Journal of the Meteorological Society of Japan, Vol. 87A, pp. 165–181, 2009
DOI:10.2151/jmsj.87A.165

165

Improvement of Rain/No-Rain Classification Methods for Microwave Radiometer
Observations over the Ocean Using a 37 GHz Emission Signature
Satoshi KIDA, Shoichi SHIGE
Department of Aerospace Engineering, Osaka Prefecture University, Sakai, Japan

Takuji KUBOTA
Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, Japan

Kazumasa AONASHI
Meteorological Research Institute, Tsukuba, Japanand

Ken’ichi OKAMOTO
Faculty of Environmental and Information Studies, Tottori University of Environmental Studies, Tottori, Japan(Manuscript received 30 June 2008, in final form 5 November 2008)

Abstract
The rain/no-rain classification (RNC) for the Tropical Rainfall Measuring Mission (TRMM) Imager (TMI)
fails in detecting shallow rain observed by the TRMM Precipitation Radar (PR). In this study, the RNC method
is revised to use the 37-GHz emission more efficiently to identify shallow rain and is applied to the TMI
observation. The results are then evaluated against the RNC made by the PR observation, considered as the“truth.” The revised RNC method (GSMaP2) is compared with the original RNC method (GSMaP1) and the
Goddard profiling algorithm (GPROF).
GSMaP2 performs well for shallow rain behind cold fronts in the extratropics, where GSMaP1 and GPROF
fail, using the 37-GHz emission signature. Through a whole year, a global comparison shows that GSMaP2
performs better than GSMaP1 and GPROF over mid-latitudes. However, GSMaP2 fails in detecting shallow
isolated rain over sub-tropical oceans owing to a globally constant value for the vertically integrated cloudliquid water path (LWP) assumed in the forward calculation. Therefore, we parameterize the LWP as afunction of storm height from the PR observation over the region where shallow isolated rain is predominant.
GSMaP3, in which the parameterization of the LWP is applied to GSMaP2, improves detection of shallow
isolated rain over sub-tropical oceans.

1. Introduction
Satellite-borne microwave radiometers provide ob­­servations of global precipitation. Rain estimation isimportant for the study of the global water cycle andwater resources. Brightness temperatures (TBs) meas­
ured by these radiometers depend on the absorption
Corresponding author: Satoshi Kida, Department of
Aerospace Engineering, Osaka Prefecture University,
1-1 Gakuen-cho, Sakai, 599-8531, Japan
E-mail: kida@aero.osakafu-u.ac.jpⒸ2009, Meteorological Society of Japanand scattering properties of the atmosphere and theunderlying surface, which vary with frequency andpolarization. There are generally two methods for
estimating precipitation: one uses an emission signa­
ture from raindrops over the spectrum of lower fre­quencies (emission-based algorithm) and the otheruses a scattering signature from ice crystals over the
spectrum of higher frequencies (scattering-based al­­gorithm).
A number of different microwave precipitation re­­trieval algorithms have been developed over the years.

166

Journal of the Meteorological Society of JapanThe methods of estimating precipitation from satellitebone microwave radiometers are divided into two
types. First, the empirical relationship between the
TBs and the rainfall rate from ground radar datasetsor rain gauges is used to estimate the rainfall rate.
Although this method uses channels that have a highcorrelation with the rainfall rate, physical relationships
between the rainfall rate and TBs are uncertain andthis method is difficult to apply to global regions
because the relationship may reflect the characteristicsof the local regions.
Second, the relationship between TBs and rainfall
rate established by the physical principles of radiativetransfer is used to quantify the rainfall rate. Thismethod clarifies the physical relationship between therainfall rate and TBs and can be applied to various
regions as long as the profiles of the various hy­­drometeors are appropriately represented. Generally,
this method comprises two parts: a forward calculation
and retrievals. In the forward calculation, lookup
tables (LUTs) or databases showing the relationshipbetween rainfall rates and TBs are produced using
radiative transfer models (RTMs). A retrieval consistsof a rain/no-rain classification (RNC) and an esti­
mation of the rain rates over the delineated rainy area.
This study focuses on the RNC. For the RNC overland, only the scattering-based algorithm is usedbecause the contrast between surface and atmospherebecoming smaller, owing to high emissivity over land,
makes it difficult to apply the emission-basedalgorithm. In contrast, for the RNC over the ocean,either the emission-based algorithm or the scatteringbased algorithm can be used because of the low emis­
sivity over the ocean. Therefore, the RNC over theocean has higher accuracy than the RNC over land.
However, the RNC over the ocean has uncertainty forshallow rain. Petty (1997) indicated that Special
Sensor Microwave/Imager (SSM/I) rainfall algorithms
have difficulty detecting small rain areas. Thisdifficulty is due to the scale of the rain being smaller
than the 30 to 60 km resolution of the SSM/I channels
used in most emission-based algorithms. Petty (1997)
suggested that the only hope of directly sensing such
precipitation is to use the 85-GHz channel for anemission-based algorithm, which is often used for ascattering-based algorithm, because it has the highest
resolution (15 km) among the SSM/I channels.
The Tropical Rainfall Measuring Mission (TRMM)
carries two microwave sensors on the same platform
for quantitative rainfall measurement: the precipitation
radar (PR) and the TRMM microwave imager (TMI)

Vol. 87A(Kummerow et al. 1998). The PR is a nadir-looking
Precipitation Radar that measures back-scatter fromprecipitation particles (Kozu et al. 2001; Okamoto
2003; Okamoto and Shige 2008). The PR observes thethree-dimensional structure of precipitation at a fineresolution (~4 km) with a 215 km-wide swath. The
TMI is a nine-channel passive microwave radiometerbased on the SSM/I, with a 720 km-wide swath. One
key difference is the addition of a pair of 10-GHz
channels with horizontal and vertical polarizations,denoted as 10H and 10V. Another key difference isthat the spatial resolution of TMI is improved over thatof SSM/I. Using simultaneous observations by the PR
and TMI allows us to improve the retrieval of rainfrom measured TBs.
We develop a microwave precipitation retrieval
algorithm (Global Satellite Mapping of Precipitation,
GSMaP) compatible with the PR algorithm (Aonashi
et al. 1996; Aonashi et al. 2009; Kubota et al. 2007),
based on the deterministic rain-retrieval algorithm ofAonashi and Liu (1998). The GSMaP algorithm isused by the Earth Observation Research Center, Japan
Aerospace Exploration Agency (JAXA/EORC) tooffer hourly global rainfall maps in near real time.,
The GSMaP algorithm is used by Shige et al. (2008)
for scientific study. The GSMaP products are available
on the Internet at http://www.radar.aero.osakafu-u.
ac.jp/~gsmap/. The RNC method over land used in
GSMaP was developed using statistical information
for TMI TBs under no-rain conditions from PR
observations (Seto et al. 2005). In the RNC method
over the ocean, rainfall is classified mostly by the 10and 19-GHz emission signatures and the 85-GHz
scattering signature.
Over the ocean, the RNC for the TMI fails in de­­tecting shallow rain observed by PR because shallow
rain has a scale smaller than the resolution of thechannels used in the emission-based algorithm and has
little ice aloft. Here, we define shallow rain as having
a small scale (~20 km) and a precipitation top height
lower than the freezing level height (FLH). We define
deep rain as having a larger scale and a precipitation
top height higher than the FLH.
It is very difficult to use the 85-GHz emission-basedalgorithm to detect shallow rain because clouds are
optically thick at 85-GHz. The resolution of the
microwave radiometer has advanced with improve­ments in the technology and resolution of the 37-GHz
channel (~16 km) of the TMI, such that the resolution
is now comparable with that of the 85-GHz channel(~15 km) of the SSM/I. Therefore, the shallow rainMarch 2009

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indicated by Petty (1997) is expected to be directly
detected using the 37-GHz channels of the TMI for theemission-based algorithm in the RNC method.
This study aims at improving the RNC over theocean for GSMaP using the 37-GHz channel of the
TMI in an emission-based algorithm. The revised
RNC method is evaluated by comparing it with the
RNC from the PR. The datasets used in this study aredescribed in Section 2. We review the RNC method of
the Goddard profiling algorithm (GPROF) and theoriginal RNC method of GSMaP in Section 3. In
Section 4, we revise the RNC method of GSMaP to
use the 37-GHz emission signature more efficiently.
We also investigate the performance of the revised
RNC method using two case studies and global
comparisons. In Section 5, we improve the forward
model, which the RNC method depends on, over subtropical oceans. Summaries are given in Section 6.

2. DataIn this study, we focus on GSMaP for the TMI(GSMaP_TMI), because GSMaP_TMI results can becompared with the PR observations. In the forward
model, LUTs are produced using the four-stream RTM
of Liu (1998). The RTM is inputted with atmosphericvariables (freezing height, temperature, relative
humidity, surface wind, and surface temperature)provided by the Japan Meteorological Agency Global
Analysis (GANAL). In the cloud layer under the FLH,
constant values for the vertically integrated cloudliquid water content (LWP) (0.5 kg m-2) and relative
humidity (100%) are assumed (Aonashi and Liu 1998).
LUTs are calculated for each 5 × 5-degree latitudelongitude box. The RTM is also inputted with a set ofhydrometeor profiles given by PR observation. The
PR-observed hydrometeor profiles are classified intoten precipitation types. The precipitation types are
determined using the stratiform pixel ratio, stratiform
rain ratio, precipitation area, precipitation-top height,
rain intensity, and diurnal cycle from PR 2A25 alongwith the TRMM Lightning Imaging Sensor flash rates(Takayabu and Katayama 2004; Takayabu 2006).
There are six land precipitation types (severe thunder­storm, afternoon shower, shallow rain, extratropicalfrontal system, organized system, and high land rain)
and four ocean precipitation types (shallow rain,
extratropical frontal system, transition zone, and or­­ganized system).
For comparison with GSMaP_TMI we used thecurrent (version 6) level-2 standard products of the PR
and TMI published by the National Aeronautics andSpace Administration and the Japan Aerospace Ex­­ploration Agency. The level-2 standard product 2A12
(GPROF) (Olson et al. 2006) was used for the rainestimation of the TMI. The basis of GPROF is a
Bayesian framework in which retrieved precipitation isconstructed from cloud-resolving model-generated
profiles that are radiatively consistent with observation.
The level-2 standard product 2A25 (Iguchi 2007;
Iguchi et al. 2009) was used for the rain estimation of
the PR. We used 2A25 as the truth for the RNC in thisstudy because the resolution of the PR is higher thanthat of the TMI. In order to more directly compare therelatively high-resolution PR observations with theestimation from the TMI, we matched the resolutionof the PR with that of the 85-GHz TMI data. A
weighted average of the PR observations in theneighborhood of a given TMI footprint is performed.
The PR average rainfall rate is defined by

∑g R
j

j

j

		
Rave =
,
∑gj

(1)

j

where Rj is the near-surface rain from PR2A25, and x  2  y  2 
g j = exp[− ln(2)  +   ] , σ x   σ y  (2)

is a Gaussian weighting factor. Here, because the TMI
footprint takes the form of an ellipse, if the short axis
direction and the long axis direction are defined as thex-direction and y-direction, then x and y are thedistances in kilometers between the PR footprint(indexed by j) and the specified TMI footprint. Thelong axis sx and the short axis s y, which are the halffootprint dimensions of the TMI 85-GHz channels, are
2.3 km and 3.6 km. The average value of the PR
rainfall rate is used throughout the paper. The
comparison with these products is processed in the PR
swath, although both GSMaP and GPROF wereapplied to the TMI swath.

3. RNC method
In this section, the RNC methods for GPROF andthe original GSMaP are reviewed briefly. The original
RNC method of GSMaP is referred to as GSMaP1.
Because the RNC of GPROF is determined after each

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pixel is processed using screening and the full Baye­sian code, we consider the RNC method of GPROF toconsist of these processes.
3.1 RNC method of GPROF over the ocean
In GPROF, screening is performed in order to
identify regions that are non-raining. All pixels thatpass this screening are identified as “possibly rain”
and are then processed using the full Bayesian algo­rithm to quantify the rain rate, which could be zero or
non-zero.
The screening of GPROF over the ocean consists of
two processes: checking the LWP and screening out
clear ocean pixels and ice surface pixels (Fig. 1).
In the first process, the LWP (kg m-2) is estimatedusing the observed 22V and 37V TBs (denoted as
TB22V and TB37V), based on the work by Karstens
et al. (1994):

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the FLH (m) is defined as

LWP _ max = 0.25 ×

FLH
,
4000

(4)

where FLH is derived from the work by Wilheit et al.
(1991), the constant values of 0.25 and 4000 represent
the liquid water content (kg m-3) and a typical freezing
level height (m). If the LWP is less than LWP_max fora target pixel, then that pixel is identified as a no-rain
pixel.
The second process is based on the Goddard
Scattering (GSCAT) algorithm (Adler et al. 1994).
This process consists of three checks. The first checkis given by
TB85 H > 262.0, and TB 22V < 269.1.

(5)

LWP = 0.399635 log( 285 − TB 22V ) − 1.40692 log( 285 If− TB
37condition
V ) + 4.299is fulfilled for a target pixel, that pixel
this399635 log( 285 − TB 22V ) − 1.40692 log( 285 − TB37V ) + 4.299.

(3)

The maximum LWP (LWP_max) (kg m-2) based on

is identified as possibly rain. This check was originallyintended to detect ambiguity between the signals froman ice surface and possible rain, but it is used to detectrain in GPROF version 6.
The second check is to distinguish ice surfaces fromrain and is given by

TB 22V < 192.0 .

(6)

If this condition is fulfilled for a target pixel, that pixel
is identified as an ice surface.
The third check is to identify clear ocean and isgiven by
TB85 H − TB37 H > 0.5, and TB37 H ≤ 186.7 .

(7)

If this condition is fulfilled for a target pixel, that pixel
is identified as clear ocean.
If the target pixel is not identified as an ice surface
or clear ocean by these checks, then it is identified as
possible rain.
After the screening, possible rain pixels are pro­
cessed using the full Bayesian algorithm to quan­tify
the rain rate, which could be zero or non-zero.

Fig. 1. Flowchart for the RNC method of
GPROF over the ocean.

3.2 Original RNC method of GSMaP over the ocean(GSMaP1)
GSMaP1 over the ocean consists of two stages (Fig.

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2a). In the first stage, we divide the pixels into deeprain pixels and shallow-rain pixels. The deep-rain
pixels are determined by the 85-GHz scattering sig­
nature and the shallow-rain pixels are determined by
the 37-GHz emission signature.
To detect a deep-rain pixel, a first guess of the
precipitation (rain85) for all pixels within the 10-GHz
effective field of view (EFOV) (a large ellipse, as seenin Fig. 2b) with a central target pixel (the black ellipse
in Fig. 2b) is computed from the observed polarizationcorrected temperature (PCT) (Spencer et al. 1989) at
85-GHz (PCT85), using the LUTs. If rain85 is greaterthan 1 mm h-1 for any pixels within the 10-GHz EFOVof the central target pixel, the target pixel is identified
as a deep-rain pixel. (Hereafter, this iden­tification
procedure is referred to as FOV10on.) The aim ofusing FOV10on is to search for stratiform rain
occurring near convective rain.
If the target pixel is not a deep-rain pixel, the37-GHz emission signature is checked to determine
whether the pixel is a shallow-rain or no-rain pixel. To
detect a shallow-rain pixel, we use the normalized
polarization difference (Petty 1994) at 37-GHz (P37)
given by

P37 =TB37V − TB37 H,
TB37Vo − TB37 Ho(8)where TB37Vo and TB37Ho are TB37V and TB37H
in a rain-free region. In computing P37, the TB37V
and TB37H at 0 mm h-1 in the LUTs, simulated using
GANAL and cloud water, are used as TB37Vo and
TB37Ho. The condition for the determination of ashallow-rain pixel is

P37 < 1 ,(9)because P37 decreases from 1 as the rainfall rateincreases.
According to the results of the first stage of the
RNC method, emission signatures from raindrops arechecked in the second stage. For deep-rain pixels,
TB10V and TB19V are used with the conditions given
by
TB10V > TB10Vo , or TB19V > TB19Vo.

(10)

Fig. 2. (a) Flowchart for GSMaP1 and (b) schematic diagram of FOV10on. The small ellipse (solid curve)
indicates the 85-G Hz EFOV. The large ellipse (dashed curve) indicates the 10 GHz EFOV. The black pixelis the target pixel.

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Journal of the Meteorological Society of JapanFor shallow-rain pixels, TB37V is additionally usedbecause the scattering from ice particles is not pre­dominant in shallow rain. The condition is given byVol. 87A

When a shallow-rain pixel is misclassified as a deeprain pixel, the emission signatures of the shallow-rain
pixel are checked using TB10V and TB19V, whichhave weaker signatures due to the lower resolution ofthese channels compared with the scale of the shallow
TB10V > TB10Vo, or TB19V > TB19Vo, or TB37V > TB37Vo
rain. As a result, most shallow-rain pixels are
0Vo, or TB19V > TB19Vo, or TB37V > TB37Vo.
(11)
misclassified as no-rain pixels. Therefore, rain85 ischecked only for the target pixel (Fig. 3b). Rain85 is
not checked for other pixels in the 10-GHz EFOV.
(Hereafter, this procedure is referred to as FOV10off.)
4. Improvement of the RNC method
TB37V is used instead of P37 to detect shallow-rain
In this section, we revise the RNC method of pixels. TB37H is more sensitive to wind speed than
GSMaP for retrievals (Fig. 3a). We use the 37-GHz TB37V (Fig. 5). In a windy region, the difference
emission signatures more efficiently in the revised between TB37V and TB37H (denoted as dTB37) is
RNC method, referred to as GSMaP2. We also in­­ less, because TB37H, which is in P37, increases more
vestigate the performance of GSMaP2 using two case than TB37V does as the wind speed increases. In theforward calculation, the difference between TB37Vo
studies and a global comparison.
and TB37Ho (denoted as dTB37o) is computed using
the averaged wind speed in the 5 × 5-degree latitude4.1 Revised RNC method of GSMaP (GSMaP2)This study modifies two conditions (FOV10on and longitude boxes from GANAL. If there is anP37<1) in the first stage of GSMaP1 in order to use extraordinary event (e.g., a typhoon), dTB37 will be
37-GHz emission signatures more efficiently. When less than dTB37o in a windy region with nonthere are pixels whose rain85 is greater than 1mm hr -1 precipitation because the local wind speed is larger
within the 10-GHz EFOV, the central target pixel is than the averaged wind speed from GANAL. As aclassified as a deep-rain pixel in FOV10on of GSMaP1 result, P37 is less than 1, leading to a misclassificationeven if it is actually a shallow-rain pixel (Fig. 4). of the shallow rain in the windy region. Therefore,

Fig. 3. (a) Flowchart for GSMaP2 and (b) schematic diagram of FOV10off.

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4.2 Performance of GSMaP2
The performance of GSMaP2 is compared with thatof GSMaP1 and GPROF. To evaluate the performanceof the RNC, we introduce the equitable threat score(ETS) (Schaefer 1990; Wilks 2006) because we arefocusing not on the estimation of rain but on the RNC.
ETS is defined byETS =N1 − N ref
N1 − N ref + N 2 + N 3

N ref =
Fig. 4. Typical example of a shallow-rain
pixel being misclassified as a deep-rain
pixel. Gray ellipse indicates a deep-rain
pixel; white ellipse indicates a shallowrain pixel.

Fig. 5. TB37H (solid line, scale on the rightvertical axis) and TB37V (dashed line,
scale on the left vertical axis) as functionsof wind speed.

only TB37V, which is less sensitive to variability ofwind speed, is used for detecting shallow rain. The
condition is given by

TB37V > TB37Vo.

(12)

,

( N1 + N 2 )( N1 + N 3 ),N1 + N 2 + N 3 + N 4(13)where N1 is the number of pixels where rain ob­­servedby PR exists and the RNC method classifies rain, N2
is the number of pixels where rain exists and the RNC
method classifies no-rain, N3 is the number of pixels
where rain does not exist and the RNC meth­­odclassifies rain, and N4 is the number of pixels whererain does not exist and the RNC method clas­sifies
no-rain. The ETS ranges from –1/3 to 1 with the best
possible ETS being 1.
We investigated the performance of the RNC meth­ods in two cases. In the first case, rain is scatteredover the sea east of Japan (30°N–36°N, 145°E–160°E)(hereafter case 1). The other case is typhoon Olga over
the western Pacific (17°N–29°N, 125°E–137°E) (here­
after case 2).
In case 1, the PR observed scattered rain behind
cold fronts in the extratropics (Fig. 6a). Figure 7
presents a vertical cross-section along the A–A’ line in
Fig. 6a. The cloud tops observed by Visible Infrared
Scanner (VIRS) aboard TRMM reach about 4 km in
altitude, but the scattered rain top is very shallow (~3km). The ETS values of GPROF, GSMaP1, and
GSMaP2 are 0.116, 0.132, and 0.284 respectively.
Although the results of the three RNC methods co­­incide with the PR results in the broad rain region
around 34°N, 150°E, detection of the scattered rain isdifferent among the three RNC methods. In GSMaP1,
the rain pixels are not scattered but broad (Fig. 6c)
because the pixels around the deep-rain pixel are
misclassified as rain pixels by FOV10on. In contrast,
scattered shallow rain is detected well by FOV10off in
GSMaP2 (Fig. 6d) because FOV10off has a greater
chance of detecting shallow rain using the emission
signal at 37-GHz than FOV10on does. Some scatteredrain is detected by GPROF, but there are fewer suchrain pixels than in the PR results (Fig. 6b). Berg et al.

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Vol. 87A

Fig. 6. Case 1 of scattered rain associated with a post-frontal convective system for 10 January 1999 (TRMM
orbit number 6436) over the sea east of Japan (30°N–36°N, 145°E–160°E).

Fig. 7. Vertical cross-section along the A–A’line in Fig. 6a. Black bars indicate therain height of the PR observation; graybars indicate cloud heights estimated by
the Visible Infrared Scanner (VIRS).

Fig. 8. Brightness temperature at 37 GHz,
from the TMI observation for the case in
Fig. 6.

(2005) suggested that GPROF consistently misses
rainfall events behind cold fronts in the extratropics
because these systems are spatially too small to beproperly captured by the relatively large TMI foot­print. However, the 37-GHz emission signature nearlycorresponds to the rain pixels detected by PR, as seenin Fig. 8. In GPROF, the constant threshold of TB37H
used in condition (7) is less than the observed TB37H
for shallow rain, leading to the misclassification of theshallow rain. GPROF may be tuned for the tropics andmay not be applicable to systems over mid-latitude. In
contrast, the regional threshold of TB37V of the RNC
using GANAL is used in GSMaP2, and thus shallow
rain can be detected by GSMaP2.
In case 2, the PR observed rain bands associatedwith typhoon Olga (Fig. 9a). The ETS of GPROF is0.434 (Fig. 9b), the ETS of GSMaP1 is 0.303 (Fig. 9c),and the ETS of GSMaP2 is 0.505 (Fig. 9d). GSMaP2
and GPROF produce better results than GSMaP1.
GSMaP1 tends to overestimate extended rainy regions

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173

Fig. 9. Case 2 of typhoon Olga for 31 July 1999 (TRMM orbit number 9629) over the western Pacific(17°N–29°N, 125°E–137°E).

where GSMaP2 and GPROF classify no-rain. The
overestimates of GSMaP1 are caused by P37. In awindy region such as one where there is a typhoon,
dTB37 decreases because TB37H increases with windspeed more than TB37V does (Fig. 5). On the other
hand, dTB37o is computed from GANAL using the
average wind speed in the 5 × 5-degree latitudelongitude box. The averaged wind speed should be
weaker than the local wind speed. Therefore, dTB37 isless than dTB37o in a windy region. As a result, P37 isless than 1 and the windy region is misclassified as arainy region. The ETS of GSMaP2 is higher than thatof GPROF because GPROF produces a larger rainy
region than the PR. The screening in GPROF tends toclassify regions that are larger than the PR region as“possibly raining.” Although the region identified as“possibly raining” consists of both raining and nonraining regions, in the Bayesian algorithm, these re­­gions are identified as raining regions because thedatabase that is used in the Bayesian algorithm rep­

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Vol. 87A

Fig. 10. (a) Global map of the ETS difference
between GSMaP2 and GSMaP1, normal­
ized by the ETS of GSMaP1 in 1999. (b)Hovmöller diagram of the monthly zonal
average of the ETS difference between
GSMaP2 and GSMaP1.

Fig. 11. ETS difference between GSMaP and GPROF, normalized by the ETS of GPROF in 1999. The ETS
difference is between GPROF and (a) GSMaP1 and (b) GSMaP2. Hovmöller diagram of the monthly zonal
average of the ETS difference between GSMaP and GPROF, normalized by the ETS of GPROF in 1999.
The ETS difference is between GPROF and (c) GSMaP1 and (d) GSMaP2.

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resents only a small fraction of the systems that might
be observed by the TMI.
A global comparison of GPROF, GSMaP1, and
GSMaP2 is performed using the ETS for 1999. Figure
10a illustrates the difference in the ETS values for
GSMaP2 and GSMaP1, normalized by the ETS of
GSMaP1. Over mid-latitudes, GSMaP2 performs
better than GSMaP1 because GSMaP2 has improved
the RNC for scattered shallow rain. Over the tropics,
GSMaP2 also performs better than GSMaP1 because
the overestimates of rainy regions in GSMaP1 are
reduced in GSMaP2. In contrast, the ETS values of
GSMaP2 decrease over sub-tropical oceans because of
the failure to detect shallow rain. Figure 10b is a
Hovmöller diagram of zonal average ETS values in
1999. The ETS values of GSMaP2 are higher thanthose of GSMaP1 over both mid-latitudes and tropics
through the whole year. The highest ETS of GSMaP2

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is found over mid-latitudes in winter. In contrast, thelowest ETS of GSMaP2 is found over sub-tropicaloceans in winter. Figures 11a and 11b illustrate thedifference in the ETS values for GSMaP and GPROF,
normalized by the ETS of GPROF. While the ETS
values of GSMaP1 are lower than those of GPROF inalmost every region, the ETS values of GSMaP2 arehigher than those of GPROF over mid-latitudes. Over
mid-latitudes, there is a horizontal non-uniformity ofenvironments because the winter mid-latitudes areaffected by strong baroclinic waves and fronts. A
regionally varying threshold value for the RNC using
GANAL is computed in GSMaP2, while GPROF sets
the constant threshold value globally. Therefore,
GSMaP2 performs well for mid-latitudes. Figures 11c
and 11d are Hovmöller diagrams of zonal average ETS
values for GSMaP1 and GSMaP2. The ETS values of
GSMaP1 are lower than those of GPROF through theFig. 12. Shallow rain over sub-tropical ocean (19°S–9°S, 100°W–115°W) for 30 January 1999 (TRMM orbit
number 6760). (a) Rain from the PR observation. (b) TB37V from the TMI observation. (c) GPROF. (d)GSMaP2.

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Journal of the Meteorological Society of Japan

whole year. However, the ETS values of GSMaP2 arehigher than those of GPROF over the mid-latitudes.
Over the tropics, GSMaP2 performs as well as GPROF
does. Over sub-tropical oceans, the ETS values of
GSMaP2 are lower than those of GPROF. This iscaused by the failure to detect shallow rain over subtropical oceans and will be addressed in the following
section.

5. Improvement of the LWP in the forward model
5.1 Parameterization of the LWP over sub-tropicalocean (GSMaP3)
Figure 12 illustrates the case of shallow rain oversub-tropical ocean. It is found that the emission signa­tures at 37V (Fig. 12b) almost corresponds to the rain
pixels determined by the PR (Fig. 12a). The shallow
rain is detected in GPROF (Fig. 12c). However, in
GSMaP2, most of the shallow-rain areas are
misclassified as no-rain areas (Fig. 12d). Figure 13 isa scatter plot between the rainfall rate as estimated by
PR and TB37V as observed by TMI. The relationshipbetween TB37V and the surface rainfall in the LUT at
the grid point 107.5°W, 22.5°S used in this region isFig. 13.
Scatter plot of the PR surface
precipitation rate and TB37V for the case
depicted in Fig. 12. The dashed line is therelation between the rainfall rate and
TB37V in the original LUT. The solidline is the relation between the rainfall
rate and TB37V in the modified LUT.

Vol. 87A

also given in the figure. TB37Vo, which is TB37V at 0mm h-1 in the LUTs (the dashed horizontal line in Fig.
13), is higher than the observed TB37V for most of the
PR rain pixels, leading to a failure to detect theshallow rain using condition (10).
The PR rain-type classification algorithm (2A23;
Awaka et al. 2007; Awaka et al. 2009) detects shallow
rain with storm heights much lower than the freezing
level. Shallow rain consists of shallow isolated rain
and shallow non-isolated rain. When a region of shal­
low rain is isolated from other non-shallow rain areas,
this shallow rain is referred to as shallow isolated rain.
Shallow non-isolated rain is defined as shallow rainthat is not shallow isolated. The pixel counts forshallow non-isolated rain and shallow-isolated rain are
given in Figs. 14a and 14b. Over the mid-latitudes,
most of the shallow rain is shallow non-isolated, indi­
cating that it is accompanied by extratropical frontal
systems as in case 1 (Fig. 6). Over the sub-tropicalocean, the shallow rain is shallow isolated because themean descending branch of the meridional Hadley cell
suppresses the convection of clouds. Shallow isolated
rain over the sub-tropical oceans and shallow nonisolated rain at mid-latitudes should have different
microphysical properties of cloud and precipitation. In
the forward calculation, hydrometer profiles classified
into ten precipitation types are used (Takayabu andKatayama 2004; Takayabu 2006; Takayabu 2008).
Precipitation over the mid-latitudes and sub-tropicalFig. 14. (a) Shallow non-isolated rain pixel
count and (b) shallow isolated rain pixel
count in 1999 from the PR 3A25, a 5°
spatial-resolution
griddedmonthly
composite of PR 2A25 data.

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S. KIDA et al.

Fig. 15. The same as in Fig. 7 but along line
A–A’ in Fig. 12a.

oceans is respec­tively classified as extratropical front­a­ l systems and shallow rain. However, for all pre­cipitation types, it is arbitrarily assumed that there
exists a cloud layer with an LWP of 0.5 kg m-2 below
the freezing-level height, because the distinction be­­tween precipitation and cloud and its relationship to
the LWP is not entirely understood (see review by
Stephens and Kummerow 2007). This assumption may
be unrealistic for shallow isolated rain, leading tooverestimation of TB37Vo.
The LWP depends on the cloud liquid water content(kg m-3) and the cloud depth. Figure 15 depicts avertical cross-section along the A–A’ line in Fig. 12a.
The cloud height is well below the freezing height (4.2km) and close to the rain-top height. Therefore, we
simply parameterize the LWP as a function of thestorm height derived from the PR observation. The
LWP (kg m-2) is

LWP = 0.1× SH ,(14)

where SH (km) is the storm height derived from PR
standard product 3A25, a monthly composite of PR
2A25 data with gridded 5-degree spatial resolution.
We also parameterize the relative humidity, which was
assumed to be 100% below the freezing level. The
relative humidity is 100% below the SH and takes on
the values from GANAL above the SH. We employ
parameterization of the LWP only for the shallow-rain
precipitation type. The modified assumptions of the
LWP and relative humidity are applied to GSMaP2 ina version we refer to as GSMaP3.
5.2 Performance of GSMaP3
Figure 16 is the result for GSMaP3 over sub-tropicaloceans for the same case as in Fig. 11. The ETS value

Fig. 16. GSMaP3 for the case depicted inFig. 12.

of GPROF (GSMaP2) is 0.253 (0.142) (Figs. 12c and12d). The ETS of GSMaP3 is 0.251 (Fig. 16). The
shallow-rain area is detected by GSMaP3 because
TB37Vo in the LUT based on the parameterized LWP
is lower than the observed TB37V for most of the PRrain pixels (solid line in Fig. 13).
A global comparison of GSMaP3 and GSMaP1 isperformed using the ETS for 1999. Figure 17 illustrates
the difference in ETS values between GSMaP3 and
GSMaP1, normalized by the ETS of GSMaP1. Over
sub-tropical oceans, GSMaP3 performs better than
GSMaP1 because the assumptions of LWP and relative
humidity are better parameterized over sub-tropicaloceans, where shallow isolated rain is predominant. A
Hovmöller diagram of the zonal average of the ETS
values in 1999 shows that the ETS of GSMaP3 ishigher than that of GSMaP1 through the whole year.
Figure 18 presents a global comparison between
GPROF and GSMaP3 in 1999. Figure 18 demonstrates
that the ETS of GSMaP3 improves over sub-tropicaloceans where the ETS of GSMaP2 is very much lower
than that of GPROF. This is because regions of shal­
low isolated rain, where detection failed in GSMaP2,
can be detected by GSMaP3. A Hovmöller diagram of
the zonal average ETS values of GSMaP3 and GPROF
indicates that GSMaP3 performs as well as GPROF
over the sub-tropical ocean.

6. Conclusion
We revised the RNC method of GSMaP (GSMaP1).
First, we use 37-GHz emission signatures more
efficiently over the ocean to detect shallow rain in
retrievals by the GSMaP algorithm. This revised RNC

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Journal of the Meteorological Society of Japan

Vol. 87A

Fig. 17. (a) Global map of the ETS difference between GSMaP3 and GSMaP1, normalized by the ETS of
GSMaP1 in 1999. (b) Hovmöller diagram of the monthly zonal average of the ETS difference between
GSMaP3 and GSMaP1, normalized by the ETS of GSMaP1 in 1999.

Fig. 18. (a) ETS difference between GSMaP3 and GPROF, normalized by the ETS of GPROF in 1999. (b)Hovmöller diagram of the monthly zonal average of the ETS difference between GSMaP3 and GPROF,
normalized by the ETS of GPROF in 1999.

method is called GSMaP2. Second, we parameterize
an LWP as a function of storm height (SH) derived
from PR observations to detect shallow isolated rain in
the forward model. The parameterization of the LWP
is introduced into GSMaP2. This further revised RNC
method is called GSMaP3. The performance of
GSMaP2 and GSMaP3 is investigated using casestudies and global comparisons.
In the case of scattered shallow rain behind cold
fronts, GSMaP2 performs better than both GSMaP1
and GPROF because the scattered shallow rain, whichis undetectable in GSMaP1 and GPROF, can bedetected in GSMaP2. The threshold value of TB37V
over rain-free regions, which distinguishes precip­

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S. KIDA et al.

itation from cloud, is better computed using GANALin GSMaP2. Therefore, shallow rain is detected using
the 37V emission signature in GSMaP2. In the case of
typhoon Olga, an improvement from GSMaP1 to
GSMaP2 was the elimination of the spurious identi­
fication of deep rain. Windy areas are misidentified asrainy regions in GSMaP1 owing to the high sensitivityof TB37H to wind speed. In GSMaP2, a windy region
with non-precipitation is correctly identified because
TB37V is used instead of P37 for the RNC. The ETSof GSMaP2 is higher than that of GPROF because
GPROF produces a larger rainy region than the PR.
Most of the regions classified as “possibly rain” by
GPROF are raining regions, because the database thatis used in the full Bayesian algorithm represents onlya small fraction of the systems that might be observedby the TMI.
A global comparison between GSMaP1 and
GSMaP2 reveals that GSMaP2 performs better than
GSMaP1 over the mid-latitudes and tropics because
GSMaP2 has improved the RNC for scattered shallow
rain and windy areas. Through the whole year,
GSMaP2 improves over the mid-latitudes, but is worse
than GSMaP1 over sub-tropical oceans because thedetection of shallow isolated rain fails over the subtropical oceans. The highest ETS of GSMaP2 occurs
during winter in the mid-latitudes. In contrast, thelowest ETS of GSMaP2 occurs during winter oversub-tropical oceans. A global comparison between
GSMaP2 and GPROF indicates that GSMaP2 pro­duces a higher ETS than GPROF over mid-latitudes. A
threshold value for the RNC using GANAL iscomputed for each region in GSMaP2, while GPROF
globally sets a constant threshold value. Over the midlatitudes, there is a horizontal non-uniformity of envi­
ronments because the mid-latitudes are affected bystrong baroclinic waves and fronts during the winter.
Therefore, GSMaP2 performs better than GPROF. In
the future, there will be more opportunities to makeobservations of higher latitudes from satellites (e.g.,
the Global Precipitation Measurement project) (Smithet al. 2007), so achieving higher accuracy in thedetection of rain at higher latitudes is important.
Although GSMaP2 performs well over the midlatitudes, its performance becomes worse than that of
GSMaP1 over sub-tropical oceans. Over the midlatitudes, most shallow rain is non-isolated because theshallow rain is accompanied by extratropical frontal
systems. In contrast, over sub-tropical oceans, shallow
rain is shallow isolated because the mean descending
branch of the meridional Hadley cell suppresses theconvection of clouds. In GSMaP, it is arbitrarily
assumed that there exists a cloud layer with an LWP of
0.5 kg m-2 below the freezing level. This assumption
may be unrealistic for shallow isolated rain, and maylead to overestimation of TB37Vo in the forward
calculation. Therefore, we parameterize the LWP as afunction of SH derived from the PR standard product
3A25 over the region. The detection of the shallow
rain in GSMaP3 agrees with the RNC of the PR
observation over the sub-tropical ocean because theshallow isolated rain, which is not detected with
GSMaP2, can be detected using TB37V.
A global comparison of GSMaP3 and GSMaP1
shows that the ETS of GSMaP3 improves over the
sub-tropical oceans because the LWP is betterparameterized there and the shallow isolated rain canbe detected. Through the whole year, the ETS of
GSMaP3 is higher than that of GSMaP1 over almost
every region. A global comparison between GSMaP3
and GPROF shows that the ETS of GSMaP3 improves
over sub-tropical oceans, where the ETS of GSMaP2
is very much lower than that of GPROF, because
regions of shallow isolated rain that GSMaP2 failed todetect can be detected by GSMaP3. Through thewhole year, GSMaP3 performs as well as GPROF oversub-tropical oceans, although there are a few regionswith lower ETS values for GSMaP3 than for GPROF
in winter owing to simple parameterization of the
LWP.
In this study, we parameterize the LWP as afunction of SH from PR 3A25 only for shallow rain.
We consider that the parameterization of the LWP in
the forward model is imperfect because observationsof the LWP are still inadequate. Therefore, further
investigation of the distinction between precipitationand cloud and its relationship to the LWP is needed.
For the investigation, it is planned to use the cloudprofiling radar (CPR) on Cloudsat (Stephens et al.
2002).

Acknowledgements
This study was supported by the Japan Science andTechnology Corporation Core Research for Evolutional
Science and Technology. This study was also sup­ported by the Japan Aerospace Exploration Agency
Earth Observation Research Center (JAXA EORC)
TRMM project and by the Mitsui & Co., Ltd. En­­vironment Fund. The authors would like to thankProfessor T. Manabe (Osaka Prefecture University)and Dr. T. Iguchi (NICT) for valuable comments. The
authors also thank Dr. C. Kummerow for providing

180

Journal of the Meteorological Society of Japanthe TMI2A12 V6 (GPROF) algorithm code and Dr. B.
Olson for valuable comments. We would also like tothank the two anonymous reviewers for their valuablecomments.

References
Adler, R., G. Huffman, and P. Keehn, 1994: Global
tropical rain estimates from microwave-adjusted
geosynchronous IR data. Remote Sens. Rev., 11,
125–152.
Aonashi, K., A. Shibata, and G. Liu, 1996: An overocean precipitation retrieval using SSM/I mul­tichannel brightness temperatures. J. Meteor.
Soc. Japan, 74, 617–637.
Aonashi, K., and G. Liu, 2000: Passive microwave
precipitation retrievals using TMI during the
Baiu period of 1998. Part I: Algorithm descrip­tion and validation. J. Appl. Meteor., 45, 2259–
2275.
Aonashi, K., J. Awaka, M. Hirose, T. Kozu, T. Kubota,
G. Liu, S. Shige, S. Kida, S. Seto, N. Takahashi,
and Y. N. Takayabu, 2009: GSMaP passive
micro­wave precipitation retrieval algorithm:
Algorithm description and validation. J. Meteor.
Soc. Japan, 87A, 119–136.
Awaka, J., T. Iguchi, and K. Okamoto, 2007: Rain typeclassification algorithm. Measuring precipitationfrom space—EURAINSAT and the future, V.
Levizzani, P. Bauer, and F. J. Turk, Eds.,
Springer, 213–224.
Awaka, J., T. Iguchi, and K. Okamoto, 2009: TRMM
PR standard algorithm 2A23 and its performanceon bright band detection. J. Meteor. Soc. Japan,
87A, 31–52.
Berg, W., T. L'Ecuyer, and C. Kummerow, 2006:
Rainfall climate regimes: The relationship ofregional TRMM rainfall biases to the envi­
ronment. J. Appl. Meteor. Climatol., 45, 434–
454.
Iguchi, T., 2007: Space-borne radar algorithms, Mea­
suring precipitation from space-EURAINSAT
and the future, V. Levizzani, P. Bauer, and F. J.
Turk, Eds., Springer, 199–212.
Iguchi, T., T. Kozu, J. Kwiatkowski, R. Meneghini, J.
Awaka, and K. Okamoto, 2009: Uncertainties in
the rain profiling algorithm for the TRMM
Precipitation Radar. J. Meteor. Soc. Japan, 87A,
1–30.
Karstens, U., C. Simmer, and E. Ruprecht, 1994:
Remote sensing of cloud liquid water. Meteor.
Atmos. Phys., 54, 157–171.

Vol. 87A

Kozu, T., T. Kawanishi, H. Kuroiwa, M. Kojima, K.
Oikawa, H. Kumagai, K. Okamoto, M.
Okumura, H. Nakatsuka, and K. Oikawa, 2001:
Development of Precipitation Radar onboard theTropical Rainfall Measuring Mission satellite.
IEEE Trans. Geosci. Remote Sens., 39, 102–116.
Kubota, T., S. Shige, H. Hashizume, K. Aonashi, N.
Takahashi, S. Seto, M. Hirose, Y. Takayabu, T.
Ushio, K. Nakagawa, K. Iwanami, M. Kachi,
and K. Okamoto, 2007: Global precipitation map
using satellite-borne microwave radiometers by
the GSMaP Project: Production and Vali­dation.
IEEE Trans. Geosci. Remote Sens., 45, 2259–
2275.
Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J.
Simpson, 1998: The Tropical Rainfall Measuring
Mission (TRMM) sensor package. J. Atmos.
Oceanic Technol., 15, 809–817.
Liu, G., 1998: A fast and accurate model for
microwave radiance calculation. J. Meteor. Soc.
Japan, 76, 335–343.
Okamoto, K., 2003: A short history of the TRMM
Precipitation Radar. Cloud Systems, Hurricanes
and the Tropical Rainfall Measurement Mission(TRMM): A Tribute to Dr. Joanne Simpson., No.
51, Amer. Meteor. Soc., 187–195.
Okamoto, K., and S. Shige, 2008: TRMM Precipitation
Radar and its observation results. IEICE Trans.
Commun. Japan, J91-B, 723–733, (in Japanese).
Olson, W. S., C. D. Kummerow, S. Yang, G. W. Petty,
W.-K. Tao, T. L. Bell, S. A. Braun, Y. Wang, S.
E. Lang, D. E. Johnson, and C. Chiu, 2006:
Precipitation and latent heating distributions
from satellite passive microwave radiometry.
Part I: Improved method and uncertainties. J.
Appl. Meteor. Climatol., 45, 702–720.
Petty, G., 1994: Physical retrievals of over-ocean rain
rate from multichannel microwave imagery. Part
I: Theoretical characteristics of normalized
polarization and scattering indices. Meteor.
Atmos. Phys., 54, 79–99.
Petty, G., 1997: An intercomparison of oceanic
precipitation frequencies from 10 SSM/I rain
rate algorithms and shipboard present-weather
reports. J. Geophys. Res., 102, 1757–1777.
Schaefer, J. T., 1990: The critical success index as anindicator of warning skill. Wea. Forecating, 5,
570–575.
Seto, S., N. Takahashi, and T. Iguchi, 2005: Rain/no-rain classification methods for microwave
radiometer observations over land using statis­

March 2009

S. KIDA et al.

tical information for brightness temperatures
under no-rain conditions. J. Appl. Meteor., 44,1243–1259.
Shige, S., T. Watanabe, H. Sasaki, T. Kubota, S. Kida,
and K. Okamoto, 2008: Validation of western andeastern Pacific rainfall estimates from the TRMM
PR using a radiative transfer model. J. Geophys.
Res., 113, D15116, doi:10.1029/2007JD009002.
Smith, E. A., G. Asrar, Y. Furuhama, A. Ginati, C.
Kummerow, V. Levizzani, A. Mugnai, K.
Nakamura, R. Adler, V. Casse, M. Cleave, M.
Debois, J. Durning, J. Entin, P. Houser, T.
Iguchi, R. Kakar, J. Kaye, M. Kojima, D.
Lettenmaier, M. Luther, A. Mehta, P. Morel, T.
Nakazawa, S. Neeck, K. Okamoto, R. Oki, G.
Raju, M. Shepherd, E. Stocker, J. Testud, and E.
Wood, 2007: International Global precipitation
measurement (GPM) program and mission: an
overview, Measuring precipitation from space EURAINSAT and the future, V. Levizzani, P.
Bauer, and F. J. Turk, Eds., Springer, 611–633.
Spencer, R., H. Goodman, and R. Hood, 1989: Pre­
cipitation retrieval over land and ocean with the
SSM/I: Identification and charac­teristics of thescattering signal. J. Atmos. Oceanic Tech­nol., 6,
254–273.
Stephens, G. L., D. G. Vane, R. J. Boain, G. G. Mace,K. Sassen, Z. Wang, A. J. Illingworth, E. J.

181

O’Connor, W. B. Rossow, S. L. Durden, S. D.
Miller, R. T. Austin, A. Benedetti, and C.
Mitrescu, 2002: The cloudsat mission and thea-train. Bull. Amer. Meteor. Soc., 83, 1771–1790.
Stephens, G. L., and C. D. Kummerow, 2007: The
remote sensing of clouds and precipitation fromspace: A review, J. Atmos. Sci., 64, 3742–3765.
Takayabu, T., 2006: Rain-yield per flash calculated
from TRMM PR and LIS data and its rela­tionship to the contribution of tall convective
rain. Geophys. Res. Lett., 33, L18705, doi:10.
1029/2006GL027531.
Takayabu, T., and M. Katayama, 2004: Low-latitudes
rainfall characteristics and its meteorologicalfactors analyzed with mesoscale statistics ofTRMM PR data, presented at the 1st AOGS,Singapore, Paper 57-OOA–A1683.
Takayabu, Y. N., 2008: Observing rainfall regimes
using TRMM PR and LIS data. GEWEX News,
18, No. 2, 9–10.
Wilheit, T. T., A. T. C. Chang, and L. S. Chiu, 1991:
Retrieval of monthly rainfall indices frommicrowave radiometric measurements using
probability distribution functions. J. Atomos.
Oceanic Technol., 8, 118–136.
Wilks, D. S., 1995: Statistical Methods in the
Atmospheric Sciences. Academic Press, 263 pp.