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Improvement of High-Resolution Satellite Rainfall Product for Typhoon Morakot(2009) over Taiwan
AINA TANIGUCHI, SHOICHI SHIGE, MUNEHISA K. YAMAMOTO, AND TOMOAKI MEGA
Graduate School of Science, Kyoto University, Kyoto, JapanSATOSHI KIDA, TAKUJI KUBOTA, AND MISAKO KACHI
Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, JapanTOMOO USHIO
Department of Electrical, Electronic, and Information Engineering, Osaka University, Suita, JapanKAZUMASA AONASHI
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan(Manuscript received 13 March 2013, in final form 27 July 2013)
ABSTRACTThe authors improve the high-resolution Global Satellite Mapping of Precipitation (GSMaP) product for
Typhoon Morakot (2009) over Taiwan by using an orographic/nonorographic rainfall classification scheme.
For the estimation of the orographically forced upward motion used in the orographic/nonorographic rainfall
classification scheme, the optimal horizontal length scale for averaging the elevation data is examined andfound to be about 50 km. It is inferred that as the air ascends en masse on the horizontal scale, it becomes
unstable and convection develops. The orographic/nonorographic rainfall classification scheme is extended to
the GSMaP algorithm for all passive microwave radiometers in orbit, including not just microwave imagers
but also microwave sounders. The retrieved rainfall rates, together with infrared images, are used for the highresolution rainfall products, which leads to much better agreement with rain gauge observations.

1. Introduction
Typhoon Morakot (2009) produced a huge amount of
rainfall and had a catastrophic impact on Taiwan (Wuand Yang 2011; Wu 2013). The extreme amount of rainfall triggered severe flooding and enormous mudslides
in southern Taiwan, which caused so-called gauge denialsituations, where ground rain gauges were either washedaway or damaged.
Under gauge denial situations, one possible substitutefor conventional gauges for surface hydrological applications, such as flood and landslides, is high-resolution
satellite rainfall products (0.18–0.258 latitude–longitude
and 0.5–3 hourly), available by combining data obtained

Corresponding author address: Shoichi Shige, Graduate Schoolof Science, Kyoto University, Kiashirakawa-Oiwake, Sakyo, Kyoto
606-8502, Japan.
E-mail: shige@kugi.kyoto-u.ac.jp
DOI: 10.1175/JHM-D-13-047.1
Ó 2013 American Meteorological Society

by microwave radiometers (MWRs) in low Earth orbit andby infrared radiometers (IRs). Following the great successof the Tropical Rainfall Measuring Mission (TRMM;
Kummerow et al. 1998), which carries the first spaceborne
precipitation radar (PR; Kozu et al. 2001; Okamoto 2003),
the development of high-resolution satellite rainfall products has accelerated (see reviews in Gebremichael andHossain 2010). However, Chen et al. (2013) suggested thathigh-resolution satellite rainfall products, such as the
TRMM Multisatellite Precipitation Analysis (TMPA;
Huffman et al. 2007), the Climate Prediction Center
Morphing product (CMORPH; Joyce et al. 2004), and thePrecipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (Sorooshian et al.
2000), cannot be used as a substitute because these products
demonstrated poor performance over the mountainous
terrain of Taiwan during heavy rainfall events.
Ge et al. (2010) simulated this event numerically andconcluded that the extreme amount and distribution of

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rainfall was mainly a response to Taiwan’s terrain anda strong monsoon surge. The identification of the poor
performance of high-resolution satellite rainfall products over mountainous areas can be found in Kubota
et al. (2009) for Japan and in Dinku et al. (2010) for
Africa and South America. Kubota et al. (2009) suggested that one of the main reasons for such errors is that
MWR algorithms might underestimate heavy rainfall
associated with shallow orographic rainfall systems.
There have been few studies on the improvement of
MWR rain retrieval for mountainous regions. Following
the study by Vicente et al. (2002) on the improvement ofIR-based rain retrieval, Kwon et al. (2008) developed
topographic correction factors, as a function of terrain
slope, low-level wind, and moisture parameters for the
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. Recently, Shige et al. (2013, hereafter S13)
improved the performance of rainfall estimates made by
the Global Satellite Mapping of Precipitation (GSMaP)
MWR algorithm (hereafter GSMaP_MWR; Aonashi
et al. 2009; Kubota et al. 2007) from TMI data for theKii Peninsula, which is a region of heavy precipitationin Japan for which satellite methods of estimating the
maximum rainfall amounts have been shown to bepoor (Negri and Adler 1993; Kubota et al. 2009). They
did not introduce orographic correction factors to the
GSMaP_MWR algorithm, but rather they incorporated a
dynamic selection of lookup tables (LUTs) that areappropriate for heavy orographic rainfall based on an
orographic/nonorographic rainfall classification scheme.
In this study, the GSMaP_MWR algorithm with theorographic/nonorographic rainfall classification scheme,
developed by S13, is applied to Typhoon Morakot (2009).
As a first step, we focus on observations derived from the
TRMM satellite overpass of Typhoon Morakot overTaiwan. Second, the GSMaP_MWR algorithm is applied
to MWRs on other satellites, and then high temporal
resolution interpolation for the GSMaP_MWR products
is obtained from cloud-top motion derived from two
successive IR images and a Kalman filter (hereafterreferred to as GSMaP_MVK; Ushio et al. 2009). The
GSMaP_MVK estimates are compared with a relatively
dense rain gauge network over Taiwan Island.

2. Data
The GSMaP_MWR algorithm consists of a forward
calculation part to calculate LUTs showing the relationship between the rainfall rate and brightness temperatures Tb with a radiative transfer model (RTM) and

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a retrieval part to estimate the precipitation rate from theobserved Tb using the LUTs. Here, the GSMaP_MWR
overland algorithm is described. For further details, refer
to Aonashi et al. (2009).
From forward calculations with a four-stream RTM(Liu 1998), LUTs showing the relationship between therainfall rate and Tb were computed daily for 5.08 3 5.08
latitude–longitude boxes. The RTM calculation requires
information on atmospheric variables and precipitationrelated variables. In this study, atmospheric temperature,
freezing-level height (FLH), and surface temperature areadapted from the Japan Meteorological Agency (JMA)
Climate Data Assimilation System (JCDAS), which isthe continuation of the Japanese 25-yr Reanalysis project(JRA-25; Onogi et al. 2007).
The convective and stratiform precipitation models
for precipitation-related variables such as hydrometeor
profiles 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 ratioof 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 classified statistically trimonthly.
The convective and stratiform precipitation profiles of PRdata are averaged over prescribed precipitation ranges
for each precipitation type. In this averaging, profiles
relative to the FLH are used to exclude the effects ofatmospheric temperature variations. The database of
precipitation types and profiles makes it possible for thealgorithm to accommodate trimonthly variations in typical hydrometeor profiles.
First, we focus on observations derived from a TRMM
satellite overpass of Typhoon Morakot over Taiwan. A
comparison of TMI estimates with nearly coincident PR
estimates is very useful for the development and validation of rainfall retrievals for TMI. For the comparison
with the GSMaP_MWR estimates, we use the PR 2A25
version 6 product (Iguchi et al. 2009) and the TMI 2A12
version 6 product derived from the GPROF algorithm(Kummerow et al. 2001; McCollum and Ferraro 2003;
Olson et al. 2006; Wang et al. 2009).
Observations derived from other microwave imagers,
such as the Advanced Microwave Scanning Radiometerfor the Earth Observing System (AMSR-E), the Special
Sensor Microwave Imager (SSM/I), and Special Sensor
Microwave Imager/Sounder (SSMIS) as well as the Advanced Microwave Sounding Unit (AMSU) microwave
sounder, are used to obtain high-resolution rainfall mapsof Typhoon Morakot over Taiwan. The GSMaP_MWR
over land algorithm for AMSU (Shige et al. 2009) isDECEMBER 2013

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FIG. 1. Observations derived from a TRMM satellite overpass of Typhoon Morakot over Taiwan area on 8 August
2009 (TRMM orbit 66832). (a) SRTM30 elevation (m), (b) PR 2A25 near-surface rain (mm h21), (c) GPROF surface
rain (mm h21), and (d) GSMaP_MWR surface rain (mm h21) estimated from TMI-observed Tbs. The observation
times of PR and TMI were 2043 and 2044 UTC, respectively.

almost the same as that for microwave imagers, except
for the process of making the LUTs, where differencesin scanning geometry between imagers (conical scanning) and sounders (cross-track scanning) are taken
into account.
High temporal resolution interpolation for the
GSMaP_MWR products (GSMaP_MVK) is obtainedwith cloud-top motion derived from two successive IR
images and a Kalman filter (Ushio et al. 2009). For thecomparison with the GSMaP_MVK estimates, we use the
TMPA near-real-time version product (3B42RT). Multisatellite MWR rainfall estimates are calibrated by the
TRMM estimates, and the geostationary IR rainfall estimates are made by calibrating IR Tbs with the MWR
rainfall estimates using a histogram-matching technique(Huffman et al. 2007). The TMPA 3B42RT rain estimates consist of the MWR estimates where available andthe IR estimates otherwise. The rain gauge data are notused in the TMPA 3B42RT products for calibration. The
temporal resolution is 3 hours, and the spatial resolution
is 0.258 3 0.258 latitude–longitude. We also use hourly
precipitation data observed at 403 rain gauge stations in
Taiwan, which are provided by Taiwan’s Central Weather
Bureau. The geographical distribution of the rain gauge
stations is mentioned later.

3. Results and discussion
a. TRMM overpass
Figure 1 shows the observations derived from a TRMM
satellite overpass of Typhoon Morakot over Taiwan on
8 August 2009. High surface rainfall rates of .25 mm h21
were detected on the western side of the Central Mountain Range running southwest to northeast in Taiwan(Fig. 1a) in the PR data (Fig. 1b). Large surface rainfall

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FIG. 2. Precipitation profiles for (a) convective and (b) stratiform rain used by the original GSMaP_MWR algorithm to produce a LUT
for rain retrieval in the case shown in Fig. 1d, (c) convective and (d) stratiform rain for the orographic rainfall case in the Kii Peninsula
derived by S13, and (e) convective and (f) stratiform rain over the land of Taiwan observed by the PR for the case shown in Fig. 1b. The
abscissa is the rainfall rate (mm h21) and the ordinate is the height difference (km) from the FLH. Each curve is colored relative to surface
rainfall rate.

events, such as those seen in the PR data, were not detected in the surface rainfall data retrieved from the TMI
data by the GPROF algorithm (Fig. 1c), nor in the original GSMaP_MWR algorithm surface rainfall estimatedfrom the TMI-observed Tbs (Fig. 1d).
In the GSMaP_MWR algorithm, the surface rainfall
estimate is obtained mainly through estimates from thepolarization-corrected temperatures (Spencer et al. 1989)
at 85 GHz (PCT85) using LUTs, which depend greatly
on precipitation profiles inputted into the RTM calculations. S13 compared the precipitation profiles used in theoriginal GSMaP_MWR (Figs. 2a,b) with the orographic
precipitation profiles (Figs. 2c,d) observed by PR over
the Kii Peninsula and used for the improvement of

orographic rainfall estimates. The orographic precipitation profiles had much lower precipitation top heights
than those of the original profiles, which was consistent
with the fact that heavy rainfall from shallow clouds inthis area has been reported by previous studies (Takeda
et al. 1976; Takeda and Takase 1980; Sakakibara 1981).
Warm-rain processes are enhanced by low-level orographic lifting of maritime air, such that rainfall develops
at a low level in the growing convective elements andprecipitates while the clouds are still developing. PCT85
in the LUT calculated from the original precipitation
profiles decreases more rapidly with rainfall rate thanthat in the LUT calculated from orographic precipitation
profiles (see Fig. 3 of S13) because the thickness of the ice

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woro 5Dh
5 VH  $hDt

Q 5 2$  VH q,

FIG. 3. As in Fig. 1d, but for surface rain rates estimated by the
GSMaP_MWR algorithm from the TMI data using LUTs calculated from the orographic rain profiles shown in Figs. 2c and 2d.

layer in the original precipitation profiles is greater thanthat in the orographic precipitation profiles. Therefore,
for a given PCT85, the LUT obtained from the original
precipitation profiles gives a rainfall rate that is lower
than that obtained from orographic precipitation profiles,
leading to an underestimation. Figures 2e and 2f show
precipitation profiles over Taiwan obtained from PR
data for the case shown in Fig. 1b. Their precipitation top
heights are lower than those of the originals (Figs. 2a,b),
suggesting that precipitation was enhanced when the
moist maritime air was forced by the topography of
Taiwan to ascend, causing significant precipitation throughthe activated warm-rain process, as described above.
This is consistent with the results of Tao et al. (2011) who
showed that ice processes have only a secondary effect on
surface rainfall for Typhoon Morakot based on the sensitivity of physics parameterization schemes in a highresolution simulation. Therefore, it is reasonable to adopt
the orographic precipitation profiles in Figs. 2c and 2d for
the Taiwan case.
Figure 3 presents surface rainfall rates estimated by
the GSMaP_MWR algorithm from TMI using the LUTs
calculated from orographic precipitation profiles. Heavy
rainfall not detected by the original GSMaP_MWR
algorithm was retrieved, but this shows an overestimation over a broad area. Accordingly, it is necessary to
introduce the orographic/nonorographic classification
scheme, developed by S13, to determine where to apply
the orographic precipitation profiles. The orographic/
nonorographic classification scheme is based on theorographically forced upward motion woro (m s21) andmoisture flux convergence Q (s21) as follows:

and

(1)
(2)

where VH is the horizontal surface wind (m s21) fromJCDAS and q is the water vapor mixing ratio (kg kg21)
calculated from JCDAS surface specific humidity. Notethat in this study, h(x, y) is the elevation (m) derived
from the Shuttle Radar Topography Mission (SRTM30;
Werner 2001; Farr et al. 2007) with a horizontal grid
spacing of approximately 1 km. The orographically forced
upward motion determines the location of considerable
condensation of water vapor produced in the ascending
air mass caused by the orographic effect, while the moisture flux convergence provides sufficient water vapor tosustain the precipitation. S13 defined threshold values
for these parameters and then applied the orographic
precipitation profiles to those pixels where the thresholdconditions were met.
Several studies have suggested that orographic influence on precipitation is described better by a smoothed
topography (Pedgley 1970; Vicente et al. 2002; Kwon
et al. 2008); thus, it would be better to adopt a smoothed
topography in calculating woro. Therefore, the optimal
horizontal length scale for averaging the elevation data
as h in Eq. (1) was examined, which was not discussed
in S13.
First, we averaged the original elevation data of 1-km
horizontal resolution (Fig. 1a) within the horizontal
length scale of 10, 50, 80, and 100 km and then calculated
woro using wind data from JCDAS with the minimum
time difference from the TRMM observation, as shownin Fig. 4. Note that the wind data were interpolated using
the four nearest gridpoint values to calculate woro at any
given point. Next, correlation coefficients between PR
2A25 near-surface rain in Fig. 1b and woro calculated
from Eq. (1) with the elevation averaged within thehorizontal length scale of 10, 50, 80, and 100 km, together
with the original 1-km resolution data, were examined in
the region of upward motion (i.e., positive woro).
The results are shown in Fig. 5. For original elevation
data with 1-km resolution and averaged elevation data
within 10 km, the low-resolution wind is equivalent tothe constant wind and woro only represents the detailed
topography (Fig. 4a), which results in a weak correlation.
On the other hand, for elevation data averaged within
80 and 100 km, woro does not have maximums (Fig. 4c)that correspond to the marked rainfall rate maximums
found in Fig. 1b, resulting in the relatively weak correlation. A relatively high correlation coefficient was
obtained for elevation data averaged within 50 km.
Compared with Fig. 1b, the areas with upward motion

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FIG. 4. Orographically forced upward motion woro (m s21) over Taiwan area estimated using Eq. (1) with surface wind data fromJCDAS at 1800 UTC 8 Aug 2009 and SRTM30 data: (a) original, 1 km, and averaged within the horizontal length scale of (b) 50 km and(c) 100 km. Vectors are JCDAS surface wind (m s21).

stronger than 0.1 m s21 in Fig. 4b correspond well to thoseareas with rainfall rates greater than 20 mm h21, andrelatively weak rainfall rates are found in those areas withdownward motion. Thus, it is inferred that as the air ascends en masse on the horizontal scale, about 50 km inthis case, it becomes unstable and convection develops.
Here, we chose to adopt the elevation data averaged
within the horizontal length scale of 50 km in calculatingworo. Moisture flux convergence was calculated with theoriginal resolution of the JCDAS data. The entire landof Taiwan is within the convergence region of the moisture flux (i.e., positive Q), and Q was more than 0.5 s21,
especially in the heavy rainfall area (not shown), which
satisfied the condition of Q for an orographic rainfall pixelin S13. Therefore, we used the same condition for thedetermination of an orographic rainfall pixel as in S13:
woro . 0:1 (m s21 ) andQ . 0:5 3 1026 (s21 ) .

Range in Fig. 1d is greatly improved, and the rainfall
distribution pattern in Fig. 1b is also estimated successfully, demonstrating the effectiveness of the GSMaP_
MWR algorithm with the orographic/nonorographic
rainfall classification scheme.

b. High-resolution satellite rainfall products
In this paper, the GSMaP_MWR algorithm with theorographic/nonorographic rainfall classification scheme
is applied not only to TMI but also to all spaceborne
MWRs: AMSR-E, SSM/I, SSMIS, and AMSU. Note that(3)

For an orographic rainfall pixel determined by condition (3), rainfall rates are estimated using LUTs thatare calculated from the orographic precipitation profiles(Figs. 2c,d). On the other hand, for a nonorographic
rainfall pixel, rainfall rates are estimated using LUTs thatare calculated from the original precipitation profiles(Figs. 2a,b). In this revised GSMaP_MWR algorithm,
LUTs calculated from the appropriate precipitation profiles are selected dynamically according to atmosphericconditions derived from 6-hourly JCDAS data, whereas
the original algorithm only deals with trimonthly variations in typical hydrometeor profiles.
Figure 6 presents surface rainfall estimated by the
GSMaP_MWR algorithm with the orographic/
nonorographic rainfall classification scheme. Underestimation on the western slope of the Central Mountain

FIG. 5. Correlation coefficients between PR 2A25 near-surface
rain shown in Fig. 1b and woro estimated using elevation data withfive horizontal resolutions (1, 10, 50, 80, and 100 km). Calculations
for correlation coefficients were made only for the upward motion
regions.

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FIG. 6. As in Fig. 1d, but for surface rain rate estimated by the
GSMaP_MWR algorithm with the orographic/nonorographic rainfall classification scheme from the TMI data.

the GSMaP_MWR algorithm using sounding channels(Shige et al. 2009) is applied to AMSU, whereas the
GSMaP_MWR algorithm using imaging channels(Aonashi et al. 2009) is applied to SSMIS.
High temporal resolution interpolation for the original
GSMaP_MWR products and the revised GSMaP_MWR
products with the orographic/nonorographic rainfall
classification scheme is obtained by cloud-top motion
derived from two successive IR images and a Kalmanfilter (Ushio et al. 2009). Figure 7 shows the time sequencecomparison of surface rainfall estimates from the TMPA
3B42RT, the original GSMaP_MVK, and the revised
GSMaP_MVK over Taiwan from 1800 UTC 8 August
2009 to 0300 UTC 9 August 2009. Because the temporal
resolution of the TMPA 3B42RT product is 3 h, the
GSMaP_MVK products with 1-h resolution are averaged
over the subsequent 3 h from the data time for comparison. As shown in Fig. 4, the interaction of southwesterly
flow with the topography leads to upward motion during
this period, which is consistent with previous studies(Ge et al. 2010; Tao et al. 2011), and thereby the revised
GSMaP_MWR estimates heavy rainfall using the LUTs
calculated from orographic precipitation profiles. Therefore, the revised GSMaP_MVK derived from the revised
GSMaP_MWR products estimates continuous heavy
rainfall over the mountain area, whereas 3B42RT and theoriginal GSMaP_MVK do not estimate such heavy rain.
Rainfall enhancement in the revised GSMaP_MVK
occurs on the eastern side of the mountains (Fig. 7l).
Observations derived from a TRMM satellite overpass
over Taiwan on 0315 UTC 9 August 2009 (TRMM orbit
66836) indicate that this is an overestimation by the1865revised GSMaP_MWR due to misclassifying nonorographic rain pixels. One of main reasons for the misclassification is the coarse resolution of JCDAS (1.258
latitude/longitude and 6 hourly).
Rainfall estimates from ground rain gauge observations, the TMPA 3B42RT, the original GSMaP_MVK,
and the revised GSMaP_MVK have been accumulated from 0000 UTC 6 August 2009 to 2400 UTC 10
August 2009 to yield a 5-day total rainfall, as shown in
Fig. 8, together with the geographical distribution ofrain gauge stations over Taiwan. For quantitative
comparison, ground rain gauge observations and satellite products were interpolated into a 0.088 latitude andlongitude grid.
Both the TMPA 3B42RT and the original GSMaP_MVK products underestimate the enormous amount of
rainfall observed by rain gauges for that period. However,
the revised GSMaP_MVK estimated successfully suchheavy rainfall and its distribution pattern is in good
agreement with that of the rain gauge observations,although it is difficult to compare them over the mountainarea where there are few rain gauge stations (see Fig. 8f).
Conversely, this supports the advantage of satellite observation in detecting heavy rainfall over mountain areas
where an observation network is sparse.
Figure 9 shows the comparison of the probability ofdetection (POD), false alarm ratio (FAR), and the critical success index (CSI). All three products have similar
POD, FAR, and CSI values for rainfall thresholds of 100–
500 mm. The TMPA 3B42RT has zero values of POD
and CSI for rainfall thresholds greater than 800 mm because it does not have rainfall estimates that exceed thatthreshold (Fig. 8b). The original GSMaP_MVK haverainfall estimates greater than 800 mm (Fig. 8c), but theareas with rainfall estimates greater than 800 mm do notcorrespond to those observed by rain gauges, resulting inlarge FAR values, particularly at thresholds of 900 and1000 mm. The FAR value of the revised GSMaP_MVK
increases with threshold, demonstrating that the revised
GSMaP_MVK overpredicts the intense rainfall area.
However, its FAR values at thresholds of 900 and 1000 mm
are lower than those of the original GSMaP_MVK. The
revised GSMaP_MVK has much higher CSI values thanboth the TMPA 3B42RT and the original GSMaP_MVK
for rainfall thresholds greater than 1000 mm, demonstrating that the revised GSMaP_MVK has better ability
to capture heavy rainfall.
Chen et al. (2013) showed that the CMORPH product(Joyce et al. 2004), as well as the TMPA 3B42RT, poorly
estimated the heavy rainfall associated with Typhoon
Morakot. The high temporal resolution interpolation of
the rainfall estimated using data from MWRs and IR
data for the CMORPH product is closer to that for the1866

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FIG. 7. Consecutive images from 1800 UTC 8 Aug 2009 to 0300 UTC 9 Aug 2009 with 3-hourly intervals from (a)–(d)TMPA 3B42 surface rain (mm h21), (e)–(h) the original GSMaP_MVK surface rain (mm h21), and (i)–(l) revised
GSMaP_MVK surface rain (mm h21). The GSMaP_MVK rainfall rates are the averaged values in the next 3 hours from thedata time.

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FIG. 8. Accumulated rainfall over Taiwan area from 0000 UTC 6 Aug 2009 to 2400 UTC 10 Aug 2009 [mm(5-days)21] for 0.088 resolution. (a) Rain gauge observations (gray areas in land mean ‘‘no data’’), (b) TMPA
3B42RT estimates, (c) the original GSMaP_MVK estimates, (d) the revised GSMaP_MVK estimates, (e) SRTM30
elevation (m), and (f) the geographical distribution of rain gauge stations in Taiwan (cross-shape symbols). The
shading represents SRTM30 elevation (m).

GSMaP_MVK product than for the TMPA 3B42RT.
On the other hand, the CMORPH product shares therainfall estimated using the GPROF algorithm from
MWR data (Fig. 1c) with the TMPA 3B42RT product.

Therefore, the main reason for failing to estimate heavy
rainfall associated with Typhoon Morakot is due to the
MRW algorithm, rather than the technique of combining
MWR and IR data, which is consistent with the findings

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FIG. 9. Comparison of (a) POD, (b) FAR, and (c) CSI based on different rainfall thresholds[mm (5-days)-1].

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of Kubota et al. (2009). Note that the results of the
GSMaP_MVK near-real-time version product, where theKalman filter model is applied only to the propagation
process forwards in time, are almost the same as thoseof the GSMaP_MVK product, where the Kalman filter
model is applied to the propagation process forwards
and backward in time. On the other hand, Chen et al.
(2013) showed that the TMPA 3B42, which is adjusted
by gauged measurements, presented less heavy rainfall
than the TMPA 3B42RT, probably because of theprocess of rescaling the TMPA 3B42RT to the monthly
rain gauge data.
As signals of heavy rainfall are indispensable for floodand landslide analysis/prediction applications (Harris
et al. 2007; Hong et al. 2007), it is a remarkable advancefor these applications that the revised GSMaP algorithm
succeeds in detecting heavy orographic rainfall continuously in the sense of time and space, while the present
algorithms fail. The results indicate that the revised
GSMaP algorithm is quite effective for the Taiwan caseif the horizontal length scale for averaging the elevation data is appropriate for calculating woro and if thethresholds in Eq. (3) are chosen properly. These would bea primary issue for future study. The horizontal lengthscale and thresholds appropriate for the orographic/
nonorographic rainfall classification scheme might varywith differing situations (topography, wind speeds, etc.),
and they must be determined objectively. Moreover,another issue that is not addressed is the precipitation
profiles used in the forward calculating part of the
GSMaP_MWR algorithm. The orographic precipitation
profiles (Figs. 2c,d) used in the revised GSMaP_MWR
have extremely low precipitation top heights comparedwith the actual precipitation profiles in this Taiwan case,as derived from PR data (Figs. 2e,f), and this might leadto an overestimation. Thus, there is the need to constructmore representative orographic precipitation profiles forbetter estimations by analyzing the relationships between
those parameters that represent orographic effects, suchas the orographically forced upward motion, and those
properties of vertical hydrometeor structures obtained
from PR observation such as the precipitation water path
(Masunaga et al. 2002).

4. Summary
The GSMaP_MWR algorithm with the orographic/
nonorographic rainfall classification scheme, developedby S13, is applied for Typhoon Morakot (2009), for which
the current satellite rainfall products have been shown ina previous study to estimate poorly the heavy rainfall(Chen et al. 2013). The classification scheme is based on
orographically forced upward motion and moisture flux

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convergence, which are calculated from elevation and
JCDAS data.
According to the correlation between the PR surface
rain and orographically forced upward motion calculated
from terrain data averaged within various horizontal
length scales, there is an optimal horizontal length scale atwhich the air ascends en masse, becomes unstable, andthen convection develops. The GSMaP_MWR algorithmwith the orographic/nonorographic rainfall classification scheme estimates successfully the heavy orographic
rainfall associated with Typhoon Morakot, and the underestimation by the present GSMaP_MWR algorithm
was improved significantly. Applying the revised GSMaP_
MWR algorithm to all MWRs (imagers and sounders)and combining them with IR data using the GSMaP_MVK algorithm, we succeed in estimating the heavy
orographic rainfall associated with Typhoon Morakot
with high temporal and spatial resolution.
The results suggest that this revised algorithm is effective for the improvement of rainfall estimates overmountainous areas when the resolution of the elevation
data and the thresholds to decide the original precipitation profiles, or the orographic precipitation profiles,
are chosen properly. However, the orographic rain profiles used for the improvement have quite low precipitation top heights, which might lead to overestimation insome cases. For better estimates, it is necessary to construct more representative profiles of orographic rainfall,to investigate the optimum horizontal length scale foraveraging the elevation data used in calculating the orographically forced upward motion, and to determine aproper threshold to decide where to apply the orographic
precipitation profiles. For the GSMaP products, which
are available in near real time (about 4 hours after observation) via the Internet (Kachi et al. 2011), analysis
data such as the JMA global analysis or forecasting data
should be used instead of the reanalysis data used here.
Despite these remaining issues, this study shows a remarkable advance in high-resolution satellite rainfall
products and in the possibility of their application to floodand landslide analysis/prediction because signals of heavy
rainfall are vital for these applications (Harris et al. 2007;
Hong et al. 2007). The revised GSMaP algorithm succeeds in detecting heavy orographic rainfall continuously
in the sense of time and space.
Acknowledgments. This study is supported 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 and Co., Ltd. The authors thank the three anonymous reviewers for their constructive comments that
helped improve the clarity of the paper.

1870

JOURNAL OF HYDROMETEOROLOGYAPPENDIX
Definitions of Acronyms
AMSR-EAMSU
CMORPH
CSI
FAR
FLH
GPROF
GSMaP
GSMaP_MVK
GSMaP_MWR
IR
JCDAS
JMA
JRA-25
LUT
MWR
PCT85
POD
PR
RTM
SRTM30
SSM/I
SSMIS
TMI
TMPA
TMPA
3B42RT
TRMM

Advanced Microwave Scanning Radiometer for the Earth Observing
SystemAdvanced Microwave Sounding Unit
Climate Prediction Center Morphing
Critical success index
False alarm ratio
Freezing-level height
Goddard profiling
Global Satellite Mapping of Precipitation
GSMaP algorithm based on moving
vector with Kalman filter
GSMaP algorithm for microwave
radiometers
Infrared radiometer
JMA Climate Data Assimilation System
Japan Meteorological Agency
Japanese 25-yr Reanalysis
Lookup table
Microwave radiometer
Polarization-corrected temperatures
at 85 GHz
Probability of detection
Precipitation Radar
Radiative transfer model
Shuttle Radar Topography Mission
30 arc-second elevation
Special Sensor Microwave Imager
Special Sensor Microwave Imager/
Sounder
TRMM Microwave Imager
TRMM Multisatellite Precipitation
Analysis
TMPA 3B42 near-real-time version
product
Tropical Rainfall Measuring Mission
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