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SEKARANOM AND MASUNAGA

37

Origins of Heavy Precipitation Biases in the TRMM PR and TMI Products
Assessed with CloudSat and Reanalysis Data
ANDUNG BAYU SEKARANOMa
Graduate School of Environmental Studies, Nagoya University, Furo-cho Chikusa-ku, Nagoya, Japan

HIROHIKO MASUNAGA
Institute for Space-Earth Environmental Research, Nagoya University, Furo-cho Chikusa-ku, Nagoya, Japan(Manuscript received 15 January 2018, in final form 7 November 2018)
ABSTRACTThis study aims to characterize the background physical processes in the development of those heavy
precipitation clouds that contribute to the Tropical Rainfall Measuring Mission (TRMM) active and passive
sensor differences. The combined global observation data from TRMM, CloudSat, and European Centre for
Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) from 2006 to 2014 were
utilized to address this issue. Heavy rainfall events were extracted from the top 10% of the rain events fromthe Precipitation Radar (PR) and TRMM Microwave Imager (TMI) rain-rate climatology. Composite
analyses of CloudSat and ERA-Interim were conducted to identify the detailed cloud structures and thebackground environmental conditions. Over tropical land, TMI tends to preferentially detect deep isolated
precipitation clouds for relatively drier and unstable environments, while PR identifies more organizedsystems. Over the tropical ocean, TMI identifies heavy rainfall events with notable convective organizationand clear regional gradients between the western and eastern Pacific Ocean, while PR fails to capture theeastward shallowing of convective systems. The PR–TMI differences for the moist and stable environments
are reversed over tropical land.

1. Introduction
Precipitation estimates derived from satellite observations are now widely used in various studies related tometeorology and climatology. One of the earliest precipitation estimation methods was developed by using
infrared (IR) sensors obtained from geostationary satellites (Griffith et al. 1978). IR sensors measure cloud-top
temperatures as a proxy of surface precipitation estimations, although these measurements are known to serve as
indirect estimations. Currently, precipitation estimations
using passive microwave (PMW) radiometers are widely
Denotes content that is immediately available upon publication
as open access.
aAdditional affiliation: Faculty of Geography, Gadjah Mada
University, Yogyakarta, Indonesia.

Corresponding author: Andung Bayu Sekaranom, andungbayu@
geo.ugm.ac.id

used, and many satellites carry these instruments. PMW
radiometers use a more direct approach of measuring
emission signals from liquid-phase hydrometeor contents aswell as scattering signals from ice-phase hydrometeors inside precipitating clouds (Wilheit 1986; Wilheit et al. 1994).
A further breakthrough was brought about by the TropicalRainfall Measuring Mission (TRMM) (Kummerow et al.
1998). The TRMM was equipped with a Precipitation Radar (PR) to be used as its active sensor in addition to the
TRMM Microwave Imager (TMI), which acted as thepassive sensor (Kummerow et al. 1998; Simpson et al. 1996).
Various studies have attempted to explore the originsof the rain-rate differences between PR and TMI(Masunaga et al. 2002; Yamamoto et al. 2008; Zagrodnik
and Jiang 2013; Liu and Zipser 2014; Henderson et al.
2017), but only a few studies were focused on the extreme events (Sekaranom and Masunaga 2017), whichhave a close relationship with disasters. The estimationof the heavy rain rates from satellites remains challenging because of the large uncertainties involved in
the estimation process. From a PR point of view, rain

DOI: 10.1175/JAMC-D-18-0011.1
Ó 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).

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JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY

rates are determined by the relationships between radarreflectivity Z and rain rate, and between the specificattenuation k and the received reflectivity Z (Iguchi
et al. 2000). The Z of the PR sensor within the Rayleighlimit is a product of the sixth moment of the drop size
distribution (DSD), which is written as follows:Z5ð‘

N(D)D6 dD,(1)0

where N(D) is the particle size distribution as a functionof diameter D (Heymsfield et al. 2002). The above
DSD–reflectivity relationship of the 13.8-GHz radar
frequency is subject to considerable attenuation whenlarge raindrops are present, as is typical for heavy rainfall events. To correct attenuation, a hybrid method involving the Hitschfeld–Bordan and surface referencetechnique is utilized (Iguchi et al. 2000, 2009). Previous
studies have found that precipitation estimation biases
derived from the PR arise from the assumptions in the
DSD and attenuation correction (Iguchi et al. 2009;
Kozu et al. 2009; Schumacher and Houze 2000). Someground validations indicate that the PR often shows
rain-rate values that underestimate the heavy rain rates(Henderson et al. 2017; Kirstetter et al. 2013; Zagrodnik
and Jiang 2013), especially for convective rains (Iguchi
et al. 2009). Rasmussen et al. (2013) pointed out thatthese differences in extremes over land are likely to berelated to mixed-phase and/or frozen hydrometers.
In contrast to PR, TMI uses a physical-based assumption to derive the relationship between the brightness
temperature and rain rate (TB –R). In the standard TMI
algorithm, TMI utilizes a Bayesian approach to calculate
the probability of the rain rate by comparing the retrieved
and observed microwave signals using a hydrometeor
profile database (Kummerow and Giglio 1994; Kummerow
et al. 2001). Low-frequency TB values of the TMI results
are less sensitive to the DSD assumptions than those fromthe PR (Masunaga et al. 2002). However, TMI has its own
uncertainties when characterizing the ice-phase hydrometeors from the 85-GHz channel (Shige et al. 2004;
Zagrodnik and Jiang 2013). Moreover, unlike PR, TMI
cannot measure vertical rain structures and, therefore, depends entirely upon assumed vertical cloud structures(Kummerow et al. 2001). TMI has another disadvantage
when estimating rain rate over land surfaces because of thenoise generated by surface emissions. Therefore, over land
surfaces, the TMI algorithm depends entirely on the
85-GHz channel (Wang et al. 2009). Another significant
issue of the TMI algorithm is related to its general
underestimation trend due to the beamfilling effect(Kummerow 1998; Kummerow et al. 2004). Since TMI
has a coarser resolution than PR, especially for the

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emission channels, biases due to deep isolated precipitation
systems can be more significant than they would be for PR(Liu and Zipser 2014). Last, although TMI TB does notstrongly depend on DSD, biases could be generated by theconversion of the liquid water contents to rain rates fromthe DSD assumption (Masunaga et al. 2002).
Previous works have demonstrated that the biases
between PR and TMI rain estimation are influenced by
the design and performance of retrieval algorithms.
Over land, Zagrodnik and Jiang (2013) showed that
TMI has an issue with higher rain-rate retrieval.
Petković and Kummerow (2017) found that the frequency of occurrence for specific rainfall regimes containing different profiles of ice particles was essential for
describing TMI–PR rainfall differences. The TMI estimation depends mostly on 85-GHz channels over land,
which is more sensitive to ice particles (Gopalan et al.
2010; Liu and Zipser 2014). The TMI land algorithm ingeneral estimates higher rainfall associated with higher
ice-scattering signals, while PR is not affected by such
constraints (Sekaranom and Masunaga 2017). Over theocean, the discrepancies between the two rain retrievals
are largely associated with the precipitation homogeneity (Kummerow 1998; Liu and Zipser 2014; Carr et al.
2015) and convective–stratiform variability within the
TMI–PR footprint (Kirstetter et al. 2015; Carr et al.
2015). The TMI is less sensitive to inhomogeneous rain
because of coarse-resolution channels (63 km 3 37 km at
10 GHz) (Kummerow et al. 1998), which makes TMI
estimates biased to heavier rain from larger precipitation systems (Liu and Zipser 2014; Carr et al.
2015). Henderson et al. (2017) showed that TMI tends to
produce a higher estimation for the organized stratiform
rain and a lower estimation for the deep-isolated convective rain than those of PR. Further, Henderson et al. (2018)
found that the central and west Pacific basins TMI–PR
differences were related to regional changes in the level ofprecipitation organization due to ENSO variability. Thismight be associated with increasing stratiform fraction
during El Niño events (Schumacher and Houze 2006).
Masunaga et al. (2002) identified that the differences
between PR and TMI are associated with different DSD
assumptions. The different DSD assumption, however,also depends on whether the precipitation is classified asconvective or stratiform (Iguchi et al. 2000). In general,the proportion of convective and stratiform rains are
influenced by their precipitation organization and its
corresponding thermodynamics environment. Excessive
moisture at the lower to midtroposphere could produce
convective outbreaks over a large area (Sherwood 1999;
Lucas et al. 2000). Higher humidity also could be produced by the convective feedback, which moistens thesurrounding environment and makes favorable to future

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SEKARANOM AND MASUNAGA

convection (Tompkins 2001). Those sustaining convective
systems, in general, could be associated with largely organized systems with considerable stratiform components.
In contrast to the precipitation systems developed in amoister environment, smaller precipitation systems tend
to be developed at a dryer environment (Schumacher andHouze 2006; Hill and Lackmann 2009). The small precipitation systems could also be associated with isolated
convective cells (Schumacher and Houze 2003), whichcontain no or fewer stratiform components than thelargely organized systems. It is therefore useful to characterize the PR–TMI heavy rain estimation biases in termof precipitation organization and its corresponding thermodynamics environment.
Using previous studies involving the PR and TMI
comparisons and validations (Berg et al. 2006; Furuzawa
and Nakamura 2005; Shige et al. 2006), we can characterize the differences of the PR and TMI results, particularly for heavy rain events. It has been shown by
Sekaranom and Masunaga (2017) that over some tropical land areas, PR tends to detect heavy rain rates withlower storm-top heights (STH) better than TMI at any
given rain rate. Furthermore, the corresponding reflectivity profiles show a significant increase below themelting layer compared to that of TMI. These findings
suggest that PR detects extreme warm rain events withsignificant collision and coalescence processes near thesurface (Hamada et al. 2015; Song and Sohn 2015).
Hamada et al. (2015) and Song et al. (2017) showed thatthe rain with lower STH identified by PR is associatedwith a more humid but more stable environment. Songet al. (2017) further argued that the extreme events
identified by PR could occur in a more stable environment since the higher humidity enhances the graupel
production in the midtroposphere. The above differences are less significant over the ocean than over theland. Sekaranom and Masunaga (2017) showed thatthe differences of the STH as a function of rain rate over
the ocean are not as distinct as those over land. However, studies by Liu and Zipser (2014) showed that TMI
identifies greater precipitation areas than PR over the
global ocean. From the above studies, we could inferthat these extreme biases are related to the amounts ofice particles at aloft and/or the scale of precipitation
systems and convective variability that might be notclearly identified from the climatological biases.
Although previous results characterized well the PR–
TMI uses for estimating rain rates, the reasons for why thedifferent preferences exist were left unresolved. This
study aims to assess the origins of the biases, particularly
for heavy rain events. Identifying the origin of the heavy
rain biases could help in developing more accurate satellite estimations, which are useful in disaster early-warning

39

systems and understanding the future climate. To address
this issue, the corresponding cloud structures fromCloudSat and the background environmental profiles
from ERA-Interim are utilized. CloudSat provides information about cloud droplets, which are not captured
by PR. The utilization of CloudSat in this research gives
more detailed background analysis to explain the originof the biases.
The description of the datasets and explanations of theresearch methodology are shown in detail in section 2.
The PR–TMI differences when estimating heavy rainfall
events are shown in section 3. The differences are further
examined using the CloudSat cloud structures accompanying the PR–TMI differences and are shown in section 4.
The corresponding environmental profiles are shown insection 5. The discussion of the differences in the physicalprocesses and their relationships with convective structure organizations are shown in section 6.

2. Methodology
a. Dataset
This study utilizes the TRMM precipitation data fromPR and TMI as the core dataset for the comparative
analysis. The 94-GHz radar reflectivity data fromCloudSat are used to explain the different cloud structures associated with the PR and TMI heavy rainfall
events. Several environmental variables from ERAInterim were used to explain the thermodynamic environments involved in the formation of PR and TMI
heavy rainfall events. The detailed information for eachdataset is shown in Table 1.
The TRMM product used in this research is the version 7 level 2 product. The level 2 product contains theinstantaneous precipitation data from TRMM PR and
TMI. The PR precipitation dataset analyzed in this work
was obtained from the near-surface rain rates from the
TRMM PR 2A25 product (NASA 2011c). This producthas an approximately 5 km 3 5 km horizontal resolution
at nadir and a 250-km swath width (Iguchi et al. 2000;
Kummerow et al. 1998). The TMI precipitation dataset
was obtained from the TRMM TMI 2A12 product(NASA 2011b). The horizontal resolution is coarser
than that of PR but has a broader swath width than PR(6900 km) (Kummerow et al. 1998). The TMI surface
flags from the same product are used to classify
the surface types and separate the study area into ocean
and land surfaces. To further examine the details of
the differences originating from the emission and icescattering patterns obtained by the PMW sensor, thebrightness temperature dataset from TRMM TMI 1B11
is also analyzed (NASA 2011a).

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TABLE 1. Data description.
Platform

Instrument

Description

Resolution

TRMM

PR

13.8-GHz radar

4.3 km (at nadir)

TRMM

TMI

CloudSatERA-Interim

CPR—

Passive microwave
imager
94-GHz radar
ECMWF Re-Analysis

Varies from 7 km 3 5 km (85 GHz)
to 63 km 3 37 km (10 GHz)
1.4 km 3 1.7 km
Gridded 0.758

The cloud properties corresponding to the PR and TMI
heavy rainfall events are derived from CloudSat, which
has operated from 2006 until present. CloudSat carries a
94-GHz Cloud Profiling Radar (CPR) that captures thevertical cloud profiles (Stephens et al. 2002; Austin et al.
2009). By design, the CPR measurements provide only
cross-section profiles of the clouds below its track with ahorizontal resolution of approximately 1.4 km 3 1.7 km.
This resolution is approximately 3 times finer than that of
TRMM PR. In this analysis, the CPR reflectivity datafrom the CloudSat level 2B Geometrical Profile product(2B-GEOPROF) are used (CloudSat DPC 2017). Cloudflag data stored in the same product are also obtained toclassify the cloud and noncloud states. The rain-type datafrom the CloudSat 2C Precipitation Column product(2C-PRECIP-COLUMN) are also analyzed to classify
the observed rain events into convective and stratiform
precipitation events. The CloudSat measurements arematched in space and time with the TRMM.
The ERA-Interim data contain atmospheric reanalysis products with a horizontal grid resolution of
approximately 0.758 3 0.758 and contain 37 verticallevels, spanning from 1000 to 1 hPa (Dee et al. 2011).
The ERA-Interim dataset is available for 1979 to thepresent (ECMWF 2017), which covers the TRMM andCloudSat observational periods. The data are available
for 4 times a day. In this analysis, the air temperature,
specific humidity, relative humidity, and vertical velocity data are used to explain the physical processes corresponding to the PR–TMI differences.

b. Data preprocessing
The global tropical areas are divided into several domains to identify the regional differences in the PR and
TMI estimations of extreme rain events. The regions arefurther classified into ocean and land subregions. Overthe tropical ocean, the domains are divided into the 1)
global tropical ocean (GlobalTropics; 158S–158N, 1808–
1808), 2) Maritime Continent (MaritimeCont; 158S–158N,
908–1508E), 3) tropical west Pacific (WestPac; 158S–158N,
1508E–1808), 4) tropical central Pacific (CentralPac;

Target parameters
Near-surface rain rate (2A25), corrected
radar reflectivity (2A25)
Near-surface rain rate (2A12), brightness
temperatures (1B11)
Radar reflectivity
Air temperature, specific humidity, relative
humidity, and vertical velocity profiles

158S–158N, 1308W–1808), and 5) tropical east Pacific(EastPac; 158S–158N, 908–1308W). Over the tropical land,
the domains were divided into the 1) global tropical land
(GlobalTropics; 158S–158N, 1808–1808), 2) Maritime
Continent (MaritimeCont; 158S–158N, 908–1508E), 3)
South America (SouthAm; 358S–158N, 458–808W), and 4)
central Africa (CentralAf; 158S–158N, 158–458E). The
domains are shown in Fig. 1. The analysis spans 2006–14,
when TRMM, CloudSat, and ERA-Interim data are
available.
First, the precipitation data from PR and TMI are
plotted onto a 0.258 3 0.258-resolution grid, following
Sekaranom and Masunaga (2017). All sample data
points (raining and nonraining pixels) inside each grid
point are averaged to represent the gridpoint rain-ratevalue. This process ensures that the influences of thedifferences of the original PR and TMI resolutions when
capturing precipitation are minimized. A heavy rainfall
event database is then constructed by taking the uppermost 10% of the grid rain rates by using rain-rate
distribution defined in each region. The uppermost 10%
thresholds for each region are shown in Table 2. Using
the 10% threshold as an extreme criterion rather thanusing an even higher threshold ensures that the combined datasets capture sufficient observations. The database contains information about the grid rain rate, thegrid center coordinate, the maximum rain rate inside thegrid, the geolocations of the maximum rain rates inside
the grid, and the recording times of each identified heavy
rainfall event.
The TMI brightness temperature data are also gridded following the same rules as the PR and TMI precipitation data. Over the ocean surface, the brightness
temperature data from all nine TMI channels are analyzed. Only the brightness temperature data from the85-GHz vertical and horizontal polarization channels
are used for the land surfaces. The ocean and land surfaces are also determined using the TMI surface flag at
the 0.258 3 0.258 grid resolution. If more than two-thirdsof the grid is dominated by ocean flags, the grid is classified as an ocean grid. A similar criterion was used forJANUARY 2019

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SEKARANOM AND MASUNAGA

FIG. 1. Regional domains in this analysis. Shaded colors show the number of CloudSat deep
convective cases over 1) the global tropics, 2) the Maritime Continent, 3) the west Pacific, 4) theeast Pacific, 5) South America, and (6) central Africa.

the land grids. Grids that did not fulfill the above conditions were not considered in this analysis.
The combination of TRMM-CloudSat data are achievedby using a similar method to that described by Masunaga(2012). In this analysis, the precipitation events identified
by the TRMM are utilized as the coordinates and time
centers to be matched with the CloudSat data. Utilizing the
temporal differences between the TRMM and CloudSat
data, the precipitation cloud properties before, at, and after extreme events are reconstructed. Although the temporal changes could be easily obtained, identifying theheavy precipitation clouds at a 0.258 3 0.258 resolution
could produce inaccurate results since CloudSat has a very
narrow field of view (FOV). Therefore, only slices of the
TRMM gridded data that overlap with the CloudSat FOV(1.4 km 3 1.7 km) are considered. The coordinates withthe maximum rain rates inside each TRMM grid are utilized to ensure that CloudSat captures the center of theheavy precipitation systems. Only grids where the CloudSat coordinates are centered between 25 and 15 km fromthe coordinate of the TRMM maximum rain rate are
selected. In addition, CloudSat cross sections thatoccupy less than 80% of the length of each grid(’15 CloudSat profiles within each grid) are removed.
The maximum time difference between the TRMM
and CloudSat observations is set to 61.5 h. These strict
criteria limit the number of samples from CloudSat toabout 80 samples over the land domains (Table 3). The
1.5-h time window is selected because narrower timelags (20, 40, and 60 min) have been found to show
consistent results among each other except for andenhanced statistical noise owing to the reduced
sample size.
Two-dimensional (horizontal distance–height) composite cloud fractions are constructed to diagnose the
PR and TMI extreme precipitation cloud structures. The
composite cloud fractions are generated by using asimilar method to that described by Igel et al. (2014).

In the first step, clouds are identified using the minimum
detected reflectivity threshold of 228 dBZ. The corresponding heavy precipitating clouds are identified by
iteratively classifying contiguous adjacent cloud pixels
surrounding the cloud pixels located at the nearest distance to the TRMM maximum rain inside each grid. The
iteration process will classify contiguous cloud pixels
as a single cloud. Therefore, only the single cloud closest
to the coordinate of the maximum rain rate inside eachgrid point is considered when building the composite
cloud fractions. Each single cloud from a set of observations are stacked together into composite cloud
structure to represent the cloud vertical and horizontal
extents. Although the precipitating systems have various
sizes and shapes associated with them (e.g., Nesbitt et al.
2006), the composite of the heavy precipitating clouds
are considered to statistically represent the ‘‘average’’cloud structure. The minimum number of cloud composite is about eight observations over the Maritime
Continent land and higher for other regions, so thecomposite structure is unlikely to be distorted by a single, exceptional event.
To further explain the PR and TMI extreme precipitation cloud differences, the convective and stratiform cloud fractions are calculated using the CloudSat
rain-type flags. The convective:stratiform ratios are

TABLE 2. The uppermost 10% rain-rate thresholds to determine
extreme rain events at each domain.
Ocean

Land

Region

PR

TMI

PR

TMI

GlobalTropics
MaritimeCont
WestPac
EastPac
SouthAm
CentralAf

10.86
11.43
10.95
10.55
—
—

9.16
9.31
9.18
9.38
—
—

11.78
11.39
—
—
11.79
12.27

12.42
10.81
—
—
11.11
13.68

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calculated for each 1.5-h bin for the 6 h before and after
the heavy rain rates were identified by the TRMM.
In contrast to the TRMM instantaneous rain rates, the
ERA-Interim dataset has 6-h observation intervals andcoarser resolutions than the gridded precipitation database. In this analysis, those ERA-Interim grid coordinates nearest to the corresponding TRMM grid are
obtained to represent the environmental profiles. Only
the ERA-Interim data that fall between 1.5 h before andafter the TRMM extreme events are considered. The
potential temperature and specific humidity profiles arethen averaged for each ocean and land region. The
convective available potential energy (CAPE) is calculated to explain the relationship between the PR–TMI
extreme rain rates with environmental instability:#ð EL "
u(z) 2 uy (z)dz,gCAPE 5uy (z)
LFC
where LFC is the level of free convection, EL is theheight of the equilibrium level (neutral buoyancy), u(z)is the virtual potential temperature of the specific parcel,uy (z) is the virtual potential temperature of the environment, and g is the acceleration due to gravity. The
CAPE was averaged for each 1.5-h bin from 24 h before
and after the extreme events observed in the TRMM,
similarly to the method described by Masunaga (2012).

3. TRMM PR and TMI differences
In this section, the observed differences between the PR
and TMI when identifying heavy precipitation events aredemonstrated. First, the joint distribution plots of the collocated TMI 85-GHz minimum polarization corrected
brightness temperatures (PCTs) versus the rain rates areshown. The 85-GHz PCTs could be utilized as a proxy of
the amount of ice particles near the cloud top. Lower
85-GHz PCTs indicate the existence of abundant ice particles from the observed rain event. The 85-GHz PCTs were
calculated using the TB differences between the vertical
TBV and horizontal polarizations TBH , following Zagrodnik
and Jiang (2013), where PCT 5 1:818TBV 2 0:818TBH .
Plots of the probability distribution functions (PDFs)
of the collocated TMI brightness temperatures corresponding to the PR and TMI uppermost 10% rain rates
are also compared in this section. Over the ocean domains, the PDF plots for the 10-, 19-, 21-, 37-, and 85-GHz
brightness temperature data are presented. Over the landdomains, only the 85-GHz brightness temperature data
are shown. The plots of the collocated TMI 85-GHz
minimum PCTs versus the rain rates over each ocean area
are shown in Fig. 2. This figure indicates that both PR and

VOLUME 58

TABLE 3. Total number of observed CloudSat-collocated profiles
for the TRMM rain rate greater than the uppermost 10%.
Ocean

Land

Region

PR

TMI

PR

TMI

GlobalTropics
MaritimeCont
WestPac
EastPac
SouthAm
CentralAf

2092
457
339
175
—
—

1703
396
268
174
—
—

730
103
—
—
432
176

940
80
—
—
491
220

TMI show a broad relationship between the near-surface
rain and the collocated 85-GHz PCTs. The broad relationship indicates that heavy rain occurrence do notstrictly associated with the amount of ice particles. A
similar result was obtained by Sekaranom and Masunaga(2017) over the Maritime Continent. The figure also
shows that for TMI, the upper-end distributions of extreme events are found at higher PCTs compared to PR.
In Fig. 2, the white lines pinpoint the PCT where themaximum number of events are observed for rain rates
above the uppermost 10%. The TMI lines correspond toslightly higher PCTs at some regions compared to PR.
This indicates that TMI estimates heavy rain events withfewer ice particles than identified by PR.
The plots of the collocated TMI 85-GHz minimum
PCTs versus the rain rates over the land domains areshown in Fig. 3. This figure shows the clear contrast between PR and TMI. The PR joint distribution plots show
similar relationships for the ocean domains. In contrast to
PR, TMI shows a strong relationship between the twoparameters. This strong relationship is expected since the
TMI land algorithm has a ‘‘built in’’ linear relationshipbetween the rain rates and 85-GHz TB values (Sekaranomand Masunaga 2017). As a result, TMI tends to detect
higher numbers of events at lower PCTs (about 160–180 K
above the uppermost 10% rain events, as shown via thewhite lines in Fig. 3). This confirms that heavy rain events
identified by TMI over land are accompanied by a large
amount of ice particles. Extreme rain events identified by
PR, on the other hand, do not directly depend on theamount of ice particles similarly to over ocean.
The PDF plots of the collocated TMI 85-GHz brightness temperatures for the PR and TMI extreme events
over the ocean domains are shown in Fig. 4. This figure
shows that the TMI exhibits a contrast between the
Maritime Continent to the eastern Pacific Ocean. The
Maritime Continent is characterized by warmer sea surface temperatures (SST) because of its position between
the Pacific and the Indian Ocean warm pools (Ramage1968). The PR extreme events tend to have lower 10-GHz
brightness temperatures than the TMI extremes over theJANUARY 2019

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43

FIG. 2. Joint distribution of collocated minima TMI 85-GHz PCTs vs (a)–(d) PR rain rates and (e)–(h) TMI rain rates for ocean regions.
(left to right) Global tropics, Maritime Continent, west Pacific, and east Pacific. The color bar indicates the number of data points. The
white lines indicate the brightness temperature where the rain frequency above the uppermost 10% reached a maximum.

Maritime Continent. The TMI extreme events produce
slightly lower brightness temperatures at 85-GHz than the
PR extreme events over the Maritime Continent. This contrasts with the eastern Pacific Ocean, where the TMI extreme
events have higher brightness temperatures at 85-GHz than
the PR extremes. The brightness temperature contrast indicates that TMI identifies more heavy rain events with
abundant ice particles over the Maritime Continent than PR
identifies, and the opposite is true over the east Pacific Ocean.
The plots of the collocated TMI 85-GHz brightness temperature PDFs for the PR and TMI extreme events over theland domains are shown in Fig. 5. All the regions show that
the TMI extreme events are characterized by lower brightness temperatures than the PR extreme events at the rightmost edge of the distribution, while the TMI PDF is skewed
slightly warmer than the PR PDF. The difference in skewness indicates that TMI tends to identify more rain events
associated with abundant ice particles over all the land domains than PR identifies because of the tight assumption ofrain rates and ice particles in the TMI land algorithm.

4. CloudSat heavy precipitation cloud structure
differences
A comparison between the PR and TMI datasets
suggests that the active and passive TRMM sensors are

systematically different in their methods of identifying
heavy precipitation events. Further analysis focused on
the detailed cloud structures would be beneficial for
explaining the PR–TMI extreme precipitation event
differences. In this section, the differences between the
PR–TMI extreme events are compared by utilizing the
CloudSat observations. The composite cloud fractions
and convective and stratiform cloud fractions fromCloudSat are examined next. Last, the PR–TMI differences in identifying heavy rain events are examined interms of the convective organizations. Since many variables could be utilized to characterize this organization(Rickenbach and Rutledge 1998), in this research, theorganizations are simply defined with regard to thehorizontal scales of the systems. The more organizedsystems are associated with larger cloud structures andvice versa. This assumption is supported by CloudSat
observation from different time windows that shows thatthe horizontal cloud extents are continuously increasing
after the convective peaks are reached.
The CloudSat cloud fractions associated with the PR
and TMI extremes over the ocean domains are shown in
Fig. 6. TMI displays regional variations from the Maritime Continent to the eastern Pacific Ocean. Over the
Maritime Continent, the TMI extreme events correspond to taller and horizontally extensive clouds.

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JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGYVOLUME 58

FIG. 3. As in Fig. 2, but for the land domains.

However, the heights continuously decrease toward theeastern Pacific Ocean. Over the eastern Pacific Ocean,
the TMI tends to identify shallow clouds. (Liu andZipser 2013) showed that shallow–wide clouds are
predominant in this area. TMI somehow detects theseclouds as extremes rather than detecting the taller
clouds identified by PR. Moreover, the differencesamong the ocean regions identified by PR do not display
this gradual change. Shige et al. (2008) showed that PR
captures the contrast between the western and eastern
Pacific Oceans that mainly occur during La Niña. In thepresent results, the PR rainfall over the eastern Pacific
Ocean produces a similar profile to that of the western
Pacific Ocean, possibly because this work exclusively
targeted extreme events.
The corresponding PR and TMI extreme precipitation
cloud fractions over the land domains are shown in Fig. 7.
The CloudSat cloud fractions over the land domains differ from those over the ocean domains. All land domains
show that the TMI extreme events are confined to narrow
convective cells. In contrast, the PR extreme events are
generated by more horizontally extensive cells with lower
cloud-top heights than those of TMI. Sekaranom and
Masunaga (2017) showed that the PR extremes withrelatively low STHs could generate greater rain rates(.30 mm h21) than the TMI extremes with taller STHs.
Yamamoto et al. (2008) found a time lag between thepeaks of the convective rainfall events detected by PR

and TMI. This implies that the PR and TMI detect
different stages of convection. In this research, theCloudSat convective and stratiform fractions were used
to characterize the degrees of convective organization.
The CloudSat convective and stratiform fractions corresponding to the PR and TMI extreme events over theocean and the land domains are shown in Fig. 8. A different feature is observed between the Maritime Continent and the eastern Pacific Ocean. Over the Maritime
Continent, an earlier convective peak is identified by
TMI, occurring approximately 3 h before the extremes
detected by the TRMM, while the PR convective peakoccurs 1.5 h before. Liu and Zipser (2014) showed that
TMI tends to detect greater precipitation areas than PR.
The greater precipitation areas could be generated bymore horizontally extensive cells with earlier convective
peaks than those detected by PR.
From the above results, we can characterize the PR–
TMI differences in terms of the convective organizations. The degree of organization of the systems also canbe characterized based on the time differences between
the peaks. Over the Maritime Continent ocean, TMI
captures more organized systems than PR. This result isreversed over the eastern Pacific Ocean, where TMI
captures less organized systems than PR. Over land, thePR in general captures more organized systems than
TMI. Over the Maritime Continent land, the less organized systems identified by TMI are associated with aJANUARY 2019

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FIG. 4. Collocated TMI brightness temperatures for the PR and TMI extreme events over the oceans for (left to right) eachregion and (top to bottom) the brightness temperature channels.

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FIG. 5. As in Fig. 4, but for the land domains.

later convective peak than PR, whereas over SouthAmerica and central Africa, the less organized precipitation systems identified by TMI are associatedwith a larger convective fraction (Figs. 8g,h).

5. Influence of environmental conditions on the
PR–TMI rain-rate estimation differences
To further study the physical processes in the precipitation cloud development, comparisons of theenvironmental profiles from ERA-Interim are given inthis section. First, the potential temperature and humidity profile differences corresponding to the PR and
TMI extreme events are compared. The influence of theinstabilities in the PR and TMI heavy precipitation
cloud development is further explained using thecorresponding CAPE.
The corresponding PR–TMI environmental profile
differences over the ocean domains are shown in Fig. 9.
The results show that the TMI extreme events occurred

FIG. 6. CloudSat collocated cloud fractions for the (a)–(d) PR extreme events and (e)–(h) TMI extreme events over the examined ocean
domains. (a),(e) Global tropical ocean, (b),(f) Maritime Continent, (c),(g) west Pacific, and (d),(h) east Pacific.

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47

FIG. 7. As in Fig. 6, but for the land domains. (a),(e) Global tropical land, (b),(f) Maritime Continent, (c),(g) South America, and(d),(h) central Africa.

in more humid environments than those of PR. A contrast is also observed from the Maritime Continentcompared to the eastern Pacific Ocean. Lower heights at
which the humidity difference is largest are observed
over east Pacific Ocean compared to the Maritime
Continent ocean. Over the Maritime Continent andwest Pacific Ocean, the TMI extreme events correspondto higher humidities throughout the troposphere. Overthe eastern Pacific Ocean, the humidity excesses for the
TMI extremes are reversed and become humidity deficits above 500 hPa and near the surface.
The PR extreme events over the ocean domains
generally have slightly warmer potential temperatures near the surface, but have colder values above the
500-hPa level than those of the TMI extreme events. The
Maritime Continent–eastern Pacific Ocean contrast isalso present in the potential temperature profiles. As
identified by PR, the Maritime Continent is characterized by lower potential temperatures in the midtroposphere and shows only slight differences from the TMI
values near the surface. Over the eastern Pacific Ocean,
the near-surface temperature differences become larger.
The corresponding PR–TMI environmental profile
differences over the land domains are shown in Fig. 10.
This figure shows that the PR extreme events occur at
higher specific humidities than the TMI extreme events

for all identified regions, except near the surface, which
differs from the results for the ocean extremes. Higher
specific humidities were observed, especially at the
850–500-hPa level. Furthermore, the corresponding PR
extreme events show colder potential temperatures near
the surface than those of the TMI extreme events. The
greater humidity but lower potential temperature differences corresponding to the PR extreme events werealso identified by Hamada et al. (2015) and Song andSohn (2015). However, the ocean has considerably different thermodynamic characteristics than those of theland domains, and clouds cannot be explained by theabove results.
To examine the possible temporal change of theabove environmental profile differences, temporal plotsof the specific humidity and potential temperature overthe global tropical land and ocean are shown in Fig. 11.
The figure shows that over land, a deepening of thelower-tropospheric cold bias and a rapid development ofdry bias toward the time of the precipitation occurrence
are observed in association with the TMI extremes
compared to the PR extremes. The overall patterns arenevertheless qualitatively similar to what has been seen
at t 5 0 (Figs. 9 and 10). Over ocean, the temporalchanges are even less significant compared to the land
environmental profiles.

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FIG. 8. CloudSat convective fractions for the PR extreme events (red line) and TMI extreme events (green line) over the (a)–(d) ocean and(e)–(h) land. The shaded color represents the 95% confidence interval.

The CAPE plots corresponding to the PR and TMI
extreme events over the ocean and land domains areshown in Fig. 12. In general, the highest rain rates aredetected with downward slopes after the CAPE drops

following a maximum that resembles tropical deep
convective clouds (Masunaga 2012). In general, the PR
extreme events over the ocean domains have higher
CAPE values than the TMI extreme events. The ocean

FIG. 9. Potential temperature anomalies (pot.temp_diff; orange) and specific humidity anomalies (shum_diff; blue) from the ERA-Interim
over ocean regions in terms of the PR–TMI differences.

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49

FIG. 10. As in Fig. 9, but for the land domains.

CAPEs are also higher for the western Pacific comparedto the eastern Pacific, with the CAPE reaching a maximum over the Maritime Continent. TMI extremes prefer slightly more stable environments than the PR
extremes as implied by the potential temperature
profiles shown earlier.
Over the land domains, the corresponding TMICAPEs are greater than the PR CAPEs with notable
diurnal variations. This result indicates that the TMI extreme events occur in more unstable environments than
the PR extreme events, differing from the ocean composite. Regional differences of the CAPE in Fig. 12 showthat heavy rain events over the Maritime Continent(land) and central Africa in general are generated byhigher CAPE than over South America. Lower CAPE
might be associated with precipitation that is generated
by the more oceanic environment over South America(Masunaga et al. 2002; Sekaranom and Masunaga 2017).
Extreme rain events with high CAPE are identified overcentral Africa, which is often associated with strong afternoon heating (Liu and Zipser 2005). The strong instability is possibly related to the higher rain rates
estimated by TMI than PR for the general rain events(Liu and Zipser 2014). However, for extreme rain events,
the PR captures some events that are associated with amore stable environment than identified by TMI.

6. Discussion and conclusions
This study investigates the precipitation biases fromthe TRMM active–passive sensors due to cloud and environmental properties. The combination of the TRMM,
CloudSat, and ERA-Interim data provides comprehensive

information to explain the PR–TMI estimation differences.
The general properties of the PR–TMI differences in
identifying the heavy rainfall events are further discussed inthis section.
Noticeable differences between the PR and TMI extreme events are observed over the land domain. The clear
ice-scattering signatures (Fig. 3) and horizontally confined
cloud structures (Fig. 7) of the TMI extremes indicate that
the TMI heavy rain events are associated with isolated
deep convective clouds over land (Fig. 13b, right). Plotsfrom the ERA-Interim data show that the TMI extreme
events over land occur in drier free-troposphere conditions
but in more unstable environment than the PR extreme
events. The CAPE patterns (Fig. 12) also indicate that thediurnal forcing strongly controls the heavy rain events,
primarily via afternoon surface heating (Yamamoto et al.
2008). The more unstable environment produces higher
cloud tops and can produce more abundant ice particles
aloft (Song et al. 2017), which can then produce a stronger
ice-scattering signal.
In contrast to the deep isolated clouds identified by the
TMI, the PR extremes over land tend to be associatedwith more organized systems (Fig. 13b, left). The absenceof a strong relation between the PR rain rates and the TMI
85-GHz PCTs (Fig. 3) implies that the PR extremes are
not always associated with a substantial amount of icephase hydrometeors aloft (Furuzawa and Nakamura 2005;
Sekaranom and Masunaga 2017). The extreme precipitation clouds corresponding to the PR extreme eventsshow more horizontally extensive convective cells thanthose associated with the TMI heavy rainfall events(Fig. 7). The PR heavy rainfall events are associated withmoister environments in the free troposphere as well as

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FIG. 11. Temporal changes of the potential temperature anomalies and specific humidity anomalies fromthe ERA-Interim before and after heavy rain events identified by the TRMM. The plots are for global tropical(a),(c) ocean and (b),(d) land in terms of the PR–TMI differences.

more stable environments than the TMI heavy rainfall
events (Fig. 12). This more stable environment produces
lower cloud-top heights and less confined cloud structures,
which include warm rain extremes (Song et al. 2017).
These warm rain extremes may be missed by TMI becauseof the lack of ice particles.
The TMI extremes are not essentially different fromthe PR extremes over ocean domains in terms of their
PCTs (Fig. 2). However, the collocated TMI microwave
emissions indicate regional differences of TMI over theocean domains (Fig. 4) that also differ from the PR extreme events. Over the eastern Pacific Ocean, the collocated CloudSat cloud-top heights corresponding to the
TMI extreme events are lower than those of the PR

extreme events (Fig. 6). It appears that their horizontal
extent is much larger than those of individual cumulus
clouds (Liu and Zipser 2013), although it is smaller thanfound in other oceanic regions. (Fig. 13a, bottom right).
The shallow horizontally extensive clouds are associatedwith moister environments near the surface but drier
environments than those of their PR counterparts at
approximately the 500-hPa level (Fig. 9). The shallow
horizontally extensive clouds were also found in a morestable environment than that identified by PR (Fig. 12).
In contrast to the shallow horizontally extensive clouds,
the higher cloud-top heights associated with the PR
heavy rainfall events correspond to more unstableenvironments.

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51

FIG. 12. Temporal changes of the collocated CAPE from the ERA-Interim data for the PR extreme events (red) and TMI extreme
events (green).

Comparisons among the ocean domains show a regional transition of the extreme precipitation cloud
structures from the eastern to western parts of the
Pacific Ocean. Over the western Pacific Ocean, theCloudSat profiles show that the horizontal extents areslightly broader for the TMI extremes than for the PR
heavy rainfall events (Fig. 6). The TMI convective
fraction is greater, and its peak occurs earlier than the
PR convective peak, which implies that TMI detects
heavily organized precipitation systems (Fig. 13a, top
right). Based on the corresponding environmental
conditions, the TMI extreme events tend to occur inmore humid and stable environments than the PR extreme events (Fig. 12). In the case of TMI extreme
events, the moister environment (Fig. 9) could produce
more extensive convective cloud systems. The moisterenvironment could be partially due to rain evaporationin stratiform clouds, which moistens the atmosphere.
A comparison over the ocean domain suggests thatthe beamfilling effect (Kummerow 1998) has a strong
influence on the TMI heavy rain-rate estimation. Overboth the eastern and western Pacific Oceans, TMI captures horizontally extensive clouds despite the cloud-top
heights being lower over the eastern Pacific Ocean.
This result suggests that TMI tends to detect larger

precipitation areas than PR, as identified by Liu andZipser (2014). Furthermore, the result shows that thisstrong emission might be associated with higher stratiform
fractions. As identified by Henderson et al. (2017), TMI
tends to estimate higher rain rates for stratiform rain
than PR.
This paper attempts to explain the differences between the TRMM PR and TMI when identifying
heavy rainfall events over the global tropical lands
and oceans. The results show that PR and TMI detectheavy rainfall events from distinct physical processes
that can be explained in terms of precipitation organization. Characterizing this organization could provide a potential application for reducing the biases
between the PR and TMI rain-rate estimations. The
results presented in this paper also represent a potential contribution to improving satellite precipitation estimations and their derived products when
active–passive satellite sensors are combined.
The future works of this research will focus onwhether different degrees of precipitation organization
could be utilized to reduce the rain estimation biases
between the PR and TMI and on whether the bias pattern as a function of organization is consistent over time,
particularly across different ENSO phases.

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FIG. 13. Illustration of the PR–TMI differences when identifying heavy rainfall events as associatedwith organized precipitation. (a) The contrast between the eastern and western Pacific Oceans; (b) the
differences over land.

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Acknowledgments. This research is funded by Lembaga
Pengelola Dana Pendidikan (LPDP), Ministry of
Finance Republic of Indonesia, with the Contract
PRJ-1875/LPDP/2014. The TRMM PR (2A12) andTMI (2A25) datasets analyzed in this work are available
online from the NASA Precipitation Measurement Mission (PMM) archives (https://pmm.nasa.gov/data-access/downloads/TRMM). The CloudSat data are provided
by http://cloudsat.atmos.colostate.edu/data. The ERAInterim data are available at https://www.ecmwf.int/en/
forecasts/datasets/reanalysis-datasets/ERA-Interim.

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