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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 9, SEPTEMBER 2009

The GSMaP Precipitation Retrieval Algorithm forMicrowave Sounders—Part I: Over-Ocean Algorithm
Shoichi Shige, Member, IEEE, Tomoya Yamamoto, Takeaki Tsukiyama, Satoshi Kida, Hiroki Ashiwake,
Takuji Kubota, Member, IEEE, Shinta Seto, Kazumasa Aonashi, and Ken’ichi Okamoto, Member, IEEE

Abstract—We develop an over-ocean rainfall retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) basedon the Global Satellite Mapping of Precipitation (GSMaP) microwave radiometer algorithm. This algorithm combines an emissionbased estimate from brightness temperature (Tb) at 23 GHz anda scattering-based estimate from Tb at 89 GHz, depending on ascattering index (SI) computed from Tb at both 89 and 150 GHz.
Precipitation inhomogeneities are also taken into account. The
GSMaP-retrieved rainfall from the AMSU (GSMaP_AMSU) is
compared with the National Oceanic and Atmospheric Administration (NOAA) standard algorithm (NOAA_AMSU)-retrieved
data using Tropical Rainfall Measuring Mission (TRMM) data asa reference. Rain rates retrieved by GSMaP_AMSU have better
agreement with TRMM estimates over midlatitudes during winter.
Better estimates over multitudes over winter are given by theuse of Tb at 23 GHz in the GSMaP_AMSU algorithm. It wasalso shown that GSMaP_AMSU has higher rain detection than
NOAA_AMSU.
Index Terms—Microwave radiometer (MWR), microwave
sounder, precipitation, rain-rate retrieval.

I. I NTRODUCTIONR

AIN maps have been produced using data from passive
microwave radiometers (MWRs) currently in orbit suchas the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) [1], the Advanced Microwave ScanningRadiometer for the Earth Observing System (AMSR-E) aboard
the National Aeronautics and Space Administration (NASA)
Aqua satellite [2], and the Special Sensor Microwave/Imager

Manuscript received October 1, 2008; revised February 19, 2009. First
published June 5, 2009; current version published August 28, 2009. This
work was supported by the Japan Aerospace Exploration Agency under the
5th TRMM RA.
S. Shige, T. Yamamoto, T. Tsukiyama, S. Kida, and H. Ashiwake arewith the Department of Aerospace Engineering, Osaka Prefecture University,
Sakai 599-8531, Japan (e-mail: shige@aero.osakafu-u.ac.jp; tomoya@aero.
osakafu-u.ac.jp; t-tsukiyama@aero.osakafu-u.ac.jp; kida@aero.osakafu-u.
ac.jp; h-ashiwake@aero.osakafu-u.ac.jp).
T. Kubota is with the Earth Observation Research Center, Japan
Aerospace Exploration Agency, Tsukuba 305-8505, Japan (e-mail: kubota.
takuji@jaxa.jp).
S. Seto is with the Institute of Industrial Science, The University of Tokyo,
Tokyo 153-8505, Japan (e-mail: seto@rainbow.iis.u-tokyo.ac.jp).
K. Aonashi is with the Forecast Research Department, MeteorologicalResearch Institute, Japan Meteorological Agency, Tsukuba 305-0052, Japan(e-mail: aonashi@mri-jma.go.jp).
K. Okamoto was with the Department of Aerospace Engineering, Osaka
Prefecture University, Sakai 599-8531, Japan. He is now with the Faculty ofEnvironmental and Information Studies, Tottori University of Environmental
Studies, Tottori 689-1111, Japan (e-mail: kokamoto@kankyo-u.ac.jp).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TGRS.2009.2019954(SSM/I) aboard satellites of the Defense Meteorological Satellite Program [3], [4]. Information obtained by the TRMM
precipitation radar (PR) [5]–[7] accelerated the developmentof rainfall retrieval from spaceborne passive MWRs. One algorithm using precipitation-related variable models and retrieval
methods based on TRMM observation studies is the Global
Satellite Mapping of Precipitation (GSMaP) MWR algorithm(hereinafter referred to as GSMaP_MWR) [8], [9].
Despite the improved rainfall estimates using data frompassive MWRs, the challenge remains to further fill information
gaps through more frequent satellite observations. One way
to fill gaps is to use infrared (IR) data, which is available
frequently over most areas of the globe from geostationary andpolar-orbiting satellites. High-temporal-resolution interpolation
for GSMaP_MWR is obtained by cloud-top motion derived
from two successive IR images and Kalman filter (hereinafterreferred to as GSMaP_MVK) [10]. However, the relationshipbetween cloud-top motion and precipitation feature motion iscomplex [11], possibly leading to errors.
Passive MWRs are generally of two types: imagers andsounders. Imagers such as the TMI, AMSR-E, and SSM/I havechannels suitable for monitoring precipitation. Sounders suchas the Advanced Microwave Sounding Unit (AMSU) [12], [13]aboard the National Oceanic and Atmospheric Administration(NOAA) satellites and the first satellite of the Meteorological
Operational satellite program (MetOp-A) are primarily developed for profiling atmospheric temperature and moisture using
opaque spectral regions. To optimize the sensor performance,
20 channels are divided among three separate total-power
radiometers, namely, AMSU-A1, AMSU-A2, and AMSU-B(NOAA-18 and MetOp-A replaced AMSU-B with the similar Microwave Humidity Sounder). Each radiometer uses a
cross-track scanner to view the Earth, where AMSU-A1 andAMSU-A2 (denoted collectively as AMSU-A) have fields ofview (FOVs) of 48 km at nadir and 149 km × 79 km at limb,
while AMSU-B has FOVs of 16 km at nadir and 52 km × 27 km
at limb. The window channels at 23.8, 31.4, 50.3, 89, and
150 GHz are used to retrieve several important parameters related to the hydrological cycle and expand the AMSU capability
beyond that of temperature and moisture profiling [14].
Two AMSU-based rainfall retrieval algorithms have beendeveloped. One is a neural-network-based algorithm developed
at the Massachusetts Institute of Technology, Cambridge [15],[16]. The algorithm is trained using a cloud-resolving model.
The other is the Microwave Surface and Precipitation Products System (MSPPS) Day-2 rainfall algorithm for the AMSU
and has been developed at NOAA (hereinafter referred to as

0196-2892/$26.00 © 2009 IEEE

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SHIGE et al.: GSMaP PRECIPITATION RETRIEVAL ALGORITHM FOR MICROWAVE SOUNDERS INOAA_AMSU) [14]. Because there have been four AMSU
instruments in orbit since the launch of NOAA18 in May
2005, together with five microwave imagers (TMI, AMSR-E,
and SSM/I), there have been more observations of rainfall intime and space, with swaths being ∼2200 km wide. Anotheradvantage of the four AMSU sensors on the NOAA satellites
and MetOp-A is that they are typically spaced about 4 h in time,thus giving a better representation of the diurnal cycle. Three
AMSU sensors have been incorporated into high-spatial- andtemporal-resolution precipitation products such as the TRMM
Multisatellite Precipitation Analysis (TMPA) [17], Climate Precipitation Center Morphing Technique (CMORPH) [18], andNaval Research Laboratory Blended Satellite Technique (NRLBlended) [19], [20]. Different precipitation retrieval algorithms
are applied to different passive microwave radiometric sensors
for subsequent use in these high-resolution precipitation products. For example, in the current TMPA, passive microwave
FOVs from the TMI, AMSR-E, and SSM/I are converted toprecipitation estimates at the TRMM Science Data and Information System (TSDIS) with sensor-specific versions of
the Goddard profiling (GPROF) algorithm [21]–[24], while
those from AMSU are converted to precipitation estimates at
the National Environmental Satellite, Data, and Information
Service (NESDIS) with the NOAA_AMSU algorithm [14].
In this paper, we develop a rainfall retrieval algorithm
for AMSU (hereinafter referred to as GSMaP_AMSU) thatshares at a maximum a common algorithm framework with
the GSMaP_MWR algorithm [8], [9]. An hourly global rainfall
map in near real time (about 4 h after observation) basedon the GSMaP algorithm [8]–[10] has been provided by theJapan Aerospace Exploration Agency (JAXA)/Earth Observation Research Center [25]. In the current system, hightemporal-resolution interpolation of the rainfall estimated using
data from microwave imagers (TMI, AMSR-E, and SSM/I) is
obtained using the IR information. Offline tests showed that the
IR interpolation of GSMaP (GSMaP_MVK) is more efficient
using rain estimations derived from microwave sounder data
based on the NOAA_AMSU algorithm in addition to those derived from microwave imager data [26]. The NOAA_AMSU algorithm is a scattering algorithm that mainly relies on data from
AMSU-B channels, as will be described later (Section II-B).
Over land, only the scattering-based algorithm can be usedbecause the contrast between surface and atmosphere becomesless owing to high emissivity over land, making it difficult toapply the emission-based algorithm. On the other hand, overocean, both the emission- and the scattering-based algorithm
can be used because of low emissivity over ocean. Here,
we develop rain retrieval over ocean for the GSMaP_AMSU
algorithm, which combines an emission-based estimate frombrightness temperature (Tb) data at 23 GHz and a scatteringbased estimate from Tb data at 89 GHz.
II. D ATA
A. Space–Time Matchup of AMSU Data Against TRMM Data
In principle, rainfall retrievals using data from the PR and
TMI aboard the TRMM satellite are superior to those using
data from AMSU instruments, which have coarser FOVs and3085

channels for profiling atmospheric temperature and moistureinstead of precipitation. The PR provides height information
based upon the time delay of the precipitation-backscatteredreturn power and allows vertical profiles of precipitation to beobtained directly. The TMI is equipped with channels suitable
for monitoring precipitation; in particular, a 10-GHz channel
has a nearly linear relationship between brightness temperatures and rain rate. Thus, a comparison of AMSU estimatesagainst TRMM estimates is very useful for the development
and validation of AMSU rainfall retrievals. In this paper, wematched TRMM rainfall products and NOAA-15, NOAA-16,
and NOAA-17 AMSU retrievals over ocean for two months ofJanuary and July 2005. A total of 75 significant rain events(44 events for January 2005 and 31 events for July 2005) for
matched TRMM-AMSU orbits were found within a 20-min
window. When instantaneous imagery was compared, either PR
or TMI estimates were averaged for the AMSU-B FOV. The antenna pattern for the AMSU-B was approximated by a Gaussian
weighting function with the same 3-dB beam width as for theactual antenna pattern. We applied the rainfall retrieval algorithm (GSMaP_AMSU) to the matched AMSU/TRMM data
and validated the estimates using the GSMaP_MWR estimatesfrom the TMI data (hereinafter referred to as GSMaP_TMI) as
references. We also used the current (version 6) level-2 standard
products of the PR and TMI, which are published by NASA and
JAXA. The level-2 standard product 2A12 (GPROF algorithm)
was used for the rain estimation from the TMI data [22],[23]. The basis of GPROF is a Bayesian framework, in which
retrieved precipitation is constructed from cloud-resolving
model-generated profiles that are radiatively consistent withthe observation [21]. The level-2 standard product 2A25 wasused for the rain estimation from the PR data [27]–[29].
The PR operates at a single frequency of 13.8 GHz so that
the PR2A25 algorithm corrects the attenuation in the measured radar reflectivity factor Zm and estimates the attenuationcorrected radar reflectivity factor Ze. The attenuation correction
is based on a hybrid method that provides a smooth transition
between the Hitschfeld–Bordan [30] method, which performs
well at low attenuations, and the surface reference technique[31], for which the relative error decreases with increasing
path-integrated attenuation. This method is termed the“a-adjustment” method [32].
B. NOAA_AMSU (MSPPS Day-2) Rainfall Algorithm
The MSPPS Day-2 rainfall algorithm for the AMSU(NOAA_AMSU) was developed at NOAA [14]. The algorithm
originates from the works of Weng and Grody [33] and, subsequently, Zhao and Weng [34]. A simultaneous retrieval of
the ice water path (IWP) and ice-particle effective diameter(De) from Tb data at 89 and 150 GHz was performed throughtwo processes: simplifying the radiative transfer equation into a
two-stream approximation and estimating the cloud-base andcloud-top Tbs through the use of AMSU measurements at
23.8 and 31.4 GHz. The rain rate was computed based on an
IWP and rain-rate relation derived from the GPROF algorithm
database, which contains the profiles of various hydrometeors
generated from the cloud-resolving models.

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 9, SEPTEMBER 2009

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

The weakness of the NOAA_AMSU algorithm is that only
precipitation that is detectable from a scattering signature canbe estimated [17], [18]. Recently, a new correction has been developed for the AMSU-A cloud liquid water content to fill in thegaps of NOAA_AMSU retrievals over ocean [35]. In this paper,rain estimates derived using the improved NOAA_AMSU algorithm were used.
C. GSMaP_MWR Algorithm
The GSMaP_MWR algorithm is a PR-consistent advanced
MWR algorithm [8], [9]. The basic idea of the GSMaP_MWR
algorithm is to find the optimal rainfall that gives radiativetransfer model (RTM) FOV-averaged Tbs that fit best the observed Tbs [36], [37]. The GSMaP_MWR algorithm consistsof the forward calculation part to calculate the lookup tables(LUTs) showing the relationship between rainfall rates andTbs with an RTM, and the retrieval part to estimate precipitation rates from the observed Tbs using the LUTs. The forward calculation part of the GSMaP_MWR algorithm, which
the GSMaP_AMSU algorithm shares with the GSMaP_MWR
algorithm (Fig. 1), is described in detail.
The RTM calculation requires information on atmosphericvariables, as well as precipitation-related variables. Atmospheric temperature, freezing-level height (FLH), surface
winds, and surface temperature are adapted from the Japan
Meteorological Agency (JMA) global analysis (GANAL). Similarly, sea surface temperature is adapted from JMA merged
satellite and in situ data global daily sea surface temperaturesin the global ocean. As for relative humidity, the constant valueof 100% is assumed.
The convective and stratiform precipitation models for
precipitation-related variables (hydrometer profiles, drop-size
distribution (DSD), etc.) are constructed for ten 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 from the PR2A25
data, together with the ratio between PR precipitation rates andTRMM lightning imaging sensor flash rates [38]–[40]. Precipitation types consist of six land types (severe thunderstorm,
afternoon shower, shallow convection, extratropical cyclone,organized convection, and highland rain) and four ocean types(shallow convection, extratropical cyclone, transition zone, andorganized convection). Global distributions of the precipitation
types in 2.5◦ × 2.5◦ latitude–longitude boxes are statisticallyclassified trimonthly.
The convective and stratiform precipitation profiles of
PR2A25 data are averaged over prescribed precipitation ranges
for each precipitation type. In this averaging, profiles relative
to FLH are used to exclude the influence of atmospherictemperature variations [9]. The database of precipitation types
and profiles makes it possible for the algorithm to deal withtrimonthly variation of typical hydrometeor profiles.
For rain DSD, a gamma distribution of raindrop size isassumedN (D) = N0 Dμ exp(−ΛD)
where N (D) is the number concentration for particles with diameter D, μ = 3, and N0 and Λ are the parameters to be determined. For convective precipitation, N0 and Λ are determinedusing the DSD parameter estimated from the “a-adjustment”
method of the PR2A25 algorithm [41]. For stratiform
precipitation, the standard values of N0 and Λ assumed in the
PR2A25 algorithm are used.
On the other hand, conventional models were used for frozen
and mixed-phase particle-size distribution that could not be
estimated from the TRMM PR observation. The exponential
distribution is used for the drop-size distribution model of

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SHIGE et al.: GSMaP PRECIPITATION RETRIEVAL ALGORITHM FOR MICROWAVE SOUNDERS I

snow and graupel. The refractivity of convective and stratiform
frozen particles is calculated, assuming them as the mixtureof ice and air with an empirically prescribed constant density(200 kg · m−3 ). Particle-size distribution and refractivity for
mixed-phase stratiform precipitation (between FLH minus
1 km and FLH) were parameterized in terms of atmospherictemperature [42], [43], while mixed-phase convective precipitation was neglected.
From forward calculations with a four-stream RTM [44],LUTs showing the relationship between rainfall rates and Tbs
were computed daily in 5.0◦ × 5.0◦ latitude–longitude boxes.
Surface rainfalls have been retrieved from the TMI, SSM/I,
AMSR, and AMSR-E using the GSMaP algorithm [45].
III. A LGORITHM D EVELOPMENT
A flowchart of the GSMaP_AMSU algorithm is shown in
Fig. 1. Similar to the GSMaP_MWR algorithm and other MWR
rain retrievals, the GSMaP_AMSU algorithm consists of twoparts: the forward calculations for making the LUTs and the retrievals from the Tb data of satellite-borne MWRs (i.e., AMSU).

In the retrieval process, we used values of the LUTs interpolated
linearly using the four neighboring boxes.
It is seen from Fig. 2(a) that Tb23 increases with rainfall
rate and becomes saturated at a certain rain rate, which decreases with increasing LZA. In this example, the rain rate
for saturation is about 12.0 mm · h−1 for LZA = 0 (near thenadir), while it is about 10.0 mm · h−1 for LZA = 60 (nearthe limb). Tb31 also increases with rainfall rate but becomessaturated at a smaller rain rate than Tb23 does [Fig. 2(b)]. On
the other hand, Tb89 either does not change greatly or decreases
with increasing rain rate for this low-rainfall-rate range, as well
as Tb150. The main feature of the low-rainfall-rate range isthat the increase in rainfall rate increases values of Tb23. The
rainfall rate range that is lower than 12.0 mm · h−1 for LZA = 0(10.0 mm · h−1 for LZA = 60) is therefore referred to as the“emission regime.”
It is also noted that Tb23 at 0 mm · h−1 in the LUTs
(Tb23LUT0 ) for LZA = 60 is larger than that for LZA = 0
because the path length through which the atmosphere isviewed by cross-track scanners increases with LZA. Here,“diff_Tb23LUT ” is defined as follows:
diff_Tb23LUT = Tb23LUTsat − Tb23LUT0

A. Forward Calculation
The left panel of Fig. 1 describes the process of making
the LUTs. Some modifications have been made to the processof making the LUTs in the GSMaP_MWR algorithm for its
application to the AMSU data, taking the differences between
imagers and sounders into account. While conical scanning radiometers such as the TMI preferentially scan at a constant slant
path angle, the AMSU radiometer uses cross-track scanning toview the Earth. The variations in path lengths through which theatmosphere is viewed by cross-track scanning should be taken
into account. For the AMSU, the received polarization also
varies with scan angle because of the rotating-reflector/fixedfeed horn antenna design. This is different from that of imagers
using a conical scanning mechanism, which receive a fixedpolarization independent of the scan. At a given scan angle θs ,the normalized surface emitted radiation (i.e., emissivity) εsseen by the AMSU contains mixed vertical εV and horizontal
εH polarizations (the very small cross-polarized contribution
due to imperfect cross-polarization isolation in the antenna isneglected), i.e.,εs = εV (θ) cos2 θs + εH (θ) sin2 θs

3087

(1)

where the local zenith angle θ (LZA) varies as a function of scanangle θs [46]. The LUTs are produced for each scan angle from
RTM calculations using (1). For a sea surface, the emissivity
components εV and εH are calculated using the Fresnel formulafor calm seas [47], together with an empirical model that
includes the effects of wind-driven foam and surface roughness
on emissivity [48], while for a land surface, they are set at 0.9.
Fig. 2 shows an example, used in the GSMaP_AMSU
algorithm, of the LUT for Tbs at 23.8 GHz (Tb23), 31.4 GHz(Tb31), 89 GHz (Tb89), and 150 GHz (Tb150) for midlatitudes
during winter (“extratropical cyclone over ocean” at the grid
point (162.5◦ E, 32.5◦ N) on January 1, 2005). The LUT has
60 lines for LZAs between the nadir and limb in intervals of 2◦ .

(2)where Tb23LUTsat is Tb23 for saturation in the LUTs. A
larger diff_Tb23LUT for LZA = 0 than that for LZA = 60
indicates that Tb23 represents rainfall rate more unambiguously, because Tb23 in the LUT increases more rapidly with
rainfall. Therefore, diff_Tb23LUT indicates certainty of theemission-based rainfall retrieval using Tb23, and the variations in diff_Tb23LUT should be taken into account by thealgorithm.
For rainfall rates that are greater than the rain rates for
Tb23 saturation, Tb89 decrease rapidly [Fig. 2(c)], while Tb23
slightly decreases with an increase in rainfall rate. Clearly,
in this rainfall rate range, the emission for raindrops is nolonger a useful indicator of rainfall rate. Only the signal of
scattering by ice particles at 89 GHz can provide information
on precipitation. Tb150 also decreases with rainfall [Fig. 2(d)].
However, Tb89 is more suited for directly sensing precipitation intensity information within the main rain layers than
Tb150, because the response to snow and graupel lowers
the Tb and increases strongly with frequency. We refer tothe rainfall rate range that is higher than 12.0 mm · h−1 for
LZA = 0 (10.0 mm · h−1 for LZA = 60) as the “scattering
regime.”It should be noted that the rainfall rate range of the emission
and scattering regimes are not constant and vary for the LUTs,
which are computed daily in 5.0◦ × 5.0◦ latitude–longitude
boxes. Fig. 3 shows the LUTs for low latitudes (“organized
convection over ocean” at the grid point (157.5◦ E, 2.5◦ N)
on January 1, 2005). In this case, the rainfall rate range thatis lower than 10.0 mm · h−1 for LZA = 0 (3.0 mm · h−1 for
LZA = 60) is referred to as the “emission regime.” It is also
noted that Tb23LUT0 for low latitudes [Fig. 3(a)] is higher than
Tb23LUT0 for midlatitudes during winter [Fig. 2(a)]. This isbecause there is a larger amount of water vapor at low latitudes
than at midlatitudes during winter. Therefore, diff_Tb23LUT

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 9, SEPTEMBER 2009

Fig. 2. Examples of Tb LUTs for “extratropical cyclone over ocean” at
the grid point (162.5◦ E, 32.5◦ N) on January 1, 2005, at (a) 23.8 GHz,(b) 31.4 GHz, (c) 89.0 GHz, and (d) 150 GHz used in the GSMaP_AMSU
algorithm. The horizontal and vertical axes denote the rain rates and calculatedTbs, respectively. Color denotes the LZA between the nadir and limb inintervals of 2◦ , as indicated by the value bar at the bottom of the figures.

Fig. 4. LUTs for Tbs at 23.8 GHz for LZAs of (a) 0◦ , (b) 30◦ , and (c) 60◦ ,corrected using the inhomogeneities for “extratropical cyclone over ocean” at
the grid point (162.5◦ E, 32.5◦ N) on January 1, 2005, as shown in Fig. 2(a).
The horizontal and vertical axes denote the rain rates and calculated Tbs,
respectively. Color denotes the precipitation inhomogeneity between zero andtwo (with steps of 0.1), as indicated by the value bar at the bottom of the figures.

relations between rainfall and brightness temperatures. Precipitation in the real atmosphere is assumed to be lognormally
distributed in the horizontal, as observed in [49] and [50]. It isthen possible that the RTM-derived Tb–rain relations are modified as a function of the variability ζ using a method similar tothe one used in the study of [51] and the GSMaP_MWR algorithm [8], [9]∞
Tbcorrect (R, ζ) =

Tb(R)

1
(2π)1/2 ζR


0

× exp

Fig. 3. Same as Fig. 2 except for “organized convection over ocean” at thegrid point (157.5◦ E, 2.5◦ N) on January 1, 2005.

for low latitudes is smaller than that for midlatitudes during winter, indicating that Tb23 represents rainfall rate more
ambiguously.
Radiative transfer calculations that assume homogeneous
rainfall distributions fail to properly reproduce the observed−12[ln(R)
−
μ]
dR
2ζ 2

(3)

where R is the rain rate, R is the mean rain rate of the lognormal distribution ζ 2 = ln(σ 2 + 1)2 , with σ being thevariability of the rainfall; μ = ln(R) − 0.5ζ 2 ; Tb(R) is the
RTM-derived Tb–rain relation; and Tbcorrect is the Tb–rain
relation corrected by the lognormal distribution. Figs. 4 and 5
show the LUTs for Tb23 and Tb89 corrected using ζ from thatshown in Fig. 2(a) and (c). It is seen from Fig. 4 that the rain
rates for Tb23 saturation increase with the value of ζ, whichmeans that the rainfall rate range of the emission regime isextended.
B. Retrieval
The right panel of Fig. 1 shows the process of retrieving fromthe AMSU Tbs. Here, we used an Advanced TIROS Operational Vertical Sounder and Advanced Very High Resolution

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SHIGE et al.: GSMaP PRECIPITATION RETRIEVAL ALGORITHM FOR MICROWAVE SOUNDERS I

Fig. 5.

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Fig. 6. Precipitation top height observed by the PR plotted versus SI (K)for (a) diff_LUT23 < 50 K, (b) 50 K < diff_LUT23 < 60 K, (c) 60 K <diff_LUT23 < 70 K, and (d) diff_LUT23 > 70 K from the 75 AMSU/TRMM
matched-up cases. The horizontal lines indicate the FLHs.

Same as Fig. 4 except for 89.0 GHz.

Radiometer Preprocessing Package (AAPP) [52] to obtain the
AMSU Tb data. We developed the retrieval process, retaining
the basic structure of the GSMaP_MWR algorithm. Emission
signatures are mainly used to determine rainfall, while scattering signatures are used to help define the nature of precipitation,
similar to the SSM/I algorithm in the study of [53].
Taking advantage of 150 GHz of the AMSU, an SI is defined
as follows:
SI = (Tb89 − Tb89LUT0 ) − (Tb150 − Tb150LUT0 )(4)

where Tb89LUT0 and Tb150LUT0 are Tb89 and Tb150 at
0 mm · h−1 in the LUTs, respectively. Because the response
to snow and graupel lowers the Tb and increases strongly
with frequency, the Tb reduction is higher at 150 GHz than at
89 GHz [Fig. 2(c) and (d)]. Fig. 6 shows relationships between
the SI calculated from AMSU-B data and the PR-observed
precipitation top height for each diff_Tb23LUT range. Freezingheights increase with diff_Tb23LUT , which is consistent withthe comparison of the LUT for midlatitudes during winter[Fig. 2(a)] and that for low latitudes [Fig. 3(a)] in Section III-A.
It is seen that thickness between PR precipitation top height and
FLH increases with AMSU-B SI. This result is consistent withthat from the previous study (see [54, Fig. 3]). In the retrieval
process, SI is used to help define the nature of precipitation.
At the beginning of this process, rain or no-rain flags are
identified by deterministic methods. The rain/no-rain classification (RNC) over ocean consists of two processes. In the firstprocess, emission signature from Tb31 is utilized, because tests
show that utilization of Tb23 results in higher false-alarm ratio(FAR) due to its adjacency to the center of the water-vapor line.
Here, FAR is the fraction of positive forecasts that turn out toFig. 7. Two-dimensional probability density functions in terms of(horizontal axis) SI and (vertical axis) ζ for (left) AMSU-A FOV and (right)
AMSU-B FOV.

be wrong. Further definition of FAR is described in the study of[55]. Therefore, over ocean, rain flags are identified as
Tb31 ≥ Tb31LUT0(5)

where Tb31LUT0 is Tb31 at 0 mm · h−1 in the LUTs. It should
be noted that Tb31LUT0 is not constant and varies for the LUTs
[see Figs. 2(b) and 3(b)]. Using Tb31 allows us to detect warm
rain from clouds that lack the ice phase, which account for 31%of the total rain amount and 72% of the total rain area in thetropics [56].
It is shown from Fig. 2(b) that, for large LZA, Tb31 forstrong rain rate is smaller than Tb31LUT0 , leading to misclassification of such strong rain pixels as no-rain pixels. Therefore,
in the second check, using SI, rain flags are identified asfollows:
SI > 0.

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(6)

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Fig. 8. Weighting function w89 giving the smallest rmse between Rain2389 and PR estimate for (top) RN3 and (bottom) the remaining two raining groups (RN1
and RN2) and for each range of diff_Tb23LUT. The weighting function w89 is determined based on the AMSU/TRMM matched-up cases and interpolated byquadratic equations.

Therefore, each FOV is separated into the following fourgroups:RN0 classified as no-rain pixel by (5) and (6);RN1 classified as rain pixel only by (5);RN2 classified as rain pixel only by (6);RN3 classified as rain pixel by (5) and (6).
As already noted, the horizontal inhomogeneity of rainfall
within rather large AMSU FOVs must be taken into accountby the algorithm in order to properly compensate for nonlinearities in the Tbs versus water content relationships. Here, thevariability of the rainfall σ is defined using rain rates estimatedfrom the PR as

1
N

σ=

N


1/2
(Ri − Ravg )2

i=1

Ravg

(7)

where i represents each pixel of the PR data within the FOV
area and N is the total number of pixels in the area. Fig. 7 shows
relationships between the SI calculated from AMSU-B data andζ for the AMSU-A and AMSU-B FOVs computed from PR
data. For both AMSU-A and AMSU-B FOVs, ζ decreases with
SI. This is explained by the fact that deep convection having
large SI is generally embedded in mesoscale convective systems
having horizontal scales larger than AMSU-A and AMSU-B
FOV sizes. The values of ζ for the AMSU-A and AMSU-B
FOVs are estimated from SI based on statistical relationships
from Fig. 7 as
ζ = − 0.0165SI + 1.4050,
ζ = − 0.177SI + 1.0383,

for AMSU-A FOV
for AMSU-B FOV. (8)Since AMSU-A and AMSU-B FOV sizes vary with LZA,
the aforementioned relationships should be produced for each
LZA range. However, feature did not change greatly with LZA
range, possibly due to the small number of the AMSU/TRMM
matched-up cases. Therefore, variations in AMSU FOV sizes
are not taken into account.
The LUTs at 23 and 89 GHz are corrected using the valueof ζ estimated from SI for AMSU-A and AMSU-B FOVs,
respectively. Using the corrected LUTs, rain rates are retrieved

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SHIGE et al.: GSMaP PRECIPITATION RETRIEVAL ALGORITHM FOR MICROWAVE SOUNDERS I

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Fig. 9. Zonally averaged rain rates retrieved by GSMaP_AMSU, NOAA_AMSU, and reference algorithm [PR2A25 for (a) and (d), GSMaP_TMI for(b) and (e), and TMI2A12 for (c) and (f)] over ocean only for (a)–(c) January 2005 and (d)–(f) July 2005.

from Tb23 (Rain23) and Tb89 (Rain89). Here, Rain23 andRain89 are given by the rain rates of Tb23 and Tb89 in thecorrected LUT corresponding to the observed Tb23 and Tb89,
respectively.
The rainfall estimate at the AMSU-B FOV is given by combining estimates from emission and scattering algorithms as
Rain2389 = (1 − w89) × Rain23 + w89 × Rain89.

(9)

The weighting w89 is a function of SI and determined basedon the AMSU/TRMM matched-up cases. Fig. 8 shows w89
that gives the smallest root-mean-square error (rmse) between
Rain2389 and PR estimates for RN3, which is the majorraining group, and the remaining two raining groups (RN1
and RN2). The weighting w89 is determined for each range of
diff_Tb23LUT and then interpolated by quadratic equations. It
should be noted that the nominal and effective resolution that
retrievals produced corresponds to the AMSU-B FOV size andvaries with LZA.
Only precipitation retrievals over ocean are considered in thecurrent investigation, but those over land and coast are briefly
described. Over land, the RNC method, which is based on theprobability distribution of Tb under no-rain conditions derived
from unclassified AMSU pixels similar to the algorithm of [57],is used. Over coast, the RNC method of [58] is used. The
estimate is then given by the rain rate retrieved from Tb89 (i.e.,
Rain89).

IV. V ALIDATION OF R AINFALL R ETRIEVALS
A. Global Scale
Fig. 9 compares zonally averaged rain rates over ocean
for GSMaP_AMSU and NOAA_AMSU using PR2A25,
GSMaP_TMI, and TMI2A12 as references. Qualitatively, both
GSMaP_TMI and NOAA_AMSU have good agreement withthe reference algorithms.
Over midlatitudes (30◦ N–35◦ N) during winter (January2005), GSMaP_AMSU has good agreement with GSMaP_TMI[Fig. 9(b)]. On the other hand, NOAA_AMSU gives lower
rainfall rates than GSMaP_TMI does but higher rainfall rates
than TMI2A12 does. It is noted that GSMaP_AMSU is invery good agreement with PR2A25 for latitudes 30◦ N–35◦ N[Fig. 9(a)]. The differences between PR2A25 and TMI2A12
standard rainfall products over midlatitude regions during winter are well known (e.g., [59]). Studies [60]–[62] found biases
in the freezing-level estimates [63] used in TMI2A12 retrievals,
particularly over midlatitude regions during winter. On theother hand, the freezing heights derived from GANAL are usedin GSMaP_TMI. The basis of the TMI 2A12 algorithm is a“Bayesian” framework, in which the retrieved precipitation isconstructed from those cloud-resolving model-generated profiles that are radiatively consistent with the observation [21],[22]. Although the cloud-radiative model database supporting
the TMI2A12 V6 algorithm has been expanded, the databaseconsists of only six simulations, two of which are extratropical cyclone cases (see [23, Table 2]). On the other hand,in GSMaP_TMI, RTM calculations use a trimonthly database

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3092

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 9, SEPTEMBER 2009of precipitation types and profiles in 2.5◦ × 2.5◦ latitude–
longitude boxes derived from the PR2A25 data. Thus, compared with the supporting databases of the TMI2A12 algorithm,
those of the GSMaP_TMI algorithm are more consistent withnaturally occurring profiles at the time/location where the algorithm is applied. Furthermore, GSMaP_TMI can detect rainfall
events behind cold fronts over midlatitude regions during winter that are consistently missed by TMI2A12 [64]. Thus, it isfair to say that, over midlatitudes during winter, GSMaP_TMI
estimates are better than TMI2A12 V6 ones. The upcoming
TMI2A12 V7 estimates are larger than TMI2A12 V6 and evenlarger than PR2A25 V6 over midlatitudes during winter [65].
Therefore, over midlatitudes during winter, GSMaP_AMSU,
which has better agreement with GSMaP_TMI, is better than
NOAA_AMSU.
Better estimates over midlatitudes during winter are given by
the use of Tb23 in the GSMaP_AMSU algorithm. As discussed
in Section III-A, Tb23 in the LUT for midlatitudes during
winter [Fig. 2(a)] increases more rapidly with rainfall (i.e., large
diff_TB23LUT ) because there is a smaller amount of water
vapor at midlatitudes during winter. Therefore, Tb23 represents
rainfall rate more unambiguously over midlatitudes during winter. The path length through which the atmosphere is viewed
by cross-track scanners increases with LZA. For LZA = 60
(near the limb), Tb23 is still a useful indicator of rain rate overmidlatitudes during winter [Fig. 4(c)].
On the other hand, over the subtropical region (5◦ S–30◦ S)
for July 2005, GSMaP_AMSU gives higher rainfall than threereference algorithms [Fig. 9(d)–(f)], while NOAA_AMSU has
good agreement with three reference algorithms. GSMaP_
AMSU is developed based on the AMSU/TRMM matched-up
cases, most of which are organized rain systems, possibly
leading to overestimation of GSMaP_AMSU for scatteringrain cases over subtropical regions.
B. Case Studies
In this section, GSMaP_AMSU estimates are compared withNOAA_AMSU estimates, using 75 AMSU/TRMM matchedup cases (44 cases for January 2005 and 31 cases for July 2005).
In addition to the rmse and bias, the equitable threat score (ETS)[55], [66] is used for the evaluation of the performance of the
RNC of the AMSU algorithms. The ETS ranges from −1/3 to
1, and the best possible ETS is 1
ETS =N1 − Nref
N1 − Nref + N2 + N3

Nref =(N1 + N2 )(N1 + N3 )N1 + N 2 + N 3 + N 4(10)where N1 is the number of pixels where rain is observed by thereference algorithm and the RNC method of the AMSU algorithm (GSMaP_AMSU or NOAA_AMSU) classifies rain, N2
is the number of pixels where rain is observed by the reference
algorithm and the RNC method classifies no rain, N3 is thenumber of pixels where rain is not observed by the reference algorithm and the RNC method of the AMSU algorithm classifies
rain, and N4 is the number of pixels where rain is not observed

Fig. 10. ETSs of GSMaP_AMSU and NOAA_AMSU for the 44 AMSU/
TRMM matched-up cases for January 2005 using (a) PR2A25, (b) GSMaP_
TMI, and (c) TMI2A12 as references.

by the reference algorithm and the RNC method classifies norain. For the ETS, PR2A25, GSMaP_TMI, and TMI2A12 areused for the reference algorithm, but for the rmse and bias,
PR2A25 is not used as the reference algorithm. This is because
the rain retrieval process of GSMaP_AMSU, except for its
RNC, is developed based on comparison with PR2A25 (Fig. 8),
and the PR2A25 data are not independent.
It is seen from Figs. 10 and 11 that the ETSs of GSMaP_
AMSU are higher than those of NOAA_AMSU for all three references for most of the cases. Both RNC methods in GSMaP_
AMSU and NOAA_AMSU use the AMSU-A data. However,
the regionally varying threshold value of Tb31LUT0 is computed in the GSMaP_AMSU algorithm using atmospheric variables given by GANAL, while the AMSU-A cloud liquid watercontent estimated from Tb23 and Tb31 by assuming an isothermal atmosphere [14], [46], [67] is used by NOAA_AMSU.
Assuming an isothermal atmosphere for nonisothermal atmospheres in the AMSU-A cloud liquid water content retrieval

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SHIGE et al.: GSMaP PRECIPITATION RETRIEVAL ALGORITHM FOR MICROWAVE SOUNDERS I

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Fig. 12. RMSEs of GSMaP_AMSU and NOAA_AMSU for the 44 AMSU/
TRMM matched-up cases for January 2005 using (a) GSMaP_TMI and(b) TMI2A12 as references.

Fig. 11. Same as Fig. 10 except for the 31 AMSU/TRMM matched-up cases
for July 2005. (a) ETS: PR2A25. (b) ETS: GSMaP_TMI. (c) ETS: TMI2A12.

might be responsible for larger errors for NOAA_AMSU thanfor GSMaP_AMSU. The differences are also caused by using
SI in GSMaP_AMSU for its RNC.
A recent study [26] showed that the IR interpolation of
GSMaP (GSMaP_MVK) is more efficient using rain estimations derived from microwave sounder data based on the
NOAA_AMSU algorithm in addition to those derived frommicrowave imager data. This is caused by the more accurate
rain detection by the sounders than the IR sensors that observe
cloud-top temperature. Therefore, using the estimates retrievedby GSMaP_AMSU, which has higher rain detection than
NOAA_AMSU, may lead to better GSMaP_MVK estimates.
It is seen from Figs. 12(a), 13(a), 14(a), and 15(a) thatthe rmse and bias of GSMaP_AMSU are lower than those of
NOAA_AMSU when GSMaP_TMI is used as a reference. On
the other hand, the rmse and, especially, bias of NOAA_AMSU
are lower than that for GSMaP_AMSU when TMI2A12 isused as a reference [Figs. 12(b), 13(b), 14(b), and 15(b)]. In

the NOAA_AMSU algorithm, the relationship between surface
rain rate and IWP is derived using the TMI2A12 (GPROF)
algorithm database. These results suggest that physically consistent algorithms, which share a database or an LUT, should
be applied to passive microwave imagers and sounders because
consistency in the rain products from passive microwave imagers and sounders is very important in combining them. Development of a rain retrieval algorithm for microwave sounders
sharing at a maximum a common algorithm framework withthe algorithm for other microwave imagers is also a key issue
for the upcoming Global Precipitation Mission [68] where
microwave sounders are now considered as important sensors
for achieving three-hourly rainfall sampling.
V. S UMMARY AND F UTURE W ORKGlobal precipitation maps have been retrieved from passive
MWRs currently in orbit such as the TMI, AMSR-E, and SSM/I
using the GSMaP_MWR algorithm. Despite the improved rainfall estimates from passive MWRs, the challenge remains tofurther fill in information gaps through more frequent satellite
observations. In this paper, we developed an over-ocean rainfall retrieval algorithm for the AMSU based on the GSMaP
MWR algorithm. This algorithm combined an emission-based
estimate from Tb at 23 GHz and a scattering-based estimate
from Tb at 89 GHz, depending on an SI computed from Tb atboth 89 and 150 GHz. Precipitation inhomogeneity was also
taken into account.
The GSMaP-retrieved rainfall from the AMSU was compared with the NOAA-standard-algorithm-retrieved data using

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 9, SEPTEMBER 2009

Fig. 13. Same as Fig. 12 except for the 31 AMSU/TRMM matched-up cases
for July 2005. (a) RMSE: GSMaP_TMI. (b) RMSE: TMI2A12.

Fig. 15. Same as Fig. 14 except for the 31 AMSU/TRMM matched-up cases
for July 2005. (a) Bias: GSMaP_TMI. (b) Bias: TMI2A12.

tudes during winter were given by the use of Tb at 23 GHz in the
GSMaP_AMSU algorithm. On the other hand, GSMaP_AMSU
overestimated rain rates over subtropical regions. This is probably because GSMaP_AMSU was developed based on the
AMSU/TRMM matched-up cases, most of which are organizedrain systems.
Comparisons using 75 AMSU/TRMM matched-up cases
showed that GSMaP_AMSU had higher rain detection than
NOAA_AMSU. Therefore, using the estimates retrieved by
GSMaP_AMSU may lead to better GSMaP_MVK estimates.
It was also shown that physically consistent algorithms, whichshare a database or an LUT, should be applied to passive
microwave imagers and sounders because consistency in therain products from passive microwave imagers and sounders isvery important in combining them.
Implementing GSMaP_AMSU rain retrieval will bring moreaccuracy to a GSMaP global rainfall map where AMSU estimates are not used now.
ACKNOWLEDGMENT

Fig. 14. Bias of GSMaP_AMSU and NOAA_AMSU for the 44 AMSU/
TRMM matched-up cases for January 2005 using (a) GSMaP_TMI and(b) TMI2A12 as references.

the TRMM data as a reference. Rain rates retrieved from
GSMaP_AMSU have better agreement with TRMM estimatesover midlatitudes during winter. Better estimates over midlati-

The authors would like to thank R. Ferraro from NOAA/NESDIS for providing rain estimates derived from the MSPPS
Day-2 algorithm, G. Liu from Florida State University,
Tallahassee, for providing his RTM, H. Murata from the JMA
for his kind guidance on using the AAPP, T. Manabe from
Osaka Prefecture University, Sakai, Japan, for his commentson this paper, and T. Mega from Osaka Prefecture University
for the helpful computing assistance. The authors would also
like to thank the two anonymous reviewers for their constructive
comments.

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SHIGE et al.: GSMaP PRECIPITATION RETRIEVAL ALGORITHM FOR MICROWAVE SOUNDERS I

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Shoichi Shige (M’08) received the B.S., M.S., andPh.D. degrees from Kyoto University, Kyoto, Japan,
in 1995, 1997, and 2001, respectively.
From 2001 to 2004, he was with the Earth Observation Research Center, Japan Aerospace Exploration Agency, as an Invited Scientist. Since 2004,
he has been with the Department of Aerospace
Engineering, Osaka Prefecture University, Sakai,
Japan, where he is currently an Assistant Professor.
His research interests include mesoscale convective
systems and satellite rainfall and heating profile
estimation.
Dr. Shige is a member of the Meteorological Society of Japan, American
Meteorological Society, American Geophysical Union, and Remote Sensing
Society of Japan.

Tomoya Yamamoto received the M.E. degree inaerospace engineering from Osaka Prefecture University, Sakai, Japan, in 2009.
He is currently with the Department of Aerospace
Engineering, Osaka Prefecture University. His
research interests include algorithm development
for spaceborne microwave radiometers andvalidation of rainfall retrievals.

Takeaki Tsukiyama received the B.E. degree inaerospace engineering from Osaka Prefecture University, Sakai, Japan, in 2008.
He is currently with the Department of Aerospace
Engineering, Osaka Prefecture University. His
research interests include algorithm development
for spaceborne microwave radiometers andvalidation of rainfall retrievals.

Satoshi Kida received the B.E. and M.E. degrees in aerospace engineeringfrom Osaka Prefecture University, Sakai, Japan, in 2005 and 2007, respectively.
He is currently with the Department of Aerospace Engineering, Osaka
Prefecture University. His research interests include algorithm development forspaceborne microwave radiometers and validation of rainfall retrievals.

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SHIGE et al.: GSMaP PRECIPITATION RETRIEVAL ALGORITHM FOR MICROWAVE SOUNDERS I

Hiroki Ashiwake received the B.E. degree in aerospace engineering from
Osaka Prefecture University, Sakai, Japan, in 2008.
He is currently with the Department of Aerospace Engineering, Osaka
Prefecture University. His research interests include algorithm development forspaceborne microwave radiometers and validation of rainfall retrievals.

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, asa Postdoctoral Fellow. From 2005 to 2007, he wasa Japanese Science Technology Agency Researcher.
Since 2007, he has been with the Earth Observation Research Center, Japan Aerospace Exploration
Agency, Tsukuba, Japan, as a Researcher. His current
research interests include algorithm development forspaceborne microwave radiometers and radars, and validation of 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.

Shinta Seto received the B.Sc., M.Sc., and Ph.D.
degrees from The University of 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 the development of spaceborne
dual-frequency precipitation radar. Since 2006, hehas been with the Institute of Industrial Science, The
University of Tokyo. His current research interests
include precipitation retrieval using microwave remote sensing and its application to water cycle studies.

3097

Kazumasa Aonashi received a Bachelor degreefrom Meteorological College, Kashiwa, Japan, in
1982 and the Ph.D. degree in meteorology from The
University of Tokyo, Tokyo, Japan, in 1997.
Since 1988, he has been with the Forecast
Research Department, Meteorological ResearchInstitute, Tsukuba, Japan. His areas of expertise
include passive microwave precipitation retrieval andassimilation of satellite microwave radiometer data
into cloud-resolving numerical weather prediction
models.

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 joined the Communications Research Laboratory (CRL, currently the National Institute of Information and Communications Technology), Tokyo, in
1973. He was the Associate Director General of CRL
in 1999. He was with Osaka Prefecture University,
Sakai, Japan, as a Professor of aerospace engineeringfrom 2000 to 2008. He has been with Tottori University of Environmental Studies, as a Professor ofenvironmental management since 2008 in the Faculty of Environmental andInformation Studies. 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 domestic International Union of Radio
Science-F in Japan in 1997–2000, and he was the Chair of the IEEE Geoscienceand Remote Sensing Chapter of the Japan Council in 1998–2000. He was the
President of the Remote Sensing Society of Japan in fiscal years 2004 and 2005.

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