The 3rd ASIAN WATER CYCLE Symposium
December 2-4, 2007, Beppu, Oita, JAPANHIGH PRECISION AND HIGHRESOLUTION GLOBAL PRECIPITATION
MAP FROM SATELLITE DATA
GSMaP(Global Satellite Mapping of Precipitation)
Ken’ichi Okamoto(1), Toshio Iguchi(2), NobuhiroTakahashi(2), Koyuru Iwanami(3), Kazumasa Aonashi(4),Riko Oki(5), Misako Kachi(5), Takuji Kubota(5)
(1)
(2)
(3)
(4)
(5)

Osaka Prefecture Univ., Japan
National Inst. of Info. and Comm. Tech., Japan
National Research Inst. for Earth Science and Disaster Prevention, Japan
Meteorological Research Institute, JapanJapan Aerospace Exploration Agency

Global Precipitation Map1

Research Goals

1hr, 0.1ﾟ×0.1ﾟ resolution

Production of high precisionand high resolution globalprecipitation map by using
satelliteborne microwave
radiometer data
− Microwave radiometer− TRMM/PR− GEO’s IR

Evaluation, dissemination,utilization of precipitation map

Microwave Radiometer Data

AMSR-E, AMSR

TMI/TMI, PR

SSM/I×3

AMSU-A, B

Development of reliablemicrowave radiometer algorithm− Production of precipitation
physical model by using
TRMM PR data

– Contribution to theIPWG/PEHRPP– Application to flood
prediction and warning− Algorithm consistent withPR– GPM
GPM satellites

Four pillars of the research
GEO
meteorological Production of global precipitation maps
satellite

TRMM/TMIImprovementof algorithm

Aqua/
AMSR-EADEOOS-II/
AMSR

DMSP/
SSM/I

Algorithm development
Observation data

IR data

Cloudmotion
vectoralgorithm

Precipitation
mapdatabaseHigh temporalresolution mapFeedback

Internet

Look-up table

Data
dissemination
Ground radar observation

Estimation
ofrain rate

COBRA data

Radiative transfer equation
Precipitation physical model development

Localprecipitation
map
TRMM/PRGlobalprecipitation
map byTRMM/PR

Database of
precipitation
structure

Observation by TRMM/PR

Rain type classification
Precipitation profile
DSD
Melting layer
Snow

Precipitation
profileBasis of Rain Rate Retrieval by
Microwave Radiometers
Radiation Transfer
Equation
Observation
Data
Verification
Algorithm

Rain rate
Retrieval

Precipitation Physical
Model
Look-up
Table

Rain rate
Rain type, Rain profile
Rain drop size distribution
Melting layer, Snow, e.t.c.

• Satellites observe the brightness temperature, integration of radiation andscattering power.
• The relation between rain rate and brightness temperature is tabulated byassuming precipitation physical model and calculating the radiativetransfer equation. The rain rates giving the nearest brightness
temperature values to the observed ones are considered to be the most4
appropriate estimation.

Developments of Precipitation Physical Model
Improvementof scattering
algorithm

Precipitation
profile model

Mixed ice and waterSnow Particle
ModelClassification ofRain Types

Freezing Level

Melting Layer

Liquid water

Melting layer
model

Rain drop size
distribution(DSD) model

5

Composition of GSMaP ProductsTRMM
TMIAqua
AMSR-EADEOS-IIAMSR

DMSP
SSM/I×3

GSMaP
Microwave Radiometer Algorithms
Rain rates retrieved by eachsatellite microwave radiometer

Combination

1-hour data by TRMM/TMI,
Aqua/AMSR-E, ADEOSII/AMSR, DMSP/SSMI(F13, 14, 15)

Combined
Product
0.25º grid

6 hours
1 day
1 monthGeostationary Satellite
IR Cloud
Motion Vector
Combined IR/
Microwave Radiometer
Products
0.1º grid·1 hourGlobal Precipitation Map by GSMaP_TMI, JJA, DJF(1998-2005)

Comparison of TRMM rain rates by using TRMM/PR,TMI/GPROF, TMI/GSMaP algorithms (1998-2006)

Ocean
PR 2A25V6
TMI 2A12 V6
GSMaP_TMI
V4.8.4borealwinterborealspringborealsummerborealautumn1998-2006
average
PR swath
only

8Comparison of TRMM rain rates by using TRMM/PR,
TMI/GPROF, TMI/GSMaP algorithms (1998-2006)Land
PR 2A25V6
TMI 2A12 V6
GSMaP_TMI
V4.8.4borealspringborealwinter1998-2006
average
PR swath
onlyborealsummerborealautumn9Integrated 6-hour microwave radiometer
precipitation map (GSMaP_MWR)
MWR(TMI+AMSR+AMSR-E+F13, 14, 15 SSM/I)

10

Comparison of monthly rain rates by groundbased rain gauge (GPCC) with GSMaP_MWR
z GPCC Monthly Precipitation(Monitoring) Product （ Rudolfet al. 2005）
 ground-based rain gauge
 about 7000 rain gauges in theworld
 1.0ﾟ ×1.0ﾟ monthly averagez Analysis method• Tropical Area （20S～20N）• There are at least 2 rain
gauges in the 1.0ﾟ ×1.0ﾟ
• 2003-2005

Regression line : y=1.20 x + 30.8 (mm/month)

Correlation coefficient : 0.79

11Production of high temporal (1 hr)/high spatial (0.1ﾟ×0.1ﾟ)resolution precipitation map (GSMaP)
Algorithm flow to predict the movement of raining areas by applying the cloud
motion vector of the past 1 hour estimated from the IR cloud image
Infrared (IR) Data
11.4 μm Geo IR
Present
11.4 μm Geo IR
1 hour before

1 hr Moving Vector

Microwave Radiometer (MWR) Data
Predicted GSMaP
1 hr MWR
Present

Kalman Filter

GSMaP Data
GSMaP
1 hour before

GSMaP
Present

Combined global precipitation map-MW radiometer + cloud motion vector with Kalman filter(0.1ﾟ , 1 hour, 8-10 July 2005)
MVK: MWR(TMI+AMSR+AMSR-E+F13, 14, 15 SSM/I)+IR Cloud Motion Vector +Kalman Filter

13

Combined precipitation map around Japan-MW radiometer + cloud motion vector withKalman filter- (0.1 ﾟ , 1 hour, 9-10 July 2005)

14

Combined Typhoon Banyan’s precipitation map-MW radiometer + cloud motion vector with Kalman filter(0.1ﾟ , 1 hour, 25 July 2005)

15

Example of validation of GSMaP_MVK
using Radar-AMeDAS (8 July 2005)
GSMaP_MVKRadar-AMeDAS

16

Evaluation of various high resolution precipitation map
using Radar-AMeDAS rain map
Daily variation of correlation coefficient (0.25ﾟ×0.25ﾟ）) July, 2005
Correlation
1GSMaP_MVK
GSMaP_MVK+

0.9

Correlation

CMORPH
0.8

3B42RT

0.7

CMORPH: NOAA/CPC
3B42RT: NASA/GSFC

0.6

0.5
7/2

7/3

7/4

7/5

7/6

7/7

7/8

7/9

7/10

7/11

Date

GSMaP_MVK shows high correlation with Radar-AMeDAS throughout the period.
GSMaP_MVK+, produced by adding NOAA AMSU rain rates to GSMaP_MVK, shows particularly
17high correlation.

Dissemination of research products
Global precipitation map

Dissemination ofGSMaP data

Application to the flood
prediction and warning• DVD-R• Data release via
the Internet

GSMaPPrecipitation
dataParticipation in
the international project
IPWG (International Precipitation
Working Group) /PEHRPP (PilotEvaluation of High Resolution
Precipitation Product)

IPWG/PEHRPP
18

Construction of System for Near-Real-Time
Global Rainfall Maps by GSMaP algorithms
JAXA/EORC has started to release global rainfall data in near real time(about four hours after observations) on the Internet using GSMaPalgorithms.
Look-up Tables for MWR
retrievals (once a day)

Production of Global Rainfall Map (hourly)X

X + 3 hours

X + 4 hours

Microwave Radiometer data
TMI, AMSR-E, SSM/I(F13,14,15)

Atmospheric varibales

GEO IR data

JMA Global analysis (GANAL)JMA MGDSST

Merged data by JWA from MTSAT,METEOSAT,GOES

ftp
Objectiveanalysis
SSTftp
RTM
Calculation

Kalman filter noise table(computed byonce a week)

LUT for
MWR

MWR
IR

decoding

Rainfallretrieval fromeach MWR
sensorBlended
MWR-IR
algorithm

Globalrainfallmaps

PublicServer

GSMaP NRT System in JAXA/EORC
Global Rainfall Map in Near Real Time by
JAXA/EORC
http://sharaku.eorc.jaxa.jp/GSMaP/19Summary
Academic contributions
Microwave radiometer rain retrieval algorithm
Combined microwave and IR radiometer algorithm
Production and validation of the global precipitation maps

International contributions
GPM project
Participation in the PEHRPP/IPWG
Social contributions
Construction of System for Near-Real-Time Global Rainfall
Maps by GSMaP algorithms(JAXA)
Contribution to the joint research by Public Works ResearchInstitute and JAXA to use satellite rain data for the flood
prediction and warning system especially in Asian countries.

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