242

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGYVOLUME 52

Improvement of TMI Rain Retrievals in Mountainous Areas
SHOICHI SHIGE
Graduate School of Science, Kyoto University, Kyoto, JapanSATOSHI KIDA
Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, JapanHIROKI ASHIWAKE
Graduate School of Engineering, Osaka Prefecture University, Sakai, JapanTAKUJI KUBOTA
Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, JapanKAZUMASA AONASHI
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan(Manuscript received 19 March 2012, in final form 24 August 2012)
ABSTRACT
Heavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive
microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping ofPrecipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM)
Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification
scheme is developed on the basis of orographically forced upward vertical motion and the convergence of
surface moisture flux estimated from ancillary data. Lookup tables derived from orographic precipitation
profiles are used to estimate rainfall for an orographic rainfall pixel, whereas those derived from original
precipitation profiles are used to estimate rainfall for a nonorographic rainfall pixel. Rainfall estimates madeusing the revised GSMaP algorithm are in better agreement with estimates from data obtained by the radar on
the TRMM satellite and by gauge-calibrated ground radars than are estimates made using the original
GSMaP algorithm.

1. Introduction
Observations by microwave radiometers (MWRs) in
low Earth orbit and by infrared radiometers (IRs) ingeostationary Earth orbit have been combined to improve satellite rainfall estimates on the basis of retaining
the strengths of the individual techniques since the work
by Adler et al. (1993), who estimated monthly rainfall.
With the great success of the Tropical Rainfall Measuring

Corresponding author address: Shoichi Shige, Graduate Schoolof Science, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyoku, Kyoto, 606-8502, Japan.
E-mail: shige@kugi.kyoto-u.ac.jp
DOI: 10.1175/JAMC-D-12-074.1
Ó 2013 American Meteorological Society

Mission (TRMM; Simpson et al. 1996), which carries thefirst spaceborne precipitation radar (PR) (Kozu et al.
2001; Okamoto 2003), the development of MWR algorithms has accelerated. The improved MWR algorithms
and the greater number of MWRs in orbits encourage
us to develop high-resolution satellite rainfall products(0.18–0.258 latitude/longitude and 0.5–3 hourly) by combining data obtained by MWRs and IRs such as the
TRMM Multisatellite Precipitation Analysis (TMPA;
Huffman et al. 2007), the Climate Prediction Center
Morphing product (CMORPH; Joyce et al. 2004), theSelf-Calibrating Multivariate Precipitation Retrieval(SCaMPR; Kuligowski 2002), the Precipitation Estimation from Remotely Sensed Information using Artificial

JANUARY 2013

SHIGE ET AL.

Neural Networks (PERSIANN; Sorooshian et al. 2000),the Passive Microwave-Infrared product (PMIR; Kidd
et al. 2003), the Naval Research Laboratory-Blended
product (NRL; Turk and Miller 2005), and the Global
Satellite Mapping of Precipitation product (GSMaP;
Ushio et al. 2009). These datasets have been used forflood and landslide analysis/prediction (e.g., Hong et al.
2007). Reviews of satellite rainfall products and their surface hydrologic applications are found in Gebrenuchaeland Hossain (2010).
Surface hydrologic applications of satellite rainfall
products are still at the experimental stage because of
the poor performance of satellite estimates over land,particularly over mountainous areas. Negri and Adler(1993) showed that IR techniques did poorly in estimating the rain maxima in Japan associated with shallow
orographic (warm) rain systems (Takeda et al. 1976;
Takeda and Takase 1980; Sakakibara 1981). Althoughbrightness temperatures Tb obtained from MWR observations have a more direct relationship with rainfall
rates than IR cloud-top Tb, MWR algorithms underestimate rainfall associated with shallow orographic
rain systems. Todd and Bailey (1995) showed that an
MWR algorithm underestimates rain rates associatedwith shallow orographic rain systems over upland areas ofWales and northwestern England in the United Kingdom. Kubota et al. (2009) more recently showed poor
verification results of the high-resolution satellite rainfall
products for mountainous regions over Japan and suggested that one of the main reasons for such error is that
MWR algorithms underestimate heavy rainfall associated with shallow orographic rainfall systems. Satellite
methods underestimate heavy rainfall associated withshallow orographic rainfall systems because they assume
that heavy rainfall results from deep clouds. Some of therainiest areas around the world, however, such as thewest coast of India during the summer monsoon, aredominated by shallow orographic rain systems (Houze
1993; Liu and Zipser 2009).
Efforts have been made to improve the accuracyof satellite estimates over complex-terrain areas where
ground-radar and rain gauge measurements are limited. Vicente et al. (2002) developed a topographic
correction technique for an IR-based rain retrieval
such as the Hydro-Estimator (Scofield and Kuligowski2003). Kwon et al. (2008) recently developed topographic correction factors for terrain of the Korean
Peninsula in the Goddard profiling (GPROF) algorithm(Kummerow et al. 2001; McCollum and Ferraro 2003;
Olson et al. 2006; Wang et al. 2009), which is the
TRMM Microwave Imager (TMI) facility algorithm, asa function of terrain slope, low-level wind, and moistureparameters.

243

In this paper, we improve the performance of rainfall
estimates made by the GSMaP algorithm from TMI data
(Aonashi et al. 2009; Kubota et al. 2007) for the Kii
Peninsula, which is a heavy-rainfall region in Japan forwhich satellite methods of estimating the rain maximum
have been shown to be poor (Negri and Adler 1993;
Kubota et al. 2009). Following the study of Kwon et al.
(2008), we use parameters of terrain slope, low-level
wind, and moisture. We introduce not topographicalcorrection factors to the GSMaP algorithm, however,
but rather the dynamic selection of lookup tables(LUTs) that are appropriate for orographic heavy
rainfall, and we estimate rainfall rates from observed Tb
using the LUTs.

2. DataIn this paper, we use the data obtained by the PR and
TMI on board the TRMM satellite. The PR, an active
microwave sensor, provides height information that isbased on the time delay of the precipitation-backscatteredreturn power and allows vertical profiles of precipitation to be obtained directly over the global tropics. The
TMI, a passive microwave sensor, measures radiancesthat are the end product of the integrated effects ofelectromagnetic absorption/emission and scattering
through a precipitating cloud along the sensor view path.
Although the PR algorithm has weaknesses such as assumptions about the raindrop size distribution (Iguchi
et al. 2000, 2009; Shige et al. 2008), rainfall retrievals
using data from the PR are in principle superior to thoseusing data from the TMI, particularly over land. The
lower contrast between the surface and atmosphere due
to high and variable emissivity over land makes it difficult to use an emission signature from raindrops overthe spectrum of lower frequencies of the TMI; hence,a scattering signature from ice crystals over the spectrum of higher frequencies is mainly used. Thus, a comparison of TMI estimates with PR estimates is usefulfor the improvement and validation of TMI rainfall
retrievals.
The GSMaP algorithm consists of a forward-calculation
part to calculate LUTs showing the relationship between the rainfall rate and Tb with a radiative transfer
model (RTM) and a retrieval part to estimate the precipitation rate from the observed Tb using the LUTs.
Here, the GSMaP overland algorithm is described. For
details, refer to Aonashi et al. (2009).
From forward calculations with a four-stream RTM(Liu 1998), LUTs showing the relationship between
the rainfall rate and Tb were computed daily in 5.08 3
5.08 latitude–longitude boxes. The RTM calculation
requires information on atmospheric variables and244

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGYVOLUME 52

FIG. 1. Case study of orographic rainfall over the Kii Peninsula on 30 Jul 2004 (TRMM orbit No. 38228): (a) PR 2A25 near-surface rain
rate (mm h21), (b) TMI PCT85 (K), (c) GSMaP1 surface rain (mm h21), and (d) GTOPO30 elevation (m). The observation times of PR
and TMI data were 1534 and 1533 UTC, respectively.

precipitation-related variables. In this study, atmospherictemperature, freezing-level height (FLH), and surface temperature are adapted from the Japanese 25-year Reanalysis(JRA-25) (Onogi et al. 2007), which was completed using
the Japan Meteorological Agency (JMA) numerical assimilation and forecast system and has been continued as
the JMA Climate Data Assimilation System (JCDAS).
The convective and stratiform precipitation models
for precipitation-related variables such as hydrometersprofiles are constructed for seven land precipitation
types. Precipitation types are determined in terms of thestratiform pixel ratio, stratiform rain ratio, precipitationarea, precipitation-top height, rain intensity, and diurnal
cycle obtained from the PR data, together with the ratio
between PR precipitation rates and TRMM Lightning
Imaging Sensor flash rates (Takayabu 2006, 2008).
Global distributions of the precipitation types in 2.58 3
2.58 latitude–longitude boxes are statistically classified
trimonthly. The convective and stratiform precipitation
profiles of PR data are averaged over prescribed precipitation ranges for each precipitation type. In thisaveraging, profiles relative to the FLH are used to exclude the effect of atmospheric temperature variations.
The database of precipitation types and profiles makes it

JANUARY 2013

SHIGE ET AL.

245

FIG. 2. Precipitation profile models for (a) convective and (b) stratiform rainfall rain used by the GSMaP1 algorithm to produce a LUT for rain retrieval in the case shown in Fig. 1c; (c) convective and (d) stratiform rainfall rainobtained by averaging the convective and stratiform precipitation profiles observed by the PR for the case shown in
Fig. 1a. The ordinate is the height difference from the FLH.

possible for the algorithm to deal with trimonthly variation
in typical hydrometeor profiles.
The rain/no-rain classification method of Seto et al.
(2005) was used over land, and that of Kubota et al.

(2007), which is an improvement of the McCollum andFerraro (2005) method, was used over coasts. The surface rainfall estimate is obtained by combining estimatesfrom the polarization-corrected temperatures (Spencer

246

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY

FIG. 3. LUTs for PCT85 calculated from precipitation profiles
shown in Figs. 2a and 2b (black line) and those shown in Figs. 2cand 2d (red line).

et al. 1989) at 85 GHz (PCT85) and 37 GHz (PCT37)
using LUTs.
The Radar-Automated Meteorological Data Acquisition System (AMeDAS) precipitation analysis (hereinafter RA; Makihara et al. 1996; Makihara 2007) for2004 is used to validate the GSMaP estimates. The RA
data are a 1-h accumulated-precipitation estimate created from a composite of JMA operational 10-min-cycle
radar data, calibrated by rain gauges of the AMeDAS
network. Spatial and temporal resolutions of the RA
have been upgraded by the JMA. For the period between June 2003 and December 2005, the spatial resolution was 0.0258 latitude and 0.03 1258 longitude (about
2.5 km 3 2.5 km) and the temporal resolution was
30 min. The RA data have been used by Negri andAdler (1993), and Kubota et al. (2007, 2009) to validate
satellite rainfall products and at the International Precipitation Working Group regional validation site inJapan (Kidd et al. 2010).

3. Algorithm improvements
a. Case studies
Figure 1 presents a case of orographic rainfall over theKii Peninsula on 30 July 2004, associated with Typhoon
Namtheun, which was examined by Kubota et al. (2009).
High surface rain rates of .25 mm h21 were detectedaround Owase (348N, 1368E), where mountains higher
than 500 m are very close to the coastline running
northeast to southwest (Fig. 1d), in version 6 of the PRdata (Fig. 1a) and in the RA data (Kubota et al. 2009,
their Fig. 15a). Large surface rainfall events, such as

VOLUME 52

those seen in radar observations, were not detected in
the surface rain data retrieved from TMI data by theoriginal GSMaP algorithm (hereinafter GSMaP1; Fig.
1c) or by version 6 of the GPROF algorithm (Kubota
et al. 2009, their Fig. 15d). In both algorithms, surface
rain rates over land are computed mainly according toobserved PCT85 (Fig. 1b).
Figure 2 shows the convective and stratiform precipitation profiles used by the GSMaP1 algorithm forthe case shown in Fig. 1. For all prescribed precipitation
ranges, precipitation-top heights reach 6 km above theFLH for convective rain and 3 km above the FLH for
stratiform rain, which is a manifestation of active coldrain processes, and are representative of rain over land.
The convective and stratiform precipitation profiles
obtained from the data for the case of orographic rainfall are shown in Figs. 2c and 2d. Throughout the prescribed precipitation range, precipitation-top heights do
not reach 3 km above the FLH for either convective or
stratiform rain, demonstrating weaker cold-rain processes and being remarkably different from the precipitation profiles used by the GSMaP1 algorithm shownin Figs. 2a and 2b. Heavy rainfall from shallow clouds inthis area has been reported by previous studies (Takeda
et al. 1976; Takeda and Takase 1980; Sakakibara 1981).
Figure 3 shows the PCT85 dependence on the surface
rain rate (i.e., LUTs) calculated using original precipitation profiles (Figs. 2a,b) and orographic precipitation profiles (Figs. 2c,d). The GSMaP algorithm
uses not only PCT85 but also PCT37 to reduce theoverestimation of rainfall from scattering signaturesassociated with tall precipitation where the thickness of
the ice layer between the precipitation-top height andthe FLH is greater than 4 km (Aonashi et al. 2009). In
this case, the scattering signatures are not strong, and the
GSMaP algorithm therefore mainly uses PCT85. It isseen that PCT85 calculated from the original precipitation profiles decreases more rapidly with therainfall rate than that calculated from orographic precipitation profiles, because the thickness of the ice layer
in the original precipitation profiles is greater than thatin the orographic precipitation profiles. Therefore, fora given PCT85 (e.g., 250 K), the LUT obtained from theoriginal precipitation profiles gives a rain rate (e.g.,
3 mm h21) that is lower than that obtained from orographic precipitation profiles (e.g., 10 mm h21), leading
to underestimation by the GSMaP1 algorithm.
Figure 4 presents a case of orographic rainfall over theKii Peninsula on 30 March 2004 that is associated witha frontal cyclone. In this case, the PR algorithm estimates heavy rainfall on the southeast coast (Fig. 4a),which is in agreement with the RA data (not shown).
Also for this case, the surface rain rates estimated by theJANUARY 2013

SHIGE ET AL.

247

FIG. 4. Similar to Fig. 1, but for 30 Mar 2004 (TRMM orbit No. 36320): (a) PR 2A25 near-surface rain rate (mm h21), (b) TMI PCT85(K), (c) GSMaP1 surface rain (mm h21), and (d) GSMaP2 surface rain (mm h21). The observation times of PR and TMI data were 0550
and 0551 UTC, respectively.

GSMaP1 algorithm (Fig. 4c) from the TMI-observed
PCT85 (Fig. 4b) are lower than PR estimates. The
GPROF algorithm also underestimates surface rainfall (not shown). Figure 4d shows surface rainfall
rates estimated by the GSMaP algorithm using the
LUTs calculated from orographic precipitation profiles (hereinafter GSMaP2). The LUT calculated fromorographic precipitation profiles provides better agreement between the GSMaP2 estimates and PR estimatesalong the southeast coast. The GSMaP2 estimates are
greater than the PR estimates over all regions except thesoutheast coast, however.

The above results indicate the importance of separating precipitation profiles (and the LUTs derived fromthem) according to rain type (i.e., orographic or nonorographic rainfall) for radiometer rainfall estimates.
This is a situation similar to that for radar rainfall estimates, in which separating raindrop size distributions(and radar reflectivity–rainfall rate relationships derived from them) according to rain type is important(Rosenfeld and Ulbrich 2003). In particular, for shallow
orographic rainfall produced by low-level orographic
lifting of maritime air (Fig. 5), raindrop size distributions
have an extremely small median volume diameter in248

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGYVOLUME 52

FIG. 5. Conceptual model of warm-rain processes enhanced by lowlevel orographic lifting of maritime air.

comparison with all other types. Hence, raindrop size
distribution assumptions account for a gross radar underestimation of the orographic rainfall as well as nondetection of some shallow convection because of groundclutter. A larger number of small raindrops for orographic rainfall is attributed to active cloud-drop coalescence and warm-rain processes. The cloud-drop
coalescence in maritime clouds with large cloud drops is
so fast that rainfall develops low in the growing convective elements and precipitates while the clouds arestill growing. This is consistent with the precipitation
profiles without substantial upper-level ice particles
shown in Figs. 2c and 2d. The area focused on in this study
is a coastal region, and maritime air is thus orographically
lifted.
Here, we develop an orographic/nonorographic rainfall classification scheme and dynamically select the
LUTs that are calculated from orographic precipitation
profiles or from original precipitation profiles (Fig. 6).
As reviewed by Lin (2007) and Houze (2012), mechanisms by which mountains and hills affect precipitating
clouds are extremely complicated and are highly dependent on many factors. In this study, only the case inwhich warm-rain processes are enhanced by low-level
orographic lifting of maritime air (Fig. 5) is considered.
The orographically forced vertical motion may beroughly estimated from the lower boundary condition
for flow over mountains (Lin 2007):
w5Dh
5 VH  $h ,Dt

(1)where h(x, y) is the terrain height as a function of x and y
and VH is the surface horizontal wind. Figure 7a shows
the orographically forced vertical motion estimatedfrom Eq. (1). Upward motion is found along the southeast coast where strong rainfall rates are estimated by
the PR. Here, the horizontal wind in a 0.18 cell is generated by spatially interpolating JRA data on a grid with
1.258 resolution, and terrain height in a 0.18 cell isFIG. 6. Flowchart for GSMaP with an orographic/nonorographic
rainfall classification scheme (GSMaP3).

generated by spatially smoothing U.S. Geological Survey global 30 arc-s elevation data (GTOPO30) witha horizontal grid spacing of approximately 1 km.
The condition for the determination of an orographic
rainfall pixel is
w . 0:1 (m s21 ) .

(2)

Besides the orographically forced vertical motion, theheavy orographic rainfall requires the convergence of
surface moisture flux Q, and, therefore, the condition forthe determination of an orographic rainfall pixel is givenbyQ . 0:5 3 1026 (s21 ) .

(3)

The 6-hourly JRA data with minimum time differencewith the TRMM observations are employed for the conditions in Eqs. (2) and (3). On the other hand, the JRAdata at 0000 UTC are used for LUTs that are calculated
daily, because of computational costs. As shown in Fig. 6,
for an orographic rainfall pixel determined by the conditions in Eqs. (2) and (3), the LUTs derived from orographic precipitation profiles (Figs. 2c,d) are used toestimate rainfall. On the other hand, for a nonorographic
rainfall pixel, the LUTs derived from original precipitation
profiles (Figs. 2a,b) are used to estimate rainfall. Hereinafter, the GSMaP algorithm that dynamically selects the
LUTs according to orographic/nonorographic rainfall
classification is referred to as GSMaP3.
Figures 7b and 7c show the areas in which the orographic LUT was used by GSMaP3 and the GSMaP3
estimates, respectively. Dynamic selection of the LUTs
on the basis of orographic/nonorographic rainfall classification gives good agreement between the GSMaP3

JANUARY 2013

SHIGE ET AL.

249

FIG. 7. As in Fig. 4, but for (a) orographically forced verticalmotion (cm s21) estimated using Eq. (1) with surface horizontal
winds from JRA data at 0600 UTC 30 Mar 2004, (b) the areasin which the orographic LUT was used by GSMaP3, and (c)GSMaP3 surface rain (mm h21).

estimates and PR estimates over all regions. The areas
determined by the condition in Eq. (2) are included inthose determined by the condition in Eq. (3). Therefore,
the areas in which the orographic LUT was used by
GSMaP3 (Fig. 7b) correspond to those determined by
Eq. (2). A threshold in Eq. (2) can be selected liberally(i.e., detection of the areas with weak upward motion at
the expense of misclassifying nonorographic rain pixels)
or conservatively (i.e., detection of only upward motion
above a certain threshold at the expense of eliminatingsome orographic rain pixels). This is a situation similar
to that for rain/no-rain classification methods for MWR
observations (Ferraro et al. 1998). The threshold in

Eq. (2) seems to be conservative for the case shown in
Fig. 7 and should be parameterized as is done for rain/norain threshold values of the cloud liquid water path (Kidaet al. 2009, 2010), but this is left for future studies. The roleof the condition in Eq. (3) will be described in section 3b.

b. Comparisons with the RA
Figure 8a depicts the RA rain rates averaged during
summer (June–August 2004) over the Kii Peninsula
when the swath of the TMI passes. The GSMaP1 algorithm did poorly in estimating the rain maximum over theKii Peninsula (Fig. 8c). Large negative values below
500 mm month21 found in areas with heavy orographic

250

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGYVOLUME 52

FIG. 8. Averages rain rates for (left) summer (June–August 2004) and (right) winter (January, February, and December 2004) for (a),(b)RA, (c),(d) GSMaP1, and (e),(f) GSMaP3.

JANUARY 2013

SHIGE ET AL.

251

FIG. 9. Rain-rate differences (a),(b) between GSMaP1 and RA (i.e., GSMaP1 2 RA) and (c),(d) between GSMaP3 and RA (i.e.,
GSMaP3 2 RA) for (left) summer (June–August 2004) and (right) winter (January, February, and December 2004).

rainfall (Fig. 9a) indicate underestimation of the GSMaP1
there. This result is consistent with those reported by
Negri and Adler (1993) and Kubota et al. (2009). On theother hand, the rain maximum can be seen in the GSMaP3
rain rates (Fig. 8e). Underestimation by the GSMaP1
algorithm for summer is mitigated by orographic/
nonorographic rainfall classification in the GSMaP3
algorithm, especially in coastal regions. Although there
are still negative values in Fig. 9c, the GSMaP3 estimates
are in better agreement with the RA estimates than are
the GSMaP1 estimates. As discussed earlier, the threshold in Eq. (2) seems to be conservative and orographic/
nonorographic rainfall classification may involve incorrectly eliminating some orographic rain pixels.

The RA rain rates averaged during winter (January,
February, and December 2004) shown in Fig. 8b are much
lower than those during summer shown in Fig. 8a. Correspondingly, differences between GSMaP1 and RA for
winter (Fig. 9b) are much smaller than those for summer(Fig. 9a). In contrast to the results for summer, the GSMaP3
rain rates (Fig. 8f) and differences between GSMaP3
and RA (Fig. 9d) for winter are the same as those for
GSMaP1 (Figs. 8d, 9b). The results suggest that orographic LUTs are used in summer but not in winter.
To clarify why orographic LUTs are not used in winter,rain is estimated by the GSMaP algorithm using a modified orographic/nonorographic rainfall classification inwhich an orographic rainfall pixel is determined only by

252

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGYVOLUME 52

FIG. 10. As in Fig. 9, but for differences between GSMaP4 and RA (i.e., GSMaP4 2 RA).

the condition in Eq. (2), which procedure is referred toas GSMaP4. For summer, differences between GSMaP4
and RA (Fig. 10a) are almost the same as those between
GSMaP3 and RA (Fig. 9c) except at some locations
with large positive values. In contrast, for winter, overestimation in GSMaP1 and GSMaP3 is exaggerated
by orographic/nonorographic rainfall classification in
GSMaP4 (Fig. 10b). Therefore, because of the condition in Eq. (3), orographic rain is not detected by theorographic/nonorographic rainfall classification during
winter. This is consistent with the fact that heavy rainfall
associated with shallow orographic rain systems isobserved in summer (Takeda et al. 1976; Takeda andTakase 1980; Sakakibara 1981).

4. Summary and conclusions
In this paper, we improved the performance of rainfall
estimates made by the GSMaP algorithm (Aonashi et al.
2009; Kubota et al. 2007) from the TMI data over the Kii
Peninsula, which is a heavy-rainfall region in Japan forwhich satellite methods have been shown in previous studies to poorly estimate the rain maximum (Negri and Adler
1993; Kubota et al. 2009). We developed an orographic/
nonorographic rainfall classification scheme that is basedon orographically forced upward vertical motion and theconvergence of surface moisture flux, and we dynamicallyselected LUTs that are calculated from orographic precipitation profiles or from original precipitation profiles.
Rainfall estimates made by the revised GSMaP algorithm
are in much better agreement with PR and RA estimates
than are those made by the original GSMaP algorithm.
Some of the rainiest areas of the world, such as thewest coast of India during the summer monsoon, aredominated by shallow orographic rain systems (Houze
1993; Liu and Zipser 2009). The orographic/nonorographic
rainfall classification scheme has the potential to significantly improve radiometer rainfall estimates overthese areas. For the global application of the revisedalgorithm, however, it is necessary to examine mechanisms by which mountains and hills affect precipitating
clouds and thus shape precipitation profiles. As reviewed
by Lin (2007) and Houze (2012), the mechanisms are
extremely complicated. In this study, only the simplest
mechanism, enhancement by orographically forced upward vertical motion, is considered. Even this low-level
orographic enhancement depends on the cloud microstructure and is complicated. Rosenfeld and Ulbrich(2003) suggested that the low-level enhancement would
probably be weaker in more microphysically continental
clouds with small cloud drops than in more microphysically maritime clouds with large cloud drops. Therefore,
precipitation profiles would probably have more upperlevel ice particles in more microphysically continental
clouds than in more microphysically maritime clouds.
Numerical assimilation and forecast systems are continuously improving, leading to better reanalyses. Hodges
et al. (2011) showed that differences in extratropical cyclones are largest between the older lower-resolution
JRA-25 and the newer high-resolution reanalyses, particularly in the Southern Hemisphere. Comparisons oforographic and nonorographic rainfall classification
schemes using reanalyses are interesting but are left to
future studies.
Acknowledgments. The authors thank Prof. C.
Kummerow from Colorado State University for a suggestion at the initial stage of this study. This study is supported

JANUARY 2013

SHIGE ET AL.

by the Earth Observation Research Center, the Japan
Aerospace Exploration Agency, a Grant-in-Aid for Scientific Research (KAKENHI), and the Environment
Fund of Mitsui & Co., Ltd. The authors thank threeanonymous reviewers for their constructive comments that
improved the clarity of the paper.
REFERENCES
Adler, R. F., A. J. Negri, P. R. Keehn, and I. M. Hakkarinen, 1993:
Estimation of monthly rainfall over Japan and surrounding
waters from a combination of low-orbit microwave and geosynchronous IR data. J. Appl. Meteor., 32, 335–356.
Aonashi, K., and Coauthors, 2009: GSMaP passive microwave
precipitation retrieval algorithm: Algorithm description andvalidation. J. Meteor. Soc. Japan, 87A, 119–136.
Ferraro, R. R., E. A. Smith, W. Berg, and G. J. Huffman, 1998: A
screening methodology for passive microwave precipitation
retrieval algorithms. J. Atmos. Sci., 55, 1583–1600.
Gebrenuchael, M., and F. Hossain, Eds., 2010: Satellite Rainfall
Applications for Surface Hydrology. Springer, 327 pp.
Hodges, K. I., R. W. Lee, and L. Bengtsson, 2011: A comparison of
extratropical cyclones in recent reanalyses ERA-Interim, NASA
MERRA, NCEP CFSR, and JRA-25. J. Climate, 24, 4888–4906.
Hong, Y., R. F. Alder, A. Negri, and G. J. Huffman, 2007: Floodand landslide applications of near real-time satellite rainfall
products. Nat. Hazards, 43, 285–294.
Houze, R. A., Jr., 1993: Cloud Dynamics. Academic Press, 573 pp.
——, 2012: Orographic effects on precipitating clouds. Rev. Geophys., 50, RG1001, doi:10.1029/2011RG000365.
Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-global, multi-year,
combined-sensor precipitation estimates at fine scale. J. Hydrometeor., 8, 38–55.
Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and K. Okamoto,2000: Rain-profiling algorithm for the TRMM precipitation
radar. J. Appl. Meteor., 39, 2038–2052.
——, ——, J. Kwiatkowski, R. Meneghini, J. Awaka, andK. Okamoto, 2009: Uncertainties in the rain profiling algorithm for the TRMM precipitation radar. J. Meteor. Soc. Japan,
87A, 1–30.
Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004:
CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at highspatial and temporal resolution. J. Hydrometeor., 5, 487–503.
Kida, S., S. Shige, T. Kubota, K. Aonashi, and K. Okamoto, 2009:
Improvement of rain/no-rain classification methods for microwave radiometer observations over ocean using the 37-GHz
emission signature. J. Meteor. Soc. Japan, 87A, 165–181.
——, ——, T. Manabe, T. S. L’Ecuyer, and G. Liu, 2010: Cloudliquid water path for the rain/no-rain classification method
over ocean in the GSMaP algorithm. Trans. Japanese Soc.
Artif. Intell. Aerosp. Tech. Japan, 8, 19–23.
Kidd, C. K., D. R. Kniveton, M. C. Todd, and T. J. Bellerby, 2003:
Satellite rainfall estimation using combined passive microwave and infrared algorithms. J. Hydrometeor., 4, 1088–1104.
——, R. Ferraro, and V. Levizzani, 2010: The fourth International
Precipitation Working Group workshop. Bull. Amer. Meteor.
Soc., 91, 1095–1099.
Kozu, T., and Coauthors, 2001: Development of precipitation radar
onboard the Tropical Rainfall Measuring Mission (TRMM)
satellite. IEEE Trans. Geosci. Remote Sens., 39, 102–116.

253

Kubota, T., and Coauthors, 2007: Global precipitation map using
satelliteborne microwave radiometers by the GSMaP project:
Production and validation. IEEE Trans. Geosci. Remote Sens.,
45, 2259–2275.
——, T. Ushio, S. Shige, S. Kida, M. Kachi, and K. Okamoto, 2009:Verification of high resolution satellite-based rainfall estimates around Japan using gauge-calibrated ground radar dataset. J. Meteor. Soc. Japan, 87A, 203–222.
Kuligowski, R. J., 2002: A self-calibrating real-time GOES rainfall
algorithm for short-term rainfall estimates. J. Hydrometeor., 3,
112–130.
Kummerow, C., and Coauthors, 2001: The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimationfrom passive microwave sensors. J. Appl. Meteor., 40, 1801–
1820.
Kwon, E.-H., B.-J. Sohn, D.-E. Chang, M.-H. Ahn, and S. Yang,
2008: Use of numerical forecasts for improving TMI rain retrievals over the mountainous area in Korea. J. Appl. Meteor.
Climatol., 47, 1995–2007.
Lin, Y.-L., 2007: Mesoscale Dynamics. Cambridge, 630 pp.
Liu, C., and E. J. Zipser, 2009: ‘‘Warm rain’’ in the tropics: Seasonal and regional distribution based on 9 yr of TRMM data.
J. Climate, 22, 767–779.
Liu, G., 1998: A fast and accurate model for microwave radiancecalculations. J. Meteor. Soc. Japan, 76, 335–343.
Makihara, Y., 2007: Steps towards decreasing heavy rain disasters
by short-range precipitation and land-slide forecast using
weather radar accompanied by improvement of meteorological operational activities (in Japanese). Tenki, 54, 21–33.
——, N. Uekiyo, A. Tabata, and Y. Abe, 1996: Accuracy of RadarAMeDAS precipitation. IEICE Trans. Commun., 79, 751–762.
McCollum, J. R., and R. R. Ferraro, 2003: Next generation of
NOAA/NESDIS TMI, SSM/I, and AMSR-E microwave land
rainfall algorithms. J. Geophys. Res., 108, 8282, doi:10.1029/
2001JD001512.
——, and ——, 2005: Microwave rainfall estimation over coasts.
J. Atmos. Oceanic Technol., 22, 497–512.
Negri, A. J., and R. F. Adler, 1993: An intercomparison of threesatellite infrared rainfall techniques over Japan and surrounding waters. J. Appl. Meteor., 32, 357–373.
Okamoto, K., 2003: A short history of the TRMM precipitation
radar. Cloud Systems, Hurricanes and the Tropical Rainfall
Measurement Mission (TRMM): A Tribute to Dr. Joanne
Simpson, Meteor. Monogr., No. 51, Amer. Meteor. Soc., 187–
195.
Olson, W. S., and Coauthors, 2006: Precipitation and latent heating
distributions from satellite passive microwave radiometry.
Part I: Improved method and uncertainties. J. Appl. Meteor.
Climatol., 45, 702–720.
Onogi, K., and Coauthors, 2007: The JRA-25 Reanalysis. J. Meteor.
Soc. Japan, 85, 369–432.
Rosenfeld, D., and C. W. Ulbrich, 2003: Cloud microphysicalproperties, process, and rainfall estimation opportunities.
Radar and Atmospheric Science: A Collection of Essays in
Honor of David Atlas, Meteor. Monogr., No. 52, Amer. Meteor. Soc., 237–258.
Sakakibara, H., 1981: Heavy rainfall from very shallow convective
clouds. J. Meteor. Soc. Japan, 59, 387–394.
Scofield, R. A., and R. J. Kuligowski, 2003: Status and outlook of
operational satellite precipitation algorithms for extremeprecipitation events. Wea. Forecasting, 18, 1037–1051.
Seto, S., N. Takahashi, and T. Iguchi, 2005: Rain/no-rain classification methods for microwave radiometer observations over

254

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY

land using statistical information for brightness temperatures
under no-rain conditions. J. Appl. Meteor., 44, 1243–1259.
Shige, S., T. Watanabe, H. Sasaki, T. Kubota, S. Kida, andK. Okamoto, 2008: Validation of western and eastern Pacificrainfall estimates from the TRMM PR using a radiativetransfer model. J. Geophys. Res., 113, D15116, doi:10.1029/
2007JD009002.
Simpson, J., C. Kummerow, W.-K. Tao, and R. F. Adler, 1996: On
the Tropical Rainfall Measuring Mission (TRMM). Meteor.
Atmos. Phys., 60, 19–36.
Sorooshian, S., K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and
D. Braithwaite, 2000: Evaluation of PERSIANN systemsatellite-based estimates of tropical rainfall. Bull. Amer.
Meteor. Soc., 81, 2035–2046.
Spencer, R. W., H. M. Goodman, and R. E. Hood, 1989: Precipitation retrieval over land and ocean with SSM/I: Identification and characteristics of the scattering signal. J. Atmos.
Oceanic Technol., 6, 254–273.
Takayabu, Y. N., 2006: Rain-yield per flash calculated from
TRMM PR and LIS data and its relationship to the contribution of tall convective rain. Geophys. Res. Lett., 33, L18705,
doi:10.1029/2006GL027531.
——, 2008: Observing rainfall regimes using TRMM PR and LIS
data. GEWEX News, 18, 9–10.

VOLUME 52

Takeda, T., and K. Takase, 1980: Radar observation of rainfall
system modified by orographic effects. J. Meteor. Soc. Japan,
58, 500–516.
——, N. Moriyama, and Y. Iwasaka, 1976: A case study of heavy
rain in Owase area. J. Meteor. Soc. Japan, 54, 32–41.
Todd, M. C., and J. O. Bailey, 1995: Estimates of rainfall over theUnited Kingdom and surrounding seas from the SSM/I using
the polarization corrected temperature algorithm. J. Appl.
Meteor., 34, 1254–1265.
Turk, F. J., and S. D. Miller, 2005: Toward improving estimates ofremotely sensed precipitation with MODIS/AMSR-E
blended data techniques. IEEE Trans. Geosci. Remote Sens.,
43, 1059–1069.
Ushio, T., and Coauthors, 2009: A Kalman filter approach to
the global satellite mapping of precipitation (GSMaP) fromcombined passive microwave and infrared radiometric data.
J. Meteor. Soc. Japan, 87A, 137–151.
Vicente, G. A., J. C. Davenport, and R. A. Scofield, 2002: The roleof orographic and parallax corrections on real time high resolution satellite rainfall estimation. Int. J. Remote Sens., 23,
221–230.
Wang, N.-Y., C. Liu, R. Ferraro, D. Wolff, E. Zipser, andC. Kummerow, 2009: TRMM 2A12 land precipitation product—
Status and future plans. J. Meteor. Soc. Japan, 87A, 237–253.