IRアルゴリズム全球降水マップ作成

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
＊Microwave Radiometer
＊Rain Radar
＊Combined
＊Vertical Profile
＊Melting Layer Model
＊Z-R Relation and Rain Type
＊Non uniformity
＊Snow (Dry, Wet)
＊Attenuation by Cloud
CRL（Precipitation Radar：
5 GHz, 13.8 GHz, 95 GHz,
Wind Profiler: 400MHz)
National Research Institute for
Earth Science and Disaster
Prevention（Precipitation
Radar：10 GHz, 35 GHz,
95GHz )
Improvement
of
Algorithm
Improvement of
Physical Model
of Precipitation
Global
Rainfall
Map
Satellite Data
(TRMM, DMSP,
Aqua, ADEOS-II)
Geostationary
Satellite IR Data
Ground-based
Radar
•0.25 deg/ 1day precipitation map
only from the microwave
radiometers
•0.1 deg/ 1hour precipitation map
from combined IR and MW
radiometers
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
Aqua
ADEOS2
DMSP/F13
DMSP/F14
DMSP/F15
Precipitation
data
Merging
Coordination
1-Day covering area of 6 Satellites with
microwave radiometers(TRMM, Aqua,
ADEOS2, DMSP(F13, F14, F15))
•With 1 day resolution, the 6
satellites cover the whole globe.
•We still have sampling error
especially when we think of higher
resolution such as 3 hours/0.1
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 SunSynchronous-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
Split Window
降水マップ作成
11.4 μm Geo IR
Present
11.4 μm Geo IR
1 hour before
1 hr Moving Vector
Intermediate data
Microwave Radiometer (MWR) Data
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Kalman filtering
1 hr MWR
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GSMap Data
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GSMaP
1 hour before
^
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x((k  1)T )  A(kT ) x(kT )  B(kT )u(kT )  L( y(kT )  C x(kT ))
GSMaP
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
^
^
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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
状態
方程式
推定雨量
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レーダアメダスとの誤差
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観測方程式
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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
・Sampling error analysis
• Validation by using the Radar
network in Japan
・Interpolation from
cloud motion
・Split window
analysis
・Combination of
microwave
radiometers
Integration of MW and IR data
Comparison between GPROF and our
algorithm for the TMI data

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