JAPAN’s GV Strategy and Plans JAPAN’s GV Strategy and Plans for GPMfor GPM

K. Nakamura (HyARC/Nagoya K. Nakamura (HyARC/Nagoya Univ.) Univ.)

and S. Shimizu (JAXA)and S. Shimizu (JAXA)

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.

Rain attenuation Surface Reference Method

Drop Size Distribution External Parameter (In the algorithm)

PR algorithm concept

Snow

Rain

Melting Layer

Stratiform H

eig

ht

Radar reflectivity

PR

Snow

Rain

Melting Layer

Stratiform

KaPR

Heig

ht

KuPR

Radar reflectivity

Rain Region: Dual Frequency Drop Size Distribution (N0, D0)

Rain attenuationsIce/Snow Region: insufficient for three parameters : (N0, D0,

DPR algorithm concept

Accurate rainfall estimation using differential attenuation method(DSD parameter estimation)

Discrimination of snow and rain using differential attenuation method

Sensitive observation by the KaPR

Detectable range of KuPR (14 GHz)

Detectable range of KaPR (35 GHz)

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 gase

s Other difficulties

Beam filling: same as TRMM/PR

Beam matching new problem

GPM/DPR Calibration and Validation

Calibration (by ARC)Engineering values

Algorithm

Physical 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) , …

Verification

Transmit power,Received power,Antenna beam direction

Validation

Assumption(Initial values)

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.

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 tas

k of external cal. for GPM/DPR) using multiple ARCs• Algorithm specific validation for each rain retrieval algorith

m of DPR will be required.– For this purpose, we need to develop new paradigm of algorithm v

alidation 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.

GV New Paradigm Example with PR/DPR

Rain (snow) water content (PWC(h))Density ( (h))Drop Size Disribution, etc

Zm14

Zm35

Rain rate(R(h))

Retrieval Algorithm

Hydrometeor (Rain, Snow, Graupel, etc.)

forward calculation

GV dataVertical velocity (v(D))

Particle typesDSD, v(D)Non-Uniformity, etc.

Rain Rate(R(h))

Remote Sensing GV algorithm

In-situmeasurement

True values in Nature

Assumption

Particle types

DSD(h) GV algorith

m

Compare

CompareSynthesized Nature

Retrival

Numerical m

odels

DSD(h), v(D), Particle type, Zm, PWC, etc

Water vaporCloud water content （ Liquid, Solid）OxygenAerosolSea Surface TemperatureNoise, etc

v(D)

Compare

Compare

Compare

（ Iguchi, 2004）

Reproduce physical parameters for forward calculation from ground-based observation using GV algorithms

Reflectivity (Ze), Rain Rate (R)

① ②

③

④

⑤

⑥

⑦

⑨⑩

⑧

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.

Candidates for ＧＰＭ ＧＶ Supersite

Okinawa （ 26N, 128E)

Wakkanai（ 45.5N, 142E)

International Arctic Environmental Research Project Group -Upper air observation by VHF radar

Okinawa Subtropical Environment Remote Sensing Center - C-band multiparameter radar, wind profiler, etc.

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.

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 h

ave 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 so

lid particles.• Spectrum differences in C, Ku, Ka and W for detection of terminal velo

city 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.

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.