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Global Precipitation Map Using Satellite-Borne
Microwave Radiometers by the GSMaP Project:
Production and Validation
Takuji Kubota, Member, IEEE, Shoichi Shige, Hiroshi Hashizume, Kazumasa Aonashi,
Nobuhiro Takahashi, Member, IEEE, Shinta Seto, Masafumi Hirose, Yukari N. Takayabu,
Tomoo Ushio, Member, IEEE, Katsuhiro Nakagawa, Member, IEEE, Koyuru Iwanami,
Misako Kachi, and Ken’ichi Okamoto, Member, IEEE

Abstract—This paper documents the production and validationof retrieved rainfall data obtained from satellite-borne microwave
radiometers by the Global Satellite Mapping of Precipitation(GSMaP) Project. Using various attributes of precipitation derived
from Tropical Rainfall Measuring Mission (TRMM) satellite data,
the GSMaP has implemented hydrometeor profiles derived from
Precipitation Radar (PR), statistical rain/no-rain classification,and scattering algorithms using polarization-corrected temperatures (PCTs) at 85.5 and 37 GHz. Combined scattering-based
surface rainfalls are computed depending on rainfall intensities.
PCT85 is not used for stronger rainfalls, because strong depressions of PCT85 are related to tall precipitation-top heights. Therefore, for stronger rainfalls, PCT37 is used, with PCT85 used forweaker rainfalls. With the suspiciously strong rainfalls retrieved
from PCT85 deleted, the combined rainfalls correspond well to the
PR rain rates over land. The GSMaP algorithm for the TRMM
Microwave Imager (TMI) is validated using the TRMM PR,
ground radar [Kwajalein (KWAJ) radar and COBRA], and Radar
Automated Meteorological Data Acquisition System (AMeDAS)
precipitation analysis (RA). Monthly surface rainfalls retrieved
from six microwave radiometers (GSMaP_MWR) are comparedwith the gauge-based dataset. Rain rates retrieved from the TMI

Manuscript received June 1, 2006; revised December 22, 2006. This work
was supported by the R&D of Hydrological Modeling and Water Resources
System in Core Research for Evolutional Science and Technology of the Japan
Science and Technology Agency.
T. Kubota and H. Hashizume are with the Japan Science and Technology Agency, Osaka Prefecture University, Osaka 599-8531, Japan (e-mail:kubota@ieee.org).
S. Shige and K. Okamoto are with the Department of Aerospace Engineering, Osaka Prefecture University, Osaka 599-8531, Japan.
K. Aonashi is with Meteorological Research Institute, Japan Meteorological
Agency, Ibaraki 305-0052, Japan.
N. Takahashi is with Research and Standards Division, National Institute ofInformation and Communications Technology, Tokyo 184-8795, Japan.
S. Seto is with the Institute of Industrial Science, University of Tokyo, Tokyo
153-8505, Japan.
M. Hirose and M. Kachi are with Earth Observation Research Center, Japan
Aerospace Exploration Agency, Ibaraki 305-8505, Japan.
Y. N. Takayabu is with the Center for Climate System Research, Universityof Tokyo, Chiba 277-8568, Japan.
T. Ushio is with the Department of Electrical, Electronic, and Information
Engineering, Osaka University, Osaka 565-0871, Japan.
K. Nakagawa is with Okinawa Subtropical Environment Remote-Sensing
Center, National Institute of Information and Communications Technology,
Okinawa 901-0411, Japan.
K. Iwanami is with the Storm, Flood, and Landslide Research Department,
National Research Institute for Earth Science and Disaster Prevention, Ibaraki
305-0006, Japan.
Digital Object Identifier 10.1109/TGRS.2007.895337(GSMaP_TMI) are in better agreement with the PR estimatesover land everywhere except over tropical Africa in the boreal
summer. Validation results of the KWAJ radar and COBRA
show a good linear relationship for instantaneous rainfall rates,
while validation around Japan using the RA shows a good relationship in the warm season. Poor results, connected to weakprecipitation cases, are found in the cold season around Japan.
Index Terms—Microwave radiometer, precipitation, rain-rate
retrieval, validation.

I. I NTRODUCTIONR

ECENTLY, extensive studies have been conducted on
global warming and climate changes [1]. Increasing
global surface temperatures can lead to changes in precipitation associated with changes in atmospheric circulation, amore active hydrological cycle, and increases in water-holding
capacity throughout the atmosphere. Therefore, monitoring andanalyzing precipitation rates using global precipitation datasets
are necessary for climate-change detections. However, raingauge stations are mainly located over land and placed sparsely
in the tropics. In contrast, precipitation estimates derived fromsatellite remote sensing can offer the prospect of near-global
climatologies. Thus, more accurate and higher precision global
precipitation maps are needed for many researchers.
Passive microwave radiometer observations have attracted
the attention of many studies because they can measure theemitted radiation from rainwater and the scattering causedby cloud ice and snow. Using large observation swaths andhigh observation frequencies, there is a great expectation for
global precipitation maps produced by microwave radiometers.
The problems of using sensor algorithms to retrieve rainfall
rates have been revealed through the analyses of observation
results of the Tropical Rainfall Measuring Mission (TRMM)
satellite, developed under a U.S.–Japan joint mission, carrying
rain observation sensors such as the TRMM Microwave Imager(TMI) and the Precipitation Radar (PR) [2], [3]. The Global
Precipitation Measurement (GPM) mission is an expanded
follow-on mission to TRMM with a TRMM-like core satellite
carrying dual-frequency PR (DPR) and a GPM Microwave
Imager (GMI) onboard, and accompanied by constellationsof satellites equipped with microwave radiometers provided
by international partners. There is an increasing interest in

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advanced retrieval algorithms for microwave radiometers andglobal precipitation maps utilizing these microwave radiometerdata. The over-ocean algorithms utilize the emission information by rainwater at lower microwave frequencies [4]–[8],whereas the over-land algorithms use the scattering information
by cloud ice at higher microwave frequencies [9]–[13]. Overcoast algorithms are discussed in [14]. The algorithms canbe also divided into two groups: deterministic rain-retrieval
algorithms [4], [5], [15]–[18] and probabilistic rain-retrieval
algorithms [6]–[8], [19]–[22].
The Global Satellite Mapping of Precipitation (GSMaP)
Project [23] started in November 2002. The aims of the GSMaP
Project are the development of an advanced microwave radiometer algorithm based on the deterministic rain-retrieval
algorithm of [16] and the production of precise high-resolution
global precipitation maps. In this project, the algorithms aredeveloped based on physical models of precipitation including
melting layers and particle-size distribution. The information
obtained by the PR is introduced in order to share a common
precipitation model between the microwave radiometers and the
PR algorithms.
This paper documents the production and the validationof rainfall retrievals from satellite-borne microwave radiometers by the GSMaP Project. Datasets are briefly described in
Section II. The retrieval algorithms of microwave radiometers
are documented in Section III. Section IV demonstrates thevalidation of retrievals. The GSMaP retrievals are comparedto TRMM PR rainfalls, the TMI standard product, groundradar observations, gauge-adjusted radar data, and gauge-based
estimates. Summaries are given in Section V.
II. D ATA
A. Production by the GSMaP ProjectIn the GSMaP Project, surface rainfalls have been retrievedby the algorithm for brightness temperatures (Tbs) from the
TRMM TMI for eight years (1998–2005), from the Advanced
Microwave Scanning Radiometer for the Earth Observation
System (AMSR-E) for three years (2003–2005) aboard the National Aeronautics and Space Administration (NASA)’s Aqua
satellite, from AMSR aboard the Japan Aerospace Exploration Agency’s (JAXA) Advanced Earth Observing Satellite-II(ADEOS-II) for seven months (April–October 2003), andfrom three Special Sensor Microwave/Imager (SSM/I) aboard
the Defense Meteorological Satellite Program for three years(2003–2005). The product combined with these microwave
radiometer-derived rainfall estimates is referred to as the
GSMaP_MWR. The product of the TMI-only retrievals isreferred to as the GSMaP_TMI. The GSMaP Project has also
been developing algorithms combined with the passive microwave and geostationary Earth orbit (GEO) infrared (IR)radiometers. High temporal interpolation of the GSMaP_MWR
is obtained by the morphed technique [24] and the Kalmanfilter [25] using the IR information. This product is beyond thescope in this paper, because this paper describes the retrievals ofsatellite-borne microwave radiometers and their validation (seethe GSMaP Project website [26] and follow-on papers for the
microwave-GEO-IR-combined algorithm).

B. Brightness Temperatures (Tbs)
TMI is a nine-channel linear-polarized passive microwave
radiometer with five frequencies (10.7, 19.35, 21.3, 37, and85.5 GHz) [2]. Each frequency has one vertical (v) and one
horizontal (h) channel, except for 21 GHz (v-only). We use TMI
1B11 (Algorithm Version “6.5”) for the Tbs. The swath widthof the TMI is 760 km.
The SSM/I is a seven channel passive microwave radiometer operating at four frequencies (19.35, 22.235, 37.0, and85.5 GHz) with dual-polarization in all except the 22.235-GHz
frequency (v-only). The Tbs of SSM/I are obtained from
Remote Sensing Systems.
The AMSR-E has six frequencies with dual-polarization(6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz) [27]. Tbs at
6.925 GHz are not used in the GSMaP algorithm. The algorithm
only utilizes the Tbs of AMSR at 10.65, 18.7, 23.8, 36.5, and89.0 GHz, although the AMSR has an eight frequency withdual-polarization except two vertical channels in the 50 GHz.
We use the Tbs of L1B Version 2 of AMSR-E and AMSR
provided by JAXA.
C. PR 2A25
The TRMM PR is the first spaceborne precipitation radar,and its 128-element active phased-array system operates at
13.8 GHz [28]–[30]. It can provide the 3-D structure of the rainfall, particularly of the vertical distribution, and obtain quantitative rainfall measurements over land, as well as over ocean.
The swath width of the PR is 215 km and narrower than that of
the TMI. We use PR 2A25 Version 6 and also use the TRMM
3G68 product distributed by the TRMM Science Data andInformation System (TSDIS) office for the 0.5 × 0.5◦ analysis.
The 2A25 algorithm estimates the true effective reflectivity
factor Ze at 13.8 GHz at each radar resolution cell from themeasured vertical profiles of reflectivity factor Zm. The rainfall
rate is then calculated from the estimated Ze [30]. Recently,
Shige et al. [31] investigated the consistency between TMIobserved Tbs at 10 GHz and those simulated from PR 2A25 V5
and V6 rain profiles for intertropical convergence zone (ITCZ)
rain systems during the warm phase of the 1997/1998 El Nino
Southern Oscillation, using a radiative transfer model (RTM).
They showed that simulated Tbs from PR 2A25 V6 are higher
than those from PR 2A25 V5 and exhibit better agreement withthe observed Tbs.
D. TMI 2A12
TMI 2A12 is a product of the Goddard profiling algorithm (GPROF) [6], [32], [33]. The basis of the TMI 2A12
algorithm is a Bayesian framework, in which retrieved precipitation are constructed from those cloud resolving model(CRM)-generated profiles that are radiatively consistent withthe observation. We use the 2A12 Version 6 for the comparison
with the TMI retrievals of the GSMaP.
Major improvements in Version 6 are the following:1) greater diversity in the supporting CRM database;2) adjustment of CRM ice microphysics;

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3) inclusion of the effects of mixed-phase precipitation in
CRM simulations;4) definition of cloud and precipitation properties in simulated footprints;
5) new geographic database;6) calculation of the “background” rain-free radiance field;7) reformulation of the convective/nonconvective rain-areaconstraint.
(see the detailed descriptions in [32] and [33] on improvements
made in 2A12 V6).
E. Radar Rain Data at Okinawa (COBRA)
A field campaign of observing precipitation in Okinawa,
Japan (okn-baiu04), was conducted in the 2004 rainy season(May 22 to June 6) with the National Institute of Information
and Communications Technology, Okinawa bistatic polarimetric radar known as “COBRA” [34]. The main radar of the
COBRA system is a ground-based monostatic-pulse Doppler
radar located on Okinawa Island (26.59◦ N, 128.06◦ E), using
a single wave (5340 MHz) in the C-band [35]. In the campaign, COBRA was operated in 10-min cycles with the 3-D
observation scan taking 6 min and the range–height indicatorscan taking 4 min. In this paper, the COBRA precipitation
dataset Version 1.0 at the elevation of 0.5 km is used for fourcases selected for overpasses of the TMI, i.e., 3 : 00–3 : 09 UTC
June 1, 2 : 00–2:09 UTC June 2, 1 : 10–1 : 19 UTC June 3, and
22 : 00–22 : 09 UTC June 9. The analyzed area was from 127◦ E
to 129.1◦ E, and from 25.6◦ N to 27.5◦ N.
F. Radar Rain Data at Kwajalein (KWAJ) (2A53)
The 2A53 Version 5 is the 2-km spatial-resolution instantaneous rainfall product observed by the radar located on KWAJ
(8.72◦ N, 167.73◦ E) [36], [37], provided by NASA’s Goddard
Space Flight Center (GSFC) Distributed Active Archive Center.
The radar is a 3-D scanning S-band dual-polarization Doppler
weather radar [38]. The area is defined as the area of the circle
with a 150-km radius centered on the radar. The data inside of
the 15-km radius are not used in order to avoid clutter due toradar reflection off the ocean surface. Over 99% of the KWAJ
radar area is ocean.
We chose ten cases for overpasses of the TMI during May
2003, that is, 9 UTC May 4, 6 UTC May 11, 19 UTC May 11,
18 UTC May 14, 4 UTC May 15, 2 UTC May 18, 16 UTC
May 18, 1 UTC May 19, 11 UTC May 26, and 10 UTC
May 29. These cases were selected in terms of rain areas above
1 mm/h.
G. Radar-Automated Meteorological Data Acquisition System(AMeDAS) Precipitation Analysis (RA)The RA is a 1-h accumulated-precipitation estimate produced by the Japan Meteorological Agency (JMA) [39], [40].
Spatial and temporal resolutions of the RA have been upgraded by the JMA. During the period between June 2003 andDecember 2005, the spatial resolution was 0.025◦ of latitude
and 0.03125◦ of longitude (about 2.5 × 2.5 km2 ) and thetemporal resolution was 30 min. The JMA operated 20 ground-2261

based weather radars as of October 1999, and their detection
range now covers almost all of the Japanese Islands. AMeDAS
includes 1320 automatic surface-weather stations. The densityof the AMeDAS rain-gauge network is approximately one station in each 17 × 17 km2 area. The RA data are created from acomposite of JMA operational radars calibrated by rain gauges(the AMeDAS network and gauges operated by the ministry ofland, infrastructure, and transport and the prefectural offices),and the calibration technique is described in [39].
Observation areas of the RA are 500 km2 per radar. In thispaper, they are limited by circles with radii of 150 km fromthe stations at Nase, Okinawa, and Ishigakijima and by circles
with radii of 200 km from the other radar sites. Nase, Okinawa,and Ishigakijima are islands located south of 30◦ N, and theirobservation areas are mostly ocean. These limitations are due toapparent biases of the RA, possibly because of detection losses
of low precipitation-top heights far from the radar sites and poor
calibration over oceanic areas by sparse gauges.
Sampling errors of satellite estimates were investigated using
the RA dataset by previous papers [40]–[42]. In order to avoid
the sampling errors, RA data are daily averaged only over thearea where the swath of the TMI passes, as will be described in
Section IV-C.
H. Global Precipitation Climatology Centre (GPCC)
Rain-Gauge Analyses
The land-surface monitoring product is constructed by the
GPCC operated by the National Meteorological Service ofGermany [43], [44]. The product is based on about 7000 raingauge stations worldwide, comprising mostly of synoptic andmonthly climate reports collected from the global telecommunications network in real time and supplemented by otherworldwide data collections, such as the Monthly Climatic Datafor the World. Sophisticated quality control is performed before
carrying out the analyses. We use the monthly product of the
GPCC with 1.0 × 1.0◦ boxes that have at least a gauge.
III. D ESCRIPTION OF R ETRIEVAL A LGORITHM
A. Algorithm Developments by the GSMaP ProjectIn the GSMaP Project, the deterministic rain-retrieval algorithm is utilized for the production of global rainfall estimatesfor Tbs from the microwave radiometers. The retrieval algorithm is based on the study in [16]. The basic idea of thealgorithm is to find the optimal precipitation that gives RTM
field-of-view (FOV)-averaged Tbs that fit best with the observed Tbs. A flowchart of the current version “V4.7.2” isillustrated in Fig. 1.
The left panel of Fig. 1 describes the process of making the lookup tables (LUTs) that show the relationship between rainfall rates and Tbs. From forward calculations witha four-stream RTM [45], LUTs for homogeneous precipitation are constructed. Daily LUTs are computed in 5.0 ×
5.0◦ latitute–longitude boxes, with atmospheric variables being
given by the JMA Global Analysis (GANAL). Freezing-level
heights (FLHs) are computed daily by cubic spline interpolation
for the vertical layer of temperature data of the GANAL.

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Fig. 1. Flowchart for the GSMaP rain-retrieval algorithm in this paper.

The RTM calculation uses PR-observed hydrometeor profiles, classified by eight precipitation types. Precipitation types
are determined in terms of stratiform pixel ratio, stratiform rain
ratio, precipitation area, precipitation-top height, rain intensity,and diurnal cycle [46] from the PR 2A25. Precipitation types
consist of five land types (afternoon shower, shallow convection, extratropical cyclone, organized convection, and the
Tibetan Plateau) and three ocean types (shallow convection, extratropical cyclone, and organized convection). Global distributions of the precipitation types in 2.5 × 2.5◦ latitude–longitude
boxes are statistically classified trimonthly using the 2A25.
Precipitation profiles of the 2A25 data are averaged for eachtype and binned with rainfall intensities separated into either theconvective or stratiform cases, for the period of 1998–2004. In
this calculation, the profiles are arranged with altitudes relative
to FLHs. The database of precipitation types and profiles makes
it possible for the algorithm to deal with trimonthly variation of
typical hydrometeor profiles.
The RTM is inputted with the precipitation profiles from thedatabase and with the FLHs from GANAL. In the current algorithm, dynamic separation such as [32] is not utilized for the
FOV between the convective and the stratiform rain. Instead,
we use a fixed ratio for weighted averages between the LUTs of
the convective and stratiform rain. The ratio is then computedfrom pixel ratios of convective and stratiform rainfalls, binned
with rainfall intensities for precipitation types using the 2A25.
The drop-size distribution model in Marshall–Palmer [47]is used for rain, and the exponential distribution is used forsnow and graupel. For snow, the slope parameter is a Sekhon–
Srivastava [48].
The Nishitsuji melting-layer model [49], [50] is utilized in
the RTM calculation for stratiform rain and is also used in

the TRMM PR standard algorithm [30]. The Nishitsuji model
was developed through propagation experiments of satellite-toground microwave links under sleet and snow conditions. It
parameterizes volume water fraction and dielectric constant of
mixed-phase particles in terms of temperature and explains the
microwave attenuation of the melting layer. Tbs are computedby the RTM with 32 vertical layers consisting of 13 standard
pressure levels below 100 hPa and 19 levels with 50-m intervals
in a 1-km range bracketing the stratiform rain melting layer.
For convective rain, the RTM calculation uses 13 vertical layers(only standard pressure levels).
Fig. 2 shows an example, used in the GSMaP_TMI algorithm, of the LUT for Tbs at 10-GHz vertical polarization [Tbs(10v)] and Type 8 (organized convection over ocean) at the grid
point (2.5◦ E, 2.5◦ N) on January 1, 1998. The LUT has 21lines for each precipitation inhomogeneity between zero andtwo (with steps of 0.1), assuming that precipitation in the real
atmosphere is lognormally distributed in the horizontal. Similartables are computed for the other frequencies at the grid point.
In the retrieval process, we use values of the LUTs interpolated
linearly by the neighboring four boxes.
The right panel of Fig. 1 shows the process of the retrievalsfrom the Tbs of satellite-borne microwave radiometers. At the
beginning of this process, the first guess of precipitation iscomputed from the observed polarization-corrected temperature (PCT) at 85.5 GHz (PCT85) using the LUTs. PCT85 [51]is given by
PCT85 = 1.81 Tb(85v) − 0.81 Tb(85h).
The rain rate retrieved from PCT85 is referred to as the
Rainpct85.

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Fig. 2. Example of the LUT for Tbs (10v) and Type 8 (organized convection
over ocean) at the grid point (2.5◦ E, 2.5◦ N) on January 1, 1998 used in the
GSMaP_TMI algorithm. The horizontal and vertical line denotes rain rates andcalculated Tbs, respectively. Solid lines labeled “0,” “1,” and “2” denote values
with precipitation inhomogeneity of “0,” “1,” and “2,” respectively.

Next, rain or no-rain flags are identified by deterministic
methods. The algorithm for the TMI uses surface-type information derived from the land–sea database of the TSDIS
toolkit.
Over land for the TMI, the method by the study in [52] isused for rain/no-rain classification (RNC). This RNC method
is based upon statistical information for the TMI Tbs underno-rain conditions of the PR 2A25. Tbs (85v) under no-rain
conditions are estimated from the database and the observed
Tbs (21v). When the observed Tbs (85v) are larger than theestimated values, “no-rain” flags are identified. For radiometersof polar-orbit satellites, databases are computed from the fittingof observed Tbs (89v) to a normal distribution. Tbs (89v) underno-rain conditions are estimated from the database and theobserved Tbs (23v) and compared to the observed Tbs (89v)[53]. We use screening of Tbs (23v) < 260.0 K, or surface
temperatures < 273.2 K, with no-rain for snow.
Over ocean, rain flags are identified by three conditions:
Rainpct85 larger than 1 mm/h; at least one pixel of Rainpct85
larger than 1 mm/h within the scope of 10-GHz effective
FOV (EFOV); or by normalized polarization differences [5] at
37 GHz (P37) of less than one.
Rain flags are identified over the coast by using a modified
method of the study in [14]. The condition of “T85H > 257,
T22V < 269.1” is not used in our algorithm because it often
leads to false rainfalls during the winter in midlatitude coastal
areas. We use a condition of surface temperatures < 273.2 K
for a no-rain state over coastal areas. McCollum and Ferraro’s
procedure [14] defined ambiguous classifications as values
lying between classes (63) and (66) in their paper. In this paper,
ambiguous classes (63)–(65) in the method [14] are results,
using their method, are classified as “possible rain,” and an
ambiguous class (66) is classified as “no-rain.” A scattering
threshold of “Rainpct85 > 1,” instead of their PCT cutoff
threshold, is applied to possible-rain cases. This scattering

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Fig. 3. Histogram showing sum of rain rates over land during 1998. Lines
connected through cross, open circle, and solid circle marks indicate rain
rates of PR 2A25, Rainpct85, and Rainpct37, respectively. The width of thehistogram bin is 1 mm/h. This analysis is done for 0.1 × 0.1◦ boxes with PR
overpasses.

threshold of the GSMaP seems to be suitable except at the
winter midlatitudes, as will be shown in Fig. 13.
Precipitation inhomogeneities are estimated from theRainpct85 classified as “rain” by the RNC method within ascope of a 10-GHz EFOV, according to the method similar tothe one used in the study in [18]. The LUTs are corrected using
the inhomogeneities by assuming the aforementioned lognormal distribution.
Rainfalls are estimated from the scattering information using Tbs of high frequencies (e.g., 37 and 85.5 GHz for the
TMI). This scattering algorithm is documented in the next
section.
Over ocean, rainfalls are estimated also from the emission
information using Tbs at low frequencies. Weighted averages
are calculated using emission-based and scattering-based retrievals, and the optimal precipitation is obtained by the iterative variation method that minimizes a cost function madeup of differences between the observed and RTM-calculatedTbs, as described in [16]. The over-ocean algorithm for the
SSM/I had to be modified because of the lack of a 10-GHz
channel. In the over-ocean algorithm for the SSM/I, thenormalized-polarization-difference-based [5] rain estimates areused instead of the original v-polarized 19-GHz Tb-based rainestimates for such strong rain, because the 19-GHz Tb saturates
during strong rain (e.g., above 10 mm/h) [54].
In the algorithm, rainfall retrievals are 0 mm/h when increments of the PCT85 are positive for scattering-based estimatesor when increments of the Tbs(19v) are negative for emissionbased estimates. Here, increments are the differences between
the observed Tbs and the Tbs computed by the RTM for the
no-rain state.
We do not retrieve rainfalls when FLHs are less than 500 m
over the ocean in the current algorithm. Cases that satisfy
the low FLH condition are not analyzed for the GSMaP and
GPROF products in this paper. The algorithm for snowfall has
been left for future work.

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Fig. 4. Case study on a MCS over western Africa on July 3, 1998 (orbit number: 3429). Spatial resolution of all panels is 0.125 × 0.125◦ . (a) Estimated surface
rain rate (in millimeters per hour) of PR 2A25. (b) Precipitation-top height (in kilometers) of the 2A25. (c) PCT37 of the TMI (in Kelvin). (d) PCT85 of the TMI(in Kelvin). (e) Rainpct37 (in millimeters per hour). (f) Rainpct85 (in millimeters per hour).

B. Scattering Algorithm Using 85 and 37 GHz
The scattering algorithm of the GSMaP [55] utilizes 85.5 and37 GHz PCT (PCT85 and PCT37) [51], [56], [57]. Here, PCT37
is given by
PCT37 = 2.17 Tb(37v) − 1.18 Tb(37h).
The histogram in Fig. 3 shows the sum of rain rates during
January–December 1998. Rainpct85 and Rainpct37 are therain rates (in millimeters per hour) estimated from the PCT85
and PCT37, respectively. They are compared to the estimatedsurface rain rates of the PR 2A25 (Rainpr). Here, orbit datasets
are plotted on a grid of 0.1 × 0.1◦ latitude/longitude boxes,
and values observed by both the TMI and PR over land are
analyzed. Fig. 3 shows that the Rainpct85 corresponds wellto the Rainpr below 10 mm/h. On the other hand, suspicious
rainfalls above 30 mm/h, not found in the Rainpr, are distinctly
found in the Rainpct85.
Using the numerical-model simulation, Mugnai et al. [58]
investigated vertical sources of radiation that contribute to the
top-of-atmosphere Tbs in terms of the four frequencies corresponding to the SSM/I. They demonstrated that the 85-GHz
signals were emissions from the upper level liquid and ice
scattering in the upper reaches of tall precipitation clouds.

Therefore, the PCT85 has a large sensitivity to precipitationtop height for tall convection, whereas it is intimately related
to surface rainfall for moderate convection. Fig. 4 shows a casestudy on a mesoscale convective system (MCS) over western
Africa on July 3, 1998. Precipitation cores of the MCS arefound in estimates of the PR 2A25 [Fig. 4(a)], and there aresmall rain rates between cores. On the other hand, precipitationtop heights [Fig. 4(b)] are tall even over the area between cores.
Precipitation-top heights are tallest in the convective region
around 11◦ N, 8◦ W. Here, precipitation-top height is definedas the top bin above 0.3 mm/h of the 2A25, as per [59] and[60]. The pattern of the PCT85 [Fig. 4(d)] is more similar tothat of the precipitation-top height than surface rain rates ofthe 2A25. The areas of low PCT85, which are connected to theareas of very large Rainpct85 [Fig. 4(f)], are nearly all locatedin the areas of tall precipitation-top height. These results are
consistent with the study in [58].
In Fig. 3, the Rainpct37 is close to the Rainpr above
20 mm/h, although the Rainpct37 deviates largely from theRainpr below 10 mm/h. In Fig. 4(e), the Rainpct37 does notshow strong rainfalls above 100 mm/h, while the Rainpct85
shows. Moreover, the area of relatively high PCT37 [Fig. 4(c)],which corresponds to that of weak Rainpct37, is found over
the area between cores. On the other hand, relatively small

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Fig. 5. Cumulative histogram of rain rates over land during 1998. Lines
connected through cross, open circle, and solid circle marks indicate rain ratesof PR 2A25, Rainpct85, and Rainpct3785, respectively. Histogram bins are
determined only by PR rain rates. The width of the bin is 1 mm/h. This analysis
is done for 0.25 × 0.25◦ boxes with PR overpasses.

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Fig. 6. Zonally averaged rain rates over ocean-only for (a) December, January,
and February, (b) March, April, and May, (c) June, July, and August, and(d) September, October, and November during 1998–2005. Solid, long-dashed,and short-dashed lines indicate rain rates of PR 2A25, TMI 2A12, and
GSMaP_TMI, respectively.

gradients of the PCT37 are found over the edges of the rain
area, which can be connected to poor correspondences of the
Rainpct37 to the Rainpr for weak rain rates. Thus, because
strong Rainpct85 is closely related to the tall precipitationtop height, we use the Rainpct85 for small rainfalls and theRainpct37 for strong rainfalls.
Estimations combined with the Rainpct85 and the Rainpct37,
referred to as Rainpct3785 (in millimeters per hours), aresummarized as follows:
Rainpct3785 = W × Rainpct37 + (1 − W ) × Rainpct85
W = 0

for Rainpct85 ≤ 10

W = (Rainpct85 − 10)/10

for 10 < Rainpct85 < 20

W = 1

for Rainpct85 ≥ 20

where W is the weighting function.
Fig. 5 shows the cumulative histogram of rain rates during January–December 1998, comparing the Rainpct85 andRainpct3785 to the Rainpr. Rain rates observed by both the
TMI and the PR over land are analyzed with 0.25 × 0.25◦
latitude–longitude boxes. In this analysis, histogram bins are
determined only by Rainpr intensities. Fig. 5 shows that the
Rainpct3785 corresponds well to the Rainpr, whereas the
Rainpct85 overestimates heavily. Thus, the Rainpct3785 is used
as retrievals over land and coast in the GSMaP algorithm. Tbs
at 36.5 and 89.0 GHz are used for retrievals from AMSR-E
and AMSR.
IV. V ALIDATION OF THE R AINFALL R ETRIEVALS
A. Comparison With TRMM PR Datasets
In the TRMM satellite, it is possible to compare estimations
from the TMI and the PR directly and synchronously. Using
the 1998–2005 Rainpr as a reference, the GSMaP_TMI arecompared to surface rainfall rates of 2A12 by the GPROF

Fig. 7. Zonally averaged rain rates over land-only during 1998–2005. Thelines denote the same as Fig. 6.

algorithm. The gridded data are analyzed with horizontal resolution of 0.5 × 0.5◦ latitude–longitude boxes only over the PR
overpasses.
Fig. 6 compares zonally averaged rain rates over ocean of
PR 2A25, TMI 2A12, and GSMaP_TMI. Over tropical oceanssuch as the ITCZ, 2A12 rain rates are larger than Rainpr, as
shown in previous papers [3], [61]–[63]. Over tropical oceans
around the ITCZ in all seasons, GSMaP-retrieved rain rates arein better agreement with the Rainprs than the 2A12 rain rates.
On the other hand, the GSMaP tends to be larger than the 2A25
over the winter midlatitudes and smaller over the South Pacificconvergence zone and the subtropics around 15◦ N–30◦ N.
Fig. 7 compares zonally averaged rain rates over land. The
2A12 rain rates are larger than Rainpr in all seasons. On theother hand, GSMaP-retrieved rain rates are in good agreementwith the Rainprs, except for the region between 5◦ N–15◦ N in

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Fig. 8. Monthly time series of rain rates averaged over ocean areas of (a) Indian Ocean (50◦ E–90◦ E, 10◦ S–EQ), (b) ITCZ over the eastern Pacific (150◦ W–
90◦ W, 5◦ N–15◦ N), (c) equatorial Atlantic (40◦ W–0◦ E, 5◦ S–5◦ N), and (d) Northern Hemisphere midlatitude Pacific (140◦ E–160◦ W, 25◦ N–35◦ N),or land areas of (e) Amazon Basin (80◦ W–45◦ W, 10◦ S–EQ), (f) Indian Subcontinent (65◦ E–90◦ E, 5◦ N–25◦ N), (g) tropical northern Africa (5◦ E–35◦ E,
EQ–15◦ N), and (h) tropical southern Africa (10◦ E–40◦ E, 15◦ S–EQ). Red solid, green long-dashed, and blue short-dashed lines indicate rain rates of PR 2A25,
TMI 2A12, and GSMaP_TMI, respectively.

the boreal summer. Large differences are found over tropicalnorthern Africa.
In the left panels of Fig. 8, monthly rain rates of the PR
2A25, the TMI 2A12, and the GSMaP_TMI are comparedfor ocean averages over four areas. Correlation coefficients
between the monthly series of the GSMaP and the 2A25 are
0.95 for the Indian Ocean, 0.98 for the ITCZ, 0.98 for the equatorial Atlantic region, and 0.87 for the Northern Hemispheremidlatitude Pacific. Variations of the GSMaP and the 2A12 areclosely similar to those of the 2A25 over the Indian Ocean andequatorial Atlantic.
In the GSMaP algorithm, surface rainfalls over land arecomputed by the scattering algorithm using the PCT85 andthe PCT37, as described in the previous section. In the rightpanels of Fig. 8, monthly rain rates of 2A25, 2A12, and
GSMaP_TMI are compared for land averages over four areas.
Correlation coefficients between the monthly series of GSMaPand 2A25 are 0.97 for the Amazon Basin, 0.96 for the Indian
Subcontinent, 0.98 for tropical northern Africa, and 0.99 fortropical southern Africa. The GSMaP results correspond well to
the 2A25 over the Amazon Basin and tropical southern Africa,

whereas the 2A12 results are larger than the Rainpr. Variationsof the GSMaP and the 2A12 are similar to those of the 2A25
over the Indian Subcontinent. In the tropical northern Africa
region, estimates of the GSMaP and the 2A12 are larger thanthose of the 2A25.
The tendencies of the biases for the GSMaP and the 2A25
are different between tropical northern Africa and the Amazon.
The PR–TMI differences depend on storm heights for the 2A12[64], [65], and the GSMaP [66]. Precipitation systems tend
to be deeper and more intense in tropical Africa than in the
Amazon [67]. Seto et al. [65], [66] investigate the causes ofthese biases, comparing single or multiple PR pixels within a
TMI footprint for 85 GHz and the TMI pixel. They concluded
that the overestimates of the GSMaP compared to the 2A25
were due to tall precipitation with a precipitation-top height
above 8 km, which can be connected to the differences between
tropical Africa and the Amazon. Potential problems in the
GSMaP algorithm are the precipitation profiles and the scattering algorithm. The profiles are an influential component of thealgorithm, and retrievals tend to decrease over tropical Africa
in the experiment using Africa-only profiles. The scattering

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Fig. 9. Comparison of TMI retrievals with KWAJ radar data for ten selected
overpasses during May 2003. (a) GSMaP_TMI and (b) 2A12. This analysis isdone for 0.25 × 0.25◦ ocean-only boxes.

Fig. 10. Comparison of TMI retrievals with COBRA data for four selected
overpasses during June 2004. (a) GSMaP_TMI and (b) 2A12. This analysis isdone for 0.25 × 0.25◦ boxes.

algorithm noted in Section III-B uses a simple scheme in thecurrent algorithm, and more elaborate techniques are necessary
in view of regional dependence.
A candidate for the problem in the 2A25V6 algorithm ispath-integrated attenuation estimates by the surface-referencetechnique. This technique assumes that the true radar surface
cross section remains the same inside and outside the raining area [68], [69]. By this assumption, attenuation-correctedsurface-backscattering cross sections are underestimated overland, leading to underestimates of rain rates [70]–[72]. The underestimates due to this are particularly distinct over the tropical
Africa area (10◦ N–15◦ N), although they cannot account for all
differences of the TMI–PR differences.

GSMaP data and 0.64 for the 2A12 data. Root mean-squareerrors (rmses) are 1.78 mm/h for the GSMaP and 1.79 mm/h
for the 2A12. Correlation coefficients between the GSMaP and
the KWAJ are 0.66 over ocean only and 0.65 over coast only,
while all pixels are “ocean” over the radar area in the 2A12.
Fig. 10 shows a scatter plot of the GSMaP_TMI and theradar rain data from Okinawa (COBRA) with 0.25 × 0.25◦
resolution. Here, four overpasses during June 2004 are selected,
as noted in Section II-E. Both panels of this figure also show
a good linear relationship. The correlation coefficients (with251 degrees of freedom) are 0.82 for the GSMaP data and 0.84
for the 2A12 data. The rmses are 1.37 mm/h for the GSMaP and1.12 mm/h for the 2A12. Note that surface types are different
between the GSMaP and the 2A12 over the observation areaof the COBRA, as will be discussed in the next section. The
ratio of “ocean” pixels to total pixels in the area is 29% in the
GSMaP data, while the ratio is 69% in the 2A12 data.
Thus, the GSMaP retrievals correspond well to the two
ground-radar rain rates, and although both algorithms are quite
different, the validation results of the GSMaP are similar to
those of the 2A12.

B. Comparison With the Ground-Radar Datasets
The GSMaP algorithm utilizes information derived from the
TRMM PR, and it is necessary that the retrievals of the GSMaP
algorithm be validated with the dataset independent of the
TRMM PR. In this section, the GSMaP_TMI is compared with
two ground-radar datasets.
Fig. 9 shows a scatter plot of the GSMaP_TMI and the radarrain data at KWAJ with 0.25 × 0.25◦ resolution. Here, ten overpasses during May 2003 are selected, as noted in Section II-F.
This figure does not show the points for which rain from one
or both instruments is less than 0.01 mm/h. Both panels of
the figure show a good linear relationship. The correlation
coefficients (with 1137 degrees of freedom) are 0.65 for the

C. Comparison With the RA
The RA is created from estimates of 20 radars calibrated byrain gauges. They are useful for the validation of the microwave
retrievals because of the good calibration and relatively large
observation ranges that cover almost all of the Japanese islands.

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Fig. 11. (a) Rainfalls of RA on 21 : 00 UTC June 1, 2004. Gray regions indicate observation areas of the RA not included in the TMI overpass. (b) Daily mean
rainfalls of RA averaged within the TMI swath on June 1, 2004. Analyzed areas are limited by the distances from the radar sites. Lower panels show surface typesof (c) 2A12V6 and (d) GSMaP_TMI (TSDIS toolkit). Blue, red, and green denote ocean, coast, and land, respectively. Here, (c) and (d) show only grid points that
hold in the same surface types as the FOV with 0.25 × 0.25◦ resolution.

The TRMM satellite visits the RA observation areas at a few
times in a day, and RA data are selected according to when
the swath of the TMI passes [see Fig. 11(a)]. This selection
was calculated half-hourly during the period between January
2004 and December 2005. After the initial data selection, daily
averages were calculated using the selected RA in the area
limited by the distances from the radar sites [Fig. 11(b)].
In this section, four validation indexes are utilized in addition
to the spatial correlation and the rmse. The Heidke skill score(HSS) indicates the degree of consistency between the two withrespect to the binary classification of a grid box as raining or
not raining [11]. The bias score is the ratio of the number of yes
forecasts (TMI retrievals giving “raining”) to the number of yes
observations (RA observing “raining”) [73]. The probabilityof detection (POD) is the ratio of correct forecasts to thenumber of times the forecasted event occurred. The false-alarm
ratio (FAR) is the fraction of yes forecasts that turn out to be
wrong. Further definitions of the statistics are described in the
study in [73]. In 2 × 2 contingency tables, the categories are< 1 and >= 1 mm/day as used in the study in [24]. Statistics
are computed for daily estimates and averaged monthly during
2004–2005 with 0.25 × 0.25◦ resolution.

Surface types are different between the GSMaP data (using
the TSDIS toolkit) and the 2A12V6 data. The 2A12V6 uses
a revised geographic database described in the study in [32].
Fig. 11(c) and (d) shows examples of surface-type distributions
over the RA observation area and shows that coastal areas are
smaller in the 2A12 data [Fig. 11(c)] than in the GSMaP data
[Fig. 11(d)]. Thus, statistics are calculated using only the grid
points of the same surface type for both the GSMaP and the2A12. Border areas that include different surface types of the
FOV in a grid point are excluded in this analysis.
Figs. 12–14 indicate monthly series of RA validation during
2004–2005 for ocean, coast, and land, respectively. Note thatthe grid points of “land,” illustrated in green in the lower
panels of Fig. 11, are located in the mountainous regionsin Japan. Spatial correlation and HSS show distinct seasonalvariations in all surface types, while the differences of thealgorithms are small, in particular, over ocean. Overall, resultsover ocean are best, and results over land are worst in surface
types for the correlation and the HSS. Correlation coefficientsof the GSMaP for ocean, coast, and land are 0.66, 0.55, and0.40 during May–October and are 0.49, 0.33, and 0.13 during
November–April, respectively.

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Fig. 12. Time series of RA validation during January 2004–December 2005, with statics generated every month using daily estimates for ocean-only. Solid andopen circles indicate the GSMaP_TMI and 2A12, respectively.

Poor results in the winter season are examined further. The
effectiveness of the retrievals can be related to areas and intensities of precipitations, in addition to the surface temperature.
Area ratios of the number of observations above 1 mm/h tothe number of all observations are computed using the above
daily RA with 0.25◦ resolution during 2004–2005. Here, thethreshold of 1 mm/h is selected because the algorithm overland and coast uses scattering signals, and we use the thresholdthat is not very sensitive to weak precipitations. In order to
identify days with weak precipitations, we use the criterionof the ratio < 1%, which means that the “precipitation areas
above 1 mm/h are less than 1% of the RA.” The ratios averaged
during the two years were 5.4%, 4.8%, and 4.5% for ocean,
coast, and land, respectively. The ratios of days that satisfy the
aforementioned criterion are 37%, 52%, and 67% for ocean,
coast, and land, respectively. Results of the analyses using thecriterion are summarized in Table I. Correlation coefficients arehigher when weak-precipitation days are excluded than when
only those days are used, and this is particularly noticeable incoast and land areas. These analyses suggest low effectivenessfor weak-precipitation cases over coast and land.
Large bias scores show the overestimates of precipitation
area in the boreal winter over coast and land. The POD is low
in the boreal winter over coast and land, while it is relatively
high over ocean throughout the year. Spatial correlations of the
GSMaP data are slightly larger than those of the 2A12 data overcoastal areas, while the rmses of the GSMaP data are larger. The

FAR also shows a clear seasonal cycle for coast and land, andit indicates that false precipitations are found more often in the
boreal winter.
Thus, in the boreal summer, spatial correlations are high,detections of precipitations are relatively high, and false precipitations are lower over all surface types. On the other hand, inthe boreal winter over coast and land, there are overestimates ofprecipitation area, detections of precipitations are low, and false
precipitations are found more often, which can be due to thewrong identification of the precipitation areas in the algorithms.
The POD is high over ocean throughout the year. Large bias
scores and high FARs are connected with the poor correlationsin the weak-precipitation days. These results show that theefficiency of the TMI retrievals is better in warm seasons thanin the cold season around Japan.
D. Comparison With Gauge-Based Datasets
Comparison of surface rainfalls retrieved from microwave radiometers with ground-based estimates from gauge datasets canbe valuable, although there are significant uncertainties associated with sampling errors and the systematic errors of gauges.
Monthly estimates of the GSMaP_MWR are compared withthe gauge-based dataset of the GPCC. Fig. 15 shows a scatterplot of the GSMaP_MWR and the GPCC over 15◦ S–15◦ N
during January 2004–December 2005. This analysis is donewith 1.0 × 1.0◦ boxes with at least a gauge. The figure shows a2270

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Fig. 13. Same as Fig. 12 except for coast-only.

good linear relationship between the two, although rain rates of
the GPCC tend to be larger than those of the GSMaP_MWR.
Correlation coefficients are calculated between monthly rain
rates of the GSMaP_MWR and the GPCC with the resulting
correlation coefficient of 0.80 with 5972 degrees of freedom
for Fig. 15. The linear regression model is as follows:y = 1.08 x + 21.9 (in millimeters per month).
Here, x is the independent variable illustrating the monthly rain
rate of the GSMaP_MWR, and y in the dependent variableshowing the monthly rain rates of the GPCC. The positive
slope and intercept values of the regression model indicate
the underestimates of the GSMaP_MWR with reference to
the GPCC.
V. S UMMARYThe GSMaP Project started in November 2002 with the aimof developing a PR-consistent advanced microwave radiometer
algorithm based on the study in [16], and the production of precision high-resolution global precipitation maps. In this project,
the algorithms have been improved in terms of PR-observedhydrometeor profiles, statistical RNC method, implement of
the melting-layer model, and scattering algorithms using ascattering-based method combining PCT85 and PCT37 data,
referred to as Rainpct3785. By this method, suspicious strong

rainfalls of retrievals from PCT85 are deleted with the resultthat the combined Rainpct3785 method corresponds well to the
PR rain rates.
We have retrieved surface rainfalls from the TMI for eight
years (1998–2005), from the AMSR-E for three years (2003–
2005), from the AMSR for seven months (April–October2003), and from the SSM/I for three years (2003–2005). We
combined these rainfall retrievals with the data derived fromthe microwave radiometers (GSMaP_MWR).
The GSMaP_TMI rain rates are compared to the GPROF
retrievals using the PR data as the reference during 1998–2005.
The differences between PR 2A25 and TMI 2A12 are large,
in particular, over land. In contrast, the GSMaP_TMI is ingood agreement with the 2A25 over land except for Africa(5◦ N–15◦ N) in the boreal summer.
The instantaneous estimates of TMI retrievals are compared with rain rates of two ground-radar datasets, KWAJ and
COBRA, with 0.25 × 0.25◦ resolutions. For ten overpasses
during May 2003, the correlation coefficients using the KWAJ
are 0.65 for the GSMaP and 0.64 for the 2A12, with rmses of
1.78 mm/h for the GSMaP and 1.79 mm/h for the 2A12. For
four overpasses during June 2004, the correlation coefficientsusing the COBRA are 0.82 for the GSMaP and 0.84 for the
2A12 with rmses of 1.37 mm/h for the GSMaP and 1.12 mm/h
for the 2A12. These results show a good linear relationshipbetween the TMI retrievals of the GSMaP and two rainfall
datasets of the ground radar.

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Fig. 14. Same as Fig. 12 except for land-only.
TABLE I
CORRELATION COEFFICIENTS BETWEEN THE GSMaP_TMI AND THE
RA FOR SURFACE TYPES (OCEAN, COAST, AND LAND) DURING
MAY–OCTOBER OR NOVEMBER–APRIL. THE RESULTS ARE
ALSO SHOWN USING ONLY WEAK-PRECIPITATION DAYSAND E XCLUDING T HOSE S AME D AYS

The daily estimates of the TMI retrievals around Japan arecompared with RA precipitations during 2004–2005. Spatial
correlation and the HSS between the TMI retrievals and the
RA show that distinct seasonal variations occur in all surface
types, although the differences of the algorithms are small, inparticular, over ocean. Results of the correlation and the HSS
are best over ocean surfaces and worst over land surfaces.
Correlation coefficients of the GSMaP are high from May to
October and relatively low from November to April. Correlation coefficients of the GSMaP are high throughout the year
when cases with weak-precipitation cases are excluded. During
the boreal summer, there are relatively high detection rates
of precipitations and less false precipitations over all surface
types. On the other hand, during the boreal winter over coastand land, there are overestimates of precipitation area, low

Fig. 15. Comparison of GSMaP_MWR monthly estimates with rain-gauge
analyses over 15◦ N–15◦ S during January 2004–December 2005. This analysis
is done for 1.0 × 1.0◦ boxes with at least a gauge. Dashed line shows a linear
regression line.

detections, and more frequent false precipitations. These poor
results during the winter lead to the low correlations in the weak
precipitation days. These results show that the efficiency of the
TMI retrievals around Japan is greater in the warm seasons
than in cold.

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Monthly estimates of the GSMaP_MWR are compared withthe gauge-based dataset of the GPCC. The correlation coefficient is 0.80 between the GPCC and the GSMaP_MWR
over the tropics during 2004–2005. There is a good linear
relationship, although the GPCC tends to be larger than the
GSMaP.
The GSMaP Project has also been developing algorithms
combined with the passive microwave and GEO IR radiometers. High temporal interpolation of the GSMaP_MWR can beobtained using the morphed technique and the Kalman filter
using IR information, and this will be described in future
papers.
ACKNOWLEDGMENT
This paper is part of the GSMaP Project led by Prof.
K. Okamoto (Osaka Prefecture University, Osaka, Japan) andunder the Core Research for Evolutional Science and Technology program of the Japan Science and Technology Agency.
The authors would like to thank the members of the GSMaP
Project for the many valuable comments and assistance they
have provided and to Y. Iida, S. Kida, and T. Watanabe (Osaka
Prefecture University) for the helpful computing assistance.
They would also like to thank the Associate Editor and theanonymous reviewers for the valuable comments, as well as
Dr. G. Liu (Florida State University) for the RTM code.
Figures were drawn using the Grid Analysis and Display
System and GFD-DENNOU Library. TRMM PR data and Tbs
data of microwave radiometers, TMI 1B11, AMSR L1B, and
AMSR-E L1B, were provided by JAXA under the cooperative research agreement between Osaka Prefecture University
and JAXA. The authors would also like to thank the GPCC
and the TSDIS. Radar rain data at KWAJ (2A53) were provided by the NASA GSFC Distributed Active Archive Center.
The RA Dataset was provided by JMA. The GSMaP Project
website can be found at http://www.radar.aero.osakafu-u.ac.jp/~gsmap/.
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Takuji Kubota (M’07) received the B.S., M.S., andPh.D. degrees from Kyoto University, Kyoto, Japan,
in 1999, 2001, and 2004, respectively.
From 2004 to 2005, he was with the Disaster
Prevention Research Institute, Kyoto University,
as a Postdoctoral Researcher and he worked on predictability of tropical precipitation. Since 2005, hehas been a Japanese Science Technology Agency
Postdoctoral Researcher at Osaka Prefecture University, Osaka, Japan. His current research interests arein algorithm development for spaceborne microwave
radiometers and in validation of the rainfall retrievals.
Dr. Kubota is a member of the Remote Sensing Society of Japan, Meteorological Society of Japan, American Meteorological Society, and American
Geophysical Union.

Shoichi Shige received the B.S., M.S., and Ph.D.
degrees from Kyoto University, Kyoto, Japan, in
1995, 1997, and 2001, respectively.
From 2001 to 2004, he was with Earth Observation Research Center, Japan Aerospace Exploration
Agency, as an Invited Scientist. In 2004, he was with
the Department of Aerospace Engineering, Osaka
Prefecture University, Osaka, Japan, where he is currently a Research Associate. His research interests
include mesoscale convective systems and satellite
rainfall and heating profiles estimation.

Hiroshi Hashizume received the B.S. degree in engineering from the
Kanazawa University, Ishikawa, Japan, in 1996, and the Ph.D. degree in Earthenvironmental science from Hokkaido University, Sapporo, Japan, in 2001.
From 2001 to 2003, he was with the Jet Propulsion Laboratory, Pasadena,CA, as a Postdoctoral Researcher. He is currently a Japan Science Technology
Agency Postdoctoral Researcher at the Osaka Prefecture University, Osaka,
Japan. He has been working on scientific analysis of Earth remote-sensing data
to understand the role of air–sea interaction in the global climate. He is also
working on algorithm development on retrieving rainfall rate from the series ofspaceborne microwave radiometers—SSM/I, TRMM, TMI, and AMSR.

Kazumasa Aonashi received a degree from the Meteorological College, Kashiwa, Japan, in 1982, and
the Ph.D. degree in meteorology from the Universityof Tokyo, Tokyo, Japan, in 1997.
Since 1988, he has been with the Forecast
Research Department at the the Meteorological Research Institute, Tsukuba, Japan. His areas of expertise are in passive microwave precipitation retrieval
and assimilation of satellite-microwave radiometerdata into cloud-resolving numerical weather prediction models.

Nobuhiro Takahashi (M’05) received the B.S.,
M.S., and Ph.D. degrees from Hokkaido University, Sapporo, Japan, in 1988, 1990, and 1994,
respectively.
In 1994, he joined the Communications ResearchLaboratory (currently the National Institute of Information and Communications Technology), Tokyo,
Japan, where he is engaged in the research of theTropical Rainfall Measuring Mission and Global
Precipitation Measurement in the Research andStandards Division.
Dr. Takahashi is a member of the American Meteorological Society andMeteorological Society of Japan.

Shinta Seto received the B.Sc., M.Sc., and Ph.D. degrees from the Universityof Tokyo, Tokyo, Japan, in 1998, 2000, and 2003, respectively.
From 2003 to 2006, he was with the National Institute of Information
and Communications Technology (formerly, the Communications ResearchLaboratory), Tokyo, as a Postdoctorate Researcher and he worked on thedevelopment of spaceborne dual-frequency Precipitation Radar. Since 2006, hehas been in the Institute of Industrial Science at the University of Tokyo. His
current research interests are precipitation retrieval using microwave remotesensing and its application to water-cycle studies.

Masafumi Hirose received the B.S. degree in space aeronautics from the Tokyo
Metropolitan Institute of Technology, Hino City, Japan, in 1998, and the M.S.
and Ph.D. degrees in meteorology from Nagoya University, Nagoya, Japan, in
2000 and 2004, respectively.
He is an Aerospace Project Research Associate with the Earth Observation
Research and Application Center at the Japan Aerospace Exploration Agency,
Ibaraki, Japan. His research interests include radar meteorology and precipitation climatology.
Dr. Hirose is a member of the Japan Meteorological Society.

Yukari N. Takayabu received the Ph.D. degree in geophysics from the
University of Tokyo, Tokyo, Japan, in 1993.
From 1987 to 2000, she was with the National Institute for Environmental
Studies. In 2000, she was with the Center for Climate System Studies at the
University of Tokyo, where she is currently an Associate Professor. Since 2006,
she has also been serving as a part-time Group Leader at the Institute of Observational Research for Global Change, Japan Agency for Marine–Earth Scienceand Technology. Her current interests include dynamics of tropical atmosphericdisturbances coupled with cumulus convection and multiscale interactions frommesoscale convective systems to global-scale climate variations. She is alsointerested in the characterization of cloud and precipitation systems utilizing
various kinds of spaceborne measurements, with recent emphases on analyzing
TRMM data.
Dr. Takayabu was the recipient of the Society’s Award of the Japan Meteorological Society in 1998.

Tomoo Ushio (M’00) received the B.S., M.S., and Ph.D. degrees from Osaka
University, Osaka, Japan, in 1993, 1995, and 1998, respectively, all in electricalengineering.
From 1998 to 2000, he was with the Global Hydrology and Climate Center,
Huntsville, AL, as a Postdoctorate Researcher. In 2000, he was with the
Department of Aerospace Engineering, Osaka Prefecture University. In 2006,
after being an Assistant Professor at Osaka Prefecture University, he waswith the Department of Electrical, Electronic, and Information Engineering,Osaka University, where he is currently an Associate Professor. His research
specialties are radar-based remote-sensing techniques and passive and active
remote sensing of the atmosphere from spaceborne platforms.

Katsuhiro Nakagawa (M’06) received the B.Sc.
degree in engineering from Kinki University, Osaka,
Japan, in 1993, and the M.Sc. and Ph.D. degrees in
global environment engineering from Kyoto University, Kyoto, Japan, in 1995 and 1998, respectively.
Since 1998, he has been with the National Instituteof Information and Communications Technology(formerly, Communications Research Laboratory).
He has been engaged in the development of the multiparameter weather radar (COBRA) in Okinawa,
Japan.
Dr. Nakagawa is a member of the American Meteorological Society and theJapan Society of Civil Engineers.

KUBOTA et al.: GLOBAL PRECIPITATION MAP USING SATELLITE-BORNE MICROWAVE RADIOMETERS

Koyuru Iwanami received the B.Sc., M.Sc., and Dr.Sci. degrees from
Hokkaido University, Sapporo, Japan, in 1985, 1987, and 1991, respectively.
In 1991, he was with the National Research Institute for Earth Scienceand Disaster Prevention, Japan. He is currently with the Storm, Flood, andLandslide Research Department, National Research Institute for Earth Scienceand Disaster Prevention, Ibaraki, Japan. He has been engaged in research on
cloud and precipitation using X-band polarimetric radar and dual-frequency
cloud radars.
Dr. Iwanami is a member of the American Meteorological Society, Meteorological Society of Japan, and Japanese Society of Snow and Ice.

Misako Kachi received the B.Sc. degree in Earth and planetary physics from
Hokkaido University, Sapporo, Japan, in 1993, and the M.Sc. degree in Earthand planetary science from the University of Tokyo, Tokyo, Japan, in 1995.
In 1996, she was with the National Space Development Agency of Japan.
From 2003 to 2005, she was with the Japanese Ministry of Education, Culture,
Sports, Science, and Technology, as a secretariat member for the ad hocGroup on Earth Observations and the Earth Observation Summits. Since 2005,
she has been with the Earth Observation Research Center, Japan Aerospace
Exploration Agency, Ibaraki, Japan. Her research interests include microwave
remote sensing and its application to other research/operational fields.

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Ken’ichi Okamoto (M’83) was born in Akashi,
Japan, in 1946. He received the Dr.Sci. degree fromthe University of Tokyo, Tokyo, Japan, in 1973.
He was with the Communications ResearchLaboratory (CRL, currently, the National Instituteof Information and Communications Technology),
Tokyo, in 1973. He was the Associate Director
General of CRL in 1999. He has been with Osaka
Prefecture University, Osaka, Japan, as Professorof aerospace engineering since April 2000. He has
been engaged in research on remote sensing of the
Earth’s environment by various types of radars, including the Tropical Rainfall
Measuring Mission Precipitation Radar.
Dr. Okamoto was the Chair of the IEEE Geoscience Remote Sensing chapterof the Japan Council in 1998–2000. He was the President of the Remote Sensing
Society of Japan in FY 2004 and 2005.