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 Modification of Aonashi’s algorithm 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 Improvement of Algorithm Global Rainfall Map ＊Vertical Profile ＊Melting Layer Model ＊Z-R Relation and Rain Type ＊Non uniformity ＊Snow (Dry, Wet) ＊Attenuation by Cloud Improvement of Physical Model of Precipitation Satellite Data (TRMM, DMSP, Aqua, ADEOS-II) Geostationary Satellite IR Data 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 ) 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) Precipitation data TRMM Aqua　　 ADEOS2 DMSP/F13 DMSP/F14 DMSP/F15　　　 Merging Coordination 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. 1-Day covering area of 6 Satellites with microwave radiometers(TRMM, Aqua, ADEOS2, DMSP(F13, F14, F15))

The area around Japan has mean rain rate 0 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.

降水マップ作成 Infrared (IR) Data Microwave Radiometer (MWR) Data 1 hr MWR 10.8 μm Geo IR Present 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 Kalman filtering 1 hr MWR Present GSMap Data GSMaP 1 hour before 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

Kalman Filterによる最適解

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

High quality precipitation map 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 analysis ・Combination of microwave radiometers ・Sampling error analysis Validation by using the Radar network in Japan Integration of MW and IR data

Comparison between GPROF and our algorithm for the TMI data