JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, D24101, doi:10.1029/2010JD014279, 2010

Comparison of rain fractions over tropical and sub‐tropicalocean obtained from precipitation retrieval algorithms
for microwave sounders
Satoshi Kida,1,2 Shoichi Shige,2 and Takeshi Manabe1
Received 30 March 2010; revised 21 July 2010; accepted 1 September 2010; published 17 December 2010.

[1] We compare the fractional occurrence of precipitation (rain fraction) over ocean
derived using the Global Satellite Mapping of Precipitation algorithm for the Advanced
Microwave Sounding Unit (GSMaP_AMSU) and the Microwave Surface andPrecipitation Products System Day 2 rainfall algorithm (NOAA_AMSU) for the
Kwajalein radar site and over tropical and subtropical ocean. The rain fractions of
GSMaP_AMSU and NOAA_AMSU are lower than that of Kwajalein radar estimates
because of failure to detect areas of light rain. Over tropical and subtropical ocean, therain fraction of GSMaP_AMSU is closer to that obtained using a microwave imager(MWI) and little different from that of Tropical Rainfall Measuring Mission Precipitation
Radar (PR) data, whilethe rain fraction of NOAA_AMSU is much smaller than thatobtained using MWI or PR data. In the case of the edge of the South PacificConvergence Zone where the PR observes scattered shallow rain, while NOAA_AMSU
fails to detect the scattered rain, GSMaP_AMSU detects the scattered rain throughconsideration of the scattering index, which is the difference in brightness temperature(Tb) between 89 and 150 GHz. Although the scattering index is designed on the basisthat Tb decreases in response to scattering by precipitation at these frequencies andincreases rapidly with frequency, there are emission and scattering regimes. Furthermore,
the scattering index also responds to emission in light rain with a low concentrationof cloud liquid water. As a result, the light rain pixel can be detected using thescattering index to take advantage of the emission signature from raindrops.
Citation: Kida, S., S. Shige, and T. Manabe (2010), Comparison of rain fractions over tropical and sub‐tropical ocean obtained
from precipitation retrieval algorithms for microwave sounders, J. Geophys. Res., 115, D24101, doi:10.1029/2010JD014279.

1. Introduction[2] Accurate observation of the global distribution of
precipitation has long been a major scientific goal. Global
rainfall maps with high spatial and temporal precision are
required not only for scientific research but also for hydrologic applications, agricultural management, and weather
forecasts. In spite of its importance, estimation of precipitation with sufficient accuracy and resolution on a global basis
is difficult because of the great variability of precipitation in
space and time. One of the best approaches to capture global
precipitation is to use data from passive microwave radiometers (MWRs) aboard low Earth‐orbiting (LEO) satellites
and data from infrared radiometers (IRs) aboard geostationary satellites. Data from MWRs have a strong physicalrelationship with hydrometeors that result in surface precipitation, but an individual LEO satellite provides very sparse
1
Department of Aerospace Engineering, Osaka Prefecture University,
Sakai, Japan.
2
Graduate School of Science, Kyoto University, Kyoto, Japan.

Copyright 2010 by the American Geophysical Union.
0148‐0227/10/2010JD014279

sampling of the time‐space occurrence of precipitation. On
the other hand, data from IRs have high temporal and spatial
resolution, but have a weaker relationship with surface precipitation than the data from MWRs because IR channels
measure cloud‐top temperature, which does not always correlate well with rainfall.
[3] High‐precision and high‐temporal global rainfall maps
are produced by combining data from IRs and MWRs to takeadvantage of the strengths of each. In producing global
rainfall maps, the data from IRs are mainly used to fill thegaps in the estimations of precipitation from MWR data
because the estimations of precipitation by IRs are poor.
There are two main types of techniques to combine data fromIRs and MWRs. One involves the manipulation of IR data in astatistical fashion to mimic the behavior of MWR‐derived
precipitation estimates (e.g., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA),
Huffman et al., 2007). This technique produces microwave‐
calibrated IR estimates that match the microwave‐based
fractional precipitation coverage. The other technique is touse precipitation estimates derived from MWR observations
exclusively and transport their features using spatial propagation information obtained from IR data during periods

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when instantaneous MWR data are not available at a location(e.g., Climate Precipitation Center Morphing Technique,
CMORPH, Joyce et al., 2004; Global Satellite Mapping ofPrecipitation Moving Vector with the Kalman Filter method,
GSMaP_MVK, Ushio et al., 2009). In both techniques, thefractional occurrence of precipitation obtained from theglobal precipitation maps depends on the fractional occurrence of precipitation obtained from MWRs. Therefore, theaccuracy of rain detection by the precipitation retrieval
algorithm for MWRs is important in producing accurate
global rainfall maps.
[4] Passive MWRs aboard LEO satellites generally
consist of two types: imagers and sounders. Microwave
imagers (MWIs) such as the TRMM Microwave Imager(TMI) [Kummerow et al., 1998], the Advanced Microwave
Scanning Radiometer for the Earth Observation System(AMSR‐E) aboard the Aqua satellite [Kawanishi et al.,2003], and the Special Sensor Microwave Imager (SSM/I)[Hollinger et al., 1990; Colton and Poe, 1999] of the
Defense Meteorological Satellite Program (DMSP) havechannels suitable for observing precipitation such as those at
10 and 19 GHz, which are suitable for observing the emission signal from raindrops, and those at 37 and 85 GHz,
which are suitable for observing the scattering signal fromice aloft. On the other hand, microwave sounders (MWSs)
such as the Advanced Microwave Sounding Unit (AMSU)
aboard the National Oceanic and Atmospheric Administration (NOAA) satellites [Saunders et al., 1995; Mo, 1996] areprimarily developed for profiling atmospheric temperatureand moisture using opaque spectral regions. To optimize thesensor performance, 20 channels are divided among three
separate total‐power radiometers: AMSU‐A1, AMSU‐A2,
and AMSU‐B (NOAA‐18 replaced AMSU‐B with thesimilar Microwave Humidity Sounder). AMSU‐A has
window channels at 23.8, 31.4, 50.3, and 89 GHz and the
50–60 GHz oxygen band. AMSU‐B has window channels
at 89 and 150 GHz and around the 183 GHz water vapor
line. 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 they expand the
AMSU capability beyond that of temperature and moisture profiling. Recently, a new type of MWR suitable for
precipitation retrieval and temperature and moisturesounding, such as the Special Sensor Microwave Imager‐
Sounder (SSMIS) [Kunkee et al., 2008], has been carriedby satellites.
[5] Three precipitation retrieval algorithms for MWSs
have been developed. One is the Microwave Surface andPrecipitation Products System (MSPPS) Day 2 algorithm for
AMSU (hereinafter referred to as NOAA_AMSU), which
was developed at NOAA [Ferraro et al., 2005]. Several
researchers found that rainfall derived using early versionsof the precipitation retrieval algorithm for the AMSU‐B
channels differs in many respects from rainfall derived
using MWI estimation techniques. The AMSU‐B algorithm
detects solid hydrometeors, but not liquid. The MWIs
similarly sense only solid hydrometeors over land, thus the
AMSU‐B estimates are roughly comparable for land areas.
On the other hand, over ocean, the MWIs sense not only
solid hydrometeor but also liquid. As a result, over ocean,
the AMSU‐B retrievals are less accurate due their inability
to detect warm rain from clouds that lack the ice phase,

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which accounts for 31% of the total rain amount and 72%of the total rain area in the tropics [Lau and Wu, 2003].
Thus, the MWS retrievals over ocean are not considered to
be included in the baseline Global Precipitation Measurement (GPM) sampling [Hou et al., 2008]. The latest version
of NOAA_AMSU has been improved using the emission
signals from cloud liquid water (CLW) in AMSU‐A channels
to retrieve rain that has little or no ice aloft [Vila et al., 2007].
The second is a neural network‐based algorithm developed at
the Massachusetts Institute of Technology (Cambridge, MA,
USA; hereinafter referred to as MIT_AMSU) [Surussavadee
and Staelin, 2008a, 2008b]. The algorithm is trained using acloud‐resolving model. The third is the Global Satellite
Mapping of Precipitation (GSMaP) algorithm for AMSU
(hereinafter referred to as GSMaP_AMSU) [Shige et al.,2009], which is based on the GSMaP algorithm for MWIs.
The data set of NOAA_AMSU is generated operationally andis available on the Cooperative Institute for Climate Studies(CICS) server (http://cics.umd.edu/∼lcao/datasets.html).
[6] While TMPA and CMORPH use precipitation estimates derived from MWS data based on the NOAA_AMSU
algorithm in addition to those derived from MWI data, thecurrent GSMaP_MVK uses only precipitation estimatesderived from MWI data. Offline tests showed that
GSMaP_MVK is more effective when taking rain estimatesderived from MWS data using the NOAA_AMSU algorithm in addition to those derived from MWI data using the
GSMaP algorithm [Kubota et al., 2009]. It is planned to use
rain estimates derived from MWS data using the GSMaP
algorithm. Thus, in this study, we compare the fractional
occurrences of precipitation over ocean estimated by
GSMaP_AMSU and NOAA_AMSU algorithms. The data setof MIT_AMSU is not included in this study because data setsof MIT_AMSU have not been obtained at this point. We also
evaluate the fractional occurrences of precipitation using
ground validation (GV) radar data, the rain estimates fromother microwave imagers, and TRMM Precipitation Radar(PR) [Kozu et al., 2001; Okamoto and Shige, 2008].

2. Data
2.1. Microwave Imager[7] For comparison with the precipitation retrieval algorithms for MWSs, we use the rain product retrieved by the
GSMaP algorithm for the TMI (hereinafter this algorithm isreferred to as GSMaP_TMI). The basic idea of the GSMaP
algorithm is to find the optimal rainfall that gives the radiative transfer model (RTM) FOV‐averaged Tb values that
fit best the observed Tb values [Aonashi et al., 2009; Kubota
et al., 2007]. The GSMaP algorithm consists of a forward
calculation part to calculate lookup tables (LUTs), whichgive the relationship between the rainfall rate and Tb, via aradiative transfer model (RTM), and a retrieval part toestimate precipitation rates from the observed Tb values
using the LUTs. In the forward calculation part, LUTs areproduced using the four‐stream RTM developed by Liu[1998]. Atmospheric variables (freezing height, temperature, relative humidity, surface wind, and surface temperature) provided by the Japan Meteorological Agency Global
Analysis (GANAL) in a 5 × 5‐degree latitude‐longitude box
are used as input into the RTM. The retrieval calculation

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part consists of a rain/no‐rain classification (RNC) and an
estimation of the rain rates over the delineated rainfall area.
[8] In the RNC over ocean for GSMaP, Tb in the 37 GHz
channel with vertical polarization (Tb37v) is used to detectscattered shallow rain at midlatitude [Kida et al., 2009]
because Tb in the 37 GHz channel with horizontal polarization (Tb37h) is more sensitive to the surface wind speed,
and its use results in the misclassification of rainy areas in
the region where the local wind speed is higher than the
averaged wind speed of GANAL. However, early work forthis study showed that the RNC method using Tb37v misses
some shallow rain in the tropics because the sensitivity of
Tb37v to light rain is lower than that of Tb37h. Thus, we
use not only Tb37v but also the normalized polarizationdifference [Petty, 1994] at 37 GHz (P37), which is a combination of Tb37v and Tb37h for the RNC. Because of thehigh sensitivity of Tb37h to the surface wind speed, the
RNC method over ocean is selected from Tb37v and P37
according to the surface wind speed. P37 is used in theregion where the difference in wind speed of GANAL
between a 1.25 × 1.25‐degree latitude‐longitude box and a
5 × 5‐degree latitude‐longitude box is within 2.0 m s−1,
while Tb37v is used in the other region.
[9] GSMaP_TMI arbitrarily assumed a cloud layer with a
CLW path of 0.5 kg m−2 and relative humidity of 100%
below the freezing level [Aonashi and Liu, 2000] because
the distinction between precipitation and cloud and its
relationship with the CLW path is not entirely understood[Stephens and Kummerow, 2007]. However, this assumption may be unrealistic for the region where shallow rain is
predominant because the CLW path depends on the CLWcontent (kg m−3) and cloud depth. Therefore, over the region
where shallow rain is predominant, the parameterization of
the CLW path as a function of storm height [Kida et al.,2009] is applied:
CLW ¼ 0:1  SH

ð1Þ

where SH (km) is the storm height derived from the PR
standard product 3A25, which is a monthly composite of
TRMM PR data with gridded 5 degree spatial resolution.
We also parameterize the relative humidity, which was
assumed to be 100% below the freezing level. The relative
humidity is 100% under the SH and values from GANAL
above the SH. In this study, the parameterization is applied
to GSMaP_TMI.
[10] We also used the current (version 6) level 2 standard
products of the TRMM TMI published by the National
Aeronautics and Space Administration (NASA) and theJapan Aerospace Exploration Agency (JAXA). The level 2
standard product 2A12 is the rain estimation of the TMI
produced by the Goddard PROFiling (GPROF) algorithm(hereinafter referred to as GPROF_TMI) [Kummerow et al.,
2001; Olson et al., 2006]. The basis of GPROF_TMI is a
Bayesian framework in which retrieved precipitation isconstructed from cloud‐resolving, model‐generated profiles
that are radiatively consistent with observations.
2.2. Microwave Sounder[11] We compare the fractional occurrences of precipitation
estimated by two precipitation algorithms for MWSs overocean: the Microwave Surface and Precipitation Products

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System (MSPPS) Day 2 rainfall algorithm (NOAA_AMSU)[Weng and Grody, 2000; Zhao and Weng, 2002; Ferraro
et al., 2005; Vila et al., 2007] and the Global Satellite
Mapping of Precipitation Algorithm (GSMaP_AMSU) [Shige
et al., 2009]. Polar‐orbiting satellites such as the NOAA
satellite collect two samples per day at about the same
nominal times, and thus mean estimates inferred from theseobservations incur an intrinsic diurnal bias. On the other
hand, the TRMM, which is in a non‐Sun‐synchronous orbit,
reduces the diurnal bias because of its precession throughthe diurnal cycle in a period of about 46 days. Therefore, toreduce the diurnal bias incurred by the AMSU observation,
we use data from the four AMSU sensors aboard the NOAA
satellites (NOAA‐15, NOAA‐16, NOAA‐17, and NOAA‐18),
which are typically spaced about 4 h in time, thus giving abetter representation of the diurnal cycle. The RNC method
in each algorithm is briefly described in the following subsections because the fractional occurrences of precipitation
are affected by the rain detection.
2.2.1. MSPPS Day 2 Rainfall Algorithm(NOAA_AMSU)[12] The algorithm originates from the works of Weng and
Grody [2000] and Zhao and Weng [2002]. The ice water
path (IWP) and ice particle effective diameter (De) were
simultaneously retrieved from Tb at 89 and 150 GHz(hereinafter referred to as Tb89 and Tb150, respectively)
through two processes: simplifying the radiative transfer
equation into a two‐stream approximation and estimating
the cloud‐base and cloud‐top Tb values from AMSU measurements at 23.8 and 31.4 GHz. The rain rate was computed on the basis of an IWP and rain rate relation derived
from the GPROF [Kummerow et al., 2001; Olson et al.,2006] algorithm database, which contains the profiles ofvarious hydrometeors generated from cloud‐resolving
models. The rain rate is computed when the IWP is greaterthan or equal to 0.05 kg m−2 and De is greater than or equal
to 0.3 mm if the IWP and De retrievals fall within an
acceptable range (between 0 and 3.0 kg m−2 and between
0 and 3.5 mm, respectively).
[13] Additionally, because the rain retrieval based on the
IWP can estimate only precipitation that is detectable from ascattering signature, the CLW path retrieved from AMSU‐A
channels and the convective index (CI) calculated using the
AMSU‐B moisture channels are used as a proxy for
retrieving rain that has little or no ice. In detecting shallow
rain, NOAA_AMSU uses the CLW path as a proxy for
retrieving rain and employs a high CLW path of 0.4 kg m−2
as a threshold of RNC to avoid false signatures in moist,
nonraining clouds and uncertainty in the AMSU‐A CLW
calculations [Vila et al., 2007]. The rain rate is computedfrom the relationship between the rain rates and the CLW
path when there is a lack of ice structure (Tb89 < Tb150)
and the rain rate derived from the IWP is greater than orequal to 0 mm h−1 and the CLW path is greater than thethreshold value of 0.4 kg m−2.
2.2.2. GSMaP Algorithm for a Microwave Sounder(GSMaP_AMSU)[14] GSMaP_AMSU is a precipitation algorithm developed for an MWS and based on the GSMaP algorithm for an
MWI [Shige et al., 2009]. Like the GSMaP algorithm for an
MWI, GSMaP_AMSU consists of a forward calculation partto calculate LUTs giving the relationship between the rainfall

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2001 [Shimizu et al., 2009]. Before processing the precipitation retrieval, each pixel is classified into one of three raincategories (“rain certain,” “rain possible,” and “no rain”).
The labeling of pixels as rain possible often results fromnoise, and the current standard product 2A25 does notprovide rainfall retrievals for rain possible pixels because of
the serious contamination of no rain pixels among rain
possible pixels [Takahashi and Iguchi, 2008]. Therefore, the2A25 algorithm underestimates rain coverage.

Figure 1. Schematic of the conversion of the rainfall ratefrom a footprint to a grid.
rate and Tb using a developed RTM and a retrieval part toestimate precipitation rates from the observed Tb values using
the LUTs.
[15] In the retrieval part, a rain pixel is classified before
the rainfall rate is estimated. The method of RNC over ocean
comprises two processes. In the first process, an emission
signature at 31 GHz is used to detect warm rain from clouds
that lack the ice phase. The condition for the determinationof a rainy pixel in the emission‐based method isTb31 > Tb31LUT0

ð2Þ

where Tb31LUT0 is Tb31 at 0 mm h−1 in the LUT. In thesecond process, an SI is computed from Tb89 and Tb150:
SI ¼ ðTb89  Tb89LUT0 Þ  ðTb150  Tb150LUT0 Þ

ð3Þ

where Tb89LUT0 and Tb150LUT0 are Tb89 and Tb150 at
0 mm h−1 in the LUTs, respectively. Because scattering fromice lowers Tb and the Tb reduction is greater at 150 GHz thanat 89 GHz, the condition for the determination of a rainy
pixel is2.4. Ground‐Based Radar[18] GV data used in this study are the official TRMM
2A53 rain products for Kwajalein (KWAJ) produced by the
TRMM Satellite Validation Office and available from the
Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The 2A53 product provides theinstantaneous rain rate at 2 km × 2 km horizontal resolution,
extending 150 km from the respective GV radar. In thisstudy, the GV radar data were processed using the official
Version 7 algorithm of the 2A53 product. The algorithm
applies the window probability matching method (WPMM)[Rosenfeld and Wolff, 1995] to the statistical determinationof rain rates from radar reflectivities. The GV processing
system and descriptions of the GV algorithms are detailed in
Wolff et al. [2005]. In the present study, the 2 km data wereaveraged over 0.1 degree grids for comparison with thesatellite estimates.
2.5. Conversion to Gridded Data
[19] To compare the rain estimates at the same resolution,
we remapped each rain estimation derived from the MWI
and MWS into a 0.1 degree global grid. When the center ofa gridbox is included by a single footprint, the gridbox isassigned with the rainfall rate of the footprint. On the other
hand, when the center of a gridbox is included by multiple
footprints, the rainfall rate within the gridbox is averaged
according to the distance from the center of a footprint. The
weighted average rainfall rate in a grid is defined by
X
Rgrid ¼i

Wi  Ri
X
;
Wi

ð5Þ

i

SI > 0:ð4Þ

in which(  2 )!
xi 2
yWi ¼ exp  lnð2Þ 
þ i
i
i

−2

[16] A cloud layer with a CLW path of 0.5 kg m below
the freezing level is arbitrarily assumed by GSMaP_AMSU
as well as GSMaP_TMI. In this study, over the region whereshallow rain is predominant, the parameterization of the CLW
path given by equation (1) is applied to GSMaP_AMSU.
2.3. Satellite‐Borne Radar[17] The current (version 6) level 2 standard product 2A25
[Iguchi, 2007; Iguchi et al., 2009] is used for the rain estimation of the PR. The 2A25 algorithm estimates the true
effective reflectivity factor Ze at 13.8 GHz in each radar
resolution cell from the vertical profiles of the measured
reflectivity factor Zm. The rainfall rate is then calculated
from the estimated Ze. The PR has a minimum detectablereflectivity of 17 dBZ, which corresponds to approximately
0.7 mm h−1 after the TRMM satellite orbit boost on August

ð6Þ

where i is the number of the footprint and Ri is the rainfall
rate of the footprint i of the MWRs. A position of (xi, yi)
indicates a position of the center of the gridbox in a system of
coordinates with their origin at the center of the footprint i.
The MWR footprint takes the form of an ellipse, and thelong‐axis direction and short‐axis direction are defined as thex‐direction and y‐direction (Figure 1). We employ a shortaxis sx and long axis sy of 3.6 and 2.3 km for the TMI, which
are half the footprint dimensions for the 85 GHz channel,
which has the smallest footprint of any channel used in thealgorithm. The AMSU footprint size varies for each scanangle because the AMSU‐B/MHS radiometer employs
cross‐track scanning to view the Earth. Therefore, we

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Figure 2. Variation of the lengths of the short axis sx andlong axis sy as functions of the off‐nadir angle for AMSU.
employ variable FOVs of 16 km × 16 km at nadir and
52 km × 27 km at limb for the AMSU‐B/MHS (Figure 2),
from which the precipitation products for MWSs are
retrieved.
[20] The 2 km data from the Kwajalein radar and the 5 km
data from the PR were simply averaged over a 0.1 degree
grid because the resolutions of the Kwajalein radar data andPR data are finer than the 0.1 degree grid.

3. Results
3.1. Comparison With the Rain Fraction Measured
at the Kwajalein Radar Site[21] The rain fraction f is defined by
f ¼

Nrain
Nall

ð7Þ

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mitigated the temporal sampling errors with respect to thenoncontiguous sampling of the satellites as a source of
uncertainty. The rain fractions of the KR estimates range from
24.9% to 26.2%. The rain fractions of the satellite estimatesare 10.5% for GSMaP_AMSU, 7.6% for NOAA_AMSU,
15.6% for GSMaP_TMI, 15.0% for GPROF_TMI, and12.9% for the PR. Because the Kwajalein radar, which has
finer horizontal resolution (2 km) and higher sensitivity
than the satellites, can detect isolated convective rain and alarge area of stratiform rain having a low rain rate [Schumacherand Houze, 2000], the rain fractions estimated by the satellites
are half those of the KR estimates. The rain fractions of
GSMaP_AMSU and NOAA_AMSU are lower than the rain
fractions of GSMaP_TMI, GPROF_TMI, and the PR. The
rain fraction of GSMaP_AMSU is greater than that of
NOAA_AMSU, indicating that GSMaP_AMSU detects alarger rain area than NOAA_AMSU.
[23] Figure 4 shows the rain fractions as cumulative
fractions, being functions of the rain rate. The rain fractionsof GSMaP_AMSU, NOAA_AMSU, and the PR are small
for a rain rate lower than 0.1 mm h−1. This indicates that
GSMaP_AMSU, NOAA_AMSU, and the PR fail to detect
an area of light rain with a rain rate less than 0.1 mm h−1. In
the case of NOAA_AMSU, a very small rain fraction forrain rates less than 1.0 mm h−1 and a rapid increase in therain fraction around 1.0 mm h−1 are found. The rain fractionof GSMaP_AMSU increases continuously in the same way
as the rain fractions of other estimates for a rain rate greaterthan 0.1 mm h−1.
[24] Table 1 shows the cumulative rain fractions between
0.0 and 1.0 mm h−1 and between 1.0 and 10.0 mm h−1.
The rain fraction of MWSs between 0.0 and 1.0 mm h−1
are lower than those of MWIs and PR. On the other hand,
the rain fraction of MWSs between 1.0 and 10.0 mm h−1

where Nrain is the number of rain gridboxes and Nall is thenumber of gridboxes observed by the satellite. It is important to note that the rain fraction is sensitive to the spatial
scale.
[22] Figure 3 compares the rain fractions of the Kwajaleinradar estimates (KR estimates) and the satellite estimates for2007. For comparison, all satellite estimates and KR estimates were matched in both time and space, which effectively

Figure 3. Satellite estimate versus Kwajalein radar estimate of the rainfall fraction in 2007.

Figure 4. Cumulative rain fractions as functions of the rain
rate for Kwajalein radar, GSMaP_AMSU, NOAA_AMSU,
GSMaP_TMI, GPROF_TMI, and the PR.

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Table 1. Cumulative Rain Fractions Between 0.0 and 1.0 mm h−1
and Between 1.0 and 10.0 mm h−1

GSMaP_AMSU
NOAA_AMSU
GSMaP_TMIGPROF_TMI
PR

0 ∼ 1 mm h−1

1 ∼ 10 mm h−1

4.72
1.34
11.3
10.9
8.49

5.80
6.05
4.32
3.91
4.16

are slightly higher than those of MWIs and PR. This
indicates that the rain detection of MWSs is almost
comparable to those of MWIs and PR for a moderate
rain rate.
3.2. Tropical and Subtropical Ocean[25] Figure 5 shows the rain fractions of GSMaP_AMSU,NOAA_AMSU, GSMaP_TMI, GPROF_TMI, and the PR
for a 2 degree longitude‐latitude box. In the Intertropical
Convergence Zone (ITCZ) and South Pacific Convergence
Zone (SPCZ), where there are generally large rain amounts,
high rain fractions (∼10%) for all MWRs are found. On theother hand, in subtropical oceans, where the mean descending branch of the meridional Hadley cell suppresses

D24101the convection of clouds, low rain fractions (∼6%) arefound. The rain fractions over ocean between 30°S and 30°N
are 10.1% for GSMaP_AMSU, 7.2% for NOAA_AMSU,
13.3% for GSMaP_TMI, 13.4% for GPROF_TMI, and9.3% for the PR. The rain fractions of GSMaP_AMSU are
between the rain fractions of the PR and TMI, while the rain
fraction of NOAA_AMSU is the smallest. On the other
hand, the rain amounts over ocean between 30°S and 30°N
are 79.0 mm month−1 for GSMaP_AMSU, 82.8 mm month−1
for NOAA_AMSU, 82.3 mm month−1 for GSMaP_TMI,
80.6 mm month−1 for GPROF_TMI, and 77.9 mm month−1
for PR. The rain amount of GSMaP_AMSU is lower thanthose of MWIs, while the rain amount of NOAA_AMSU ishigher than those of MWIs. Figure 6 shows the zonal averaged rain fractions of GSMaP_AMSU, NOAA_AMSU,
GSMaP_TMI, GPROF_TMI and PR. The rain fractions of
all products are at their peak around 7°N. The rain fractions of GSMaP_AMSU and NOAA_AMSU have bias lower
than that of MWIs over tropical and subtropical oceans. The
comparison between GSMaP_AMSU and PR shows theexcellent agreement of the rain fraction from 30°S to 2°N.
On the other hand, the rain fraction of GSMaP_AMSU islarger than that of PR in the region between about 2°N and
12°N and lower in the region between about 12°N and

Figure 5. Rain fractions for (a) GSMaP_AMSU, (b) NOAA_AMSU, (c) GSMaP_TMI, (d) GPROF_TMI,
and (e) the PR in a 2 degree latitude‐longitude box.
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D24101Figure 6. Zonal averaged rain fractions for GSMaP_AMSU, NOAA_AMSU, GSMaP_TMI, GPROF_TMI,
and PR.
28°N. Although the global averages for GSMaP_AMSU
and PR are similar, regional differences exist. Figures 7a,7b, 7c, and 7d show differences in the rain fractions
between the MWS and MWI. The rain fraction of
GSMaP_AMSU is lower than that of the MWI in parts of
the ITCZ and SPCZ. On the other hand, the rain fractionof NOAA_AMSU is much lower than that of the MWI
over tropical and subtropical ocean. This result is consistent with the result obtained at the Kwajalein radar site
(Figure 3). Figures 7e and 7f show the difference in therain fraction between the MWS and PR. While the rain
fraction of GSMaP_AMSU is close to that of the PR overFigure 7. Difference in the rain fraction between (a) GSMaP_AMSU and GSMaP_TMI, (b) NOAA_AMSU and GSMaP_TMI, (c) GSMaP_AMSU and GPROF_TMI, (d) NOAA_AMSU and GPROF_TMI,(e) GSMaP_AMSU and PR, and (f) NOAA_AMSU and the PR.
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D24101Figure 8. The same as Figure 5 but the rain fractions of the satellite estimates for a rain rate less than
1.0 mm h−1.

the western ITCZ, over the eastern ITCZ, the rain fractionof GSMaP_AMSU is greater than that of the PR. Precipitation systems over the eastern Pacific exhibit a numberof significant differences when compared with those over thewestern Pacific warm pool [Berg et al., 2002]. Shige et al.
[2008] hypothesized that the PR underestimates the rainfall
rate because of the higher concentration of smaller raindrops
in clouds over the eastern Pacific than over the western
Pacific. The high concentration of small raindrops in clouds
over the eastern Pacific may lead to the failure of the PR todetect rain, resulting in the low rain fraction recorded by the
PR over the eastern ITCZ. Over subtropical ocean such as
the edge of the SPCZ, the rain fraction of NOAA_AMSU
is lower than that of the PR, but the rain fraction of
GSMaP_AMSU is comparable to that of the PR.
[26] Figure 8 shows the rain fraction of the satellite estimates for a rain rate lower than 1.0 mm h−1. The rain
fractions over ocean between 30°S and 30°N are 5.0% for
GSMaP_AMSU, 1.3% for NOAA_AMSU, 9.1% for
GSMaP_TMI, 9.4% for GPROF_TMI, and 5.8% for the
PR. The rain fraction of GSMaP_AMSU is comparable to
the rain fraction of the PR and is half the rain fractions of
GSMaP_TMI and GPROF_TMI. On the other hand, therain fraction of NOAA_AMSU is the lowest of all rain

fractions and is small (∼4%) over tropical and subtropicaloceans. This result shows that NOAA_AMSU misses lightrain with a rain rate lower than 1.0 mm h−1 over tropical andsubtropical oceans.
[27] Figure 9 presents the case that the AMSU observations match the PR observation at the edge of the SPCZ
(36.2°S–6.2°S, 125.5°W–95.5°W). The time lag between
the PR and AMSU is about 470 seconds. In this case, thePR observes scattered rain (Figure 9a). GSMaP_AMSU
can detect scattered shallow rain in the middle of theswath (Figure 9b), while NOAA_AMSU misses the scatteredshallow rain (Figure 9c). Figure 9d shows the freezing height
derived from GANAL on a 1.25‐degree grid and the rain top
height derived from PR2A25. The rain top observed by the
PR is below the freezing height in this case. In this region,
because the mean descending branch of the meridional
Hadley cell suppresses the convection of clouds, shallow rain
is predominant. Figure 9e shows the CLW retrieved from
AMSU‐A channels. Over the region including the scatteredshallow rain, a CLW path of about 0.25 kg m−2 is found. To
detect shallow rain, NOAA_AMSU uses the CLW path asa proxy for retrieving rain and employs a high‐CLW pathof 0.4 kg m−2 as a threshold value of RNC to avoid false
signatures in moist, nonraining clouds and uncertainty in

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D24101Figure 9. Case in which the AMSU observation matches the PR observation on 20 September 2007
(TRMM orbit number 56103) at the edge of the SPCZ (36.2°S–6.2°S, 125.5°W–95.5°W). Rain derived
from (a) PR, (b) GSMaP_AMSU and (c) NOAA_AMSU. (d) Freezing height derived from GANAL in a
1.25 degree grid and rain top height derived from PR. (e) CLW path derived using the NOAA_AMSU.
The thick lines are the edge of the AMSU swath, and the thin lines are the edge of the PR swath.
the AMSU‐A CLW calculations [Vila et al., 2007].
Therefore, the threshold value of RNC is higher than the
CLW path over the region including the scattered shallow
rain, which leads to no‐rain classification.

[28] Figure 10 shows the result of RNC for GSMaP_
AMSU with different RNC threshold values of the CLW
path. In GSMaP_AMSU, each footprint is separated into
four RNC categories using equations (2) and (4). RN0 is

Figure 10. Result of rain classification in GSMaP_AMSU with (a) parameterization of the CLW path as afunction of the storm height, which is typically 0.25 kg m−2, and (b) 0.5 kg m−2 CLW. RN0 is classified as ano‐rain pixel by equations (2) and (4), RN1 is classified as a rain pixel only by equation (2), RN2 is classified
as a rain pixel only by equation (4), and RN3 is classified as a rain pixel by equations (2) and (4).
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D24101that Tb89 for 0.25 kg m−2 CLW represents the rain rateunambiguously because Tb89 for the CLW of 0.25 kg m−2
in the LUT increases more rapidly with rainfall. On theother hand, Tb150 varies only slightly for a rain rate lessthan 2.0 mm h−1 and decreases above 2.0 mm h−1 becauseof scattering from ice. Thus, although the SI is designed on
the basis that ice scattering lowers Tb and increases rapidly
with frequency, there are emission and scattering regimes
and the SI also responds to emission in light rain with a
low CLW path. As a result, the light rain pixel can bedetected using the SI to take advantage of the emission
signature from raindrops at 89 GHz.

4. Summary

Figure 11. Brightness temperature as a function of therain rate. The solid line is the brightness temperature at
89 GHz, and the dashed line is the brightness temperature
at 150 GHz. The dashed‐dotted line shows the SI computed by equation (3). The different colors indicate different RNC threshold values of CLW: blue lines are for0.25 kg m−2, red lines for 0.50 kg m−2, and green lines for0.75 kg m−2.
the classification of a no‐rain pixel according to equations (2)and (4), RN1 is the classification of a rain pixel according to
only equation (2), RN2 is the classification of a rain pixel
according to only equation (4), and RN3 is the classificationof a rain pixel according to equations (2) and (4). For
GSMaP_AMSU with the parameterization of the CLW path
as a function of storm height [Kida et al., 2009], which istypically 0.25 kg m−2 in this region, the scattered rain isclassified as RN2, which is derived only from the SI(Figure 10a). For GSMaP_AMSU with a threshold of 0.5 kg
m−2 for the RNC, which is used in the original version of
GSMaP_AMSU, the scattered shallow rain is classified as norain (Figure 10b). The emission‐based method [equation (2)]that uses the AMSU‐A channels fails to detect scatteredshallow rain because the scattered shallow rain is less than
the resolution of the AMSU‐A channels used.
[29] Figure 11 shows the LUT for Tb89 and Tb150 withthree different CLW thresholds (0.25, 0.5, and 0.75 kg m−2).
Tb89 increases with the rain rate below 2.0 mm h−1 becauseof the emission from the raindrops and is saturated at about
2.0 mm h−1. Here, “diff_Tb89LUT” is defined asdiff Tb89LUT ¼ Tb89LUTsat  Tb89LUT0

ð8Þ

where Tb89LUTsat is Tb89 at saturation in the LUTs.
diff_Tb89LUT is large for the CLW threshold of 0.25 kg m−2,which is found in the shallow rain over subtropical ocean(Figure 9e), and small for 0.75 kg m−2 because the emission signature is weaker for 0.25 kg m−2 CLW than for
0.75 kg m−2 CLW. A larger value of diff_Tb89LUT for0.25 kg m−2 CLW than for 0.75 kg m−2 CLW indicates[30] In this study, we compare the fractional occurrence of
precipitation (rain fraction) obtained using two microwave
precipitation algorithms for MWSs.
[31] At the Kwajalein radar site, the rain fractions ofsatellite‐based estimates are not even half the rain fractionsof Kwajalein radar estimates (KR estimates) because theKR, which has finer horizontal resolution and higher
sensitivity, can detect isolated convective rain and a large
area of stratiform rain with a low rain rate. The rain
fractions of GSMaP_AMSU and NOAA_AMSU are lower
than the rain fractions of MWI algorithms (GSMaP_TMI
and GPROF_TMI) and the PR. Small rain fractions of
GSMaP_AMSU, NOAA_AMSU, and the PR result fromthe failure to detect the light rain area with a rain rate lessthan 0.1 mm h−1. For NOAA_AMSU, there is a very smallrain fraction for rain rates less than 1.0 mm h−1 and a rapid
increase in the rain fraction around 1.0 mm h−1. The rain
fraction of GSMaP_AMSU increases continuously in thesame way as the rain fractions of other estimates for a rain
rate greater than 0.1 mm h−1.
[32] In the comparison of the rain fractions between the
MWS and MWI over tropical and subtropical oceans, therain fractions of GSMaP_AMSU and NOAA_AMSU havebias lower than that of MWIs over tropical and subtropicaloceans. The rain fraction of GSMaP_AMSU is lower thanthat of the MWI in parts of the ITCZ and SPCZ. On theother hand, the rain fraction of NOAA_AMSU is much
lower than that of the MWI over tropical and subtropicalocean. The rain fraction is lower for NOAA_AMSU than for
GSMaP_AMSU because NOAA_AMSU fails to detect thelarge rain area with a rate less than 1.0 mm h−1. In thecomparison of the rain fractions between the MWS and PR,
although the global averages for GSMaP_AMSU and PR
are similar, the rain fraction of GSMaP_AMSU is larger
than that of the PR in the eastern ITCZ, possibly because thePR algorithm fails to detect the rain owing to the higher
concentration of smaller raindrops in clouds over the eastern
Pacific. On the other hand, over subtropical ocean such as
the edge of the SPCZ, the rain fraction of NOAA_AMSU islower than that of the PR.
[33] In the case of the edge of the SPCZ, where the PR
observes scattered shallow rain, while NOAA_AMSU fails
to detect the scattered rain, GSMaP_AMSU detects some ofthe scattered rain. For light rain, Tb89 increases with the rain
rate below 2.0 mm h−1 because of the emission from thecloud and raindrops and is saturated at about 2.0 mm h−1.
A larger diff_Tb89LUT for 0.25 kg m−2 CLW than for

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0.75 kg m−2 CLW indicates that Tb89 for 0.25 kg m−2
CLW represents the rain rate unambiguously. Therefore,
although the SI is designed on the basis that Tb decreases in
response to scattering by precipitation at these frequencies
and increases strongly with frequency, there are emission andscattering regimes and the SI also responds to emission inlight rain with a low CLW path. As a result, the light rain
pixel can be detected using the SI to take advantage of theemission signature from raindrops. Therefore, it is suggested
that the MWS retrievals over ocean are incorporated intohigh spatial and temporal resolution precipitation products,
although the MWS retrievals over ocean are not considered
to be included in the baseline GPM sampling [Hou et al.,2008].
[34] In this study, we compare the rain fractions overtropical and subtropical ocean. On the other hand, the rain
fractions outside tropical and subtropical ocean have notbeen compared. However, in the future, there will be more
opportunities to make observation of higher latitudes fromsatellite in the GPM Mission [Smith et al., 2007], so the rain
fraction at higher latitudes is important. Thus, we willcompare the rain fractions outside tropical and subtropicalocean in future work.
[35] Acknowledgments. This study is supported by the Mitsui & Co.,
Ltd., Environment Fund and by the Japan Aerospace Exploration Agency/
Earth Observation Research Center (JAXA/EORC) TRMM project. The
authors thank Professor K. Okamoto (Tottori University of Environmental
Studies) for valuable comments. The authors also thank Dr. G. Liu for providing the RTM code.

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S. Kida and S. Shige, Graduate School of Science, Kyoto University,
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