1. 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)

2. Outline  GSMaP Project in Japan –Objectives –Outline  Modification of Aonashi ’ s algorithm –Comparison with 2A12  Integration of IR into MWR data –Initial results

3. Global Satellite Mapping of Precipitation Project (GSMaP) ・ 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) 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)

4. Improvement of Algorithm Improvement of Physical Model of Precipitation Ground-based Radar Global Rainfall Map 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 ) Satellite Data (TRMM, DMSP, Aqua, ADEOS-II) Outlines of the GSMaP project Geostationary Satellite IR Data 0.25 deg/ 1day precipitation map only from the microwave radiometers 0.1 deg/ 1hour precipitation map from combined IR and MW radiometers

5. TRMM/TMI 1998 年 7 月の 1 ヶ月平均の例 Our algorithmGPROF 2mm/h

6. Error near Himalayan mountain 85GHz scattering data base without precipitation from TRMM/PR observation

7. Zonal mean of precipitation Black : TRMM/PR Green: GPROF Red: our algorithm

9. TRMM Aqua ADEOS2 DMSP/F13 DMSP/F14 DMSP/F15 1-Day covering area of 6 Satellites with microwave radiometers(TRMM, Aqua, ADEOS2, DMSP(F13, F14, F15)) Production of Global Precipitation Map Precipitation data Merging Coordination Global precipitation map(3hours, 1 week) 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.

10. 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) Grid box size 100km Grid box size 500km The area around Japan has mean rain rate 0.2mm/h with Radar-AMeDAS Composites Data.

11. 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)

12. 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)

13. 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.

14. 降水マップ作成 11.4 μm Geo IR 1 hour before 10.8 μm Geo IR Present 11.4 μm Geo IR Present Infrared (IR) Data Microwave Radiometer (MWR) Data 1 hr Moving Vector GSMap Data Intermediate data Split Window GSMaP 1 hour before 1 hr MWR Present Kalman filtering

18. 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.

19. Thank you ! ありがとう ! 謝謝 Vielen Dank Merci Gracias Grazie

20. Kalman Filter による最適解

21. カルマンフィルタによる定式 化 初期値（真値） 誤差 移動ベクトルによる 雨域行列 推定雨量 状態 方程式 観測方程式 マイクロ波放射計データと レーダアメダスとの誤差

22. High quality precipitation map Global precipitation map only from microwave radiometers Integration of MW and IR data ・ Interpolation from cloud motion ・ Split window analysis ・ Combination of 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 ・ Sampling error analysis Validation by using the Radar network in Japan

23. Comparison between GPROF and our algorithm for the TMI data