ClickHere

GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L13803, doi:10.1029/2006GL026350, 2006

for

Full
ArticleValidation of rainfall estimates from the TRMM precipitation radarand microwave imager using a radiative transfer model:
1. Comparison of the version-5 and -6 products
S. Shige,1 H. Sasaki,1 K. Okamoto,1 and T. Iguchi2
Received 20 March 2006; revised 8 May 2006; accepted 24 May 2006; published 6 July 2006.

[1] The Tropical Rainfall Measuring Mission (TRMM)
version-6 rainfall products show the reduced bias between
Precipitation Radar (PR) and TRMM Microwave Imager(TMI) rainfall estimates during the 1997/1998 El Niñoevent noted in version 5, but need to be verified. We
investigate consistency between TMI-observed brightness
temperatures (TBs) at 10 and 19 GHz channels and thosesimulated from the PR and TMI rainfall estimates using aradiative transfer model. Simulated TBs from PR V6
exhibits better agreement with observed ones than thosefrom PR V5, implying the algorithm improvements.
However, discrepancies at 19 GHz suggest that
uncertainty in the assumed drop size distribution still
remains in PR V6. Simulated TBs from TMI V6 also
exhibits better agreement with observed ones than thosefrom TMI V5. However, the simulated 10-GHz TBs fromTMI V6 exhibits more scatter against TMI-observed ones
than those from PR V6 do. Citation: 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 theversion-5 and -6 products, Geophys. Res. Lett., 33, L13803,
doi:10.1029/2006GL026350.

1. Introduction[2] The Tropical Rainfall Measuring Mission (TRMM)
satellite has been in operation for more than 8 years,
providing the distribution of rainfall throughout the Tropics
using microwave observations from the Precipitation Radar(PR) and the TRMM Microwave Imager (TMI). The PR is
the first space-borne precipitation radar and can provide
height information based upon the time delay of the
precipitation-backscattered return power. This allows us
for vertical profiles of precipitation to be obtained directly
over the global Tropics. On the other hand, the 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.
Comparisons of tropical mean rainfall time series indicatethat there are large discrepancies between the TRMM
version-5 rainfall products derived by the PR (2A25) andTMI (2A12) profiling algorithms during the 1997/1998 El
Niño (warm) event [Robertson et al., 2003].
1
Department of Aerospace Engineering, Osaka Prefecture University,
Osaka, Japan.
2
National Institute of Information and Communications Technology,
Tokyo, Japan.

Copyright 2006 by the American Geophysical Union.
0094-8276/06/2006GL026350$05.00[3] Several improvements have been implemented tocreate the most recent algorithm, the TRMM version 6algorithms. The TRMM version-6 rainfall products show
the reduced bias during the warm event, but need to beverified. The most common method of verifying satelliteretrieved rain estimates is to directly compare to groundvalidation measurements (ground truth) derived from rain
gauge networks, ground weather radar, or a combination of
the two. Wolff et al. [2005] showed satellite retrievals fromthe V6 of TMI, PR, and Combined algorithms are wellagree within 10% of TRMM Ground Validation (GV)
estimates at the Kwajalein site, which is located on theeastern boundary of western Pacific warm pool. Yang et al.
[2006] also compared TMI V6 estimates to TRMM GV
estimates at the Kwajalein site, as well as PR V5 and TMI
V5 estimates. However, during the El Niño event, larger
discrepancies between the TRMM estimates are found overeastern Pacific where there are no TRMM GV sites. Thus,
we must rely on indirect strategies to achieve validation.
Viltard et al. [2000] investigated the consistency in TMIobserved brightness temperatures (hereinafter TBs) andthose simulated from PR2A25 V4 rain profiles for a casein the central Pacific. In this paper, the consistency between
TMI-observed TBs at lower frequency channels (10.7 and19.4 GHz) and those simulated from PR2A25 V5 and V6
rain profiles for the intertropical convergence zone (ITCZ)
precipitation systems during the 1997/1998 El Niño event isstatistically investigated using a radiative transfer model.
Although this study lays weight on validation of PR2A25
using independent TMI data, the consistency between
TMI-observed TBs and those simulated from TMI2A12
V5 and V6 rain profiles are also investigated to see theirperformance.

2. Approach[4] Figure 1 shows the schematic flow diagram of
PR2A25 and TMI2A12 algorithms and the procedure forvalidating the TRMM rainfall products.
[5] The PR operates at a single frequency (13.8 GHz) sothat PR2A25 algorithm corrects the attenuation in themeasured radar reflectivity factor Zm and estimates theeffective radar reflectivity factor Ze [Iguchi et al., 2000].
The attenuation correction is based on a hybrid of the
Hitschfeld –Bordan (H-B) solution and the surface referencetechnique (SRT). The coefficient a in the k-Ze relationshipk = aZbe is adjusted in such a way that the path-integrated
attenuation (PIA) estimated from the H-B solution matches
the SRT estimate of PIA where k is the specific attenuation
due to precipitation. Major improvements in V6 are as
follows: a) Inclusion of the effects of attenuation due toL13803

1 of 5

L13803

SHIGE ET AL.: VALIDATION OF RAINFALL ESTIMATES

L13803Figure 1. Diagram showing the schematic flow diagram of PR2A25 and TMI2A12 algorithms and the procedure forvalidating the TRMM rainfall products. The question mark means to examine consistency between TMI-observedbrightness temperatures (TBs) and those simulated from the TRMM rainfall products.
cloud liquid water, water vapor, and molecular oxygen in theattenuation algorithm, b) Estimation of the attenuation
between the lowest range bin that is free from the mainlobe
surface clutter and the actual surface by assuming a constantslope of dBZe, c) Estimation of Ze, rain and several parameters that varies with the adjustment parameter () of a as the
expected values with respect to the posterior probability
distribution function of , d) Reevaluation of the error
estimate in the PIA by the SRT and that from the H-B
solution. See the detailed descriptions by Iguchi et al.
(manuscript in preparation, 2006) on improvements madeon 2A25 V6.
[6] On the other hand, the basis of TMI 2A12 algorithm
is on a Bayesian framework, in which retrieved precipitation
are constructed from those cloud-resolving model (CRM)generated profiles that are radiatively consistent with theobservation [Kummerow and Coauthors, 2001]. Major
improvements in V6 are: a) Greater diversity in the supporting cloud-radiative model database, b) Adjustment of CRM
ice microphysics, c) Inclusion of the effects of mixed-phase
precipitation in CRM simulations, d) Definition of cloudand precipitation properties in simulated footprints, e) New
geographic database, f) Calculation of the ‘‘background’’rain-free radiance field, g) Reformulation of the convective/
non-convective rain area constraint. See the detailed
descriptions by Olson et al. [2006] and Yang et al. [2006]
on improvements made on 2A12 V6.
[7] Due to the scarcity of reliable validation data anddifficulties associated with the collocation of validation dataand satellite measurements, we investigate the consistency
in observed and simulated TBs. Here, the simulated TBs arederived from PR2A25 and TMI2A12 rain profiles. In thisstudy, we focus on lower-frequency channels (10.7 GHz and19.4 GHz). This is because lower-frequency brightness
temperatures are well correlated with total liquid water pathand because less uncertainties in approximative radiativetransfer at lower-frequency channels are expected than at
higher-frequency channels (37 GHz and 85.5 GHz) where
scattering by ice-phase precipitation is significant.
[8] The radiative transfer model (RTM) developed by Liu[1998] is used to calculate TBs. All radiance slant paths
through the three-dimensional domain are considered independently. The antenna pattern for each frequency is ap-

proximated by a Gaussian weighting function with the same
3-dB beamwidth as the actual antenna pattern.
[9] The upwelling microwave radiances over ocean depend on the sea surface characteristics and atmosphericconstitutes. In this study, mean atmospheric temperaturefrom the 6-hourly ERA40 reanalysis data with minimum
time difference with the TRMM observations are employed.
The column water vapor (CWV), liquid water path (LWP),
sea surface wind, and sea surface temperature are adopted
from the TMI products provided by Remote Sensing Systems. The water vapor profiles are determined from CWV,
following Masunaga and Kummerow [2005]. Our simulated
TBs in clear-air areas show very good agreement with TMI
measurements. For TBs simulations from PR2A25 estimates, the cloud water profiles in nonraining areas are
determined from LWP, following Masunaga and Kummerow. In raining areas, we used the cloud water profiles
from CRM simulations based on which the PR2A25 V6
algorithm estimates the attenuation by cloud water. We
utilized two drop size distribution (DSD) models of a
gamma distribution corresponding to convective and stratiform rain assumed in PR algorithm (T. Kozu et al.,Estimation of rain drop size distribution parameters using
statistical relations between multiparameter rainfall remotesensing data, preprints of 29th International Conference on
Radar Meteorology, 1999). A change in the DSD due toalternations in a accommodating the SRT estimate of PIA isalso taken into account. For PR stratiform rain profiles witha bright band, Klaassen’s [1990] melting-layer parameterization is used, following Kim et al. [2004]. For TBs
simulations from TMI2A12 estimates, we used cloud estimates present in the TMI2A12 and the Marshall – Palmer
distribution assumed in CRMs that make up the cloudradiative model database.
[10] For comparisons, the emission index (EI) is definedas 1  P and used instead of brightness temperature toisolate the microwave signal due to precipitation particles.
Here, P is the normalized polarization difference [Petty,
1994] given by

2 of 5P¼

TBV  TBH;
TBV ;clear  TBH;clear

ð1Þ

L13803

SHIGE ET AL.: VALIDATION OF RAINFALL ESTIMATES

Figure 2. (a) Differences of rainfall estimates (mm day1)
between PR2A25 V6 and TMI2A12 V6 for February of
1998; (b) Time series of monthly mean rainfall estimates(mm day1) from PR2A25 and TMI2A12 for the selectedarea extending from 0 to 10S latitude and from 140 to
150W longitude indicated by a box in Figure 2a. Only TMI
data matched within PR swath are used.

L13803indicates that PR2A25 V5 underestimates rainfall in the
ITCZ. The simulated EI10s from PR2A25 V6 are higher
than those from PR2A25 V5, and exhibit better agreementwith observed EI10s, especially for higher values. This is
explained by the inclusion of attenuation corrections forwater vapor and cloud liquid water. A more relative weightto the SRT PIA in V6 than V5 due to changing of theweighting factors between the PIA estimated by the SRT
and that by H-B solution also accounts for the better
agreement in V6 than V5.
[ 13 ] Frequency plots of the EI10s simulated fromTMI2A12 V5 and V6 rain profiles against corresponding
TMI-observed EI10s are presented in Figures 3c and 3d,respectively. The EI10s simulated from TMI2A12 V5 arehigher than the observed EI10s, especially for the higher
range (associated with heavy rainfall). This indicates that
TMI2A12 V5 overestimates rainfall in the ITCZ. Thus, both
PR2A25 and TMI2A12 algorithms account for the inconsistencies in the TRMM V5 rainfall products. The simulated
EI10s from TMI2A12 V6 are lower than those fromTMI2A12 V5, and exhibit better agreement with observed
EI10s for higher values. Yang et al. [2006] attributed the
reduced high bias in TMI2A12 V6 to the expanded cloudradiative model database supporting the V6 algorithm, as
well as the more consistent and specific convective/stratiform rain separation procedure, which together reduce thehigh bias in estimated stratiform rains previously noted inwhere 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 and/or precipitation. For the observed P, the background brightness temperatures are estimated following themethod of TMI 2A12 V6 [Olson et al., 2006]. For thesimulated P, the back ground brightness temperatures arecalculated with all cloud and precipitation water contents set
to zero. The EI is 0 for 0 rain rate and increases with the rain
rate.

3. Results and Discussions[11] Figure 2a indicates that rainfall for February 1998
estimated by PR2A25 V6 is smaller than that estimated by
TMI2A12 V6 over a well-defined ITCZ (associated withheavy rainfall) which is present in the central and eastern
Pacific between the equator and 10S. It is neverthelessobvious from Figure 2b that the difference between TMI
and PR estimates for February 1998 is drastically narrowing
in V6 (1.8 mm day1, or 11%) compared with V5 (7.5 mm
day1, or 46%) for the area from 0 to 10S latitude andfrom 140 to 150W longtitude where larger differences arefound in Figure 2a. The procedures described in theprevious section are applied to 6 months of TRMM data,from January to June 1998, over this selected area. The
number of TRMM orbits used in this study is 129. Only the
TMI footprints for each channels of which 80% are coverd
with PR rain pixels are analyzed.
[12] Frequency plots of the 10-GHz EIs (hereafter EI10s)
simulated from PR2A25 V5 and V6 rain profiles against
corresponding TMI-observed EI10s are presented in Figures
3a and 3b, respectively. The EI10s simulated from PR2A25
V5 are much lower than the observed EI10s, especially forthe higher range (associated with heavy rainfall). This

Figure 3. Frequency plots of the 10-GHz emission indexes(EIs) simulated from (a) PR2A25 V5, (b) PR2A25 V6,(c) TMI2A12 V5, and (d) TMI2A12 V6 rain profiles against
corresponding TMI-observed 10-GHz EIs. The average(solid line) and standard deviation (error bars) of simulated
EI for each histogram bin along the horizontal line areplotted. The dashed lines are the 1:1 lines.

3 of 5

L13803

SHIGE ET AL.: VALIDATION OF RAINFALL ESTIMATES

L13803saturates is lower than for 19 GHz than for 10 GHz. Thus,
comparing radiative characteristics of 19 GHz implies
validating weaker rain. When attenuation is small or marginal (i.e., weaker rainrate), the weight is shifted toward the
PIA estimate from the H-B solution in PR2A25. In such acase, the SRT perturbs the initial DSD model only slightly,
and an appropriate selection of the initial DSD model isvery important. Therefore, less agreement between TMIobserved EIs and those simulated from PR2A25 at 19 GHz
than 10 GHz is attributed to inappropriate selection of theinitial DSD model used in PR2A25 V6 that remains thesame as in PR2A25 V5.
[16] In contrast to PR2A25, the simulated EI19s fromTMI2A12 V6 rain profiles are good agreement with TMIobserved ones as well as those from TMI2A12 V5 (Figures
4c and 4d). The simulated EI19s from TMI2A12 V6 exhibit
less scatter against TMI-observed ones than those fromTMI2A12 V5 do. It is also noticed that the EI19s simulated
from TMI2A12 V6 exhibit less scatter against TMIobserved ones than the EI10s simulated from TMI2A12
V6 do. This indicates that the 19 GHz channel makes moresignificant contribution in TMI2A12 than the 10 GHz
channel because of their higher resolution.

4. Summary and Conclusion
Figure 4. Same as Figure 3 except for the 19-GHz EIs
simulated from (a) PR2A25 V5, (b) PR2A25 V6,(c) TMI2A12 V5, and (d) TMI2A12 V6 rain profiles
against corresponding TMI-observed 19-GHz EIs.
V5. These improvements may account for the better agreement in V6 than V5. On the other hand, the scatter of thesimulated EI10s from TMI2A12 V6 increases as the observed EI10s. Although the 10 GHz channel is used in
TMI2A12 but not in PR2A25, the scatter of the simulated
EI10s from TMI2A12 V6 is higher than that of thesimulated EI10s from PR2A25 V6 (Figures 3b and 3d).
This indicates that the 10 GHz channel does not makesignificant contribution in TMI2A12 because of their low
resolution. Although the footprint of 10 GHz channel isvery large, this channel provides total liquid water pathestimates and avoids saturation from such heavy rainfall.
Thus, the 10 GHz channel may be very important, although
the information from it is underused in the current
TMI2A12.
[14] Frequency plots of the 19-GHz EIs (hereafter EI19s)
simulated from PR2A25 V5 and V6 rain profiles against
corresponding TMI-observed EI19s are presented in Figures
4a and 4b, respectively. The simulated EI19s from PR2A25
V6 are somewhat higher than those from PR2A25 V5,especially for the higher range. However, the simulated
EI19s from PR2A25 V6 remain lower than the observed
ones, implying underestimation of rain. For example, thesimulated EI19 from PR2A25 V6 is about 0.4 for theobserved EI19 of 0.5.
[15] The RTM calculations assuming horizontally uniform rain with a 5 km-depth show that EI19s of 0.4 and 0.5
imply rain rates of about 3 and 4 mm hr1, respectively.
Since absorption of microwaves by liquid cloud and rainincreases with frequency, the water content for which TB[17] In this study, we have shown that simulated EI10s
from PR2A25 V6 rain profiles exhibit better agreement with
TMI-observed EI10s than those from PR2A25 V5 rain
profiles during the 1997/1998 El Niño event. This is
explained by the inclusion of attenuation corrections forwater vapor and cloud liquid water, and a more relative
weight to the SRT PIA in V6 than V5. The simulated EI10s
from TMI2A12 V6 also exhibit better agreement withobserved one than those from TMI2A12 V5. However,the simulated EI10s from TMI2A12 V6 exhibit more scatter
against TMI-observed ones than those from PR2A25 V6 do,
implying that the 10 GHz channel does not make significant
contribution in TMI2A12. On the other hand, the simulated
EI19 from PR2A25 V6, as well as from PR 2A25 V5, arelower than the observed one. This is attributed to inappropriate selection of the initial DSD model in PR2A25.
[18] Only precipitation systems over the central Pacificduring the 1997/1998 El Niño event was considered in thecurrent investigation. This study will be extended to examine regional and seasonal biases that exist between the
PR2A25 V6 and TMI2A12 V6 algorithms.
[19] Acknowledgments. This study is supported by the fund of Japan
Science and Technology Corporation - Core Research for Evolution
Science and Technology (CREST). We wish to thank G. Liu of Florida
State University for providing his radiative transfer model. We also thank
K. Aonashi of Meteorological Research Institute for his kind guidance on
using the radiative transfer model. Discussions with N. Takahashi of NICTand T. Kozu of Shimane University were appreciated. We also thank C.
Kummerow for providing the TMI 2A12 V6 algorithm code. The TRMM
products were provided by Japan Aerospace Exploration Agency (JAXA).
ERA40 data was 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 TMI was obtained from the
Remote Sensing Systems Web site.

References
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.

4 of 5

L13803

SHIGE ET AL.: VALIDATION OF RAINFALL ESTIMATES

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, IEEE Conf. Publ., 137(H1), 39 – 44.
Kummerow, C., et al. (2001), The evolution of the Goddard profilingalgorithm (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.
Masunaga, H., and C. D. Kummerow (2005), Combined radar and radiometer analysis of precipitation profiles for a parametric retrieval algorithm, J. Atmos. Oceanic Technol., 22, 909 – 929.
Olson, W. S., et al. (2006), Precipitation and latent heating distributions from satellite passive microwave radiometry. part I: Improved
method and uncertainties, J. Appl. Meteorol. Climatol., 45, 702 –720.
Petty, G. W. (1994), Physical retrievals of over-ocean rain rate from multichannel microwave imagery. part I: Theoretical characteristics of normalized polarization and scattering indices, Meteorol. Atmos. Phys., 54, 79 –99.

L13803Robertson, 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.
Viltard, N., C. Kummerow, W. S. Olson, and Y. Hong (2000), Combined
use of the radar and radiometer of TRMM to estimate the influence ofdrop size distribution on rain retrievals, J. Appl. Meteorol., 39, 2103 –2114.
Wolff, D. B., et al. (2005), Ground validation for the Tropical Rainfall
Measuring Mission (TRMM), J. Atmos. Oceanic Technol., 22, 365 – 380.
Yang, S., W. Olson, J. J. Wang, T. L. Bell, E. A. Smith, and C. D.
Kummerow (2006), Precipitation and latent heating distributions fromsatellite passive microwave radiometry. part II: Evaluation of estimatesusing independent data, J. Appl. Meteorol. Climatol., 45, 721 – 739.


T. Iguchi, National Institute of Information and Communications
Technology, 4-2-1 Nukui-Kitamachi, Tokyo 184-8795, Japan.
S. Shige, H. Sasaki, and K. Okamoto, Department of Aerospace
Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Osaka 5998531, Japan. (shige@aero.osakafu-u.ac.jp)

5 of 5