range is considered to reasonably
capture extreme rain events in the study area where
extreme events often occur in a duration of approximately about 5 h for rain rates higher than 15 mm h21(approximately equal to the uppermost 1% PR threshold) (Liu et al. 2015).
In addition to the spatial and temporal differences,
there is also a difference in how each pixel is categorized
as rain or nonrain from PR and TMI. In general, there

JULY 2017

1871

SEKARANOM AND MASUNAGA

are three main groups of nonrain/rain flag classifications
from PR, consisting of no-rain, rain-possible, and raincertain fields (TRMM PR Team 2011). A pixel is classified as rain in PR when its echo exceeds a certain
threshold. Furthermore, when the echo is weak but
higher than the threshold or when it is possibly influenced by clutter contamination, it is labeled as a rainpossible field. Since the minimum rain-rate detectability
is approximately 0.5 mm h21 (;17 dBZ) from PR andconsiderably less for TMI, we set this value as thethreshold for determining no-rain/rain fields. Thus, only
grids with a rain rate higher than 0.5 mm h21 are considered as raining.
This study treats ocean, land, and coast separately
since different surface types potentially becomes asource of error in the estimation. This separation isintended to address the different modes of the TMI algorithm depending on the surface types, especially because of the limitation of the land algorithm in using
emission channels. The surface type classification is
assigned for each grid from the matched-up dataset
based on the TMI 2A12 surface flag. However, since thespatial resolution of the matched-up dataset is different
from the original TMI spatial resolution, the grid surface
types are determined based on the dominant TMI surface type with each grid box. If more than two-thirds of agrid box is filled with TMI land pixels, then that grid box
is classified as land surface and vice versa for ocean. A
grid box that is identified as neither land or ocean isdefined as coast.

c. Statistical intercomparison
The preprocessing routine described in the previous
section generates a combined dataset that consists of
PR, TMI, and TMPA rain estimates with a 0.258 3 0.258
spatial resolution and 3-hourly temporal resolution.
First, the three datasets are compared in terms of thedomain-mean climatology over a range from very lightrain to the heaviest rain. We focus on the relative differences among the products, and no ground-based observation data are utilized for the validation.
The mean rain rate is computed in two different
manners: conditional and unconditional rain rates. The
conditional means are calculated with raining grid boxes
only (grid with rain rate . 0.5 mm h21), while the unconditional means represent the average of all gridboxes irrespective of raining or nonraining. The meanvalues are obtained separately for different surface
types of ocean, land, and coast–mixed surfaces. We also
study the potential difference in moderately or extremely heavy rainfall using several rain-rate thresholds.
Eight thresholds of 0.5, 1, 5, 10, 15, 20, 30, and 40 mm h21
are examined, below which all rain rates are excluded.

Second, differences in the rain-rate estimation isassessed in further detail between each pair among thethree products, that is, PR–TMI, PR–TMPA, and TMI–
TMPA. Absolute nonrain events, in which both of thevalues are equal to #0.5 mm h21, are excluded from theanalysis. The dissimilarities between the first and second
data from each pair are computed to identify a positiveor negative difference value. This positive (negative)
difference value implies that a higher (lower) rain-rate
estimation exists from the first data to the second data
in a pair based on the following function:
D51
å (Rx,i 2 Ry,i ),
n i

(1)

where D is the difference values, n is the total number ofdata, Rx,i and Ry,i are the first and second data in eachpair, and i denotes the individual data samples.
A few additional analyses of the difference values are
conducted to account for the fact that rain screening is
not homogeneous across the three products. Following
Habib et al. (2009) and Zagrodnik and Jiang (2013), we
calculate the hit signal difference (HD), miss signal
difference (MD), and false signal difference (FD). The
HD is calculated when both the first and the second data
in a pair contain a finite rain rate within the same grid
box:
HD 5

1
å (Rx,i,H 2 Ry,i,H ),
n i

(2)

where HD is the hit signal difference, and Rx,i,H and Ry,i,H
are the first and second rain data samples for Rx,i . 0 and
Ry,i . 0. The MD is calculated with the samples where thefirst data misses the rain events detected by the second
data in a pair:
MD 5

1
åR ,
n i y,i,T(3)

where MD is the miss signal difference, and Ry,i,T is thesecond data samples for Rx,i . 0 and Ry,i . 0. The FD isdefined as the inverse to the MD:
FD 5

1
åR ,
n i x,i,T(4)

where FD is the false signal difference, and Rx,i,T is thefirst data samples for Rx,i . 0 and Ry,i . 0.
Subsetting each data pair only for the HD component,
the conditional root-mean-square difference (RMSD) is
calculated and decomposed into its systematic and random differences (Habib et al. 2009; Zagrodnik and Jiang2013):

1872

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGYsﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
RMSD 5 RMSDs 1 RMSDr 5
å (Rx,i 2 Ry,i )2 ,n i
(5)VOLUME 56

where R0x,i 5 a 1 b 3 Ry,i and a and b are the regression
constants.
Finally, the exceedance probability distribution is
utilized to assess the interproduct differences in extreme
rainfall. The exceedance values represent the occurrence probability at or above a certain rain rate, following the formulasuch as fixed thresholds, regional uppermost values, andannual maximum values. In this work, extreme rain eventsare defined as the uppermost 1% from the data over theentire region. It is assumed that regional variations of extreme rainfall are relatively small because of the uniform
climate characteristics of the study area.
Ice-scattering signals in the TMI 85-GHz brightness
temperature are a proxy of heavy convective rainfall
over all three surfaces, although such signals are moreheavily relied upon over land than over ocean. Differences between PR and TMI estimation that arise fromthe ice-scattering signal have also been reported, particularly between deep convection and warm rain events(Nesbitt et al. 2000; Zagrodnik and Jiang 2013). Regarding this issue, we identify the extremes as a functionof polarization-corrected brightness temperature(PCT), which is calculated based on the vertical andhorizontal 85-GHz brightness temperatures received by
TMI (Spencer et al. 1989):

P 5 m/(n 1 1) ,PCT 5 1:818TBV 2 0:818TBH ,where RSMDs is systematic difference, RSMDr is random difference, and n is total number of the data in apair with rain rate . 0.5 mm h21. The systematic error is
extracted by linear regression assﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
RMSDs 5
å (R0x,i 2 Ry,i )2 ,
n i

(6)

(7)

where P is the exceedance probability, m is the data rankordered from the highest rain rate to the prescribed
threshold of rain rate, and n is the total number of
each data.

d. Extreme rainfall analysis
In this study, extreme rainfall is examined in terms ofpassive microwave ice-scattering signals, radar stormtop heights, and vertical rain profiles. Those parameters
are useful to characterize clouds with extreme precipitation, particularly for the case of deep convections.
A strong deep convective cloud, expected to be heavily
precipitating, generally contains a large amount of ice
particles. The microwave scattering due to such ice
particles would not only produce a striking decrease in
the measured TMI 85-GHz brightness temperature but
result in a notable PR echo-top height. In this study, the85-GHz brightness temperature data from TMI 1B11
(NASA 2011d), storm-top height data from PR 2A23
(NASA 2011e), and vertical rain profile data from PR
2A25 are utilized. All of the data are gridded as previously described.
There are various methods for categorizing nonextreme
and extreme rain events. For example, Salahuddin andCurtis (2011) categorized extreme events based on fixed
thresholds of 10, 20, and 40 mm for the entire study area.
Hamada et al. (2015) defined the uppermost 0.1% data
distribution at each 2.58 3 2.58 as extreme events. Furthermore, because of large variations accompanying different classification methods, Kiktev et al. (2003) used
multiple classification methods to define the extremes,(8)

where PCT is the polarization-corrected brightness
temperature, TBV is the vertically polarized brightness
temperature, and TBH is the horizontally polarized
brightness temperature at TMI 85 GHz. For identifying
the extremes as a function of PCT, the definition of extreme in this research is slightly different than the general definition. In this research, an extreme is derived
from the uppermost 1% of rain events at each 5-K
PCT bin.

3. Intercomparison of mean, difference value, and
RMSD
The results of mean, difference value, and RMSD
analyses are presented in this section. Approximately
two-thirds of the study domain is ocean (66.14%), while
the remainder is coast (22.77%) and land (11.09%)(Table 1). A comparison of the unconditional and conditional means for each surface type is presented inTable 1. This table shows that TMPA has the highest
unconditional mean when compared with PR and TMI
for all surface types, that is, 0.21, 0.32, and 0.23 mm h21
for ocean, land, and coast, respectively. In contrast to
the unconditional mean, TMPA has the lowest value forany surface type as observed from the conditional mean.
TMI tends to produce the highest conditional means
when compared with PR and TMPA. The TMI conditional mean for land is 2.74 mm h21 and for coast is
2.55 mm h21, while PR produces the highest conditional mean over ocean (2.41 mm h21). The differences between PR–TMI conditional means are further

JULY 2017

1873

SEKARANOM AND MASUNAGATABLE 1. Total number of grid boxes (including nonraining grids), unconditional mean (average of all grid boxes $0 mm h21), andconditional mean (average of all grid boxes for .0.5 mm h21) based on each surface type.
Total number of grid boxes

PR
TMI
TMPA

Unconditional mean

Conditional mean

Ocean

Land

Coast

Ocean

Land

Coast

Ocean

Land

Coast

14 578 000 (66.14%)

2 444 200 (11.09%)

5 019 500 (22.77%)

0.19
0.17
0.21

0.28
0.26
0.32

0.20
0.14
0.23

2.41
2.04
1.97

2.55
2.74
2.40

2.48
2.55
2.41

investigated based on its original pixel-by-pixel standard
deviations within each TMPA grid resolution. The PR
standard deviations for ocean, land, and coast are 3.60,
3.87, and 3.82, while TMI standard deviations are 1.67,
2.59, and 2.55. In general, PR yields higher rain-rate
variations than TMI within the 0.258 3 0.258 grid resolution. It is possible that rain events detected by PR
contain smaller precipitating areas than TMI because
the PR swath is narrower than the TMI swath.
To obtain more detailed information than the meanvalues from all rain rates, it is useful to identify the rain
occurrence above specific rain-rate thresholds as a fraction of the total number of observations, as shown in
Table 2. A comparison of rain frequencies over ocean,land, and coast shows that generally, the land surface
often has the highest rain frequency. As identified by

TABLE 2. Percentage of grid boxes for rain rate . 0.5 to .
40 mm h21 based on each algorithm and surface type. Surface
maximums are displayed in bold font.
Percent of total data
Number of grid boxes.0.5 mm h21.1 mm h21
.5 mm h21
.10 mm h21.15 mm h21.20 mm h21.30 mm h21.40 mm h21

Surface

PR

TMI

TMPA

Ocean
Land
Coast
Ocean
Land
Coast
Ocean
Land
Coast
Ocean
Land
Coast
Ocean
Land
Coast
Ocean
Land
Coast
Ocean
Land
Coast
Ocean
Land
Coast

8.014910.9565
8.1611
5.07737.51535.42640.9077
1.33620.96310.23290.30190.23800.09010.08690.08710.03350.03920.02720.00600.00410.00510.00130.00070.00128.24399.34885.46974.81
6.81093.84740.67441.20650.65850.12080.37310.13930.03940.13300.04520.01670.04680.01520.00480.00550.00200.00110.00040.0002

10.8422
13.15109.34356.74069.05156.27590.7026
1.39441.03070.11730.28330.21330.02670.07340.06840.00890.02170.02260.00190.00260.00350.00050.00060.0007

Qian (2008), this contrast likely results from the strong
afternoon heating over land that draws large moisture
flux from the ocean, which then condenses into rain in
the central part of the islands. The rain rate remaining
the highest over land holds for light rain to heavy rainfrom .0.5 to .20 mm h21. Furthermore, this table also
provides support for the previous result related to thelarge number of very light rain rates identified by
TMPA. In this case, TMPA has a higher frequency forvery light rain rates (.0.5 and .1 mm h21) but decreases to less than PR and TMI for the higher thresholds (.5 mm h21 and above). At the middle-to-upper
range, PR tends to identify a higher number of rain
events relative to the other two estimates. The higher
PR frequency is particularly found over ocean and coastfrom rain rates . 5 to . 40 mm h21 thresholds. In contrast to PR, TMI tends to identify higher rain frequencies for medium to heavy rain over land, from .10
to .30 mm h21. Although this table can define thegeneral characteristics of rain frequencies, a closer lookat each specific rain rate is required. A detailed analysis
was obtained by further elaboration with a more comprehensive statistical analysis based on cumulative
contribution, as presented in the next section.
The result of difference value analysis (HD, MD, andFD) is presented in Table 3, and the RMSD between
each pair of products is shown in Table 4. Both of theanalyses show that PR–TMI often produces the largest
difference when compared with the other data pairs,

TABLE 3. Cross matrix of HD, MD, and FD of rain rate between
each pair out of the three algorithms (mm h21). The results areclassified based on each surface type from left to right. The HD,
MD, and FD are shown from top to bottom. The highest difference
for each surface is displayed in bold font.
Ocean

HD
MD
FD

PR
TMI
PR
TMI
PR
TMI

Land

Coast

TMI

TMPA

TMI

TMPA

TMI

TMPA

0.43
—
20.88
—
0.90
—

0.09
20.30
20.92
20.82
0.79
0.61

0.09
—
21.37
—
1.22
—

20.05
20.20
21.22
21.06
0.86
0.74

0.63
—
21.27
—
1.33
—

20.05
20.77
21.21
21.44
0.87
0.73

1874

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGYTABLE 4. Cross matrix of conditional RMSDs and the contribution of systematic RMSDss (mm h21) between each algorithm(for rain rate . 0.5 mm h21). The highest RMSD at each surface isdisplayed in bold font.
Ocean

RMSD
RMSDs

PR
TMI
PR
TMI

Land

Coast

TMI

TMPA

TMI

TMPA

TMI

TMPA

2.01
—
0.35
—

1.62
1.36
0.29
0.44

2.67
—
1.35
—

1.98
1.92
0.57
0.49

2.57
—
1.04
—

2.00
1.95
0.61
1.19

regardless of surface types. The PR–TMI high MD and
FD imply that the majority of the differences are closely
related to the nonrain and rain classifications by eachproduct. Based on the conditional RMSD, TMI–TMPA
is the smallest in the total RMSD for all three surfaces(1.36, 1.92, and 1.95 mm h21 for ocean, land, and coast,
respectively). However, the systematic component of
the RMSD shows that the difference between PR and
TMPA is smaller over ocean and coast.

4. Rain-rate contribution
This section presents an analysis of the probability
distribution of rainfall in terms of cumulative probability, exceedance probability, and mean rain rate as afunction of the TMI 85-GHz PCT.
The first result, shown in Fig. 2, provides the cumulative probability contribution among the three datasets.
The main feature is that PR provides the smallest contribution to low and medium rain rates when comparedwith TMI and TMPA. The contribution of rain rate fromthe minimum up to 10 mm h21 is approximately 70%–
80% for PR, while higher contributions are achieved by
TMI and TMPA in the same range. The TMPAfs cumulative contribution is higher than PR and TMI for any

VOLUME 56

given rain-rate value, particularly over ocean and land.
The higher cumulative contribution is possibly generated by a large number of light rain events, which areresponsible to the TMPAfs low conditional means
shown in Table 1. The TMI contribution, when compared with TMPA, shows a similar pattern. However,
TMI has a smaller contribution than TMPA for low-tomedium rain rates, particularly over land. The TMI
contribution is approximately 80% at 10 mm h21 overland, which is close to PR. Over coast, the TMI contribution is consistent with TMPA.
The exceedance probability is presented in Fig. 3. The
result indicates that PR generally produces a higher
exceedance probability than TMI and TMPA overocean and coast. This result provides an explanation for
Table 2, where PR identifies a higher number of rain
events .10 and .20 mm h21 over ocean and coast thanthe other two datasets, while TMI is higher over land. It
appears that the TMI land algorithm produces a higher
exceedance probability than PR between 10 and
25 mm h21. However, the TMI exceedance probability
rapidly decreases after .25 mm h21. This result is further examined in section 5.
Figure 4 shows a comparison of the mean rain rate as afunction of TMI 85-GHz PCT. Over land, all the algorithms produced similar results when TMI 85-GHz
PCT . 200 K, while for lower PCTs, TMI tends toshow higher rain rates than the other two datasets. The
higher TMI rain rate below 200-K PCT is also observed
over coast, but the spread occurs at lower PCT (at approximately 160 K). This spread presumably explains
why TMI exhibits a higher frequency of rain events for arain rate of .10 mm h21. It could be implied that thehigher exceedance probability of TMI over land shownin Fig. 3 is due to the frequent rain events with PCTs
below 240 K. The excess of TMI rain for low PCTs is notobserved over ocean, which may be expected because

FIG. 2. Cumulative contribution to mean rain rate (%) from PR (solid black line), TMI (dashed black line), and TMPA (dashed gray line)
for (a) ocean, (b) land, and (c) coast, calculated from each 0.258 square grid from 1998 to 2014.

JULY 2017

SEKARANOM AND MASUNAGA

1875

FIG. 3. Probability of exceedance for PR (solid black line), TMI (dashed black line), and TMPA (dashed gray line) based on each 0.258
square grid from 1998 to 2014. A rain rate with corresponding exceedance value close to 1 indicates a higher probability to occur. The plots
represent each surface: (a) ocean, (b) land, and (c) coast.

the TMI land algorithm relies entirely on the icescattering channels.

5. Comparison of extreme rainfall
The previous result in Fig. 4 shows that the ice scattering may not be tightly linked to the surface rainfall. In
particular, the TMI land algorithm is known for being
insensitive to light rain without ice particles (Furuzawa
and Nakamura 2005; Rajendran and Nakazawa 2005;
Zagrodnik and Jiang 2013). The apparent inconsistency
between the exceedance probability plot (Fig. 3b) andrain-rate–PCT plot (Fig. 4b) over land is further investigated in this section. Figure 5 presents a twodimensional histogram of rain rate and TMI PCT. The
relationship between rain rate and PCT is broadly
spread except for the TMI land and coast, in which therain rate is tightly correlated with PCT. This result islikely because the TMI land and coast algorithms do not
rely on the emission channels. Although the mean rain

rate as a function of PCT (shown as black plus signs in
Fig. 5) indicates that the TMI land and coast algorithms
are higher than PR and TMPA at an intermediate rain
range, the histogram shows that the TMI rain–PCT
curve over land and coast rarely contains rain rates
higher than 20 mm h21 except at the lowest PCTs.
However, PR rain can exceed 20 mm h21 over a wide
range of PCTs over the three surfaces.
Figure 6 shows the PCT–rain relation as plotted inFig. 4 but calculated from the samples limited to the uppermost 1% rain rate. PR consistently has the highest
rain rate for all PCTs over land and coast when comparedwith TMI and TMPA. This result is in contrast with themean rain-rate–PCT plot presented in Fig. 4, where TMI
is the highest over land for an intermediate range of rain
rates when compared with the two other datasets. Comparison of the two figures shows that there are differences
related to the range of the data distribution inside the
TMI land algorithm, especially between the mean and theuppermost values as a function of PCTs.

FIG. 4. Plots of TMI minimum 85-GHz PCT vs averaged rain rate from PR (filled circles), TMI (plus signs), and TMPA (times signs).
The PCT values are obtained from the minimum value inside collocated 1/48 square grid boxes from 1998 to 2014. Each point represents
the mean value for a 5-K PCT bin. The result is classified into three surface types: (a) ocean, (b) land, and (c) coast.

1876

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGYVOLUME 56

FIG. 5. Fraction of the total number of rain events as a function of rain rates and TMI minimum 85-GHz PCTs, obtained from collocated
1/ 48 square grid boxes from 1998 to 2014. The data are classified by the (top) PR, (middle) TMI, and (bottom) TMPA algorithms and the(left) ocean, (center) land, and (right) coast surface types. The scale represents fractions of the total data for each plot.

A remaining question is, as shown in Fig. 5, why are theheaviest rains detected by PR not necessarily associatedwith the lowest TMI PCTs? To address this question, we
examine the storm-top heights from collocated PR 2A23
associated with extreme rain rates of the uppermost 1%
for each PCT bin in Fig. 7. The result indicates that PR,
TMI, and TMPA could produce comparable results overocean. As identified by the three estimates, the storm-top
height is from approximately 6 km at 10 mm h21 toapproximately 8–10 km at 30 mm h21 over ocean. However, it appears that TMI yields a higher storm-top height
for any given rain rate over land and coast relative to PR
and TMPA. Over land, TMI indicates storm-top heightsfrom approximately 8 km at 10 mm h21 to 12 km at
30 mm h21. The extreme events derived from PR and
TMPA, in contrast, exhibit lower storm-top heights overland: from approximately 5 km at 10 mm h21 to 10 km at
30 mm h21. Because TMI does not directly observe

FIG. 6. As in Fig. 4, but for the average of the uppermost 1% rain rate at each 5-K 85-GHz TMI minimum PCT bin.

JULY 2017

SEKARANOM AND MASUNAGA

1877

FIG. 7. Plots of collocated PR storm-top height as a function of the uppermost 1% rain rates from PR (filled circles), TMI (plus signs),
and TMPA (times signs). The uppermost 1% are obtained using TRMM observations from 1998 to 2014. The storm-top height data are
obtained from the average value of collocated PR2A23 at 1/ 48 square grid boxes and classified into (a) ocean, (b) land, and (c) coast.

surface rain rates but rather relies on the empirical relationship with ice-scattering signals, this relationshipmay be somewhat too exaggerated (i.e., excessive ice
scattering for a given surface rain rate) for extreme
rainfall.
The relation of surface rain with PCT is further examined in terms of vertical rain profiles for 1% extreme
rainfall. Figure 8 shows the PR vertical rain profiles averaged within the extreme subset identified by PR (red),
TMI (green), and TMPA (blue) while considering thePCT values. The plots are categorized into four PCT
ranges: ,160, 160–200, 200–240, and .240 K. Over ocean,the vertical profiles agree well among the three products
for all PCT ranges. At the lowest 85-GHz PCTs (,160 K),
the near-surface rain reaches approximately 22–
23 mm h21. The near-surface rain rates are decreasing at
higher PCTs: approximately 15, 10, and 3 mm h21 for 160–
200-, 200–240-, and .240-K PCTs, respectively. Over land,
the vertical rain profiles disagree among the products ascompared with over the ocean. The PR-identified extreme
rain profiles exhibit a significant increase in rain rate from
5 km to near the surface. In contrast, such a downward
increase is more modest for the TMI-identified extremes.
This PR–TMI difference resulted in the TMI’s lower nearsurface rain. For PCTs ,160 K, the near-surface rain rate
is approximately 12–13 mm h21 when extremes are defined by PR, while the surface rain rate reaches only approximately 10 mm h21 for the TMI-sorted subset. Thiskind of difference is also found for PCTs of 160–200 and
200–240 K. Comparison of the vertical rain profiles overthe coast also indicates differences that are similar to thoseover land.
One might wonder whether the present statistics fromMC precipitation are also representative of global
rainfall characteristics. To address this question, we
present the results of exceedance probabilities obtained

from the global tropics and a few representative regions
across the tropics (Fig. 9). The global exceedance
probabilities (with MC excluded), as shown in Figs. 9aand 9b, imply that a similar condition also exists on a
global scale. Over global land, the TMI probability isslightly higher than the PR probability at midrange extremes (approximately 15–30 mm h21). Although thepattern is similar, MC has a higher PR probability above
30 mm h21, while the TMI probability rapidly decreases
at lower rain rates than the global average. Nevertheless, the overall characteristics found previously for MC
also apply to the global tropics.
The exceedance probability with four other regions is
also shown in Fig. 9. Plots of ocean probabilities are
represented by east Pacific (Fig. 9c) and west Pacific(Fig. 9d), while land is represented by South America(Fig. 9d) and central Africa (Fig. 9f). The probabilities
are homogeneous over ocean but have large variations
over land. The plot over South America produces ahigher PR probability at all ranges when compared with
the TMI, and it yields a different pattern than that observed for MC over global land. Central Africa shows acontrasting result, in which the TMI probability is higher
than PR for rain rates higher than 10 mm h21. The PR
exceedance probability over central Africa in this case islower than the PR exceedance probability at the global
scale. The regional contrast suggests that South America
provides a relatively oceanic environment for precipitation to develop, while Africa is under a highly
continental condition (Masunaga et al. 2005).

6. Conclusions and discussion
The results of this study highlight the properties ofdifferent rain-rate estimation among PR, TMI, and
TMPA over MC for both nonextreme and extreme cases

1878

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGYVOLUME 56

FIG. 8. Collocated PR 2A25 vertical rain-rate mean for PR uppermost 1% extremes (red), TMI uppermost 1% extremes (green), and
TMPA uppermost 1% extremes (blue), as classified by four TMI minimum 85-GHz PCT ranges [(top) ,160, (top middle) 160–200,(bottom middle) 200–240, and (bottom) .240 K] and (a) ocean, (b) land, and (c) coast. The uppermost 1% are obtained using TRMM
observations from 1998 to 2014. Plus signs indicate 95% confidence interval. Solid color lines on the right represent the corresponding
freezing heights, while dashed ones represent the corresponding PR 2A23 storm-top heights.

JULY 2017

SEKARANOM AND MASUNAGA

1879

FIG. 9. Comparison of PR (black solid), TMI (black dashed), and TMPA (gray dashed) exceedance probabilities calculated using longterm data from 1998 to 2014 for (a) global ocean, (b) global land, (c) east Pacific (ocean) 908–1308W, 158S–158N, (d) South America (land)
458–808W, 358S–108N, (e) west Pacific (ocean) 1308–1658E, 58–208N, and (f) central Africa (land) 158–458E, 158S–158N.

using long-term data records from 1998 to 2014. The
results revealed that the rain rate in general and in theextreme cases has its own characteristics attributed
to the differences: 1) For the nonextreme rain events(below 10 mm h21), TMI and TMPA have a higher probability of detection than PR over the three surfaces. 2)
For moderately heavy rain events (between 10 and
25 mm h21), the TMI algorithm over land detects more
frequent rain events than PR and TMPA. 3) However,the detected extreme events from TMI land algorithm
decreased at above 30 mm h21. Further analysis showsthat the TMI extreme events are heavily accompaniedwith the strongest ice-scattering signals and highest
storm-top heights, while the PR extremes show a weaker
relationship between the three variables.
The PR–TMI differences in representing the uppermost 1% extremes highlight the known weakness in the
TMI land algorithm. The TMI land estimates depend on
the existence of ice particles aloft in precipitating clouds
since they assume a tight relationship between icescattering signals and surface rain rates. Heavy rain
events are generally associated with stronger convection, which contains ice particles aloft. However, it has

been shown that the rain-rate–ice-scattering relationship is not always robust. Very intense rain events
identified by PR are not associated with the highest
storm-top height and are thus unlikely with the mostabundant ice particles aloft. The comparison with theglobal exceedance probability provided in the previous
section indicates that higher extreme events are notnecessarily accompanied by extreme ice-scattering signals, similar to Hamada et al. (2015), while the TMI landalgorithm by design always predicts the heaviest rainsfrom the lowest PCT. The factors contributing to thehigher PR extremes are now under investigation.
There are several remarks related to this study. First,
despite the PR–TMI extreme contrast over land, it appears that both estimates are statistically consistent overocean, which is possibly due to the use of both emission
and ice-scattering channels for TMI. Second, although
TMI often provides similar results as TMPA for light rain
rates, there are large estimation differences for medium
to heavy rain events, particularly over ocean and coast.
Comparison of the uppermost 1% yields higher conformity between PR and TMPA in terms of ice-scattering
signals, storm-top heights, and vertical rain profiles.

1880

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY

However, the results also suggest that extreme eventsfrom both of the sources are not quite similar, although
TMPA tends to provide rain estimates between PR and
TMI. The main reason between this difference is still
unclear. It is possible that the temporal mismatch between instantaneous PR rainfall and 3-hourly snapshot
rain data from TMPA may have a significant contribution to this condition (Prakash et al. 2012). Further,since the extreme cases are selected from 1% of theupper distribution, the large number of very light rain
events from TMPA could also introduce a bias tothe 1% threshold, which determines the extremes.
Changing the 1% extreme threshold into a higher
specific value does increase the TMPA extreme means.
However, a similar comparison using two specificthresholds (10 and 15 mm h21) shows no significant
differences in the extreme properties, particularly thevertical rain-rate profiles.
Acknowledgments. This research is funded by Lembaga
Pengelola Dana Pendidikan (LPDP)–Ministry of FinanceRepublic of Indonesia, with Contract PRJ-1875/LPDP/
2014. Hiro Masunaga is grateful to the support of The
Japan Society of Promotion of Science (JSPS) Grant-inAid for Scientific Research (KAKENHI) (B) (26287113).
The TRMM PR (2A12), TMI (2A25), and TMPA
(3B42) datasets analyzed in this work are available onlinefrom NASA Precipitation Measurement Mission (PMM)archives (https://pmm.nasa.gov/data-access/downloads/trmm).
REFERENCES
Adhikari, P., Y. Hong, K. R. Douglas, D. B. Kirschbaum,
J. Gourley, R. Adler, and G. R. Brakenridge, 2010: A digitizedglobal flood inventory (1998–2008): Compilation and preliminary results. Nat. Hazards, 55, 405–422, doi:10.1007/
s11069-010-9537-2.
Aldrian, E., and R. Dwi Susanto, 2003: Identification of threedominant rainfall regions within Indonesia and their relationship to sea surface temperature. Int. J. Climatol., 23,
1435–1452, doi:10.1002/joc.950.
As-Syakur, A. R., T. Tanaka, T. Osawa, and M. S. Mahendra, 2013:
Indonesian rainfall variability observation using TRMM
multi-satellite data. Int. J. Remote Sens., 34, 7723–7738,
doi:10.1080/01431161.2013.826837.
Berg, W., T. L’Ecuyer, and C. Kummerow, 2006: Rainfall climate
regimes: The relationship of regional TRMM rainfall biases tothe environment. J. Appl. Meteor. Climatol., 45, 434–454,
doi:10.1175/JAM2331.1.
Chang, C.-P., Z. Wang, J. McBride, and C.-H. Liu, 2005: Annual
cycle of Southeast Asia–Maritime Continent rainfall and theasymmetric monsoon transition. J. Climate, 18, 287–301,
doi:10.1175/JCLI-3257.1.
Fu, Y., and G. Liu, 2003: Precipitation characteristics in midlatitude East Asia as observed by TRMM PR and TMI.
J. Meteor. Soc. Japan, 81, 1353–1369, doi:10.2151/jmsj.81.1353.

VOLUME 56

Furuzawa, F. A., and K. Nakamura, 2005: Differences of rainfall
estimates over land by Tropical Rainfall Measuring Mission(TRMM) Precipitation Radar (PR) and TRMM Microwave
Imager (TMI)—Dependence on storm height. J. Appl. Meteor., 44, 367–383, doi:10.1175/JAM-2200.1.
Gopalan, K., N.-Y. Wang, R. Ferraro, and C. Liu, 2010: Statusof the TRMM 2A12 land precipitation algorithm.
J. Atmos. Oceanic Technol., 27, 1343–1354, doi:10.1175/
2010JTECHA1454.1.
Habib, E., A. Henschke, and R. F. Adler, 2009: Evaluation of
TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over
Louisiana, USA. Atmos. Res., 94, 373–388, doi:10.1016/
j.atmosres.2009.06.015.
Hamada, A., Y. N. Takayabu, C. Liu, and E. J. Zipser, 2015: Weaklinkage between the heaviest rainfall and tallest storms. Nat.
Commun., 6, 6213, doi:10.1038/ncomms7213.
Huffman, G. J., and D. T. Bolvin, 2013: TRMM and other data
precipitation data set documentation. NASA TRMM Doc., 40
pp.
——, and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear,
combined-sensor precipitation estimates at fine scales.
J. Hydrometeor., 8, 38–55, doi:10.1175/JHM560.1.
Ichikawa, H., and T. Yasunari, 2006: Time–space characteristics ofdiurnal rainfall over Borneo and surrounding oceans as observed by TRMM-PR. J. Climate, 19, 1238–1260, doi:10.1175/
JCLI3714.1.
Iguchi, T., and R. Meneghini, 1994: Intercomparison of singlefrequency methods for retrieving a vertical rain profile fromairborne or spaceborne radar data. J. Atmos. Oceanic Technol., 11, 1507–1516, doi:10.1175/1520-0426(1994)011,1507:
IOSFMF.2.0.CO;2.
——, T. Kozu, R. Meneghini, J. Awaka, and K. Okamoto, 2000:
Rain-profiling algorithm for the TRMM Precipitation
Radar. J. Appl. Meteor., 39, 2038–2052, doi:10.1175/
1520-0450(2001)040,2038:RPAFTT.2.0.CO;2.
Kiktev, D., D. M. Sexton, L. Alexander, and C. K. Folland, 2003:
Comparison of modeled and observed trends in indices ofdaily climate extremes. J. Climate, 16, 3560–3571, doi:10.1175/
1520-0442(2003)016,3560:COMAOT.2.0.CO;2.
Kripalani, R. H., and A. Kulkarni, 1997: Rainfall variability overSouth-east Asia—Connections with Indian monsoon and
ENSO extremes: New perspectives. Int. J. Climatol., 17,1155–1168, doi:10.1002/(SICI)1097-0088(199709)17:11,1155::
AID-JOC188.3.0.CO;2-B.
Kumar, R., A. Varma, A. Mishra, R. Gairola, I. Das, A. Sarkar, and
V. Agarwal, 2009: Comparison of TRMM TMI and PR version
5 and 6 precipitation data products under cyclonic weather
conditions. IEEE Trans. Geosci. Remote Sens. Lett., 6, 378–
382, doi:10.1109/LGRS.2009.2014257.
Kummerow, C., and L. Giglio, 1994: A passive microwave techniquefor estimating rainfall and vertical structure information fromspace. Part I: Algorithm description. J. Appl. Meteor., 33, 3–18,
doi:10.1175/1520-0450(1994)033,0003:APMTFE.2.0.CO;2.
——, W. S. Olson, and L. Giglio, 1996: A simplified scheme forobtaining precipitation and vertical hydrometeor profiles frompassive microwave sensors. IEEE Trans. Geosci. Remote
Sens., 34, 1213–1232, doi:10.1109/36.536538.
——, W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998: The
Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809–817, doi:10.1175/
1520-0426(1998)015,0809:TTRMMT.2.0.CO;2.

JULY 2017

SEKARANOM AND MASUNAGA——, and Coauthors, 2001: The evolution of the Goddard profilingalgorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 1801–1820, doi:10.1175/
1520-0450(2001)040,1801:TEOTGP.2.0.CO;2.
Liu, J., C. D. Doan, S.-Y. Liong, R. Sanders, A. T. Dao, andT. Fewtrell, 2015: Regional frequency analysis of extreme
rainfall events in Jakarta. Nat. Hazards, 75, 1075–1104,
doi:10.1007/s11069-014-1363-5.
Masunaga, H., T. Iguchi, R. Oki, and M. Kachi, 2002: Comparisonof rainfall products derived from TRMM microwave imager
and precipitation radar. J. Appl. Meteor., 41, 849–862,
doi:10.1175/1520-0450(2002)041,0849:CORPDF.2.0.CO;2.
——, T. S. L’Ecuyer, and C. D. Kummerow, 2005: Variability in thecharacteristics of precipitation systems in the tropical Pacific.
Part I: Spatial structure. J. Climate, 18, 823–840, doi:10.1175/
JCLI-3304.1.
Mori, S., and Coauthors, 2004: Diurnal land–sea rainfall peak migration over Sumatera Island, Indonesian Maritime Continent, observed by TRMM satellite and intensive rawinsonde
soundings. Mon. Wea. Rev., 132, 2021–2039, doi:10.1175/
1520-0493(2004)132,2021:DLRPMO.2.0.CO;2.
NASA, 2011a: TRMM product level 2A Precipitation Radar (PR)rainfall rate and profile, version 7.00. GES DISC, accessed 22
March 2015. [Available online at https://disc.sci.gsfc.nasa.gov/
precipitation/documentation/TRMM_README/TRMM_
2A25_readme.shtml.]——, 2011b: TRMM Microwave Imager (TMI) level 2A hydrometeor profile product, version 7.00. GES DISC, accessed 22 March2015. [Available online at https://disc.gsfc.nasa.gov/precipitation/
documentation/TRMM_README/TRMM_2A12_readme.
shtml.]——, 2011c: TRMM product 3B42, version 7.00. GES DISC, accessed
22 March 2015. [Available online at https://disc.gsfc.nasa.gov/
precipitation/documentation/TRMM_README/TRMM_3B42_
readme.shtml.]——, 2011d: TRMM Microwave Imager (TMI) level 1B calibrated
brightness temperature (TB) product, version 7.00. GES DISC,
accessed 22 March 2015. [Available online at https://disc.gsfc.
nasa.gov/precipitation/documentation/TRMM_README/
TRMM_1B11_readme.shtml.]——, 2011e: TRMM product level 2A Precipitation Radar (PR)rain characteristics (2A23), version 7.00. GES DISC, accessed 22
March 2015. [Available online at https://disc.gsfc.nasa.gov/
precipitation/documentation/TRMM_README/TRMM_2A23_
readme.shtml.]
Nesbitt, S. W., E. J. Zipser, and D. J. Cecil, 2000: A census of
precipitation features in the tropics using TRMM: Radar,ice scattering, and lightning observations. J. Climate,

1881

13, 4087–4106, doi:10.1175/1520-0442(2000)013,4087:
ACOPFI.2.0.CO;2.
Prakash, S., C. Mahesh, R. M. Gairola, and P. K. Pal, 2012: Comparison of high-resolution TRMM-based precipitation products during tropical cyclones in the north Indian Ocean. Nat.
Hazards, 61, 689–701, doi:10.1007/s11069-011-0055-7.
Qian, J.-H., 2008: Why precipitation is mostly concentrated overislands in the Maritime Continent. J. Atmos. Sci., 65, 1428–
1441, doi:10.1175/2007JAS2422.1.
Rajendran, K., and T. Nakazawa, 2005: Systematic differences
between TRMM 3G68 PR and TMI rainfall estimates and thepossible association with life cycle of convection. SOLA, 1,
165–168, doi:10.2151/sola.2005-043.
Ramage, C. S., 1968: Role of a tropical ‘‘Maritime Continent’’ inthe atmospheric circulation. Mon. Wea. Rev., 96, 365–370,
doi:10.1175/1520-0493(1968)096,0365:ROATMC.2.0.CO;2.
Rosenfeld, D., 2007: Cloud top microphysics as a tool for precipitation measurements. Measuring Precipitation from Space,
Springer, 61–77.
Salahuddin, A., and S. Curtis, 2011: Climate extremes in Malaysia
and the equatorial South China Sea. Global Planet. Change,
78, 83–91, doi:10.1016/j.gloplacha.2011.05.001.
Seto, S., and T. Iguchi, 2007: Rainfall-induced changes in actual
surface backscattering cross sections and effects on rain-rate
estimates by spaceborne precipitation radar. J. Atmos. Oceanic Technol., 24, 1693–1709, doi:10.1175/JTECH2088.1.
Spencer, R. W., H. M. Goodman, and R. E. Hood, 1989: Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal. J. Atmos.
Oceanic Technol., 6, 254–273, doi:10.1175/1520-0426(1989)006,0254:
PROLAO.2.0.CO;2.
Tangang, F. T., L. Juneng, E. Salimun, P. N. Vinayachandran, Y. K.
Seng, C. J. C. Reason, S. K. Behera, and T. Yasunari, 2008: On
the roles of the northeast cold surge, the Borneo vortex, theMadden-Julian oscillation, and the Indian Ocean dipole during
the extreme 2006/2007 flood in southern peninsular Malaysia.
Geophys. Res. Lett., 35, L14S07, doi:10.1029/2008GL033429.
TRMM PR Team, 2011: Tropical Rainfall Measuring Mission(TRMM) Precipitation Radar algorithm: Instruction manualfor version 7. JAXA/NASA Tech. Rep., 170 pp. [Available
online at http://www.eorc.jaxa.jp/TRMM/documents/PR_
algorithm_product_information/pr_manual/PR_Instruction_
Manual_V7_L1.pdf.]
Zagrodnik, J. P., and H. Jiang, 2013: Investigation of PR and TMI
version 6 and version 7 rainfall algorithms in landfalling
tropical cyclones relative to the NEXRAD stage-IV multisensor precipitation estimate dataset. J. Appl. Meteor. Climatol., 52, 2809–2827, doi:10.1175/JAMC-D-12-0274.1.