Click
Here

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, D15116, doi:10.1029/2007JD009002, 2008

for

Full
ArticleValidation of western and eastern Pacific rainfall estimates fromthe TRMM PR using a radiative transfer model
S. Shige,1 T. Watanabe,1 H. Sasaki,1 T. Kubota,1,2 S. Kida,1 and K. Okamoto1
Received 18 May 2007; revised 7 March 2008; accepted 24 March 2008; published 8 August 2008.

[1] Rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM)

Precipitation Radar (PR) facility algorithm over the eastern Pacific are lower than thosefrom the TRMM Microwave Imager (TMI) facility algorithm and a PR-consistentadvanced microwave radiometer algorithm, while they are in good agreement over thewestern Pacific. We investigate the consistency between TMI-observed brightness
temperatures (TBs) for the 19 GHz channel and those simulated from the PR rainfall
estimates using a radiative transfer model. Discrepancies between observed TBs andsimulated TBs from the PR are larger for the eastern Pacific than for the western
Pacific, indicating that PR underestimates rainfall over the eastern Pacific. It ishypothesized that drop size distributions (DSDs) in the eastern Pacific have stronger
maritimity (i.e., more small to medium sized raindrops) than the initial DSD model ofthe PR algorithm, representative of the ‘‘data-rich’’ western Pacific, leading tounderestimation of rain by the PR. Differences in the adjustable parameter of the PR
algorithm, implying changes in the DSD, and the vertical structure of PR-observedreflectivity over the western and eastern Pacific support the hypothesis.
Citation: Shige, S., T. Watanabe, H. Sasaki, T. Kubota, S. Kida, and K. Okamoto (2008), Validation of western and eastern Pacificrainfall estimates from the TRMM PR using a radiative transfer model, J. Geophys. Res., 113, D15116, doi:10.1029/2007JD009002.

1. Introduction[2] The Tropical Rainfall Measuring Mission (TRMM)[Simpson et al., 1988, 1996; Kummerow et al., 2000] is thefirst satellite to carry both active and passive microwave
sensors for measuring precipitation, leading to significantly
better and more consistent rainfall estimates throughout theTropics. The TRMM Precipitation Radar (PR) is the firstspace-borne precipitation radar and provides height information based upon the time delay of the precipitationbackscattered return power and allows vertical profiles ofprecipitation to be obtained directly over the global Tropics[Kozu et al., 2001; Okamoto, 2003]. The TRMM Microwave
Imager (TMI) measures radiances that are the end product of
the integrated effects of electromagnetic absorption/emission
and scattering through a precipitating cloud along the sensor
viewpath.
[3] By comparison, the more direct measurement ofhydrometeors by the PR seems to have less uncertainty.
The PR estimates, therefore, have been commonly used asa reference to validate rainfall estimates from passive
microwave imagers [e.g., Yang et al., 2006; Kubota etal., 2007]. The PR operates at a single frequency of
13.8 GHz so that the PR2A25 algorithm corrects theattenuation in the measured radar reflectivity factor Zm
1
Department of Aerospace Engineering, Osaka Prefecture University,
Sakai, Osaka, Japan.
2
Japan Science and Technology Agency, Kawaguchi, Saitama, Japan.

Copyright 2008 by the American Geophysical Union.
0148-0227/08/2007JD009002$09.00

and estimates the attenuation-corrected radar reflectivity
factor Ze [Iguchi and Meneghini, 1994; Iguchi et al.,2000]. The attenuation correction is based on a hybrid
method that provides a smooth transition between the
Hitschfeld –Bordan [Hitschfeld and Bordan, 1954] method,which performs well at low attenuations, and the surface
reference technique [Meneghini et al., 2000], for which therelative error decreases with increasing path-integrated
attenuation (PIA). This method is termed the ‘‘a-adjustment’’ method [Iguchi and Meneghini, 1994]. The coefficient a in the kp  Ze relationship.
kp ¼ aZeb

ð1Þ

is adjusted in such a way that the PIA estimated fromthe H-B method matches the SRT estimate of PIA where kp is
the specific attenuation due to precipitation. Althoughspecific attenuation in precipitation at TRMM PR frequencies is predominantly due to absorption and is roughly
proportional to the volume of rain, the reflectivity is
proportional to the sixth power of raindrop diameter, andthus sensitive to variant properties of the drop size
distribution (DSD), which is the greatest weakness of
the PR.
[4] The differences between PR2A25 [Iguchi et al., 2000;
Iguchi, 2007] and TMI2A12 [Kummerow et al., 2001;
Olson et al., 2006] standard rainfall products over theeastern Pacific are well known [Berg et al., 2002, 2006].
The most common method of verifying satellite-retrieved
rain estimates is to directly compare to ground validation
measurements (ground truth) derived from rain gauge net-

D15116

1 of 9

D15116

SHIGE ET AL.: VALIDATION OF TRMM PR RAINFALL ESTIMATES

D15116

Figure 1. Rainfall estimates from version 6 of (top) PR2A25 and (middle) TMI2A12 retrieval
algorithms for the 3-month period from SON 2000. (bottom) Rainfall difference map between version 6of the PR2A25 and TMI2A12 retrieval algorithms for the 3-month period from SON 2000. The maps
were created from the 3G68 data set, which matches the instantaneous PR2A25 and TMI2A12 estimateson a 0.5°  0.5° grid. Boxes indicating the locations of (a) the selected western Pacific (2.5° – 12.5°N,
130°– 160°E) and (b) the eastern Pacific (5.0° –15°N, 120°– 150°W) are also shown.
works, ground weather radar, or a combination of the two.
However, precipitation has been poorly observed over theoceans owing to sparse observations. In particular, very few
in situ measurements of rainfall are available for the eastern
Pacific intertropical convergence zone (ITCZ) [Janowiak etal., 1995]. It has also proven to be extremely difficult tomake accurate ground-based measurements of precipitationfrom ships [e.g., Yuter and Parker, 2001]. Thus we must
rely on indirect strategies for validation. Smith and Hollis[2003] evaluated and validated the TRMM facility rain
profile algorithms by an algorithm-to-algorithm intercomparison analysis in the context of physical hypothesis
testing. Berg et al. [2006] have attempted to identify
globally observable physical variables that can be used asa proxy for PR/TMI differences and, by association, thecorresponding regime-dependent cloud properties. Shige etal. [2006, hereinafter SAL] investigated the consistency
between TMI-observed brightness temperatures (hereinafterTBs) and those simulated from PR2A25 V5 and V6 rain
profiles for the ITCZ precipitation systems during the 1997/
1998 El Niño event when there were large discrepancies
between the TRMM version-5 rainfall products [Robertson

et al., 2003]. Simulated TBs at 10 GHz from PR V6 havebetter agreement with observed TBs than do TBs from PR
V5, implying algorithm improvements. However, discrepancies at 19 GHz suggest that uncertainty in the assumed
DSD still remains in PR V6.
[5] In this study, the previous work of SAL is extended tovalidate PR2A25 V6 over the western and eastern Pacificduring a normal year using independent TMI-observed TB
data.

2. Approach[6] The present study uses the same approach as thatshown in Figure 1 of SAL. The difference is that SAL
examined the consistency between TMI-observed TBs andthose simulated at 10 and 19 GHz, while this work
examines only the consistency at 19 GHz. Because thepopulation of heavy rainfall is very small during a normal
year, comparing radiative characteristics at 10 GHz, which
implies validating heavy rain, is meaningless. While SAL
investigated the consistency between TMI-observed TBs
and those simulated from PR2A25 V6 and TMI2A12 V6rain profiles, this study only investigates the consistency

2 of 9

D15116

SHIGE ET AL.: VALIDATION OF TRMM PR RAINFALL ESTIMATES

D15116

Figure 2. Time series of monthly mean rainfall estimates from PR2A25, TMI2A12, COMB2B31 and
GSMaP_TMI for (a) the western Pacific and (b) the eastern Pacific.
between TMI-observed TBs and those simulated from
PR2A25 V6 rain profiles. It was shown by SAL that the
19 GHz channel significantly contributes to the rain retrievalof the TMI2A12 algorithm, and thus comparing TMIobserved TBs to those simulated from TMI2A12 V6 rain
profiles is meaningless as a validation.
[7] The four-stream plane-parallel radiative transfer
model (RTM) developed by Liu [1998] is used to calculate
TBs. RTM calculations are performed for each 4 km  4 km
PR pixel before the TBs are averaged for the radiometerfield of view (FOV). The antenna pattern for the 19 GHz
channel is approximated by a Gaussian weighting function
with the same 3-dB beam width as the actual antenna pattern,taking into account the rotation of the TMI antenna. Allradiance slant paths through the three-dimensional domain
are considered independently. Horizontal displacement in
the direction of sight is caused by signal contributions
from layers other than those near the surface that are
projected onto the footprint of the observation [Baueret al., 1998]. Thus at frequencies (37 and 85 GHz) sensitive
to ice particle scattering the projection effect is mostsignificant. On the other hand, the 19 GHz channel issensitive to rainfall at lower-levels, and the FOV for the
19 GHz channel is larger than those of higher frequency
channels. Hence the slant-path approximation produces
only slightly higher simulated TBs from PR2A25 rain
profiles, resulting in better agreement with TMI-observed
TBs.
[8 ] Column water vapor (CWV), liquid water path
(LWP), sea surface wind, and sea surface temperature(SST) are adopted from the TMI products provided by
Remote Sensing Systems, while mean atmospheric temperature from the 6-hourly ERA40 reanalysis data with minimum time difference with the TRMM observations isemployed. For PR data, we use two DSD models of a
gamma distribution corresponding to convective and stratiform rain assumed in PR algorithm [Iguchi et al., 2000]. Achange in the DSD due to a variation in a accommodating
the SRT estimate of PIA is also taken into account. For PR
stratiform rain profiles with a bright band, the melting-layer
parameterization of Klaassen [1990] is used, following Kim
et al. [2004]. Details on the simulation setting were provided in SAL and Sasaki et al. [2007]. Sasaki et al. [2007]
showed the effects of using different DSD models on
simulated brightness temperature at 19 GHz are negligibly
small, while those at 10 GHz are considerably large, whichis consistent with the results from Coppens and Haddad[2000] that show the effect of varying the DSD is smallest

between 16 and 19 GHz. Thus validation using the simulation of 19 GHz radiances from PR rain profiles, which aremainly a function of water content, is robust.
[9] For comparison, the emission index (EI) is defined as
1  P and used instead of TB to isolate the microwave
signal due to precipitation particles [Grecu et al., 2004].
Here, P is the normalized polarization difference [Petty,
1994] given by
P¼

TBV  TBH;
TBV ;clear  TBH;clear

ð2Þ

where TBV and TBH are the vertically and horizontally
polarized brightness temperatures and TBV,clear and TBH,clear
are background brightness temperatures in the absence ofcloud or precipitation. For the observed P, the backgroundbrightness temperatures are estimated following the methodof TMI 2A12 V6 [Olson et al., 2006]. For the simulated P,
the background brightness temperatures are calculated withall cloud and precipitation water contents set to zero. The EI
is zero for a zero rain rate and increases with rain rate.

3. Results[10] Figure 1 shows a comparison of rainfalls fromversion 6 of the TRMM PR2A25 and TMI2A12 standard
products over the Pacific for the period from September toNovember (SON) 2000. Two regions in the ITCZ wereselected for comparison. One region is over the western
Pacific warm pool and the other encompasses the region of
maximum differences between PR and TMI estimates overthe eastern Pacific as shown in Figure 1. Both selected
regions are of the same dimensions and are over ocean-onlyareas in the ITCZ. The figure clearly shows large differences in eastern Pacific ITCZ rainfall, with a negative bias
for PR estimates relative to the TMI estimates.
[11] Time series are shown in Figure 2, indicating there
are larger biases for the selected eastern Pacific than for theselected western Pacific during the period from January
1998 through July 2001. Increasing bias was found after
August 2001 when the TRMM satellite was boosted from350 km to 402.5 km to conserve fuel. The effects of theboost on an active sensor, PR, should be larger than thoseon a passive sensor, TMI, but an investigation is beyond thescope of the study. The differences between PR and TMI
estimates for October 2000 are 6% over the western Pacificand 29% over the eastern Pacific, respectively. The difference over the eastern Pacific is larger than the difference

3 of 9

D15116

SHIGE ET AL.: VALIDATION OF TRMM PR RAINFALL ESTIMATES

D15116

Figure 3. Histogram showing sum of rain rates at 0.5° resolution from PR2A25, TMI2A12,
COMB2B31 and GSMaP_TMI for SON 2000 over (a) the western Pacific and (b) the eastern Pacific.
over the central Pacific during the 1997/1998 El Niño(February 1998) event, which was found to be with 11%
by SAL. TMI2A12 estimates greater rainfall over theeastern Pacific than over the western Pacific, while
PR2A25 estimates slightly less rainfall over the eastern
Pacific than over the western Pacific. Such discrepancies
in west-east gradients of rainfall are crucial for ENSO
indices based on the zonal gradient of rainfall in theequatorial Pacific [Curtis and Adler, 2000].
[12] The basis of the TMI 2A12 algorithm is based on a‘‘Bayesian’’ framework, in which retrieved precipitation isconstructed from those cloud-resolving model (CRM)generated profiles that are radiatively consistent with theobservation [Kummerow et al., 1996, 2001]. Although thecloud-radiative model database supporting the TMI2A12
V6 algorithm has been expanded, the database consists of
only six simulations [Olson et al., 2006, Table 2]. The
shallower convective events common to the eastern Pacificmay not be represented in the cloud-radiative model database supporting the TMI2A12 V6 algorithm, leading to an
overestimation of rain in the eastern Pacific.
[13] As references, time series of rainfall estimates using
two other algorithms are also shown in Figure 2. One is the
TRMM combined PR and TMI 2B31 (hereinafter
COMB2B31) algorithm [Haddad et al., 1997]. Over both
the western and eastern Pacific, COMB2B31 estimates arelarger than PR2A25 estimates. Although theoretically theradar-radiometer estimates may lead to better results than
the radar-only estimates, PR2A25 products rather than
COMB2B31 products have been used in a great manystudies. One possible limitation of the COMB2B31 algorithm, which was designed primarily with operational goals
for TRMM, resides in the fact that the TBs associated with agiven reflectivity profile are not explicitly calculated using
physical models, but are estimated based on a priori
statistical relationships. Consequently, the errors in theestimated TBs may be larger than those derived from aphysical model.
[14] The other algorithm is the Global Satellite Mapping
Project (GSMaP) rain-retrieval algorithm [Kubota et al.,2007], which is a PR-consistent advanced microwave radiometer algorithm based on the study by Aonashi and Liu[2000]. The basic idea of the GSMaP algorithm is to findthe optimal rainfall that gives RTM FOV-averaged TBs that

fit best the observed TBs. Using RTM, lookup tables
showing the relationship between rainfall rates and TBs
were computed daily in 5° 5° latitude-longitude boxes,
with atmospheric variables (freezing level height, temperature, relative humidity, surface winds and surface temperature) given by the Japan Meteorological Agency (JMA)
Global Analysis (GANAL). RTM calculations use a trimonthly database of precipitation types and profiles in
2.5  2.5° latitude-longitude boxes derived from thePR2A25 data, together with the melting-layer model usedin the PR2A25 algorithm. Thus compared with the supporting databases of the TMI2A12 algorithm, those of the
GSMaP algorithm are more consistent with naturally occurring profiles at the time/location where the algorithm isapplied. Here, the current version ‘‘4.8.4’’ of the GSMaP
algorithm for TMI (hereinafter GSMaP_TMI) was used. In
spite of differences in the supporting databases and theprocess of retrievals from TBs, the GSMaP_TMI estimates
are similar to the TMI2A12 estimates in that they are larger
than PR estimates over the eastern Pacific, while they are ingood agreement over the western Pacific.
[15] The histogram in Figure 3 shows the sum of rain
rates during SON 2000 from PR2A25, TMI2A12,
COMB2B31 and GSMaP_TMI at 0.5° resolution. Though
the TMI2A12 estimates are smaller than PR2A25 estimatesat very light rain rates (0 mm h1 < R  1 mm h1) and areslightly larger than PR2A25 estimates at light-to-moderate
rain rates (3 mm h1 < R  10 mm h1), the histogramshows good agreement between TMI2A12 and PR2A25
over the western Pacific. The difference at very light rain
rates is probably due to the sensitivity of the TMI2A12
algorithm, which is limited primarily by the difficulty of
distinguishing precipitating from nonprecipitating clouds.
On the other hand, the sum of rain rates in the range
2 mm h1 < R  10 mm h1 is much larger for TMI2A12
than for PR2A25 over the eastern Pacific. While thedifference in the sum of rain rates in the range
2 mm h1 < R  10 mm h1 between TM2A12 andPR2A25 is 1453.62 mm month1 over the western Pacific,
the difference is 3829.17 mm month1 over the eastern
Pacific, resulting in variability of the biases across the
Pacific. COMB2B31 estimates are larger than PR2A25
estimates below 5 mm h1, while COMB2B31 estimates
are smaller than PR2A25 estimates above 5 mm h1, over

4 of 9

D15116

SHIGE ET AL.: VALIDATION OF TRMM PR RAINFALL ESTIMATES

D15116

Figure 4. Frequency plots of the 19 GHz emission indexes (EIs) simulated from PR2A25 rain profilesfor (a) the western Pacific and (b) the eastern Pacific against corresponding TMI-observed 19 GHz EIs.
The average (solid line) and standard deviation (error bars) of simulated EI for each histogram bin along
the horizontal line are plotted. The dashed lines are the 1:1 lines.
both the western and eastern Pacific, perhaps reflectingthat the TBs associated with a given reflectivity profile
are estimated based on a priori statistical relationships.
The GSMaP_TMI estimates are in very good agreementwith the PR2A25 estimates over the western Pacific,
except for below 2 mm hr1. The larger negative bias
below 2 mm hr1 in the GSMaP_TMI estimates than in
the TMI2A12 estimates may be caused by the rain/norain threshold being 0.5 kg m2 of cloud water in
the GSMaP_TMI algorithm [Aonashi and Liu, 2000],
while the threshold is 0.3 kg m2 of cloud water in the
TMI2A12 algorithm [Berg et al., 2006]. On the other
hand, the sum of rain rates in the range 3 mm h1 < R 
10 mm h1 is much larger for GSMaP_TMI than for
PR2A25 over the eastern Pacific. While the difference inthe sum of rain rates in the range 3 mm h1 < R  10 mm h1
between GSMaP_TMI and PR2A25 is 52.39 mm month1
over the western Pacific, that it is 2360.18 mm month1 overthe eastern Pacific. Although the database of the GSMaP
algorithm is more consistent with PR2A25 rain profiles thanis the TMI2A12 algorithm database, a large bias is still foundover the eastern Pacific, which motivates us to validate the
PR2A25 estimates using a RTM.
[16] The procedures described in the previous section
were applied to the 3 months (SON 2000) of TRMM data,over the selected western and eastern Pacific regions. The
numbers of TRMM orbits used in this study were 770 and
776 for the western and eastern Pacific, respectively. Only
the TMI footprints for each channels of which 80% arecovered with PR rain pixels were analyzed. Frequency plotsof the 19-GHz EIs (hereafter EI19s) simulated from
PR2A25 rain profiles against corresponding TMI-observed
EI19s for the selected western and eastern Pacific regions
are presented in Figures 4a and 4b, respectively. The
simulated EI19s from PR2A25 are lower than the observed
EI19s over the western Pacific. Similar discrepancies havebeen found for precipitation systems over the central Pacificduring the 1997/1998 El Niño event by SAL. Nevertheless,the simulated EI19s from PR2A25 over the western Pacifichave much better agreement with the observed ones thanthose over the eastern Pacific do. The simulated EI19s from
PR2A25 are significantly lower than the observed EI19s
over the eastern Pacific, especially for the low-to-medium
range (smaller attenuation), resulting in an upward concave
shape with respect to the observed EI19s. This indicates that
PR2A25 underestimates rainfall over the eastern Pacific.
[17] Iguchi et al. [2000] noted the weight in the aadjustment procedure of the PR2A25 algorithm shifts
toward the SRT when attenuation is large. In such cases,
the initial value of a assumed in the PR2A25 algorithm
has little effect on the final Ze near the surface, because itis essentially determined by the measured Zm and the PIA
at the surface. As a result, the a and b coefficients of theradar reflectivity – rainfall rate (Z– R) relation R = aZb are
only weakly dependent on the initial values. Whenattenuation is small, however, the SRT perturbs the initialchoices of a and b only slightly. Therefore inappropriate
selection of the initial DSD model is responsible for theunderestimation of rain in the low-to-medium range.
[18] The initial DSD models used in PR2A25, made froma collection of Z– R relations measured near the ocean fromwidely distributed locations around the world, were representative of ‘‘maritime’’ distributions. Bringi et al. [2003]
found ‘‘maritime-like’’ and ‘‘continental-like’’ clusters of
DSD parameters by analyzing polarimetric radar and disdrometer data. ‘‘Maritime-like’’ DSDs are characterized bya higher concentration of smaller sized drops, while ‘‘continental-like’’ DSDs are characterized by a lower concentration of larger sized drops. Rosenfeld and Ulbrich [2003]
indicated that rainfall rate increases for a given reflectivity
with increasing maritimity of the clouds, by a factor of morethan three (see their Figure 10.5), which is the manifestationof major differences in the DSDs for maritime and continental clouds. The collection of Z– R relations, from which

5 of 9

D15116

SHIGE ET AL.: VALIDATION OF TRMM PR RAINFALL ESTIMATES

D15116

Figure 5. Convective  for SON 2000 obtained from PR3A25. Grids have 0.5° spatial resolution andare monthly composites of PR2A25.
the initial DSD models used in PR2A25 were made,includes Z– R relations from the ‘‘data-rich’’ western Pacific[Stout and Mueller, 1968; Tokay and Short, 1996], but not
those from the ‘‘data-poor’’ eastern Pacific. Thus it ishypothesized that the DSDs of rainfall over the eastern
Pacific have stronger maritimity (i.e., more small to medium
sized raindrops) than does the initial DSD model of
PR2A25, leading to underestimation of the rain by the
PR2A25 algorithm when the SRT perturbs the initial DSD
model only slightly.
[19] Iguchi [2007] noted that all variations in the kp  Ze
relationship (equation (1)) in PR2A25 are included in thevariation of a as
kp ðrÞ ¼ ð DSDÞa0 ðrÞZeb0 ðrÞ

ð3Þ

where b 0 is assumed to be independent of range r. The
variation in a0(r) with r includes changes due to the phasestate difference, temperature difference, and rain type
difference. The variation in a due to the changes in the
DSD is taken into account by the adjustable factor (DSD).
A value of  less than unity implies more large raindrops andfewer small drops for the same rainfall rate, while a value of greater than unity implies fewer large raindrops and more
small drops.
[20] Chandrasekhar et al. [2005] demonstrated the DSD
parameters estimated from  obtained from PR2A25 are in
fairly good agreement with DSD retrieval from coincident
dual-polarization ground data over Florida and Brazil. Theysuggested that similar comparisons over ocean would bebetter, because the SRT is inaccurate over land but much
more accurate over ocean [Meneghini et al., 2000]. Figure 5
shows a map of  for SON 2000 obtained from PR3A25,
0.5° spatial resolution monthly composite of PR2A25 data.
Because the a-adjustment procedure based on the SRT is
more useful for large attenuation scenarios,  for convective
rain is shown. It can be seen that  over the eastern Pacific islarger than unity and  over the western Pacific. This
supports the hypothesis that the DSDs of rainfall over theeastern Pacific have stronger maritimity than do the initial
DSD model of PR2A25 and DSDs of rainfall over thewestern Pacific.
[21] The RTM calculations at 19 GHz are not sensitive
to the DSD assumptions, but those at 10 GHz are very
sensitive [Coppens and Haddad, 2000; Sasaki et al.,2007]. Coppens and Haddad [2000] showed largermean-diameter DSDs (such as the Marshall and Palmer’s[1948] distribution (hereinafter MP) and the logarithmic[Feingold and Levin, 1986]) produce higher TBs at 10
GHz, whereas smaller-mean-diameter DSDs (such as thatof Sekhon and Srivastava [1971] and to a lesser extent thatof Willis and Tattelman [1989]) produce lower TBs at
10 GHz. Sasaki et al. [2007] showed that MP produces TBs
at 10 GHz simulated from PR2A25 rain profiles that are
higher than TMI-observed TBs for the ITCZ precipitation
systems during the 1997/1998 El Niño event, whereas the
PR DSD models (gamma distribution) produce TBs at
10 GHz lower than TMI-observed TBs as shown by SAL.
This is because MP assumes there are more large drops
than does a gamma distribution with a negative shapeparameter, which is downwardly concave. From the dependence of RTM calculations on the DSD, we expect rain
retrievals with larger-diameter DSDs to estimate lower rain
rates for a given brightness temperature than rain retrievalswith smaller-diameter DSDs. In fact, the version ‘‘4.7.2’’of the GSMaP algorithm using MP estimates lower rain
rates over ocean than the current version ‘‘4.8.4’’ using a
gamma distribution with negative shape parameter constructed from the  database obtained from PR2A25 (Kozu
et al., manuscript in preparation, 2008). Although it ispossible that TMI2A12 overestimates rain rates over theeastern Pacific due to factors such as rain detection
problems, any overestimation will not be due to its DSD
assumption (MP).

4. Discussions[22] To support the hypothesis that the DSDs of rainfall
over the eastern Pacific have stronger maritimity than thoseover the western Pacific, we examined differences in thevertical structure of precipitation between the western andeastern Pacific. Here, we used contoured-frequency-byaltitude diagrams (CFADs [Yuter and Houze, 1995]) of
PR2A25 V6 estimated Ze for convective rain and stratiform
rain (Figure 6).
[23] Noticeable differences in convective distributions
between the western and eastern Pacific occur at low levels.
The shallow convective mode extending up to 3 km withlow (25 dBZ) radar reflectivity is evident for the eastern
Pacific (Figure 6b), while it is less evident in the western
Pacific (Figure 6a). This mode is representative of warm

6 of 9

D15116

SHIGE ET AL.: VALIDATION OF TRMM PR RAINFALL ESTIMATES

D15116

Figure 6. Contoured-frequency-by-altitude diagrams (CFADs) of PR2A25 attenuation-corrected radarreflectivity Ze (dBZ) for convective rain over (a) the western Pacific and (b) the eastern Pacific andfor stratiform rain over (c) the western Pacific and (d) the eastern Pacific for SON 2000. The bin sizeis 1 dBZ.
rain and includes shallow, isolated echoes, which have been
reclassified as convective in V6 of the TRMM PR rain-typeclassification algorithm (2A23) [Awaka et al., 2007] after asuggestion by Schumacher and Houze [2003]. Previous
studies have noted larger populations of shallow convection
over ocean than over land [e.g., Short and Nakamura, 2000;
Takayabu, 2002]. Thus larger populations of shallow convection in the eastern Pacific suggest stronger maritimity of
convection over the eastern Pacific than that over thewestern Pacific. The difference in populations of shallow
convection results in differences in the shape of convective
heating and drying profiles between the western and eastern
Pacific estimated from TRMM PR data [Shige et al., 2007,2008].
[24] The deep convective mode possesses high (25 dBZ)
radar reflectivity at low-levels and extends to greater heights
over the western and eastern Pacific. Even for the deep
convective mode, there are important differences in the
distributions for the western and eastern Pacific. Zipser
and Lutz [1994] showed that the maritime vertical profile
of radar reflectivity has a maximum reflectivity at the lowest
level and a very rapid decrease in reflectivity with height
beginning just above the freezing level, while the continental
profiles exhibit maximum reflectivity somewhat above thesurface and have a gradual decrease in reflectivity withheight above the freezing level. The profiles over the western
Pacific have a very rapid decrease in reflectivity with height
beginning just above the freezing level, similar to themaritime vertical profiles examined by Zipser and Lutz
[1994]. On the other hand, the profiles over the eastern
Pacific have maximum reflectivity at levels lower than thosefor which there is maximum reflectivity over the western

Pacific and a very rapid decrease in reflectivity with height
beginning well below the freezing level.
[25] Zipser and Lutz [1994] hypothesized that the larger
negative vertical gradient of reflectivity above the freezing
level for the maritime profile than for the continental profile
is a direct result of the characteristically weaker updrafts
observed in maritime clouds than in continental clouds,
which is consistent with the scarcity of lightning discharges
in maritime clouds. The rapid decrease in reflectivity withheight beginning well below the freezing level over theeastern Pacific suggests weaker updrafts over the eastern
Pacific than over the western Pacific. Weaker updrafts help
promote growth by coalescence by giving droplets moretime to interact and advect less cloud water available for
growth of ice hydrometeors aloft, leading to DSDs withsmall median drop diameters [Rosenfeld and Ulbrich,
2003].
[26] The stratiform distributions (Figures 6c and 6d) showa reflectivity enhancement near the melting level (near 5 km)
associated with the radar bright-band. Significant differences in stratiform distributions between the western andeastern Pacific occur above the melting level. The CFADsof the eastern Pacific have a less probable occurrence of
20 dBZ compared with the western Pacific. Similar toconvection, this indicates the development of weak ice
precipitation in the eastern Pacific.
[27] The convective-stratiform classification of the DSD
is taken into account by the PR2A25 algorithm, where thesame Ze translates to smaller R in stratiform rain comparedwith convective rain. This is based on the observation by
Tokay and Short [1996] who found that stratiform rain
contains more large raindrops and fewer small to medium
raindrops than does convective rain using surface-based

7 of 9

D15116

SHIGE ET AL.: VALIDATION OF TRMM PR RAINFALL ESTIMATES

D15116

Figure 7. Schematic illustration of precipitation features over the (a) western and (b) eastern Pacific.
The smaller sizes of asterisks and dots for precipitation features over the (a) western and (b) eastern
Pacific. The smaller sizes of asterisks and dots for precipitation features over the eastern Pacific comparedwith those over the western Pacific indicate DSDs with smaller median diameters.
observations over the western Pacific. It should be notedthat shallow, isolated echoes were classified as stratiform
rain in V5 of the TRMM PR rain-type classification
algorithm, which led to even greater underestimation ofwarm rain than in V6. Although there have been disputes on
the convective-stratiform classification [Yuter and Houze,
1997; Atlas et al., 1999, 2000], properties of the drop size
spectra from the stratiform classification are consistent withthe dominant growth processes of stratiform precipitation,
which are vapor deposition onto existing ice particles andthe collection of snow generated by the mesoscale updraftthat develops in the upper levels in the stratiform regionswith higher tops frequently seen in the western Pacific.
However, the passage of the particles through the region ofmesoscale updraft may not be sufficient for the growth oflarge drops in the stratiform regions with lower tops overthe eastern Pacific. Thus stratiform rain over the eastern
Pacific probably produces more small to medium size
raindrops compared with the western Pacific, leading tounderestimation of rain by the PR2A25 algorithm.

5. Summary and Conclusion[28] The differences between PR2A25 [Iguchi et al.,
2000; Iguchi, 2007] and TMI2A12 [Kummerow et al.,
2001; Olson et al., 2006] standard rainfall products overthe eastern Pacific are well known [Berg et al., 2002, 2006].
Rainfall for TMI2A12 is greater than for PR2A25 over theeastern Pacific, while the products are in good agreement
over the western Pacific, and this results in different west-east
gradients of rainfall. The GSMaP_TMI algorithm [Kubota etal., 2007], which is a PR-consistent advanced microwave
radiometer algorithm based on the study by Aonashi and Liu[2000], also gives rainfall greater than that given by the
PR2A25 algorithm over the eastern Pacific, while the results
are in good agreement over the western Pacific.
[29] Here, we have shown that discrepancies between
TMI-observed TBs for the 19 GHz channel and thosesimulated from the PR rainfall estimates over the eastern
Pacific are larger than those over the western Pacific,
especially for rainfall in the lower to medium range (small
attenuation). It is hypothesized that the DSDs of rainfall
over the eastern Pacific have stronger maritimity (i.e., more
small to medium sized raindrops) than the initial DSD
model of PR2A25, representative of the ‘‘data-rich’’ western Pacific, leading to an underestimation of rain. The
hypothesis is consistent with the convective  from the aadjustment procedure in the PR2A25 algorithm.

[30] A schematic illustration of precipitation features
over the western and eastern Pacific is presented in
Figure 7. There are large differences in the verticalstructure of precipitation between the western and eastern
Pacific. A larger population of warm rain and development of weak ice precipitation in convective rain are found in
the eastern Pacific compared with the western Pacific,
suggesting weaker updrafts [Zipser and Lutz, 1994]. Weaker
updrafts help promote growth by coalescence by giving
droplets more time to interact and advect less cloud water
available for the growth of ice hydrometeors aloft. Similar tothe convective rain, the development of weaker ice precipitation in stratiform rain is found over the eastern Pacificcompared with the western Pacific. Active coalescence andless ice precipitation lead to DSDs with smaller median
diameters [Rosenfeld and Ulbrich, 2003]. Thus the differences in the vertical structure of precipitation support thenotion that the maritimity of DSDs is stronger for the eastern
Pacific than for the western Pacific.
[31] Finally, we caution against using the present findings
as quantitative validation of the passive algorithms(TMI2A12 and GSMaP_TMI) since other uncertainties
remain.
[32] Acknowledgments. This study is supported by funding from theJapan Aerospace Exploration Agency/Earth Observation Research Center(JAXA/EORC) TRMM project and Japan Science and Technology (JST)Corporation, Core Research for Evolution Science and Technology(CREST). We wish to thank G. Liu from Florida State University forproviding his RTM. We also thank K. Aonashi from the MeteorologicalResearch Institute (MRI) for his kind guidance on using the RTM.
Discussions with T. Iguchi and N. Takahashi from the National Instituteof Information and Communications Technology (NICT), T. Kozu from
Shimane University, and T. Nakazawa from the MRI are appreciated. We
also thank C. Kummerow for providing the TMI2A12 V6 algorithm code.
The authors thank three anonymous reviewers for their constructive comments. The TRMM products were provided by JAXA. ERA40 data were
provided from the European Center for Medium-range Weather Forecasts(ECMWF). The CWV, LWP, sea surface wind, and sea surface temperature
data set retrieved from the TMI was obtained from the Remote Sensing
Systems website.

References
Aonashi, K., and G. Liu (2000), Passive microwave precipitation retrievals
using TMI during the baiu period of 1998. part I: Algorithm description
and validation, J. Appl. Meteorol., 39, 2024 – 2037.
Atlas, D., C. W. Ulbrich, F. D. Marks Jr., E. Amitai, and C. R. Williams(1999), Systematic variation of drop size and radar-rainfall relations,J. Geophys. Res., 104, 6155 – 6169.
Atlas, D., C. W. Ulbrich, F. D. Marks Jr., R. A. Black, E. Amitai, P. T.
Wills, and C. E. Samsury (2000), Partitioning tropical oceanic convective
and stratiform rains by draft strength, J. Geophys. Res., 105, 2259 – 2267.
Awaka, J., T. Iguchi, and K. Okamoto (2007), Rain type classification
algorithm, in Measuring Precipitation From Space - EURAINSAT and

8 of 9

D15116

SHIGE ET AL.: VALIDATION OF TRMM PR RAINFALL ESTIMATES

the Future, edited by V. Levizzani, P. Bauer, and F. J. Turk, pp. 213 – 224,
Springer, New York.
Bauer, P., L. Schanz, and L. Roberti (1998), Correction of three-dimensional effects for passive microwave remote sensing of convective clouds,
J. Appl. Meteorol., 37, 1619 – 1632.
Berg, W. K., C. Kummerow, and C. A. Morales (2002), Differences between
east and west Pacific rainfall systems, J. Clim., 15, 3659 – 3672.
Berg, W. K., T. L’Ecuyer, and C. Kummerow (2006), Rainfall climate
regimes: The relationship of regional TRMM rainfall biases to the environment, J. Appl. Meteorol., 45, 434 – 454.
Bringi, V. N., V. Chandrasekar, H. Hubbert, E. Gorgucci, W. L. Randeu,
and M. Schoenhuber (2003), Rindrop size distribution in different climate
regimes from disdrometer and dual-polarized radar analysis, J. Atmos.
Sci., 60, 354 – 365.
Chandrasekhar, V., W. Li, and B. Zafar (2005), Estimation of raindrop size
distribution from spaceborne radar observations, IEEE Trans. Geosci.
Remote Sens., 43, 1078 – 1086.
Coppens, D., and Z. S. Haddad (2000), Effects of raindrop size distribution
variations on microwave brightness temperature calculation, J. Geophys.
Res., 105, 24,483 – 24,489.
Curtis, S., and R. Adler (2000), ENSO indices based on patterns of satellitederived precipitation, J. Clim., 15, 2786 – 2793.
Feingold, G., and Z. Levin (1986), The lognormal fit of raindrop spectra fromfrontal convective clouds in Israel, J. Appl. Meteorol., 25, 1346 – 1363.
Grecu, M., W. S. Olson, and E. N. Anagnostou (2004), Retrieval of precipitation profiles from multiresolution, multifrequency active and passive microwave observations, J. Appl. Meteorol., 43, 562 – 575.
Haddad, Z. S., E. A. Smith, C. Kummerow, T. Iguchi, M. L. Farrar, S. L. D.
M. Alves, and W. S. Olson (1997), The TRMM day-1 radar/radiometer
combined rain-profiling algorithm, J. Meteorol. Soc. Jpn., 75, 799 – 808.
Hitschfeld, W., and J. Bordan (1954), Errors inherent in the radar measurement of rainfall at attenuating wavelengths, J. Atmos. Sci., 11, 58 – 67.
Iguchi, T. (2007), Space-borne radar algorithms, in Measuring Precipitation
From Space - EURAINSAT and the Future, edited by V. Levizzani,
P. Bauer, and F. J. Turk, pp. 199 – 212, Springer, New York.
Iguchi, T., and R. Meneghini (1994), Intercomparison of single-frequency
methods for retrieving a vertical rain profile from airborne or spaceborne
radar data, J. Atmos. Oceanic Technol., 11, 1507 – 1516.
Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and K. Okamoto (2000),
Rain-profiling algorithm for the TRMM precipitation radar, J. Appl.
Meteorol., 39, 2038 – 2052.
Janowiak, J. E., P. A. Arkin, P. Xie, M. Morrissey, and D. R. Legates(1995), An examination of the east Pacific ITCZ rainfall distribution,
J. Clim., 8, 2810 – 2823.
Kim, M.-J., J. A. Weinman, and R. A. Houze Jr. (2004), Validation ofmaritime rainfall retrievals from the TRMM microwave radiometer,
J. Appl. Meteorol., 43, 847 – 859.
Klaassen, W. (1990), Attenuation and reflection of radio waves by a melting
layer of precipitation, Proc. IEEE, 137, 39 – 44.
Kozu, T., et al. (2001), Development of precipitation radar onboard theTropical Rainfall Measuring Mission (TRMM) satellite, IEEE Trans.
Geosci. Remote Sens., 39, 102 – 116.
Kubota, T., et al. (2007), Global precipitation map using satelliteborne
microwave radiometers by the GSMaP project: Production and validation, IEEE Trans. Geosci. Remote Sens., 45, 2259 – 2275.
Kummerow, C., W. S. Olson, and L. Giglio (1996), A simplified scheme forobtaining precipitation and vertical hydrometer profiles from passive
microwave sensors, IEEE Trans. Geosci. Remote Sens., 34, 1213 – 1232.
Kummerow, C., et al. (2000), The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit, J. Appl. Meteorol.,39, 1965 – 1982.
Kummerow, C., et al. (2001), The evolution of the Goddard Profiling
Algorithm (GPROF) for rainfall estimation from passive microwave sensors, J. Appl. Meteorol., 40, 1801 – 1820.
Liu, G. (1998), A fast and accurate model for microwave radiance calculations, J. Meteorol. Soc. Jpn., 76, 335 – 343.
Marshall, J. S., and W. M. Palmer (1948), The distribution of raindrops withsize, J. Atmos. Sci., 5, 165 – 166.
Meneghini, R., T. Iguchi, T. Kozu, L. Liao, K. Okamoto, J. A. Jones,
and J. Kwiatkowski (2000), Use of the surface reference technique forpath attenuation estimates from the TRMM precipitation radar, J. Appl.
Meteorol., 2053 – 2070.
Okamoto, K. (2003), A short history of the TRMM precipitation radar, in
Cloud Systems, Hurricanes and the Tropical Rainfall Measurement Mission(TRMM): A Tribute to Dr. Joanne Simpson, Meteorol. Monogr., 51, pp.
187 – 195, Am. Meteorol. Soc., Boston, Mass.
Olson, W. S., et al. (2006), Precipitation and latent heating distributions
from satellite passive microwave radiometry. part I: Improved method
and uncertainty estimates, J. Appl. Meteorol., 40, 702 – 720.

D15116

Petty, G. W. (1994), Physical retrievals of over-ocean rain rate frommultichannel microwave imagery. part I: Theoretical characteristics ofnormalized polarization and scattering indices., Meteorol. Atmos. Phys.,54, 79 – 99.
Robertson, F. R., D. E. Fitzjarrald, and C. D. Kummerow (2003), Effects ofuncertainty in TRMM precipitation radar path integrated attenuation oninterannual variations of tropical oceanic rainfall, Geophys. Res. Lett.,
30(4), 1180, doi:10.1029/2002GL016416.
Rosenfeld, D., and C. W. Ulbrich (2003), Cloud microphysical properties,
process, and rainfall estimation opportunities, in Radar and Atmospheric
Science: A Collection of Essays in Honor of David Atlas, Meteorol.
Monogr., 52, pp. 237 – 258, Am. Meteorol. Soc., Boston, Mass.
Sasaki, H., S. Shige, and K. Okamoto (2007), Improvements of brightness
temperature simulations for validation of TRMM rainfall products (inJapanese), J. Remote Sens. Soc. Jpn., 27, 153 – 166.
Schumacher, C., and R. A. Houze Jr. (2003), The TRMM precipitation
radar’s view of shallow, isolated rain, J. Appl. Meteorol., 42, 1519 – 1524.
Sekhon, R. S., and R. C. Srivastava (1971), Doppler radar observations ofdrop size distributions in a thunderstorm, J. Atmos. Sci., 28, 983 – 994.
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. Comparison of the version5 and -6 products, Geophys. Res. Lett., 33, L13803, doi:10.1029/
2006GL026350.
Shige, S., Y. N. Takayabu, W.-K. Tao, and C.-L. Shie (2007), Spectral retrieval of latent heating profiles from TRMM PR data. part II: Algorithm
improvement and heating estimates over tropical ocean regions, J. Appl.
Meteorol., 46, 1098 – 1124.
Shige, S., Y. N. Takayabu, and W.-K. Tao (2008), Spectral retrieval of latentheating profiles from TRMM PR data. part III: Estimating apparent
moisture sink profiles over tropical oceans, J. Appl. Meteorol., 47,
620 – 640.
Short, D. A., and K. Nakamura (2000), TRMM radar observations ofshallow precipitation over the tropical oceans, J. Clim., 13, 4107 – 4124.
Simpson, J., R. F. Adler, and G. R. North (1988), A proposed satellite
Tropical Rainfall Measuring Mission (TRMM), Bull. Am. Meteorol.
Soc., 69, 278 – 295.
Simpson, J., C. Kummerow, W.-K. Tao, and R. F. Adler (1996), On the
Tropical Rainfall Measuring Mission (TRMM), Meteorol. Atmos. Phys.,
60, 19 – 36.
Smith, E. A., and T. D. Hollis (2003), Performance evaluation of level-2
TRMM rain profile algorithms by intercomparison and hypothesis testing, in Cloud Systems, Hurricanes and the Tropical Rainfall Measurement Mission (TRMM): A Tribute to Dr. Joanne Simpson, Meteorol.
Monogr., 51, pp. 207 – 222, Am. Meteorol. Soc., Boston, Mass.
Stout, G. E., and E. A. Mueller (1968), Survey of relationships between
rainfall rate and radar reflectivity in the measurement of precipitation,
J. Appl. Meteorol., 7, 465 – 474.
Takayabu, Y. N. (2002), Spectral representation of rain features and diurnal
variations observed with TRMM PR over the equatorial area, Geophys.
Res. Lett., 29(12), 1584, doi:10.1029/2001GL014113.
Tokay, A., and D. A. Short (1996), Evidence from tropical raindrop spectraof the origin of rain from stratiform versus convective clouds, J. Appl.
Meteorol., 35, 355 – 371.
Willis, P. T., and P. Tattelman (1989), Drop-size distributions associatedwith intense rainfall, J. Appl. Meteorol., 28, 3 – 15.
Yang, S., W. Olson, J. J. Wang, T. L. Bell, E. A. Smith, and C. D. Kummerow(2006), Precipitation and latent heating distributions from satellite passive
microwave radiometry. part II: Evaluation of estimates using independentdata, J. Appl. Meteorol., 45, 721 – 739.
Yuter, S. E., and R. A. Houze Jr. (1995), Three-dimensional kinematic andmicrophysical evolution of Florida cumulonimbus. part II: Frequency
distribution of vertical velocity, reflectivity, and differential reflectivity,
Mon. Weather Rev., 123, 1941 – 1963.
Yuter, S. E., and R. A. Houze Jr. (1997), Measurements of raindrop size
distributions over the Pacific warm pool and implications for Z-R relations, J. Appl. Meteorol., 36, 847 – 867.
Yuter, S. E., and W. S. Parker (2001), Rainfall measurement on ship revisited:
The 1997 PACS TEPPS cruise, J. Appl. Meteorol., 40, 1003 – 1018.
Zipser, E. J., and K. R. Lutz (1994), The vertical profile of radar reflectivityof convective cells: A strong indicator of storm intensity and lightning
probability?, Mon. Weather Rev., 122, 1751 – 1759.


S. Kida, K. Okamoto, H. Sasaki, S. Shige, and T. Watanabe, Department
of Aerospace Engineering, Osaka Prefecture University, 1-1 Gakuen-cho,
Sakai, Osaka 599-8531, Japan. (shige@aero.osakafu-u.ac.jp)
T. Kubota, Earth Observation Research Center, Japan Aerospace
Exploration Agency, 2-1-1, Sengen, Tsukuba, Ibaraki 305-8505, Japan.

9 of 9