Journal of the Meteorological Society of Japan, Vol. 87A, pp. 119–136, 2009
DOI:10.2151/jmsj.87A.119

119

GSMaP Passive Microwave Precipitation Retrieval Algorithm : Algorithm Description
and Validation
Kazumasa AONASHI
Meteorological Research Institute, Tsukuba, Japan

Jun AWAKA
Department of Marine Biology and Sciences, Tokai University, Sapporo Campus, Sapporo, Japan

Masafumi HIROSE
Faculty of Science and Technology, Meijo University, Nagoya, Japan

Toshikaki KOZUInterdisciplinary Faculty of Science and Engineering, Shimane University, Matsue, Japan

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

Guosheng LIU
Department of Meteorology, Florida State University, Tallahassee, Florida, USA

Shoichi SHIGE, Satoshi KIDA
Department of Aerospace Engineering, Osaka Prefecture University, Sakai, Japan

Sinta SETO
Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

Nobuhiro TAKAHASHINational Institute of Information and Communications Technology, Koganei, Japan
andYukari N. TAKAYABU
Center for Climate System Research, The University of Tokyo, Kashiwa, Japan(Manuscript received 29 June 2008, in final form 23 December 2008)

Corresponding author: Kazumasa Aonashi, Meteorological Research Institute, 1-1 Nagamine, Tsukuba,
305-0052, Japan
E-mail: aonashi@mri-jma.go.jp©2009, Meteorological Society of Japan

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Abstract
This paper describes a precipitation-retrieval algorithm for the Tropical Rainfall Measuring Mission(TRMM) Microwave Imager (TMI) that was developed under the Global Satellite Mapping of Precipitation
project (GSMaP) by improving the authors’ previous algorithm. The basic idea of the GSMaP algorithm is
to find the optimal precipitation for which the brightness temperatures (TBs) calculated by the radiativetransfer model (RTM) fit best with the observed TBs. The main improvements of the GSMaP algorithm
over the authors’ previous work are as follows: (1) use of precipitation-related variable models (precipitation profiles, drop-size distribution, etc.) and precipitation detection and inhomogeneity estimation methods based on TRMM observation studies; (2) use of scattering signals of the TMI Polarization-Corrected
Temperature (PCT) at 37 and 85 GHz (PCT37, PCT85) and scattering-signal correction for tall precipitation (thickness between precipitation top level and freezing level (Dtop) larger than 6 km ) over land andcoastal areas.
In order to validate the GSMaP algorithm, we compared its retrievals from TMI TBs in 1998 with the
TRMM Precipitation Radar (PR) and Goddard Profiling Algorithm (GPROF) retrievals (2A12 version 6).
The results show that (1) over land and coastal areas, the GSMaP retrievals agreed better with PR than
GPROF for tall precipitation (Dtop > 4 km) weaker than 10 mm h-1, while both GSMaP and GPROF underestimated PR precipitation rates for precipitation heavier than 10 mm h-1; (2) over ocean, the GSMaP retrievals agreed better with PR than GPROF for precipitation heavier than 10 mm h-1, while GSMaP slightly
overestimated precipitation weaker than 10 mm h-1 compared to PR and GPROF; (3) The GSMaP algorithm
failed to detect some precipitation areas weaker than 2 mm h-1 over sub-tropical oceans.
Experimental algorithms with different precipitation-related variable models and retrieval methods using scattering signals were applied to TMI TBs in July 1998 to examine the effect of the above improvements to the GSMaP algorithm. The results show that the improvement of the precipitation profile alleviated the underestimation of precipitation heavier than 10 mm h-1 over land and coastal areas, that the combined use of new physical-related variable models alleviated the underestimation of precipitation heavier
than 10 mm h-1 over ocean, and that the use of PCT37 and scattering-signal correction reduced the overestimation of tall precipitation (Dtop > 4 km) weaker than 10 mm h-1 over land and coastal areas.

1. Introduction
Satellite passive microwave radiometer (MWR)
brightness temperatures (TBs) give information onemission from raindrops and scattering by frozen
particles. MWRs recently have become the principal
sensors for global precipitation retrieval, since theseemission and scattering signals (lower-frequency TB
increases and higher-frequency TB depressions froma precipitation-free atmosphere) have a more direct relationship with precipitation rates than infrared radiometer (IR) cloud-top TBs (Ebert and Manton 1998).
MWR precipitation retrieval algorithms are generally classified as either statistical or physical. Statisticalalgorithms (e.g., Grody 1991; McCollum and Ferraro2003) require observed precipitation data sets to derive the empirical relationship between precipitation
rates and TBs. Physical algorithms (e.g., Panegrossi
et al. 1998; Kummerow et al. 2001; Aonashi and Liu
2000) require a priori models for precipitation-related

variables and radiative-transfer models (RTMs) in order to calculate look-up tables (LUTs) relating to precipitation rates and TBs.
Before the TRMM launch, global informationon the precipitation-related variables was sparse.
Accordingly, conventional statistical algorithms depended on observed precipitation data sets taken overlimited areas. Conventional physical algorithms resorted to precipitation-related variable models basedon a small number of field campaigns (Aonashi and
Liu 2000, hereafter referred to as AL) or cloud-resolving model outputs (Panegrossi et al. 1998; Kummerow
et al. 2001).
Recent studies using TRMM data, however, haverevealed the global distribution of various precipitation-related variables, such as particle size and precipitation types (e.g., Kozu et al. 2009; Takayabu 2006).
The TRMM data have also enabled us to research thestatistical relationship between TBs and the precipitation-related variables (e.g., Seto et al. 2005; Kubota etMarch 2009

K. AONASHI et al.

al. 2007, 2009; Kida et al. 2009).
Under the Global Satellite Mapping of Precipitation
project (GSMaP), we developed a new physical precipitation retrieval algorithm (hereafter referred to as the
GSMaP algorithm) by improving the AL algorithm
based on the above studies. The main improvementsin the GSMaP algorithm are as follows: (1) use of precipitation-related variable models and precipitation detection and inhomogeneity-estimation methods basedon TRMM observation studies; (2) use of the scattering signals of Polarization-Corrected Temperature(PCT) at 37 and 85 GHz (PCT37, PCT85) and scattering-signal correction for tall precipitation over landand coastal areas.
The purpose of the present study is to describe
the GSMaP algorithm and to report its validation results. To this end, we compared its retrievals from
TRMM Microwave Imager (TMI) TBs in 1998 with
the TRMM Precipitation Radar (PR) and the Goddard
Profiling Algorithm (GPROF) retrievals. We also applied experimental algorithms with different precipitation-related variable models and retrieval methods,
using scattering signals of TMI TBs in July 1998 to
examine the effects of the above improvements in the
GSMaP algorithm.

2. Data used in the present study
2.1 TMITMI is a five-frequency, nine-channel, conicalscanning MWR that measures at 10.7, 19.7, 21.3, 37,
and 85.5 GHz (hereafter referred to as 10, 19, 21, 37,
and 85 GHz) with an incident angle of 52.8 degrees.
As seen in Table 1, each frequency has one vertically(V) and one horizontally (H) polarized channel, except for the water-vapor absorption band at 21 GHz,
which has only a vertical polarization channel. Table

Table 1. TMI Frequencies, Polarizations, and Beam effective FOVs.

Frequency(GHz)

Polarization

Beam effective
FOVs (km)

10.65

V&H

63 x 37

19.35

V&H

30 x 18

21.3

V

23 x 18

37.0

V&H

16 x 9

85.5

V&H

7x5

121

1 also gives beam effective Fields-Of-Views (FOVs)
for each TMI channel. The swath width of TMI islimited to 760 km because of the low orbital altitudeof TRMM (about 400 km).
In the present study, we used TMI 1B11 version 6
for the MWR TB data. The surface condition flagsof the 1B11 TBs were classified into land, ocean, andcoast, based on the land/ocean coverage within the 10
GHz FOVs.
2.2 PR
PR is a nadir-looking weather radar measuring
back-scattered energy from precipitating particles at
13.8 GHz. The horizontal and vertical resolutions of
PR are 4.3 km and 250 m. PR has a swath width of
215 km, though low-level echoes near swath boundaries are contaminated by ground clutter.
In the validation, we used PR 2A25 version 6 for
the PR precipitation data. PR 2A25 includes precipitation profiles, rain flags for various kinds (convective,
stratiform, bright band, etc.), and range bin numbersof various precipitation-related variables (top level,bright-band levels, etc.) (Iguchi 2007; Iguchi et al.
2009).
2.3 GPROF
In the validation, we used TMI 2A12 version 6 precipitation data, which is the output of GPROF (thecurrent NASA operational rainfall algorithm). GPROF
retrieves instantaneous rainfall using a Bayesian approach to match the observed TBs to those calculated
from CRM variables. More information on the retrieval algorithm is available in Kummerow et al. (2001)
and Olson et al. (2006).

3. Algorithm description
The basic idea of the GSMaP algorithm is to determine the precipitation rates that yield RTM-calculated
TBs that best fit with TMI TBs, the same as that of
AL. The GSMaP algorithm employs PCT37 and
PCT85 over land and coast and TBs with vertical polarization at 10, 19, and 37 GHz (TB10v, TB19V, andTB37v), in addition to PCT37 and PCT85 over ocean.
The GSMaP algorithm consists of a forward-calculation part to calculate the LUTs and a retrieval part toestimate precipitation rates from the observed TBs using the LUTs.
3.1 Forward calculation
As Fig. 1 schematically illustrates, the RTM calculation requires information on atmospheric variables

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the Japan Meteorological Agency (JMA) Global
ANALysis data (GANAL), since GANAL gives a priori, global information on these variables with certain
accuracy. Similarly, we adopted the sea-surface temperature from the JMA merged satellite and in situ
data on global daily sea-surface temperatures in theocean (MGDSST). For relative humidity and CLWC,
we assumed the same constant values, 100% and 0.5
kg m-2, as AL.
The LUTs were calculated for every 5 x 5 degree
point within the latitude-longitude coordinates, as we
will describe in Section 3.1.c. Thus, we incorporated
the above variables for the 5 x 5 degree points into the
RTM calculation.

Fig. 1. Schematic illustration of information
required for the RTM calculation.

b. Precipitation-related variable models
We adopted the following convective and stratiform
precipitation models for the precipitation-related variables:(temperature, relative humidity, freezing level height(FLH), cloud liquid water content (CLWC), etc.), surface variables (surface temperature, sea-surface windspeed), and precipitation-related variables (precipitation profiles, precipitating-particle size distribution,refractivity of frozen and mixed-phased particles, horizontal inhomogeneity of precipitation, etc.).
This subsection will describe the above physicalvariables and the RTM calculation methods used in
the GSMaP algorithm. (We will discuss the precipitation inhomogeneity in Section 3.2 since it was estimated from the observed TBs.) As described in the introduction, the main improvement in the forward calculation part over AL is the use of new precipitationrelated variable models, as summarized in Table 2.

(1) Precipitation profile
We constructed statistical precipitation profile
models using TRMM observations. To this end, we
first classified PR data into 10 types (six over land,
four over ocean and coast) using the PR precipitation
parameters (rain area, stratiform rain-area fraction,
precipitation-top level, etc.) and the ratio between the
PR precipitation rates and TRMM Lightning Imaging
Sensor (LIS) flash rates (Takayabu 2006).
We then produced convective and stratiform precipitation profile models for these types by averaging
the PR convective and stratiform precipitation profiles
over prescribed precipitation ranges for each type. In
this averaging, we used profiles relative to FLH in order to exclude the influence of atmospheric temperature variations (Kubota et al. 2007).

a. Atmospheric and surface variables
We adopted the atmospheric temperature, FLH,
and surface temperature over land and coast from

Table 2. Main differences in precipitation-related variable models of the AL and GSMaP algorithms.

Precipitation-related variables

AL

GSMaP

Precipitation profile

Constant vertical gradients

The PR profiles averaged for eachprecipitation types

Raindrop size distribution

Marshall-Palmer distribution

Kozu et al. (2008) model

Mixed-phase particle size distribution and refractivity

Neglected

Nishitsuji et al. (1983) model for
stratiform precipitation

March 2009(2) Precipitating-particle size distribution models
For the raindrop-size distribution, we constructedstatistical models for each precipitation type using
globally available PR observations (Kozu et al. 2009).
To this end, we first assumed a gamma distribution
for the raindrop size:
N(D) = N0D µ exp(–λD),

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K. AONASHI et al.

(1)

We then derived convective and stratiform LUTs
with various horizontal precipitation inhomogeneities
using the approximation of AL. This approximation
converted TBs for homogeneous precipitation (TBh)
into those for inhomogeneous precipitation (TBi), assuming a lognormal distribution of the precipitation:i

TB = ∫

1

exp[−

1

2

h

(1n( pr ) −1n( pr )) ]TB d 1n ( pr ) ,2ξ2πξ
where N is the number concentration for particles
with diameter D, µ=3, and N0 and λ are parameters
to 1
1
i
2
h
TB = ∫− 2 (1n( pr ) −1n( pr )) ]TB d 1n ( pr ) ,(2)
be determined. For convective precipitation,
weexp[caliξ
22πξ factors
brated N0 and λ using attenuation-corrected
derived from the PR data. For stratiform precipitation,
we used the standard values for N0 and λ of PR 2A25 where ln(pr) is the natural logarithm of the precipitaversion 6. We then averaged the above values for each tion, and 1n ( p r ) and ξ are the average and the stanprecipitation type to produce statistical models of dard deviation of ln(pr).
raindrop-size distribution.
The LUTs used in the retrieval part were weighted
Conventional models were used for frozen and averages of the above convective and stratiform LUTs.
mixed-phase particle-size distributions that could The weights were determined by the statistical frenot be estimated from the TRMM observations. For quency distribution of the PR convective and stratithe frozen-particle size distribution, we applied the form precipitation for each precipitation type and surMarshall Palmer distribution to both convective and face precipitation rate.
stratiform precipitation, similar to AL. The particlesize distribution for mixed-phase stratiform precipi- 3.2 Retrieval part
tation (between FLH minus 1 km and FLH) was paThis subsection will describe the procedures of therameterized in terms of atmospheric temperature retrieval part, schematically illustrated in Fig. 2. As(Nishituji et al. 1983). We neglected mixed-phase con- described in the introduction, the main improvements
vective precipitation.
in the retrieval part over AL are as follows: (1) new
precipitation detection and inhomogeneity estimation(3) Refractivity of frozen and mixed-phase particles
We calculated the refractivity of convective andstratiform frozen particles, assuming them to be a
mixture of ice and air with an empirically prescribed
constant density (200 kg m-3), similar to AL. The refractivity of mixed-phase stratiform particles wasparameterized in terms of atmospheric temperature(Nishituji et al. 1983).
c. RTM calculation methods
First, we calculated the convective and stratiform
LUTs for horizontally homogeneous precipitation
by incorporating the above atmospheric-, surface-,and precipitation-related variables into the RTM program of Liu (1998). This program computed TBs for aplane-parallel atmosphere using a four-stream approximation. In this program, all precipitation particles
were assumed to be spherical, and the absorption andscattering coefficients and the phase functions werecomputed based on the Mie theory.

2

Fig. 2. Schematic illustration of proceduresin the retrieval part of the GSMaP algorithm.

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Journal of the Meteorological Society of Japanmethods based on TRMM observation studies; (2) a
new scattering retrieval method using PCT37 and
PCT85 and scattering-signal correction for tall precipitation.
a. Detection of precipitating areas
Over land, we adopted the detection methods ofSeto et al. (2005). We calculated the scattering indices from TMI TB85v and TB21v, and judged that precipitating areas had scattering indices larger than thestatistically determined thresholds. Since this method
mistook snow cover for precipitation, we regarded allareas with surface temperatures lower than 273.2 K as
precipitation-free.
Over coastal areas, we used the method of Kubota
et al. (2007). This is an improvement of McCollum
and Ferraro (2005) that detects precipitating areas using PCT85 scattering signals and a decision tree ofseveral empirical conditions for TMI TBs.
Over ocean, we adopted the method of Kida et al.
(2009). We judged that precipitating areas had either
PCT85 scattering or TB37v emission signals, whichwere expressed by the respective TMI PCT85 depressions and TB37v increases from the LUT TBs for 0mm h-1.
b. Estimation of precipitation inhomogeneity
We used high-resolution PCT85 to estimate theVol. 87A

standard deviation of the natural logarithm of the precipitation (SDNLPR), similar to AL.
First, we converted the PCT85 scattering signals
into precipitation (Rain85) using LUTs for a prescribed value of SDNLPR (=1.0), and calculated a first
guess of the inhomogeneity (Sigma85) from Rain85
within the TB10v FOVs.
This first guess was then adjusted based on the statistical relationship between Sigma85 and SDNLPR
calculated from PR (Kubota et al. 2009). The LUTs
for this adjusted value were used in the following retrieval procedures.
c. Precipitation retrieval using scattering signals
As scattering signals for precipitation retrieval,
we chose PCT37 in addition to PCT85, in spite of
the coarser resolution and smaller signals of PCT37.
This was based on the following comparison results
between the TMI scattering signals and the thickness between the PR precipitation top levels and FLH(Dtop). Figure 3 illustrates the relationship between
Dtop and the scattering-signal ratios of the TMI observations to the RTM calculation from the PR precipitation, over land, for July 2004. This indicates that
the TMI PCT85 scattering signals were much moresensitive to Dtop than the RTM-calculated PCT85
scattering signals, and that the TMI PCT37 scattering
signals were practically independent of Dtop, except

Fig. 3. Scatter diagram between Dtop (in m) and the scattering-signal ratios of the TMI observations to the
RTM calculation based on the PR precipitation for (a) PCT85 and (b) PCT37 over land, for July 2004.

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K. AONASHI et al.

Fig. 4. Scatter diagram between (a) Dtop (in m) and TB85v minus TB21v (in K) in precipitating areas overland and (b) Dtop and PCT85 (in K) in precipitating areas over coast, for July 2004.

for tall precipitation (Dtop > 6 km).
Based on the above results, we combined the
PCT85 and PCT37 scattering signals for the retrieval
as follows.
(1) The LUTs for PCT37 were statistically corrected
for tall precipitation over land and coast.
(2) Rain85 was adjusted by precipitation retrieved
from PCT37 (Rain37).
The LUT correction for tall precipitation required
Dtop estimation using TMI TBs. Over land (coast), we
employed TB85v minus TB21v (PCT85) in precipitating areas as the indices of Dtop (see Fig. 4) and statistically estimated Dtop from these variables.
We derived the statistical ratios of the TMI PCT37
scattering signals to those calculated from the PR surface precipitation and their dependency on Dtop, asseen in Fig. 3. We then corrected the LUTs for PCT37,
using these statistical values for the TMI-estimatedtall precipitation over land and coast.
Next, we retrieved Rain37 from the PCT37 scattering signals using the corrected LUTs. We adjusted
Rain85 based on Rain37, multiplying Rain85 by 10
GHz-FOV averages of the ratio of Rain37 to Rain85.
We output these adjusted values as precipitation retrievals using scattering signals (scattering retrieval),
comprising the outputs of the GSMaP algorithm overland and coast.

d. Precipitation retrieval using emission signals overocean
For retrieval over ocean, emission signals at lower
frequencies were available in addition to the scattering
signals. Similar to AL, the GSMaP algorithm foundthe optimal precipitation over ocean by minimizing a
cost function (J) that was a weighted sum of squaresof the differences between the observation and theforward calculation for TB10v and TB19v:
km

{

[

1
TB 0f ( yˆ k ) − TB if yˆ k :r ( xˆ j )2
σ2ff k =1J = ∑∑

]} ,
2

(3)

where r ( xˆ j ) is the precipitation at point x̂ j , ŷk is thelocation of a TMI pixel, T B 0f and T B if represent the
TMI TBs and the FOV-averaged TBs calculated fromr ( xˆ j ) at frequency f. σƒ is the standard deviation of thedifferences between T B 0f and T B if . Here ∑ representskmsummation for f=10 and 19 GHz, and ∑k = 1 is the summation of TMI pixels over ocean.
We derived the first guess for this minimization byadjusting the scattering retrievals based on precipitation retrieved from TB10v (Rain10v). In this adjustment, we multiplied the scattering retrievals by the 10
GHz-FOV averages of the ratio of scattering retrievals to Rain10v. For shallow precipitation with zeroprecipitation retrievals, we employed precipitation retrieved from TB37v (Rain37v) as the first guess.
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Fig. 5. GMS5 IR TB image for 18 UTC,
June 1, 1998.
D

Vol. 87A

Fig. 7. GMS5 IR TB image for 15 UTC,
June 5, 1998.
D

&

$

%
E

E

&㵭

%㵭
Fig. 6. Surface precipitation rates (in mmh-1) for June 1, 1998.
(a) PR for OP 2931 (18 UTC).
(b) GSMaP precipitation retrievals for OP
2931 (18 UTC).

Fig. 8. Surface precipitation rates (in mmh-1) for June 5, 1998.
(a) PR for OP 2992 (15 UTC).
(b) GSMaP precipitation retrievals for OP
2992 (15 UTC).

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K. AONASHI et al.

We obtained the optimal precipitation over ocean
by AL’s method of solving the gradient equation of
the cost function.

4. Algorithm validation
We applied the GSMaP algorithm to TMI TBs forsome Baiu (Japanese rainy season) cases in 1998. We
also used the GSMaP algorithm for retrieving TMI
global precipitation distributions for January, April,
July, and October 1998. We then validated these precipitation retrievals using the PR precipitation rates.
We also compared the GSMaP and GPROF global
precipitation retrievals (2A12 version 6) for July 1998.
4.1 Baiu cases
In order to examine both over-land and over-ocean
retrievals, we chose a precipitation case around
Kyushu Island and a Baiu disturbance case near
Okinawa.
a. Precipitation around Kyushu (18 UTC, June 1,
1998)
Figure 5 is a Japanese Geostationary Meteorological
Satellite 5 (GMS5) IR TB image for 18 UTC on June
1. At this time, an extra-tropical low with fronts wasmoving eastward over the East China Sea. Most ofJapan was covered with low-TB clouds, and precipitation was observed over a wide region of westernJapan.
Figure 6 presents the PR surface precipitation rates
and TMI precipitation rates retrieved with the GSMaP
algorithm for TRMM Over-Path (OP) 2931 (~18 UTC)
within the dashed-line box in Fig. 5. This indicates
that, over land and coast, many precipitation retrievals fell into a range of 3 to 10 mm h-1, and that heavier precipitation (at Point A, for example) was not retrieved by the GSMaP algorithm. Accordingly, the
GSMaP algorithm overestimated (underestimated) PR
surface precipitation weaker than 3 mm h-1 (heavier
than 10 mm h-1) over land and coast.
Over ocean, the GSMaP algorithm retrieved precipitation heavier than 10 mm h-1 (Points B and B’),
while we found small-scale positional errors between
the PR and the retrieved precipitation in heavy-precipitation areas.
b. Baiu disturbance near Okinawa (15 UTC, June 5,
1998)
Figure 7 is a GMS5 IR TB image taken at 15 UTC
on June 5. At this time, the Baiu front extended overthe sea from around Okinawa to south of Honshu

127

Island. Low-TB clouds covered a wide region along
the front, and meso-scale convective clouds were scattered around Okinawa.
Figure 8 presents the PR surface precipitation rates
and TMI precipitation rates retrieved with the GSMaP
algorithm for OP2992 (~15 UTC) within the dashedline box in Fig. 7. Large-scale patterns in the precipitation retrievals agreed well with those measured by
PR over ocean. Though we did find small-scale positional errors between the PR and the retrieved precipitation in heavy-precipitation areas (at Points C and C’,for example). The GSMaP algorithm underestimatedthe maximum values of heavy precipitation overocean, while it tended to overestimate the precipitation rates for the surrounding areas.
4.2 Global precipitation distributions for January,
April, July, and October, 1998
We calculated the 10 GHz-FOV average of the instantaneous values of the GSMaP retrievals and the
PR surface precipitation rates for January, April, July,and October 1998. In this calculation, we employed
PR data around the swath centers (width 100 km) to
avoid ground-clutter contamination. We then classified these averaged values using surface condition
flags for the 10 GHz FOVs in order to examine the
GSMaP algorithm’s performance over land, coast, andocean.
Figure 9 presents scatter diagrams of the over-land
GSMaP retrievals and PR surface precipitation rates
for the four months. The correlation coefficients between the over-land retrievals and PR were 0.6923 to
0.7677, with the correlation degrading for precipitation heavier than 10 mm h-1. The scattering patterns
varied little among the four months. On average, theover-land retrievals underestimated the PR precipitation, particularly in the heavy precipitation range (>10 mm h-1).
Figure 10 presents scatter diagrams of the overcoast GSMaP retrievals and the PR surface precipitation rates for the four months. The over-coast retrievals had error characteristics (underestimation ofheavy precipitation) similar to the over-land retrievals.
Though the correlation between the retrievals and PR
over coast (0.6233 to 0.7075) was slightly worse thanthat over land. We think that this is mainly due to estimation error in the surface emission over coastal
FOVs, which are a mixture of ocean and land areas.
Figure 11 presents scatter diagrams of the overocean GSMaP retrievals and PR surface precipitation
rates for the four months. The over-ocean retrievals

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Fig. 9. Scatter diagrams of the over-land GSMaP precipitation retrievals and PR surface precipitation rates (inmm h-1) for (a) January, (b) April, (c) July, and (d) October 1998.

Fig. 10. The same as Fig. 9, but for over-coast GSMaP precipitation retrievals.

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K. AONASHI et al.

129

Fig. 11. The same as Fig. 9, but for over-ocean GSMaP precipitation retrievals.

agreed well with PR precipitation, as indicated by thehigher correlation (0.8697 to 0.8831). It should also be
noted that the scattering patterns varied little among
the four months. However, we found a slight overestimation of PR precipitation weaker than 10 mm h-1.
Next, we checked global maps of the monthly-mean
precipitation rates of the GSMaP algorithm and theiranomalies from PR (Figs. 12 and 13) in order to see
the regional variations in the precipitation retrieval errors. The result suggests that the GSMaP algorithm
tended to overestimate heavy-precipitation areas along
the inter-tropical and sub-tropical convergence zones
and the mid-latitude storm tracks, while it failed todetect some precipitation areas weaker than 2 mm h-1
over sub-tropical oceans. In contrast, we did not findsystematic biases over land, except for mountainous
regions like the southern slopes of the Himalayas,
where orography-induced shallow convection was predominant.
4.3 Comparison with GPROF for July 1998
We calculated the 10 GHz-FOV average of the instantaneous values of the GPROF retrievals for July
1998, using the procedures described in Section 4.2.

Figure 14a (b) is a scatter diagram of the over-land
GPROF (GSMaP) retrievals and PR surface precipitation rates for this month. Each point is classified interms of Dtop. While GPROF overestimated tall precipitation (Dtop > 4 km) weaker than 10 mm h-1, the
GSMaP retrievals agreed better with PR than GPROF
for this range. Since GPROF used only 85 GHz TBs
as scattering signals, we think that this overestimation
was caused by their sensitivity to tall precipitation.
Both GSMaP and GPROF underestimated the PR precipitation rates for precipitation heavier than 10 mm
h-1, while GSMaP had larger retrieval errors for thisrange. We also found that over-coast retrievals had error characteristics similar to the over-land retrievals(Figure not shown).
Figure 14c (d) presents a Dtop-classified scatter
diagram of the over-ocean GPROF (GSMaP) retrievals and PR surface precipitation rates for July 1998.
While GPROF underestimated precipitation heavier
than 10 mm h-1, the GSMaP retrievals agreed betterwith PR than GPROF for this range. In contrast, the
GPROF retrievals corresponded slightly better with
PR precipitation rates than did the GSMaP retrievalsfor precipitation weaker than 10 mm h-1. These error

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E

F

G

Vol. 87A

Fig. 12. Global maps of the monthly mean precipitation rates (in mm h-1) in the GSMaP retrievals for (a)January, (b) April, (c) July, and (d) October 1998.

characteristics did not depend on Dtop, in contrast to
the over-land scattering retrievals. Thus, we infer thatthey were caused by variable-model differences between the two algorithms related to the emission signals (e.g., rain profile, drop-size distribution, mixedphase refractivity, etc.). The effects of these variablemodel differences on GSMaP retrievals will be discussed in Section 5.1.

5. Retrieval experiments

As described in Section 3, the main improvementsin the GSMaP algorithm over AL are: (1) new precipitation-related variable models; (2) new precipitationdetection and inhomogeneity-estimation methods; (3)a new scattering-retrieval method using PCT37 and
PCT85 and scattering-signal correction for tall precipitation. In this section, we focus on the effects ofthe precipitation-related variable models and the scattering-retrieval method on the precipitation retrievals.
(Precipitation-detection and inhomogeneity estima-

Fig. 13. Global maps of monthly mean precipitation-rate anomalies (in mm h-1) between the GSMaP retrievals
and PR for (a) January, (b) April, (c) July, and (d) October 1998.

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K. AONASHI et al.

E㪞㪪㪤㪸㪧 㫉㪼㫋㫉㫀㪼㫍㪸㫃㩷㩿㫄㫄㪆㪿㫉㪀㪞㪧㪩㪦㪝㩷㫉㪼㫋㫉㫀㪼㫍㪸㫃㩷㩿㫄㫄㪆㪿㫉㪀D㪧㪩㩷㫇㫉㪼㪺㫀㫇 㫉㪸㫋㪼㩷㩿㫄㫄㩷㪿㫉㪄㪈㪀㪧㪩㩷㫇㫉㪼㪺㫀㫇 㫉㪸㫋㪼㩷㩿㫄㫄㩷㪿㫉㪄㪈㪀G
㪞㪪㪤㪸㪧 㫉㪼㫋㫉㫀㪼㫍㪸㫃㩷㩿㫄㫄㪆㪿㫉㪀㪞㪧㪩㪦㪝㩷㫉㪼㫋㫉㫀㪼㫍㪸㫃㩷㩿㫄㫄㪆㪿㫉㪀F

㪧㪩㩷㫇㫉㪼㪺㫀㫇 㫉㪸㫋㪼㩷㩿㫄㫄㩷㪿㫉㪄㪈㪀㪧㪩㩷㫇㫉㪼㪺㫀㫇 㫉㪸㫋㪼㩷㩿㫄㫄㩷㪿㫉㪄㪈㪀

Fig. 14. Scatter diagrams of the retrievals and PR surface precipitation rates (in mm h-1) for July 1998 for (a)GPROF over land, (b) GSMaP over land, (c) GPROF over ocean, and (d) GSMaP over ocean. Red, green,
and blue dots are points with respective Dtop > 4 km, 2-4 km, and < 2 km.

tion methods have already been reported by Seto et al.
2005; Kubota et al. 2007, 2009; and Kida et al. 2009.)
To evaluate these effects, we developed experimental algorithms by switching off some of the above improvements. We applied these algorithms to TMI TBs
for July 1998, and compared the outputs with the corresponding GSMaP retrievals.
5.1 Experiments on the precipitation-related variable
modelsFor this purpose, we developed an experimental al-

gorithm (EXP1) that used the AL models for all of thevariables in Table 2 (other procedures were the sameas GSMaP).
Figure 15a is a scatter diagram of the over-land
EXP1 retrievals and PR surface precipitation rates forJuly 1998. Comparing this with Fig. 9c, we found that
EXP1 exhibits a more severe underestimation of PR
precipitation heavier than 5 mm h-1. This means thatthe new precipitation-related variable models alleviated this underestimation. We also checked the overland retrieval increments between GSMaP and EXP1

132

Journal of the Meteorological Society of Japanfor this month. The results indicated significant differences in the increments, depending on the precipitation type (Section 3.1.b). For example (Fig. 15b), we
had large positive increments for type 5, where organized medium top-level precipitation was dominant,while the increments were small for type 1 (tall thunderstorms).
Figure 15c is a scatter diagram of the over-oceanVol. 87A

EXP1 retrievals and PR surface precipitation rates forthis month. Comparing this with Fig. 11c, we foundthat EXP1 underestimated PR-measured precipitation
heavier than 10 mm h-1 and that the new precipitationrelated variable models alleviated this underestimation. We also found that the (GSMaP – EXP1) increments were large over the tropical ocean, compared tothe mid-latitudes of the winter hemisphere (< 25 S), as

Fig. 15.
(a) Same as Fig. 9c, but for the EXP1 retrievals.
(b) Over-land precipitation retrieval differences (GSMaP minus EXP1) for July 1998. Red and blue are for
precipitation types 1 and 5.
(c) Same as Fig. 11c, but for the EXP1 retrievals.
(d) Over-ocean precipitation retrieval differences (GSMaP minus EXP1) for July 1998. Pink and green are
for the tropical and winter-hemisphere mid-latitudes (< 25 S).

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K. AONASHI et al.

133

Fig. 16. PCT85 scattering-signal differences (in K) between GSMaP and the experiments for homogenous
precipitation of 10 mm h-1 at 00 UTC, July 1, 1998 for (a) GSMaP minus EXP1 and (b) GSMaP minus
EXP2.

seen in Fig. 15d.
We created further experimental algorithms thatchanged only the precipitation profile (EXP2), theraindrop-size distribution (EXP3), or the mixedphase refractivity (EXP4) from the AL model in orderto check the effect of each variable model. We thencompared the forward-calculated TBs of EXP1-4 and
GSMaP for 00 UTC on July 1, 1998.
Figure 16a (b) illustrates the differences between
GSMaP and EXP1 (EXP2) in the calculated PCT85
scattering signals for homogeneous precipitation of 10
mm h-1. The differences between GSMaP and EXP3,4
were negligible (Figure not shown). This means thatthe new precipitation-profile model played a dominant role in improving over-land and –coast GSMaP
retrievals through its forward calculation of the scattering signals.
Figure 17 presents the differences between GSMaPand EXP1-4 in the calculated TB10v emission signals for the same case as in Fig. 16. This indicatesthat each of the three variable-model improvements

significantly impacted the calculation of the emission
signals, as opposed to the scattering signals. The newmodels for precipitation profiles and raindrop size
distribution reduced the calculated emission signals
except in the winter hemisphere mid-latitudes, whereemission from rain became small because of low
FLH. In contrast, the new mixed-phase model raised
the calculated emission signals, in particular over the
winter hemisphere mid-latitudes, where emission frommixed-phase precipitation became dominant. We consider that the combined effect of these new variable
models provided more realistic LUTs that improveover-ocean GSMaP retrievals, particularly for heavy
precipitation.
5.2 Experiments on scattering retrieval
For this purpose, we developed an experimental algorithm (EXP5) that used only PCT85 as the scattering signals, and we switched off the scattering-signal
correction for tall precipitation (other procedures were
the same as GSMaP). We also created an experimen-

Fig. 17. TB10v emission-signal differences (in K) between GSMaP and the experiments for homogenous precipitation of 10 mm h-1 at 00 UTC, July 1, 1998.
(a) GSMaP minus EXP1; (b) GSMaP minus EXP2; (c) GSMaP minus EXP3; (d) GSMaP minus EXP4.

134

Journal of the Meteorological Society of Japantal algorithm (EXP6) that only switched off the scattering-signal correction for tall precipitation.
Figure 18a (b) is a Dtop-classified scatter diagramof the over-land EXP5 (EXP6) retrievals and PR surface precipitation rates for July 1998. EXP5 extremely
overestimated tall precipitation (Dtop > 4 km) weaker than 10 mm h-1. EXP6 gave better retrievals than
EXP5 by generally reducing the precipitation rates,
though EXP6 still overestimated tall precipitation.
Comparing this with Fig. 14b, we found that the improvement in the scattering retrieval greatly alleviatedthe overestimation of tall precipitation (Dtop > 4 km)
and enhanced the correspondence with PR over land.
While the use of PCT37 contributed to this alleviation, it also exacerbated the underestimation of precipitation heavier than 10 mm h-1. At the same time, thescattering-signal correction of tall precipitation wasessential in diminishing the extreme overestimation.

6. Summary and discussion
This paper described a GSMaP passive microwave
precipitation-retrieval algorithm developed by improving the AL algorithm. The main improvements in
the GSMaP algorithm over AL are as follows: (1) use
of precipitation-related variable models and precipitation detection and inhomogeneity estimation methods
based on TRMM observation studies; (2) use of scattering signals of PCT37 and PCT85 and scattering-

signal correction of tall precipitation over land andcoast.
We validated the GSMaP retrievals using PR and
GPROF data for 1998. The results are as follows:(1) Over land and coast, the GSMaP retrievals agreed
better with PR than GPROF for tall precipitation(Dtop > 4 km) weaker than 10 mm h-1, while both
GSMaP and GPROF underestimated PR precipitation
rates for precipitation heavier than 10 mm h-1.
(2) Over ocean, the GSMaP retrievals agreed betterwith PR than GPROF for precipitation heavier than 10mm h-1, while GSMaP slightly overestimated precipitation weaker than 10 mm h-1 compared with PR and
GPROF .
(3) The GSMaP algorithm failed to detect some precipitation areas weaker than 2 mm h-1 over sub-tropical oceans.
The retrieval experiment results indicate that the
improvement in the precipitation profile alleviated theunderestimation of precipitation heavier than 10 mm
h-1 over land and coast, that the combined use of new
physical-related variable models alleviated the underestimation of precipitation heavier than 10 mm h-1
over ocean, and that the use of PCT37 and scatteringsignal correction reduced the overestimation of tall
precipitation (Dtop > 4 km) weaker than 10 mm h-1
over land and coast.
The validation and retrieval experiment results,E㪜㪯㪧㪍㩷㪩㪼㫋㫉㫀㪼㫍㪸㫃㩷㩿㫄㫄㩷㪿㫉㪄㪈㪀D㪜㪯㪧㪌㩷㪩㪼㫋㫉㫀㪼㫍㪸㫃㩷㩿㫄㫄㩷㪿㫉㪄㪈㪀Vol. 87A

㪧㪩㩷㫇㫉㪼㪺㫀㫇 㫉㪸㫋㪼㩷㩿㫄㫄㩷㪿㫉㪄㪈㪀㪧㪩㩷㫇㫉㪼㪺㫀㫇 㫉㪸㫋㪼㩷㩿㫄㫄㩷㪿㫉㪄㪈㪀

Fig. 18. Scatter diagrams of the retrievals and PR surface precipitation rates (in mm h-1) during July 1998 for(a) EXP5 over land and (b) EXP6 over land. Red, green, and blue dots are points with respective Dtop > 4
km, 2-4 km, < 2 km.

March 2009

however, indicate that the GSMaP algorithm hada problem in identifying heavy precipitation areas
over land and coast, and underestimated precipitation heavier than 10 mm h-1. This is mainly because
the forward calculation exaggerated the PCT37 scattering signals for this precipitation range, comparedto the TMI observation. Thus, solution of this problem requires improvement in the precipitation-related
variable models and RTM program related to highfrequency scattering signals. We will work on thefollowing points to achieve this improvement: (1) the
introduction of the scattering effects of non-sphericalfrozen particles into the RTM program; (2) the search
for a frozen-particle size distribution and density thatgives LUTs close to the statistical relationship between the TMI scattering signals and PR precipitation
rates.
The validation and retrieval experiment resultsover ocean suggest that the GSMaP algorithm chosethe appropriate precipitation-related variable models
in terms of the emission signals. Although the results
also suggest a slight overestimation of weak precipitation surrounding heavy precipitation areas. One possible cause of this is that the low-frequency TBs in
the surrounding areas became larger than those of
the plane-parallel RTM calculation, since TMI observes TBs along slant paths (Kummerow et al. 2004).
Hence, the overestimation could be due to the neglectof the ‘slant-path effect’ in the forward calculation.
However, there is a possibility that PR underestimated the weak precipitation, as Shige et al. (2006, 2008)
has pointed out. Accordingly, we will carefully examine the causes of this problem in an effort to reduce
the overestimation.
We also found a detection problem over sub-tropicaloceans where shallow precipitation is predominant.
Kida et al. (2009) pointed out that the over-ocean precipitation-detection method of the GSMaP algorithm
is less accurate than GPROF over these areas, and thatthe detection problem arose from the empirically determined, global assumptions concerning relative humidity (100%) and CLWC (0.5 kg m-2) in the forward
calculation. In order to improve the over-ocean precipitation detection method, we need to locate shallow
precipitation regions and provide more realistic humidity and CLWC for these regions.

Acknowledgementsof135

K. AONASHI et al.

The present study was supported by “Productiona High-Precision, High-Resolution Global

Precipitation Map Using Satellite Data” (the GSMaP
project) under the “Core Research for Evolutional
Science and Technology” program of the Japan
Science and Technology Agency. The authors thankall team members of the GSMaP project, in particular Drs. Ken’ichi Okamoto, Tottori University ofEnvironmental Studies, and Toshio Iguchi, National
Institute of Information and Communications
Technology. The GANAL and MGDSST data were
provided by the Numerical Prediction Division of
JMA.

References
Aonashi, K., and G. Liu, 2000: Passive microwave
precipitation retrievals using TMI during the
Baiu period of 1998. Part 1: Algorithm description and validation. J. Appl. Meteor., 39, 2024–
2037.
Ebert, E. E., and M. J. Manton, 1998: Performance ofsatellite rainfall estimation algorithms during
TOGA COARE. J. Atmos. Sci., 55, 1537–1557.
Grody, N., 1991: Classification of snow cover and precipitation using the special sensor microwave
imager, J. Geophys. Res., 96(D4), 7423–7435.
Iguchi, T., 2007: Space-borne radar algorithms in
Measuring precipitation from space-EURAINSAT and the future, V. Levizzani, P. Bauer, andF. J. Turk, Eds., Springer, 199–212.
Iguchi, T., T. Kozu, J. Kwiatkowski, R. Meneghini, J.
Awaka, and K. Okamoto, 2009: Uncertainties
in the rain profiling algorithm for the TRMM
Precipitation Radar. J. Meteor. Soc. Japan, 87A,
1–30.
Kida, S., S. Shige, T. Kubota, K. Aonashi, and K.
Okamoto, 2009: Improvement of rain/no-rainclassification methods for microwave radiometer observations over the ocean using a 37 GHz
emission signature. J. Meteor. Soc. Japan, 87A,
165–181.
Kozu, T., T. Iguchi, T. Kubota, N. Yoshida, S. Seto,
J. Kwiatkowski, and Y. N. Takayabu, 2009:
Feasibility of raindrop size distribution parameter estimation with TRMM Precipitation Radar.
J. Meteor. Soc. Japan, 87A, 53–66.
Kubota, T., S. Shige, H. Hashizume, K. Aonashi, N.
Takahashi, S. Seto, M. Hirose, Y. N. Takayabu,
K. Nakagawa, K. Iwanami, T. Ushio, M. Kachi,
and K. Okamoto, 2007: Global precipitation
map using satelliteborne microwave radiometersby the GSMaP project : Production and validation. Trans. Geosci. Remote Sens., 45, 7, 2259–

136

Journal of the Meteorological Society of Japan

2275.
Kubota, T., S. Shige, K. Aonashi, and K. Okamoto,2009: Development of nonuniform beamfillingcorrection method in rainfall retrievals for passive microwave radiometers over ocean using
TRMM observations. J. Meteor. Soc. Japan,
87A, 153–164.
Kummerow, C., Y. Hong, W. S. Olson, S. Yang, R. F.
Adler, J. McCollum, R. Ferraro, G. Petty, D. B.
Shin, and T. T. Wilheit, 2001: The Evolution of
the Goddard Profiling Algorithm (GPROF) for
rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 1801–1820.
Kummerow, C., P. Poyner, W. Berg, and J. ThomasStahle, 2004: The effects of rainfall inhomogeneity on climate variability of rainfall estimated from passive microwave sensors. J. Atmos.
Oceanic Technol., 21, 624–638.
Liu, G., 1998: A fast and accurate model for microwave radiance calculation. J. Meteor. Soc.
Japan, 76, 335–343.
McCollum, J. R., and R. R. Ferraro, 2003: Next
generation of NOAA/NESDIS TMI, SSM/I,
and AMSR-E microwave land rainfall algorithms. J. Geophys. Res., 108(D8), 8382,
doi:10.1029/2001JD001512.
McCollum, J. R., and R. R. Ferraro, 2005: Microwave
rainfall estimation along coasts. J. Atmos.
Ocean. Technol., 22, 497–512.
Nishitsuji, A., M. Hoshiyama, J. Awaka, and Y.
Furuhama, 1983: An analysis of propagative
character at 34.5 GHz and 11.5 GHz between
ETS-II satellite and Kasima station –On the
precipitation model from stratus. IEICE Trans.,
Vol. J66-B, 1163–1170, (in Japanese).
Olson, W. S., C. D. Kummerow, S. Yang, G. W. Petty,Vol. 87A

W.-K. Tao, T. L. Bell, S. A. Braun, Y. Wang,
S. E. Lang, D. E. Johnson, and C. Chiu, 2006:
Precipitation and latent heating distributions
from satellite passive microwave radiometry.
Part I: Improved method and uncertainties. J.
Appl. Meteor. Climatol., 45, 702–720.
Panegrossi, G., S. Dietrich, F. S. Marzano, A. Mugnai,
E. A. Smith, X. Xiang, G. J. Tripoli, P. K. Wang,and J. P. V. Poiares Baptista, 1998: Use of cloudmodel microphysics for passive microwavebased precipitation retrieval: Significance ofconsistency between model and measurement
manifolds. J. Atmos. Sci., 55, 1644–1673.
Seto, S., N. Takahashi, and T. Iguchi, 2005: Rain/norain classification methods for microwave radiometer observations over land using statisticalinformation for brightness temperatures underno-rain conditions. J. Appl. Meteor., 44. 1243–
1259.
Shige, S., H. Sasaki, K. Okamoto, and T. Iguchi, 2006:
Validation of rainfall estimates from the TRMM
Precipitation Radar and microwave imager using a radiative transfer model: 1. Comparisonof the version-5 and -6 products. Geophys. Res.
Lett., 33, L13803, doi:10.1029/2006GL026350.
Shige, S., T. Watanabe, H. Sasaki, T. Kubota, S.
Kida, and K. Okamoto, 2008: Validation ofwestern and eastern Pacific rainfall estimatesfrom the TRMM PR using a radiative transfer model. J. Geophys. Res., 113, D15116,
doi:10.1029/2007JD009002.
Takayabu, N. Y., 2006: Rain-yield per flash calculated from TRMM PR and LIS data and its relationship to the contribution of tall convective rain. Geophys. Res. Lett., 33, L18705,
doi:10.1029/2006GL027531.