1. Kazumasa Aonashi (MRI/JMA)
PEHRPP Workshop　 Dec. 3, 2007 Geneva
GSMaP Passive Microwave
Precipitation Retrieval
Algorithm
Kazumasa Aonashi (MRI/JMA)

2. Passive Microwave Precipitation Retrieval
GSMaP Retrieval Algorithm
Global Satellite Mapping of Precipitation Project started in 2003.
Leader: Prof. Ken’ich Okamoto (Osaka Pref. Univ.)
Funded by JST/CREST
The goal is to produce accurate precip map using mainly satellite microwave radiometer.
Passive microwave precip retrieval (Aonashi)
Passive microwave precip+ IR wind (Dr. Ushio)

3. Outline Introduction Algorithm Description
Forward Calculation (precip cloud models)
Retrieval Part
Validation (TRMM PR, Ground-based Obs.)
Future Directions
Improvement of Scattering part
Summary

4. Algorithm Description
Forward Calculation
Retrieval Part

5. Basic Idea of the Retrieval Algorithm
Observed
TBs
Retrieval Calculation
Forward calculation
Precip Cloud
Models
RTM
Screening
Inhomogeneity
estimation
Scattering part
Radiation part
Look-up
Table
FLH
Precip Profiles
DSD
Mixed phase
inhomogeneity
Precip.
Find the optimal precipitation that gives RTM-calculated TBs
fitting best with the observed TBs:
PCT37, PCT85 (land)
TB10v,TB19v, PCT37, PCT85 (sea)

6. Precipitating Cloud Model for forward calculation
Precipitation type
Stratiform/
Convective
Precipitation Profile model
Frozen
Precip
Freezing
Level Height
Freezing Level
Rain
Mixed-phase model
Particle Size Distribution
Atmosphere & Surface
(GANAL)

7. Parameters used in the Algorithm: Atmospheric & surface variables
Temperature bias of GANAL against sonde
Atmospheric variables (Temp,FLH), surface variables(Ts, SSW, SST)
are derived from the Global Analysis data of JMA
Freezing
Level Height
for Jan.1,
2003

8. Precipitation Profile Model
Precip type classification
Data base
10 types (land 6, sea 4) are classified from TRMM PR data
(2.5 deg, 3 monthly)
Precip Profile
(land) 0: thunderstorm, 1: shower, 2: shallow, 3: frontal rain, 4: organized rain 5: highland
(sea) 6: shallow 7:frontal rain,
8:transit, 9:organized rain
Precip profile data base
Height from 1 deg level [km]
Example：
TRMM PR averaged preciptation profiles for each type, surface precip, conv/stra
1℃ level
Rainfall rate [mm/h]

9. Particle Size Distribution
DSD for rain: Kozu model (2A25 average distribution
calibrated with averaged epsillon)
epsillon =1 for stratiform rain
PSD for frozen particles: Marshall-Palmer distribution
Data base of conv. Epsillon
Averaged for each precip type

10. Nishitsuji (Mixed-Phase) Model for Stratiform Rain
On the basis of the filed experiment, the following parameters are modeled
Volume liquid water fraction （Pw)
shape parameter of the dielectric constant (U)
DSD parameter (B) is a function of Pw
Density ρ=√Pw
Fall velocity Magono-Nakamura(1965) for snow and Foot and Du Toit for rain
Pw and U profile
Relationship between B and Pw B

11. LUT calculation (1) TBs for homogeneous precip
Radiative Transfer Code (Liu,1998)
One-dimensional model (Plane-parallel)
Mie Scattering (Sphere)
4 stream approximation
Calculate TBs for homogeneous, convective & stratiform precip with each precip types.

12. LUT calculation (2): LUTs for inhomo precip
LOOK-UP TABLE
(LUT)
The calculated TBs are converted into TBs for inhomogeneous precip with Aonashi and Liu’s method (2000).
LUT used for retrieval is weighted average of convective & stratiform TBs.
STD of Log(Pr) is estimated from STD of Log(rain85) statistically.

13. Flow of the Retrieval Part
Observed TBs
Screening of Precip Areas
LOOK-UP TABLE
(LUT)
rain flag
Inhomo. Estimation / LUT selection
Inhomogeneity (STD of Log (Pr))
First-guess of Precipitation (scattering)
rain37  PCT37+LUT
rain85  PCT85+LUT
Minimization of Σ(TBc-TBo)**2
TB10v,TB19v
Over Ocean
Retrivals
Over Land
Retrievals

14. Validation
Comparison with
TRMM PR &
Ground-based
observations

15. Multi-Satellite Precip Composite （GSMaP_MWR, daily precip）
TMI+AMSR+AMSR-E+SSM/I (F13, F14, F15), 0.25ﾟ×0.25ﾟ 15

16. Time Sequence of TRMM precip. GSMaP, GPROF, PR (37S～37N 1998-2006)
PR 2A25V6
TMI 2A12V6
GSMaP
V4.8.4 海
Sea
PR swath only
Land 16

17. Zonal Mean TRMM Precip over Ocean GSMaP, GPROF, PR (1998～2006)
PR 2A25V6
TMI 2A12V6
GSMaP
V4.8.4
PR swath only 17

18. Comparison with ground-based radar (0.25 x 0.25 deg in lat-lon grid)
COBRA(Okinawa)
4 cases in June 2004
Kwajalein Radar
10 cases in May 2003
Corretation：0.82（No:253）
RMSE：1.37 mm/hr
Correlation：0.65（No:1139）
RMSE：1.78 mm/hr

19. Zonal Mean TRMM Precip over Land GSMaP, GPROF, PR (1998～2006)
PR 2A25V6
TMI 2A12V6
GSMaP
V4.8.4
PR swath only 19

20. Comparison with GPCC data
GSMaP_MWR：monthly mean preciptation (1x1 deg)
GPCC Monthly Precipitation (Monitoring) Product （Rudolf et al. 2006）：
Correlation:
0.80 for 45S～45N
（sample: 69440）
0.85 for 15S～15N
（sample: 2177）
Fitting：
y=1.14 x
[40S～40N]
y=1.21 x
[15S～15N]

21. Ratio of TMI scattering signals to PR in terms of precip top level (July, 1998)
(Rain37/rainsurf)
(Rain85/rainsurf)
PR top level –FLH (m)
PR top level –FLH (m)
Precip is over-(under-)estimated for PR high (low) top level.
(Rain85/rainsurf) is sensitive to PR top level.

22. Future Directions PSD, densities of frozen particles
Scattering properties of
Non-spherical particles

23. Estimation of realistic PSD, density etc.
PSD (field campaign)
RTM simulation
Observed
Radar &
MWR data

24. Scattering properties of non-spherical frozen particles (Liu, 2004)
DDA :Each of the dipoles is subject to an electric field which is the sum of the incident wave and the electric fields due to all of the other dipoles.
 An actual target is approximated by an array of dipoles.
From Mishchenko et al (2000)
Non-
Sphere
SP=(D-D0)/(Dmax-D0)
Keeping the single scattering properties
Sphere D
Dmax
D0: diameter of solid sphere

25. Summary GSMaP passive microwave precipitation retrieval algorithm:
Atmopheric & surface variables from GANAL
Precip profile, DSD & inhomo. from PR statistics
Mixed-phase model
The retrieved precipitation agreed well with PR, radar data over ocean.
The over-land algorithm underestimated the GPCC precipitation, and showed bias in terms of precipitation top level.
Introduction of the scattering of non-spherical particles, realistic PSD etc.

26. END
Thank you.

27. Satellite Observation (TRMM) Back scattering from Precip.
3 mm-3cm
（１００－１０GHｚ）
Microwave Radiometer
10μm
Infrared Imager
Radiation from Rain
Scattering by Frozen
Particles
Cloud Top Temp.
Scattering
Cloud Particles
Frozen Precip.
Snow Aggregates
１９GHｚ
８５GHz
Radar
2cm
Melting Layer 0℃
Back scattering from Precip.
Radiation
Rain Drops
SST, Winds

28. GSMaP MWR algorithm

29. Particle Size Distribution Model
全球で取得可能なDSDパラメータ(TRMM PRのε)の分布と、降水タイプ分類のパターンの類似性から、降水タイプ分類と関係づけたデータベース
地上ディスドロメータデータによる検証
TRMM PRから推定したZ-R関係の係数aとディスドロメータから推定したaの比較．コトタバン（西スマトラ・山岳地帯）

30. GSMaP_TMI（気候値・ 平均） 30

31. 現状のアルゴリズムをTMIに適用したリトリーバル値とPR（降水レーダ）の地上降水強度の比較（９８年７月）
降水トップの高い（低い）降水を過大（過小）評価する
特に８５ＧＨｚの散乱シグナルは降水トップへの感度が大きい

32. Evaluation using PR Match-up data
We were able to generate 141 AMSR-E vs. PR match-up data within observation time difference 5 minutes for Jan,Feb,Mar,Jun,Jul,Aug This figure shows distribution of the match-up locations.
Total:141

33. Evaluation using PR Match-up data
Distribution of the bias
△ △ △ △ △ △

34. Evaluation using PR Match-up data
This figure shows the histogram of BIAS which are calculated from 141 match-up data.
Liu 　　　　　　Petty 　　　　　Aonashi
Average=0.08
Average=0.12
Average=-0.04

35. Evaluation using PR Match-up data
Distribution of the RMSE
△ △ △ △
<

36. Evaluation using PR Match-up data
This figure shows the frequency of RMSE which are calculated from 141 match-up data.
Liu 　　　　　　Petty 　　　　　Aonashi
Average=1.40
Average=1.40
Average=1.33