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The Global Satellite Mapping of Precipitation (GSMaP) project:
Integration of microwave and infrared radiometers for a global
precipitation map
Tomoo Ushio*, K. Okamoto, F. Isoda, Y. Iida (Osaka Prefecture Univ.)
K. Aonashi, T. Inoue (MRI)
N. Takahashi, T. Iguchi, H. Hanado (NICT)
K. Iwanami (NIED)
Outline
GSMaP Project in Japan
– Objectives
– Outline
Modification of Aonashi’s algorithm
– Comparison with 2A12
Integration of IR into MWR data
– Initial results
Global Satellite Mapping of Precipitation Project (GSMaP)
More than 20 scientists from 8 institutions in Japan. (NICT, MRI, JAXA, Osaka
Pref. Univ., Tokyo Univ., Shimane Univ., Hokkaido-Tokai Univ., NIED)
(PI: Ken’ichi Okamoto, Osaka Prefecture University)
・To produce highly accurate and high spatial resolution maps of global
precipitation by mainly using satelliteborne microwave radiometer.
- Everyday, 0.25×0.25 degree/ 1 day resolution.
- Microwave Radiometer (TRMM, DMSP×3, Aqua, ADEOS-II),PR(TRMM)
- Integration to Geostationary satellite IR data
・To develop reliable microwave radiometer algorithm
- Consistent algorithm with PR algorithm based on the common physical model
of precipitation
・To establish technique to produce rainfall map by using satellite-borne
microwave radiometer data for the future mission(GPM)
Outlines of the GSMaP project
Consistent Algorithm Based on
Common Physical Model of
Precipitation Improvement Global
＊Microwave Radiometer of Rainfall
＊Rain Radar Algorithm Map
＊Combined
＊Vertical Profile
＊Melting Layer Model Improvement of Satellite Data
＊Z-R Relation and Rain Type Physical Model
＊Non uniformity
(TRMM, DMSP,
＊Snow (Dry, Wet) of Precipitation Aqua, ADEOS-II)
＊Attenuation by Cloud
Geostationary
CRL（Precipitation Radar： Satellite IR Data
5 GHz, 13.8 GHz, 95 GHz,
Wind Profiler: 400MHz) •0.25 deg/ 1day precipitation map
Ground-based only from the microwave
National Research Institute for
Radar radiometers
Earth Science and Disaster •0.1 deg/ 1hour precipitation map
Prevention（Precipitation from combined IR and MW
Radar：10 GHz, 35 GHz, radiometers
95GHz )
TRMM/TMI
1998年7月の1ヶ月平均の例
Our algorithm GPROF 2mm/h
Error near Himalayan mountain
85GHz scattering data base without precipitation from TRMM/PR observation
Zonal mean of precipitation
Black : TRMM/PR Green: GPROF Red: our algorithm
Black : TRMM/PR Green: GPROF Red: our algorithm
Production of Global Precipitation Map
Global precipitation map(3hours, 1 week)
TRMM Precipitation
Aqua data
ADEOS2
DMSP/F13
DMSP/F14 Merging
DMSP/F15 Coordination
•With 1 day resolution, the 6
satellites cover the whole globe.
•We still have sampling error
especially when we think of higher
1-Day covering area of 6 Satellites with resolution such as 3 hours/0.1
microwave radiometers(TRMM, Aqua,
ADEOS2, DMSP(F13, F14, F15)) degree.
The area around Japan has mean rain rate
0.2mm/h with Radar-AMeDAS Composites Data.
Grid box size 100km
Grid box size 500km
Average Sampling Error of rainfall per each period by Five Sun-
Synchronous-Orbit Satellites’ Group plus TRMM(TMI) in 500km and 100km
grid box using Radar-AMeDAS Composites Data during 36 months. (Y. Iida,
2003)
Needs for the combined algorithm
If we want the 0.1 deg./1 hour resolution precipitation
map only from 6 currently available MWR, we would
have more than 500% sampling error.
In order to reduce these errors, we use the Infrared
Radiometers (IR) data which do not have any
sampling problems. (large algorithm errors)
Moving vector approach
This method was recently introduced by Joyce
et al. [2004].
Advantage
– MWR based approach (not Tb but cloud motion)
– Fast processing time
Disadvantage
– Physically simple assumption (not based on
dynamics and thermodynamics)
What, When, Where, and How do we
analyze for?
Purpose: To draw the global precipitation map with
0.1 degree/1 hour resolution
What: 1hour global IR data from
Goddard/DAAC and TMI/2A21 data
When: August 3 to 4, 2000
Where: -35 to 35 in latitude, 0 to 360 in longitude
How: By interpolating precipitation between TMI
overpasses using the cloud motion inferred
from 1 hour IR Tb.
Infrared (IR) Data
10.8 μm Geo IR
Present
降水マップ作成
Split Window
11.4 μm Geo IR
Present
11.4 μm Geo IR
1 hour before 1 hr Moving Vector Intermediate data
Microwave Radiometer (MWR) Data
x1 (kT) 0 . . . 0 x1 (kT) A10 v1 (kT)
Kalman filtering . A 0 . . 0 . 0 .
21
1 hr MWR . 0
. 0 . . . 0 u(kT) .
. 0 0 . 0 . . 0 .
Present x5 (kT) 0
0 0 A54 0 x5 (kT) 0
v5 (kT)
x1 ((k 1)T )
. A A ‥A
65 54 21 0 0 x1 (kT )
. 0 A76 A65‥A32 0 .
. 0 0 A109 A98‥A65 x5 (kT )
x5 ((k 1)T )
^ ^ ^
GSMap Data x((k 1)T ) A(kT) x(kT) B(kT)u(kT) L( y(kT) C x(kT))
GSMaP GSMaP
1 hour before
Summary and future directions
GSMaP project in Japan was introduced.
Precipitation estimates by TMI/Aonashi’s algorithm
shows good correlation over land and ocean. But we
still have ice/snow covering problems.
Initial results of the global precipitation map with 0.1
deg./1 hr resolution from IR and TMI combined
algorithm were introduced and demonstrated.
We are going to examine the possibility of using the
bi-spectral and Kalman filtering approach.
Lightning data also will be included.
Thank you !
ありがとう!
謝謝
Vielen Dank
Merci
Gracias
Grazie
x((k 1)T ) A(kT) x(kT) B(kT)u(kT)
Kalman Filterによる最適解
^ ^ ^
x((k 1)T ) A(kT ) x(kT ) B(kT )u (kT ) L( y(kT ) C x(kT ))
カルマンフィルタによる定式化
移動ベクトルによる
推定雨量 初期値（真値）
雨域行列
x1 (kT ) 0 . . . 0 x1 (kT ) A10 v1 (kT )
. A 0 . . 0 . 0 .
21 誤差
. 0 . 0 . . . 0 u (kT ) .
. 0 0 . 0 . . 0 .
x5 (kT ) 0 0 0 A54
x5 (kT ) 0
0 v5 (kT )
状態
x1 ((k 1)T )
方程式 A A ‥A
. 65 54 21 0 0 x1 (kT )
. 0 A76 A65‥A32 0 .
.
0 0 A109 A98‥A65 x5 (kT )
x5 ((k 1)T )
x1 (kT )
.
マイクロ波放射計データと
y (kT ) 0 0 0 0 I . w(kT )
レーダアメダスとの誤差
.
観測方程式 x5 (kT )
Global precipitation map only from microwave radiometers
・Improvements of Aonashi’s algorithm
・Compariton betwenn ＴＲＭＭ/PR・
TMI(GPROF and Aonashi’s algorithm)
・Application to AMSR-E, SSM/I data
・
Ｖａｌｉｄａｔｉｏｎ
High quality precipitation map
・Interpolation from
cloud motion
・Split window
・Sampling error analysis analysis
• Validation by using the Radar ・Combination of
network in Japan microwave
radiometers
Integration of MW and IR data
Comparison between GPROF and our
algorithm for the TMI data
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