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Detection and Tracking of Tropical Convective Storms Based on Globally Gridded Precipitation
Measurements: Algorithm and Survey over the Tropics
HANII TAKAHASHI,a,b MATTHEW LEBSOCK,b ZHENGZHAO JOHNNY LUO,c HIROHIKO MASUNAGA,d AND
CINDY WANGeaJoint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Californiab
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CaliforniacDepartment of Earth and Atmospheric Sciences, City University of New York, City College, New Yorkd
Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japane
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey(Manuscript received 30 July 2020, in final form 22 January 2021)

ABSTRACT: This paper is the first attempt to document a simple convection-tracking method based on the IMERG
precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now, precipitation
datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian
accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air
masses. Convection-tracking algorithms have traditionally been developed on the basis of brightness temperature (Tb) fromsatellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in a moist environment; this situation cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, anadvantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into
convective storms, which provides us more-complete life cycle records of convective storms than traditional IR-based
tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective
systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis
reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining
after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 h after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps toelucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric
thermodynamics.
KEYWORDS: Tropics; Storm tracks; Precipitation; Cloud tracking/cloud motion winds; Databases; Microwave observations

1. Introduction
An attempt to understand the variability of precipitation life
cycles was originally made nearly three decades ago using
visible and infrared (IR) geostationary satellite imaging(Griffith et al. 1978). Since then, there has been a growing literature about convective evolutions and their associated precipitation life cycles based on analyses of convection-tracking
methods using satellite IR retrievals.
Convection tracking was originally done manually (e.g.,
Martin and Schreiner 1981; Velasco and Fritsch 1987; Miller
and Fritsch 1991; Laing and Fritsch 1993a,b; Rowell and
Milford 1993). Although it revealed interesting aspects of
convective evolution, such manual tracking was limited in its
sample size and consistency because it was labor intensive andcould be subjective. To overcome the limitations of manual
tracking, Woodley et al. (1980) and Williams and Houze (1987)

Supplemental information related to this paper is available atthe Journals Online website: https://doi.org/10.1175/JAMC-D-200171.s1.
Corresponding author: Hanii Takahashi, hanii.takahashi@jpl.
nasa.gov

proposed automatic tracking algorithms. Following these twopioneering studies, the literature based on automated convection tracking has grown. The classic way to identify andtrack deep convective systems is to use a cold brightness temperature (Tb) threshold from satellite IR images to define
continuous areas of deep convective cloudiness. The selectionof Tb varies from analysis to analysis, but usually ranges
from 208 to 255 K (Fiolleau and Roca 2013a). Since this range
of Tb corresponds to a range of altitudes greater than 5 km,
the number of convective objects tracked, their extent, andlifetime are sensitive to the choice of Tb.
The tracking algorithms also vary from one study to another.
The most popular algorithm has been the area overlapping
technique that estimates the fraction of the geographicaloverlap between convective clouds on two successive images(Arnaud et al. 1992; Williams and Houze 1987). One of themost notable is Machado et al. (1998), which developed analgorithm using the International Satellite Cloud Climatology
Project (ISCCP) Pixel Level Cloud Product (DX) datasets(Rossow and Schiffer 1999) to track the evolution of mesoscaleconvective systems through their life cycles. Such datasets arecalled ISCCP convection-tracking (CT) datasets. In the
ISCCP-CT datasets, connected IR pixels with Tb , 245 K
and with Tb , 220 K are identified as convective systems andconvective clusters, respectively. The ISCCP-CT datasets

DOI: 10.1175/JAMC-D-20-0171.1
Ó 2021 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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cover latitudes , 558 for more than 30 years, which gives us
opportunities to study convective properties and features ofmesoscale convective systems in different life stages (e.g.,
Takahashi and Luo 2014; Vant Hull et al. 2016).
While the existing approaches to tracking convective storms
have resulted in a great deal of progress in our understandingof convective storms, their motivation is not always to monitor
the evolution of precipitation per se, but to monitor the evolution of convective storms that are convecting and precipitating. There are fundamental limitations of using IR retrievals
based on Tb thresholds for the purpose of tracking precipitation evolution. First, thinner in situ cirrus clouds (unassociatedwith active convection) are IR semitransparent and lookwarmer, and thus such clouds can be excluded when colder Tb
thresholds are applied (Machado et al. 1998). However, someof thicker in situ cirrus clouds can be nearly as cold as convective storms and thus they can be incorrectly identified as
deep convective systems. Second, even when cold Tbs are associated with convection, there is a poor correlation between
Tb and precipitation rate (e.g., Karbalaee et al. 2017a,b). To
monitor both cloud and precipitation evolution, which is our
purpose of this study, we argue that these shortcomings can be
mitigated if a tracking algorithm is applied to precipitation
patterns instead of IR imagery.
Liu et al. (2008) compiled a precipitation feature databasefrom measurements of radar and visible, IR, and microwave(MW) radiometers on the Tropical Rainfall Measuring
Mission (TRMM; Kummerow et al. 1998) satellite. This
TRMM-based cloud and precipitation feature database has
been used to study the regional variations of rainfall characteristics, vertical structure of rainfall, convective properties, aswell as diurnal variations of precipitation (e.g., Xu and Zipser
2011; Luo et al. 2013). However, TRMM, a low-Earth-orbiting
satellite flying over a certain location several times per day,is limited in sampling frequency, which makes it difficult tomonitor hourly scale variability critical to precipitation cycles.
For this purpose, a gridded high-spatiotemporal-resolution precipitation product is required.
Such precipitation products exist. For example, the National
Oceanic and Atmospheric Administration (NOAA) Climate
Prediction Center morphing technique (CMORPH; Joyce
et al. 2004) and TRMM 3B42 (Huffman et al. 2007) have aspatial resolution of 0.258 and a temporal resolution of 3 h, and
the Global Satellite Mapping of Precipitation (GSMaP;
Kubota et al. 2007; Ushio et al. 2009), one of the Global
Precipitation Measurement (GPM; Hou et al. 2014) mission
products like Integrated Multisatellite Retrievals for GPM(IMERG), has a spatial resolution of 0.18 and a temporal resolution of 1 h. Previous studies used them for a variety of research topics: regional model evaluations (e.g., Beucher et al.
2014; Zhang and Anagnostou 2019), physics of costal convection (Coppin et al. 2020), precipitation duration (White et al.
2017), diurnal cycles (Janowiak et al. 2005), and Madden–
Julian oscillation (Kerns and Chen 2016, 2020). A previous
study also made a comparison between gridded precipitation
like 3B42 and IR based gridded data, which showed no improvements using gridded precipitation (Dias et al. 2017).
However, many previous studies demonstrated that the approach

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of merging IR and precipitation provided some insight into thelife cycle of isolated tropical cold cloud systems (Imaoka andNakamura 2012), tropical mesoscale convective systems(Fiolleau and Roca 2013a,b), midlatitude deep convective
systems (Feng et al. 2012), and deep convective system (Futyan
and Del Genio 2007). The present study further explores theutility of global precipitation datasets with focus on propertiesof the precipitation life cycle including systematic differences
between cloud-top temperature and surface precipitation,
which have not been well documented before.
Beginning in 2014, GPM released a new generation of the
precipitation product IMERG (Huffman et al. 2020), which
offers global precipitation monitoring with a spatial resolution of 0.18 and a temporal resolution of 0.5 h. This highspatiotemporal-resolution precipitation product has not yet
received a systematic validation at its full resolution although
its regional errors and biases are well documented (e.g., Gosset
et al. 2018; Anjum et al. 2018; Sharifi et al. 2019), and issues due
to inhomogeneity in space and time also have been reported(e.g., Tan et al. 2019). Although there have been some limitations and uncertainties in IMERG (see section 5), the qualityof IMERG has improved as the versions of IMERG have been
constantly updated. The latest version, IMERG V06B, wasreleased on June 2019, and a recent study of Tang et al. (2020)
claims that the IMERG V06B outperforms many satellite
precipitation products such as TRMM 3B42, CMORPH,PERSIANN-CDR (Ashouri et al. 2015), Climate Hazards
Group Infrared Precipitation with Stations (CHIRPS; Funket al. 2014), and Soil Moisture to Rain (SM2RAIN; Brocca
et al. 2014). Taking advantage of this opportunity, we develop
an algorithm to identify and track tropical convective storms
based on the latest version of IMERG V06B.
The present paper is the first, to our knowledge, to use
IMERG to conduct convection tracking. Therefore, themain purpose of this paper is to document a simple convectiontracking method based on the newly developed IMERG
product to generate an IMERG-based convection-tracking
dataset (IMERG-CT). It is worth pointing out that up to now
precipitation datasets have been Eulerian accumulations.
However, with IMERG-CT, it is now possible to estimate
not only the time evolution of instantaneous rain intensity
but also the lifetime-accumulated precipitation amount ofeach convective storm. Therefore, this precipitation-based
convection-tracking algorithm provides the highest global
spatiotemporal resolution of any algorithm to date thatable to monitor precipitation evolution from the Lagrangian
perspective.
Moreover, since we also include some information on cloud
tops based on IR imagery in IMERG-CT, the link between theevolution of precipitation and convective cloud development
can be examined. Thus, another purpose of this paper is todemonstrate the utility of IMERG-CT through investigating
the linkage between precipitation and cloud properties such as
Tb. Therefore, in addition to IMERG precipitation product,
we use the latest ISCCP product, ISCCP H-series pixel-levelglobal (HXG), to get information of Tb. Moreover, lidar andradar products based on CloudSat observations are used toevaluate the IMERG-CT algorithm. These satellite datasets

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used in this study are described in the next section. Section 3
explains the algorithms of IMERG-CT. Some evaluations andsensitivity tests for IMERG-CT are conducted in section 4.
Results based on statistical and composite analysis are presented in section 5, followed by section 6, where the key findings are summarized and discussed.

405

2. Datafor conducting the ISCCP-CT dataset described in the introduction. In 2017, the new pixel-level cloud product called HXG
was released, which has higher spatial sampling of ;10 km(0.18 latitude/longitude) when compared with DX. (ISCCP-HXG
is available at https://www.ncdc.noaa.gov/isccp/isccp-data-access,which covers the period from 1982 to 2018.) Detailed information
about the ISCCP-HXG cloud product is provided by Younget al. (2018).

a. IMERG

d. Instantaneous comparison between IMERG and HXG

The IMERG product 3IMERGHH is used to estimate
gridded surface precipitation. The horizontal and temporal
resolutions of this latest IMERG product, 3IMERGHH, havebeen improved from 0.258 to 0.18 latitude/longitude spatial
resolution (;10 km in the tropics), and from a 3-hourly to a
30-min temporal resolution. This offers global precipitation
monitoring based on a merged retrieval using algorithms developed by several groups: TRMM Multisatellite Precipitation
Analysis (TMPA; Huffman et al. 2007), Climate Prediction
Center Morphing with Kalman Filter (CMORPH-KF; Joyce
and Xie 2011), and microwave recalibrated PERSIANN-CCS(Hong et al. 2004). Basically, the algorithms intercalibrate andmerge ‘‘all’’ satellite microwave rain estimators, together with
MW-calibrated IR satellite estimators, precipitation gauge
analyses, and combined radar–radiometer precipitation estimations where TRMM and GPM observations are available, atfine time and space scales over the whole globe. It is critical tonote that although the IMERG data product uses infraredobservations to fill in spatial and temporal gaps, the infrared isanchored to much more reliable precipitation estimates by
microwave observations (both preceding and following theanalysis time) wherever available. [Huffman et al. (2020)
provides an overview of the IMERG product and the data are
available at https://storm.pps.eosdis.nasa.gov/storm/.] In thisstudy, we use ‘‘precipitaionCal,’’ or multisatellite precipitation
estimates with gauge calibration for precipitation rate.

Although the heaviest rainfall is often assumed to be associated with the tallest convective towers, a previous study(Hamada et al. 2015) suggests that the linkage between intensity of rainfall and convective development remains weaksince warm rain can be heavier than cold rain under a certain
environment. It is also known that cirrus anvils can be as cold as
convective cores, but they do not produce heavy rain likeconvective cores. Therefore, it is interesting to explore the link
between cloud-top temperature (i.e., infrared brightness temperatures) from ISCCP-HXG and precipitation rate fromIMERG. ISCCP-HXG has a lower temporal resolution (3 h)than IMERG (30 mins), yet ISCCP-HXG has the same
spatial resolution as IMERG (0.18 latitude/longitude),which makes it easier to explore an instantaneous link between them. Figure 1 shows instantaneous comparisons between precipitation (.0.01 mm h21) and Tb with different
thresholds, and as expected, correlations between Tb andprecipitation (.0.01 mm h21) are low as shown in Table 1. This
result indicates that IR imagery alone has difficulties in monitoring precipitation. IMERG and other precipitation productsof similar design attempt to push this limit by incorporating
satellite microwave measurements wherever available, although the accuracy of estimates is not always assured when
the absence of microwave observations are filled in by infraredobservations. This is a primary motivation to use IMERG precipitation rate rather than IR Tb to identify and track rain events.
We further test if light rainfall (precipitation . 0.5 mm h21) andrelatively heavy precipitation (precipitation . 5 mm h21) correlate better with some Tb, but spatial correlations get even lower(Table 1). It is very interesting to see that a decrease or
increase of Tb thresholds does not change the correlations
between precipitation rate and Tb by much. This result
suggests that the evolution of rainfall intensity does not always follow that of cloud-top development and suggests thatthe peak of cloud top that reaches minimum Tb and the peakof rain event that reaches maximum precipitation do notnecessarily happen simultaneously. Therefore, it is interesting to further explore the temporal separation between
the two peaks to understand the life cycles of tropical convective storms, which is a central motivation for developing
IMERG-CT.

b. CloudSat
CloudSat (http://www.cloudsat.cira.colostate.edu/) is a polarorbiting satellite that overpasses the equator at ;0130/1330
local time. CloudSat carries a 94-GHz Cloud Profiling Radar(CPR), which has a horizontal resolution of 1.7 km along trackby 1.4 km across track as well as a vertical resolution of 480 m
oversampled at 240 m. CPR is sensitive to both cloud- andprecipitation-size particles and has the capability of providing
vertical structures of clouds. In this study, we use information
about cloud layers from 2B–Geometrical Profiling Product(GEOPROF)-lidar, and the cloud mask and the calibrated
radar reflectivity profiles from the 2B-GEOPROF product(Marchand et al. 2008). An overview of the CloudSat mission
is summarized in Stephens et al. (2008).

c. ISCCP-HXG
Since 1982, ISCCP has provided global cloud information based on geostationary satellite data. A pixel-levelcloud product called DX, whose temporal and spatial
resolutions are limited to mesoscale (i.e., 30-km mapped
pixel and a 3-h time interval), has been widely used, especially

3. Definition of tropical convective storm and tracking
algorithm
Our primary focus is to track the evolution of precipitationin tropical convective storms. Therefore, we apply a tracking
algorithm over IMERG by identifying precipitation cores

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FIG. 1. Tropical maps of (a)–(d) Tb from ISCCP-HXG based on different thresholds as labeled and (e) precipitation rate (.0.01 mm h21)
from IMERG at 0000 UTC 1 Jan 2008.

(PCs) and precipitation systems (PSs) between 208S and 208N
latitude.

a. Precipitation cores and precipitation systems
Similar to ISCCP-CT, we use two thresholds to make a distinction between PCs and PSs. Previous studies based on TRMM
Precipitation Rader (PR) have defined a rain cell as adjacentgrid boxes showing the precipitation rate $ 0.5 mm h21 (e.g.,
Jiang et al. 2011; Li et al. 2018), which is the lowest light precipitation that TRMM PR can capture. From TRMM, thislowest light precipitation rate threshold ($ 0.5 mm h21) captures
97% of surface rain amount (Schumacher and Houze 2003),
whereas the most frequently detected heavy rain rate overtropical and subtropical ocean is 5 mm h21 (Berg et al. 2010).

Figure 2 demonstrates how these thresholds associate withtropical storms in IMERG: precipitation rate $ 5 mm h21(Fig. 2a) captures most of the heavy rain cells near the convection, whereas precipitation rate $ 0.5 mm h21 (Fig. 2b) also
TABLE 1. Spatial correlation coefficients between Tb and PR withdifferent thresholds at 0000 1 Jan 2008.

Tb , 280 K
Tb , 260 K
Tb , 245 K
Tb , 220 K

PR. 0.01 mm h21

PR. 0.5 mm h21

PR. 5 mm h21

20.27
20.27
20.26
20.26

20.23
20.22
20.21
20.23

20.16
20.16
20.16
20.20

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FIG. 2. IMERG precipitation rates for (a) precipitation $ 5 mm h21, (b) precipitation $ 0.5 mm h21, and the largest(c) PC and (d) PS over 908–1508E and from 88S to 208N observed on 0730 UTC 9 May 2008.

includes light precipitation as it captures the majority ofrainfall (;70%) in IMERG.
We use these two thresholds to make a distinction between
PCs and PSs: a PC is identified as adjacent rainy pixels with thehigher threshold ($5 mm h21) and a PS is identified using thelower threshold ($0.5 mm h21) when at least one PC is embedded in the PS. In this way PSs are defined to be larger than
the PCs and inclusive of the core area. Examples of the largest
segmented cluster of PC and its corresponding PS over thisregion are demonstrated in Figs. 2c and 2d, respectively. The
sensitivity to different thresholds will be discussed in section 4.

b. Tracking algorithm
A 3D labeling is applied for PCs based on a 10-connected
spatiotemporal neighborhood, composed of an 8-connected
spatial neighborhood and a 2-connected temporal neighborhood, similar to Fiolleau and Roca (2013a). In other words,
pixels are defined to be connected if their edges or corners
touch, and two adjoining pixels (one time step before andafter) are considered to be part of the same object (Fig. 3a).
This 3D labeling connects individual PCs into a single family bygiving a unique family number. In each time step of a PC, a PS
in which the PC is embedded is identified in a way that Fig. 2
illustrates: the PC in Fig. 2c is embedded in the PS in Fig. 2d,and it is possible that more than one PC share the same PS
when PCs are closely located. Then, information of location,
intensity, and area of the PC, together with the PS in which the
PC is embedded, are saved. In this way, we are able to track theevolution of tropical convective storms from PCs and theirassociated PSs (Fig. 4), including stratiform areas. We also
create a flag when a PC experiences merging or splitting. We
refer to our convection-tracking dataset to as IMERG-CT andthe list of variables available in IMERG-CT is summarized inTable 2.
Vigorous rainfall can be produced by warm-topped systems
in moist environment, which cannot be captured by traditional
IR-based tracking but is observed in IMERG-CT. Therefore,
an advantage of IMERG-CT is its ability to include the

FIG. 3. Illustrations of (a) the 10 connectivity (an 8-connected spatial neighborhood and a 2-connected temporalneighborhood) and (b) the 6 connectivity (a 4-connected spatial neighborhood and a 2-connected temporalneighborhood).

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FIG. 4. A demonstration of tracking the evolution of precipitation from a precipitation core andits associated precipitation system.

previously missing information of shallow clouds that growinto convective storms, which provides us more complete life
cycle records of convective storms than traditional IR-based
tracking does. This means that IMERG-CT may also containPCs that are only associated with shallow clouds that never
have chance to develop deep, because such clouds can produce
precipitation rate . 5 mm h21, which is our definition of PCs.
In this study, we would like to exclude such clouds to focus only
on life cycles of larger tropical convective storms.
To exclude IMERG-CT families whose PCs are all associated with shallow clouds, we also track life cycles of Tb basedon ISCCP-HXG. Recall that the frequency of the ISCCP-HXG
data is every 3 h so it is only available once every 6 IMERG
time steps. We store the information of Tb for PCs and PSs in
IMERG-CT when the observed time between ISCCP-HXG
and IMERG matches, and make an additional requirement: an IMERG-CT family must have at least one PC thathas minimum Tb , 245 K at some point during the life
cycle, the same threshold that is applied to select convective systems in ISCCP-CT. Adding information of Tb into
IMERG-CT also helps us in investigating the life cycle correlations between precipitation and Tb and exploring the potential temporal separation between peaks of precipitation
and Tb.

4. Evaluation and sensitivity testa. Comparison with observations from CloudSat
To evaluate the utility of the additional requirement that Tb
must reach below the 245 K threshold in at least one instantin the whole lifetime, one month (January 2008) of CloudSat
observations (2B-GEOPROF and 2B-GEOPROF-lidar) arecollocated with IMERG when the observed time between

CloudSat and IMERG matches. Since CloudSat CPR and IMERG
have resolutions of 1.4 and 10 km, respectively, ;10 pixels
of CloudSat CPR profiles fall into an IMERG grid box.
Therefore, maximum values of cloud-top height (CTH) andcloud thickness based on 2B-GEOPROF-lidar are composited over all PCs, and histograms of these cloud properties are compared between IMERG-CT families withminimum Tb , 245 K and those with minimum Tb . 245 K
in Figs. 5a–d. Results clearly show that the requirementof minimum Tb , 245 K can eliminate the majority of low
clouds whose CTH and cloud thickness are less than 10 km.
Note that CloudSat does not necessarily detect all the deep
clouds within a 0.18 3 0.18 grid box due to the nadir sampling

TABLE 2. List of variables available in IMERG-CT.
Family number (unique by year)
Lifetime of this family as number of PS it contains
Max precipitation rates in this family (mm h21)
System total precipitation in this family (kg)
Max PC area in this family (km2)
Max PS area in this family (km2)
Min Tb in this family (K)Min and max lat in this family
Min and max lon in this family
Flag of merging or splitting
Time (year, month, day, and hour)
Min, max, and mean precipitation over each PS (mm h21)
Area total precipitation over each PS (kg)Min, max, and mean Tb over each PS (K)Area of each PC and PS (km2)
Min and max latit over each PS
Min and max lon over each PS
Fraction of ocean

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FIG. 5. Histograms of maximum (top) CTH and (bottom) cloud thickness based on 2B-GEOPROF-lidar that are collocated over (a),(d)all PCs; (b),(e) single-layered PCs; and (c),(f) multilayered PCs, with an assumption that IMERG-CT families must contain at least one PC
that has minimum brightness temperature (Tb) , 245 K (red) and one that has minimum Tb . 245 K (blue).

and therefore some of the remaining PCs that CloudSat suggests may in fact have deeper cloud in another portion of thegrid box. However, another possibility is that some of theseremaining cases are multilayered cases that have shallow precipitation beneath a radiometrically cold cirrus that may not be
detected by the CloudSat radar. To investigate this possibility,
maximum values of CTH (Figs. 5b,c) and cloud thickness(Figs. 5e,f) over single- and multilayered PCs are compared.
Results confirm that the multilayered cases only explain someremaining cases of shallow clouds, and thus IMERG itself has
limitations in excluding shallow clouds. To this end, inclusionof supplementary IR data helps.
CloudSat CPR profiles based on 2B-GEOPROF collocatedwith PCs and PSs can be conveniently summarized in Fig. 6
through contoured-frequency-by-altitude diagrams (CFAD;
Yuter and Houze 1995). Both PCs and PSs have a similar
bowlike shape, which is typical of deep convective systems
observed by millimeter-wavelength radar from space. The
CFAD of PCs shows more occurrence of large radar echo(;10 dBZ) at higher altitudes and more severe attenuation inradar signals at the lower levels than the CFAD of PSs. Thesefeatures are consistent with the definition of the PCs as having
larger rain rates than PSs. Both the PC and PS CFAD show
evidence of a radar bright band that is indicative of a melting
layer associated with stratiform precipitation. This is a clear

indication that PCs should not be interpreted as only representing
convective cores although they may contain convective coreswithin them.

b. Sensitivity test1) 10 VERSUS 6 CONNECTIVITIESAs we described above, we apply a 10-connectivity criterion
for the 3D labeling. Here, we also test a six-connectivity criterion, composed of a four-connected spatial neighborhood
and a two-connected temporal neighborhood, to examine how
sensitive the convection tracking is to the selection of connectivities. For the six-connectivity criterion, pixels are defined
to be connected if their edges touch, and two adjoining pixels(one-time step before and after) are considered to be a part ofthe same object (Fig. 3b). Two months (January and February2008) of IMERG-CT based on the 10 and 6 connectivities arecompared in Table 3, which lists the sample sizes of total storm
families, fraction of storms that have lifetimes of more than
one day or less than 30 min, and that have merging or
splitting cases.
Results without (with) applying the assumption of minimum
Tb , 245 K show that number of storm families increases by
1.2 times (1.1 times) when using the 6 connectivity rather than
the 10 connectivity. This is reasonable because the 10-connectivity

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TABLE 3. IMERG-CT dataset based on 10 and 6 connectivities
without or with (in parentheses) applying the assumption of minimum Tb , 245 K.

Total storm family (cases)
Lifetime . 1 day (%)
Lifetime 5 1 step (%)
Merging or splitting (%)

10 connectivity

6 connectivity

642 035 (183 906)
0.06 (0.20)
66.2 (35.5)
5.4 (16.7)

754 312 (201 060)
0.04 (0.17)
71.3 (41.2)
4.2 (14.2)

0.9 mm h21 for PSs and those from 1 to 9 mm h 21 for PCs togenerate one month (January 2008) of IMERG-CT using
10-connectivities and the assumption of minimum Tb , 245 K.
Results are summarized and compared in Table 4. It is clearthat the number of storm families decreases as a thresholdincreases, but the rest of variables are not too sensitive toPS thresholds, especially when PC threshold $ 5 mm h21. We
also find out that PC thresholds higher than 5 mm h21 often
fail to capture the entire image of a PC and are likely to breakthe PC into several subclusters, and PS thresholds lower
than 0.5 mm h21 are not capable of separating individual
PSs because multiple clusters are erroneously connected together. In Fig. 7, we use precipitation rate $ 20 mm h21 andprecipitation rate $ 0.01 mm h21 as examples to demonstrate
the aforementioned issues. As shown in Fig. 7a, precipitation
FIG. 6. CFADs based on CloudSat CPR reflectivities that arecollocated with (a) PC and (b) PS, and (c) the differences between them.

criterion allows more opportunities for pixels to connect witheach other, thus has larger families, and thus has fewer labeledfamilies. The fraction of storms whose lifetime exceed morethan a day is similar between the two connectivity criteria, but
the 6-connectivity criterion favors storms whose lifetime is lessthan 30 mins by 5.1% (5.7%) relative to the 10-connectivity
criterion. We would like to minimize the fraction of storms
whose lifetime is less than 30 mins because they only show up in
one single image and do not allow us to track their life cycles.
We also would like to reduce storms that have merging or splittingcases, but the fractional difference is only 1.2% (2.5%) between
the 6- and 10-connectivity criteria. Therefore, we decided to use
the 10-connectivity rather than the 6-connectivity criterion inthis study.

TABLE 4. IMERG-CT dataset based on different precipitationthreshold for PC and PS with 10 connectivities and the assumptionof minimum Tb , 245 K.
Total
Merging
PC
PS
storm
or
threshold threshold family Lifetime . Lifetime 5 1 splitting(mm h21) (mm h21) (cases) 1 day (%)
step (%)
(%)
1

3

5

2) PRECIPITATION RATE THRESHOLDS
The convection tracking is sensitive to the selection of
precipitation rate thresholds since it determines the characteristics of PCs and PSs. To test how sensitive the list ofvariables used in Table 3 is to the selection of precipitationthresholds, the IMERG-CT with different precipitationthresholds is examined. Several previous studies referredrain intensity lower than 1 mm h 21 as light precipitation(e.g., Tian et al. 2018; Tao et al. 2019; Wang et al. 2020) andrain intensity greater than 10 mm h 21 as strong precipitation (e.g., Maranan et al. 2020) or convective precipitation(Cui et al. 2020). Therefore, we apply thresholds from 0.1 to

7

9

0.1
0.3
0.5
0.7
0.9
0.1
0.3
0.5
0.7
0.9
0.1
0.3
0.5
0.7
0.9
0.1
0.3
0.5
0.7
0.9
0.1
0.3
0.5
0.7
0.9

19 79317 66716 13314 67913 29515 82815 43515 180
14 91014 637
12 757
12 59912 493
12 39812 301
10 20910 14610 101
10 061
10 016
8353
8318
8294
8271
8353

0.39
0.44
0.48
0.52
0.58
0.32
0.32
0.33
0.34
0.34
0.15
0.15
0.15
0.15
0.15
0.10
0.10
0.10
0.10
0.10
0.06
0.06
0.06
0.06
0.06

40.3
40.3
39.8
39.1
37.8
38.2
38.2
38.0
37.8
37.5
36.3
36.2
36.0
36.0
36.0
34.2
34.2
34.0
34.0
34.0
33.4
33.4
33.3
33.2
33.4

14.9
15.9
16.7
17.8
19.0
16.5
16.8
17.0
17.2
17.5
16.4
16.6
16.7
16.8
16.9
17.0
17.1
17.2
17.2
17.3
16.6
16.7
16.7
16.7
16.6

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FIG. 7. As in Fig. 2, but using different thresholds: (a),(c) $ 20 mm h21 and (b),(d) $ 0.01 mm h21.

rate $ 20 mm h21 corresponds to very intense rainfall, tracingout only a portion of heavy rain cells in the inner rainbands ofa tropical cyclone. As a result, the largest PC in this region
becomes too small (Fig. 7c). Precipitation rate as low as
0.01 mm h21 in Fig. 7b, on the other hand, corresponds tovery light rain, which ends up connecting multiple PSs together. Therefore, the largest PS in this region becomes toolarge (Fig. 7d).

3) TROPICAL DOMAIN
Some storms that originally form in the tropics can move tothe subtropics as they travel through their lifetime. Since thesample size of storms also depends on location, it is important
to know how many tropical convective storms travel outside oftropics. Therefore, we extended our tracking algorithm to
408S–408N for one month (January 2008). Results show that
only 0.56% of tropical convective storms starting within 208S–
208N end up outside of 208S–208N. Therefore, we decided touse the range of 208S–208N for our analysis, realizing that lessthan 1% of them will leave the domain at some point in their
lifetime.

4) CUTOFF SIZE FOR PCS
Although we are using a relatively higher rain rate todefine a PC, single pixel overlaps between PCs at two adjacenttimes easily connect different convective systems that are notrelated. To reduce uncertainty in artificially connecting precipitation systems and prolonging the lifetime of the precipitation systems, the cutoff size for IMERG-CT is discussed. For
example, a system moves 9 km in 30 min if the propagation
speed is 5 m s21, or 18 km in 30 min if the propagation speed is
10 m s21. This means that if we assume a system is square, asystem smaller than 400 km2 will likely not overlap itself in

30 min. Therefore, PC sizes are restricted to be greater than
400 km2.

5. Limitations and uncertainties in IMERG data
Although there are some advantages of using IMERG to
study precipitation events, there are inherent limitations anduncertainties in IMERG data, which are worth discussing.
First, unlike IR-based convection tracking where data homogeneity can be achieved (e.g., Fiolleau et al. 2020), IMERG
lacks homogeneity in space and time as various sensors (e.g.,geostationary IR, microwave sounders and imagers) measure
precipitation at different locations and times of the day. Since
retrievals and calibrations are not consistent among different
sensors, it can cause ‘‘flashing’’ in the precipitation fields ascontinuous overpasses in a particular location are filled withdifferent sensors (Huffman et al. 2015). Moreover, due to theinhomogeneous nature of the IMERG diurnal sampling,
precipitation can fluctuate around the thresholds when thethresholds are exceeded only for certain MW sensors (e.g., Tanet al. 2019). These issues in the performance of the IMERG
algorithm can limit the consistency of consecutive snapshotsthat are needed for IMERG-CT.
Second, IMERG has the selection of a hard-coded thresholdfor clipping of the intense rain rates. In the IMERG V06, thisvalue is set to 200 mm h21, which is different from the TRMM
PR 300 mm h21 threshold (Huffman et al. 2015). This indicatesthat IMERG clips all the values above 200 mm h21 although
the TRMM PR is valid at this range. Also, one of the algorithm
modules used in IMERG for the IR-based interpolation of
microwave precipitation estimates is the Kalman filter, whichtends to smooth out extreme features. These may limit analyzing extreme values in the life cycle and impact the ability to

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FIG. 8. The global distribution of precipitation rates for (top) IMERG, (middle) GSMaP, and (bottom) CMORPH cut off at (left)
0.01 mm h21 and (right) 0.5 mm h21 thresholds observed on 0000 UTC 1 Jan 2008.

properly describe the mean precipitation properties of thesystems. In addition to the Kalman filter, another module usedin IMERG is the IR-based PERSIANN (Hsu et al. 1997, 1999,
2007; Sorooshian et al. 2000, 2002; Nguyen et al. 2017, 2018)
estimate, which is using the PERSIANN-CSS (Nguyen et al.
2018) version that already includes its own cloud clustering
algorithm. The use of such cloud clustering with different
thresholds may potentially distort the definition of IMERGCT. This impact, however, should be minor since sensitivity
tests with GSMaP and CMORPH, neither of which adopts the
PERSIANN-CSS approach, in lieu of IMERG as demonstrated below do not qualitatively alter the results.
Third, the IMERG product depends critically on MW
retrievals by the Goddard profiling algorithm (GPROF)(Kummerow et al. 2015). This means that continental retrievals
depend on microwave-scattering signals from solid hydrometeors. Thus, IMERG results are less reliable for shallow
precipitation without ice processes over land. Therefore,
IMERG-CT can contain some uncertainty for shallow clouds
over land (Zhou et al. 2019). It is noted that satellite estimates
are calibrated to gauge-network data over land for IMERG so
the known deficiencies in continental warm-rain retrieval fromspace are partially mitigated.
A recent study comparing IMERG V06 and radar-based
product in MCSs finds that IMERG consistently overestimate rainfall amounts from light and moderate precipitation (,10 mm h21), particularly precipitation between 1 and

10 mm h21 over the continental United States (Cui et al. 2020).
Another recent study also pointed out that IMERG tends tooverestimate frequently occurring weak convective events,
while it tends to underestimate rarely occurring strong mesoscale convective systems, resulting in an error compensation(Maranan et al. 2020). The accuracy of IMERG retrievals,
namely at lower rain rates has not been well established.
The abovementioned limitations and uncertainties can impact our results based on IMERG-CT. A useful approach toquantify such uncertainties is to repeat the current analysis
with other precipitation datasets in lieu of IMERG to see how
the results change. In this way, the potential issues specific to
IMERG would be able to separate from general limitations
common to different products. To this end, we compare
IMERG with GSMaP and CMORPH. IMERG overall captures wider areas of precipitation than GSMaP/CMORPH
and there are some systematic product-dependent biases inprecipitation rates. However, the distributions of precipitation events with thresholds of precipitation rate .0.5 mm h21(our definition of PS) are comparable between IMERG and
GSMaP/CMORPH (Fig. 8). We also generate one-day statistics of CT datasets based on CMORPH and GSMaP andcompare the results with IMERG-CT on the same day (Table S1and Figs. S1 and S2 in the online supplemental material).
Results clearly show that they all share similar aspects of thecluster properties, except for the maximum precipitation
rate. IMERG tends to have higher maximum precipitation

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FIG. 9. Histograms of lifetime duration of each storm family, together with maximum precipitation, maximum
area of PC, and maximum area of associated PS among each storm family that has no merging or splitting cases(blue) and that has some merging or splitting cases (red). Histograms are normalized so that the red and blue barheights add to 100% separately.

rate than GSMaP/CMORPH. This is consistent with Masunaga
et al. (2019), which shows that in the 99th percentile extremes, IMERG exceeds GSMaP and CMORPH at all latitudes. Moreover, the flashing effect is not noticeable when
precipitation . 5 mm h21. The comparison suggests that clippingof intense rainfall and using of Kalman filter did not entirely remove extreme features in IMERG, and the lack of homogeneity
and associated flashing effect in IMERG data do not bring large
uncertainties in IMERG-CT when we use PC . 5 mm h21.
Although there are some inherent limitations and uncertainties
in IMERG data, they seem not to bring serious errors in IMERGCT. Also, as IMERG V06 made great improvement on studying
the diurnal cycle compering to the previous version of V05 (Tanet al. 2019), IMERG continues to be improved with known issues
solved by the developers, and these improvements flow down to
our IMERG-CT. This is a flexible framework of the method andkeeps evolving as the IMERG gets improved in the future.

a. Surveys over the tropics
Based on the evaluations and sensitivity tests, we use precipitation rate $ 5 mm h21 and precipitation rate $ 0.5 mm h21
thresholds to define PCs and PSs, respectively. We finalize theprocedure by using 10-connectivity 3D labeling and applying
the condition of at least one occurrence of minimum Tb ,245 K and generate two years (January–December 2007 and2008) of IMERG-CT over 208S–208N to track the evolution oftropical convective storms. Here we show some of the statisticalproperties of the tracked convective systems. Because the primary
purpose of this paper is to describe the tracking algorithm anddataset, this is not a comprehensive exploration. Rather we seek
to highlight some interesting features of the dataset.

b. Statistics of convective storm properties
Histograms of lifetime duration and maximum precipitation
rate for IMERG-CT, as well as maximum area of PCs and theirassociated PSs are investigated in Fig. 9 through a comparison
between IMERG-CT families without (red) and with (blue)
merging/splitting storms. Note that about 17% of IMERG-CT
families have merging/splitting storms (Table 3) and histograms
are normalized so that each red and blue bar heights add to100% separately. IMERG-CT families with merging/splitting
storms have longer lifetime durations than those without, andstorms with longer lifetimes tend to be bigger and to produce
stronger precipitation rate than those with shorter lifetimes.
Therefore, it is not surprising to see IMERG-CT families withmerging/splitting storms tend to have higher maximum precipitation rates and larger maximum area of PCs and PSs thatthose without merging/splitting storms.

c. Spatial distributions
To understand how the tropical convective storms in
IMERG-CT are distributed over the tropics (208S–208N), we

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FIG. 10. Tropical maps of (a) occurrence frequency, (b) lifetime, (c) maximum precipitation, (d) area of precipitating systems, and(e) area of precipitating cores of precipitating storms in 58 3 58 boxes over 208S–208N. Those grid boxes with occurrence frequency of
precipitating storms less than 0.01% are left blank.

explore the spatial patterns of occurrence frequency, together
with annual mean value of lifetime duration, intensity of precipitation, and size of IMERG-CT families (Fig. 10). Similar to
Liu et al. (2008), Fig. 10a shows that tropical convective storms
are most prevalent over the following five regions: tropical
Africa, Amazon, tropical warm pool, eastern Pacific intertropical convergence zone (ITCZ), and Atlantic ITCZ, where
deep convective clouds appear prominently (e.g., Liu et al.
2007; Takahashi and Luo 2014). Figure 10b shows that continental storms generally have shorter lifetime durations than
oceanic storms, which is consistent with previous studies (e.g.,
Futyan and Del Genio 2007), and long-lived storms are concentrated around 208N over the Arabian Sea, the Bay of
Bengal, the northeast Pacific Ocean, and the northeast Atlantic
Ocean, or around 208S over the Indian Ocean. These long-lived
storms around 208N are observed during northern summer(June, July, and August), and those around 208S are observed
during southern summer (December, January, and February).
Extratropical cyclones are generally more frequent during thewinter season near 408N/S latitudes. Therefore, these longlived storms in local summer can be due to the invasion of

extratropical cyclones into the lower latitudes during thesummer season. Maximum precipitation rate (Fig. 10c) is thehighest over the warm pool, and remains high over Africa,
Amazon, eastern Pacific ITCZ, and Atlantic ITCZ, similar to
the regions in Fig. 10a where deep convective clouds areprevalent. The largest areal coverages by PSs (Fig. 10d) arealso observed over the warm pool, ITCZ, and Amazon. For thearea of PCs (Fig. 10e), a clear land–ocean contrast is found:
PCs are likely to be smaller over land than ocean. The land–
ocean contrast is also present in the PS area, although not as
striking as in the PC area. It is evident when compared between
the Maritime Continent and the surrounding oceans. As foundin many previous studies (e.g., Houze and Cheng 1977; Chenet al. 1996; Machado et al. 1998), larger storms tend to belonger lived, which is evident from the land–ocean contrast in
this study.

d. Life cycles
Figure 11 shows the life cycle evolution of the following
IMERG-CT variables, sorted by lifetime duration categories:
PC area, PS area, fraction of PC to PS area, maximum

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FIG. 11. Evolution of PC area, PS area, fraction of PC area to PS area, maximum
precipitation rate, average precipitation rate, minimum Tb, and average Tb sorted bydifferent durations of lifetime.

precipitation rate, average precipitation rate, minimum Tb,
and average Tb. We have 140 418 samples for 1–5 h duration,
20 467 samples for 6–10 h duration, 5265 samples for 11–15 hduration, 1873 samples for 16–20 h duration, and 827 samples
for 21–25 h duration. The evolutions of PC and PS areas haveabout the same shape over all lifetimes, and they increase to
peak size at about midlife. The storm families with longer
lifetime show larger PC and PS areas, colder Tb, and higher
precipitation rate at their peaks. These results are consistent
with Machado et al. (1998) and Inoue et al. (2009), althoughthese previous works focused on clouds while our current study
focuses on precipitation. The initial growth rates of IMERGCT variables are also explored as in Machado et al. (1998),
which suggested that they can be good indicators for the lifetime duration of storms. We examine the initial growth rates(difference between first two time steps) and find that the initial growth rates of PC area, PS area, and maximum precipitation rate tend to be higher for long-lasting storm families,
which is consistent with other studies (e.g., Machado andLaurent 2004). It is also found that Tb reaches its minimum
earlier than the PS area reaches its maximum, which is consistent
with other studies (McAnelly and Cotton 1989; Arnaud et al.

1992; Futyan and Del Genio 2007; Takahashi and Luo 2014). For
the precipitation rate (both maximum and averaged), the peakcomes usually before the PC or PS area reach its peak. It is interesting to point out that the evolution of fraction of PC to PS
area follows that of precipitation rate, reaching its maximum atabout the same time precipitation rate reaches maximum.
Therefore, the storm families with longer lifetime have a higher
precipitation rates and a larger fraction of PC to PS area.

e. Dependence of system total precipitation of
PS characteristics
Figure 11 demonstrates how IMERG-CT can be used to
study precipitation evolution from the Lagrangian perspective.
Based on IMERG-CT, we can also estimate system total
precipitation by integrating area total precipitation of a PS
over its lifetime. This means that we can now study total
rainfall based on Lagrangian accumulations rather thanEulerian accumulations, which is a very important step indiagnosing cloud-precipitation process following the evolutionof air masses. Here, we investigate how the amount of system
total precipitation depends on lifetime and intensity of storm
families. Figures 12a and 12b show system total precipitation

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FIG. 12. (a) System-total precipitation and (b) occurrence frequencies as functions of system maximum precipitation (x axis) andsystem lifetime (y axis). Black- and magenta-outlined boxes in(b) are examples of weaker shorter-lived PS families and stronger
longer-lived PS families, respectively.

and occurrence frequency of storm families as functions ofsystem lifetime and highest precipitation rate among a storm
family (system maximum precipitation rate), respectively.
Results show that system total precipitation is equally sensitive
to lifetime and precipitation rates as weaker shorter-lived PS
families produce less system total precipitation than stronger
longer-lived PS families, which is expected. However, it isworth pointing out that those weaker shorter-lived PS families
are more frequently occurring relative to those stronger
longer-lived PS families. For example, 51.9% of PS families fall
into the black box in Fig. 12b, yet they only contribute 0.1% of
total precipitation over the tropics. On the other hand, although
only 0.5% of PS families fall into the pink box in Fig. 12b, they
contribute 48.6% of total precipitation over the tropics. Based on
Fig. 10, it is known that stronger longer-lived PS families are likely
to be larger systems. These results highlight that large portionof tropical rainfall are due to rarely occurring PS families thatare probably mesoscale-organized type of convective systems(e.g., Mohr et al. 1999; Rossow et al. 2013).

f. Linkage between rainfall and cloud-top temperature
As discussed before (section 2d), correlations between instantaneous precipitation rate and Tb images are low because
higher clouds do not always generate more rainfall than lower
clouds. However, some consistent patterns start to show up

VOLUME 60

when comparisons are made between composite life cycles ofprecipitation and Tb: storm families with longer lifetime tend
to have higher precipitation rate and colder Tb than the storm
families with shorter lifetime (Fig. 11). To further clarify thelinkages between precipitation and Tb through investigating
the time lag between precipitation and Tb, we investigate thecoevolution between precipitation and Tb for each IMERGCT family by anchoring Tb on precipitation through a composite method similar to the one introduced by Masunaga(2012, 2013).
The composite time series is constructed for investigating
various properties of convective systems in terms of the evolution before and after peak precipitation. First, the time lag
between the evolution of maximum precipitation rate (thehighest precipitation rate over an area of PS) and that ofminimum Tb (the lowest Tb over an area of PS) is investigated.
Since each family has its own timing to reach its own peak valueof maximum precipitation rate that is highly variable from 5 to
197 mm h21, we estimate fractional maximum precipitation
rate relative to the peak value of maximum precipitation rateduring the life cycle of each family and set composite time to be
0 when the maximum precipitation reaches the peak value
during its lifetime. For minimum Tb, we examine the anomaly
against its coldest value within the family since the anomaly
better illustrates the magnitude of variability than raw infrared
Tb. We compare the evolution of fractional maximum precipitation rate and the anomalous minimum Tb as a function ofcomposite hours built around the maximum precipitation rate.
In this way, fractional maximum precipitation rate is by construction 100% when the composite time is 0, and the temporalvariation of minimum Tb relative to the hour of peak precipitation can be clearly visualized. We do not include the families
that have multiple peaks of maximum precipitation rate orminimum Tb. In the same manner, the composite time series ofdifferent parameters are also investigated, and results are
summarized in Fig. 13.
Figures 13a and 13c show the temporal sequence of fractional maximum precipitation rate and anomalous minimum
Tb, and that of fractional maximum precipitation rate andaverage Tb, respectively. Minimum Tb reaches its peak atcomposite time equals to 0, which means that temporal separations between maximum precipitation and minimum Tb is
within 3 h or the temporal resolution adopted in this study. Aninteresting point of this composite analysis lies in the difference
between the behaviors of minimum Tb and average Tb. Unlike
minimum Tb that has rapid decreases after the convective rainpeaks, average Tb mostly stay largely the same for 5 h after the
peaks. Considering the definitions of these Tb measures,
minimum Tb most likely represents the cloud top of convective
core, while average Tb characterizes the rest of the storms,
mainly stratiform rain and cirrus anvils (Machado and Rossow
1993). The fact that minimum Tb drops off quickly while average Tb stays largely the same for an extended period of timesuggests that convective precipitation has a much shorter duration than stratiform precipitation. Figure 13 reveals the signature of stratiform anvils persisting well after the storm has
produced the maximum rainfall, which may have significant
implications for the time evolution of the latent and radiative

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FIG. 13. The composite time series of (a) maximum precipitation rate and minimum Tb, (b) area total precipitationand minimum Tb, (c) maximum precipitation rate and average Tb, and (d) area total precipitation and average Tb.

heating profiles of the storms. Our study highlights the importance of joint analysis of cloud and precipitation data intime sequence, which helps elucidate the underlying dynamic
processes producing tropical rainfall and its resultant effects on
the atmospheric thermodynamics.
Replacing maximum precipitation rate with total precipitation (PS area total at one time), we further investigate a timelag between the evolution of total precipitation and minimum
Tb and that between total precipitation and average Tb, which
are shown in Figs. 13b and 13d, respectively. Results again
indicate that time delays between total precipitation andminimum Tb is within 3 h, and average Tb mostly stay the samefor 5 h after convective rain peaks although minimum Tb have
rapid decreases after the peak of total precipitation. Therefore,
the signature of stratiform anvils is robust and can be captured
by IMERG-CT dataset.

6. Summary
This study is the first attempt to detect and track the evolution of tropical convective storms using the high spatiotemporal imagery of precipitation from IMERG. It differs fromprior efforts to track tropical convective storms in using
precipitation as the tracked variable instead of the Tb. We
demonstrate that precipitation can be a natural variable
upon which to base a convective storm tracking algorithm and
Tb does have the natural advantage as a filter of shallow precipitation. This capability is maintained in the algorithm described here through the imposition of a once-per-storm Tb
threshold. An additional motivation for using the IMERG
data is the purely practical consideration that among existing gridded datasets, IMERG offers 30-min temporal resolution whereas ISCPP-HXG offers only 3-hourly resolution.
The convection-tracking algorithm used in this study goes asfollows:
1) Identify PCs (precipitation $ 5 mm h21) and PSs (precipitation $ 0.5 mm h21) between 208S and 208N latitude.
2) A 3D labeling (latitude/longitude/time) is applied for PCs
based on a 10-connected spatiotemporal neighborhood(Fiolleau and Roca 2013a) in order to connect individual
PCs into a single family by giving a unique family number.
3) Each single family must have at least one time step that has
minimum Tb , 245 K in order to exclude shallow clouds

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that never grow into deep convective storms. Information
on minimum Tb is calculated from the collocated ISCCPHXG available every 3 h.
4) PC sizes are restricted to be greater than 400 km2.
Using this algorithm, we generate one year of a convectiontracking dataset, named IMERG-CT. IMERG-CT has been
evaluated against CloudSat and previous studies in this paper,
which confirms that this simple algorithm of convection
tracking based on IMERG performs comparably to existing
tracking algorithms. However, an advantage of IMERG-CT is
its ability to include the previously missing information ofshallow clouds that grow into convective storms, which provides us more complete life cycle records of convective storms
than traditional IR-based tracking does. We emphasize that up
to now precipitation datasets have been Eulerian accumulations whereas with IMERG-CT, it is now possible to study boththe time evolution of ‘‘instantaneous’’ precipitation rate but
also lifetime-accumulated precipitation amount over eachconvective storm, which is a very important step in diagnosing
cloud-precipitation processes in tropical convective storms.
Some preliminary characteristics of IMERG-CT shown here
are as follows:
1) Continental storms have shorter lifetime durations than
oceanic storms, which is consistent with previous studies(e.g., Futyan and Del Genio 2007).
2) Maximum precipitation is the highest over the warm pool,and relatively high over Africa, Amazon, eastern Pacific
ITCZ, and Atlantic ITCZ, where deep convective clouds
appear prominently, which is consistent with previous
studies (e.g., Liu et al. 2008).
3) The storm families with longer lifetimes show larger PC and
PS areas, colder Tb, and higher precipitation at their peaks,
which is consistent with previous studies (e.g., Machado
et al. 1998; Inoue et al. 2009).
4) The initial growth rates of PC area, PS area, and maximum
precipitation are higher for long-lasting storm families,
which is consistent with previous studies (e.g., Machado andLaurent 2004).
5) The largest PC begins to decrease in size earlier than the PS
does, which is similar to Machado et al. (1998), and minimum Tb reaches its minimum earlier than the PS area
reaches its maximum, which is consistent with previous
studies (McAnelly and Cotton 1989; Arnaud et al. 1992;
Futyan and Del Genio 2007; Takahashi and Luo 2014).
6) Nearly half the total precipitation amount over the tropics
is produced by rarely occurring (less than 1%) strong longlived PS families, which are probably associated withmesoscale-organized type of convective systems (e.g., Mohret al. 1999; Rossow et al. 2013).
7) Composite analysis reveals a link between precipitation andTb: the signature of stratiform anvils hanging around after
the storm has produced the maximum rainfall as average
Tb mostly stay almost constant for 5 h after the peak ofprecipitation.
This study demonstrates the utility of IMERG-CT to analyze the evolution of tropical convective storms and investigate

VOLUME 60

the linkage between precipitation and Tb. Since IMERG has
high spatiotemporal resolutions, IMERG-CT might give direct
insight on moist convective processes from Lagrangian perspectives. A similar analysis should be applied to extratropicalfrontal convection. Our future plan is to extend IMERG-CT tomultiple years and to study convective evolution and its relation to the thermodynamic environment.
Acknowledgments. The research was carried out at the Jet
Propulsion Laboratory, California Institute of Technology,
under a contract with the National Aeronautics and Space
Administration (NASA). The authors thank William Rossow
and three anonymous reviewers for insightful comments anddiscussions.
Data availability statement. The data of IMERG can be
retrieved from the STORM Processing Center (https://storm.pps.eosdis.nasa.gov/storm/), CloudSat observations
can be retrieved from the CloudSat Data Processing Center(www.cloudsat.cira.colostate.edu), ISCCP-CT and ISCCP-HXG
can be retrieved from https://isccp.giss.nasa.gov/outgoing/
PICKUP/CT/ and https://www.ncdc.noaa.gov/isccp/isccp-dataaccess, respectively.
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