JAPAN’s GV Strategy and Plans for GPM

1. JAPAN’s GV Strategy and Plans for GPM K. Nakamura (HyARC/Nagoya Univ.) and S. Shimizu (JAXA)

2. Objectives of Japanese GPM Cal/Val • To confirm the reliability of the GPM standard products, • To quantify the error of the products and confirm the characteristics, • To clarify the origin of the error of the products and feed it back to modify the algorithms and • To validate the algorithms using the physical parameters observed or estimated from the ground validation activities.

3. PR algorithm concept Stratiform Height Snow PR Melting Layer Rain Radar reflectivity Rain attenuation  Surface Reference Method Drop Size Distribution  External Parameter (In the algorithm)

4. Rain Region: Dual Frequency Drop Size Distribution (N0, D0) Rain attenuations DPR algorithm concept Detectable range of KaPR (35 GHz) Detectable range of KuPR (14 GHz) Height Stratiform Sensitive observation by the KaPR Discrimination of snow and rain using differential attenuation method Snow KuPR KaPR Melting Layer Rain Accurate rainfall estimation using differential attenuation method (DSD parameter estimation) Radar reflectivity Ice/Snow Region: insufficient for three parameters : (N0, D0, r)

5. GPM/DPR vs TRMM/PR on algorithm Attenuation TRMM/PR (Ku-band) (Rain) DSD uncertainty GPM/DPR KuPR (Ku-band) (Rain) KaPR (Ka-band) (Rain) + (Cloud) + (Water Vapor) + (gases) Rain attenuation correction will be improved. New uncertain terms: attenuation by cloud, water vapor, and gases Other difficulties Beam filling: same as TRMM/PR Beam matching new problem

6. GPM/DPR Calibration and Validation Engineering values Verification Algorithm Physical values Calibration (by ARC) Transmit power,Received power,Antenna beam direction Assumption(Initial values) Precip. type classification (Conv./Strat.),Particle type (Rain/Snow/Graupel),(DSD (Drop Size Distribution)),Temp. & humidity profile,Melting layer model,Gaseous attenuation, … Precip. rate/accumulation,Precip. type classification (Conv./Strat.),Particle type (Rain/Snow/Graupel),DSD (Drop Size Distribution) , … Validation

7. From TRMM experiences • Simple comparison is never enough. • Ground-based radar data (especially radar reflectivity value) are depended on the radars. • TRMM is too good to be validated by regression-based traditional validation. • Temporal/spatial mismatching is still problem. • Precise and comprehensive precipitation system measurement is required. • Physical validation may be more important for radar rain retrieval as well as microwave rain retrieval. • Very few occasions of simultaneous observations between GV instruments and satellite, especially PR.

8. Japanese GV activities • Japanese calibration and validation will focus on DPR in GPM. • More accurate and sensitive cal/val analyses will be required. • Validation for snow rate will be required for DPR. • Post-launch beam matching measurement between two radars (new task of external cal. for GPM/DPR) using multiple ARCs • Algorithm specific validation for each rain retrieval algorithm of DPR will be required. • For this purpose, we need to develop new paradigm of algorithm validation and collect many kinds of physical parameters for Special validation sites are required for the physical validation. •  We need to establish Super sites for DPR GV • (Okinawa, Wakkanai) • Statistical comparison with long-term precipitation data using operational data. • For this purpose, we need to collect operational raingauge data (e.g. AMeDAS data) and other operational data.

9. GV New Paradigm Example with PR/DPR ⑨ ⑩ Compare ① ② ⑦ ③ Compare ⑤ ⑥ ④ True values in Nature Reflectivity (Ze), Rain Rate (R) Compare Hydrometeor (Rain, Snow, Graupel, etc.) Remote Sensing GV algorithm Rain Rate (R(h)) GV data Vertical velocity (v(D)) DSD(h), v(D), Particle type, Zm, PWC, etc Rain (snow) water content (PWC(h)) Density ( (h)) Drop Size Disribution, etc In-situ measurement ⑧ Compare GV algorithm Synthesized Nature Retrival Numerical models Reproduce physical parameters for forward calculation from ground-based observation using GV algorithms DSD(h) Assumption v(D) Particle types DSD, v(D) Non-Uniformity, etc. Particle types Compare Water vapor Cloud water content （Liquid, Solid） Oxygen Aerosol Sea Surface Temperature Noise, etc forward calculation Zm14 Zm35 Retrieval Algorithm Rain rate (R(h)) （Iguchi, 2004）

10. Key issues for success of GV activities • How do we synthesize physical parameters from GV data? • We need to collect appropriate observation data. • We need to investigate and collect existing observation data. Whether are existing datasets enough for reproducing physical parameters for forward calculation or not? • New observation for GV will be need before launch of GPM-Core satellite. • We need to establish GV algorithms for reproducing physical parameters. • We need to validate the physical parameters retrieved by GV observations. • We need to make Zm data by forward calculation.

11. Candidates for ＧＰＭ ＧＶ Supersite Campaign observation in Okinawa was carried out in May and June 2004 for CREST-GSMaP activity. Now we start to investigate the data for GPM GV. • International Arctic Environmental Research Project Group • Upper air observation by VHF radar Wakkanai （45.5N, 142E) Okinawa Subtropical Environment Remote Sensing Center - C-band multiparameter radar, wind profiler, etc. Okinawa （26N, 128E)

12. Issues • Validation for solid precipitation • Algorithms and validation methods for retrieval of solid precipitation have not established. (Physical parameters for DPR algorithm development have not been clear.) • Density, N0, D0 Snow rate • N0 and D0 can be derived by dual frequency radar for rain rate. But we have three parameters for snow. Statistics of snow density is required. • We will try to get upper layer data above melting level at Okinawa. • Conventional method using polarization radar for the classification of solid particles. • Spectrum differences in C, Ku, Ka and W for detection of terminal velocity of snow. • We need to collect snow rate and other physical parameters in NiCT Wakkanai during winter season using wind profilers, Ku/W-band radars, multi-parameter radar, etc before launch of GPM-core satellite. • Continuous validation analyses using statistical methods will be needed after the launch.

13. Summary • DPR is steadily being developed by JAXA and NiCT for the launch of GPM-Core satellite in winter on 2010. • Japanese calibration and validation will focus on DPR in GPM. • New GV paradigm for DPR is proposed. We are now designing Japanese GV plan based on the new paradigm. • Construction of adequate physical parameter database for forward calculation is the most important and concerning problem.