Journal of the Meteorological Society of Japan, Vol. 87A, pp. 203–222, 2009
DOI:10.2151/jmsj.87A.203

203

Verification of High-Resolution Satellite-Based Rainfall
Estimates around Japan Using a Gauge-Calibrated Ground-Radar Dataset
Takuji KUBOTA
Earth Observation Research Center, Japan Aerospace and Exploration Agency, Tsukuba, Japan

Tomoo USHIO
Department of Electrical, Electronic, and Information Engineering, Osaka University, Suita, Japan

Shoichi SHIGE, Satoshi KIDA
Department of Aerospace Engineering, Osaka Prefecture University, Sakai, Japan

Misako KACHI
Earth Observation Research Center, Japan Aerospace and Exploration Agency, Tsukuba, Japanand

Ken'ichi OKAMOTO
Faculty of Environmental and Information Studies, Tottori University of Environmental Studies, Tottori, Japan(Manuscript received 30 June 2008, in final form 31 October 2008)

Abstract
Global rainfall products of high spatial and temporal resolutions have been provided using combined
data from passive microwave (PMW) sensors in low Earth orbit and infrared (IR) radiometers in geostationary Earth orbit (GEO). This study compared six satellite rainfall estimates around Japan with reference
to a ground-radar dataset calibrated by rain gauges provided by the Japan Meteorological Agency (JMA)from January through December 2004. Validation results tended to be better for the products with temporalinterpolation based upon the morphed technique using GEO IR information. Satellite estimates were poor
for light rainfall during the warm season and for very heavy rainfall.
Further analyses of satellite estimates were conducted in terms of data sources and surface types.
Effective performance by the merger of PMW sounders over the ocean was verified by radar validation, in
addition to the best results of the PMW imagers. Overall, validation results over the ocean were best, andresults over mountainous regions were worst. Performance was poor over coasts and small islands, due tothe problem of PMW retrievals. This study focused on hydrometeor profiles of orographic heavy rainfall
over the Japanese Archipelago, which could be related to the poor performance of satellite estimates invery heavy rainfall.

Corresponding author: Takuji Kubota, Earth Obser­
vation Research Center, Japan Aerospace Exploration
Agency, 2-1-1 Sengen, Tsukuba-city, Ibaraki 305-8505,
Japan
E-mail: kubota@ieee.org©2009, Meteorological Society of Japan

1. Introduction
There has recently been increasing interest in highresolution precipitation datasets based on satellite remote sensing. Global precipitation products capable ofhigh spatial (0.25° or finer) and temporal (three-hour204

Journal of the Meteorological Society of Japanor shorter) resolutions have been provided by developers using combined data from passive microwave(PMW) sensors in low Earth orbit (LEO) and infrared(IR) radiometers in geostationary Earth orbit (GEO)(e.g., Sorooshian et al. 2000; Joyce et al. 2004; Turkand Miller 2005; Huffman et al. 2007; Ushio et al.
2009). For practical use of these datasets, Gottschalck
et al. (2005) considered them as potential input to the
Global Land Data Assimilation System, and Hong etal. (2007) proposed flood and landslide applicationsof near-real-time satellite rainfall data.
The development of precipitation datasets has
been enhanced since the launch of the TropicalRainfall Measuring Mission (TRMM) satellite in1997. Developed under a United States-Japan joint
mission, TRMM carries rain observation sensors
such as the TRMM Microwave Imager (TMI) and
the Precipitation Radar (PR) (Kummerow et al.
1998). Accurate measurement of precipitation by the
PR and simultaneous observations by the TMI and
PR have greatly advanced algorithm development.
An expanded follow-on mission of the TRMM, theGlobal Precipitation Measurement (GPM) satellite,
is planned. This mission will include a TRMM-likecore satellite carrying a Dual-Frequency Precipitation
Radar (DPR) and a GPM Microwave Imager (GMI),
and constellations of satellites equipped with PMW
radiometers (Smith et al. 2007).
Satellite precipitation datasets have been evaluatedusing rain gauges and ground-based radar in regional-scale domains (e.g., Gottschalck et al. 2005; Xieet al. 2007; Tian and Peters-Lidard 2007; Tian et al.
2007). The International Precipitation Working Group(IPWG) and the Program to Evaluate High-Resolution
Precipitation Products (PEHRPP) have establisheda program for in situ validation of daily rainfall estimates by operational satellite algorithms (Arkin etal. 2005; Ebert et al. 2007). The IPWG study built
upon satellite precipitation validation studies, such as
the Precipitation Intercomparison Project (PIP) (e.g.,Smith et al. 1998; Adler et al. 2001) and the Algorithm
Intercomparison Projects of the Global Precipitation
Climatology Project (GPCP) (e.g., Ebert et al. 1996).
As Ebert et al. (2007) pointed out, it is important thatstrengths and limitations of satellite precipitation data
be understood so that the data can be interpreted correctly. The Global Satellite Mapping of Precipitation(GSMaP) Project (Okamoto et al. 2005, 2007) has
contributed to IPWG and PEHRPP activities by verifying satellite data around Japan using gauge-calibrated radar data provided by the Japan MeteorologicalVol. 87A

Agency (JMA).
In this study, satellite rainfall estimates were validated, and their strengths and limitations were examined around Japan. Specifically, GSMaP rainfall
estimates by the PMW–IR blended algorithm werevalidated in detail. Ushio et al. (2009) developed aKalman filter technique for the GSMaP algorithm, although their paper did not feature evaluations of the
products from overall algorithms developed by the
GSMaP Project. The present study investigated the
GSMaP products; and issues of surface types, sensors, and techniques of algorithms were analyzed forthe domain around Japan. Section 2 describes thedata and method. Section 3 documents comparisons
between various satellite estimates. Section 4 demonstrates the validation of the GSMaP products, in termsof data sources, techniques, and surface types. Section
5 focuses on orographic heavy rainfall. Section 6 presents a summary.

2. Data and method
2.1 Analyzed period
Satellite rainfall estimates were validated withradar data for the period from January throughDecember 2004. Ten record-breaking tropical storms
and typhoons made landfall in Japan between Juneand October (Kim et al. 2005; Nakazawa 2006).
Moreover, in mid-July 2004 two well-known heavy
rainfall events were observed in Niigata-Fukushima
Prefectures and Fukui Prefecture, causing severe
economic and social losses in Japan. During 2004,
no events of El Niño/Southern Oscillation occurred.
The analyzed period was limited to one year, withno guarantee that the present results would be reproduced in other years. However, during 2004, satellite
sensors associated with this paper were not changed.
Moreover, spatial and temporal resolutions of the radar dataset remained fixed, while the resolutions were
upgraded by JMA. Thus, one merit in the analyses ofthe year 2004 was a constant number of instruments,
by which discussions of algorithm problems could beconcentrated on.
2.2 Data in this study
The PMW sensors in LEO were generally of two
types: imagers and sounders. Through 2004, five
PMW imagers were in operation in LEO: the TRMM
TMI, the Advanced Microwave Scanning Radiometerfor the Earth Observation System (AMSR-E) aboard
the National Aeronautics and Space Administration(NASA) satellite Aqua (Kawanishi et al. 2003), andMarch 2009

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three Special Sensor Microwave/Imager (SSM/I) unitsof the Defense Meteorological Satellite Program. The
over-ocean algorithm for the PMW imagers in rain
retrievals used information of emitted radiation fromrainwater with low-frequency channels (e.g., 10 GHz
and 19 GHz channels), in addition to scattering withhigh-frequency channels (e.g., 85 GHz channel); theover-land and over-coast algorithms used only scattering signals because of high and variable emissivityof the land surface (Wilheit et al. 1991; Grody 1991;
Petty 1994; Kummerow et al. 2001; McCollum andFerraro 2003; Olson et al. 2006; Kubota et al. 2007).
For scattering techniques, the relationship to the rain
rate was less direct than for emission, because thescattering was primarily due to the frozen hydrometeors above freezing level (Wilheit 1986). Three sounders (e.g., the advanced microwave sounding unit-B(AMSU-B) aboard National Oceanic and Atmospheric
Administration (NOAA) satellites) were in operation
through 2004. Rainfall estimates from the AMSU-B
were based on scattering information from highfrequency channels such as the 89 and 150 GHz
channels. In GEO, the Geostationary Operational
Environment Satellite (GOES-9) Visible Infrared
Spin-Scan Radiometer (VISSR) operated over Japan

in 2004.
a. Production by the GSMaP ProjectIn the GSMaP Project, surface rainfall was estimated by data from PMW and GEO IR radiometers.
The name convention of the GSMaP products is summarized in Table 1. The end product of the GSMaP
project was called GSMaP_MVK (hereafter, GSMaP).
The GSMaP resolution was hourly, at 0.1° × 0.1° latitude/longitude. GSMaP estimates were achieved by
the temporal interpolation of PMW retrievals usinga PMW–IR blended algorithm composed of a morphed technique (Joyce et al. 2004) and a Kalman filter(Ushio et al. 2009) using IR information. For computation of the PMW retrievals, estimates from the
PMW imagers were retrieved by the algorithm of the
GSMaP Project (Aonashi and Liu 2000; Kubota et al.
2007, 2009; Aonashi et al. 2009). Rainfall estimatesfrom the AMSU-B were provided by the NOAA
Microwave Surface and Precipitation Products System(Zhao and Weng 2002; Ferraro et al. 2005), which
was improved by Vila et al. (2007). In overlapped areas of imagers and sounders, averages of both wereused over land and coasts, and only imagers were usedover the ocean, due to the advantage of low-frequency

Table 1. Naming convention of rainfall products in the GSMaP Project.

Product

Data source

IR technique

Note

GSMaP_MVK(GSMaP)

PMW imagers, PMW
sounders,
GEO IR radiometers

Morphing and Kalmanfilter, by forward andbackward processes

End product by the
GSMaP project

Experimental
product name

Data source

IR technique

Note

G_3B41

PMW imagers, PMW
sounders, GEO IR
radiometers

Same as in TMPA 3B41

Test of the IR technique

G_NRT

PMW imagers, PMW
sounders, GEO IR
radiometers

Morphing and Kalmanfilter, by forward process

Test of the IR technique

G_IVK

PMW imagers,
GEO IR radiometers

Morphing and Kalmanfilter, by forward andbackward processes

No use of PMW sounders

G_MWI

PMW imagers

None

No use of PMW sounders
and GEO IR radiometers

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channels of the imagers. The GSMaP project did notnormalize the various rainfall estimates.
In addition, experiment datasets were analyzed inthis study (Table 1). The product that merges rainfall
estimates derived from the PMW-imagers is referred
to as G_MWI. The product from the blended algorithm using G_MWI estimates is named as G_IVK.
The purpose was to evaluate effects using the PMW
sounders. To test the IR technique, the temporal interpolation of PMW retrievals was performed experimentally using estimates of the TRMM Multi-satellite
Precipitation Analysis (TMPA) 3B41 data; this product is named as G_3B41.
In the PMW–IR blended algorithm, rainfall estimates were interpolated by propagating forward
and backward in time. For faster processing than for
GSMaP_MVK, we computed estimates in a process
named as G_NRT for near-real-time requirements, using an algorithm of the forward-only process.
b. T
 RMM Multi-satellite Precipitation Analysis(TMPA) 3B41, 3B42RT, and 3B42
The TMPA 3B42RT product is a combination of
the PMW product (3B40) and PMW-calibrated GEO
IR product (3B41), provided by the NASA Goddard
Space Flight Center. The 3B40 product is calibrated
by the TMI Goddard Profiling Algorithm (GPROF;
Kummerow et al. 2001; Olson et al. 2006) precipitation estimate for 3B42RT, and the TMI–PR combinedalgorithm (Haddad et al. 1997) precipitation estimate
for the 3B42. The 3B41 product uses 3B40 to correct
combined GEO IR data by the histogram-matching
technique. Histograms of time-space matched combined PMW precipitation rates and IR brightness temperatures (Tbs) are used to create spatially varying
calibration coefficients that convert IR Tbs to precipitation rates. The 3B42RT data are monthly combinedwith Climate Assessment and Monitoring System(CAMS) monthly rain gauge analysis developed by
the Climate Prediction Center (CPC) (Xie and Arkin
1996). This monthly combined analysis is then used
to create monthly scaling factors for each grid box,
which are applied to 3B42RT to obtain 3B42. 3B42
was the only product using gauge data in addition tosatellite data that was analyzed in this study. A fulldescription of the algorithm was given by Huffman etal. (2007). The horizontal resolution was 0.25° × 0.25°
latitude/longitude, and the temporal resolutions were
3 hours for 3B42RT and 3B42 and 1 hour for 3B41.
Since 3B42 corresponded to the middle of the 3-hourly period, the data were 1.5 hours out of phase with

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the other datasets, and radar datasets for validating
the TMPA were averaged during the correspondingtime period.
c. N
 OAA Climate Prediction Center Morphing
technique (CMORPH)
CMORPH uses motion vectors derived from halfhourly GEO IR imagery to propagate the precipitation estimates derived from PMW data. The shapeand intensity of the precipitation features are modified (morphed) during the time between PMW sensor
scans by time-weighted linear interpolation. For a fulldescription of the algorithm, see Joyce et al. (2004).
The dataset is provided by the NOAA CPC. The resolution is half-hourly at 8 km × 8 km nominal resolution. The CPC also provides QMORPH estimates,
which are similar to CMORPH estimates, except that
the PMW precipitation features are propagated via IR
data only forward in time, although QMORPH wasnot analyzed for this study.
d. P
 recipitation Estimation from Remotely Sensed
Information using Artificial Neural Networks(PERSIANN)
The PERSIANN dataset, provided by the Universityof California, Irvine, uses a neural network techniqueto estimate rainfall rates from GEO IR data. The neural network is calibrated with PMW data and can beconstantly updated as new data become available. The
neural network is then used to obtain precipitation estimates. A full description of the algorithm was given by Sorooshian et al. (2000) and Hsu et al. (1997).
The resolution of the global data of the PERSIANN
is 3-hourly at 0.25° × 0.25° latitude/longitude spatial
resolution.
e. N aval Research Laboratory-Blended satellite
technique (NRL-Blended)
In the NRL-Blended technique, GEO IR data are
first spatially averaged to the resolution of PMW data.
The GEO IR data are then paired with the PMW data
and used to calculate the IR rainfall threshold andrain-rate distribution for 2° grid boxes. The GEO IR
data are then registered onto a global 0.1° grid, andan inverse-distance weighted mean is calculated fromthe nearest 2° grid box and the surrounding eight 2°
grid boxes. A full description of the algorithm wasgiven by Turk and Miller (2005) and Turk and Mehta(2007). The dataset is provided by the Naval ResearchLaboratory. The resolution is 3-hourly at 0.25° ×
0.25° latitude/longitude. Archived NRL-blended dataMarch 2009

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started on January 28, 2004; the analyzed period was
February 1 to December 31, 2004, due to data availability.
f. R adar-Automated Meteorological Data Acquisition
System (AMeDAS) precipitation analysis
Radar-AMeDAS precipitation analysis data (hereafter, radar data) are a 1-hour accumulation precipitation estimate produced by JMA (Makihara et al.
1996; Makihara 2000, 2007). The data are createdfrom a composite of JMA operational 10 min cycle
radar data, calibrated by rain gauges (AMeDAS network and gauges operated by the Ministry of Land,

Fig. 1. (a) Analyzed area with surface types
from the TSDIS toolkit. Blue denotes
ocean, red denotes coast, and green denotes land. (b) GTOPO30 elevation map.

207

Infrastructure and Transport and the prefectural offices). The calibration technique is described in
Makihara (2000). JMA has operated 20 ground-based
weather radars since October 1999, and their detection range now covers almost all of the JapaneseArchipelago. AMeDAS includes 1320 automatic surface weather stations. The density of the AMeDAS
rain gauge network is one station in each 17 km by 17
km area.
From June 2003 to December 2005, the spatial
resolution was 0.025° latitude and 0.03125° longitude(2.5 km 2.5 km) and the temporal resolution was 30
min. Observation areas were 500 km 2 per radar. The
radar estimate was calibrated by rain gauges in viewof the Z-R relationship modification. Over sea, theradar estimates were calibrated by the mean relationship over land in a radar scan, or previous relationship(Shinpo 2001), and the difficulty of modification byrain gauges was recognized. Note that the calibration
over the ocean is uncertain and that results may beunreliable there. In this study, the observation areas
were limited to within 150 km of the oceanic stations
at Nase, Okinawa, and Ishigakijima, and to within
200 km of the other radar sites. Nase, Okinawa, andIshigakijima are small islands located south of 30°N,
and their observation areas are mostly ocean. Figure
1a depicts the analyzed area, which was the same asthat analyzed by Kubota et al. (2007) for validationof the TMI retrievals. These limitations were due toFig. 2. A distribution of sample size for analyzed 3-hourly radar data per grid with ahorizontal resolution of 0.25° × 0.25° latitude/longitude.

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apparent biases of the radar data, possibly because of
detection losses of low precipitation top heights far
from the radar sites and poor calibration over oceanic
areas, due to the sparsity of gauges.
2.3 Method of verification using radar data
Satellite estimates were validated for non-snowfall
regions because snowfalls were beyond the scopeof this study. Using routine surface meteorological
data at three weather stations in Japan, Matsuo et al.
(1981) found that precipitation types were dependent
on surface air temperature and surface relative humidity. Here, a simple threshold of surface temperatures above 4°C was adopted for non-snowfall areas.
Surface temperature data were derived by linear interpolation of JMA Global Analysis (GANAL) data. The
original resolution of the GANAL data was 6-hourlyfor 1.25° × 1.25° latitude/longitude. Figure 2 depicts a
distribution of sample size for analyzed 3-hourly radar
data. The amount of the data decreased in the northern area to about 60%. Note that this threshold may
allow warm regions above 4°C with snow-covered
land or sea ice.
This study used the four scenarios presented inTable 2, which is a 2 × 2 contingency table, in addition
to a well-known correlation coefficient and root meansquare error (RMSE). In Table 2, a represents correctly estimated rain events, b represents events whenrain was estimated but did not occur in the validation
data, c represents events when rain was not estimatedbut did occur in the validation data, and d represents
correctly estimated no-rain events. The probabilityof detection (POD) is the ratio of correct raining estimates to the number of raining events observed.
POD was defined as POD = a / (a + c) , where elements a through c are the number of occurrences ofeach scenario in Table 2. This statistic, also known as
the hit rate, is an index of rain detection ability. The
false-alarm ratio (FAR) is the fraction of raining esti-

Table 2. A 2 × 2 contingency table for evaluation.
Elements a through d are assigned the observed event
counts in each category.
radar observing
rain

radar observing
no-rainsatellite estimate
giving rainabsatellite estimate
giving no-raincdVol. 87A

mates that turned out to be wrong, or an index of false
rainfall. The FAR was defined as FAR = b / (a + b) .
Threat score (TS), which is not used in this study, isdefined as TS = a / (a + b + c) and gives the numberof correct raining estimates divided by the total number of occasions for which a raining event is observedor estimated. A modified version, the equitable threatscore (ETS) is defined asETS =a − arefa − aref + b + c,

where aref = (a + b) (a + c) / n and n is the sample
size. ETS is a modification of the TS that takes into
account the number of hits aref that could be expected,
due purely to random chance (cf., Ebert et al. 2007).
Because the sample size n is required to compute aref,
ETS depends on the number of correct no-raining estimates (the d scenario in Table 2), unlike the TS. The
frequency bias (FB) is the ratio of the estimated to observed rain areas, FB = (a + b) / (a + c). Here, FB = 1
for unbiased estimates, indicating that the number of
estimated raining events is the same as the number ofFig. 3. Monthly time series of satellite-based
rainfall estimates and the radar data averaged in the analyzed area for January
through December 2004. Red line denotes GSMaP data, green line denotes
CMORPH data, blue line denotes 3B42
data, light blue line denotes 3B42RT data,orange line denotes NRL-blended data,
and violet line denotes PERSIANN data.
The magenta line with open circles denotes radar data. The horizontal resolution of the datasets is 0.25° × 0.25° latitude/longitude.

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209

Fig. 4. Time series of radar validation for January through December 2004 using estimates in terms of (a),(c) spatial correlation coefficients and (b), (d) RMSEs. Daily estimates are analyzed in Figs. (a) and (b),and 3-hourly estimates are analyzed in (c) and (d). Monthly averaged statistics are displayed in the panels.
Lines for the satellite data are the same as in Fig. 3. The horizontal resolution of the datasets is 0.25° ×
0.25° latitude/longitude.

observed raining events. A further description of thestatistics was given by Wilks (2006). Statistics werecomputed for daily, 3-hourly, or hourly estimates andaveraged monthly.
The radar dataset is a 1-hour accumulation dataset from a collection of instantaneous scans, while asatellite estimate is based on instantaneous observation. Rain areas of 1-hour accumulation data tend tobe slightly larger than those of instantaneous data because rainfall areas propagate within an hour. Thus,
the POD and FB are expected to be slightly smallerusing the radar data rather than the instantaneousverification dataset, while the FAR is rather reliable.

This is less effective for estimates averaged over longer time-scales. In practice, satellite estimates havelarge errors, owing to their algorithms. Therefore, thispaper focuses on validations of the algorithms.
Surface-type information is from the land–sea database of the TRMM Science Data and Information
System (TSDIS) toolkit, a software library of functions provided for TRMM science algorithm developers.

3. C omparisons between various satellite estimates
This section compares six satellite estimates from

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Journal of the Meteorological Society of Japanvarious national centers and universities with the radar data. Figure 3 plots a monthly series of the radar
data and the satellite-based estimates averaged in theanalyzed area. In this area, rainfall amounts werelarge in May through June, as a result of two typhoon
landfalls and the stationary Baiu front, and in Augustthrough October, as a result of seven typhoon landfalls and the stationary Akisame front, in addition tocontributions by extratropical cyclones. The variationin 3B42 data, which was adjusted by gauge information, corresponded well to that in the radar data. The
3B42RT data were overestimated, while other data
were underestimated, based on radar data. Figures 4
and 5 plot time series of radar validation for January
through December 2004, with statistics averaged
for each month. The statistics were computed using
3-hourly estimates for Figs. 4c and d, or daily aver-Vol. 87A

aged estimates for the other figures, with a horizontal
resolution of 0.25° × 0.25° latitude/longitude. In Fig.
4a for daily comparison, spatial correlation coefficients were high during the boreal summer and low
during the boreal winter. Performances of satellite
estimates tended to be better during the warm season, which was consistent with validation results for
the United States (Gottschalck et al. 2005; Ebert et al.
2007) and Australia (Ebert et al. 2007), in addition toresults of the PIP-3 (Adler et al. 2001). The correlation coefficients (RMSEs) of GSMaP and CMORPH
were higher (smaller) than those of the other products.
RMSEs were large in the 3B42RT data, while those of
the 3B42 data, which were adjusted by gauges, were
smaller than the 3B42RT RMSEs.
Figures 4c and d present the results for 3-hourly estimates; they exhibit features similar to those of Figs.

Fig. 5. Same as Fig. 4 for daily estimates, except that (a) ETS, (b) FB, (c) POD, and (d) FAR are presented.

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Fig. 6.
Time series of radar validationfor 3-hourly estimates of GSMaP and
G_3B41 data. Monthly averaged (a)spatial correlation coefficients and (b)RMSEs are displayed in the panels. Dotsdenote GSMaP data, and open circles denote G_3B41 data.

4a and b. The correlation coefficients were lower thanthose in Fig. 4a, and the RMSEs were larger thanthose in Fig. 4b. This result indicates that the 3-hourly
satellite estimates were not as accurate as the daily estimates. For the 3-hourly estimates, the results provided by GSMaP and CMORPH were better than thoseof the other products, which were the same as for thevalidation results of daily estimates.
Figure 5 presents the 2 × 2 contingency table statistics. The ETS indicates similar but noisy features in
the correlation. The FB indicates underestimation ofrain areas in the satellite estimates. Case studies revealed that these underestimates were largely related
to mistaken rain detection, probably due to algorithm
problems, and it seems that the differences in the orig-

211

inal instantaneous and 1-hour accumulation data were
not very influential in daily estimates, as noted in
Section 2. The POD values of GSMaP and CMORPH
tended to be higher than for the other products. The
FAR values were higher during the cold season thanduring the warm season, indicating that false rainfall
was found in the satellite estimates more frequently.
The FAR values of the 3B42 data adjusted by thegauges were relatively small, especially during the boreal winter.
The superiority of GSMaP and CMORPH could be
related to the temporal interpolation of the PMW estimates based upon the morphed technique. Figure 6
clearly demonstrates this in the validation results of
3-hourly GSMaP and G_3B41 estimates. Here, the
GSMaP and G_3B41 procedures were the same, except for the IR interpolation method. Correlation coefficients for GSMaP were 0.2 higher than those for
G_3B41, and the RMSEs of the GSMaP were smaller
than those of the G_3B41. Thus, the IR interpolation
method can contribute significantly to differences invalidation results, as indicated in Figs. 4 and 5.
In the above analyses, a threshold of 0.0 mm day-1
was used to discriminate between rain (above 0.0
mm day-1) and no rain (0.0 mm day-1). For validationof rainfall above specific rain rates, four thresholds(1.0, 5.0, 10.0, and 20.0 mm day-1) were tested. The
results are plotted in Fig. 7 for the ETS, using daily
estimates for six rainfall products. High values of
ETS were found for moderate thresholds (1 to 5 mm
day-1) during the warm season for all products, including the gauge-adjusted 3B42. This result indicates thatsatellite estimates are poor for light rainfall during thewarm season. The ETS values, excluding light rainfall
for the 3B42, are comparable to those for the GSMaPand CMORPH, while the ETS values for all data of
the 3B42 are lower (Fig. 5a). The direct conversion
from the IR Tbs to the rain rates in the TMPA method
could have been strongly affected by a weak relationship between rain intensity and cloud top temperature
for light rainfall. For all products, ETS values for thethreshold of 20.0 mm day-1 were lower than thosefor the thresholds of 1.0 or 5.0 mm day-1. This result
suggests that their performances were poor in very
heavy rainfall. A possible reason will be discussed in
Section 5.

4. Comparison between GSMaP productsThis section compares GSMaP satellite estimateswith radar data. Further analyses were performed interms of data sources, techniques, and surface types

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Vol. 87A

Fig. 7. Time series of the ETS using daily estimates in terms of (a) GSMaP, (b) CMORPH, (c) NRL-Blended,(d) PERSIANN, (e) 3B42RT, and (f) 3B42 data. Monthly averaged statistics are displayed in the panels.
Open circles denote results for a threshold of 1.0 mm day-1, triangles denote results for a threshold of 5.0
mm day-1, plusses denote results for a threshold of 10.0 mm day-1, and crosses denote results for a thresholdof 20.0 mm day-1. Dots are the same as in Fig. 5a (i.e., a threshold of 0.0 mm day-1). The horizontal resolution of the datasets is 0.25° × 0.25° latitude/longitude.

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to reveal their strengths and limitations.
Figure 8 plots a time series of radar validation for
January through December 2004, with statistics averaged for each month. The statistics were computed
using daily averaged estimates with a horizontal resolution of 0.25° × 0.25° latitude/longitude. The resultsfrom G_MWI, which was based on only retrievals of
the PMW imagers, had the worst correlation, RMSE,
and POD, and the best FAR. The POD results revealed that rainfall detection was poor, due to sampling errors by the PMW imagers. Such sampling errors, which have been discussed in many papers (e.g.,
Bell and Kundu 1996, 2000), could have led to poorcorrelations and RMSEs. However, the PMW imagers were able to measure the emission signals fromthe precipitation, and false signals of rainfall wereless frequent, as revealed in the results for the FAR.
Moreover, GSMaP performed slightly better than G_
IVK (Fig. 8). As demonstrated by Ushio et al. (2009),
a longer time after the last microwave satellite overpass led to larger errors in estimates using the morphed technique.
Furthermore, differences among the sensors were
examined in hourly estimates. In the GSMaP algorithm, the PMW imagers, PMW sounders, and GEO

Fig. 8. Time series of radar validation using
daily estimates in terms of (a) correlation
coefficient, (b) RMSE, (c) POD, and (d)FAR. Monthly averaged statistics are displayed in the panels. Dots denote GSMaP
data; open circles denote G_IVK data;and crosses denote G_MWI data. The
horizontal resolution of the datasets is
0.25° × 0.25° latitude/longitude.

213

Fig. 9. Time series of validation over theocean using hourly estimates of the
GSMaP data for different satellite sensors. Monthly averaged statistics are
displayed in the panels. Dots denote the
PMW imager, open circles denote the
PMW sounder, and crosses denote theblend of the PMW–IR sensors. The horizontal resolution of the datasets is 0.1° ×
0.1° latitude/longitude.

IR radiometers were used as data sources, with consideration of differences in the sensor datasets notedin Section 2.2. To quantify the effects, validation using radar data was performed over the ocean for eachsensor. Figure 9 presents the results for different data
sources using hourly estimates of the GSMaP with ahorizontal resolution of 0.1° × 0.1° latitude/longitude.
An estimate denoted as a “blend” was computed by
the GSMaP blended PMW–IR algorithm in areas
with no overpass of the microwave sensors, and thisestimate was quite different from a direct estimate of
the IR data. Sampling problems as shown in analysesof Fig. 8 are not related here because Fig. 9 presents
results of hourly validations. Three kinds of satellite
estimates in the analysis of Fig. 9 corresponded to simultaneously different areas among them, and comparisons of averages of the statistics became meaningful.
Overall, results for the PMW imagers were better
than those for the other data sources. Because microwave emission and scattering were more directly related to precipitation than to cloud-top temperaturefrom the IR observation, the PMW algorithms generally provided more accurate instantaneous rainfall estimates than the IR algorithms did (Ebert et al. 1996;

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Smith et al. 1998). Correlation coefficients for estimates using the PMW imagers were 0.1 higher thanthose for estimates using the PMW sounders and theblend of PMW–IR sensors. Correlation coefficientsfor the PMW sounders were slightly higher than thosefor the blend of PMW–IR sensors. The POD values for
the PMW sounders were relatively low, while the FAR
values were also low. This result was consistent withresults for the algorithm developed by NOAA based
upon scattering information of the PMW sounders.
The AMSU-B had high-frequency channels with goodFig. 10.
Differences in validation results
using hourly estimates of GSMaP and
GSMaP_NRT data. The panels are thesame as in Fig. 9, except that all surface
types are presented. Dots denote GSMaP
results, and open circles denote GSMaP_
NRT results.

Fig. 11.
Time series of radar validation
for GSMaP data for January throughDecember 2004 using 3-hourly estimatesin terms of (a) correlation coefficient, (b)RMSE, (c) POD, and (d) FAR. Monthly
averaged statistics are displayed in thepanels. Dots denote ocean, open circles
denote coast, and triangles denote land.
The horizontal resolution of the datasets
is 0.25° × 0.25° latitude/longitude.

Fig. 12. (a) Averages of the radar data during 2004 and (b) differences between the
GSMaP and the radar (i.e., GSMaP minus
radar), averaged during 2004. The horizontal resolution of the datasets is 0.25° ×
0.25° latitude/longitude.

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sensitivity to rainfall involving ice particles above thefreezing level, and poor sensitivity to shallow rainfall. Thus, the results demonstrated less detection andfewer false signals of over-ocean rainfall by the PMW
sounders. Over land, estimates from the PMW sounders were equivalent to those from the PMW imagers
in terms of scattering-based methods, as describedin Section 2.2. These results demonstrated that effective performance by merges of PMW sounders wasverified over ocean, due to less false rainfall with the
PMW sounders.
In the GSMaP PMW–IR blended algorithm, rainfall
features were propagated via IR data both forward andbackward in GSMaP, while they were only propagatedforward in G_NRT, the merit of which was a fasterprocessing time. The relationship between GSMaPand G_NRT was similar to that between CMORPH
and QMORPH of the NOAA CPC. Differences in validation results for GSMaP and G_NRT are indicatedin Fig. 10. Owing to the inclusion of backward processes in GSMaP, the GSMaP results were better thanthe G_NRT results. Differences between correlations
and RMSEs were larger during the boreal summer
than during the boreal winter, due to short-lived convection during summer and rainfall associated withtyphoons between June and October. Differences in
the POD were large in the warm season, while differences in the FAR were nearly constant throughout theyear, suggesting that relatively low detection of rainfall using G_NRT was more apparent during warm
seasons. These results implied that these differences
would be larger in the tropics, where short-lived convection was observed frequently.
Differences in validation results were examinedby surface type. Figure 1a reveals surface-type distributions over the analyzed area. Note that the grid
points of “land” (green in the figure) were locatedin the mountainous regions in Japan, as seen in the
topographic data of Fig. 1b from the GTOPO30 dataset (Row et al. 1995) provided by the United States
Geological Survey. Figure 11 presents time series ofradar validation for GSMaP over ocean, coast, andland, with statistics averaged for each month. The
statistics were computed for 3-hourly estimates witha horizontal resolution of 0.25° × 0.25° latitude/longitude. The spatial correlation indicated distinct seasonal variations for all surface types. Overall, results over
the ocean had the best correlation, and results overmountainous regions (land) had the worst. This result
was closely related to ocean algorithms that used thelow-frequency channels of the PMW imagers, since215

the algorithms for the PMW imagers differed for different surface types as noted in Section 2.2. Kubota
et al. (2007) found a similar seasonal variation in thevalidation results of retrievals from the TMI for different surface types around Japan. The FAR indicatedthat false rainfall is found over mountainous regions
more often in the boreal winter, which is closely related to the rain/no-rain identification problem of the
PMW algorithm for snow-covered land.

Fig. 13. (a) Temporal correlation and (b)RMSE between 3-hourly estimates of
the radar and GSMaP data for January
through December 2004. The horizontal
resolution of the datasets is 0.25° × 0.25°
latitude/longitude.

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Fig. 14. Same as Fig. 13, except that POD
and FAR are presented.

The previous validation in this paper was made forspatial evaluations. Here, temporal variations withindividual pixels of 0.25° × 0.25° latitude/longitude
were analyzed using 3-hourly estimates for January
through December 2004. Figure 12a depicts rain ratesof the radar, averaged during 2004. Note that a threshold of the temperature was used in this study for analysis of non-snowfall areas, as described in Section
2.3. Heavy rainfall was found in the south of Shikoku
Island (133°E, 33°N), the southeast of Kii Peninsula(135.5°E, 33.5°N), and the southern slopes of the
Akaishi Mountains (138°E, 35°N). These regions are
well-known for their frequently heavy orographic

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rainfall associated with warm, moist southeasterly
winds at low altitude. When the air flowing over rugged terrain is potentially unstable, the lifting inducedby the terrain can lead to the formation of orographic
clouds of cumuli or cumulonimbi, which can be very
important precipitation producers (cf., Houze 1993).
Figure 12b depicts differences between the GSMaPand the radar data. Large negative values below 1500
mm year-1 found in areas with heavy orographic rainfall indicate underestimation of the GSMaP there,
which will be discussed in the next section. Positive
values over the ocean could be closely related to atendency of the PMW retrievals. Kubota et al. (2007)
demonstrated that TMI retrievals of the GSMaP tended to be larger than those of the PR 2A25 (Iguchi etal. 2000; Iguchi 2007) over the mid-latitudes during
boreal winter.
Temporal correlation and RMSE results for GSMaP
are presented in Fig. 13, and POD and FAR results
are presented in Fig. 14. Here, signals at the boundaries of radar observation areas over the ocean may
have had radar problems, as noted in Section 2.2.
Relatively high temporal correlation coefficients were
found over the ocean, and only the ocean had correlations above 0.8 (Fig. 13a). Low correlations below 0.6
were scattered over coasts, such as Okinawa Island(128°E, 26.5°N) and the northern coast of Kyusyu
Island (129.5°E, 33°N). The POD values were below
0.4 over these islands (Fig. 14a). This is related to arain/no-rain identification problem of the PMW algorithm over coasts. The PMW algorithm of the GSMaP
Project used a modification of the McCollum andFerraro (2005) method (Kubota et al. 2007). However,as discussed in McCollum and Ferraro (2005), thePMW footprint over coasts is a mixture of radiometrically cold ocean and radiometrically warm land
surfaces, and the performances of the algorithms areworse over coasts than over land and ocean. Moreover,correlations below 0.6 are found in coastal areas (37°N
to 40°N) in the Sea of Japan, and are closely related
to mesoscale convective systems observed frequently
during winter. For Hokkaido Island (141°E, 43°N), thecorrelations below 0.6 were related to the low POD
values in Fig. 14a. Because the FAR was relatively
low for Hokkaido Island, the screening of snow cover
resulted in rain detection errors for the rain/no-rain
identification method. Large RMSE values (Fig. 13b)
were found for coastal areas with frequently heavy
orographic rainfall. In Fig. 14b, the large FAR values
found for the Hida Mountains (138°E, 36.8°N) wererelated to false rainfall due to snow cover. The largeMarch 2009

T. KUBOTA et al.

217

Fig. 15. Case study of orographic rainfall over Kii Peninsula on July 30, 2004. The spatial resolution of all
panels is 0.125° × 0.125° latitude/longitude. (a) ground-based radar data, (b) PR 2A25 near-surface rain rate
(mm h-1), (c) GSMaP_TMI surface rain (mm h-1), (d) TMI 2A12 surface rain (mm h-1), (e) TMI PCT85 (K),(f) PR 2A25 precipitation-top height (km). The observation time of the ground-based radar was 15:00 to
15:59Z, that of PR was 15:34Z, and that of TMI data was 15:33Z (TRMM orbit number: 38228).

FAR values also found for the ocean east of Hokkaido
Island were related to false rainfall due to sea ice.

5. Discussion
Large errors of the GSMaP were found in areas
with frequently heavy orographic rainfall over theJapanese Archipelago. One reason for these errors
was the relatively low POD values (Fig. 14a), due tothe rain/no-rain identification problem over coasts.
However, the identification problem over coasts wasusually problematic for weak rainfall with a small Tb
depression due to scattering. Another reason could
be related to heavy rainfall. We focused on hydrometeor vertical profiles for orographic rainfall, whichwere influential in the PMW retrievals. Figure 15
presents a case study of orographic rainfall over theKii Peninsula on July 30, 2004. The spatial resolu-

tion of all panels in Fig. 15 is 0.125° × 0.125° latitude/
longitude. In this case, strong easterly winds above
10 m s-1, associated with Typhoon Namtheun, wereobserved from 12:00UTC July 30 to 01:00UTC July
31 in surface meteorological data at Owase (136°E,
34°N), where mountains higher than 500 m are very
close to the coastline running northeast to southwest(Figs. 1b and 16a). In the ground-based radar data
(Fig. 15a) and the PR 2A25 Version 6 data (Fig. 15b),
large surface rain rates above 25 mm h-1 were detectedaround Owase at 15:00UTC on July 30. Large surface
rainfall events, such as those seen in radar observations, were not detected for the two products for whichsurface rain rates were retrieved from the TMI data,
which observed simultaneously with the PR. The TMI
retrievals were produced by the GSMaP algorithm(GSMaP_TMI) (Fig. 15c) and the 2A12 Version 6 by

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Journal of the Meteorological Society of Japan

the GPROF algorithm (Fig. 15d). In both algorithms,
surface rain rates over land were computed based on
observed Tbs at 85.5 GHz. Figure 15e presents polarization-corrected temperatures at 85.5 GHz (PCT85)
defined by Spencer et al. (1989). The pattern for
PCT85 was more similar to that of the precipitation-

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top height presented in Fig. 15f than the 2A25 surface
rain rates. A similar relationship between PCT85 andthe precipitation-top height was reported by Kubota
et al. (2007) for a mesoscale convective system overwestern Africa. For the present study, the precipitation-top height was defined as the top bin above 0.3
mm h-1 in 2A25. Depressions of PCT85 over orographic rainfall around Owase were similar to thoseover the ocean east of Owase, the PR rainfall intensities of which were weak. According to the PCT85 pattern, GSMaP_TMI and 2A12 surface rain rates wereweak for the orographic rainfall around Owase andthe ocean east of Owase.
In a case study of heavy rain in the Owase area using range height indicator (RHI) radar data, Takeda
and Takase (1980) demonstrated continuous rain
originating from mid-level clouds and a remarkable increase in radar echo intensity with decreasingheight below the bright band when an easterly wind
was prevalent. Figure 16b depicts a vertical cross section of precipitation along the track of the PR for theorographic rainfall around Owase. At 12:00UTC on
July 30, the freezing level height was 5.2 km fromthe JMA GANAL. A large increase in rain rates
was observed below the freezing level, and rain rates
above 80 mm h-1 were observed in the heavy rainfallarea, In contrast, these features were not found over
the ocean (Fig. 16c), while precipitation top heights
around 0.3 mm h-1 (Fig. 16c) were similar to those in
Fig. 16b. Thus, PMW scattering associated with ice
particles above the freezing level was relatively weakin the orographic heavy rainfall, and retrieval using
high-frequency channels could be regarded as small.
Underestimation of rainfall by the PMW algorithms
was closely related to the large errors in areas of orographic rainfall. This result also implied that the samefeatures for orographic heavy rainfall could be foundoutside Japan (e.g., in moist monsoonal regions suchas South Asia).

6. SummaryFig. 16. (a) Elevation map of Kii Peninsulaarea. (b) Vertical cross section of precipitation by TRMM/PR for the same case as
in Fig. 15 over the A-B segment of Fig.
16a. (c) Same as (b), except over the C-D
segment of Fig. 16a. The unit in (b) and(c) is mm h-1. Rain rates above 20 m h-1
are shaded.

Six high-resolution satellite rainfall estimates werecompared with ground-based radar data (JMA RadarAMeDAS precipitation analysis) as a reference forJanuary through December 2004 over regions withsurface temperatures above 4°C. For 3-hourly or daily averaged estimates with a horizontal resolution of
0.25° × 0.25° latitude/longitude, radar validation results tended to be better during the boreal summer
and worse during the boreal winter. The validation results of GSMaP and CMORPH, which used the tem-March 2009

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T. KUBOTA et al.

poral interpolation based on the morphed techniqueusing GEO IR information, were better than those ofother products. Satellite estimates were poor for lightrainfall during the warm season and for very heavy
rainfall.
Further analyses of satellite estimates were performed in terms of data sources and surface types todetermine their strengths and limitations. The rainfall estimates over the ocean were more efficient using PMW sounders in addition to PMW imagers. Thisfinding is related to less false rainfall being detectedby the PMW sounders. Overall, validation results over
the ocean were best, and results over mountainous regions were worst. Rainfall estimates were poor overcoasts and small islands, due to the rain/no-rain identification problem over coasts. Heavy rainfall during
2004 was located in areas with frequent orographic heavy rainfall over the Japanese Archipelago, andlarge errors in satellite estimates were detected there.
This result could be closely related to issues of hydrometeor vertical profiles in the PMW retrievals, related
to the poor satellite estimates in very heavy rainfall.
In this study, the analyzed period was limited to
one year, and one of the merits for the year 2004 wasa constant number of instruments. A sampling issue isinvolved in choosing a single year over multiple years,
and results of longer periods may be desired. This
study compared satellite rainfall estimates with reference to a ground radar dataset calibrated by rain gauges. However, the calibration of the radar by gauges is
uncertain over the ocean, and results may be unreliable there. Satellite estimates are validated for nonsnowfall regions, and validations of snowfalls are beyond the scope of this study. This issue has been left
for future work.

Acknowledgments
This study is supported by the Global Satellite
Mapping of Precipitation (GSMaP) Project under the JST-CREST, and by the Mitsui & Co., Ltd.
Environment Fund. The GSMaP Project website is
http://www.radar.aero.osakafu-u.ac.jp/~gsmap/. The
authors would like to thank Dr. K. Aonashi and Mr.
H. Eito (Meteorological Research Institute, Japan
Meteorological Agency) and Dr. T. Iguchi (National
Institute of Information and Communications
Technology) for their valuable comments, and D.
Katagami, T. Watanabe and H. Ashiwake (Osaka
Prefecture University) for helpful computing assistance. They also thank the editor and the anonymous
reviewers for their valuable comments. The datasetof the Radar-AMeDAS Precipitation Analysis wasprovided by the Japan Meteorological Agency. The
IPWG website (http://www.isac.cnr.it/~ipwg/IPWG.
html) and the PEHRPP website (http://essic.umd.
edu/~msapiano/PEHRPP/) provided information on
precipitation products.

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