Comparison of Diurnal Variation of Precipitation System
Observed by TRMM PR, TMI and VIRS
Munehisa K. Yamamoto∗1 , Fumie A. Furuzawa2,3 and Kenji Nakamura3
1: Graduate School of Environmental Studies, Nagoya University
2: Japan Science and Technology Agency
3: Hydrospheric Atmospheric Research Center, Nagoya University

1. Introduction

3. Results

Diurnal variation of precipitation system is one of
the interesting topics for meteorology. Infrared databy geostationary satellites are useful to detect thediurnal cycle of cloud activity. However, precipitation
activity is not always corresponding to cloud activity.
The relationship between the cloud and precipitation
is not fully understood.
The Tropical Rainfall Measuring Mission (TRMM)
satellite bears potential to study the diurnal cycle
due to its orbital characteristics by long time accumulation. TRMM has multiple sensors such as
Precipitation Radar (PR), TRMM Microwave Imager(TMI) and Visible and Infrared Scanner (VIRS), andTRMM can observe the same precipitation and cloudsystems simultaneously. In addition to that, PR estimates not only precipitation amount but also precipitation characteristics (convective and stratiform)
and vertical structure.
In this study, we compared diurnal variations observed by the TRMM onboard sensors. The regional
characteristics of diurnal variation is also examined.

3.1

2. DataThis study used TRMM version 5 standard products in 1998–2003, June–July–August (JJA). Variables are summarized in Table 1. Datasets for PR,
TMI and VIRS are processed in the following order. At first, the raw data are averaged into 0.25◦ by0.25◦ grid boxes. VIRS and TMI data are adjustedto PR swath to decrease the sampling effect. Next,
the averaged data are accumulated every hour localtime. And the peak local time of maximum rainfall(minimum brightness temperature) for PR and TMI(VIRS) are detected. In addition to these data, 3B42
version 6 surface rain data are also utilized as thereference of less sampling error.

Diurnal variation of precipitation revealedby 3B42

Figure 1a shows the peak local time of maximum
rain rate derived from 3B42 averaged for 6 years inJJA. Generally, the peak local time corresponds toprevious studies. For example, over land, afternoon
peak rainfall appears on the plain such as westernand southeastern United States, Ganges River Basinand the Yangzi River Basin, and also on the TibetanPlateau. On the contrary, nighttime peak rainfall appears on the Rocky Mountain, the Deccan Plateau,and so on. A few hours time shift of the peak time
are also illustrated. While the time shift from the latenight to early morning appears over land such as central United States, coastal Amazon, southern slopesof Himalaya, the time shift from the early morning
to early afternoon appears over ocean near the coast
such as Gulf of Mexico, west coast of United States,
maritime continent and Bay of Bengal.
The strength of diurnal variation is shown in Fig.
1b. As an index of the strength of diurnal variation,
Normalized Differential Rainfall Index (NDRI) is defined byRRmax − RR
NDRI =(1)RRmax + RR
where RRmax is the rain rate at peak local time andRR is the averaged rain rate. The larger NDRI appears, the stronger the diurnal variation is. Whilelarge NDRI (> 0.3) are mainly represented over theland region, for example, middle and west UnitedStates, Tibetan Plateau, maritime continent, some island and the coastal region of Amazon, small NDRI
are represented over the oceanic region such as theIntertropical Convergence Zone (ITCZ) and northwestern Pacific. However, large NDRI appears evenfor oceanic region such as Gulf of Mexico, maritimecontinent, and western Bay of Bengal.

3.2

Comparison of peak local time among thesensors*1 Corresponding

author address: Munehisa K. Yamamoto, Hydrospheric Atmospheric Research Center, Nagoya University, Furo-cho, Chikusa-ku,Nagoya, 464-8601, Japan.
TEL/FAX: +81-52-789-5467
E-mail: yamamoto@satellite.hyarc.nagoya-u.ac.jp

The spatial distributions of peak local time derived
from PR, TMI and VIRS are shown in Fig. 2a, b,c, respectively. Compared with the distribution ofpeak local time derived from 3B42 (Fig. 1a), thatderived from PR, TMI and VIRS are generally similar especially for the region where the large NDRI isillustrated (Fig. 1b), although the variations of peak

local time are large. The peak time shifts also illustrate for PR, TMI and VIRS. However, the shifts areindistinct compared with 3B42. For VIRS, nighttime
peak appears over north and south Africa, Australiaand eastern Pacific and Atlantic Ocean in the midlatitude. These regions correspond to dry region, so
the nighttime peak show the diurnal variation of surface temperature. Hereafter, we pay attention to theregions where NDRI is large.
Making comparison with PR, TMI and VIRS, systematic time shifts appeared. For example, over thewestern and eastern United States, India and Tibetan Plateau, 12–16 LT peak dominants in PR. ForTMI and VIRS, 14–18 LT peak become dominant.
Over the oceanic region such as Gulf of Mexico andwest Bay of Bengal, 12–16 LT peak is more distinct
over offshore in VIRS. Figure 3 shows the histogramsof the peak time difference among the sensors, thatis TMI−PR, VIRS−PR and VIRS−TMI, over theglobal ocean, global land and some land regions. Asa common feature, no time shift dominants over allof the regions. Focusing on broad component, many
distributions are shifted to the right over the land region, especially for North America. In other words,
the peak local time for PR is earlier than that for
TMI and the peak local time for PR and TMI is earlier than that for VIRS. The shift is more distinct for
VIRS−PR and VIRS−TMI.

3.3

Causes of time shifts among the sensors

Figure 4 shows the fraction of convective rain frequency to the total rain derived from PR 2A25. Convective rain dominated regions appear in such as theUnited States, Amazon and the maritime continent,
Gulf of Mexico and the western Bay of Bengal. Theseregions correspond to large NDRI region. However,
over the Tibetan Plateau, the convective rain fraction
is small in spite of large NDRI. Basically, the rain isclassified as convective in the case of large reflectivefactor (>39 dBZ) (Awaka 1998). Convective but relatively weak and shallow precipitation may dominantso that the convective rain fraction is small.
Above results suggest us to hypothesize that thesensors’ signatures at each stage of convective rainsystems cause the time shift. A schematic diagramof the development of convective rain system is illustrated in Fig. 5. PR directly detects near surfacerain at the initial to mature stage. TMI mainly observed the deep convection and retrieve strong rainwith solid hydrometeor in the mature stage. VIRS
detects the deep convective clouds and the anvil frommature to decaying stages. PR and TMI observe
the rain and hydrometeor directly, and then the peaktime difference is small. VIRS observes the cloud top
height so that the peak time difference is large.
In order to remove the cirrus-type clouds such asanvils, a split window technique is introduced (Inoue2000). For VIRS, brightness temperature difference(BTD) is defined as BT(ch. 4) − BT(ch. 5). We

selected some thresholds of BTD: 1.5, 2.5 and 3.5 K.
If the BTD is more than these thresholds, the pixel
was eliminated. In other words, cirrus-type clouds
are strongly removed by small BTD thresholds (notshown). We find that morning peak become muchclear especially for the United States, western India
and northwestern Atlantic. While the relatively weak
convective rain dominated region such as the eastpart of India, Middle Africa shows small time shift.
These features support to our hypothesis.

4. Summary and Conclusion
This study examined characteristics of diurnal variation using TRMM for 6 years orbit. The characteristics of diurnal variation from combined productand each sensor is consistent with previous studies.
Making comparison among the sensors, the systematic peak time shift is illustrated. The order is PR
first, followed by TMI and VIRS by a few hours.
These regions correspond to the regions where thestrong diurnal variation appears. The time shift may
be caused by the sensors’ characteristics in the development of convective precipitation and cloud system.

References
Awaka, J., 1998: Algorithm 2A23 — rain type classification. IHAS and ESTO, Eds., Proc. Symp.
on the Precipitation Observation from Non-Sun
Synchronus Orbit, 215–220, Nagoya, Japan.
Inoue, T., 2000: Early results on comparison between
cloud information by VIRS and rain information
by PR/TMI. Adv. Space Res., 25, 977–980.

Table 1. Datasets for this study.
Sensor Product Ver. Description
VIRS

1B015

IR Brightness Temperaturech. 4 (10.8 µm), ch. 5 (12.0 µm)

TMI

2A125Surface rain rate

PR2A255Near surface rain rate

COMB3B426

Surface rain rate(a) LT(maxRR) 98-03 JJA 3B4240200-20-40060021204681801012240
1416300
182022360[LT]24(b) NDRI (RR(maxLT)-RR(ave))/(RR(maxLT)+RR(ave)) 98-03 JJA 3B4240
200-20-40060

120180240300

360[NDRI]
0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65

Figure 1. (a) Peak local time of maximum precipitation and (b) NDRI (definition is in text)
derived from 3B42 for 1998–2003 in JJA. For (b), pixels with few rain amount where the averaged
rain was less than 10 mm month-1 are blanked out.

(a) LT(maxRR) 98-03 JJA PR2A2540200-20-40060120180240300360180240300360180240300

360
[LT]
24(b) LT(maxRR) 98-03 JJA TMI2A1240200-20-40060120(c) LT(minBT) 98-03 JJA VIRS1B01
40200-20-400600212046810121416182022Figure 2. Peak local time of maximum precipitation derived from (a) PR and (b) TMI and (c)peak local time of minimum brightness temperature derived from VIRS for 1998–2003 in JJA.

(c) Asia(b) Global Land

10VIRS-PR

100

Freq. [%]

Freq. [%]

Freq. [%](d) North America

100100

10VIRS-TMI

TMI-PR1

1-12 -10 -8 -6 -4 -2 0 2 4
LT diff. [hr]61-12 -10 -8 -6 -4 -2 0 2 4
LT diff. [hr]8 10 1268 10 12-12 -10 -8 -6 -4 -2 0 2 4LT diff. [hr]68 10 1268 10 12(e) South America(a) Global Ocean
100

10040˚(d)(c)
20˚Freq. [%]

Freq. [%]100˚10(e)-20˚

10-40˚0˚60˚120˚180˚240˚0˚300˚11-12 -10 -8 -6 -4 -2 0 2 4LT diff. [hr]68 10 12-12 -10 -8 -6 -4 -2 0 2 4LT diff. [hr]Figure 3. Histograms of peak local time difference over (a) the global ocean, (b) the global land,land regions of (c) Asia, (d) North America and (e) South America for TMI-PR (solid line),
VIRS-PR (dotted line), and VIRS-TMI (dash-dotted line).

Conv. fraq. (freq.(conv)/freq.(all)) 98-03 JJA PR2A2540200-20-400600.1

120

0.2

0.3

1800.4

240

0.5

0.6

300
0.7

0.8

360
[ratio]0.9

Figure 4. Fraction of convective rain frequency to the total rain derived from PR2A25 for 1998–
2003 in JJA.

InitialMature

Decaying
VIRS

TMI
PRTimeFigure 5. Schematic diagram of the relationship between the sensors’ signature and developmentof convective precipitation system. Clouds show the stages of development. Blue line and red line
show the rain rate for each stage for PR and TMI, respectively. Black line is same as PR and TMI
but for brightness temperature.