Journal of the Meteorological Society of Japan, Vol. 87A, pp. 137–151, 2009
DOI:10.2151/jmsj.87A.137

137

A Kalman Filter Approach to the Global Satellite Mapping of Precipitation (GSMaP)
from Combined Passive Microwave and Infrared Radiometric Data
Tomoo USHIO, Kazushi SASASHIGE
Department of Electronic, Electronics and Information Engineering, Osaka University, Suita, Japan

Takuji KUBOTAJapan Aerospace Exploration Agency/Earth Observation Research Center, Tsukuba, Japan

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

Ken’ichi OKAMOTO
Tottori University of Environmental Studies, Tottori, Japan

Kazumasa AONASHI
Meteorological Research Institute, Tsukuba, Japan

Toshiro INOUE
Center for Climate System Research, The University of Tokyo, Kashiwa, Japan

Nobuhiro TAKAHASHI, Toshio IGUCHINational Institute of Information and Communications Technology, Koganei, Japan

Misako KACHI, Riko OKI
Japan Aerospace Exploration Agency/Earth Observation Research Center, Tsukuba, Japan

Takeshi MORIMOTO and Zen-Ichiro KAWASAKI
Department of Electronic, Electronics and Information Engineering, Osaka University, Suita, Japan(Manuscript received 6 June 2008, in final form 25 November 2008)

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Abstract
A system has been developed and implemented that integrates passive microwave radiometer data with
infrared radiometer data in order to have high temporal (1 hour) and spatial (0.1 degree) resolution global
precipitation estimates. The product (GSMaP_MVK) is produced based on a Kalman filter model thatrefines the precipitation rate propagated based on the atmospheric moving vector derived from two
successive IR images. The proposed method was evaluated and compared with other high-resolution
precipitation products and the ground-based data collected by the Automated Meteorological Data
Acquisition System (AMeDAS) near Japan. It was clearly shown that the approach described in this paper
performed better than without the Kalman filter, and the time series of the hourly global precipitation patterndemonstrated the potential capabilities for weather monitoring and typhoon tracking. The GSMaP_MVK
product achieved a score comparable to the CMORPH and the 3B42RT products.

1. Introduction
Accurate observation of the global distribution of
precipitation has long been a major scientific goal.
Highly accurate time- and space-resolution estimatesof precipitation on a global basis are required not
only for scientific research, but also hydrologic
applications, agricultural management, and weather
forecasts. In spite of its importance, due to the great
variability of precipitation in space and time,
estimation of precipitation with sufficient accuracy
and resolution on a global basis is difficult, andconsequently, our knowledge of precipitation is un­­satisfactory. One of the best approaches to capture
global precipitation is to use data from spaceborne
sensors, and we have several satellites to observe
precipitation passively or actively. Among these areobservations by microwave radiometers from low
earth orbit satellites, which have proven to be fairly
accurate.
The GPM (Global Precipitation Measurement)
project is led mainly by the United States and Japan,
and is now being actively promoted in Europe,
France, India, and China with international coop­eration. In this project, the microwave radiometers
observing microwave emission from rain will beplaced on many low-orbit satellites, to reduce theinterval to about 3 hours in observation time for eachlocation on the earth. However, the problem ofsampling error arises if the global precipitation
Corresponding author: Tomoo Ushio, Department ofElectronic, Electronics and Information Engineering,Osaka University, 2-1 Yamadaoka, Suita, Osaka 5650871, Japan
E-mail: ushio@comm.eng.osaka-u.ac.jp©2009, Meteorological Society of Japanestimates are less than three hours. Therefore, it isnecessary to utilize a gap-filling technique to gen­erate precipitation maps with high temporal reso­
lution, which is quite important for operational uses
such as flash flood warning systems.
As is well known, mapping of global precipitation
using satellite data has already been performed bynumerous researchers, and an increasing number ofsatellite-based rainfall products, for example
TMPA-RT (Huffman et al. 2007), NRLgeo (Turkand Miller 2005), PERSIANN (Sorooshian et al.
2000), CMORPH (Joyce et al. 2004), PMR (Kidd etal. 2003), and so forth, are now available in near realtime over the Internet. Among them, the WCRP
(World Climate Research Program) GPCP (Global
Precipitation Climatology Project) has succeeded in
producing precipitation estimates on a monthly
2.5-degree grid with two decades of data (1979 topresent) (Adler et al. 2003), and the most recent
product named TMPA (The Tropical Rainfall
Measuring Mission Multisatellite Precipitation Anal­ysis) provides 3-hour real time rainfall analysis on a0.25-degree grid (Huffman et al. 2007). In the GPCP,
the infrared radiometer, the microwave radiometer,
and the data of a rain gauge on the ground are used,
and the precipitation estimates are generated mainly
from the visible–infrared radiometer data of thesynchronous meteorological satellite based on thehistogram matching for the long term with theprecipitation estimates from the microwave radi­ometer. On the other hand, CMORPH (CPC Morph­ing technique) provides a global precipitation map
with 30 minute 0.073 degree resolution in a different
way from the GPCP. In CMORPH, two pieces of theinfrared images at 30-minute time intervals are used
to calculate the moving vector of the cloud, and fromthe moving vector image in time, the rain rates

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estimated from a microwave radiometer arepropagated along with the moving vector. Thus theprecipitation rates during the gap between micro­
wave radiometer overpasses are interpolated, anddecrease the sampling error. So far, the CMORPH
product has showed excellent scores on a 1-day,
0.25-degree comparison in terms of correlation
coefficient, RMS error, and several statistical pa­­
rameters through the evaluation activities in PEHRPP(Pilot Evaluation of the High Resolution Precipitation
Products).
Although the CMORPH product is one of the bestproducts at present, there are some weaknesses. In
the product, the brightness temperature determinedusing the infrared radiometer is used only to calculate
the movement of the rainy area, and the directtransformation of the precipitation does not comefrom brightness temperature itself. The precipitation
is estimated only from the microwave radiometers.
So, when a microwave radiometer passed over a
ground location again because the rain is moved
without changing precipitation on the movement
vector, the precision of the precipitation estimation
deteriorates. In order to compensate for this, the
CMORPH method introduces a backward propagation
technique in time and then combines both forward
and backward estimates. Thus the dynamical errors
resulting from precipitation system growth and decay
are reduced.
The Global Satellite Mapping of Precipitation(GSMaP) project was established by the Japan
Science and Technology Agency (JST) in 2002 to
produce global precipitation products with highresolution and high precision from not only micro­
wave radiometers but also geostationary infrared
radiometers. The GSMaP’s goal is to develop a
microwave radiometer algorithm compatible with a
Precipitation Radar algorithm and eventually to
produce a global precipitation map with hightemporal and spatial high resolution, for instance,
produced on daily basis with grid spacing of 0.25
degree of latitude by 0.25 degree of longitude, bycomprehensively analyzing only satellite data. In
addition, providing hourly precipitation data on a
global 0.1 degree grid is added to the goals of the
project, which is the subject of this paper.
When mapping global precipitation with 0.1
degree/1 hour resolution in this research, an approachsimilar to but different from CMORPH is taken forthe mapping of precipitation. In this study, we use
the moving vector derived from two successive IR

139

images to propagate the rainy area from microwave
radiometry, basically similar to CMORPH; how­ever, we focus on the relationship between rainfall
strength and IR brightness temperature Tb . In thisproject, the Kalman filter is applied to the rainfall
rate after propagation along with the moving vector
to update the rate on the IR Tb . We refer to thisKalman filter–based product as GSMaP_MVK, shortfor GSMaP moving vector with Kalman filter
method, while the moving vector–only approach islabeled GSMaP_MV. Using this technique, a nearreal time system named GSMaP_MVK_RT, whichsimply contains only the propagation process forward
in time, was developed (Noda et al. 2007). In thispaper, the full system (GSMaP_MVK), whichcontains both the forward and backward propagation
processes, is described and assessed in various timescales from other ground- and satellite-based
products.

2. DataThis section describes the input data sets that areused in this study. As is stated above, the microwave
radiometer (MWR) on low earth orbit (LEO) and theinfrared radiometer on geostationary satellites arequite complementary with each other for monitoring
the precipitation. The data used here are from themicrowave radiometers in low earth orbits and theinfrared radiometers in geosynchronous orbits.
The MWR sensors used in this study are SSM/I(Special Sensor Microwave/Imager), TMI (TRMM
Microwave Imager), and AMSR-E (Advanced
Microwave Scanning Radiometer for EOS), whosecharacteristics are listed in Table 1. We use theconverted precipitation data from the observedbrightness temperature at microwave frequencies as
inputs to our system. The algorithms to retrieve thesurface precipitation rate are described in Aonashi etal. (1996) and Kubota et al. (2007) for microwave
radiometers. The basic idea of the Aonashi algorithm
Table 1. Characteristics of microwave radiometers usedin this study.

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is to find the optimal rain intensity that matches thebrightness temperature calculated from the radiativetransfer model with the observed brightness temper­
ature at several microwave frequencies on theplatform. Since we do not deal with the algorithm
development of the microwave radiometer data to
retrieve the surface rain rate in this paper, we do notstate the algorithm techniques explicitly here.
The IR data sets used in this study are supplied
from the CPC (Climate Prediction Center) of NOAA.
The CPC acquires the data of the infrared radiometerfrom geostationary satellites (Janowiak et al. 2001)
and supplies to the public the composite data setsthrough the Man–computer Interactive Data Access
System (McIDAS; Lazzari et al. 1999). The latitude
and longitude resolution of the data are 0.03635
degree (4 km at the equator). The latitude range is
60°N–60°S. The temporal resolution is about 30
minutes. In this data set, three major defects arecorrected: (1) navigation error, (2) limb darkening,and (3) intersatellite calibration problems. Detailed
information on these errors and associated corrections
is provided by Joyce et al. (2001).

3. Methodology
The combination techniques used for producing the
0.1 degree/1 hour resolution global precipitation map
in this paper are based on the morphing techniquedescribed by Joyce et al. (2004). A brief summary ofVol. 87A

the CMORPH technique is given here and then the
proposed method for extending the propagation
technique using a Kalman filter is described. In Fig.
1, the algorithm flow of this product (GSMaP_MVK)
is shown.
3.1 Advection vector and rainfall propagation
Given two consecutive images of the IR brightness
temperature with one-hour resolution at t = 0 and t =1, the two-dimensional cross correlation coefficient
can be calculated. If the image at t = 1 is lagged withrespect to t = 0 image spatially in the longitudinaland latitudinal directions, the correlation is calculated
for the given spatial offset. By repeating thisprocedure for the various offsets and searching forthe horizontal offsets that yield the maximal
correlation, the advection vector is determined. In
this study, in order to speed up computation, the lowresolution correlation of bin-averaged images is firstcomputed and then zoomed in recursively to obtain
the optimal offsets. This stepwise procedure issignificantly effective for quickly computing the shift
showing the greatest correlation. In our estimation,
the global advection fields in every 3.25 degree canbe calculated in 5 minutes with a 3 GHz Linuxcomputer.
Once the vector fields are obtained globally fromthe correlation-maximizing method described above,
the rainfall area retrieved from the passive microwave

Fig. 1. Flow chart of the algorithm developed in this study.

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radiometer is propagated forward in time by theadvection vector derived from the IR images. In
addition, the rainfall area is spatially propagatedbackward in time using the same advection vector
used in the forward propagation. And then, theoptimal estimates of rainfall rate are computed by
calculating the weighted average from the forward
and backward propagated rainfall rates using theinverse of temporal distance from passive micro­
wave radiometer scan to propagation time to weightthe propagated rainfall from each scan.
The spatial resolution of the GSMaP_MVK producthas a minimum of 0.1 degree, while the resolution of
the original IR image is 0.03635 degree at theequatorial region. This minimum bound of 0.1 degree
basically comes from the resolution limit of the
microwave radiometer data at high frequency, sug­gesting the difficulty to enhance the resolution below
10 km (at the equator).
3.2 Kalman filter
A possible main source of error with the CMORPH
technique is that the advection analysis is the only
process that describes the temporal variation of the
precipitation system, although the weighted average
called “morphing” is performed. The actual rain rateintensifies or decays during the storm life cycle,
while the cloud system moves horizontally. The
tracking of the cloud system can be achieved by
calculating the advection vector from the consecutive
IR images, but measurements that can giveinformation on precipitation fluctuation with 1-hourresolution on a global basis are rare. Though the
Geo-IR measurements are not directly related toprecipitation, their information on cloud top height isavailable nearly all the time over the globe and is
statistically correlated with surface rainfall rate withlarge variances. This leads us to use a Kalman filter
to provide better feedback information to moreaccurately represent the temporal variation of pre­cipitation system.
Let Xk be the precipitation rate at time k, which isthe propagated precipitation rate forward in time.
After the rain pixel is propagated along with moving
vector, the predicted precipitation rate at time k + 1 is
not necessarily the same as the actual rain rate at thetime. If its uncertainty is represented as w, which isthe so-called system noise, then we have
Xk+1 = Xk + w.

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(1)

Figure 2 shows a histogram of the uncertainty inprecipitation rate after 1 hour from its propagationcompared with the rain rate retrieved from themicrowave radiometer. In this figure, the regional
differences are ignored and all the data all over the
globe are collected. Note that it is normally
distributed, showing that the propagated precipitation
rate can be viewed as a random variable, whichenables us to take the Kalman filter approach.
At each time step, the only parameter of theFig. 2. Histogram of the uncertainty in pre­cipitation rate after 1 hour from its
propagation compared with the rain rate
retrieved from the microwave radiometer.

Fig. 3. Geostationary satellite IR brightness
temperature (ordinate) relative to pre­cipitation estimated from microwave
radiometers (abscissa) during July 2005.

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Fig. 4.
Distribution of the IR brightness
temperature at 3 mm h–1.

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figure, the brightness tem­perature is nonlinearlycorrelated with the surface precipitation rate withlarge variance. In Fig. 4, the histogram of thebrightness temperature at the precipitation rate of 3mm h–1 is shown as one example. Note that it isnormally distributed. From Figs. 3 and 4, it is shownthat at each time step of the propagation along withmoving vector a noisy measurement of the true
precipitation is made from the IR observation. Let y
be the brightness tem­perature that is estimated fromthe Geo-IR meas­urement. Let v be the measurement
error as shown in Fig. 4, which is so-called
observation noise. Then we haveyk = HXk + v,

measurement available to give the feedback infor­
mation is the brightness temperature at the thermal-IR
wavelength. In Fig. 3, the relationship between the IR
brightness temperature and the precipitation rate fromthe microwave radiometer is shown. As seen in this(2)

where H is a coefficient constant.
As is evident from Fig. 3, the actual relationshipbetween IR brightness temperature (Tb ) and surface
precipitation rate is quite nonlinear with large

Fig. 5. Schematic illustration combining the precipitation fields forward and backward in time. The whitebelts denote the coverage of the microwave sensors.

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variances. To apply the Kalman filter on a linear
basis, the linearization approximation is needed andhas been performed in every 1 mm h–1 at present
study.
Based on equations (1) and (2), the Kalman gain iscomputed and then the modified precipitation rate is
obtained. Here, the system under consideration isassumed to be in a steady state in order to reduce thecomputation cost. However, the relationships between
IR Tb and the precipitation rate and between moved
precipitation rate and the actual rain rate are strongly
dependent on season and location. Hence, both
system noise and observation noise are varying withtime and location. In this study, we introduce themonthly based steady Kalman filter, which allows
both the system noise and observation noise to varyfrom month to month. In addition, the relationshipbetween IR Tb and precipitation rate varies from one
region to the next even in the same month. In thisstudy, to keep enough samples and to stabilize theKalman filter, the noise tables have been created forevery 10 degrees in latitude over land and ocean.
After applying the Kalman filter to the propagatedrainy pixel stated above, the same propagation and
Kalman filter procedure is applied to the rainy pixel
backward in time as is illustrated in Fig. 5. At time t,
the microwave radiometer passes over, and the rainy
pixel is repeatedly propagated and the Kalman filter
refines the precipitation rate as Rt f,i after i hours later.
The revisited rainy pixel is also repeatedly propagatedbackward in time and the Kalman filter refines theprecipitation rate as Rt f, j , which is the estimated
precipitation rate j hours before the most recent
microwave radiometer overpass. In this case, theoptimal estimate R̂ is the weighted averageR̂ t = R f t ,iσjσi
+ Rbt, j,
σ j +σiσ j +σi(3)where sigma is the root-mean-square uncertainty inthe estimates after the i or j hours from the micro­
wave radiometer overpass.

4. Results and discussion
4.1 Example
The GSMaP_MVK has been computed for Jan. 1,
2003 to Dec. 31, 2006, and the processing still
continues to obtain the long-time products. The top
panel in Fig. 6 shows an example of the GSMaP_MVK product in July 15, 0 UTC 2005. Note that the143

global precipitation with 0.1-degree and 1-hourresolution has been produced by interpolating therainfall rates between the microwave radiometer
overpasses using the Geo-IR data sets. The global
pattern of the precipitation distribution is typical forthis season. While isolated scattered showers,
probably from convective type rainfall, are seen in
the tropics, band-type rain events appear at midlatitudes. Far south of Japan the typhoon Haitang canalso be seen, which brought heavy rain recorded in
the Taiwan area after a few days. The second panelin the left row in Fig. 6 shows one-day accumulationof the precipitation rate for July 3, 2005. Because the
GSMaP_MVK is a relatively new technique, the 3B42
V6 (Huffman et al. 2007) is placed on the right row
in Fig. 6 for a side-by-side comparison in the same
scale. It is clearly shown that the both panels arebasically in excellent agreement, and in the 3B42
product tiny rain pixels appear more widely scatteredthan in the GSMaP_MVK product. Rain events
exceeding 100 mm day-1 are frequently seen not onlyin the tropics but also at the middle latitudes.
Focusing on a particular event, for example, a Baiu
(Japanese summer monsoon) front is seen from the
Korean Peninsula to the Pacific side of the Japanesearchipelago with heavy rain exceeding 100 mm day-1.
With this seasonal rain front, heavy rain of more than
100 mm day-1 is readily observed in the western partof the main island of Japan on July 3 by the Japanese
Meteorological Agency and is associated with thehazardous flood that occurred there at this time. The
third panels from the top in Fig. 6 show the JJA
seasonal merged precipitation product in 2005 as one
example. In these third panels, both products haveshown good agreement, except for the ocean area
near Antarctica. As is well-known, strong pre­cipitation patterns show up on the ITCZ in the Pacificand Atlantic Oceans, with strong maxima in theeastern Pacific Ocean. Turning to the Asian Mon­
soon, the very heavy precipitation maximum isevident in the Bay of Bengal, with sharp contrast
between ocean and land. On this sharp contrast,
however, the produced product might have a tendency
to underestimate the precipitation rates, particularly
over land compared with the 3B42 product. In the
3B42, relatively high precipitation rates are seen overIndia and Bangladesh. The bottom panels in Fig. 6
display the annual precipitation map in 2005 from the
GSMaP_MVK and 3B42 products. As is expected,
the strong precipitation belt across the Pacific Ocean
along the ITCZ is evident with peaks at the both ends

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Fig. 6. Examples of the GSMaP_MVK products for various time scales. From top to the bottom, global
precipitation distribution in 1 hour, daily, monthly, and annual scales are shown. Note that the area of
35–50 degrees is not covered by the TRMM/TMI, hence the number of microwave radiometer observations
decreases in this region. The precipitation rate at 40–50 degrees seems to be lower in GSMaP_MVK thanin the 3B42. This is partly because the algorithm of the microwave radiometer does not retrieve the rain
rate if the 0 degree level is lower than 500 m.

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145

Fig. 7. Consecutive images of 12 hours with 1 hour/0.1 degree resolution from GSMaP_MVK for July 3,
showing the progression of Typhoon Nabi. For each image, the color superimposed on the IR cloud image
denotes the rain rate, while the grey belts denote the coverage by the various microwave sensors.

146

Journal of the Meteorological Society of Japanof the belt zone in both panels. In the middle
latitudes, two clear storm track patterns appear near
Honshu (the main island of Japan) and in the east
coast of North America. All these features indicatedabove suggest that the final GSMaP_MVK productshows a typical precipitation pattern from sub-daily
to annual scales and the developed algorithm behavesreasonably.
In order to show a particular event in finer scale,Fig. 7 shows 0.1 degree/1 hour resolution estimates ofprecipitation associated with the progression of
Typhoon Nabi. This typhoon occurred in August 29,
2005 at the Mariana Islands, passed over Japan, andthen became an extratropical cyclone in September 8,2005 at the Kuril Islands. The twelve hours of thecontinuous hourly images in Fig. 7 are at the mature
stage of the typhoon life cycle, and show explicitly
how the pattern and amplitude of precipitation
associated with the typhoon change as the typhoon
propagates. The time series of the embedded
precipitation distribution within the cloud patternshows the precipitation spiraling cyclonically, andthat the distribution consists of the eyewall as well as
principal and secondary rainbands that occur outside
the eyewall. Although the fine-scale precipitation
estimates particularly with less than 1 hour resolution
are quite difficult and subject to large errors on a
global basis, the overall pattern of the typhoon looks
typical showing that the produced product can be
useful for typhoon tracking and monitoring. It is quite
interesting to investigate how the eyewall and therainbands propagate and develop in a fine time scale,
which will be a theme of a future paper.
4.2 Assessment and validation
An initial validation was carried out using
microwave radiometer data for the period of July
2005. In the proposed approach, precipitation esti­mates from microwave radiometers are propagated bymoving vectors derived from Geo-IR data, and theKalman filter modification is applied to the prop­
agated rain rate. This procedure is repeated forward
in time until the next microwave radiometer
overpasses. When the next microwave radiometerarrives, the propagated rain pixels are overwritten by
precipitation estimates from an updated microwave
scan. At the time of this overwrite, there are twosimultaneous estimation of precipitation: one is fromthe Kalman filter output and another is from thenewest passive microwave radiometer. Since we haveprecipitation estimates with and without Kalman

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filter (i.e., moving vector only), we can quantitatively
estimate the error of the proposed method comparedwith the moving vector only approach if the
precipitation estimates from the updated microwave
scans are regarded as truth. An advantage of thisassessment is that a more general evaluation ispossible than that of the ground-based radar data
because the validation area is not restricted in any
particular area or time like ground-based rain gauges
or radars.
Figure 8 shows the correlation of GSMaP_MV and
GSMaP_MVK precipitation estimates using only theforward process with microwave radiometer
estimation over one month (July 2005) as a functionof time from the past microwave overpass in thetropics (0–10°N) and the extratropics (20–30°N) overland and ocean. It is shown how the correlation
coefficient behaves as a function of time after the
past microwave overpass. When the Kalman filter isapplied to the propagated rain pixels, a difference
between with and without Kalman filter is not seen
in one or two hours after the last microwave
overpasses. However, in several hours later, theproposed Kalman filter method tends to make adifference. For example, at four hours later, thecorrelation coefficient at this time over ocean intropics shows about 0.35 for GSMaP_MVK, while the
GSMaP_MV shows 0.23. This difference tends to belarger as the time passes, demonstrating the beneficial
effect of the proposed Kalman filter approachcompared with the moving vector only approach.
This trend is common in all cases in Fig. 8.
Concerning at the latitude and land/ocean de­­pendence, the correlations over ocean are better thanover land, probably due to the microwave radiometer
algorithm performance. It seems that the extratropicalcase over ocean behaves better than the tropics caseover ocean. However, overall, the tropical andextratropical difference is not so large. In thehorizontal axis of this figure, 6 hours from the last
microwave overpass is chosen as the maximum value,
because the five microwave radiometers (TMI,AMSR-E, and SSM/Is) can cover about 88% of thewhole earth in a 6-hour interval, while 25% iscovered in 1-hour intervals. If we process the data
before the TRMM launching, the available microwave
sensors are SSM/Is only and the really observablearea at a certain time interval decreases by least
several tens of percent, with the result that theeffective time of the Kalman filter approach will
increase.

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147

Fig. 8. Correlation coefficient between GSMaP and precipitation estimated from microwave radiometers forthe period of July 2005 in the tropics and the extra-tropics over land and ocean. Solid line denotes the
Kalman filter method, while the dotted line denotes the moving vector only method.

The validation results so far have been for theassessment of the proposed technique. The actual
GSMaP_MVK final product, which has one hour/0.1
degree resolution, is composed of many precipitation
estimates not only from the microwave radiometerbut also from the Kalman filter modification at
various hours after the last microwave radiometer
overpasses. A comparison of the near real time
prodproductuct (GSMaP_MVK_RT) with RADARAMeDAS has been done in Noda et al. (2007).
Taking the discussion one step further in this paper,
the GSMaP_MVK product, which includes bothforward and backward process in time, is validated

and assessed in several temporal and spatial scales.
First, the GSMaP_MVK product is evaluated on anhourly scale using the RADAR-AMeDAS data sets
on a 0.1 degree/1 hour basis. Second, the daily scaleevaluation is carried out as a part of the IPWG
(International Precipitation Working Group) activities(Ebert et al. 2006). Finally, monthly scale crosscomparison with the GPCP is stated on a global
basis.
Figure 9 shows a typical result consisting of a timeseries of the 0.1 degree/1 hour resolution precipitation
estimates for 12 hours in July 25 2005 as well as the
RADAR-AMeDAS composites and IR brightness

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Fig. 9. Time series of the 1 hour precipitationfrom the RADAR-AMeDAS (dotted bars)
and GSMaP_MVK (black bars) together
with the IR brightness temperature (solidline) at the pixel at 35°N, 124°E. The
microwave estimates are at 21:00 and
0:00. The arrow at 21:00 indicates thetime of the microwave radiometer over­pass.

temperature in a moving coordinate with theatmospheric motion vector. In general, it is shownthat the GSMaP_MVK can correctly catch therainfall occurrences and adjust the rainfall rates inreference to the IR brightness temperature. However,the GSMaP_MVK product has the tendency toVol. 87A

underestimate the precipitation intensity, particularly
for strong rainfall rates more than 10 mm h–1. This isprobably because this sudden increase in rain rate did
not reflect the IR brightness temperature in this timescale.
The scattergram between the RADAR-AMeDAS
and GSMaP_MVK for July 2005 in logarithmic scaleis shown in Fig. 10. In this figure, the unit is10log(rain rate in mm h–1) [dBR]. The left panel iswith the original 1 hour/0.1 degree resolution and theright panel is with averaged 3 hour/0.25 degree
resolution. These panels show that the finer scaleestimates show significant scatter, and it is anticipatedthat the 1 hour/0.1 degree plots show more
uncertainties than the 3 hour/0.25 degree plots. In left
panel, the high-density region spreading through thevertical axis from –4 dBR in abscissa axis comesfrom the discrete observation of the RADARAMeDAS. The dotted line is the one-to-one line inboth panels. The correlation coefficients are 0.44 and0.65 for the 1 hour/0.1 degree and 3 hour/0.25 degree
resolution, respectively, and the monthly mean
precipitation rate is 0.17 mm h–1 for the GSMaP_MVK and 0.23 mm h–1 for the RADAR-AMeDAS,
suggesting that the GSMaP_MVK estimates matches
well to the RADAR-AMeDAS estimates and tends tounderestimate large precipitation values. At the
moment, this will be partly because the current
algorithm of the microwave radiometer does not

Fig. 10. Scatter plots comparing the hourly precipitation estimates from GSMaP_MVK with RADARAMeDAS with 1 hour/0.1 degree (left panel) and 3 hour/0.25 degree (right panel) resolution.

March 2009149

T. USHIO et al.

Fig. 12. Time series of the monthly corre­
lation between the GPCP and GSMaP_MVK over land and ocean.

Fig. 11. Time series of correlation coefficient
between the GSMaP_MVK, CMORPH,
3B42RT, and RADAR-AMeDAS data
during 2005.

include the topographical effect and the current IR
observation cannot be made with resolution of lessthan one hour.
To validate the daily rainfall estimates, the IPWG/
PEHRPP validation data (Ebert et al. 2007) wereused in this study. The validation sites list the several
statistical values; by picking up these values a timeseries evaluation of the GSMaP_MVK product andsome other products on a daily basis is possible. Thus
Fig. 11 shows a time series of monthly averaged daily
correlation coefficients and RMSEs for the GSMaP_
MVK, 3B42RT, and CMORPH products over Japan
in 2005. On the whole, better correlation is obtained
during the summer than the winter in every productlisted here. In July the correlation coefficient showsnearly 0.7, while in January the correlation coefficientdrops only about 0.2. Probably this is partly becauseof the difficulty of the snow/rain retrieval in passive
microwave radiometer algorithms. Generally, the
GSMaP and CMORPH products show better scoresFig. 13. A map of correlation of GSMaP_MVK against GPCP at each grid point for the period 2003–2006.

150

Journal of the Meteorological Society of Japanin terms of correlation and RMSE than the 3B42RT,
indicating that the IR moving vector approach isbasically superior to the method that converts the IR
brightness temperature to precipitation rates. The
CMORPH and GSMaP products have quite similarcorrelation overall, and it appears that the CMORPH
shows a slightly better score than the GSMaP_MVK
in spite of the Kalman filter. Probably it is because
the CMORPH uses the NOAA/AMSU microwave
sounder data in addition to the microwave radiometer
data, while the GSMaP_MVK uses only the
microwave radiometer data. On the detailed analysis
with this daily resolution, Kubota et al. (this issue)
provides a comprehensive review.
It is usually difficult to validate the highresolution product on a global basis, because thehigh-resolution ground gauge network is not nec­
essarily available all over the world. In order to assess
the GSMaP_MVK product on a global basis, thecross comparison has been done using the GPCP
product, which is well validated and calibrated from
GPCC data sets. In Fig. 12, correlation validation ofmonthly 1 degree GSMaP_MVK against GPCP
analyses is presented for Jan.–Dec. 2005. Generally
the GSMaP_MVK over ocean performs better thanover land for the entire period, probably because thealgorithm of the microwave radiometers shows abetter performance over ocean than over land (Kubota
et al. 2007). The total correlation including ocean
and land is better than 0.85 through the year,
suggesting that the GSMaP_MVK can be used forclimatological studies. In this comparison, theseasonal variation in terms of correlation is not clear.
In order to see where around the globe the GSMaP_MVK data sets agree or disagree, a map ofcorrelation of GSMaP_MVK against GPCP is shownin Fig. 13. Correlation calculations are performed forthe time series of the 48 months precipitation at eachgrid point for the period of 2003 to 2006. Note thatthe higher correlation appears mainly on the middle
to lower altitude while the correlation drops near thepolar region.
5. Summary and conclusions
An algorithm has been developed to map global
precipitation distribution with 0.1 degree/1 hour
resolution by integrating the data from the LEO
microwave radiometers and the GEO infrared
radiometers. The algorithm (GSMaP_MVK) uses the
Kalman filter to update the precipitation rate after
propagating the rain pixels along with the atmosphericVol. 87A

motion vector derived from the successive IR images.
The distribution and amplitude of the global
precipitation pattern is generally consistent with theearly studies. The proposed approach showed a betterscore than the technique without the Kalman filter.
The correlation coefficient was 0.1 better than without
the Kalman filter about 4 hours after the last
microwave overpasses, and the RMS error was
improved about 0.1 mm h–1 with the Kalman filter
technique. The IPWG daily comparisons with theradar rain gauge network in Japan showed one of thehighest scores among the typical global precipitation
products.
One of the disadvantages of this product is thedifficulty of describing the initiation of precipitation
between the microwave radiometer overpasses. The
current algorithm propagates and applies the Kalmanfilter only for the rainy pixels identified by the
microwave radiometer algorithm. Because of this, therainy area appears in the product only after the
microwave radiometer overpasses and observes thearea. This may be resolved by considering the dual
channel observation (Inoue 1987) or WV channel(Ohsawa et al. 2001); that is for future work.
Apparently, more work is needed to explicitly
evaluate the quality of the data products in several
time and spatial scales using the different data
sources, and to improve the quality of the algorithm.
Since the precipitation rate produced by the proposed
method strongly depends on the precipitation
estimates retrieved from the microwave radiometer
data, careful analysis is needed to evaluate thesources of the errors.

Acknowledgment
This work is conducted as a part of the Global
Satellite Mapping of Precipitation (GSMaP) project
supported by the Core Research for Evolutional
Science and Technology (CREST) program of Japan
Science and Technology (JST), and TRMM/RA from
JAXA.

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